안효원
2022-06-13
add
@ee5af791c93b18036de7960dc5aa045f011ffb6f
--- 20220530/.ipynb_checkpoints/cctv-checkpoint.ipynb
+++ 20220530/.ipynb_checkpoints/cctv-checkpoint.ipynb
... | ... | @@ -2,7 +2,7 @@ |
| 2 | 2 |
"cells": [ |
| 3 | 3 |
{
|
| 4 | 4 |
"cell_type": "code", |
| 5 |
- "execution_count": 16, |
|
| 5 |
+ "execution_count": 73, |
|
| 6 | 6 |
"id": "55e4cb48", |
| 7 | 7 |
"metadata": {},
|
| 8 | 8 |
"outputs": [], |
... | ... | @@ -14,8 +14,8 @@ |
| 14 | 14 |
}, |
| 15 | 15 |
{
|
| 16 | 16 |
"cell_type": "code", |
| 17 |
- "execution_count": 17, |
|
| 18 |
- "id": "f9691b90", |
|
| 17 |
+ "execution_count": 74, |
|
| 18 |
+ "id": "482ba70c", |
|
| 19 | 19 |
"metadata": {
|
| 20 | 20 |
"scrolled": true |
| 21 | 21 |
}, |
... | ... | @@ -73,22 +73,22 @@ |
| 73 | 73 |
" <tbody>\n", |
| 74 | 74 |
" <tr>\n", |
| 75 | 75 |
" <th>계</th>\n", |
| 76 |
- " <td>83,557</td>\n", |
|
| 77 |
- " <td>4,812</td>\n", |
|
| 78 |
- " <td>1,851</td>\n", |
|
| 79 |
- " <td>3,434</td>\n", |
|
| 80 |
- " <td>4,295</td>\n", |
|
| 81 |
- " <td>6,840</td>\n", |
|
| 82 |
- " <td>8,708</td>\n", |
|
| 83 |
- " <td>11,572</td>\n", |
|
| 84 |
- " <td>10,627</td>\n", |
|
| 85 |
- " <td>12,267</td>\n", |
|
| 86 |
- " <td>11,247</td>\n", |
|
| 87 |
- " <td>7,904</td>\n", |
|
| 76 |
+ " <td>83557</td>\n", |
|
| 77 |
+ " <td>4812</td>\n", |
|
| 78 |
+ " <td>1851.0</td>\n", |
|
| 79 |
+ " <td>3434.0</td>\n", |
|
| 80 |
+ " <td>4295</td>\n", |
|
| 81 |
+ " <td>6840</td>\n", |
|
| 82 |
+ " <td>8708</td>\n", |
|
| 83 |
+ " <td>11572</td>\n", |
|
| 84 |
+ " <td>10627</td>\n", |
|
| 85 |
+ " <td>12267</td>\n", |
|
| 86 |
+ " <td>11247</td>\n", |
|
| 87 |
+ " <td>7904</td>\n", |
|
| 88 | 88 |
" </tr>\n", |
| 89 | 89 |
" <tr>\n", |
| 90 | 90 |
" <th>종로구</th>\n", |
| 91 |
- " <td>1,715</td>\n", |
|
| 91 |
+ " <td>1715</td>\n", |
|
| 92 | 92 |
" <td>815</td>\n", |
| 93 | 93 |
" <td>NaN</td>\n", |
| 94 | 94 |
" <td>NaN</td>\n", |
... | ... | @@ -103,10 +103,10 @@ |
| 103 | 103 |
" </tr>\n", |
| 104 | 104 |
" <tr>\n", |
| 105 | 105 |
" <th>중구</th>\n", |
| 106 |
- " <td>2,447</td>\n", |
|
| 106 |
+ " <td>2447</td>\n", |
|
| 107 | 107 |
" <td>16</td>\n", |
| 108 |
- " <td>114</td>\n", |
|
| 109 |
- " <td>87</td>\n", |
|
| 108 |
+ " <td>114.0</td>\n", |
|
| 109 |
+ " <td>87.0</td>\n", |
|
| 110 | 110 |
" <td>77</td>\n", |
| 111 | 111 |
" <td>236</td>\n", |
| 112 | 112 |
" <td>240</td>\n", |
... | ... | @@ -118,10 +118,10 @@ |
| 118 | 118 |
" </tr>\n", |
| 119 | 119 |
" <tr>\n", |
| 120 | 120 |
" <th>용산구</th>\n", |
| 121 |
- " <td>2,611</td>\n", |
|
| 121 |
+ " <td>2611</td>\n", |
|
| 122 | 122 |
" <td>34</td>\n", |
| 123 |
- " <td>71</td>\n", |
|
| 124 |
- " <td>234</td>\n", |
|
| 123 |
+ " <td>71.0</td>\n", |
|
| 124 |
+ " <td>234.0</td>\n", |
|
| 125 | 125 |
" <td>125</td>\n", |
| 126 | 126 |
" <td>221</td>\n", |
| 127 | 127 |
" <td>298</td>\n", |
... | ... | @@ -133,10 +133,10 @@ |
| 133 | 133 |
" </tr>\n", |
| 134 | 134 |
" <tr>\n", |
| 135 | 135 |
" <th>성동구</th>\n", |
| 136 |
- " <td>3,829</td>\n", |
|
| 136 |
+ " <td>3829</td>\n", |
|
| 137 | 137 |
" <td>163</td>\n", |
| 138 |
- " <td>144</td>\n", |
|
| 139 |
- " <td>208</td>\n", |
|
| 138 |
+ " <td>144.0</td>\n", |
|
| 139 |
+ " <td>208.0</td>\n", |
|
| 140 | 140 |
" <td>107</td>\n", |
| 141 | 141 |
" <td>325</td>\n", |
| 142 | 142 |
" <td>255</td>\n", |
... | ... | @@ -151,37 +151,37 @@ |
| 151 | 151 |
"</div>" |
| 152 | 152 |
], |
| 153 | 153 |
"text/plain": [ |
| 154 |
- " 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 \\\n", |
|
| 154 |
+ " 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 \\\n", |
|
| 155 | 155 |
"구분 \n", |
| 156 |
- "계 83,557 4,812 1,851 3,434 4,295 6,840 8,708 11,572 10,627 \n", |
|
| 157 |
- "종로구 1,715 815 NaN NaN 195 150 0 261 85 \n", |
|
| 158 |
- "중구 2,447 16 114 87 77 236 240 372 386 \n", |
|
| 159 |
- "용산구 2,611 34 71 234 125 221 298 351 125 \n", |
|
| 160 |
- "성동구 3,829 163 144 208 107 325 255 967 415 \n", |
|
| 156 |
+ "계 83557 4812 1851.0 3434.0 4295 6840 8708 11572 10627 \n", |
|
| 157 |
+ "종로구 1715 815 NaN NaN 195 150 0 261 85 \n", |
|
| 158 |
+ "중구 2447 16 114.0 87.0 77 236 240 372 386 \n", |
|
| 159 |
+ "용산구 2611 34 71.0 234.0 125 221 298 351 125 \n", |
|
| 160 |
+ "성동구 3829 163 144.0 208.0 107 325 255 967 415 \n", |
|
| 161 | 161 |
"\n", |
| 162 |
- " 2019년 2020년 2021년 \n", |
|
| 163 |
- "구분 \n", |
|
| 164 |
- "계 12,267 11,247 7,904 \n", |
|
| 165 |
- "종로구 9 200 0 \n", |
|
| 166 |
- "중구 155 361 403 \n", |
|
| 167 |
- "용산구 307 617 228 \n", |
|
| 168 |
- "성동구 490 472 283 " |
|
| 162 |
+ " 2019년 2020년 2021년 \n", |
|
| 163 |
+ "구분 \n", |
|
| 164 |
+ "계 12267 11247 7904 \n", |
|
| 165 |
+ "종로구 9 200 0 \n", |
|
| 166 |
+ "중구 155 361 403 \n", |
|
| 167 |
+ "용산구 307 617 228 \n", |
|
| 168 |
+ "성동구 490 472 283 " |
|
| 169 | 169 |
] |
| 170 | 170 |
}, |
| 171 |
- "execution_count": 17, |
|
| 171 |
+ "execution_count": 74, |
|
| 172 | 172 |
"metadata": {},
|
| 173 | 173 |
"output_type": "execute_result" |
| 174 | 174 |
} |
| 175 | 175 |
], |
| 176 | 176 |
"source": [ |
| 177 |
- "cctvData = pd.read_csv('./seoul_cctv_202112.csv', encoding='cp949', skiprows=1, index_col='구분')\n",
|
|
| 177 |
+ "cctvData = pd.read_csv('./seoul_cctv_202112.csv', encoding='cp949', skiprows=1, index_col='구분', thousands=',')\n",
|
|
| 178 | 178 |
"cctvData.head()" |
| 179 | 179 |
] |
| 180 | 180 |
}, |
| 181 | 181 |
{
|
| 182 | 182 |
"cell_type": "code", |
| 183 |
- "execution_count": 18, |
|
| 184 |
- "id": "8184defd", |
|
| 183 |
+ "execution_count": 75, |
|
| 184 |
+ "id": "bcf14fbe", |
|
| 185 | 185 |
"metadata": {
|
| 186 | 186 |
"scrolled": true |
| 187 | 187 |
}, |
... | ... | @@ -239,7 +239,7 @@ |
| 239 | 239 |
" <tbody>\n", |
| 240 | 240 |
" <tr>\n", |
| 241 | 241 |
" <th>종로구</th>\n", |
| 242 |
- " <td>1,715</td>\n", |
|
| 242 |
+ " <td>1715</td>\n", |
|
| 243 | 243 |
" <td>815</td>\n", |
| 244 | 244 |
" <td>NaN</td>\n", |
| 245 | 245 |
" <td>NaN</td>\n", |
... | ... | @@ -254,10 +254,10 @@ |
| 254 | 254 |
" </tr>\n", |
| 255 | 255 |
" <tr>\n", |
| 256 | 256 |
" <th>중구</th>\n", |
| 257 |
- " <td>2,447</td>\n", |
|
| 257 |
+ " <td>2447</td>\n", |
|
| 258 | 258 |
" <td>16</td>\n", |
| 259 |
- " <td>114</td>\n", |
|
| 260 |
- " <td>87</td>\n", |
|
| 259 |
+ " <td>114.0</td>\n", |
|
| 260 |
+ " <td>87.0</td>\n", |
|
| 261 | 261 |
" <td>77</td>\n", |
| 262 | 262 |
" <td>236</td>\n", |
| 263 | 263 |
" <td>240</td>\n", |
... | ... | @@ -269,10 +269,10 @@ |
| 269 | 269 |
" </tr>\n", |
| 270 | 270 |
" <tr>\n", |
| 271 | 271 |
" <th>용산구</th>\n", |
| 272 |
- " <td>2,611</td>\n", |
|
| 272 |
+ " <td>2611</td>\n", |
|
| 273 | 273 |
" <td>34</td>\n", |
| 274 |
- " <td>71</td>\n", |
|
| 275 |
- " <td>234</td>\n", |
|
| 274 |
+ " <td>71.0</td>\n", |
|
| 275 |
+ " <td>234.0</td>\n", |
|
| 276 | 276 |
" <td>125</td>\n", |
| 277 | 277 |
" <td>221</td>\n", |
| 278 | 278 |
" <td>298</td>\n", |
... | ... | @@ -284,10 +284,10 @@ |
| 284 | 284 |
" </tr>\n", |
| 285 | 285 |
" <tr>\n", |
| 286 | 286 |
" <th>성동구</th>\n", |
| 287 |
- " <td>3,829</td>\n", |
|
| 287 |
+ " <td>3829</td>\n", |
|
| 288 | 288 |
" <td>163</td>\n", |
| 289 |
- " <td>144</td>\n", |
|
| 290 |
- " <td>208</td>\n", |
|
| 289 |
+ " <td>144.0</td>\n", |
|
| 290 |
+ " <td>208.0</td>\n", |
|
| 291 | 291 |
" <td>107</td>\n", |
| 292 | 292 |
" <td>325</td>\n", |
| 293 | 293 |
" <td>255</td>\n", |
... | ... | @@ -299,10 +299,10 @@ |
| 299 | 299 |
" </tr>\n", |
| 300 | 300 |
" <tr>\n", |
| 301 | 301 |
" <th>광진구</th>\n", |
| 302 |
- " <td>3,211</td>\n", |
|
| 302 |
+ " <td>3211</td>\n", |
|
| 303 | 303 |
" <td>35</td>\n", |
| 304 |
- " <td>57</td>\n", |
|
| 305 |
- " <td>100</td>\n", |
|
| 304 |
+ " <td>57.0</td>\n", |
|
| 305 |
+ " <td>100.0</td>\n", |
|
| 306 | 306 |
" <td>187</td>\n", |
| 307 | 307 |
" <td>98</td>\n", |
| 308 | 308 |
" <td>52</td>\n", |
... | ... | @@ -317,24 +317,24 @@ |
| 317 | 317 |
"</div>" |
| 318 | 318 |
], |
| 319 | 319 |
"text/plain": [ |
| 320 |
- " 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 2019년 2020년 \\\n", |
|
| 321 |
- "구분 \n", |
|
| 322 |
- "종로구 1,715 815 NaN NaN 195 150 0 261 85 9 200 \n", |
|
| 323 |
- "중구 2,447 16 114 87 77 236 240 372 386 155 361 \n", |
|
| 324 |
- "용산구 2,611 34 71 234 125 221 298 351 125 307 617 \n", |
|
| 325 |
- "성동구 3,829 163 144 208 107 325 255 967 415 490 472 \n", |
|
| 326 |
- "광진구 3,211 35 57 100 187 98 52 675 465 712 175 \n", |
|
| 320 |
+ " 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 2019년 \\\n", |
|
| 321 |
+ "구분 \n", |
|
| 322 |
+ "종로구 1715 815 NaN NaN 195 150 0 261 85 9 \n", |
|
| 323 |
+ "중구 2447 16 114.0 87.0 77 236 240 372 386 155 \n", |
|
| 324 |
+ "용산구 2611 34 71.0 234.0 125 221 298 351 125 307 \n", |
|
| 325 |
+ "성동구 3829 163 144.0 208.0 107 325 255 967 415 490 \n", |
|
| 326 |
+ "광진구 3211 35 57.0 100.0 187 98 52 675 465 712 \n", |
|
| 327 | 327 |
"\n", |
| 328 |
- " 2021년 \n", |
|
| 329 |
- "구분 \n", |
|
| 330 |
- "종로구 0 \n", |
|
| 331 |
- "중구 403 \n", |
|
| 332 |
- "용산구 228 \n", |
|
| 333 |
- "성동구 283 \n", |
|
| 334 |
- "광진구 655 " |
|
| 328 |
+ " 2020년 2021년 \n", |
|
| 329 |
+ "구분 \n", |
|
| 330 |
+ "종로구 200 0 \n", |
|
| 331 |
+ "중구 361 403 \n", |
|
| 332 |
+ "용산구 617 228 \n", |
|
| 333 |
+ "성동구 472 283 \n", |
|
| 334 |
+ "광진구 175 655 " |
|
| 335 | 335 |
] |
| 336 | 336 |
}, |
| 337 |
- "execution_count": 18, |
|
| 337 |
+ "execution_count": 75, |
|
| 338 | 338 |
"metadata": {},
|
| 339 | 339 |
"output_type": "execute_result" |
| 340 | 340 |
} |
... | ... | @@ -346,8 +346,8 @@ |
| 346 | 346 |
}, |
| 347 | 347 |
{
|
| 348 | 348 |
"cell_type": "code", |
| 349 |
- "execution_count": 19, |
|
| 350 |
- "id": "11fb1d65", |
|
| 349 |
+ "execution_count": 76, |
|
| 350 |
+ "id": "527eb8ab", |
|
| 351 | 351 |
"metadata": {},
|
| 352 | 352 |
"outputs": [ |
| 353 | 353 |
{
|
... | ... | @@ -390,7 +390,7 @@ |
| 390 | 390 |
" <tr>\n", |
| 391 | 391 |
" <th>0</th>\n", |
| 392 | 392 |
" <td>종로구</td>\n", |
| 393 |
- " <td>1,715</td>\n", |
|
| 393 |
+ " <td>1715</td>\n", |
|
| 394 | 394 |
" <td>815</td>\n", |
| 395 | 395 |
" <td>NaN</td>\n", |
| 396 | 396 |
" <td>NaN</td>\n", |
... | ... | @@ -406,10 +406,10 @@ |
| 406 | 406 |
" <tr>\n", |
| 407 | 407 |
" <th>1</th>\n", |
| 408 | 408 |
" <td>중구</td>\n", |
| 409 |
- " <td>2,447</td>\n", |
|
| 409 |
+ " <td>2447</td>\n", |
|
| 410 | 410 |
" <td>16</td>\n", |
| 411 |
- " <td>114</td>\n", |
|
| 412 |
- " <td>87</td>\n", |
|
| 411 |
+ " <td>114.0</td>\n", |
|
| 412 |
+ " <td>87.0</td>\n", |
|
| 413 | 413 |
" <td>77</td>\n", |
| 414 | 414 |
" <td>236</td>\n", |
| 415 | 415 |
" <td>240</td>\n", |
... | ... | @@ -422,10 +422,10 @@ |
| 422 | 422 |
" <tr>\n", |
| 423 | 423 |
" <th>2</th>\n", |
| 424 | 424 |
" <td>용산구</td>\n", |
| 425 |
- " <td>2,611</td>\n", |
|
| 425 |
+ " <td>2611</td>\n", |
|
| 426 | 426 |
" <td>34</td>\n", |
| 427 |
- " <td>71</td>\n", |
|
| 428 |
- " <td>234</td>\n", |
|
| 427 |
+ " <td>71.0</td>\n", |
|
| 428 |
+ " <td>234.0</td>\n", |
|
| 429 | 429 |
" <td>125</td>\n", |
| 430 | 430 |
" <td>221</td>\n", |
| 431 | 431 |
" <td>298</td>\n", |
... | ... | @@ -438,10 +438,10 @@ |
| 438 | 438 |
" <tr>\n", |
| 439 | 439 |
" <th>3</th>\n", |
| 440 | 440 |
" <td>성동구</td>\n", |
| 441 |
- " <td>3,829</td>\n", |
|
| 441 |
+ " <td>3829</td>\n", |
|
| 442 | 442 |
" <td>163</td>\n", |
| 443 |
- " <td>144</td>\n", |
|
| 444 |
- " <td>208</td>\n", |
|
| 443 |
+ " <td>144.0</td>\n", |
|
| 444 |
+ " <td>208.0</td>\n", |
|
| 445 | 445 |
" <td>107</td>\n", |
| 446 | 446 |
" <td>325</td>\n", |
| 447 | 447 |
" <td>255</td>\n", |
... | ... | @@ -454,10 +454,10 @@ |
| 454 | 454 |
" <tr>\n", |
| 455 | 455 |
" <th>4</th>\n", |
| 456 | 456 |
" <td>광진구</td>\n", |
| 457 |
- " <td>3,211</td>\n", |
|
| 457 |
+ " <td>3211</td>\n", |
|
| 458 | 458 |
" <td>35</td>\n", |
| 459 |
- " <td>57</td>\n", |
|
| 460 |
- " <td>100</td>\n", |
|
| 459 |
+ " <td>57.0</td>\n", |
|
| 460 |
+ " <td>100.0</td>\n", |
|
| 461 | 461 |
" <td>187</td>\n", |
| 462 | 462 |
" <td>98</td>\n", |
| 463 | 463 |
" <td>52</td>\n", |
... | ... | @@ -472,22 +472,22 @@ |
| 472 | 472 |
"</div>" |
| 473 | 473 |
], |
| 474 | 474 |
"text/plain": [ |
| 475 |
- " 구분 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 2019년 2020년 \\\n", |
|
| 476 |
- "0 종로구 1,715 815 NaN NaN 195 150 0 261 85 9 200 \n", |
|
| 477 |
- "1 중구 2,447 16 114 87 77 236 240 372 386 155 361 \n", |
|
| 478 |
- "2 용산구 2,611 34 71 234 125 221 298 351 125 307 617 \n", |
|
| 479 |
- "3 성동구 3,829 163 144 208 107 325 255 967 415 490 472 \n", |
|
| 480 |
- "4 광진구 3,211 35 57 100 187 98 52 675 465 712 175 \n", |
|
| 475 |
+ " 구분 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 \\\n", |
|
| 476 |
+ "0 종로구 1715 815 NaN NaN 195 150 0 261 85 \n", |
|
| 477 |
+ "1 중구 2447 16 114.0 87.0 77 236 240 372 386 \n", |
|
| 478 |
+ "2 용산구 2611 34 71.0 234.0 125 221 298 351 125 \n", |
|
| 479 |
+ "3 성동구 3829 163 144.0 208.0 107 325 255 967 415 \n", |
|
| 480 |
+ "4 광진구 3211 35 57.0 100.0 187 98 52 675 465 \n", |
|
| 481 | 481 |
"\n", |
| 482 |
- " 2021년 \n", |
|
| 483 |
- "0 0 \n", |
|
| 484 |
- "1 403 \n", |
|
| 485 |
- "2 228 \n", |
|
| 486 |
- "3 283 \n", |
|
| 487 |
- "4 655 " |
|
| 482 |
+ " 2019년 2020년 2021년 \n", |
|
| 483 |
+ "0 9 200 0 \n", |
|
| 484 |
+ "1 155 361 403 \n", |
|
| 485 |
+ "2 307 617 228 \n", |
|
| 486 |
+ "3 490 472 283 \n", |
|
| 487 |
+ "4 712 175 655 " |
|
| 488 | 488 |
] |
| 489 | 489 |
}, |
| 490 |
- "execution_count": 19, |
|
| 490 |
+ "execution_count": 76, |
|
| 491 | 491 |
"metadata": {},
|
| 492 | 492 |
"output_type": "execute_result" |
| 493 | 493 |
} |
... | ... | @@ -499,8 +499,8 @@ |
| 499 | 499 |
}, |
| 500 | 500 |
{
|
| 501 | 501 |
"cell_type": "code", |
| 502 |
- "execution_count": 20, |
|
| 503 |
- "id": "8fc76d77", |
|
| 502 |
+ "execution_count": 77, |
|
| 503 |
+ "id": "dd403320", |
|
| 504 | 504 |
"metadata": {},
|
| 505 | 505 |
"outputs": [ |
| 506 | 506 |
{
|
... | ... | @@ -543,7 +543,7 @@ |
| 543 | 543 |
" <tr>\n", |
| 544 | 544 |
" <th>0</th>\n", |
| 545 | 545 |
" <td>종로구</td>\n", |
| 546 |
- " <td>1,715</td>\n", |
|
| 546 |
+ " <td>1715</td>\n", |
|
| 547 | 547 |
" <td>815</td>\n", |
| 548 | 548 |
" <td>NaN</td>\n", |
| 549 | 549 |
" <td>NaN</td>\n", |
... | ... | @@ -559,10 +559,10 @@ |
| 559 | 559 |
" <tr>\n", |
| 560 | 560 |
" <th>1</th>\n", |
| 561 | 561 |
" <td>중구</td>\n", |
| 562 |
- " <td>2,447</td>\n", |
|
| 562 |
+ " <td>2447</td>\n", |
|
| 563 | 563 |
" <td>16</td>\n", |
| 564 |
- " <td>114</td>\n", |
|
| 565 |
- " <td>87</td>\n", |
|
| 564 |
+ " <td>114.0</td>\n", |
|
| 565 |
+ " <td>87.0</td>\n", |
|
| 566 | 566 |
" <td>77</td>\n", |
| 567 | 567 |
" <td>236</td>\n", |
| 568 | 568 |
" <td>240</td>\n", |
... | ... | @@ -575,10 +575,10 @@ |
| 575 | 575 |
" <tr>\n", |
| 576 | 576 |
" <th>2</th>\n", |
| 577 | 577 |
" <td>용산구</td>\n", |
| 578 |
- " <td>2,611</td>\n", |
|
| 578 |
+ " <td>2611</td>\n", |
|
| 579 | 579 |
" <td>34</td>\n", |
| 580 |
- " <td>71</td>\n", |
|
| 581 |
- " <td>234</td>\n", |
|
| 580 |
+ " <td>71.0</td>\n", |
|
| 581 |
+ " <td>234.0</td>\n", |
|
| 582 | 582 |
" <td>125</td>\n", |
| 583 | 583 |
" <td>221</td>\n", |
| 584 | 584 |
" <td>298</td>\n", |
... | ... | @@ -591,10 +591,10 @@ |
| 591 | 591 |
" <tr>\n", |
| 592 | 592 |
" <th>3</th>\n", |
| 593 | 593 |
" <td>성동구</td>\n", |
| 594 |
- " <td>3,829</td>\n", |
|
| 594 |
+ " <td>3829</td>\n", |
|
| 595 | 595 |
" <td>163</td>\n", |
| 596 |
- " <td>144</td>\n", |
|
| 597 |
- " <td>208</td>\n", |
|
| 596 |
+ " <td>144.0</td>\n", |
|
| 597 |
+ " <td>208.0</td>\n", |
|
| 598 | 598 |
" <td>107</td>\n", |
| 599 | 599 |
" <td>325</td>\n", |
| 600 | 600 |
" <td>255</td>\n", |
... | ... | @@ -607,10 +607,10 @@ |
| 607 | 607 |
" <tr>\n", |
| 608 | 608 |
" <th>4</th>\n", |
| 609 | 609 |
" <td>광진구</td>\n", |
| 610 |
- " <td>3,211</td>\n", |
|
| 610 |
+ " <td>3211</td>\n", |
|
| 611 | 611 |
" <td>35</td>\n", |
| 612 |
- " <td>57</td>\n", |
|
| 613 |
- " <td>100</td>\n", |
|
| 612 |
+ " <td>57.0</td>\n", |
|
| 613 |
+ " <td>100.0</td>\n", |
|
| 614 | 614 |
" <td>187</td>\n", |
| 615 | 615 |
" <td>98</td>\n", |
| 616 | 616 |
" <td>52</td>\n", |
... | ... | @@ -625,22 +625,22 @@ |
| 625 | 625 |
"</div>" |
| 626 | 626 |
], |
| 627 | 627 |
"text/plain": [ |
| 628 |
- " 구별 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 2019년 2020년 \\\n", |
|
| 629 |
- "0 종로구 1,715 815 NaN NaN 195 150 0 261 85 9 200 \n", |
|
| 630 |
- "1 중구 2,447 16 114 87 77 236 240 372 386 155 361 \n", |
|
| 631 |
- "2 용산구 2,611 34 71 234 125 221 298 351 125 307 617 \n", |
|
| 632 |
- "3 성동구 3,829 163 144 208 107 325 255 967 415 490 472 \n", |
|
| 633 |
- "4 광진구 3,211 35 57 100 187 98 52 675 465 712 175 \n", |
|
| 628 |
+ " 구별 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 \\\n", |
|
| 629 |
+ "0 종로구 1715 815 NaN NaN 195 150 0 261 85 \n", |
|
| 630 |
+ "1 중구 2447 16 114.0 87.0 77 236 240 372 386 \n", |
|
| 631 |
+ "2 용산구 2611 34 71.0 234.0 125 221 298 351 125 \n", |
|
| 632 |
+ "3 성동구 3829 163 144.0 208.0 107 325 255 967 415 \n", |
|
| 633 |
+ "4 광진구 3211 35 57.0 100.0 187 98 52 675 465 \n", |
|
| 634 | 634 |
"\n", |
| 635 |
- " 2021년 \n", |
|
| 636 |
- "0 0 \n", |
|
| 637 |
- "1 403 \n", |
|
| 638 |
- "2 228 \n", |
|
| 639 |
- "3 283 \n", |
|
| 640 |
- "4 655 " |
|
| 635 |
+ " 2019년 2020년 2021년 \n", |
|
| 636 |
+ "0 9 200 0 \n", |
|
| 637 |
+ "1 155 361 403 \n", |
|
| 638 |
+ "2 307 617 228 \n", |
|
| 639 |
+ "3 490 472 283 \n", |
|
| 640 |
+ "4 712 175 655 " |
|
| 641 | 641 |
] |
| 642 | 642 |
}, |
| 643 |
- "execution_count": 20, |
|
| 643 |
+ "execution_count": 77, |
|
| 644 | 644 |
"metadata": {},
|
| 645 | 645 |
"output_type": "execute_result" |
| 646 | 646 |
} |
... | ... | @@ -652,8 +652,8 @@ |
| 652 | 652 |
}, |
| 653 | 653 |
{
|
| 654 | 654 |
"cell_type": "code", |
| 655 |
- "execution_count": 21, |
|
| 656 |
- "id": "f924974d", |
|
| 655 |
+ "execution_count": 78, |
|
| 656 |
+ "id": "4c9e2cea", |
|
| 657 | 657 |
"metadata": {
|
| 658 | 658 |
"scrolled": true |
| 659 | 659 |
}, |
... | ... | @@ -740,7 +740,7 @@ |
| 740 | 740 |
"4 성동구 292672 285990 6682 46380" |
| 741 | 741 |
] |
| 742 | 742 |
}, |
| 743 |
- "execution_count": 21, |
|
| 743 |
+ "execution_count": 78, |
|
| 744 | 744 |
"metadata": {},
|
| 745 | 745 |
"output_type": "execute_result" |
| 746 | 746 |
} |
... | ... | @@ -752,8 +752,8 @@ |
| 752 | 752 |
}, |
| 753 | 753 |
{
|
| 754 | 754 |
"cell_type": "code", |
| 755 |
- "execution_count": 22, |
|
| 756 |
- "id": "7fcdba74", |
|
| 755 |
+ "execution_count": 79, |
|
| 756 |
+ "id": "1f6c133e", |
|
| 757 | 757 |
"metadata": {},
|
| 758 | 758 |
"outputs": [], |
| 759 | 759 |
"source": [ |
... | ... | @@ -762,8 +762,8 @@ |
| 762 | 762 |
}, |
| 763 | 763 |
{
|
| 764 | 764 |
"cell_type": "code", |
| 765 |
- "execution_count": 23, |
|
| 766 |
- "id": "b725e640", |
|
| 765 |
+ "execution_count": 80, |
|
| 766 |
+ "id": "f68a3330", |
|
| 767 | 767 |
"metadata": {},
|
| 768 | 768 |
"outputs": [ |
| 769 | 769 |
{
|
... | ... | @@ -848,7 +848,7 @@ |
| 848 | 848 |
"4 성동구 292672 285990 6682 46380" |
| 849 | 849 |
] |
| 850 | 850 |
}, |
| 851 |
- "execution_count": 23, |
|
| 851 |
+ "execution_count": 80, |
|
| 852 | 852 |
"metadata": {},
|
| 853 | 853 |
"output_type": "execute_result" |
| 854 | 854 |
} |
... | ... | @@ -859,8 +859,8 @@ |
| 859 | 859 |
}, |
| 860 | 860 |
{
|
| 861 | 861 |
"cell_type": "code", |
| 862 |
- "execution_count": 26, |
|
| 863 |
- "id": "60e168a3", |
|
| 862 |
+ "execution_count": 81, |
|
| 863 |
+ "id": "5877cc50", |
|
| 864 | 864 |
"metadata": {},
|
| 865 | 865 |
"outputs": [ |
| 866 | 866 |
{
|
... | ... | @@ -903,10 +903,10 @@ |
| 903 | 903 |
" <tr>\n", |
| 904 | 904 |
" <th>22</th>\n", |
| 905 | 905 |
" <td>강남구</td>\n", |
| 906 |
- " <td>6,871</td>\n", |
|
| 906 |
+ " <td>6871</td>\n", |
|
| 907 | 907 |
" <td>69</td>\n", |
| 908 |
- " <td>67</td>\n", |
|
| 909 |
- " <td>66</td>\n", |
|
| 908 |
+ " <td>67.0</td>\n", |
|
| 909 |
+ " <td>66.0</td>\n", |
|
| 910 | 910 |
" <td>580</td>\n", |
| 911 | 911 |
" <td>830</td>\n", |
| 912 | 912 |
" <td>1293</td>\n", |
... | ... | @@ -919,10 +919,10 @@ |
| 919 | 919 |
" <tr>\n", |
| 920 | 920 |
" <th>20</th>\n", |
| 921 | 921 |
" <td>관악구</td>\n", |
| 922 |
- " <td>5,149</td>\n", |
|
| 922 |
+ " <td>5149</td>\n", |
|
| 923 | 923 |
" <td>440</td>\n", |
| 924 |
- " <td>84</td>\n", |
|
| 925 |
- " <td>431</td>\n", |
|
| 924 |
+ " <td>84.0</td>\n", |
|
| 925 |
+ " <td>431.0</td>\n", |
|
| 926 | 926 |
" <td>439</td>\n", |
| 927 | 927 |
" <td>609</td>\n", |
| 928 | 928 |
" <td>622</td>\n", |
... | ... | @@ -935,10 +935,10 @@ |
| 935 | 935 |
" <tr>\n", |
| 936 | 936 |
" <th>16</th>\n", |
| 937 | 937 |
" <td>구로구</td>\n", |
| 938 |
- " <td>4,608</td>\n", |
|
| 938 |
+ " <td>4608</td>\n", |
|
| 939 | 939 |
" <td>852</td>\n", |
| 940 |
- " <td>216</td>\n", |
|
| 941 |
- " <td>349</td>\n", |
|
| 940 |
+ " <td>216.0</td>\n", |
|
| 941 |
+ " <td>349.0</td>\n", |
|
| 942 | 942 |
" <td>187</td>\n", |
| 943 | 943 |
" <td>268</td>\n", |
| 944 | 944 |
" <td>326</td>\n", |
... | ... | @@ -951,10 +951,10 @@ |
| 951 | 951 |
" <tr>\n", |
| 952 | 952 |
" <th>7</th>\n", |
| 953 | 953 |
" <td>성북구</td>\n", |
| 954 |
- " <td>4,602</td>\n", |
|
| 954 |
+ " <td>4602</td>\n", |
|
| 955 | 955 |
" <td>81</td>\n", |
| 956 |
- " <td>78</td>\n", |
|
| 957 |
- " <td>170</td>\n", |
|
| 956 |
+ " <td>78.0</td>\n", |
|
| 957 |
+ " <td>170.0</td>\n", |
|
| 958 | 958 |
" <td>229</td>\n", |
| 959 | 959 |
" <td>322</td>\n", |
| 960 | 960 |
" <td>594</td>\n", |
... | ... | @@ -967,10 +967,10 @@ |
| 967 | 967 |
" <tr>\n", |
| 968 | 968 |
" <th>11</th>\n", |
| 969 | 969 |
" <td>은평구</td>\n", |
| 970 |
- " <td>4,131</td>\n", |
|
| 970 |
+ " <td>4131</td>\n", |
|
| 971 | 971 |
" <td>14</td>\n", |
| 972 |
- " <td>3</td>\n", |
|
| 973 |
- " <td>44</td>\n", |
|
| 972 |
+ " <td>3.0</td>\n", |
|
| 973 |
+ " <td>44.0</td>\n", |
|
| 974 | 974 |
" <td>332</td>\n", |
| 975 | 975 |
" <td>329</td>\n", |
| 976 | 976 |
" <td>555</td>\n", |
... | ... | @@ -985,22 +985,22 @@ |
| 985 | 985 |
"</div>" |
| 986 | 986 |
], |
| 987 | 987 |
"text/plain": [ |
| 988 |
- " 구별 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 2019년 2020년 \\\n", |
|
| 989 |
- "22 강남구 6,871 69 67 66 580 830 1293 988 745 791 926 \n", |
|
| 990 |
- "20 관악구 5,149 440 84 431 439 609 622 688 674 595 331 \n", |
|
| 991 |
- "16 구로구 4,608 852 216 349 187 268 326 540 488 434 415 \n", |
|
| 992 |
- "7 성북구 4,602 81 78 170 229 322 594 890 867 714 253 \n", |
|
| 993 |
- "11 은평구 4,131 14 3 44 332 329 555 403 635 1057 288 \n", |
|
| 988 |
+ " 구별 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 \\\n", |
|
| 989 |
+ "22 강남구 6871 69 67.0 66.0 580 830 1293 988 745 \n", |
|
| 990 |
+ "20 관악구 5149 440 84.0 431.0 439 609 622 688 674 \n", |
|
| 991 |
+ "16 구로구 4608 852 216.0 349.0 187 268 326 540 488 \n", |
|
| 992 |
+ "7 성북구 4602 81 78.0 170.0 229 322 594 890 867 \n", |
|
| 993 |
+ "11 은평구 4131 14 3.0 44.0 332 329 555 403 635 \n", |
|
| 994 | 994 |
"\n", |
| 995 |
- " 2021년 \n", |
|
| 996 |
- "22 516 \n", |
|
| 997 |
- "20 236 \n", |
|
| 998 |
- "16 533 \n", |
|
| 999 |
- "7 404 \n", |
|
| 1000 |
- "11 471 " |
|
| 995 |
+ " 2019년 2020년 2021년 \n", |
|
| 996 |
+ "22 791 926 516 \n", |
|
| 997 |
+ "20 595 331 236 \n", |
|
| 998 |
+ "16 434 415 533 \n", |
|
| 999 |
+ "7 714 253 404 \n", |
|
| 1000 |
+ "11 1057 288 471 " |
|
| 1001 | 1001 |
] |
| 1002 | 1002 |
}, |
| 1003 |
- "execution_count": 26, |
|
| 1003 |
+ "execution_count": 81, |
|
| 1004 | 1004 |
"metadata": {},
|
| 1005 | 1005 |
"output_type": "execute_result" |
| 1006 | 1006 |
} |
... | ... | @@ -1010,17 +1010,17 @@ |
| 1010 | 1010 |
] |
| 1011 | 1011 |
}, |
| 1012 | 1012 |
{
|
| 1013 |
- "cell_type": "code", |
|
| 1014 |
- "execution_count": null, |
|
| 1015 |
- "id": "8e4b2882", |
|
| 1013 |
+ "cell_type": "markdown", |
|
| 1014 |
+ "id": "b727130c", |
|
| 1016 | 1015 |
"metadata": {},
|
| 1017 |
- "outputs": [], |
|
| 1018 |
- "source": [] |
|
| 1016 |
+ "source": [ |
|
| 1017 |
+ "# 결측치 확인" |
|
| 1018 |
+ ] |
|
| 1019 | 1019 |
}, |
| 1020 | 1020 |
{
|
| 1021 | 1021 |
"cell_type": "code", |
| 1022 |
- "execution_count": 27, |
|
| 1023 |
- "id": "b3bba330", |
|
| 1022 |
+ "execution_count": 82, |
|
| 1023 |
+ "id": "8b3b55cd", |
|
| 1024 | 1024 |
"metadata": {
|
| 1025 | 1025 |
"scrolled": true |
| 1026 | 1026 |
}, |
... | ... | @@ -1142,410 +1142,3394 @@ |
| 1142 | 1142 |
" <td>False</td>\n", |
| 1143 | 1143 |
" <td>False</td>\n", |
| 1144 | 1144 |
" </tr>\n", |
| 1145 |
+ " </tbody>\n", |
|
| 1146 |
+ "</table>\n", |
|
| 1147 |
+ "</div>" |
|
| 1148 |
+ ], |
|
| 1149 |
+ "text/plain": [ |
|
| 1150 |
+ " 구별 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 \\\n", |
|
| 1151 |
+ "0 False False False True True False False False False False \n", |
|
| 1152 |
+ "1 False False False False False False False False False False \n", |
|
| 1153 |
+ "2 False False False False False False False False False False \n", |
|
| 1154 |
+ "3 False False False False False False False False False False \n", |
|
| 1155 |
+ "4 False False False False False False False False False False \n", |
|
| 1156 |
+ "\n", |
|
| 1157 |
+ " 2019년 2020년 2021년 \n", |
|
| 1158 |
+ "0 False False False \n", |
|
| 1159 |
+ "1 False False False \n", |
|
| 1160 |
+ "2 False False False \n", |
|
| 1161 |
+ "3 False False False \n", |
|
| 1162 |
+ "4 False False False " |
|
| 1163 |
+ ] |
|
| 1164 |
+ }, |
|
| 1165 |
+ "execution_count": 82, |
|
| 1166 |
+ "metadata": {},
|
|
| 1167 |
+ "output_type": "execute_result" |
|
| 1168 |
+ } |
|
| 1169 |
+ ], |
|
| 1170 |
+ "source": [ |
|
| 1171 |
+ "cctvData.isnull().head()" |
|
| 1172 |
+ ] |
|
| 1173 |
+ }, |
|
| 1174 |
+ {
|
|
| 1175 |
+ "cell_type": "markdown", |
|
| 1176 |
+ "id": "b467ed59", |
|
| 1177 |
+ "metadata": {},
|
|
| 1178 |
+ "source": [ |
|
| 1179 |
+ "# 결측치 -> 0" |
|
| 1180 |
+ ] |
|
| 1181 |
+ }, |
|
| 1182 |
+ {
|
|
| 1183 |
+ "cell_type": "code", |
|
| 1184 |
+ "execution_count": 83, |
|
| 1185 |
+ "id": "88f4cd02", |
|
| 1186 |
+ "metadata": {},
|
|
| 1187 |
+ "outputs": [], |
|
| 1188 |
+ "source": [ |
|
| 1189 |
+ "cctvData = cctvData.fillna(0)" |
|
| 1190 |
+ ] |
|
| 1191 |
+ }, |
|
| 1192 |
+ {
|
|
| 1193 |
+ "cell_type": "code", |
|
| 1194 |
+ "execution_count": 84, |
|
| 1195 |
+ "id": "c9f416a2", |
|
| 1196 |
+ "metadata": {},
|
|
| 1197 |
+ "outputs": [ |
|
| 1198 |
+ {
|
|
| 1199 |
+ "data": {
|
|
| 1200 |
+ "text/html": [ |
|
| 1201 |
+ "<div>\n", |
|
| 1202 |
+ "<style scoped>\n", |
|
| 1203 |
+ " .dataframe tbody tr th:only-of-type {\n",
|
|
| 1204 |
+ " vertical-align: middle;\n", |
|
| 1205 |
+ " }\n", |
|
| 1206 |
+ "\n", |
|
| 1207 |
+ " .dataframe tbody tr th {\n",
|
|
| 1208 |
+ " vertical-align: top;\n", |
|
| 1209 |
+ " }\n", |
|
| 1210 |
+ "\n", |
|
| 1211 |
+ " .dataframe thead th {\n",
|
|
| 1212 |
+ " text-align: right;\n", |
|
| 1213 |
+ " }\n", |
|
| 1214 |
+ "</style>\n", |
|
| 1215 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 1216 |
+ " <thead>\n", |
|
| 1217 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 1218 |
+ " <th></th>\n", |
|
| 1219 |
+ " <th>구별</th>\n", |
|
| 1220 |
+ " <th>총계</th>\n", |
|
| 1221 |
+ " <th>2012년 이전</th>\n", |
|
| 1222 |
+ " <th>2012년</th>\n", |
|
| 1223 |
+ " <th>2013년</th>\n", |
|
| 1224 |
+ " <th>2014년</th>\n", |
|
| 1225 |
+ " <th>2015년</th>\n", |
|
| 1226 |
+ " <th>2016년</th>\n", |
|
| 1227 |
+ " <th>2017년</th>\n", |
|
| 1228 |
+ " <th>2018년</th>\n", |
|
| 1229 |
+ " <th>2019년</th>\n", |
|
| 1230 |
+ " <th>2020년</th>\n", |
|
| 1231 |
+ " <th>2021년</th>\n", |
|
| 1232 |
+ " </tr>\n", |
|
| 1233 |
+ " </thead>\n", |
|
| 1234 |
+ " <tbody>\n", |
|
| 1145 | 1235 |
" <tr>\n", |
| 1146 |
- " <th>5</th>\n", |
|
| 1147 |
- " <td>False</td>\n", |
|
| 1148 |
- " <td>False</td>\n", |
|
| 1149 |
- " <td>False</td>\n", |
|
| 1150 |
- " <td>False</td>\n", |
|
| 1151 |
- " <td>False</td>\n", |
|
| 1152 |
- " <td>False</td>\n", |
|
| 1153 |
- " <td>False</td>\n", |
|
| 1154 |
- " <td>False</td>\n", |
|
| 1155 |
- " <td>False</td>\n", |
|
| 1156 |
- " <td>False</td>\n", |
|
| 1157 |
- " <td>False</td>\n", |
|
| 1158 |
- " <td>False</td>\n", |
|
| 1159 |
- " <td>False</td>\n", |
|
| 1236 |
+ " <th>0</th>\n", |
|
| 1237 |
+ " <td>종로구</td>\n", |
|
| 1238 |
+ " <td>1715</td>\n", |
|
| 1239 |
+ " <td>815</td>\n", |
|
| 1240 |
+ " <td>0.0</td>\n", |
|
| 1241 |
+ " <td>0.0</td>\n", |
|
| 1242 |
+ " <td>195</td>\n", |
|
| 1243 |
+ " <td>150</td>\n", |
|
| 1244 |
+ " <td>0</td>\n", |
|
| 1245 |
+ " <td>261</td>\n", |
|
| 1246 |
+ " <td>85</td>\n", |
|
| 1247 |
+ " <td>9</td>\n", |
|
| 1248 |
+ " <td>200</td>\n", |
|
| 1249 |
+ " <td>0</td>\n", |
|
| 1160 | 1250 |
" </tr>\n", |
| 1161 | 1251 |
" <tr>\n", |
| 1162 |
- " <th>6</th>\n", |
|
| 1163 |
- " <td>False</td>\n", |
|
| 1164 |
- " <td>False</td>\n", |
|
| 1165 |
- " <td>False</td>\n", |
|
| 1166 |
- " <td>False</td>\n", |
|
| 1167 |
- " <td>False</td>\n", |
|
| 1168 |
- " <td>False</td>\n", |
|
| 1169 |
- " <td>False</td>\n", |
|
| 1170 |
- " <td>False</td>\n", |
|
| 1171 |
- " <td>False</td>\n", |
|
| 1172 |
- " <td>False</td>\n", |
|
| 1173 |
- " <td>False</td>\n", |
|
| 1174 |
- " <td>False</td>\n", |
|
| 1175 |
- " <td>False</td>\n", |
|
| 1252 |
+ " <th>1</th>\n", |
|
| 1253 |
+ " <td>중구</td>\n", |
|
| 1254 |
+ " <td>2447</td>\n", |
|
| 1255 |
+ " <td>16</td>\n", |
|
| 1256 |
+ " <td>114.0</td>\n", |
|
| 1257 |
+ " <td>87.0</td>\n", |
|
| 1258 |
+ " <td>77</td>\n", |
|
| 1259 |
+ " <td>236</td>\n", |
|
| 1260 |
+ " <td>240</td>\n", |
|
| 1261 |
+ " <td>372</td>\n", |
|
| 1262 |
+ " <td>386</td>\n", |
|
| 1263 |
+ " <td>155</td>\n", |
|
| 1264 |
+ " <td>361</td>\n", |
|
| 1265 |
+ " <td>403</td>\n", |
|
| 1176 | 1266 |
" </tr>\n", |
| 1177 | 1267 |
" <tr>\n", |
| 1178 |
- " <th>7</th>\n", |
|
| 1179 |
- " <td>False</td>\n", |
|
| 1180 |
- " <td>False</td>\n", |
|
| 1181 |
- " <td>False</td>\n", |
|
| 1182 |
- " <td>False</td>\n", |
|
| 1183 |
- " <td>False</td>\n", |
|
| 1184 |
- " <td>False</td>\n", |
|
| 1185 |
- " <td>False</td>\n", |
|
| 1186 |
- " <td>False</td>\n", |
|
| 1187 |
- " <td>False</td>\n", |
|
| 1188 |
- " <td>False</td>\n", |
|
| 1189 |
- " <td>False</td>\n", |
|
| 1190 |
- " <td>False</td>\n", |
|
| 1191 |
- " <td>False</td>\n", |
|
| 1268 |
+ " <th>2</th>\n", |
|
| 1269 |
+ " <td>용산구</td>\n", |
|
| 1270 |
+ " <td>2611</td>\n", |
|
| 1271 |
+ " <td>34</td>\n", |
|
| 1272 |
+ " <td>71.0</td>\n", |
|
| 1273 |
+ " <td>234.0</td>\n", |
|
| 1274 |
+ " <td>125</td>\n", |
|
| 1275 |
+ " <td>221</td>\n", |
|
| 1276 |
+ " <td>298</td>\n", |
|
| 1277 |
+ " <td>351</td>\n", |
|
| 1278 |
+ " <td>125</td>\n", |
|
| 1279 |
+ " <td>307</td>\n", |
|
| 1280 |
+ " <td>617</td>\n", |
|
| 1281 |
+ " <td>228</td>\n", |
|
| 1192 | 1282 |
" </tr>\n", |
| 1193 | 1283 |
" <tr>\n", |
| 1194 |
- " <th>8</th>\n", |
|
| 1195 |
- " <td>False</td>\n", |
|
| 1196 |
- " <td>False</td>\n", |
|
| 1197 |
- " <td>False</td>\n", |
|
| 1198 |
- " <td>False</td>\n", |
|
| 1199 |
- " <td>False</td>\n", |
|
| 1200 |
- " <td>False</td>\n", |
|
| 1201 |
- " <td>False</td>\n", |
|
| 1202 |
- " <td>False</td>\n", |
|
| 1203 |
- " <td>False</td>\n", |
|
| 1204 |
- " <td>False</td>\n", |
|
| 1205 |
- " <td>False</td>\n", |
|
| 1206 |
- " <td>False</td>\n", |
|
| 1207 |
- " <td>False</td>\n", |
|
| 1284 |
+ " <th>3</th>\n", |
|
| 1285 |
+ " <td>성동구</td>\n", |
|
| 1286 |
+ " <td>3829</td>\n", |
|
| 1287 |
+ " <td>163</td>\n", |
|
| 1288 |
+ " <td>144.0</td>\n", |
|
| 1289 |
+ " <td>208.0</td>\n", |
|
| 1290 |
+ " <td>107</td>\n", |
|
| 1291 |
+ " <td>325</td>\n", |
|
| 1292 |
+ " <td>255</td>\n", |
|
| 1293 |
+ " <td>967</td>\n", |
|
| 1294 |
+ " <td>415</td>\n", |
|
| 1295 |
+ " <td>490</td>\n", |
|
| 1296 |
+ " <td>472</td>\n", |
|
| 1297 |
+ " <td>283</td>\n", |
|
| 1208 | 1298 |
" </tr>\n", |
| 1209 | 1299 |
" <tr>\n", |
| 1210 |
- " <th>9</th>\n", |
|
| 1211 |
- " <td>False</td>\n", |
|
| 1212 |
- " <td>False</td>\n", |
|
| 1213 |
- " <td>False</td>\n", |
|
| 1214 |
- " <td>False</td>\n", |
|
| 1215 |
- " <td>False</td>\n", |
|
| 1216 |
- " <td>False</td>\n", |
|
| 1217 |
- " <td>False</td>\n", |
|
| 1218 |
- " <td>False</td>\n", |
|
| 1219 |
- " <td>False</td>\n", |
|
| 1220 |
- " <td>False</td>\n", |
|
| 1221 |
- " <td>False</td>\n", |
|
| 1222 |
- " <td>False</td>\n", |
|
| 1223 |
- " <td>False</td>\n", |
|
| 1224 |
- " </tr>\n", |
|
| 1225 |
- " <tr>\n", |
|
| 1226 |
- " <th>10</th>\n", |
|
| 1227 |
- " <td>False</td>\n", |
|
| 1228 |
- " <td>False</td>\n", |
|
| 1229 |
- " <td>False</td>\n", |
|
| 1230 |
- " <td>False</td>\n", |
|
| 1231 |
- " <td>False</td>\n", |
|
| 1232 |
- " <td>False</td>\n", |
|
| 1233 |
- " <td>False</td>\n", |
|
| 1234 |
- " <td>False</td>\n", |
|
| 1235 |
- " <td>False</td>\n", |
|
| 1236 |
- " <td>False</td>\n", |
|
| 1237 |
- " <td>False</td>\n", |
|
| 1238 |
- " <td>False</td>\n", |
|
| 1239 |
- " <td>False</td>\n", |
|
| 1240 |
- " </tr>\n", |
|
| 1241 |
- " <tr>\n", |
|
| 1242 |
- " <th>11</th>\n", |
|
| 1243 |
- " <td>False</td>\n", |
|
| 1244 |
- " <td>False</td>\n", |
|
| 1245 |
- " <td>False</td>\n", |
|
| 1246 |
- " <td>False</td>\n", |
|
| 1247 |
- " <td>False</td>\n", |
|
| 1248 |
- " <td>False</td>\n", |
|
| 1249 |
- " <td>False</td>\n", |
|
| 1250 |
- " <td>False</td>\n", |
|
| 1251 |
- " <td>False</td>\n", |
|
| 1252 |
- " <td>False</td>\n", |
|
| 1253 |
- " <td>False</td>\n", |
|
| 1254 |
- " <td>False</td>\n", |
|
| 1255 |
- " <td>False</td>\n", |
|
| 1256 |
- " </tr>\n", |
|
| 1257 |
- " <tr>\n", |
|
| 1258 |
- " <th>12</th>\n", |
|
| 1259 |
- " <td>False</td>\n", |
|
| 1260 |
- " <td>False</td>\n", |
|
| 1261 |
- " <td>False</td>\n", |
|
| 1262 |
- " <td>False</td>\n", |
|
| 1263 |
- " <td>False</td>\n", |
|
| 1264 |
- " <td>False</td>\n", |
|
| 1265 |
- " <td>False</td>\n", |
|
| 1266 |
- " <td>False</td>\n", |
|
| 1267 |
- " <td>False</td>\n", |
|
| 1268 |
- " <td>False</td>\n", |
|
| 1269 |
- " <td>False</td>\n", |
|
| 1270 |
- " <td>False</td>\n", |
|
| 1271 |
- " <td>False</td>\n", |
|
| 1272 |
- " </tr>\n", |
|
| 1273 |
- " <tr>\n", |
|
| 1274 |
- " <th>13</th>\n", |
|
| 1275 |
- " <td>False</td>\n", |
|
| 1276 |
- " <td>False</td>\n", |
|
| 1277 |
- " <td>False</td>\n", |
|
| 1278 |
- " <td>False</td>\n", |
|
| 1279 |
- " <td>False</td>\n", |
|
| 1280 |
- " <td>False</td>\n", |
|
| 1281 |
- " <td>False</td>\n", |
|
| 1282 |
- " <td>False</td>\n", |
|
| 1283 |
- " <td>False</td>\n", |
|
| 1284 |
- " <td>False</td>\n", |
|
| 1285 |
- " <td>False</td>\n", |
|
| 1286 |
- " <td>False</td>\n", |
|
| 1287 |
- " <td>False</td>\n", |
|
| 1288 |
- " </tr>\n", |
|
| 1289 |
- " <tr>\n", |
|
| 1290 |
- " <th>14</th>\n", |
|
| 1291 |
- " <td>False</td>\n", |
|
| 1292 |
- " <td>False</td>\n", |
|
| 1293 |
- " <td>False</td>\n", |
|
| 1294 |
- " <td>False</td>\n", |
|
| 1295 |
- " <td>False</td>\n", |
|
| 1296 |
- " <td>False</td>\n", |
|
| 1297 |
- " <td>False</td>\n", |
|
| 1298 |
- " <td>False</td>\n", |
|
| 1299 |
- " <td>False</td>\n", |
|
| 1300 |
- " <td>False</td>\n", |
|
| 1301 |
- " <td>False</td>\n", |
|
| 1302 |
- " <td>False</td>\n", |
|
| 1303 |
- " <td>False</td>\n", |
|
| 1304 |
- " </tr>\n", |
|
| 1305 |
- " <tr>\n", |
|
| 1306 |
- " <th>15</th>\n", |
|
| 1307 |
- " <td>False</td>\n", |
|
| 1308 |
- " <td>False</td>\n", |
|
| 1309 |
- " <td>False</td>\n", |
|
| 1310 |
- " <td>False</td>\n", |
|
| 1311 |
- " <td>False</td>\n", |
|
| 1312 |
- " <td>False</td>\n", |
|
| 1313 |
- " <td>False</td>\n", |
|
| 1314 |
- " <td>False</td>\n", |
|
| 1315 |
- " <td>False</td>\n", |
|
| 1316 |
- " <td>False</td>\n", |
|
| 1317 |
- " <td>False</td>\n", |
|
| 1318 |
- " <td>False</td>\n", |
|
| 1319 |
- " <td>False</td>\n", |
|
| 1320 |
- " </tr>\n", |
|
| 1321 |
- " <tr>\n", |
|
| 1322 |
- " <th>16</th>\n", |
|
| 1323 |
- " <td>False</td>\n", |
|
| 1324 |
- " <td>False</td>\n", |
|
| 1325 |
- " <td>False</td>\n", |
|
| 1326 |
- " <td>False</td>\n", |
|
| 1327 |
- " <td>False</td>\n", |
|
| 1328 |
- " <td>False</td>\n", |
|
| 1329 |
- " <td>False</td>\n", |
|
| 1330 |
- " <td>False</td>\n", |
|
| 1331 |
- " <td>False</td>\n", |
|
| 1332 |
- " <td>False</td>\n", |
|
| 1333 |
- " <td>False</td>\n", |
|
| 1334 |
- " <td>False</td>\n", |
|
| 1335 |
- " <td>False</td>\n", |
|
| 1336 |
- " </tr>\n", |
|
| 1337 |
- " <tr>\n", |
|
| 1338 |
- " <th>17</th>\n", |
|
| 1339 |
- " <td>False</td>\n", |
|
| 1340 |
- " <td>False</td>\n", |
|
| 1341 |
- " <td>False</td>\n", |
|
| 1342 |
- " <td>False</td>\n", |
|
| 1343 |
- " <td>False</td>\n", |
|
| 1344 |
- " <td>False</td>\n", |
|
| 1345 |
- " <td>False</td>\n", |
|
| 1346 |
- " <td>False</td>\n", |
|
| 1347 |
- " <td>False</td>\n", |
|
| 1348 |
- " <td>False</td>\n", |
|
| 1349 |
- " <td>False</td>\n", |
|
| 1350 |
- " <td>False</td>\n", |
|
| 1351 |
- " <td>False</td>\n", |
|
| 1352 |
- " </tr>\n", |
|
| 1353 |
- " <tr>\n", |
|
| 1354 |
- " <th>18</th>\n", |
|
| 1355 |
- " <td>False</td>\n", |
|
| 1356 |
- " <td>False</td>\n", |
|
| 1357 |
- " <td>False</td>\n", |
|
| 1358 |
- " <td>False</td>\n", |
|
| 1359 |
- " <td>False</td>\n", |
|
| 1360 |
- " <td>False</td>\n", |
|
| 1361 |
- " <td>False</td>\n", |
|
| 1362 |
- " <td>False</td>\n", |
|
| 1363 |
- " <td>False</td>\n", |
|
| 1364 |
- " <td>False</td>\n", |
|
| 1365 |
- " <td>False</td>\n", |
|
| 1366 |
- " <td>False</td>\n", |
|
| 1367 |
- " <td>False</td>\n", |
|
| 1368 |
- " </tr>\n", |
|
| 1369 |
- " <tr>\n", |
|
| 1370 |
- " <th>19</th>\n", |
|
| 1371 |
- " <td>False</td>\n", |
|
| 1372 |
- " <td>False</td>\n", |
|
| 1373 |
- " <td>False</td>\n", |
|
| 1374 |
- " <td>False</td>\n", |
|
| 1375 |
- " <td>False</td>\n", |
|
| 1376 |
- " <td>False</td>\n", |
|
| 1377 |
- " <td>False</td>\n", |
|
| 1378 |
- " <td>False</td>\n", |
|
| 1379 |
- " <td>False</td>\n", |
|
| 1380 |
- " <td>False</td>\n", |
|
| 1381 |
- " <td>False</td>\n", |
|
| 1382 |
- " <td>False</td>\n", |
|
| 1383 |
- " <td>False</td>\n", |
|
| 1384 |
- " </tr>\n", |
|
| 1385 |
- " <tr>\n", |
|
| 1386 |
- " <th>20</th>\n", |
|
| 1387 |
- " <td>False</td>\n", |
|
| 1388 |
- " <td>False</td>\n", |
|
| 1389 |
- " <td>False</td>\n", |
|
| 1390 |
- " <td>False</td>\n", |
|
| 1391 |
- " <td>False</td>\n", |
|
| 1392 |
- " <td>False</td>\n", |
|
| 1393 |
- " <td>False</td>\n", |
|
| 1394 |
- " <td>False</td>\n", |
|
| 1395 |
- " <td>False</td>\n", |
|
| 1396 |
- " <td>False</td>\n", |
|
| 1397 |
- " <td>False</td>\n", |
|
| 1398 |
- " <td>False</td>\n", |
|
| 1399 |
- " <td>False</td>\n", |
|
| 1400 |
- " </tr>\n", |
|
| 1401 |
- " <tr>\n", |
|
| 1402 |
- " <th>21</th>\n", |
|
| 1403 |
- " <td>False</td>\n", |
|
| 1404 |
- " <td>False</td>\n", |
|
| 1405 |
- " <td>False</td>\n", |
|
| 1406 |
- " <td>False</td>\n", |
|
| 1407 |
- " <td>False</td>\n", |
|
| 1408 |
- " <td>False</td>\n", |
|
| 1409 |
- " <td>False</td>\n", |
|
| 1410 |
- " <td>False</td>\n", |
|
| 1411 |
- " <td>False</td>\n", |
|
| 1412 |
- " <td>False</td>\n", |
|
| 1413 |
- " <td>False</td>\n", |
|
| 1414 |
- " <td>False</td>\n", |
|
| 1415 |
- " <td>False</td>\n", |
|
| 1416 |
- " </tr>\n", |
|
| 1417 |
- " <tr>\n", |
|
| 1418 |
- " <th>22</th>\n", |
|
| 1419 |
- " <td>False</td>\n", |
|
| 1420 |
- " <td>False</td>\n", |
|
| 1421 |
- " <td>False</td>\n", |
|
| 1422 |
- " <td>False</td>\n", |
|
| 1423 |
- " <td>False</td>\n", |
|
| 1424 |
- " <td>False</td>\n", |
|
| 1425 |
- " <td>False</td>\n", |
|
| 1426 |
- " <td>False</td>\n", |
|
| 1427 |
- " <td>False</td>\n", |
|
| 1428 |
- " <td>False</td>\n", |
|
| 1429 |
- " <td>False</td>\n", |
|
| 1430 |
- " <td>False</td>\n", |
|
| 1431 |
- " <td>False</td>\n", |
|
| 1432 |
- " </tr>\n", |
|
| 1433 |
- " <tr>\n", |
|
| 1434 |
- " <th>23</th>\n", |
|
| 1435 |
- " <td>False</td>\n", |
|
| 1436 |
- " <td>False</td>\n", |
|
| 1437 |
- " <td>False</td>\n", |
|
| 1438 |
- " <td>False</td>\n", |
|
| 1439 |
- " <td>False</td>\n", |
|
| 1440 |
- " <td>False</td>\n", |
|
| 1441 |
- " <td>False</td>\n", |
|
| 1442 |
- " <td>False</td>\n", |
|
| 1443 |
- " <td>False</td>\n", |
|
| 1444 |
- " <td>False</td>\n", |
|
| 1445 |
- " <td>False</td>\n", |
|
| 1446 |
- " <td>False</td>\n", |
|
| 1447 |
- " <td>False</td>\n", |
|
| 1448 |
- " </tr>\n", |
|
| 1449 |
- " <tr>\n", |
|
| 1450 |
- " <th>24</th>\n", |
|
| 1451 |
- " <td>False</td>\n", |
|
| 1452 |
- " <td>False</td>\n", |
|
| 1453 |
- " <td>False</td>\n", |
|
| 1454 |
- " <td>False</td>\n", |
|
| 1455 |
- " <td>False</td>\n", |
|
| 1456 |
- " <td>False</td>\n", |
|
| 1457 |
- " <td>False</td>\n", |
|
| 1458 |
- " <td>False</td>\n", |
|
| 1459 |
- " <td>False</td>\n", |
|
| 1460 |
- " <td>False</td>\n", |
|
| 1461 |
- " <td>False</td>\n", |
|
| 1462 |
- " <td>False</td>\n", |
|
| 1463 |
- " <td>False</td>\n", |
|
| 1300 |
+ " <th>4</th>\n", |
|
| 1301 |
+ " <td>광진구</td>\n", |
|
| 1302 |
+ " <td>3211</td>\n", |
|
| 1303 |
+ " <td>35</td>\n", |
|
| 1304 |
+ " <td>57.0</td>\n", |
|
| 1305 |
+ " <td>100.0</td>\n", |
|
| 1306 |
+ " <td>187</td>\n", |
|
| 1307 |
+ " <td>98</td>\n", |
|
| 1308 |
+ " <td>52</td>\n", |
|
| 1309 |
+ " <td>675</td>\n", |
|
| 1310 |
+ " <td>465</td>\n", |
|
| 1311 |
+ " <td>712</td>\n", |
|
| 1312 |
+ " <td>175</td>\n", |
|
| 1313 |
+ " <td>655</td>\n", |
|
| 1464 | 1314 |
" </tr>\n", |
| 1465 | 1315 |
" </tbody>\n", |
| 1466 | 1316 |
"</table>\n", |
| 1467 | 1317 |
"</div>" |
| 1468 | 1318 |
], |
| 1469 | 1319 |
"text/plain": [ |
| 1470 |
- " 구별 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 \\\n", |
|
| 1471 |
- "0 False False False True True False False False False False \n", |
|
| 1472 |
- "1 False False False False False False False False False False \n", |
|
| 1473 |
- "2 False False False False False False False False False False \n", |
|
| 1474 |
- "3 False False False False False False False False False False \n", |
|
| 1475 |
- "4 False False False False False False False False False False \n", |
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| 1476 |
- "5 False False False False False False False False False False \n", |
|
| 1477 |
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| 1478 |
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|
| 1479 |
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|
| 1480 |
- "9 False False False False False False False False False False \n", |
|
| 1481 |
- "10 False False False False False False False False False False \n", |
|
| 1482 |
- "11 False False False False False False False False False False \n", |
|
| 1483 |
- "12 False False False False False False False False False False \n", |
|
| 1484 |
- "13 False False False False False False False False False False \n", |
|
| 1485 |
- "14 False False False False False False False False False False \n", |
|
| 1486 |
- "15 False False False False False False False False False False \n", |
|
| 1487 |
- "16 False False False False False False False False False False \n", |
|
| 1488 |
- "17 False False False False False False False False False False \n", |
|
| 1489 |
- "18 False False False False False False False False False False \n", |
|
| 1490 |
- "19 False False False False False False False False False False \n", |
|
| 1491 |
- "20 False False False False False False False False False False \n", |
|
| 1492 |
- "21 False False False False False False False False False False \n", |
|
| 1493 |
- "22 False False False False False False False False False False \n", |
|
| 1494 |
- "23 False False False False False False False False False False \n", |
|
| 1495 |
- "24 False False False False False False False False False False \n", |
|
| 1320 |
+ " 구별 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 \\\n", |
|
| 1321 |
+ "0 종로구 1715 815 0.0 0.0 195 150 0 261 85 \n", |
|
| 1322 |
+ "1 중구 2447 16 114.0 87.0 77 236 240 372 386 \n", |
|
| 1323 |
+ "2 용산구 2611 34 71.0 234.0 125 221 298 351 125 \n", |
|
| 1324 |
+ "3 성동구 3829 163 144.0 208.0 107 325 255 967 415 \n", |
|
| 1325 |
+ "4 광진구 3211 35 57.0 100.0 187 98 52 675 465 \n", |
|
| 1496 | 1326 |
"\n", |
| 1497 |
- " 2019년 2020년 2021년 \n", |
|
| 1498 |
- "0 False False False \n", |
|
| 1499 |
- "1 False False False \n", |
|
| 1500 |
- "2 False False False \n", |
|
| 1501 |
- "3 False False False \n", |
|
| 1502 |
- "4 False False False \n", |
|
| 1503 |
- "5 False False False \n", |
|
| 1504 |
- "6 False False False \n", |
|
| 1505 |
- "7 False False False \n", |
|
| 1506 |
- "8 False False False \n", |
|
| 1507 |
- "9 False False False \n", |
|
| 1508 |
- "10 False False False \n", |
|
| 1509 |
- "11 False False False \n", |
|
| 1510 |
- "12 False False False \n", |
|
| 1511 |
- "13 False False False \n", |
|
| 1512 |
- "14 False False False \n", |
|
| 1513 |
- "15 False False False \n", |
|
| 1514 |
- "16 False False False \n", |
|
| 1515 |
- "17 False False False \n", |
|
| 1516 |
- "18 False False False \n", |
|
| 1517 |
- "19 False False False \n", |
|
| 1518 |
- "20 False False False \n", |
|
| 1519 |
- "21 False False False \n", |
|
| 1520 |
- "22 False False False \n", |
|
| 1521 |
- "23 False False False \n", |
|
| 1522 |
- "24 False False False " |
|
| 1327 |
+ " 2019년 2020년 2021년 \n", |
|
| 1328 |
+ "0 9 200 0 \n", |
|
| 1329 |
+ "1 155 361 403 \n", |
|
| 1330 |
+ "2 307 617 228 \n", |
|
| 1331 |
+ "3 490 472 283 \n", |
|
| 1332 |
+ "4 712 175 655 " |
|
| 1523 | 1333 |
] |
| 1524 | 1334 |
}, |
| 1525 |
- "execution_count": 27, |
|
| 1335 |
+ "execution_count": 84, |
|
| 1526 | 1336 |
"metadata": {},
|
| 1527 | 1337 |
"output_type": "execute_result" |
| 1528 | 1338 |
} |
| 1529 | 1339 |
], |
| 1530 | 1340 |
"source": [ |
| 1531 |
- "cctvData.isnull()" |
|
| 1341 |
+ "cctvData.head()" |
|
| 1532 | 1342 |
] |
| 1533 | 1343 |
}, |
| 1534 | 1344 |
{
|
| 1535 | 1345 |
"cell_type": "code", |
| 1536 |
- "execution_count": null, |
|
| 1537 |
- "id": "fcd8d10c", |
|
| 1346 |
+ "execution_count": 85, |
|
| 1347 |
+ "id": "c359a27e", |
|
| 1538 | 1348 |
"metadata": {},
|
| 1539 | 1349 |
"outputs": [], |
| 1540 |
- "source": [] |
|
| 1350 |
+ "source": [ |
|
| 1351 |
+ "for col in cctvData.columns:\n", |
|
| 1352 |
+ " if col == '구별':\n", |
|
| 1353 |
+ " continue\n", |
|
| 1354 |
+ " else:\n", |
|
| 1355 |
+ " cctvData[col] = pd.to_numeric(cctvData[col])" |
|
| 1356 |
+ ] |
|
| 1541 | 1357 |
}, |
| 1542 | 1358 |
{
|
| 1543 | 1359 |
"cell_type": "code", |
| 1544 |
- "execution_count": null, |
|
| 1545 |
- "id": "7485fb10", |
|
| 1360 |
+ "execution_count": 86, |
|
| 1361 |
+ "id": "337c268e", |
|
| 1362 |
+ "metadata": {
|
|
| 1363 |
+ "scrolled": true |
|
| 1364 |
+ }, |
|
| 1365 |
+ "outputs": [], |
|
| 1366 |
+ "source": [ |
|
| 1367 |
+ "cctvData['최근증가율'] = ( (cctvData['2021년'] + cctvData['2020년'] + cctvData['2019년'] + cctvData['2018년'] + cctvData['2017년']) / (cctvData['2016년'] + cctvData['2015년'] + cctvData['2014년'] + cctvData['2013년'] + cctvData['2012년'] + cctvData['2012년 이전']) ) * 100" |
|
| 1368 |
+ ] |
|
| 1369 |
+ }, |
|
| 1370 |
+ {
|
|
| 1371 |
+ "cell_type": "code", |
|
| 1372 |
+ "execution_count": 87, |
|
| 1373 |
+ "id": "7d40ddf3", |
|
| 1374 |
+ "metadata": {},
|
|
| 1375 |
+ "outputs": [ |
|
| 1376 |
+ {
|
|
| 1377 |
+ "data": {
|
|
| 1378 |
+ "text/html": [ |
|
| 1379 |
+ "<div>\n", |
|
| 1380 |
+ "<style scoped>\n", |
|
| 1381 |
+ " .dataframe tbody tr th:only-of-type {\n",
|
|
| 1382 |
+ " vertical-align: middle;\n", |
|
| 1383 |
+ " }\n", |
|
| 1384 |
+ "\n", |
|
| 1385 |
+ " .dataframe tbody tr th {\n",
|
|
| 1386 |
+ " vertical-align: top;\n", |
|
| 1387 |
+ " }\n", |
|
| 1388 |
+ "\n", |
|
| 1389 |
+ " .dataframe thead th {\n",
|
|
| 1390 |
+ " text-align: right;\n", |
|
| 1391 |
+ " }\n", |
|
| 1392 |
+ "</style>\n", |
|
| 1393 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 1394 |
+ " <thead>\n", |
|
| 1395 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 1396 |
+ " <th></th>\n", |
|
| 1397 |
+ " <th>구별</th>\n", |
|
| 1398 |
+ " <th>총계</th>\n", |
|
| 1399 |
+ " <th>2012년 이전</th>\n", |
|
| 1400 |
+ " <th>2012년</th>\n", |
|
| 1401 |
+ " <th>2013년</th>\n", |
|
| 1402 |
+ " <th>2014년</th>\n", |
|
| 1403 |
+ " <th>2015년</th>\n", |
|
| 1404 |
+ " <th>2016년</th>\n", |
|
| 1405 |
+ " <th>2017년</th>\n", |
|
| 1406 |
+ " <th>2018년</th>\n", |
|
| 1407 |
+ " <th>2019년</th>\n", |
|
| 1408 |
+ " <th>2020년</th>\n", |
|
| 1409 |
+ " <th>2021년</th>\n", |
|
| 1410 |
+ " <th>최근증가율</th>\n", |
|
| 1411 |
+ " </tr>\n", |
|
| 1412 |
+ " </thead>\n", |
|
| 1413 |
+ " <tbody>\n", |
|
| 1414 |
+ " <tr>\n", |
|
| 1415 |
+ " <th>0</th>\n", |
|
| 1416 |
+ " <td>종로구</td>\n", |
|
| 1417 |
+ " <td>1715</td>\n", |
|
| 1418 |
+ " <td>815</td>\n", |
|
| 1419 |
+ " <td>0.0</td>\n", |
|
| 1420 |
+ " <td>0.0</td>\n", |
|
| 1421 |
+ " <td>195</td>\n", |
|
| 1422 |
+ " <td>150</td>\n", |
|
| 1423 |
+ " <td>0</td>\n", |
|
| 1424 |
+ " <td>261</td>\n", |
|
| 1425 |
+ " <td>85</td>\n", |
|
| 1426 |
+ " <td>9</td>\n", |
|
| 1427 |
+ " <td>200</td>\n", |
|
| 1428 |
+ " <td>0</td>\n", |
|
| 1429 |
+ " <td>47.844828</td>\n", |
|
| 1430 |
+ " </tr>\n", |
|
| 1431 |
+ " <tr>\n", |
|
| 1432 |
+ " <th>1</th>\n", |
|
| 1433 |
+ " <td>중구</td>\n", |
|
| 1434 |
+ " <td>2447</td>\n", |
|
| 1435 |
+ " <td>16</td>\n", |
|
| 1436 |
+ " <td>114.0</td>\n", |
|
| 1437 |
+ " <td>87.0</td>\n", |
|
| 1438 |
+ " <td>77</td>\n", |
|
| 1439 |
+ " <td>236</td>\n", |
|
| 1440 |
+ " <td>240</td>\n", |
|
| 1441 |
+ " <td>372</td>\n", |
|
| 1442 |
+ " <td>386</td>\n", |
|
| 1443 |
+ " <td>155</td>\n", |
|
| 1444 |
+ " <td>361</td>\n", |
|
| 1445 |
+ " <td>403</td>\n", |
|
| 1446 |
+ " <td>217.792208</td>\n", |
|
| 1447 |
+ " </tr>\n", |
|
| 1448 |
+ " <tr>\n", |
|
| 1449 |
+ " <th>2</th>\n", |
|
| 1450 |
+ " <td>용산구</td>\n", |
|
| 1451 |
+ " <td>2611</td>\n", |
|
| 1452 |
+ " <td>34</td>\n", |
|
| 1453 |
+ " <td>71.0</td>\n", |
|
| 1454 |
+ " <td>234.0</td>\n", |
|
| 1455 |
+ " <td>125</td>\n", |
|
| 1456 |
+ " <td>221</td>\n", |
|
| 1457 |
+ " <td>298</td>\n", |
|
| 1458 |
+ " <td>351</td>\n", |
|
| 1459 |
+ " <td>125</td>\n", |
|
| 1460 |
+ " <td>307</td>\n", |
|
| 1461 |
+ " <td>617</td>\n", |
|
| 1462 |
+ " <td>228</td>\n", |
|
| 1463 |
+ " <td>165.615463</td>\n", |
|
| 1464 |
+ " </tr>\n", |
|
| 1465 |
+ " <tr>\n", |
|
| 1466 |
+ " <th>3</th>\n", |
|
| 1467 |
+ " <td>성동구</td>\n", |
|
| 1468 |
+ " <td>3829</td>\n", |
|
| 1469 |
+ " <td>163</td>\n", |
|
| 1470 |
+ " <td>144.0</td>\n", |
|
| 1471 |
+ " <td>208.0</td>\n", |
|
| 1472 |
+ " <td>107</td>\n", |
|
| 1473 |
+ " <td>325</td>\n", |
|
| 1474 |
+ " <td>255</td>\n", |
|
| 1475 |
+ " <td>967</td>\n", |
|
| 1476 |
+ " <td>415</td>\n", |
|
| 1477 |
+ " <td>490</td>\n", |
|
| 1478 |
+ " <td>472</td>\n", |
|
| 1479 |
+ " <td>283</td>\n", |
|
| 1480 |
+ " <td>218.552413</td>\n", |
|
| 1481 |
+ " </tr>\n", |
|
| 1482 |
+ " <tr>\n", |
|
| 1483 |
+ " <th>4</th>\n", |
|
| 1484 |
+ " <td>광진구</td>\n", |
|
| 1485 |
+ " <td>3211</td>\n", |
|
| 1486 |
+ " <td>35</td>\n", |
|
| 1487 |
+ " <td>57.0</td>\n", |
|
| 1488 |
+ " <td>100.0</td>\n", |
|
| 1489 |
+ " <td>187</td>\n", |
|
| 1490 |
+ " <td>98</td>\n", |
|
| 1491 |
+ " <td>52</td>\n", |
|
| 1492 |
+ " <td>675</td>\n", |
|
| 1493 |
+ " <td>465</td>\n", |
|
| 1494 |
+ " <td>712</td>\n", |
|
| 1495 |
+ " <td>175</td>\n", |
|
| 1496 |
+ " <td>655</td>\n", |
|
| 1497 |
+ " <td>506.994329</td>\n", |
|
| 1498 |
+ " </tr>\n", |
|
| 1499 |
+ " </tbody>\n", |
|
| 1500 |
+ "</table>\n", |
|
| 1501 |
+ "</div>" |
|
| 1502 |
+ ], |
|
| 1503 |
+ "text/plain": [ |
|
| 1504 |
+ " 구별 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 \\\n", |
|
| 1505 |
+ "0 종로구 1715 815 0.0 0.0 195 150 0 261 85 \n", |
|
| 1506 |
+ "1 중구 2447 16 114.0 87.0 77 236 240 372 386 \n", |
|
| 1507 |
+ "2 용산구 2611 34 71.0 234.0 125 221 298 351 125 \n", |
|
| 1508 |
+ "3 성동구 3829 163 144.0 208.0 107 325 255 967 415 \n", |
|
| 1509 |
+ "4 광진구 3211 35 57.0 100.0 187 98 52 675 465 \n", |
|
| 1510 |
+ "\n", |
|
| 1511 |
+ " 2019년 2020년 2021년 최근증가율 \n", |
|
| 1512 |
+ "0 9 200 0 47.844828 \n", |
|
| 1513 |
+ "1 155 361 403 217.792208 \n", |
|
| 1514 |
+ "2 307 617 228 165.615463 \n", |
|
| 1515 |
+ "3 490 472 283 218.552413 \n", |
|
| 1516 |
+ "4 712 175 655 506.994329 " |
|
| 1517 |
+ ] |
|
| 1518 |
+ }, |
|
| 1519 |
+ "execution_count": 87, |
|
| 1520 |
+ "metadata": {},
|
|
| 1521 |
+ "output_type": "execute_result" |
|
| 1522 |
+ } |
|
| 1523 |
+ ], |
|
| 1524 |
+ "source": [ |
|
| 1525 |
+ "cctvData.head()" |
|
| 1526 |
+ ] |
|
| 1527 |
+ }, |
|
| 1528 |
+ {
|
|
| 1529 |
+ "cell_type": "code", |
|
| 1530 |
+ "execution_count": 88, |
|
| 1531 |
+ "id": "d186a399", |
|
| 1546 | 1532 |
"metadata": {},
|
| 1547 | 1533 |
"outputs": [], |
| 1548 |
- "source": [] |
|
| 1534 |
+ "source": [ |
|
| 1535 |
+ "popData.drop(0, inplace=True)" |
|
| 1536 |
+ ] |
|
| 1537 |
+ }, |
|
| 1538 |
+ {
|
|
| 1539 |
+ "cell_type": "code", |
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+ " <td>성동구</td>\n", |
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| 1828 |
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|
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|
| 1830 |
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| 1831 |
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| 1840 |
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| 1841 |
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| 1861 |
+ "source": [ |
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| 1862 |
+ "popData['외국인비율'] = ((popData['외국인'] / popData['인구수'])) *100\n", |
|
| 1863 |
+ "popData['고령자비율'] = ((popData['고령자'] / popData['인구수'])) * 100" |
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| 1892 |
+ " <th></th>\n", |
|
| 1893 |
+ " <th>구별</th>\n", |
|
| 1894 |
+ " <th>인구수</th>\n", |
|
| 1895 |
+ " <th>한국인</th>\n", |
|
| 1896 |
+ " <th>외국인</th>\n", |
|
| 1897 |
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|
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| 1904 |
+ " <th>0</th>\n", |
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| 1905 |
+ " <td>종로구</td>\n", |
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+ " <td>153789</td>\n", |
|
| 1907 |
+ " <td>144683</td>\n", |
|
| 1908 |
+ " <td>9106</td>\n", |
|
| 1909 |
+ " <td>27818</td>\n", |
|
| 1910 |
+ " <td>5.921100</td>\n", |
|
| 1911 |
+ " <td>18.088420</td>\n", |
|
| 1912 |
+ " </tr>\n", |
|
| 1913 |
+ " <tr>\n", |
|
| 1914 |
+ " <th>1</th>\n", |
|
| 1915 |
+ " <td>중구</td>\n", |
|
| 1916 |
+ " <td>131787</td>\n", |
|
| 1917 |
+ " <td>122499</td>\n", |
|
| 1918 |
+ " <td>9288</td>\n", |
|
| 1919 |
+ " <td>24392</td>\n", |
|
| 1920 |
+ " <td>7.047736</td>\n", |
|
| 1921 |
+ " <td>18.508654</td>\n", |
|
| 1922 |
+ " </tr>\n", |
|
| 1923 |
+ " <tr>\n", |
|
| 1924 |
+ " <th>2</th>\n", |
|
| 1925 |
+ " <td>용산구</td>\n", |
|
| 1926 |
+ " <td>237285</td>\n", |
|
| 1927 |
+ " <td>222953</td>\n", |
|
| 1928 |
+ " <td>14332</td>\n", |
|
| 1929 |
+ " <td>39070</td>\n", |
|
| 1930 |
+ " <td>6.039994</td>\n", |
|
| 1931 |
+ " <td>16.465432</td>\n", |
|
| 1932 |
+ " </tr>\n", |
|
| 1933 |
+ " <tr>\n", |
|
| 1934 |
+ " <th>3</th>\n", |
|
| 1935 |
+ " <td>성동구</td>\n", |
|
| 1936 |
+ " <td>292672</td>\n", |
|
| 1937 |
+ " <td>285990</td>\n", |
|
| 1938 |
+ " <td>6682</td>\n", |
|
| 1939 |
+ " <td>46380</td>\n", |
|
| 1940 |
+ " <td>2.283102</td>\n", |
|
| 1941 |
+ " <td>15.847092</td>\n", |
|
| 1942 |
+ " </tr>\n", |
|
| 1943 |
+ " <tr>\n", |
|
| 1944 |
+ " <th>4</th>\n", |
|
| 1945 |
+ " <td>광진구</td>\n", |
|
| 1946 |
+ " <td>352627</td>\n", |
|
| 1947 |
+ " <td>339996</td>\n", |
|
| 1948 |
+ " <td>12631</td>\n", |
|
| 1949 |
+ " <td>51723</td>\n", |
|
| 1950 |
+ " <td>3.581972</td>\n", |
|
| 1951 |
+ " <td>14.667907</td>\n", |
|
| 1952 |
+ " </tr>\n", |
|
| 1953 |
+ " </tbody>\n", |
|
| 1954 |
+ "</table>\n", |
|
| 1955 |
+ "</div>" |
|
| 1956 |
+ ], |
|
| 1957 |
+ "text/plain": [ |
|
| 1958 |
+ " 구별 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 1959 |
+ "0 종로구 153789 144683 9106 27818 5.921100 18.088420\n", |
|
| 1960 |
+ "1 중구 131787 122499 9288 24392 7.047736 18.508654\n", |
|
| 1961 |
+ "2 용산구 237285 222953 14332 39070 6.039994 16.465432\n", |
|
| 1962 |
+ "3 성동구 292672 285990 6682 46380 2.283102 15.847092\n", |
|
| 1963 |
+ "4 광진구 352627 339996 12631 51723 3.581972 14.667907" |
|
| 1964 |
+ ] |
|
| 1965 |
+ }, |
|
| 1966 |
+ "execution_count": 95, |
|
| 1967 |
+ "metadata": {},
|
|
| 1968 |
+ "output_type": "execute_result" |
|
| 1969 |
+ } |
|
| 1970 |
+ ], |
|
| 1971 |
+ "source": [ |
|
| 1972 |
+ "popData.head()" |
|
| 1973 |
+ ] |
|
| 1974 |
+ }, |
|
| 1975 |
+ {
|
|
| 1976 |
+ "cell_type": "code", |
|
| 1977 |
+ "execution_count": 96, |
|
| 1978 |
+ "id": "619fdca3", |
|
| 1979 |
+ "metadata": {
|
|
| 1980 |
+ "scrolled": true |
|
| 1981 |
+ }, |
|
| 1982 |
+ "outputs": [ |
|
| 1983 |
+ {
|
|
| 1984 |
+ "data": {
|
|
| 1985 |
+ "text/html": [ |
|
| 1986 |
+ "<div>\n", |
|
| 1987 |
+ "<style scoped>\n", |
|
| 1988 |
+ " .dataframe tbody tr th:only-of-type {\n",
|
|
| 1989 |
+ " vertical-align: middle;\n", |
|
| 1990 |
+ " }\n", |
|
| 1991 |
+ "\n", |
|
| 1992 |
+ " .dataframe tbody tr th {\n",
|
|
| 1993 |
+ " vertical-align: top;\n", |
|
| 1994 |
+ " }\n", |
|
| 1995 |
+ "\n", |
|
| 1996 |
+ " .dataframe thead th {\n",
|
|
| 1997 |
+ " text-align: right;\n", |
|
| 1998 |
+ " }\n", |
|
| 1999 |
+ "</style>\n", |
|
| 2000 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 2001 |
+ " <thead>\n", |
|
| 2002 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 2003 |
+ " <th></th>\n", |
|
| 2004 |
+ " <th>구별</th>\n", |
|
| 2005 |
+ " <th>인구수</th>\n", |
|
| 2006 |
+ " <th>한국인</th>\n", |
|
| 2007 |
+ " <th>외국인</th>\n", |
|
| 2008 |
+ " <th>고령자</th>\n", |
|
| 2009 |
+ " <th>외국인비율</th>\n", |
|
| 2010 |
+ " <th>고령자비율</th>\n", |
|
| 2011 |
+ " </tr>\n", |
|
| 2012 |
+ " </thead>\n", |
|
| 2013 |
+ " <tbody>\n", |
|
| 2014 |
+ " <tr>\n", |
|
| 2015 |
+ " <th>23</th>\n", |
|
| 2016 |
+ " <td>송파구</td>\n", |
|
| 2017 |
+ " <td>663965</td>\n", |
|
| 2018 |
+ " <td>658338</td>\n", |
|
| 2019 |
+ " <td>5627</td>\n", |
|
| 2020 |
+ " <td>97691</td>\n", |
|
| 2021 |
+ " <td>0.847484</td>\n", |
|
| 2022 |
+ " <td>14.713276</td>\n", |
|
| 2023 |
+ " </tr>\n", |
|
| 2024 |
+ " <tr>\n", |
|
| 2025 |
+ " <th>15</th>\n", |
|
| 2026 |
+ " <td>강서구</td>\n", |
|
| 2027 |
+ " <td>579768</td>\n", |
|
| 2028 |
+ " <td>574315</td>\n", |
|
| 2029 |
+ " <td>5453</td>\n", |
|
| 2030 |
+ " <td>92558</td>\n", |
|
| 2031 |
+ " <td>0.940549</td>\n", |
|
| 2032 |
+ " <td>15.964662</td>\n", |
|
| 2033 |
+ " </tr>\n", |
|
| 2034 |
+ " <tr>\n", |
|
| 2035 |
+ " <th>22</th>\n", |
|
| 2036 |
+ " <td>강남구</td>\n", |
|
| 2037 |
+ " <td>537800</td>\n", |
|
| 2038 |
+ " <td>533042</td>\n", |
|
| 2039 |
+ " <td>4758</td>\n", |
|
| 2040 |
+ " <td>78226</td>\n", |
|
| 2041 |
+ " <td>0.884716</td>\n", |
|
| 2042 |
+ " <td>14.545556</td>\n", |
|
| 2043 |
+ " </tr>\n", |
|
| 2044 |
+ " <tr>\n", |
|
| 2045 |
+ " <th>10</th>\n", |
|
| 2046 |
+ " <td>노원구</td>\n", |
|
| 2047 |
+ " <td>514946</td>\n", |
|
| 2048 |
+ " <td>510956</td>\n", |
|
| 2049 |
+ " <td>3990</td>\n", |
|
| 2050 |
+ " <td>88345</td>\n", |
|
| 2051 |
+ " <td>0.774839</td>\n", |
|
| 2052 |
+ " <td>17.156168</td>\n", |
|
| 2053 |
+ " </tr>\n", |
|
| 2054 |
+ " <tr>\n", |
|
| 2055 |
+ " <th>20</th>\n", |
|
| 2056 |
+ " <td>관악구</td>\n", |
|
| 2057 |
+ " <td>499449</td>\n", |
|
| 2058 |
+ " <td>485699</td>\n", |
|
| 2059 |
+ " <td>13750</td>\n", |
|
| 2060 |
+ " <td>79871</td>\n", |
|
| 2061 |
+ " <td>2.753034</td>\n", |
|
| 2062 |
+ " <td>15.991823</td>\n", |
|
| 2063 |
+ " </tr>\n", |
|
| 2064 |
+ " </tbody>\n", |
|
| 2065 |
+ "</table>\n", |
|
| 2066 |
+ "</div>" |
|
| 2067 |
+ ], |
|
| 2068 |
+ "text/plain": [ |
|
| 2069 |
+ " 구별 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 2070 |
+ "23 송파구 663965 658338 5627 97691 0.847484 14.713276\n", |
|
| 2071 |
+ "15 강서구 579768 574315 5453 92558 0.940549 15.964662\n", |
|
| 2072 |
+ "22 강남구 537800 533042 4758 78226 0.884716 14.545556\n", |
|
| 2073 |
+ "10 노원구 514946 510956 3990 88345 0.774839 17.156168\n", |
|
| 2074 |
+ "20 관악구 499449 485699 13750 79871 2.753034 15.991823" |
|
| 2075 |
+ ] |
|
| 2076 |
+ }, |
|
| 2077 |
+ "execution_count": 96, |
|
| 2078 |
+ "metadata": {},
|
|
| 2079 |
+ "output_type": "execute_result" |
|
| 2080 |
+ } |
|
| 2081 |
+ ], |
|
| 2082 |
+ "source": [ |
|
| 2083 |
+ "popData.sort_values(by = '인구수', ascending=False).head()" |
|
| 2084 |
+ ] |
|
| 2085 |
+ }, |
|
| 2086 |
+ {
|
|
| 2087 |
+ "cell_type": "code", |
|
| 2088 |
+ "execution_count": 97, |
|
| 2089 |
+ "id": "0484041d", |
|
| 2090 |
+ "metadata": {},
|
|
| 2091 |
+ "outputs": [ |
|
| 2092 |
+ {
|
|
| 2093 |
+ "data": {
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|
| 2094 |
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| 2095 |
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| 2096 |
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|
| 2098 |
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| 2099 |
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| 2100 |
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| 2101 |
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|
| 2102 |
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|
| 2103 |
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|
| 2104 |
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|
| 2105 |
+ " .dataframe thead th {\n",
|
|
| 2106 |
+ " text-align: right;\n", |
|
| 2107 |
+ " }\n", |
|
| 2108 |
+ "</style>\n", |
|
| 2109 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 2110 |
+ " <thead>\n", |
|
| 2111 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 2112 |
+ " <th></th>\n", |
|
| 2113 |
+ " <th>구별</th>\n", |
|
| 2114 |
+ " <th>인구수</th>\n", |
|
| 2115 |
+ " <th>한국인</th>\n", |
|
| 2116 |
+ " <th>외국인</th>\n", |
|
| 2117 |
+ " <th>고령자</th>\n", |
|
| 2118 |
+ " <th>외국인비율</th>\n", |
|
| 2119 |
+ " <th>고령자비율</th>\n", |
|
| 2120 |
+ " </tr>\n", |
|
| 2121 |
+ " </thead>\n", |
|
| 2122 |
+ " <tbody>\n", |
|
| 2123 |
+ " <tr>\n", |
|
| 2124 |
+ " <th>23</th>\n", |
|
| 2125 |
+ " <td>송파구</td>\n", |
|
| 2126 |
+ " <td>663965</td>\n", |
|
| 2127 |
+ " <td>658338</td>\n", |
|
| 2128 |
+ " <td>5627</td>\n", |
|
| 2129 |
+ " <td>97691</td>\n", |
|
| 2130 |
+ " <td>0.847484</td>\n", |
|
| 2131 |
+ " <td>14.713276</td>\n", |
|
| 2132 |
+ " </tr>\n", |
|
| 2133 |
+ " <tr>\n", |
|
| 2134 |
+ " <th>15</th>\n", |
|
| 2135 |
+ " <td>강서구</td>\n", |
|
| 2136 |
+ " <td>579768</td>\n", |
|
| 2137 |
+ " <td>574315</td>\n", |
|
| 2138 |
+ " <td>5453</td>\n", |
|
| 2139 |
+ " <td>92558</td>\n", |
|
| 2140 |
+ " <td>0.940549</td>\n", |
|
| 2141 |
+ " <td>15.964662</td>\n", |
|
| 2142 |
+ " </tr>\n", |
|
| 2143 |
+ " <tr>\n", |
|
| 2144 |
+ " <th>10</th>\n", |
|
| 2145 |
+ " <td>노원구</td>\n", |
|
| 2146 |
+ " <td>514946</td>\n", |
|
| 2147 |
+ " <td>510956</td>\n", |
|
| 2148 |
+ " <td>3990</td>\n", |
|
| 2149 |
+ " <td>88345</td>\n", |
|
| 2150 |
+ " <td>0.774839</td>\n", |
|
| 2151 |
+ " <td>17.156168</td>\n", |
|
| 2152 |
+ " </tr>\n", |
|
| 2153 |
+ " <tr>\n", |
|
| 2154 |
+ " <th>11</th>\n", |
|
| 2155 |
+ " <td>은평구</td>\n", |
|
| 2156 |
+ " <td>477173</td>\n", |
|
| 2157 |
+ " <td>473307</td>\n", |
|
| 2158 |
+ " <td>3866</td>\n", |
|
| 2159 |
+ " <td>87241</td>\n", |
|
| 2160 |
+ " <td>0.810188</td>\n", |
|
| 2161 |
+ " <td>18.282887</td>\n", |
|
| 2162 |
+ " </tr>\n", |
|
| 2163 |
+ " <tr>\n", |
|
| 2164 |
+ " <th>20</th>\n", |
|
| 2165 |
+ " <td>관악구</td>\n", |
|
| 2166 |
+ " <td>499449</td>\n", |
|
| 2167 |
+ " <td>485699</td>\n", |
|
| 2168 |
+ " <td>13750</td>\n", |
|
| 2169 |
+ " <td>79871</td>\n", |
|
| 2170 |
+ " <td>2.753034</td>\n", |
|
| 2171 |
+ " <td>15.991823</td>\n", |
|
| 2172 |
+ " </tr>\n", |
|
| 2173 |
+ " </tbody>\n", |
|
| 2174 |
+ "</table>\n", |
|
| 2175 |
+ "</div>" |
|
| 2176 |
+ ], |
|
| 2177 |
+ "text/plain": [ |
|
| 2178 |
+ " 구별 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 2179 |
+ "23 송파구 663965 658338 5627 97691 0.847484 14.713276\n", |
|
| 2180 |
+ "15 강서구 579768 574315 5453 92558 0.940549 15.964662\n", |
|
| 2181 |
+ "10 노원구 514946 510956 3990 88345 0.774839 17.156168\n", |
|
| 2182 |
+ "11 은평구 477173 473307 3866 87241 0.810188 18.282887\n", |
|
| 2183 |
+ "20 관악구 499449 485699 13750 79871 2.753034 15.991823" |
|
| 2184 |
+ ] |
|
| 2185 |
+ }, |
|
| 2186 |
+ "execution_count": 97, |
|
| 2187 |
+ "metadata": {},
|
|
| 2188 |
+ "output_type": "execute_result" |
|
| 2189 |
+ } |
|
| 2190 |
+ ], |
|
| 2191 |
+ "source": [ |
|
| 2192 |
+ "popData.sort_values(by = '고령자', ascending=False).head()" |
|
| 2193 |
+ ] |
|
| 2194 |
+ }, |
|
| 2195 |
+ {
|
|
| 2196 |
+ "cell_type": "code", |
|
| 2197 |
+ "execution_count": 98, |
|
| 2198 |
+ "id": "fe65b767", |
|
| 2199 |
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|
| 2200 |
+ "scrolled": true |
|
| 2201 |
+ }, |
|
| 2202 |
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| 2203 |
+ {
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|
| 2204 |
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| 2205 |
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| 2206 |
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| 2207 |
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| 2210 |
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| 2213 |
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| 2216 |
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| 2217 |
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| 2218 |
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| 2219 |
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|
| 2220 |
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|
| 2221 |
+ " <thead>\n", |
|
| 2222 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 2223 |
+ " <th></th>\n", |
|
| 2224 |
+ " <th>구별</th>\n", |
|
| 2225 |
+ " <th>총계</th>\n", |
|
| 2226 |
+ " <th>2012년 이전</th>\n", |
|
| 2227 |
+ " <th>2012년</th>\n", |
|
| 2228 |
+ " <th>2013년</th>\n", |
|
| 2229 |
+ " <th>2014년</th>\n", |
|
| 2230 |
+ " <th>2015년</th>\n", |
|
| 2231 |
+ " <th>2016년</th>\n", |
|
| 2232 |
+ " <th>2017년</th>\n", |
|
| 2233 |
+ " <th>2018년</th>\n", |
|
| 2234 |
+ " <th>2019년</th>\n", |
|
| 2235 |
+ " <th>2020년</th>\n", |
|
| 2236 |
+ " <th>2021년</th>\n", |
|
| 2237 |
+ " <th>최근증가율</th>\n", |
|
| 2238 |
+ " <th>인구수</th>\n", |
|
| 2239 |
+ " <th>한국인</th>\n", |
|
| 2240 |
+ " <th>외국인</th>\n", |
|
| 2241 |
+ " <th>고령자</th>\n", |
|
| 2242 |
+ " <th>외국인비율</th>\n", |
|
| 2243 |
+ " <th>고령자비율</th>\n", |
|
| 2244 |
+ " </tr>\n", |
|
| 2245 |
+ " </thead>\n", |
|
| 2246 |
+ " <tbody>\n", |
|
| 2247 |
+ " <tr>\n", |
|
| 2248 |
+ " <th>0</th>\n", |
|
| 2249 |
+ " <td>종로구</td>\n", |
|
| 2250 |
+ " <td>1715</td>\n", |
|
| 2251 |
+ " <td>815</td>\n", |
|
| 2252 |
+ " <td>0.0</td>\n", |
|
| 2253 |
+ " <td>0.0</td>\n", |
|
| 2254 |
+ " <td>195</td>\n", |
|
| 2255 |
+ " <td>150</td>\n", |
|
| 2256 |
+ " <td>0</td>\n", |
|
| 2257 |
+ " <td>261</td>\n", |
|
| 2258 |
+ " <td>85</td>\n", |
|
| 2259 |
+ " <td>9</td>\n", |
|
| 2260 |
+ " <td>200</td>\n", |
|
| 2261 |
+ " <td>0</td>\n", |
|
| 2262 |
+ " <td>47.844828</td>\n", |
|
| 2263 |
+ " <td>153789</td>\n", |
|
| 2264 |
+ " <td>144683</td>\n", |
|
| 2265 |
+ " <td>9106</td>\n", |
|
| 2266 |
+ " <td>27818</td>\n", |
|
| 2267 |
+ " <td>5.921100</td>\n", |
|
| 2268 |
+ " <td>18.088420</td>\n", |
|
| 2269 |
+ " </tr>\n", |
|
| 2270 |
+ " <tr>\n", |
|
| 2271 |
+ " <th>1</th>\n", |
|
| 2272 |
+ " <td>중구</td>\n", |
|
| 2273 |
+ " <td>2447</td>\n", |
|
| 2274 |
+ " <td>16</td>\n", |
|
| 2275 |
+ " <td>114.0</td>\n", |
|
| 2276 |
+ " <td>87.0</td>\n", |
|
| 2277 |
+ " <td>77</td>\n", |
|
| 2278 |
+ " <td>236</td>\n", |
|
| 2279 |
+ " <td>240</td>\n", |
|
| 2280 |
+ " <td>372</td>\n", |
|
| 2281 |
+ " <td>386</td>\n", |
|
| 2282 |
+ " <td>155</td>\n", |
|
| 2283 |
+ " <td>361</td>\n", |
|
| 2284 |
+ " <td>403</td>\n", |
|
| 2285 |
+ " <td>217.792208</td>\n", |
|
| 2286 |
+ " <td>131787</td>\n", |
|
| 2287 |
+ " <td>122499</td>\n", |
|
| 2288 |
+ " <td>9288</td>\n", |
|
| 2289 |
+ " <td>24392</td>\n", |
|
| 2290 |
+ " <td>7.047736</td>\n", |
|
| 2291 |
+ " <td>18.508654</td>\n", |
|
| 2292 |
+ " </tr>\n", |
|
| 2293 |
+ " <tr>\n", |
|
| 2294 |
+ " <th>2</th>\n", |
|
| 2295 |
+ " <td>용산구</td>\n", |
|
| 2296 |
+ " <td>2611</td>\n", |
|
| 2297 |
+ " <td>34</td>\n", |
|
| 2298 |
+ " <td>71.0</td>\n", |
|
| 2299 |
+ " <td>234.0</td>\n", |
|
| 2300 |
+ " <td>125</td>\n", |
|
| 2301 |
+ " <td>221</td>\n", |
|
| 2302 |
+ " <td>298</td>\n", |
|
| 2303 |
+ " <td>351</td>\n", |
|
| 2304 |
+ " <td>125</td>\n", |
|
| 2305 |
+ " <td>307</td>\n", |
|
| 2306 |
+ " <td>617</td>\n", |
|
| 2307 |
+ " <td>228</td>\n", |
|
| 2308 |
+ " <td>165.615463</td>\n", |
|
| 2309 |
+ " <td>237285</td>\n", |
|
| 2310 |
+ " <td>222953</td>\n", |
|
| 2311 |
+ " <td>14332</td>\n", |
|
| 2312 |
+ " <td>39070</td>\n", |
|
| 2313 |
+ " <td>6.039994</td>\n", |
|
| 2314 |
+ " <td>16.465432</td>\n", |
|
| 2315 |
+ " </tr>\n", |
|
| 2316 |
+ " <tr>\n", |
|
| 2317 |
+ " <th>3</th>\n", |
|
| 2318 |
+ " <td>성동구</td>\n", |
|
| 2319 |
+ " <td>3829</td>\n", |
|
| 2320 |
+ " <td>163</td>\n", |
|
| 2321 |
+ " <td>144.0</td>\n", |
|
| 2322 |
+ " <td>208.0</td>\n", |
|
| 2323 |
+ " <td>107</td>\n", |
|
| 2324 |
+ " <td>325</td>\n", |
|
| 2325 |
+ " <td>255</td>\n", |
|
| 2326 |
+ " <td>967</td>\n", |
|
| 2327 |
+ " <td>415</td>\n", |
|
| 2328 |
+ " <td>490</td>\n", |
|
| 2329 |
+ " <td>472</td>\n", |
|
| 2330 |
+ " <td>283</td>\n", |
|
| 2331 |
+ " <td>218.552413</td>\n", |
|
| 2332 |
+ " <td>292672</td>\n", |
|
| 2333 |
+ " <td>285990</td>\n", |
|
| 2334 |
+ " <td>6682</td>\n", |
|
| 2335 |
+ " <td>46380</td>\n", |
|
| 2336 |
+ " <td>2.283102</td>\n", |
|
| 2337 |
+ " <td>15.847092</td>\n", |
|
| 2338 |
+ " </tr>\n", |
|
| 2339 |
+ " <tr>\n", |
|
| 2340 |
+ " <th>4</th>\n", |
|
| 2341 |
+ " <td>광진구</td>\n", |
|
| 2342 |
+ " <td>3211</td>\n", |
|
| 2343 |
+ " <td>35</td>\n", |
|
| 2344 |
+ " <td>57.0</td>\n", |
|
| 2345 |
+ " <td>100.0</td>\n", |
|
| 2346 |
+ " <td>187</td>\n", |
|
| 2347 |
+ " <td>98</td>\n", |
|
| 2348 |
+ " <td>52</td>\n", |
|
| 2349 |
+ " <td>675</td>\n", |
|
| 2350 |
+ " <td>465</td>\n", |
|
| 2351 |
+ " <td>712</td>\n", |
|
| 2352 |
+ " <td>175</td>\n", |
|
| 2353 |
+ " <td>655</td>\n", |
|
| 2354 |
+ " <td>506.994329</td>\n", |
|
| 2355 |
+ " <td>352627</td>\n", |
|
| 2356 |
+ " <td>339996</td>\n", |
|
| 2357 |
+ " <td>12631</td>\n", |
|
| 2358 |
+ " <td>51723</td>\n", |
|
| 2359 |
+ " <td>3.581972</td>\n", |
|
| 2360 |
+ " <td>14.667907</td>\n", |
|
| 2361 |
+ " </tr>\n", |
|
| 2362 |
+ " </tbody>\n", |
|
| 2363 |
+ "</table>\n", |
|
| 2364 |
+ "</div>" |
|
| 2365 |
+ ], |
|
| 2366 |
+ "text/plain": [ |
|
| 2367 |
+ " 구별 총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 \\\n", |
|
| 2368 |
+ "0 종로구 1715 815 0.0 0.0 195 150 0 261 85 \n", |
|
| 2369 |
+ "1 중구 2447 16 114.0 87.0 77 236 240 372 386 \n", |
|
| 2370 |
+ "2 용산구 2611 34 71.0 234.0 125 221 298 351 125 \n", |
|
| 2371 |
+ "3 성동구 3829 163 144.0 208.0 107 325 255 967 415 \n", |
|
| 2372 |
+ "4 광진구 3211 35 57.0 100.0 187 98 52 675 465 \n", |
|
| 2373 |
+ "\n", |
|
| 2374 |
+ " 2019년 2020년 2021년 최근증가율 인구수 한국인 외국인 고령자 외국인비율 \\\n", |
|
| 2375 |
+ "0 9 200 0 47.844828 153789 144683 9106 27818 5.921100 \n", |
|
| 2376 |
+ "1 155 361 403 217.792208 131787 122499 9288 24392 7.047736 \n", |
|
| 2377 |
+ "2 307 617 228 165.615463 237285 222953 14332 39070 6.039994 \n", |
|
| 2378 |
+ "3 490 472 283 218.552413 292672 285990 6682 46380 2.283102 \n", |
|
| 2379 |
+ "4 712 175 655 506.994329 352627 339996 12631 51723 3.581972 \n", |
|
| 2380 |
+ "\n", |
|
| 2381 |
+ " 고령자비율 \n", |
|
| 2382 |
+ "0 18.088420 \n", |
|
| 2383 |
+ "1 18.508654 \n", |
|
| 2384 |
+ "2 16.465432 \n", |
|
| 2385 |
+ "3 15.847092 \n", |
|
| 2386 |
+ "4 14.667907 " |
|
| 2387 |
+ ] |
|
| 2388 |
+ }, |
|
| 2389 |
+ "execution_count": 98, |
|
| 2390 |
+ "metadata": {},
|
|
| 2391 |
+ "output_type": "execute_result" |
|
| 2392 |
+ } |
|
| 2393 |
+ ], |
|
| 2394 |
+ "source": [ |
|
| 2395 |
+ "dataset = pd.merge(cctvData, popData, on='구별')\n", |
|
| 2396 |
+ "dataset.head()" |
|
| 2397 |
+ ] |
|
| 2398 |
+ }, |
|
| 2399 |
+ {
|
|
| 2400 |
+ "cell_type": "code", |
|
| 2401 |
+ "execution_count": 99, |
|
| 2402 |
+ "id": "d300cb33", |
|
| 2403 |
+ "metadata": {},
|
|
| 2404 |
+ "outputs": [], |
|
| 2405 |
+ "source": [ |
|
| 2406 |
+ "del dataset['2012년 이전']\n", |
|
| 2407 |
+ "del dataset['2012년']\n", |
|
| 2408 |
+ "del dataset['2013년']\n", |
|
| 2409 |
+ "del dataset['2014년']\n", |
|
| 2410 |
+ "del dataset['2015년']\n", |
|
| 2411 |
+ "del dataset['2016년']\n", |
|
| 2412 |
+ "del dataset['2017년']\n", |
|
| 2413 |
+ "del dataset['2018년']\n", |
|
| 2414 |
+ "del dataset['2019년']\n", |
|
| 2415 |
+ "del dataset['2020년']\n", |
|
| 2416 |
+ "del dataset['2021년']" |
|
| 2417 |
+ ] |
|
| 2418 |
+ }, |
|
| 2419 |
+ {
|
|
| 2420 |
+ "cell_type": "code", |
|
| 2421 |
+ "execution_count": 100, |
|
| 2422 |
+ "id": "30b88050", |
|
| 2423 |
+ "metadata": {},
|
|
| 2424 |
+ "outputs": [ |
|
| 2425 |
+ {
|
|
| 2426 |
+ "data": {
|
|
| 2427 |
+ "text/html": [ |
|
| 2428 |
+ "<div>\n", |
|
| 2429 |
+ "<style scoped>\n", |
|
| 2430 |
+ " .dataframe tbody tr th:only-of-type {\n",
|
|
| 2431 |
+ " vertical-align: middle;\n", |
|
| 2432 |
+ " }\n", |
|
| 2433 |
+ "\n", |
|
| 2434 |
+ " .dataframe tbody tr th {\n",
|
|
| 2435 |
+ " vertical-align: top;\n", |
|
| 2436 |
+ " }\n", |
|
| 2437 |
+ "\n", |
|
| 2438 |
+ " .dataframe thead th {\n",
|
|
| 2439 |
+ " text-align: right;\n", |
|
| 2440 |
+ " }\n", |
|
| 2441 |
+ "</style>\n", |
|
| 2442 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 2443 |
+ " <thead>\n", |
|
| 2444 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 2445 |
+ " <th></th>\n", |
|
| 2446 |
+ " <th>구별</th>\n", |
|
| 2447 |
+ " <th>총계</th>\n", |
|
| 2448 |
+ " <th>최근증가율</th>\n", |
|
| 2449 |
+ " <th>인구수</th>\n", |
|
| 2450 |
+ " <th>한국인</th>\n", |
|
| 2451 |
+ " <th>외국인</th>\n", |
|
| 2452 |
+ " <th>고령자</th>\n", |
|
| 2453 |
+ " <th>외국인비율</th>\n", |
|
| 2454 |
+ " <th>고령자비율</th>\n", |
|
| 2455 |
+ " </tr>\n", |
|
| 2456 |
+ " </thead>\n", |
|
| 2457 |
+ " <tbody>\n", |
|
| 2458 |
+ " <tr>\n", |
|
| 2459 |
+ " <th>0</th>\n", |
|
| 2460 |
+ " <td>종로구</td>\n", |
|
| 2461 |
+ " <td>1715</td>\n", |
|
| 2462 |
+ " <td>47.844828</td>\n", |
|
| 2463 |
+ " <td>153789</td>\n", |
|
| 2464 |
+ " <td>144683</td>\n", |
|
| 2465 |
+ " <td>9106</td>\n", |
|
| 2466 |
+ " <td>27818</td>\n", |
|
| 2467 |
+ " <td>5.921100</td>\n", |
|
| 2468 |
+ " <td>18.088420</td>\n", |
|
| 2469 |
+ " </tr>\n", |
|
| 2470 |
+ " <tr>\n", |
|
| 2471 |
+ " <th>1</th>\n", |
|
| 2472 |
+ " <td>중구</td>\n", |
|
| 2473 |
+ " <td>2447</td>\n", |
|
| 2474 |
+ " <td>217.792208</td>\n", |
|
| 2475 |
+ " <td>131787</td>\n", |
|
| 2476 |
+ " <td>122499</td>\n", |
|
| 2477 |
+ " <td>9288</td>\n", |
|
| 2478 |
+ " <td>24392</td>\n", |
|
| 2479 |
+ " <td>7.047736</td>\n", |
|
| 2480 |
+ " <td>18.508654</td>\n", |
|
| 2481 |
+ " </tr>\n", |
|
| 2482 |
+ " <tr>\n", |
|
| 2483 |
+ " <th>2</th>\n", |
|
| 2484 |
+ " <td>용산구</td>\n", |
|
| 2485 |
+ " <td>2611</td>\n", |
|
| 2486 |
+ " <td>165.615463</td>\n", |
|
| 2487 |
+ " <td>237285</td>\n", |
|
| 2488 |
+ " <td>222953</td>\n", |
|
| 2489 |
+ " <td>14332</td>\n", |
|
| 2490 |
+ " <td>39070</td>\n", |
|
| 2491 |
+ " <td>6.039994</td>\n", |
|
| 2492 |
+ " <td>16.465432</td>\n", |
|
| 2493 |
+ " </tr>\n", |
|
| 2494 |
+ " <tr>\n", |
|
| 2495 |
+ " <th>3</th>\n", |
|
| 2496 |
+ " <td>성동구</td>\n", |
|
| 2497 |
+ " <td>3829</td>\n", |
|
| 2498 |
+ " <td>218.552413</td>\n", |
|
| 2499 |
+ " <td>292672</td>\n", |
|
| 2500 |
+ " <td>285990</td>\n", |
|
| 2501 |
+ " <td>6682</td>\n", |
|
| 2502 |
+ " <td>46380</td>\n", |
|
| 2503 |
+ " <td>2.283102</td>\n", |
|
| 2504 |
+ " <td>15.847092</td>\n", |
|
| 2505 |
+ " </tr>\n", |
|
| 2506 |
+ " <tr>\n", |
|
| 2507 |
+ " <th>4</th>\n", |
|
| 2508 |
+ " <td>광진구</td>\n", |
|
| 2509 |
+ " <td>3211</td>\n", |
|
| 2510 |
+ " <td>506.994329</td>\n", |
|
| 2511 |
+ " <td>352627</td>\n", |
|
| 2512 |
+ " <td>339996</td>\n", |
|
| 2513 |
+ " <td>12631</td>\n", |
|
| 2514 |
+ " <td>51723</td>\n", |
|
| 2515 |
+ " <td>3.581972</td>\n", |
|
| 2516 |
+ " <td>14.667907</td>\n", |
|
| 2517 |
+ " </tr>\n", |
|
| 2518 |
+ " </tbody>\n", |
|
| 2519 |
+ "</table>\n", |
|
| 2520 |
+ "</div>" |
|
| 2521 |
+ ], |
|
| 2522 |
+ "text/plain": [ |
|
| 2523 |
+ " 구별 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 2524 |
+ "0 종로구 1715 47.844828 153789 144683 9106 27818 5.921100 18.088420\n", |
|
| 2525 |
+ "1 중구 2447 217.792208 131787 122499 9288 24392 7.047736 18.508654\n", |
|
| 2526 |
+ "2 용산구 2611 165.615463 237285 222953 14332 39070 6.039994 16.465432\n", |
|
| 2527 |
+ "3 성동구 3829 218.552413 292672 285990 6682 46380 2.283102 15.847092\n", |
|
| 2528 |
+ "4 광진구 3211 506.994329 352627 339996 12631 51723 3.581972 14.667907" |
|
| 2529 |
+ ] |
|
| 2530 |
+ }, |
|
| 2531 |
+ "execution_count": 100, |
|
| 2532 |
+ "metadata": {},
|
|
| 2533 |
+ "output_type": "execute_result" |
|
| 2534 |
+ } |
|
| 2535 |
+ ], |
|
| 2536 |
+ "source": [ |
|
| 2537 |
+ "dataset.head()" |
|
| 2538 |
+ ] |
|
| 2539 |
+ }, |
|
| 2540 |
+ {
|
|
| 2541 |
+ "cell_type": "code", |
|
| 2542 |
+ "execution_count": 101, |
|
| 2543 |
+ "id": "460ad616", |
|
| 2544 |
+ "metadata": {},
|
|
| 2545 |
+ "outputs": [], |
|
| 2546 |
+ "source": [ |
|
| 2547 |
+ "dataset.set_index('구별', inplace=True)"
|
|
| 2548 |
+ ] |
|
| 2549 |
+ }, |
|
| 2550 |
+ {
|
|
| 2551 |
+ "cell_type": "code", |
|
| 2552 |
+ "execution_count": 102, |
|
| 2553 |
+ "id": "150a3b3f", |
|
| 2554 |
+ "metadata": {},
|
|
| 2555 |
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|
| 2556 |
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|
|
| 2557 |
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| 2562 |
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+ " }\n", |
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| 2564 |
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| 2565 |
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|
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| 2566 |
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| 2567 |
+ " }\n", |
|
| 2568 |
+ "\n", |
|
| 2569 |
+ " .dataframe thead th {\n",
|
|
| 2570 |
+ " text-align: right;\n", |
|
| 2571 |
+ " }\n", |
|
| 2572 |
+ "</style>\n", |
|
| 2573 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 2574 |
+ " <thead>\n", |
|
| 2575 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 2576 |
+ " <th></th>\n", |
|
| 2577 |
+ " <th>총계</th>\n", |
|
| 2578 |
+ " <th>최근증가율</th>\n", |
|
| 2579 |
+ " <th>인구수</th>\n", |
|
| 2580 |
+ " <th>한국인</th>\n", |
|
| 2581 |
+ " <th>외국인</th>\n", |
|
| 2582 |
+ " <th>고령자</th>\n", |
|
| 2583 |
+ " <th>외국인비율</th>\n", |
|
| 2584 |
+ " <th>고령자비율</th>\n", |
|
| 2585 |
+ " </tr>\n", |
|
| 2586 |
+ " <tr>\n", |
|
| 2587 |
+ " <th>구별</th>\n", |
|
| 2588 |
+ " <th></th>\n", |
|
| 2589 |
+ " <th></th>\n", |
|
| 2590 |
+ " <th></th>\n", |
|
| 2591 |
+ " <th></th>\n", |
|
| 2592 |
+ " <th></th>\n", |
|
| 2593 |
+ " <th></th>\n", |
|
| 2594 |
+ " <th></th>\n", |
|
| 2595 |
+ " <th></th>\n", |
|
| 2596 |
+ " </tr>\n", |
|
| 2597 |
+ " </thead>\n", |
|
| 2598 |
+ " <tbody>\n", |
|
| 2599 |
+ " <tr>\n", |
|
| 2600 |
+ " <th>종로구</th>\n", |
|
| 2601 |
+ " <td>1715</td>\n", |
|
| 2602 |
+ " <td>47.844828</td>\n", |
|
| 2603 |
+ " <td>153789</td>\n", |
|
| 2604 |
+ " <td>144683</td>\n", |
|
| 2605 |
+ " <td>9106</td>\n", |
|
| 2606 |
+ " <td>27818</td>\n", |
|
| 2607 |
+ " <td>5.921100</td>\n", |
|
| 2608 |
+ " <td>18.088420</td>\n", |
|
| 2609 |
+ " </tr>\n", |
|
| 2610 |
+ " <tr>\n", |
|
| 2611 |
+ " <th>중구</th>\n", |
|
| 2612 |
+ " <td>2447</td>\n", |
|
| 2613 |
+ " <td>217.792208</td>\n", |
|
| 2614 |
+ " <td>131787</td>\n", |
|
| 2615 |
+ " <td>122499</td>\n", |
|
| 2616 |
+ " <td>9288</td>\n", |
|
| 2617 |
+ " <td>24392</td>\n", |
|
| 2618 |
+ " <td>7.047736</td>\n", |
|
| 2619 |
+ " <td>18.508654</td>\n", |
|
| 2620 |
+ " </tr>\n", |
|
| 2621 |
+ " <tr>\n", |
|
| 2622 |
+ " <th>용산구</th>\n", |
|
| 2623 |
+ " <td>2611</td>\n", |
|
| 2624 |
+ " <td>165.615463</td>\n", |
|
| 2625 |
+ " <td>237285</td>\n", |
|
| 2626 |
+ " <td>222953</td>\n", |
|
| 2627 |
+ " <td>14332</td>\n", |
|
| 2628 |
+ " <td>39070</td>\n", |
|
| 2629 |
+ " <td>6.039994</td>\n", |
|
| 2630 |
+ " <td>16.465432</td>\n", |
|
| 2631 |
+ " </tr>\n", |
|
| 2632 |
+ " <tr>\n", |
|
| 2633 |
+ " <th>성동구</th>\n", |
|
| 2634 |
+ " <td>3829</td>\n", |
|
| 2635 |
+ " <td>218.552413</td>\n", |
|
| 2636 |
+ " <td>292672</td>\n", |
|
| 2637 |
+ " <td>285990</td>\n", |
|
| 2638 |
+ " <td>6682</td>\n", |
|
| 2639 |
+ " <td>46380</td>\n", |
|
| 2640 |
+ " <td>2.283102</td>\n", |
|
| 2641 |
+ " <td>15.847092</td>\n", |
|
| 2642 |
+ " </tr>\n", |
|
| 2643 |
+ " <tr>\n", |
|
| 2644 |
+ " <th>광진구</th>\n", |
|
| 2645 |
+ " <td>3211</td>\n", |
|
| 2646 |
+ " <td>506.994329</td>\n", |
|
| 2647 |
+ " <td>352627</td>\n", |
|
| 2648 |
+ " <td>339996</td>\n", |
|
| 2649 |
+ " <td>12631</td>\n", |
|
| 2650 |
+ " <td>51723</td>\n", |
|
| 2651 |
+ " <td>3.581972</td>\n", |
|
| 2652 |
+ " <td>14.667907</td>\n", |
|
| 2653 |
+ " </tr>\n", |
|
| 2654 |
+ " </tbody>\n", |
|
| 2655 |
+ "</table>\n", |
|
| 2656 |
+ "</div>" |
|
| 2657 |
+ ], |
|
| 2658 |
+ "text/plain": [ |
|
| 2659 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 2660 |
+ "구별 \n", |
|
| 2661 |
+ "종로구 1715 47.844828 153789 144683 9106 27818 5.921100 18.088420\n", |
|
| 2662 |
+ "중구 2447 217.792208 131787 122499 9288 24392 7.047736 18.508654\n", |
|
| 2663 |
+ "용산구 2611 165.615463 237285 222953 14332 39070 6.039994 16.465432\n", |
|
| 2664 |
+ "성동구 3829 218.552413 292672 285990 6682 46380 2.283102 15.847092\n", |
|
| 2665 |
+ "광진구 3211 506.994329 352627 339996 12631 51723 3.581972 14.667907" |
|
| 2666 |
+ ] |
|
| 2667 |
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|
| 2668 |
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| 2669 |
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| 2670 |
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|
| 2671 |
+ } |
|
| 2672 |
+ ], |
|
| 2673 |
+ "source": [ |
|
| 2674 |
+ "dataset.head()" |
|
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+ ] |
|
| 2676 |
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| 2740 |
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|
| 2741 |
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|
| 2742 |
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| 2766 |
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|
| 2767 |
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|
| 2768 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 2769 |
+ " <th></th>\n", |
|
| 2770 |
+ " <th>총계</th>\n", |
|
| 2771 |
+ " <th>최근증가율</th>\n", |
|
| 2772 |
+ " <th>인구수</th>\n", |
|
| 2773 |
+ " <th>한국인</th>\n", |
|
| 2774 |
+ " <th>외국인</th>\n", |
|
| 2775 |
+ " <th>고령자</th>\n", |
|
| 2776 |
+ " <th>외국인비율</th>\n", |
|
| 2777 |
+ " <th>고령자비율</th>\n", |
|
| 2778 |
+ " </tr>\n", |
|
| 2779 |
+ " <tr>\n", |
|
| 2780 |
+ " <th>구별</th>\n", |
|
| 2781 |
+ " <th></th>\n", |
|
| 2782 |
+ " <th></th>\n", |
|
| 2783 |
+ " <th></th>\n", |
|
| 2784 |
+ " <th></th>\n", |
|
| 2785 |
+ " <th></th>\n", |
|
| 2786 |
+ " <th></th>\n", |
|
| 2787 |
+ " <th></th>\n", |
|
| 2788 |
+ " <th></th>\n", |
|
| 2789 |
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|
| 2790 |
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|
| 2791 |
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|
| 2792 |
+ " <tr>\n", |
|
| 2793 |
+ " <th>강남구</th>\n", |
|
| 2794 |
+ " <td>6871</td>\n", |
|
| 2795 |
+ " <td>136.523236</td>\n", |
|
| 2796 |
+ " <td>537800</td>\n", |
|
| 2797 |
+ " <td>533042</td>\n", |
|
| 2798 |
+ " <td>4758</td>\n", |
|
| 2799 |
+ " <td>78226</td>\n", |
|
| 2800 |
+ " <td>0.884716</td>\n", |
|
| 2801 |
+ " <td>14.545556</td>\n", |
|
| 2802 |
+ " </tr>\n", |
|
| 2803 |
+ " <tr>\n", |
|
| 2804 |
+ " <th>관악구</th>\n", |
|
| 2805 |
+ " <td>5149</td>\n", |
|
| 2806 |
+ " <td>96.152381</td>\n", |
|
| 2807 |
+ " <td>499449</td>\n", |
|
| 2808 |
+ " <td>485699</td>\n", |
|
| 2809 |
+ " <td>13750</td>\n", |
|
| 2810 |
+ " <td>79871</td>\n", |
|
| 2811 |
+ " <td>2.753034</td>\n", |
|
| 2812 |
+ " <td>15.991823</td>\n", |
|
| 2813 |
+ " </tr>\n", |
|
| 2814 |
+ " <tr>\n", |
|
| 2815 |
+ " <th>구로구</th>\n", |
|
| 2816 |
+ " <td>4608</td>\n", |
|
| 2817 |
+ " <td>109.645132</td>\n", |
|
| 2818 |
+ " <td>421163</td>\n", |
|
| 2819 |
+ " <td>396754</td>\n", |
|
| 2820 |
+ " <td>24409</td>\n", |
|
| 2821 |
+ " <td>72611</td>\n", |
|
| 2822 |
+ " <td>5.795618</td>\n", |
|
| 2823 |
+ " <td>17.240593</td>\n", |
|
| 2824 |
+ " </tr>\n", |
|
| 2825 |
+ " <tr>\n", |
|
| 2826 |
+ " <th>성북구</th>\n", |
|
| 2827 |
+ " <td>4602</td>\n", |
|
| 2828 |
+ " <td>212.211669</td>\n", |
|
| 2829 |
+ " <td>440142</td>\n", |
|
| 2830 |
+ " <td>430528</td>\n", |
|
| 2831 |
+ " <td>9614</td>\n", |
|
| 2832 |
+ " <td>74709</td>\n", |
|
| 2833 |
+ " <td>2.184295</td>\n", |
|
| 2834 |
+ " <td>16.973840</td>\n", |
|
| 2835 |
+ " </tr>\n", |
|
| 2836 |
+ " <tr>\n", |
|
| 2837 |
+ " <th>은평구</th>\n", |
|
| 2838 |
+ " <td>4131</td>\n", |
|
| 2839 |
+ " <td>223.492561</td>\n", |
|
| 2840 |
+ " <td>477173</td>\n", |
|
| 2841 |
+ " <td>473307</td>\n", |
|
| 2842 |
+ " <td>3866</td>\n", |
|
| 2843 |
+ " <td>87241</td>\n", |
|
| 2844 |
+ " <td>0.810188</td>\n", |
|
| 2845 |
+ " <td>18.282887</td>\n", |
|
| 2846 |
+ " </tr>\n", |
|
| 2847 |
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|
| 2848 |
+ "</table>\n", |
|
| 2849 |
+ "</div>" |
|
| 2850 |
+ ], |
|
| 2851 |
+ "text/plain": [ |
|
| 2852 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 2853 |
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|
| 2854 |
+ "강남구 6871 136.523236 537800 533042 4758 78226 0.884716 14.545556\n", |
|
| 2855 |
+ "관악구 5149 96.152381 499449 485699 13750 79871 2.753034 15.991823\n", |
|
| 2856 |
+ "구로구 4608 109.645132 421163 396754 24409 72611 5.795618 17.240593\n", |
|
| 2857 |
+ "성북구 4602 212.211669 440142 430528 9614 74709 2.184295 16.973840\n", |
|
| 2858 |
+ "은평구 4131 223.492561 477173 473307 3866 87241 0.810188 18.282887" |
|
| 2859 |
+ ] |
|
| 2860 |
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|
| 2861 |
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| 2862 |
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| 2863 |
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|
| 2864 |
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|
| 2865 |
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|
| 2866 |
+ "source": [ |
|
| 2867 |
+ "dataset.sort_values(by=\"총계\", ascending=False).head()" |
|
| 2868 |
+ ] |
|
| 2869 |
+ }, |
|
| 2870 |
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| 2871 |
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| 2872 |
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| 2873 |
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| 2874 |
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| 2875 |
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| 2876 |
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| 2877 |
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| 2890 |
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| 2892 |
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| 2893 |
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|
| 2894 |
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|
| 2895 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 2896 |
+ " <th></th>\n", |
|
| 2897 |
+ " <th>총계</th>\n", |
|
| 2898 |
+ " <th>최근증가율</th>\n", |
|
| 2899 |
+ " <th>인구수</th>\n", |
|
| 2900 |
+ " <th>한국인</th>\n", |
|
| 2901 |
+ " <th>외국인</th>\n", |
|
| 2902 |
+ " <th>고령자</th>\n", |
|
| 2903 |
+ " <th>외국인비율</th>\n", |
|
| 2904 |
+ " <th>고령자비율</th>\n", |
|
| 2905 |
+ " </tr>\n", |
|
| 2906 |
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|
| 2907 |
+ " <th>구별</th>\n", |
|
| 2908 |
+ " <th></th>\n", |
|
| 2909 |
+ " <th></th>\n", |
|
| 2910 |
+ " <th></th>\n", |
|
| 2911 |
+ " <th></th>\n", |
|
| 2912 |
+ " <th></th>\n", |
|
| 2913 |
+ " <th></th>\n", |
|
| 2914 |
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|
| 2915 |
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| 2916 |
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| 2917 |
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|
| 2918 |
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|
| 2919 |
+ " <tr>\n", |
|
| 2920 |
+ " <th>송파구</th>\n", |
|
| 2921 |
+ " <td>2897</td>\n", |
|
| 2922 |
+ " <td>336.295181</td>\n", |
|
| 2923 |
+ " <td>663965</td>\n", |
|
| 2924 |
+ " <td>658338</td>\n", |
|
| 2925 |
+ " <td>5627</td>\n", |
|
| 2926 |
+ " <td>97691</td>\n", |
|
| 2927 |
+ " <td>0.847484</td>\n", |
|
| 2928 |
+ " <td>14.713276</td>\n", |
|
| 2929 |
+ " </tr>\n", |
|
| 2930 |
+ " <tr>\n", |
|
| 2931 |
+ " <th>강서구</th>\n", |
|
| 2932 |
+ " <td>2744</td>\n", |
|
| 2933 |
+ " <td>223.203769</td>\n", |
|
| 2934 |
+ " <td>579768</td>\n", |
|
| 2935 |
+ " <td>574315</td>\n", |
|
| 2936 |
+ " <td>5453</td>\n", |
|
| 2937 |
+ " <td>92558</td>\n", |
|
| 2938 |
+ " <td>0.940549</td>\n", |
|
| 2939 |
+ " <td>15.964662</td>\n", |
|
| 2940 |
+ " </tr>\n", |
|
| 2941 |
+ " <tr>\n", |
|
| 2942 |
+ " <th>강남구</th>\n", |
|
| 2943 |
+ " <td>6871</td>\n", |
|
| 2944 |
+ " <td>136.523236</td>\n", |
|
| 2945 |
+ " <td>537800</td>\n", |
|
| 2946 |
+ " <td>533042</td>\n", |
|
| 2947 |
+ " <td>4758</td>\n", |
|
| 2948 |
+ " <td>78226</td>\n", |
|
| 2949 |
+ " <td>0.884716</td>\n", |
|
| 2950 |
+ " <td>14.545556</td>\n", |
|
| 2951 |
+ " </tr>\n", |
|
| 2952 |
+ " <tr>\n", |
|
| 2953 |
+ " <th>노원구</th>\n", |
|
| 2954 |
+ " <td>2492</td>\n", |
|
| 2955 |
+ " <td>112.446718</td>\n", |
|
| 2956 |
+ " <td>514946</td>\n", |
|
| 2957 |
+ " <td>510956</td>\n", |
|
| 2958 |
+ " <td>3990</td>\n", |
|
| 2959 |
+ " <td>88345</td>\n", |
|
| 2960 |
+ " <td>0.774839</td>\n", |
|
| 2961 |
+ " <td>17.156168</td>\n", |
|
| 2962 |
+ " </tr>\n", |
|
| 2963 |
+ " <tr>\n", |
|
| 2964 |
+ " <th>관악구</th>\n", |
|
| 2965 |
+ " <td>5149</td>\n", |
|
| 2966 |
+ " <td>96.152381</td>\n", |
|
| 2967 |
+ " <td>499449</td>\n", |
|
| 2968 |
+ " <td>485699</td>\n", |
|
| 2969 |
+ " <td>13750</td>\n", |
|
| 2970 |
+ " <td>79871</td>\n", |
|
| 2971 |
+ " <td>2.753034</td>\n", |
|
| 2972 |
+ " <td>15.991823</td>\n", |
|
| 2973 |
+ " </tr>\n", |
|
| 2974 |
+ " </tbody>\n", |
|
| 2975 |
+ "</table>\n", |
|
| 2976 |
+ "</div>" |
|
| 2977 |
+ ], |
|
| 2978 |
+ "text/plain": [ |
|
| 2979 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 2980 |
+ "구별 \n", |
|
| 2981 |
+ "송파구 2897 336.295181 663965 658338 5627 97691 0.847484 14.713276\n", |
|
| 2982 |
+ "강서구 2744 223.203769 579768 574315 5453 92558 0.940549 15.964662\n", |
|
| 2983 |
+ "강남구 6871 136.523236 537800 533042 4758 78226 0.884716 14.545556\n", |
|
| 2984 |
+ "노원구 2492 112.446718 514946 510956 3990 88345 0.774839 17.156168\n", |
|
| 2985 |
+ "관악구 5149 96.152381 499449 485699 13750 79871 2.753034 15.991823" |
|
| 2986 |
+ ] |
|
| 2987 |
+ }, |
|
| 2988 |
+ "execution_count": 107, |
|
| 2989 |
+ "metadata": {},
|
|
| 2990 |
+ "output_type": "execute_result" |
|
| 2991 |
+ } |
|
| 2992 |
+ ], |
|
| 2993 |
+ "source": [ |
|
| 2994 |
+ "dataset.sort_values(by=\"인구수\", ascending=False).head(5)" |
|
| 2995 |
+ ] |
|
| 2996 |
+ }, |
|
| 2997 |
+ {
|
|
| 2998 |
+ "cell_type": "code", |
|
| 2999 |
+ "execution_count": 108, |
|
| 3000 |
+ "id": "ba6ceb68", |
|
| 3001 |
+ "metadata": {
|
|
| 3002 |
+ "scrolled": true |
|
| 3003 |
+ }, |
|
| 3004 |
+ "outputs": [], |
|
| 3005 |
+ "source": [ |
|
| 3006 |
+ "import matplotlib.font_manager as fm\n", |
|
| 3007 |
+ "font_name = fm.FontProperties(fname=\"C:/Users/pc/AppData/Local/Microsoft/Windows/Fonts/NanumGothic.ttf\").get_name()\n", |
|
| 3008 |
+ "plt.rc('font', family=font_name)"
|
|
| 3009 |
+ ] |
|
| 3010 |
+ }, |
|
| 3011 |
+ {
|
|
| 3012 |
+ "cell_type": "code", |
|
| 3013 |
+ "execution_count": 109, |
|
| 3014 |
+ "id": "e49b7cdc", |
|
| 3015 |
+ "metadata": {},
|
|
| 3016 |
+ "outputs": [ |
|
| 3017 |
+ {
|
|
| 3018 |
+ "data": {
|
|
| 3019 |
+ "text/plain": [ |
|
| 3020 |
+ "<AxesSubplot:ylabel='구별'>" |
|
| 3021 |
+ ] |
|
| 3022 |
+ }, |
|
| 3023 |
+ "execution_count": 109, |
|
| 3024 |
+ "metadata": {},
|
|
| 3025 |
+ "output_type": "execute_result" |
|
| 3026 |
+ }, |
|
| 3027 |
+ {
|
|
| 3028 |
+ "data": {
|
|
| 3029 |
+ "image/png": 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\n", |
|
| 3030 |
+ "text/plain": [ |
|
| 3031 |
+ "<Figure size 720x720 with 1 Axes>" |
|
| 3032 |
+ ] |
|
| 3033 |
+ }, |
|
| 3034 |
+ "metadata": {
|
|
| 3035 |
+ "needs_background": "light" |
|
| 3036 |
+ }, |
|
| 3037 |
+ "output_type": "display_data" |
|
| 3038 |
+ } |
|
| 3039 |
+ ], |
|
| 3040 |
+ "source": [ |
|
| 3041 |
+ "dataset['총계'].plot(kind='barh', grid=True, figsize=(10, 10))" |
|
| 3042 |
+ ] |
|
| 3043 |
+ }, |
|
| 3044 |
+ {
|
|
| 3045 |
+ "cell_type": "code", |
|
| 3046 |
+ "execution_count": 110, |
|
| 3047 |
+ "id": "7108c4f0", |
|
| 3048 |
+ "metadata": {},
|
|
| 3049 |
+ "outputs": [ |
|
| 3050 |
+ {
|
|
| 3051 |
+ "data": {
|
|
| 3052 |
+ "text/plain": [ |
|
| 3053 |
+ "<AxesSubplot:ylabel='구별'>" |
|
| 3054 |
+ ] |
|
| 3055 |
+ }, |
|
| 3056 |
+ "execution_count": 110, |
|
| 3057 |
+ "metadata": {},
|
|
| 3058 |
+ "output_type": "execute_result" |
|
| 3059 |
+ }, |
|
| 3060 |
+ {
|
|
| 3061 |
+ "data": {
|
|
| 3062 |
+ "image/png": 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sImItcDnwqrbFK2hrPDPzBuCGaca4EjijY3E/sCczr164aiVJkqqpEo0dsAb45lQrI+K9wLq2RQPAZzNz6+SCzLysy36nA6cuXJmSJEnVVZXG7jnAQwAR8WfA44CTJ1dm5stb644FtgCnA5fOYtxTgNsXulhJkqQqqkpjdxZwTET0Z+YvAETEda0/VwPPp3madQVwbGufcyLirzPzf0fEjcCJXcYdBA5FxJ2Z6XV5kiSp1iIzyy0g4sXAU4CPA7+amb/WWn4dcElrXT/wd8CPAKuBTwDPAJ6QmX/VZcy9mblphuNuA7YBDAysPm3n7j0L84GWkTV9cPhI2VX0FjMrzszmZqrchtauWvpiesTY2Bj9/f1ll9FzzK24+WY2MjKyPzM3dltX6oxdRDwNOA94WWaOR8SnI+LizHz75DaZ+Zm27X8QWJ+ZtwD/NJ9jZ+Y1wDUA6zYM5q4DVZm87B3bh8Yxt2LMrDgzm5upchvdMrz0xfSIRqPB8PBw2WX0HHMrbjEzK/u/lmPAhZk5DpCZ13d87clTIuKtbe+PAx4VEee2Lbs2M69fglolSZIqrdTGLjO/2mXZ5LnhCeBgZg7PYeiJ+dQlSZLUi8qesZtSZl44j303L2QtkiRJvaCyjd1S6lu5goM+ZqewRqPh9ToFmVlxZjY35iYtT5V5pJgkSZLmx8ZOkiSpJmzsJEmSasLGTpIkqSZs7CRJkmrCxk6SJKkmbOwkSZJqwsZOkiSpJmzsJEmSasLGTpIkqSZ8pBhw5OgE63fsK7uMnrN9aJyt5laImRVnZnPTmduoj02UlgVn7CRJkmqilBm7iPhQZr609brRZZMENmXmvUtamCRJUg9b1MYuIs4DLmwdZz0wSrNpO6Vts7HMPHuGcf4IeBHwb8AR4ATgOOCazHxra5vnA2/qsvt4Zp41rw8iSZLUAxatsYuI44BXAGdlZkbETwG/kJmviYi9bZuumGmszHx1RNwHXJuZX46Is4CTM/Ptbdt8EhjuUsf18/wokiRJPWExZ+zGgYnWMY4CjwPOjoghvnfG7uAUp2NfnpmH5lNAq7l8YD5jSJIk9YrIzMUbPOIM4FeB7wJfAK5qzd7tBX4ZeMYMQ9yZmYci4hbgR/j+U7G3ZuavRMRFwJYu+/cDTwAOARdn5u1ttW0DtgEMDKw+befuPXP/oMvUmj44fKTsKnqLmRVnZnPTmdvQ2lXlFdMjxsbG6O/vL7uMnmNuxc03s5GRkf2ZubHbukVt7KbSaqr2AmfMsOkXM/OOjn33ZuamGca/AvhAeyM3nXUbBvOYc66azaZqs31onF0H/MacIsysODObm87c/LqTmTUaDYaHh8suo+eYW3HzzSwipmzsFv2/lhGxGngb8AOtRRPAFZn5DWBva1bvcuB4mjdWJPBHmfmhxa5NkiSpTpbif4OvAP4gMz8LEBH9wM2tGyAmgP9G86aKb7TWHw/8aUT8TWbeFRE3Aie2jffEjmvyvpaZ5y/B55AkSaq0pWjsvg48NyLuAB4Engk8nJnjABHxAHBaRHyK5g0XP0Zz9u4+gMzcPIdjPtz6kSRJWjaWorF7M/DrwLuBPuDzQPsM2znAbwCvofnVJ18EXpmZ35nrATNzZ5Ht+1au4KDXnxTWaDQY3TJcdhk9xcyKM7O5MTdpeVr0xq41M/eHrZ9u6+8GCjVikiRJ+n4+K1aSJKkmbOwkSZJqwsZOkiSpJmzsJEmSasLGTpIkqSZs7CRJkmrCxk6SJKkmbOwkSZJqwsZOkiSpJpbikWKVd+ToBOt37Cu7jJ6zfWicreZWiJkVZ2Yw6iMPJc2SM3aSJEk1UZkZu4j4cGb+x7b37wRel5nfiYhGl10S2JSZ9y5VjZIkSVVWicYuIp4IHOlYvIJHZhTHMvPsGca4EjijY3E/sCczr16QQiVJkiqsEo0d8FLgORGxAtgNDAEnt61fMdMAmXlZ57KIOB04dYFqlCRJqrTIzHILiFgLfAj4Y+CUzHxDa/l1wCWZeU9EXAU8AzgBOB74Wmv3LZl51zRjXwTckZm3dlm3DdgGMDCw+rSdu/cs3IdaJtb0weHOeVZNy8yKMzMYWruq8D5jY2P09/cvQjX1ZWZzY27FzTezkZGR/Zm5sdu6Uhu7iBik2dBdnJkHI+KXaTZ321uN3XZgom2XnwSe3tpn0gPA+4ETuxxiEDgE3JmZ509Vx7oNg3nMOVfN67MsR9uHxtl1oCqTvr3BzIozs7ndFdtoNBgeHl74YmrMzObG3Iqbb2YRMWVjV/Z/Lb8ObM7M7wBk5rtbp2MBbgYGaJ6m7XRx2+tGZm5uXxkRezNz0yLUK0mSVFmlNnaZOQYQEY8FrgI2AA9HBMCuzDwIXBERPwK8AXgyzbthvw28LTM/VUrhkiRJFVT2jN2kHcCHM/NmgIjoAz4aEbcC9wPvA17eavSIiB8Ero+Il093jZ0kSdJyUpUvKP46sDEiToiIlcApwLHAg9m8CPBB4KSI6Gut/yFgJfDQFONNTLFckiSptqoyY/d24JXANTS/e+5LwCsyc7y1/mXAa4DX0vzqkzuAbZl5d7fBOq+5m0nfyhUc9JE9hTUaDUa3DJddRk8xs+LMTJJmrxKNXWtW7p2tn27r/xW4dEmLkiRJ6jFVORUrSZKkebKxkyRJqgkbO0mSpJqwsZMkSaoJGztJkqSasLGTJEmqCRs7SZKkmrCxkyRJqgkbO0mSpJqoxJMnynbk6ATrd+wru4yes31onK3mVoiZFbccMhv1kYaSFogzdpIkSTVRemMXEYMR0ej4uSsifmqG/a6NiMctVZ2SJElVV/qp2Mz8MjAMEBEDwCXAp4FPtJb9MvCK1uaPAW7OzCto1v7vjWlEXAmc0TF8P7AnM69evE8gSZJUDaU3dhGxDvhx4KzWon7gG8D5EfEXwAeAvwECOAn4uW7jZOZlXcY+HTh1EcqWJEmqnMjMcguIeBVwB/C3mXmktewxwLOBfwFeBawCvg0k8JHM/F8RcR0wCNyQmX84xdgXAXdk5q1d1m0DtgEMDKw+befuPQv90WpvTR8cPlJ2Fb3FzIpbDpkNrV214GOOjY3R39+/4OPWmZnNjbkVN9/MRkZG9mfmxm7rSm3sIuJy4AXTbJLAl4B/Ar4FPBZ4EvB/gecAl2TmPRFxI3Bil/0HgUPAnZl5/lQHWbdhMI8556o5fYblbPvQOLsOlD7p21PMrLjlkNli3BXbaDQYHh5e8HHrzMzmxtyKm29mETFlY1fqfy0z883AmyffR8T1wMWZeU/bsuOAJwNHgSPANzLzaEQ8CnioNc7m9nEjYm9mblr0DyBJklQhlfjf4Ih4FvCzwGrg9RHxBeCDmTmemQ9ExL8AbwWe2tw8jgLvmDx1K0mSpAo0dhHxPOAc4D9l5pWtZacD1wCvbG32G8AnMvPVrfUrgT+LiL/JzLtLKFuSJKlySm/sgGOBtcCTIuIrNK+jG6R5w8Sk/ws8MyIawH3A04E+4IEpxpwoUkDfyhUc9JvfC2s0GoxuGS67jJ5iZsWZmSTNXumNXWZ+onUX7GXADwH3A38HXNC2zTsj4uXAHwOPAw4CF2Vm18au85o7SZKk5aD0xg4gM/cB0z4MMjPfC7x3aSqSJEnqPaU/UkySJEkLw8ZOkiSpJmzsJEmSasLGTpIkqSZs7CRJkmrCxk6SJKkmbOwkSZJqwsZOkiSpJirxBcVlO3J0gvU7pv1+ZHWxfWicreZWiJkVV6fMRn10oaRF5oydJElSTVS2sYuID7W9fl1ENCLixrZlL4qIC7rvLUmStPyUfio2Ii4EXgkcBb4IvCYzJ4CVrfXPAU4EbgMyIn4xM28CVrR+Jse5EjijY/h+YE9mXr3oH0SSJKlkpTZ2EfEDwLnA8zNzIiLeCPwS8D+An4iIBvAbwJ+0dnkh8Bzgps6xMvOyLuOfDpy6ONVLkiRVS9kzdo8GPt+aoQP4B2BD6/VtmblpcsOI+DHgFcAvRcRfA6uBP5hh/FOA2xe0YkmSpIqKzCy3gIj/DHwF+Dbw88DlmXl/RNxNsynbBpwNvAi4G/jdzPxfEbEJeHxmXte69u7ELsMPAoeAOzPz/I7jbmuNzcDA6tN27t6zKJ+vztb0weEjZVfRW8ysuDplNrR21ZIda2xsjP7+/iU7Xh2Y2dyYW3HzzWxkZGR/Zm7stq70xg4gIlYBx2Xmv7Ute0lm/mlE7KDZ0L0beAzwRuDDwEm0Grsu4+1tn+2byboNg3nMOVfN70MsQ9uHxtl1oOxJ395iZsXVKbOl/LqTRqPB8PDwkh2vDsxsbsytuPlmFhFTNnZV+a/lELAzIlbQvFP3m8BrATLz9yJiI/BzmflR4DKAiOgDHiqpXkmSpMqpSmP3u8DPZ+Z3ACLiacBVwEta6weAte07ZOanlrRCSZKkiqtKY3cEeGZE/D3NGypOBw63rf8msDsizu3Yr5GZv91lvIkuyyRJkmqtKo3dK2h+rckbgO8CnwReN7kyMz8DnDzbwTJzc5GD961cwUEf9VNYo9FgdMtw2WX0FDMrzswkafYq0di1bpq4tOw6JEmSelllHykmSZKkYmzsJEmSasLGTpIkqSZs7CRJkmrCxk6SJKkmbOwkSZJqwsZOkiSpJmzsJEmSasLGTpIkqSYq8eSJsh05OsH6HfvKLqPnbB8aZ6u5FWJmxS1EZqM+MlDSMuGMnSRJUk1UtrGLiA/NsP55EfGapapHkiSp6ko/FRsR7wGePPkW+EJm/jqwsrX+hcBlrfXHA7dl5uuAFa2fyXGuBM7oGL4f2JOZVy/eJ5AkSaqG0hu7zHxFRKwHVgOfA67pWP9x4OPw783b3inGuaxzWUScDpy6sBVLkiRVU1VOxQ4CzwHWAX/VWvbMiGhExKkAEfFaYAT4QoFxTwFuX8hCJUmSqioys7yDRzwX2Ag8FXgi8A/AY4H/A7w4MzdFxNOAtwDvBw4A/xX4LeAJwDMzc3dE3Aic2OUQg8Ah4M7MPL/j2NuAbQADA6tP27l7zyJ8wnpb0weHj5RdRW8xs+IWIrOhtasWppgeMjY2Rn9/f9ll9BQzmxtzK26+mY2MjOzPzI3d1pV9KvYLwNeABI4CDwFjmTkREce1ttkIXJSZ3wSIiAuAk9sHyczN7e8jYm9mbpruwJl5Da3Tvus2DOauA2VH0Xu2D41jbsWYWXELkdnoluGFKaaHNBoNhoeHyy6jp5jZ3JhbcYuZWam/YTLzPuC+iHgR8GqaN08QEfcAO1rbvC8inhYRWzNzV2beC/x9aybvjpJKlyRJqpzSpw4i4njgdcBPZ+bR1rK1wB8Dv9Da7NE0T9H+u8z8EvClJSxVkiSp0kpv7IAHad7E8YyI+DzNJu7Hga+3bXMPsCUihjv2/fvMfEOXMScWvkxJkqRqK72xa11P93Lg14DLge8CDeDitm1GgacUGHPzzFs9om/lCg76yKHCGo3Gsrx2aT7MrDgzk6TZK72xA8jMQ8ClZdchSZLUy6ryPXaSJEmaJxs7SZKkmrCxkyRJqgkbO0mSpJqwsZMkSaoJGztJkqSasLGTJEmqCRs7SZKkmrCxkyRJqolKPHmibEeOTrB+x76yy+g524fG2WpuhZhZcdNlNuqjACXpezhjJ0mSVBOVbewi4oa21x/ssv5dEXHc0lYlSZJUXaWeio2IY4GP8UiDuR7YmJl3A8e2bToYEY2O3U9u24+IuBI4o2ObfmBPZl69gGVLkiRVUqmNXWZ+Fzhz8n1E3ATc02XTQ8AFHcv2AA+3jXVZ504RcTpw6kLUKkmSVHWRmWXXAEBEvBgYysw3td7fDdwObAN+FPjhjl3uzcw9M4x5EXBHZt7aZd221tgMDKw+befuaYdSF2v64PCRsqvoLWZW3HSZDa1dtbTF9JCxsTH6+/vLLqOnmNncmFtx881sZGRkf2Zu7LauEo1dRGwFRoALM3O8tWwv8BbgrTPsfi3wYuDELusGac723ZmZ5081wLoNg3nMOVcVL3yZ2z40zq4D3lhdhJkVN11m3hU7tUajwfDwcNll9BQzmxtzK26+mUXElI1d2dfYnQS8Hfg4sDW/t8v8cGZ+Bhhu2/4s4JTM3N0x1PUd4+7NzE2LUbMkSVJVlT11cBg4DzgF+FhEPIrmDRF3A5cARMTxwM1AACcAx0fEJuA+4MWTM3ySJEnLXdk3TxwFjrbuaH1pZn4bICKeClwFvCQz76d5mvZ7RMQ7gDXAXUtYsiRJUmVV5XvsHgCeFRGPjojHAs8Fvj7DPg/TnMXrZmIhi5MkSeoFZZ+KnfQrwGuB3wSOAp9svZ7OLcC93VZk5uYiB+9buYKDXoRdWKPRYHTLcNll9BQzK87MJGn2KtHYZeY3gDcW3Gfv4lQjSZLUm6pyKlaSJEnzZGMnSZJUEzZ2kiRJNWFjJ0mSVBM2dpIkSTVhYydJklQTNnaSJEk1Me332EXExXxv8/cN4PPAC4AvZ+afR8SOzPy9RaxRkiRJszDTjN0/ARuB+4FnAgeBbcD/AS5vbfPCRapNkiRJBUw7Y5eZt0XEC4HPAGsz8x8j4peBW4H/1Npsque19owjRydYv2Nf2WX0nO1D42w1t0LMrLhumY36CEBJ6mraGbuIeD/w08DVwNkRcWXRA0REdLx/Z0Q8bopt39v6831FjyNJkrTczTRj9zKAiPirzDwzIv4aODDbwSNiLc1Ttq9qW7wCOCYirgWeAiTw9cw8Fzi2tc3KtjFeDVwIfKdtjBOAt2Xmta1tng+8qUsJ45l51mzrlSRJ6mXTNnYzOD4itgLj02yzBvjmFOsGMvMnZ3Gc1cC2zPzs5IKIGKZ57R8AmflJYLhzx4i4fhbjS5Ik1cJMd8WeTHOG7fiIeHrr9TuBI8CvAeuBi6cZ4jnAQ62x/gx4HHBywRr/GbgqIo62LVsJvGWG2o8DHih4LEmSpJ4VmTn1yojf4Xuvw/tSZv6PWQ8ecWNr/wsyc6y17DrgEuC6zNzUsf3ngW8BE5n5glke4yJgS5dV/cATgEPAxZl5e8d+22je4cvAwOrTdu7eM9uPpZY1fXD4SNlV9BYzK65bZkNrV5VTTA8ZGxujv7+/7DJ6ipnNjbkVN9/MRkZG9mfmxm7rZrrG7r+0v4+ITwCzauwi4sXA3wMfB95Kc4Zvqm0fTbMR+2JmnhsRH2gtvxE4ieYMIcApQHuD9rXMPB/Y0zbWFcAHOhu5Lp/tGuAagHUbBnPXgfmclV6etg+NY27FmFlx3TIb3TJcTjE9pNFoMDw8XHYZPcXM5sbcilvMzGb8DRMR72m9/D80r5mbUUQ8DTgPeFlmjkfEpyPi4sx8e9tmfxsRnwKOAvcAN3SOk5mbI+LdwCWZeW9E7O2c5ZMkSVLTbKYO1mXmSEQ8BXjJLMcdAy7MzHGAzLy+82tPMvP3gd9vXxYRm7qMFTzyXXn3zfL4kiRJy85sGrsEyMx/johvtm5KmPRwZj74fTtkfrXLssmL+SaAh6c41ndbf7bfKPFVYN/kzRMR0Wgt/0JmvrrLGA9PM74kSVJtzeVinz/hkRm0CWBrkZ0z88Jp1r289ecFbcsu55HHl81m/J1F6gHoW7mCg36TfWGNRsNrnQoys+LMTJJmr2hjl5PNlyRJkqpl2keKtfT8s2AlSZKWg9k0du9oe22TJ0mSVFEzNnaZ+aG2t59YxFokSZI0D7OZsft3mXnFYhUiSZKk+SnU2EmSJKm6bOwkSZJqwsZOkiSpJmzsJEmSasLGTpIkqSbm8kix2jlydIL1O/aVXUbP2T40zlZzK8TMuhv1kX6StCCcsZMkSaqJSjV2EbErIvpmue07I+Jxi12TJElSr6jMqdiIeD7wDOBNwH9uLRsAPgQMAbcDA8AK4D+2/jymbf8rgTM6hu0H9mTm1YtdvyRJUtlKb+xazduvAhuAnwd+PiI+CPwJ8MnMHImIvZm5KSI2Af2Z+aWI731sbWZe1mXs04FTF/szSJIkVUEVTsVeCNyWmRfSnKn7KM1G7+nASfMc+xSaM32SJEm1F5lZ3sEjfgBov05uN/B64Lut9wmMAjd2zNhdHxHXAYPADcBPAid2OcQgcAi4MzPP7zj2NmAbwMDA6tN27t6zMB9qGVnTB4ePlF1FbzGz7obWrppy3djYGP39/UtYTT2YW3FmNjfmVtx8MxsZGdmfmRu7rSv7VOxpwFPb3t8CvLDtfQLv7NjnuIh4Uuv12Zl5D/CH7RtMnrqd7sCZeQ1wDcC6DYO560DZUfSe7UPjmFsxZtbd6JbhKdc1Gg2Gh6der+7MrTgzmxtzK24xMyv1N0xm/jnw5xFxLnAe0Efz9PDngCtozsLdCjwhIv4ReBD4VyC6jyhJkrR8lT51EBFnA2cC52XmWDTving+8K7WrNtpU+x3+tJVKUmSVH1VuHniHuAE4IkRsQJYRfOmiftn2G8ceHiKdRMLVp0kSVKPKH3GLjNvi4jHAJcCa2k2dJ+mebfsdPv9yjTrNhepoW/lCg76SKPCGo3GtNdG6fuZmSRpMZXe2AFk5i00b5yQJEnSHFXhVKwkSZIWgI2dJElSTdjYSZIk1YSNnSRJUk3Y2EmSJNWEjZ0kSVJN2NhJkiTVhI2dJElSTdjYSZIk1UQlnjxRtiNHJ1i/Y1/ZZfSc7UPjbDW3QpZzZqM+tk+SFp0zdpIkSTVR2Rm7iLghM89pvW502SSBTZl575IWJkmSVFGlNnYRcSzwMR6ZOVwPbMzMu4Fj2zYdy8yzZxjrSuCMjsX9wJ7MvHphKpYkSaquUhu7zPwucObk+4i4Cbiny6YrZjHWZZ3LIuJ04NR5lChJktQzIjPLrgGAiHgxMJSZb2q9vxu4HdgGvBp4BnACcDzwtdZuWzLzrmnGvAi4IzNv7bJuW2tsBgZWn7Zz954F/DTLw5o+OHyk7Cp6y3LObGjtqjntNzY2Rn9//wJXU3/mVpyZzY25FTffzEZGRvZn5sZu6yrR2EXEVmAEuDAzx1vL9gKbgce2bfqTwNOBP25b9gDwfuDELkMPAoeAOzPz/KmOv27DYB5zzlXz+ATL0/ahcXYdqOxlmpW0nDOb612xjUaD4eHhhS1mGTC34sxsbsytuPlmFhFTNnZlX2N3EvB24OPA1vzeLvPDNK+5e1mXXS9ue93IzM0d4+7NzE0LW60kSVK1lT11cBg4DzgF+FhEPIrmjRR3A5e0TrNeERE/ArwBeDLNu2G/DbwtMz9VTtmSJEnVU/bNE0eBo607Wl+amd8GiIinAlcBL4mIAN4HvDwzD7bW/yBwfUS8fLpr7CRJkpaTqnxB8QPAsyLi0RHxWOC5wNcBWqdnHwROioi+iFgJ/BCwEnhoivEmlqBmSZKkSin7VOykXwFeC/wmcBT4ZOv1pJcBr2ltswK4A9jW+r6779N5zd1M+lau4KCPOyqs0WgwumW47DJ6iplJkhZTJRq7zPwG8MZp1v8rcOnSVSRJktR7qnIqVpIkSfNkYydJklQTNnaSJEk1YWMnSZJUEzZ2kiRJNWFjJ0mSVBM2dpIkSTVhYydJklQTNnaSJEk1UYknT5TtyNEJ1u/YV3YZPWf70Dhbza2Q5ZLZqI/ok6RSOGMnSZJUE5Vt7CLiho730fH+eRHxmqWtSpIkqbpKPRUbEccCH+ORBnM9sDEz7waO7dj8o8DZbe9XtH4mx7oSOKNjn35gT2ZevYBlS5IkVVKpjV1mfhc4c/J9RNwE3DPF5iumWD451mWdyyLidODUeZQoSZLUMyIzy64BgIh4MTCUmW9qvb8buB3YBnwd+IfMPDkifh04B3g8cF1m7p5mzIuAOzLz1i7rtrXGZmBg9Wk7d+9Z2A+0DKzpg8NHyq6ityyXzIbWrlqwscbGxujv71+w8ZYLcyvOzObG3Iqbb2YjIyP7M3Njt3WVuCs2IrYCI8CFbYtvy8xNrfWvBL4cEf8xM98BvCMihoFnttbfCJzYZehB4FBE3JmZ57evyMxrgGsA1m0YzF0HKhFFT9k+NI65FbNcMhvdMrxgYzUaDYaHF2685cLcijOzuTG34hYzs7KvsTsJeDvwcWBrfu/04Ydb2zwOeCnwC8D7I+LTmXm4fZzM3Nwx7t7JplCSJGm5KHvq4DBwHnAK8LGIeBTNGynuBi6JiBXAHuD1mXk0Il4LvAPYPNWAkiRJy1XZN08cBY627mh9aWZ+GyAingpcRXOm7ncy84ut7e+KiJeUVrAkSVKFVeV77B4AnhURj46IxwLPBb6eTV9s37DtdO1E66ebqZZLkiTVVtmnYif9CvBa4DeBo8AnW6+nlJmfAj41xbpCp2r7Vq7goI9AKqzRaCzoRfLLgZlJkhZTJRq7zPwG8May65AkSeplVTkVK0mSpHmysZMkSaoJGztJkqSasLGTJEmqCRs7SZKkmrCxkyRJqgkbO0mSpJqwsZMkSaoJGztJkqSaqMSTJ8p25OgE63fsK7uMnrN9aJyt5lZIHTIb9fF7klRZzthJkiTVxKI1dhExFBH/HBGNjp/fnWaf97b+fN9i1SVJklRXi3kq9kTg6sz8g24rI+Ja4ClAAl/PzHOBY1urV7Zt92rgQuA7bbufALwtM69tbfN84E1dDjOemWfN94NIkiT1gsVs7BJYMc36gcz8yVmMsxrYlpmfnVwQEcPAxn8/UOYngeHOHSPi+lnWKkmS1PMWs7H7CvBbEfGzwNOAfwbGgQOZ+ZoC4/wzcFVEHG1bthJ4y3Q7RcRxwAPTrN8GbAMYGFjNzqHxAiUJYE1f82YAzV4dMms0Gkt6vLGxsSU/Zh2YW3FmNjfmVtxiZhaZufCDRvQBj25bdDXweuDe1vsE3pOZmzr2+zzwLWAiM18wy2NdBGzpsqofeAJwCLg4M2+faox1GwbzmHOums3h1Gb70Di7DnhjdRF1yGyp74ptNBoMDw8v6THrwNyKM7O5Mbfi5ptZROzPzI3d1i3Wb5ifAX607f0B4IK29wk8dvJNRDyaZiP2xcw8NyI+0Fp+I3AScKS16SlAe4P2tcw8H9jTNtYVwAema+QkSZLqaFEau8z8CPCRiDgXOA/oo3kH7ueAKzLzWxExHhGfAo4C9wA3dBlnc0S8G7gkM++NiL2ds3ySJElqWrRzQhFxNnAmcF5mjkVEAM8H3gVsyszfB36/Y59N3YZq/QDct1j1SpIk9brFvNjnHppfS/LEiPgKzVOvJwH3T7PPd1t/tt8o8VVg3+TNExHRaC3/Qma+ussYD7d+Zq1v5QoO+m36hTUaDUa3DJddRk8xM0nSYlq0xi4zb4uIxwCXAmtpNnSfpvmddFPt8/LWnxe0LbscuLzAcXfOtWZJkqRetqi352XmLcAti3kMSZIkNfmsWEmSpJqwsZMkSaoJGztJkqSasLGTJEmqCRs7SZKkmrCxkyRJqgkbO0mSpJqwsZMkSaqJRf2C4l5x5OgE63fsK7uMnrN9aJyt5lZIL2c26mP3JKnynLGTJEmqidIbu2h6XUR8LCL+IiL+PCLeFRFrZ9jvnRHxuKWqU5IkqeqqcCr2bODJmfkzkwsi4oeBPcDPtS37aGa+qG2/FbQ1phFxJXBGx9j9wJ7MvHoxCpckSaqSKjR2dwFPiYinAHfSbMaeD3xtcoOIeCxwckQcm5nf7TZIZl7WuSwiTgdOXZSqJUmSKqb0xi4z/1dEXAd8APg2cC+whmZzN+m1wFuB3wW2ty2/OSJuyMw/nGL4U4DbF7xoSZKkCorMLO/gEX3Ao4GfAIaAP26t+gBwLpDALwMPZuafRMSFwLOB19A8VXtJZt4TETcCJ3Y5xCBwCLgzM8/vOPY2YBvAwMDq03bu3rPQH6/21vTB4SNlV9FbejmzobWrSjnu2NgY/f39pRy7l5lbcWY2N+ZW3HwzGxkZ2Z+ZG7utK7uxewnNWbWpJPCPwEezVWhEnJKZt7dm+S7JzHu6jLs3MzfNto51GwbzmHOuKlK6aH51x64DpU/69pRezqysrztpNBoMDw+XcuxeZm7FmdncmFtx880sIqZs7Er9DZOZfwr8aUScC5wH9NG8IeJzwFsy85sAEfHfImJ3Zh7KzMlTq58GHiyjbkmSpCoqfeogIs4GzgTOy8yxiAia19e9G/iF1mbH0PHVLJnpuVNJkqQ2pTd2wD3ACcATI+IrwGOBk4D72rb5BvDBiOi8OundmfmeLmNOLEahkiRJVVZ6Y5eZt0XEY4BLgbXA/TRPs17Yts2VwJUFxtxcpIa+lSs46OOSCms0GoxuGS67jJ5iZpKkxVR6YweQmbcAt5RdhyRJUi8r/ZFikiRJWhg2dpIkSTVhYydJklQTNnaSJEk1YWMnSZJUEzZ2kiRJNWFjJ0mSVBM2dpIkSTVhYydJklQTlXjyRNmOHJ1g/Y59ZZfRc7YPjbPV3AqpamajPlJPkmqhsjN2EfGhsmuQJEnqJaXO2EXEY4AvAf/SWvQDwI7M/DNgZWubJwHvA34U+EJru6cBdwC/npkHWttdCZzRcYh+YE9mXr2Yn0OSJKkKyj4V+xjg1szcChARm4AntG+QmV8FhiPiTzPzJa3trgdelZljbdtd1jl4RJwOnLpo1UuSJFVIZU/FLpBTgNvLLkKSJGkpRGaWd/CIxwO7O2bsHp+Z10XE3TSbsv8BnEf3U7FXZeafRsSNwIldDjEIHALuzMzzO469DdgGMDCw+rSdu/cs7IdbBtb0weEjZVfRW6qa2dDaVWWXMKWxsTH6+/vLLqPnmFtxZjY35lbcfDMbGRnZn5kbu60r+1TsdG7LzE2t19cARMRZwMmZ+fb2DTNzc/v7iNjbtm9XmXnN5LjrNgzmrgNVjqKatg+NY27FVDWz0S3DZZcwpUajwfDwcNll9BxzK87M5sbcilvMzMr+DfMA8LSIaLTe9wFvKK8cSZKk3lVqY5eZ3wWeM9X6iHg28Na2RX3AsRHxkrZl12bm9YtUoiRJUs8oe8ZuOkcz8zPA8Bz2nVjgWiRJkiqvsnfFZuZL57Hv5pm3kiRJqpcqz9gtmb6VKzjoI5UKazQalb7ovorMTJK0mCo7YydJkqRibOwkSZJqwsZOkiSpJmzsJEmSasLGTpIkqSZs7CRJkmrCxk6SJKkmbOwkSZJqwsZOkiSpJmzsJEmSasJHigFHjk6wfse+ssvoOduHxtlqboVUNbNRH6knSbVQ+Rm7iDg2It4TEZ+IiL+MiDNby18UEReUXZ8kSVJVlD5jFxE/BazPzD1TbHIBcHNmfigiVgIfAf4KWNH6mRznSuCMjn37gT2ZefXCVy5JklQtpTd2dDRoXTzMIzOLxzBFzZl5WeeyiDgdOHW+BUqSJPWCyp+KBa4HfioiPgF8HPj9AvueAty+KFVJkiRVTGRmuQVEDNFs3r4NHAc80Fo1DvwssJbmLF0/cGLr/dOBrwFjmXldRNzYWtdpEDgE3JmZ53ccdxuwDWBgYPVpO3dPdSZYU1nTB4ePlF1Fb6lqZkNrV5VdwpTGxsbo7+8vu4yeY27FmdncmFtx881sZGRkf2Zu7Lau9MauXURcD7wqM8da79cCbwAeAp4P7AM+D3yB5mzcqsy8rss4ezNz02yPu27DYB5zzlXzrn+52T40zq4DVTib3zuqmlmV74ptNBoMDw+XXUbPMbfizGxuzK24+WYWEVM2dtX7DdMmM+8CfiMinkWz1uuAZwF7gAHgD8qrTpIkqVpKbewi4nLgBW2L1gD7ImJyGjGBlwL/BTi/NZN3CLgpItYDK5ewXEmSpEortbHLzDcDb55pu4h4ALg5IjpX3QT89y67TMy/OkmSpN5S6VOxkzLzvILbb16sWiRJkqqqJxq7xda3cgUHK3zxeFU1Gg1GtwyXXUZPMTNJ0mLqhe+xkyRJ0izY2EmSJNWEjZ0kSVJN2NhJkiTVhI2dJElSTdjYSZIk1YSNnSRJUk3Y2EmSJNWEjZ0kSVJN2NhJkiTVhI8UA44cnWD9jn1ll9Fztg+Ns9XcCqlSZqM+Rk+SascZO0mSpJroiRm7iBgEru1Y/BRga2b+ZQklSZIkVU5PNHaZ+WVgGCAiBoBLgE8Dn2gtexxwExAdu64Fnp2Z9yxRqZIkSaXpicYuItYBPw6c1VrUD3wDOD8i/iIzDwMjXfa7Gji6ZIVKkiSVKDKz7BpmFBGvAu4A/jYzj7SWPQZ4NvAvmXloiv2uz8zzp1i3DdgGMDCw+rSdu/csSu11tqYPDh8pu4reUqXMhtauKruEWRkbG6O/v7/sMnqOuRVnZnNjbsXNN7ORkZH9mbmx27rKN3YRcTnwgmk2SWA1cHeXdT8KfAG4NjOvn2qAdRsG85hzrppXncvR9qFxdh3oiUnfyqhSZr1yV2yj0WB4eLjsMnqOuRVnZnNjbsXNN7OImLKxq8ZvmGlk5puBN0++j4jrgYunum4uIs4CTs7Mty9NhZIkSdVQ+cZuUkQ8C/hZmrNzr4+ILwAfzMzxciuTJEmqhp74HruIeB7wSmBXZv50Zl4G3AlcU25lkiRJ1dErM3bH0vzqkidFxFeAxwKDQLervx9u/cxa38oVHOyR642qpNFoMLpluOwyeoqZSZIWU080dpn5idZdsJcBPwTcD/wdcEGXbf8K+KulrVCSJKl8PdHYAWTmPqAaD9mUJEmqoJ64xk6SJEkzs7GTJEmqCRs7SZKkmrCxkyRJqgkbO0mSpJqwsZMkSaoJGztJkqSasLGTJEmqiZ75guLFdOToBOt3+N3HRW0fGmeruRVSdmajPjpPkmqt9MYuIp4L/F7nYuCrmXl+a5vXAb8IfDMzN7eWvQh4fGa+bynrlSRJqqrSG7vM/DtguHN5RHyo9edzgBOB24CMiF/MzJuAFa2fye2vBM7oGKYf2JOZVy9O9ZIkSdVRemPXTUSsAB5svT0A3NV6/ULgOcBNnftk5mVdxjkdOHWRypQkSaqUqt48cRLwVYDMfCAzvwY8AXgF8IcR8dfAFbMY5xTg9kWrUpIkqUJKnbGLiMuBF0yz/q+BlwIvB14EfAP4ocwciYhNwONb291I83Rtp0HgUETcOXm9niRJUl1FZpZdw7QiYgdwN/Bu4DHAG4EP05zVe3xmXtdln72ZuWmGcbcB2wAGBlaftnP3noUtfBlY0weHj5RdRW8pO7OhtavKO/gcjY2N0d/fX3YZPcfcijOzuTG34uab2cjIyP7M3NhtXemNXes6uHcDX29fDNyVmee1ttkI/GBmfrRtv+cB/Zn5513GnLGxa7duw2Aec85Vc/wEy9f2oXF2HajkZZqVVXZmvfh1J41Gg+Hh4bLL6DnmVpyZzY25FTffzCJiysauCr+VTwT+IDOvbV8YEXvb3g4Aa9vXZ+anFr80SZKk3lGFxu47wK6I6LwG7lDb628CuyPi3I5tGpn5213GnFjA+iRJknpC6Y1dZn4SeOoM23wGOLnAmJvnW5ckSVKvKb2xq4K+lSs42IPXHpWt0WgwumW47DJ6iplJkhZTVb/HTpIkSQXZ2EmSJNWEjZ0kSVJN2NhJkiTVhI2dJElSTdjYSZIk1YSNnSRJUk3Y2EmSJNWEjZ0kSVJN2NhJkiTVhI8UA44cnWD9jn1ll9Fztg+Ns9XcClnszEZ9NJ4kLWvO2EmSJNVE6TN2ETEEfBi4q2PV32bmpdPs907gdZn5ncWsT5IkqVeU3tgBJwJXZ+YfdFsZERcAF7Zt+4eZuQdYQduMY0RcCZzRsXs/sCczr17wqiVJkiqmCo1d0mzSuq/MfB/wvog4Afht4N1tq2+OiBsy8w8z87LOfSPidODUBa5XkiSpkiIzyy0gYh3wTmAl8DTgn4Fx4EBmvqa1zZnATwOPAcaA3wPeBlySmfdMM/ZFwB2ZeWuXdduAbQADA6tP27l7zwJ+quVhTR8cPlJ2Fb1lsTMbWrtq8QYvydjYGP39/WWX0XPMrTgzmxtzK26+mY2MjOzPzI3d1pU6YxcRfcB3gJe2Fl0NvB64t7V+FfD7wOeAHZmZEfFTwLM7xrmR5mnaToPAoYi4MzPPb1+RmdcA1wCs2zCYuw5UYfKyt2wfGsfcilnszEa3DC/a2GVpNBoMDw+XXUbPMbfizGxuzK24xcys7N/KPwP8aNv7A8AFbe8TeENm3vvvCzL/EiAi7gXuay3b3D5oROzNzE2LVLMkSVIlldrYZeZHgI9ExLnAeUAfzRsiPgdckZnfAoiI/w48q2P3HwR+DJhYuoolSZKqq+wZOyLibOBM4LzMHIuIAJ4PvAvYBJCZr+uy33U0G8GHlqxYSZKkCqvCFxTfA5wAPDEiVgCrgJOA++cxprN4kiRp2Sl9xi4zb4uIxwCXAmtpNnSf5pHvrpvKR4AHphhzc7flU+lbuYKDPoqpsEajUcuL9ReTmUmSFlPpjR1AZt4C3FJwn5sWqRxJkqSeVIVTsZIkSVoANnaSJEk1YWMnSZJUEzZ2kiRJNWFjJ0mSVBM2dpIkSTVhYydJklQTNnaSJEk1YWMnSZJUE5V48kTZjhydYP2OfWWX0XO2D42z1dymNOpj6iRJS8wZO0mSpJroicYuIj7YZdm7IuK4MuqRJEmqotJPxUbEOcDlwDc7Vn0xM3+99XowIhod60+mrTGNiCuBMzq26Qf2ZObVC1exJElSNZXe2AFPAP5zZn5smm2+kpkvaV8QEde1v8/Myzp3iojTgVMXokhJkqSqi8wst4CIVwGj0zV2EfEXwKOBaC1KYC3w9Mw8Os1+FwF3ZOatXdZtA7YBDAysPm3n7j1z/xDL1Jo+OHyk7Cqqa2jtqu9bNjY2Rn9/fwnV9C4zmxtzK87M5sbciptvZiMjI/szc2O3dVVo7KY6FXsXsBU4sW3Z82iegm3vwr4DvK9ju0mDwCHgzsw8f6oa1m0YzGPOuapw7cvd9qFxdh2owqRvNXW7K7bRaDA8PLz0xfQwM5sbcyvOzObG3Iqbb2YRMWVjV/pv5cy8AbgBICKuB16VmWOt9xuAczp2OUqz4Zv0yczc3L5BROzNzE2LVbMkSVIVld7YTScz7wR+LyJ+BHgD8GSap2G/DbwtMz9VZn2SJElVUmpjFxGXAy9oW7QG2BcRk+eHk+aM3fuAl2fmwdZ+PwhcHxEvz8y7lrJmSZKkqiq1scvMNwNvnmm7iHgQOCkiDgHjwA8BK4GHpthlYsGKlCRJ6hGVPhXb5mXAa4DXAiuAO4BtmXl3t407r7mbSd/KFRz08U+FNRoNRrcMl12GJElq6YnGLjP/Fbi07DokSZKqrCceKSZJkqSZ2dhJkiTVhI2dJElSTdjYSZIk1YSNnSRJUk3Y2EmSJNWEjZ0kSVJN2NhJkiTVRE98QfFiO3J0gvU79pVdRs/ZPjTO1prmNuqTSCRJPcgZO0mSpJqwsZMkSaqJ0k/FRsQgcG3H4qcAF2bmx0ooSZIkqSeV3thl5peBYYCIOA74DeAO4JbWskcBH+f7ZxdPAs7KzNHWdlcCZ3Rs0w/sycyrF6l8SZKkyii9sYuIlcBG4CXA04DVwE3AcyLis5n5EHBml/3eAjx+8n1mXtZlm9OBUxenckmSpGqJzCy3gIg3AV+jOUP3POCDwA8BPwnsB74A3A4c7tj1QeD8zLx7mrEvAu7IzFu7rNsGbAMYGFh92s7de+b/YZaZNX1w+EjZVSyOobWrFmXcsbEx+vv7F2XsujKzuTG34sxsbsytuPlmNjIysj8zN3ZbV+qMXUT8APDe1ttjgBcDnwfuAz4FJM0aP5+ZvzTNODcCJ3ZZNQgciog7M/P89hWZeQ1wDcC6DYO560Dpk5c9Z/vQOHXNbXTL8KKM22g0GB5enLHryszmxtyKM7O5MbfiFjOzsn8r/3/AyW3vbwNe0PY+aTZfx0REAzgBOA64q7X+dzPzLzJzc/ugEbE3MzctVtGSJElVVGpjl5k3AzdHxLnAeUAfzZm7zwFXZOa3Wpu+FCAizgJOzsy3l1GvJElSlZU9Y0dEnE3z5ojzMnMsIgJ4PvCu1g0Sb23bvA84NiJe0rbs2sy8fukqliRJqqbSGzvgHpqnWJ8YEV8BHkvzq0zuz8zP0PoqlIImimzct3IFB32EVGGNRmPRrkWTJEnFld7YZeZtEfEY4FJgLXA/8GngwnmMuXnmrSRJkuql9MYOIDNvofWFxJIkSZobnxUrSZJUEzZ2kiRJNWFjJ0mSVBOlP1KsCiLiPuBg2XX0oAFgyke6qSszK87M5sbcijOzuTG34uab2ZMzc3W3FZW4eaICDk71zDVNLSI+a27FmFlxZjY35lacmc2NuRW3mJl5KlaSJKkmbOwkSZJqwsau6ZqyC+hR5lacmRVnZnNjbsWZ2dyYW3GLlpk3T0iSJNWEM3aSJEk1sezvio2ILcAvAePA32Xmfy25pFJFxArgd4CNmfkzrWVdMyq6vM4iYg/wMPAE4KbMvN7cphcRf0Tzv0GPBe7IzN82s5lFxKOA9wL3Zeavmtn0IuIfgb9vvT0K/EZmprlNLyJ+GLgcCGAC+C1gBDObUkScDFzStuh0YBswyFLmlpnL9ofmL5SP8cgp6fcBP1J2XSVnsqn1l/GW6TIqurzsz7WE+R0D3GZuhXN7D/AMM5tVVr8DvBC41r9ns8rrli7LzG36zAK4ATjRzOac4QpgXxm5LfcZuzOAv8xWesBNwDBwR2kVlSwz9wJExOSiqTL6l4LLl0umxwLfxNxmLSJW0fyyzpMxs2m1/k/+MzzyGf17NrNjIuJ3gCcBH8nMj2JuM3k28FVgZ0T0A38DfA0zK2IzsJcS/q4t92vsTgS+1fb+W61lesRUGRVdvly8CfivmNuMImIwIv5/4LPA22j+H66ZTSEiTgWemJk3ty3279kMMvPMzPwvNE+J/XJEPAVzm8l64BTg9Zl5IXAq8FzMrIitNGfblvzv2nJv7L5J85qoSU9oLdMjpsqo6PLai4jXAf+YmZ/G3GaUmV/OzC3A04ALgZWY2XR+CfiRiPgT4C3AjwOrMbNZycxx4BPA0/Hf50weoHkK+6HW+5uBBzGzWYmIs4C/zcwHKeHv2nJv7P4eOCseOe/4i8AnS6yniqbKqOjyWouIXwO+k5nvby0yt1lq/cJdAfw1ZjalzHxDZv5qZr4KeCPwaZrXJprZ7J0OfA7/fc5kP80ZuknPBb6Mmc3WxcA7Wq+X/O/asr7GLjPviYj3Ah+KiHHgs5n5v8uuqyK+C9NnVHR5XUXEGcClwMcj4vTW4sto3rlobl20Tiv+JjAGHA/cmJmH/Ls2a+PAuP8+ZxYR7wGOAP3A3swcbS03tylk5r9FxMci4gM0/42OZuaNEXEsZjatiHgmcCgzvwnl/A71C4olSZJqYrmfipUkSaoNGztJkqSasLGTJEmqCRs7SZKkmrCxkyRJqgkbO0mSpJqwsZMkSaoJGztJkqSa+H8LIzq2znC8DgAAAABJRU5ErkJggg==\n", 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+ "<Figure size 720x720 with 1 Axes>" |
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+ }, |
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+ "metadata": {
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+ "needs_background": "light" |
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+ "output_type": "display_data" |
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+ } |
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+ ], |
|
| 3073 |
+ "source": [ |
|
| 3074 |
+ "dataset['총계'].sort_values().plot(kind='barh', grid=True, figsize=(10, 10))" |
|
| 3075 |
+ ] |
|
| 3076 |
+ }, |
|
| 3077 |
+ {
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+ "cell_type": "code", |
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| 3079 |
+ "execution_count": 111, |
|
| 3080 |
+ "id": "20db3ebf", |
|
| 3081 |
+ "metadata": {},
|
|
| 3082 |
+ "outputs": [ |
|
| 3083 |
+ {
|
|
| 3084 |
+ "data": {
|
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| 3085 |
+ "text/html": [ |
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| 3086 |
+ "<div>\n", |
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+ "<style scoped>\n", |
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+ " .dataframe tbody tr th:only-of-type {\n",
|
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+ " vertical-align: middle;\n", |
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+ " }\n", |
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+ "\n", |
|
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+ " .dataframe tbody tr th {\n",
|
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+ " vertical-align: top;\n", |
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+ " }\n", |
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+ "\n", |
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+ " .dataframe thead th {\n",
|
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+ " text-align: right;\n", |
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+ " }\n", |
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+ "</style>\n", |
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+ " <thead>\n", |
|
| 3102 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 3103 |
+ " <th></th>\n", |
|
| 3104 |
+ " <th>총계</th>\n", |
|
| 3105 |
+ " <th>최근증가율</th>\n", |
|
| 3106 |
+ " <th>인구수</th>\n", |
|
| 3107 |
+ " <th>한국인</th>\n", |
|
| 3108 |
+ " <th>외국인</th>\n", |
|
| 3109 |
+ " <th>고령자</th>\n", |
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| 3110 |
+ " <th>외국인비율</th>\n", |
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| 3111 |
+ " <th>고령자비율</th>\n", |
|
| 3112 |
+ " </tr>\n", |
|
| 3113 |
+ " <tr>\n", |
|
| 3114 |
+ " <th>구별</th>\n", |
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| 3115 |
+ " <th></th>\n", |
|
| 3116 |
+ " <th></th>\n", |
|
| 3117 |
+ " <th></th>\n", |
|
| 3118 |
+ " <th></th>\n", |
|
| 3119 |
+ " <th></th>\n", |
|
| 3120 |
+ " <th></th>\n", |
|
| 3121 |
+ " <th></th>\n", |
|
| 3122 |
+ " <th></th>\n", |
|
| 3123 |
+ " </tr>\n", |
|
| 3124 |
+ " </thead>\n", |
|
| 3125 |
+ " <tbody>\n", |
|
| 3126 |
+ " <tr>\n", |
|
| 3127 |
+ " <th>종로구</th>\n", |
|
| 3128 |
+ " <td>1715</td>\n", |
|
| 3129 |
+ " <td>47.844828</td>\n", |
|
| 3130 |
+ " <td>153789</td>\n", |
|
| 3131 |
+ " <td>144683</td>\n", |
|
| 3132 |
+ " <td>9106</td>\n", |
|
| 3133 |
+ " <td>27818</td>\n", |
|
| 3134 |
+ " <td>5.921100</td>\n", |
|
| 3135 |
+ " <td>18.088420</td>\n", |
|
| 3136 |
+ " </tr>\n", |
|
| 3137 |
+ " <tr>\n", |
|
| 3138 |
+ " <th>중구</th>\n", |
|
| 3139 |
+ " <td>2447</td>\n", |
|
| 3140 |
+ " <td>217.792208</td>\n", |
|
| 3141 |
+ " <td>131787</td>\n", |
|
| 3142 |
+ " <td>122499</td>\n", |
|
| 3143 |
+ " <td>9288</td>\n", |
|
| 3144 |
+ " <td>24392</td>\n", |
|
| 3145 |
+ " <td>7.047736</td>\n", |
|
| 3146 |
+ " <td>18.508654</td>\n", |
|
| 3147 |
+ " </tr>\n", |
|
| 3148 |
+ " <tr>\n", |
|
| 3149 |
+ " <th>용산구</th>\n", |
|
| 3150 |
+ " <td>2611</td>\n", |
|
| 3151 |
+ " <td>165.615463</td>\n", |
|
| 3152 |
+ " <td>237285</td>\n", |
|
| 3153 |
+ " <td>222953</td>\n", |
|
| 3154 |
+ " <td>14332</td>\n", |
|
| 3155 |
+ " <td>39070</td>\n", |
|
| 3156 |
+ " <td>6.039994</td>\n", |
|
| 3157 |
+ " <td>16.465432</td>\n", |
|
| 3158 |
+ " </tr>\n", |
|
| 3159 |
+ " <tr>\n", |
|
| 3160 |
+ " <th>성동구</th>\n", |
|
| 3161 |
+ " <td>3829</td>\n", |
|
| 3162 |
+ " <td>218.552413</td>\n", |
|
| 3163 |
+ " <td>292672</td>\n", |
|
| 3164 |
+ " <td>285990</td>\n", |
|
| 3165 |
+ " <td>6682</td>\n", |
|
| 3166 |
+ " <td>46380</td>\n", |
|
| 3167 |
+ " <td>2.283102</td>\n", |
|
| 3168 |
+ " <td>15.847092</td>\n", |
|
| 3169 |
+ " </tr>\n", |
|
| 3170 |
+ " <tr>\n", |
|
| 3171 |
+ " <th>광진구</th>\n", |
|
| 3172 |
+ " <td>3211</td>\n", |
|
| 3173 |
+ " <td>506.994329</td>\n", |
|
| 3174 |
+ " <td>352627</td>\n", |
|
| 3175 |
+ " <td>339996</td>\n", |
|
| 3176 |
+ " <td>12631</td>\n", |
|
| 3177 |
+ " <td>51723</td>\n", |
|
| 3178 |
+ " <td>3.581972</td>\n", |
|
| 3179 |
+ " <td>14.667907</td>\n", |
|
| 3180 |
+ " </tr>\n", |
|
| 3181 |
+ " </tbody>\n", |
|
| 3182 |
+ "</table>\n", |
|
| 3183 |
+ "</div>" |
|
| 3184 |
+ ], |
|
| 3185 |
+ "text/plain": [ |
|
| 3186 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 3187 |
+ "구별 \n", |
|
| 3188 |
+ "종로구 1715 47.844828 153789 144683 9106 27818 5.921100 18.088420\n", |
|
| 3189 |
+ "중구 2447 217.792208 131787 122499 9288 24392 7.047736 18.508654\n", |
|
| 3190 |
+ "용산구 2611 165.615463 237285 222953 14332 39070 6.039994 16.465432\n", |
|
| 3191 |
+ "성동구 3829 218.552413 292672 285990 6682 46380 2.283102 15.847092\n", |
|
| 3192 |
+ "광진구 3211 506.994329 352627 339996 12631 51723 3.581972 14.667907" |
|
| 3193 |
+ ] |
|
| 3194 |
+ }, |
|
| 3195 |
+ "execution_count": 111, |
|
| 3196 |
+ "metadata": {},
|
|
| 3197 |
+ "output_type": "execute_result" |
|
| 3198 |
+ } |
|
| 3199 |
+ ], |
|
| 3200 |
+ "source": [ |
|
| 3201 |
+ "dataset.head()" |
|
| 3202 |
+ ] |
|
| 3203 |
+ }, |
|
| 3204 |
+ {
|
|
| 3205 |
+ "cell_type": "code", |
|
| 3206 |
+ "execution_count": 112, |
|
| 3207 |
+ "id": "da5811c6", |
|
| 3208 |
+ "metadata": {},
|
|
| 3209 |
+ "outputs": [], |
|
| 3210 |
+ "source": [ |
|
| 3211 |
+ "dataset['CCTV비율'] = dataset['총계']/dataset['인구수'] * 100" |
|
| 3212 |
+ ] |
|
| 3213 |
+ }, |
|
| 3214 |
+ {
|
|
| 3215 |
+ "cell_type": "code", |
|
| 3216 |
+ "execution_count": 113, |
|
| 3217 |
+ "id": "350fa4e9", |
|
| 3218 |
+ "metadata": {},
|
|
| 3219 |
+ "outputs": [ |
|
| 3220 |
+ {
|
|
| 3221 |
+ "data": {
|
|
| 3222 |
+ "text/html": [ |
|
| 3223 |
+ "<div>\n", |
|
| 3224 |
+ "<style scoped>\n", |
|
| 3225 |
+ " .dataframe tbody tr th:only-of-type {\n",
|
|
| 3226 |
+ " vertical-align: middle;\n", |
|
| 3227 |
+ " }\n", |
|
| 3228 |
+ "\n", |
|
| 3229 |
+ " .dataframe tbody tr th {\n",
|
|
| 3230 |
+ " vertical-align: top;\n", |
|
| 3231 |
+ " }\n", |
|
| 3232 |
+ "\n", |
|
| 3233 |
+ " .dataframe thead th {\n",
|
|
| 3234 |
+ " text-align: right;\n", |
|
| 3235 |
+ " }\n", |
|
| 3236 |
+ "</style>\n", |
|
| 3237 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 3238 |
+ " <thead>\n", |
|
| 3239 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 3240 |
+ " <th></th>\n", |
|
| 3241 |
+ " <th>총계</th>\n", |
|
| 3242 |
+ " <th>최근증가율</th>\n", |
|
| 3243 |
+ " <th>인구수</th>\n", |
|
| 3244 |
+ " <th>한국인</th>\n", |
|
| 3245 |
+ " <th>외국인</th>\n", |
|
| 3246 |
+ " <th>고령자</th>\n", |
|
| 3247 |
+ " <th>외국인비율</th>\n", |
|
| 3248 |
+ " <th>고령자비율</th>\n", |
|
| 3249 |
+ " <th>CCTV비율</th>\n", |
|
| 3250 |
+ " </tr>\n", |
|
| 3251 |
+ " <tr>\n", |
|
| 3252 |
+ " <th>구별</th>\n", |
|
| 3253 |
+ " <th></th>\n", |
|
| 3254 |
+ " <th></th>\n", |
|
| 3255 |
+ " <th></th>\n", |
|
| 3256 |
+ " <th></th>\n", |
|
| 3257 |
+ " <th></th>\n", |
|
| 3258 |
+ " <th></th>\n", |
|
| 3259 |
+ " <th></th>\n", |
|
| 3260 |
+ " <th></th>\n", |
|
| 3261 |
+ " <th></th>\n", |
|
| 3262 |
+ " </tr>\n", |
|
| 3263 |
+ " </thead>\n", |
|
| 3264 |
+ " <tbody>\n", |
|
| 3265 |
+ " <tr>\n", |
|
| 3266 |
+ " <th>종로구</th>\n", |
|
| 3267 |
+ " <td>1715</td>\n", |
|
| 3268 |
+ " <td>47.844828</td>\n", |
|
| 3269 |
+ " <td>153789</td>\n", |
|
| 3270 |
+ " <td>144683</td>\n", |
|
| 3271 |
+ " <td>9106</td>\n", |
|
| 3272 |
+ " <td>27818</td>\n", |
|
| 3273 |
+ " <td>5.921100</td>\n", |
|
| 3274 |
+ " <td>18.088420</td>\n", |
|
| 3275 |
+ " <td>1.115164</td>\n", |
|
| 3276 |
+ " </tr>\n", |
|
| 3277 |
+ " <tr>\n", |
|
| 3278 |
+ " <th>중구</th>\n", |
|
| 3279 |
+ " <td>2447</td>\n", |
|
| 3280 |
+ " <td>217.792208</td>\n", |
|
| 3281 |
+ " <td>131787</td>\n", |
|
| 3282 |
+ " <td>122499</td>\n", |
|
| 3283 |
+ " <td>9288</td>\n", |
|
| 3284 |
+ " <td>24392</td>\n", |
|
| 3285 |
+ " <td>7.047736</td>\n", |
|
| 3286 |
+ " <td>18.508654</td>\n", |
|
| 3287 |
+ " <td>1.856784</td>\n", |
|
| 3288 |
+ " </tr>\n", |
|
| 3289 |
+ " <tr>\n", |
|
| 3290 |
+ " <th>용산구</th>\n", |
|
| 3291 |
+ " <td>2611</td>\n", |
|
| 3292 |
+ " <td>165.615463</td>\n", |
|
| 3293 |
+ " <td>237285</td>\n", |
|
| 3294 |
+ " <td>222953</td>\n", |
|
| 3295 |
+ " <td>14332</td>\n", |
|
| 3296 |
+ " <td>39070</td>\n", |
|
| 3297 |
+ " <td>6.039994</td>\n", |
|
| 3298 |
+ " <td>16.465432</td>\n", |
|
| 3299 |
+ " <td>1.100365</td>\n", |
|
| 3300 |
+ " </tr>\n", |
|
| 3301 |
+ " <tr>\n", |
|
| 3302 |
+ " <th>성동구</th>\n", |
|
| 3303 |
+ " <td>3829</td>\n", |
|
| 3304 |
+ " <td>218.552413</td>\n", |
|
| 3305 |
+ " <td>292672</td>\n", |
|
| 3306 |
+ " <td>285990</td>\n", |
|
| 3307 |
+ " <td>6682</td>\n", |
|
| 3308 |
+ " <td>46380</td>\n", |
|
| 3309 |
+ " <td>2.283102</td>\n", |
|
| 3310 |
+ " <td>15.847092</td>\n", |
|
| 3311 |
+ " <td>1.308291</td>\n", |
|
| 3312 |
+ " </tr>\n", |
|
| 3313 |
+ " <tr>\n", |
|
| 3314 |
+ " <th>광진구</th>\n", |
|
| 3315 |
+ " <td>3211</td>\n", |
|
| 3316 |
+ " <td>506.994329</td>\n", |
|
| 3317 |
+ " <td>352627</td>\n", |
|
| 3318 |
+ " <td>339996</td>\n", |
|
| 3319 |
+ " <td>12631</td>\n", |
|
| 3320 |
+ " <td>51723</td>\n", |
|
| 3321 |
+ " <td>3.581972</td>\n", |
|
| 3322 |
+ " <td>14.667907</td>\n", |
|
| 3323 |
+ " <td>0.910594</td>\n", |
|
| 3324 |
+ " </tr>\n", |
|
| 3325 |
+ " <tr>\n", |
|
| 3326 |
+ " <th>동대문구</th>\n", |
|
| 3327 |
+ " <td>2628</td>\n", |
|
| 3328 |
+ " <td>175.760756</td>\n", |
|
| 3329 |
+ " <td>352006</td>\n", |
|
| 3330 |
+ " <td>337400</td>\n", |
|
| 3331 |
+ " <td>14606</td>\n", |
|
| 3332 |
+ " <td>62211</td>\n", |
|
| 3333 |
+ " <td>4.149361</td>\n", |
|
| 3334 |
+ " <td>17.673278</td>\n", |
|
| 3335 |
+ " <td>0.746578</td>\n", |
|
| 3336 |
+ " </tr>\n", |
|
| 3337 |
+ " <tr>\n", |
|
| 3338 |
+ " <th>중랑구</th>\n", |
|
| 3339 |
+ " <td>3737</td>\n", |
|
| 3340 |
+ " <td>281.716037</td>\n", |
|
| 3341 |
+ " <td>391885</td>\n", |
|
| 3342 |
+ " <td>387350</td>\n", |
|
| 3343 |
+ " <td>4535</td>\n", |
|
| 3344 |
+ " <td>71682</td>\n", |
|
| 3345 |
+ " <td>1.157227</td>\n", |
|
| 3346 |
+ " <td>18.291591</td>\n", |
|
| 3347 |
+ " <td>0.953596</td>\n", |
|
| 3348 |
+ " </tr>\n", |
|
| 3349 |
+ " <tr>\n", |
|
| 3350 |
+ " <th>성북구</th>\n", |
|
| 3351 |
+ " <td>4602</td>\n", |
|
| 3352 |
+ " <td>212.211669</td>\n", |
|
| 3353 |
+ " <td>440142</td>\n", |
|
| 3354 |
+ " <td>430528</td>\n", |
|
| 3355 |
+ " <td>9614</td>\n", |
|
| 3356 |
+ " <td>74709</td>\n", |
|
| 3357 |
+ " <td>2.184295</td>\n", |
|
| 3358 |
+ " <td>16.973840</td>\n", |
|
| 3359 |
+ " <td>1.045572</td>\n", |
|
| 3360 |
+ " </tr>\n", |
|
| 3361 |
+ " <tr>\n", |
|
| 3362 |
+ " <th>강북구</th>\n", |
|
| 3363 |
+ " <td>3090</td>\n", |
|
| 3364 |
+ " <td>563.090129</td>\n", |
|
| 3365 |
+ " <td>302563</td>\n", |
|
| 3366 |
+ " <td>299182</td>\n", |
|
| 3367 |
+ " <td>3381</td>\n", |
|
| 3368 |
+ " <td>64333</td>\n", |
|
| 3369 |
+ " <td>1.117453</td>\n", |
|
| 3370 |
+ " <td>21.262679</td>\n", |
|
| 3371 |
+ " <td>1.021275</td>\n", |
|
| 3372 |
+ " </tr>\n", |
|
| 3373 |
+ " <tr>\n", |
|
| 3374 |
+ " <th>도봉구</th>\n", |
|
| 3375 |
+ " <td>1930</td>\n", |
|
| 3376 |
+ " <td>226.013514</td>\n", |
|
| 3377 |
+ " <td>319373</td>\n", |
|
| 3378 |
+ " <td>317366</td>\n", |
|
| 3379 |
+ " <td>2007</td>\n", |
|
| 3380 |
+ " <td>64160</td>\n", |
|
| 3381 |
+ " <td>0.628419</td>\n", |
|
| 3382 |
+ " <td>20.089363</td>\n", |
|
| 3383 |
+ " <td>0.604309</td>\n", |
|
| 3384 |
+ " </tr>\n", |
|
| 3385 |
+ " <tr>\n", |
|
| 3386 |
+ " <th>노원구</th>\n", |
|
| 3387 |
+ " <td>2492</td>\n", |
|
| 3388 |
+ " <td>112.446718</td>\n", |
|
| 3389 |
+ " <td>514946</td>\n", |
|
| 3390 |
+ " <td>510956</td>\n", |
|
| 3391 |
+ " <td>3990</td>\n", |
|
| 3392 |
+ " <td>88345</td>\n", |
|
| 3393 |
+ " <td>0.774839</td>\n", |
|
| 3394 |
+ " <td>17.156168</td>\n", |
|
| 3395 |
+ " <td>0.483934</td>\n", |
|
| 3396 |
+ " </tr>\n", |
|
| 3397 |
+ " <tr>\n", |
|
| 3398 |
+ " <th>은평구</th>\n", |
|
| 3399 |
+ " <td>4131</td>\n", |
|
| 3400 |
+ " <td>223.492561</td>\n", |
|
| 3401 |
+ " <td>477173</td>\n", |
|
| 3402 |
+ " <td>473307</td>\n", |
|
| 3403 |
+ " <td>3866</td>\n", |
|
| 3404 |
+ " <td>87241</td>\n", |
|
| 3405 |
+ " <td>0.810188</td>\n", |
|
| 3406 |
+ " <td>18.282887</td>\n", |
|
| 3407 |
+ " <td>0.865724</td>\n", |
|
| 3408 |
+ " </tr>\n", |
|
| 3409 |
+ " <tr>\n", |
|
| 3410 |
+ " <th>서대문구</th>\n", |
|
| 3411 |
+ " <td>3055</td>\n", |
|
| 3412 |
+ " <td>90.224159</td>\n", |
|
| 3413 |
+ " <td>315659</td>\n", |
|
| 3414 |
+ " <td>304819</td>\n", |
|
| 3415 |
+ " <td>10840</td>\n", |
|
| 3416 |
+ " <td>54268</td>\n", |
|
| 3417 |
+ " <td>3.434086</td>\n", |
|
| 3418 |
+ " <td>17.191970</td>\n", |
|
| 3419 |
+ " <td>0.967817</td>\n", |
|
| 3420 |
+ " </tr>\n", |
|
| 3421 |
+ " <tr>\n", |
|
| 3422 |
+ " <th>마포구</th>\n", |
|
| 3423 |
+ " <td>2496</td>\n", |
|
| 3424 |
+ " <td>191.248541</td>\n", |
|
| 3425 |
+ " <td>378686</td>\n", |
|
| 3426 |
+ " <td>368905</td>\n", |
|
| 3427 |
+ " <td>9781</td>\n", |
|
| 3428 |
+ " <td>54582</td>\n", |
|
| 3429 |
+ " <td>2.582879</td>\n", |
|
| 3430 |
+ " <td>14.413525</td>\n", |
|
| 3431 |
+ " <td>0.659121</td>\n", |
|
| 3432 |
+ " </tr>\n", |
|
| 3433 |
+ " <tr>\n", |
|
| 3434 |
+ " <th>양천구</th>\n", |
|
| 3435 |
+ " <td>3650</td>\n", |
|
| 3436 |
+ " <td>202.402651</td>\n", |
|
| 3437 |
+ " <td>450487</td>\n", |
|
| 3438 |
+ " <td>447302</td>\n", |
|
| 3439 |
+ " <td>3185</td>\n", |
|
| 3440 |
+ " <td>68627</td>\n", |
|
| 3441 |
+ " <td>0.707013</td>\n", |
|
| 3442 |
+ " <td>15.233958</td>\n", |
|
| 3443 |
+ " <td>0.810234</td>\n", |
|
| 3444 |
+ " </tr>\n", |
|
| 3445 |
+ " <tr>\n", |
|
| 3446 |
+ " <th>강서구</th>\n", |
|
| 3447 |
+ " <td>2744</td>\n", |
|
| 3448 |
+ " <td>223.203769</td>\n", |
|
| 3449 |
+ " <td>579768</td>\n", |
|
| 3450 |
+ " <td>574315</td>\n", |
|
| 3451 |
+ " <td>5453</td>\n", |
|
| 3452 |
+ " <td>92558</td>\n", |
|
| 3453 |
+ " <td>0.940549</td>\n", |
|
| 3454 |
+ " <td>15.964662</td>\n", |
|
| 3455 |
+ " <td>0.473293</td>\n", |
|
| 3456 |
+ " </tr>\n", |
|
| 3457 |
+ " <tr>\n", |
|
| 3458 |
+ " <th>구로구</th>\n", |
|
| 3459 |
+ " <td>4608</td>\n", |
|
| 3460 |
+ " <td>109.645132</td>\n", |
|
| 3461 |
+ " <td>421163</td>\n", |
|
| 3462 |
+ " <td>396754</td>\n", |
|
| 3463 |
+ " <td>24409</td>\n", |
|
| 3464 |
+ " <td>72611</td>\n", |
|
| 3465 |
+ " <td>5.795618</td>\n", |
|
| 3466 |
+ " <td>17.240593</td>\n", |
|
| 3467 |
+ " <td>1.094113</td>\n", |
|
| 3468 |
+ " </tr>\n", |
|
| 3469 |
+ " <tr>\n", |
|
| 3470 |
+ " <th>금천구</th>\n", |
|
| 3471 |
+ " <td>2411</td>\n", |
|
| 3472 |
+ " <td>222.326203</td>\n", |
|
| 3473 |
+ " <td>244891</td>\n", |
|
| 3474 |
+ " <td>230811</td>\n", |
|
| 3475 |
+ " <td>14080</td>\n", |
|
| 3476 |
+ " <td>41041</td>\n", |
|
| 3477 |
+ " <td>5.749497</td>\n", |
|
| 3478 |
+ " <td>16.758885</td>\n", |
|
| 3479 |
+ " <td>0.984520</td>\n", |
|
| 3480 |
+ " </tr>\n", |
|
| 3481 |
+ " <tr>\n", |
|
| 3482 |
+ " <th>영등포구</th>\n", |
|
| 3483 |
+ " <td>4056</td>\n", |
|
| 3484 |
+ " <td>165.792923</td>\n", |
|
| 3485 |
+ " <td>400908</td>\n", |
|
| 3486 |
+ " <td>376837</td>\n", |
|
| 3487 |
+ " <td>24071</td>\n", |
|
| 3488 |
+ " <td>62516</td>\n", |
|
| 3489 |
+ " <td>6.004121</td>\n", |
|
| 3490 |
+ " <td>15.593603</td>\n", |
|
| 3491 |
+ " <td>1.011703</td>\n", |
|
| 3492 |
+ " </tr>\n", |
|
| 3493 |
+ " <tr>\n", |
|
| 3494 |
+ " <th>동작구</th>\n", |
|
| 3495 |
+ " <td>2306</td>\n", |
|
| 3496 |
+ " <td>136.755647</td>\n", |
|
| 3497 |
+ " <td>394364</td>\n", |
|
| 3498 |
+ " <td>385483</td>\n", |
|
| 3499 |
+ " <td>8881</td>\n", |
|
| 3500 |
+ " <td>66613</td>\n", |
|
| 3501 |
+ " <td>2.251980</td>\n", |
|
| 3502 |
+ " <td>16.891248</td>\n", |
|
| 3503 |
+ " <td>0.584739</td>\n", |
|
| 3504 |
+ " </tr>\n", |
|
| 3505 |
+ " <tr>\n", |
|
| 3506 |
+ " <th>관악구</th>\n", |
|
| 3507 |
+ " <td>5149</td>\n", |
|
| 3508 |
+ " <td>96.152381</td>\n", |
|
| 3509 |
+ " <td>499449</td>\n", |
|
| 3510 |
+ " <td>485699</td>\n", |
|
| 3511 |
+ " <td>13750</td>\n", |
|
| 3512 |
+ " <td>79871</td>\n", |
|
| 3513 |
+ " <td>2.753034</td>\n", |
|
| 3514 |
+ " <td>15.991823</td>\n", |
|
| 3515 |
+ " <td>1.030936</td>\n", |
|
| 3516 |
+ " </tr>\n", |
|
| 3517 |
+ " <tr>\n", |
|
| 3518 |
+ " <th>서초구</th>\n", |
|
| 3519 |
+ " <td>4082</td>\n", |
|
| 3520 |
+ " <td>214.969136</td>\n", |
|
| 3521 |
+ " <td>416167</td>\n", |
|
| 3522 |
+ " <td>412279</td>\n", |
|
| 3523 |
+ " <td>3888</td>\n", |
|
| 3524 |
+ " <td>60678</td>\n", |
|
| 3525 |
+ " <td>0.934240</td>\n", |
|
| 3526 |
+ " <td>14.580205</td>\n", |
|
| 3527 |
+ " <td>0.980856</td>\n", |
|
| 3528 |
+ " </tr>\n", |
|
| 3529 |
+ " <tr>\n", |
|
| 3530 |
+ " <th>강남구</th>\n", |
|
| 3531 |
+ " <td>6871</td>\n", |
|
| 3532 |
+ " <td>136.523236</td>\n", |
|
| 3533 |
+ " <td>537800</td>\n", |
|
| 3534 |
+ " <td>533042</td>\n", |
|
| 3535 |
+ " <td>4758</td>\n", |
|
| 3536 |
+ " <td>78226</td>\n", |
|
| 3537 |
+ " <td>0.884716</td>\n", |
|
| 3538 |
+ " <td>14.545556</td>\n", |
|
| 3539 |
+ " <td>1.277612</td>\n", |
|
| 3540 |
+ " </tr>\n", |
|
| 3541 |
+ " <tr>\n", |
|
| 3542 |
+ " <th>송파구</th>\n", |
|
| 3543 |
+ " <td>2897</td>\n", |
|
| 3544 |
+ " <td>336.295181</td>\n", |
|
| 3545 |
+ " <td>663965</td>\n", |
|
| 3546 |
+ " <td>658338</td>\n", |
|
| 3547 |
+ " <td>5627</td>\n", |
|
| 3548 |
+ " <td>97691</td>\n", |
|
| 3549 |
+ " <td>0.847484</td>\n", |
|
| 3550 |
+ " <td>14.713276</td>\n", |
|
| 3551 |
+ " <td>0.436318</td>\n", |
|
| 3552 |
+ " </tr>\n", |
|
| 3553 |
+ " <tr>\n", |
|
| 3554 |
+ " <th>강동구</th>\n", |
|
| 3555 |
+ " <td>2809</td>\n", |
|
| 3556 |
+ " <td>203.020496</td>\n", |
|
| 3557 |
+ " <td>466472</td>\n", |
|
| 3558 |
+ " <td>462664</td>\n", |
|
| 3559 |
+ " <td>3808</td>\n", |
|
| 3560 |
+ " <td>74070</td>\n", |
|
| 3561 |
+ " <td>0.816341</td>\n", |
|
| 3562 |
+ " <td>15.878767</td>\n", |
|
| 3563 |
+ " <td>0.602180</td>\n", |
|
| 3564 |
+ " </tr>\n", |
|
| 3565 |
+ " </tbody>\n", |
|
| 3566 |
+ "</table>\n", |
|
| 3567 |
+ "</div>" |
|
| 3568 |
+ ], |
|
| 3569 |
+ "text/plain": [ |
|
| 3570 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율 \\\n", |
|
| 3571 |
+ "구별 \n", |
|
| 3572 |
+ "종로구 1715 47.844828 153789 144683 9106 27818 5.921100 18.088420 \n", |
|
| 3573 |
+ "중구 2447 217.792208 131787 122499 9288 24392 7.047736 18.508654 \n", |
|
| 3574 |
+ "용산구 2611 165.615463 237285 222953 14332 39070 6.039994 16.465432 \n", |
|
| 3575 |
+ "성동구 3829 218.552413 292672 285990 6682 46380 2.283102 15.847092 \n", |
|
| 3576 |
+ "광진구 3211 506.994329 352627 339996 12631 51723 3.581972 14.667907 \n", |
|
| 3577 |
+ "동대문구 2628 175.760756 352006 337400 14606 62211 4.149361 17.673278 \n", |
|
| 3578 |
+ "중랑구 3737 281.716037 391885 387350 4535 71682 1.157227 18.291591 \n", |
|
| 3579 |
+ "성북구 4602 212.211669 440142 430528 9614 74709 2.184295 16.973840 \n", |
|
| 3580 |
+ "강북구 3090 563.090129 302563 299182 3381 64333 1.117453 21.262679 \n", |
|
| 3581 |
+ "도봉구 1930 226.013514 319373 317366 2007 64160 0.628419 20.089363 \n", |
|
| 3582 |
+ "노원구 2492 112.446718 514946 510956 3990 88345 0.774839 17.156168 \n", |
|
| 3583 |
+ "은평구 4131 223.492561 477173 473307 3866 87241 0.810188 18.282887 \n", |
|
| 3584 |
+ "서대문구 3055 90.224159 315659 304819 10840 54268 3.434086 17.191970 \n", |
|
| 3585 |
+ "마포구 2496 191.248541 378686 368905 9781 54582 2.582879 14.413525 \n", |
|
| 3586 |
+ "양천구 3650 202.402651 450487 447302 3185 68627 0.707013 15.233958 \n", |
|
| 3587 |
+ "강서구 2744 223.203769 579768 574315 5453 92558 0.940549 15.964662 \n", |
|
| 3588 |
+ "구로구 4608 109.645132 421163 396754 24409 72611 5.795618 17.240593 \n", |
|
| 3589 |
+ "금천구 2411 222.326203 244891 230811 14080 41041 5.749497 16.758885 \n", |
|
| 3590 |
+ "영등포구 4056 165.792923 400908 376837 24071 62516 6.004121 15.593603 \n", |
|
| 3591 |
+ "동작구 2306 136.755647 394364 385483 8881 66613 2.251980 16.891248 \n", |
|
| 3592 |
+ "관악구 5149 96.152381 499449 485699 13750 79871 2.753034 15.991823 \n", |
|
| 3593 |
+ "서초구 4082 214.969136 416167 412279 3888 60678 0.934240 14.580205 \n", |
|
| 3594 |
+ "강남구 6871 136.523236 537800 533042 4758 78226 0.884716 14.545556 \n", |
|
| 3595 |
+ "송파구 2897 336.295181 663965 658338 5627 97691 0.847484 14.713276 \n", |
|
| 3596 |
+ "강동구 2809 203.020496 466472 462664 3808 74070 0.816341 15.878767 \n", |
|
| 3597 |
+ "\n", |
|
| 3598 |
+ " CCTV비율 \n", |
|
| 3599 |
+ "구별 \n", |
|
| 3600 |
+ "종로구 1.115164 \n", |
|
| 3601 |
+ "중구 1.856784 \n", |
|
| 3602 |
+ "용산구 1.100365 \n", |
|
| 3603 |
+ "성동구 1.308291 \n", |
|
| 3604 |
+ "광진구 0.910594 \n", |
|
| 3605 |
+ "동대문구 0.746578 \n", |
|
| 3606 |
+ "중랑구 0.953596 \n", |
|
| 3607 |
+ "성북구 1.045572 \n", |
|
| 3608 |
+ "강북구 1.021275 \n", |
|
| 3609 |
+ "도봉구 0.604309 \n", |
|
| 3610 |
+ "노원구 0.483934 \n", |
|
| 3611 |
+ "은평구 0.865724 \n", |
|
| 3612 |
+ "서대문구 0.967817 \n", |
|
| 3613 |
+ "마포구 0.659121 \n", |
|
| 3614 |
+ "양천구 0.810234 \n", |
|
| 3615 |
+ "강서구 0.473293 \n", |
|
| 3616 |
+ "구로구 1.094113 \n", |
|
| 3617 |
+ "금천구 0.984520 \n", |
|
| 3618 |
+ "영등포구 1.011703 \n", |
|
| 3619 |
+ "동작구 0.584739 \n", |
|
| 3620 |
+ "관악구 1.030936 \n", |
|
| 3621 |
+ "서초구 0.980856 \n", |
|
| 3622 |
+ "강남구 1.277612 \n", |
|
| 3623 |
+ "송파구 0.436318 \n", |
|
| 3624 |
+ "강동구 0.602180 " |
|
| 3625 |
+ ] |
|
| 3626 |
+ }, |
|
| 3627 |
+ "execution_count": 113, |
|
| 3628 |
+ "metadata": {},
|
|
| 3629 |
+ "output_type": "execute_result" |
|
| 3630 |
+ } |
|
| 3631 |
+ ], |
|
| 3632 |
+ "source": [ |
|
| 3633 |
+ "dataset" |
|
| 3634 |
+ ] |
|
| 3635 |
+ }, |
|
| 3636 |
+ {
|
|
| 3637 |
+ "cell_type": "code", |
|
| 3638 |
+ "execution_count": 114, |
|
| 3639 |
+ "id": "2050dc32", |
|
| 3640 |
+ "metadata": {},
|
|
| 3641 |
+ "outputs": [ |
|
| 3642 |
+ {
|
|
| 3643 |
+ "data": {
|
|
| 3644 |
+ "text/html": [ |
|
| 3645 |
+ "<div>\n", |
|
| 3646 |
+ "<style scoped>\n", |
|
| 3647 |
+ " .dataframe tbody tr th:only-of-type {\n",
|
|
| 3648 |
+ " vertical-align: middle;\n", |
|
| 3649 |
+ " }\n", |
|
| 3650 |
+ "\n", |
|
| 3651 |
+ " .dataframe tbody tr th {\n",
|
|
| 3652 |
+ " vertical-align: top;\n", |
|
| 3653 |
+ " }\n", |
|
| 3654 |
+ "\n", |
|
| 3655 |
+ " .dataframe thead th {\n",
|
|
| 3656 |
+ " text-align: right;\n", |
|
| 3657 |
+ " }\n", |
|
| 3658 |
+ "</style>\n", |
|
| 3659 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 3660 |
+ " <thead>\n", |
|
| 3661 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 3662 |
+ " <th></th>\n", |
|
| 3663 |
+ " <th>총계</th>\n", |
|
| 3664 |
+ " <th>최근증가율</th>\n", |
|
| 3665 |
+ " <th>인구수</th>\n", |
|
| 3666 |
+ " <th>한국인</th>\n", |
|
| 3667 |
+ " <th>외국인</th>\n", |
|
| 3668 |
+ " <th>고령자</th>\n", |
|
| 3669 |
+ " <th>외국인비율</th>\n", |
|
| 3670 |
+ " <th>고령자비율</th>\n", |
|
| 3671 |
+ " <th>CCTV비율</th>\n", |
|
| 3672 |
+ " </tr>\n", |
|
| 3673 |
+ " <tr>\n", |
|
| 3674 |
+ " <th>구별</th>\n", |
|
| 3675 |
+ " <th></th>\n", |
|
| 3676 |
+ " <th></th>\n", |
|
| 3677 |
+ " <th></th>\n", |
|
| 3678 |
+ " <th></th>\n", |
|
| 3679 |
+ " <th></th>\n", |
|
| 3680 |
+ " <th></th>\n", |
|
| 3681 |
+ " <th></th>\n", |
|
| 3682 |
+ " <th></th>\n", |
|
| 3683 |
+ " <th></th>\n", |
|
| 3684 |
+ " </tr>\n", |
|
| 3685 |
+ " </thead>\n", |
|
| 3686 |
+ " <tbody>\n", |
|
| 3687 |
+ " <tr>\n", |
|
| 3688 |
+ " <th>중구</th>\n", |
|
| 3689 |
+ " <td>2447</td>\n", |
|
| 3690 |
+ " <td>217.792208</td>\n", |
|
| 3691 |
+ " <td>131787</td>\n", |
|
| 3692 |
+ " <td>122499</td>\n", |
|
| 3693 |
+ " <td>9288</td>\n", |
|
| 3694 |
+ " <td>24392</td>\n", |
|
| 3695 |
+ " <td>7.047736</td>\n", |
|
| 3696 |
+ " <td>18.508654</td>\n", |
|
| 3697 |
+ " <td>1.856784</td>\n", |
|
| 3698 |
+ " </tr>\n", |
|
| 3699 |
+ " <tr>\n", |
|
| 3700 |
+ " <th>성동구</th>\n", |
|
| 3701 |
+ " <td>3829</td>\n", |
|
| 3702 |
+ " <td>218.552413</td>\n", |
|
| 3703 |
+ " <td>292672</td>\n", |
|
| 3704 |
+ " <td>285990</td>\n", |
|
| 3705 |
+ " <td>6682</td>\n", |
|
| 3706 |
+ " <td>46380</td>\n", |
|
| 3707 |
+ " <td>2.283102</td>\n", |
|
| 3708 |
+ " <td>15.847092</td>\n", |
|
| 3709 |
+ " <td>1.308291</td>\n", |
|
| 3710 |
+ " </tr>\n", |
|
| 3711 |
+ " <tr>\n", |
|
| 3712 |
+ " <th>강남구</th>\n", |
|
| 3713 |
+ " <td>6871</td>\n", |
|
| 3714 |
+ " <td>136.523236</td>\n", |
|
| 3715 |
+ " <td>537800</td>\n", |
|
| 3716 |
+ " <td>533042</td>\n", |
|
| 3717 |
+ " <td>4758</td>\n", |
|
| 3718 |
+ " <td>78226</td>\n", |
|
| 3719 |
+ " <td>0.884716</td>\n", |
|
| 3720 |
+ " <td>14.545556</td>\n", |
|
| 3721 |
+ " <td>1.277612</td>\n", |
|
| 3722 |
+ " </tr>\n", |
|
| 3723 |
+ " <tr>\n", |
|
| 3724 |
+ " <th>종로구</th>\n", |
|
| 3725 |
+ " <td>1715</td>\n", |
|
| 3726 |
+ " <td>47.844828</td>\n", |
|
| 3727 |
+ " <td>153789</td>\n", |
|
| 3728 |
+ " <td>144683</td>\n", |
|
| 3729 |
+ " <td>9106</td>\n", |
|
| 3730 |
+ " <td>27818</td>\n", |
|
| 3731 |
+ " <td>5.921100</td>\n", |
|
| 3732 |
+ " <td>18.088420</td>\n", |
|
| 3733 |
+ " <td>1.115164</td>\n", |
|
| 3734 |
+ " </tr>\n", |
|
| 3735 |
+ " <tr>\n", |
|
| 3736 |
+ " <th>용산구</th>\n", |
|
| 3737 |
+ " <td>2611</td>\n", |
|
| 3738 |
+ " <td>165.615463</td>\n", |
|
| 3739 |
+ " <td>237285</td>\n", |
|
| 3740 |
+ " <td>222953</td>\n", |
|
| 3741 |
+ " <td>14332</td>\n", |
|
| 3742 |
+ " <td>39070</td>\n", |
|
| 3743 |
+ " <td>6.039994</td>\n", |
|
| 3744 |
+ " <td>16.465432</td>\n", |
|
| 3745 |
+ " <td>1.100365</td>\n", |
|
| 3746 |
+ " </tr>\n", |
|
| 3747 |
+ " </tbody>\n", |
|
| 3748 |
+ "</table>\n", |
|
| 3749 |
+ "</div>" |
|
| 3750 |
+ ], |
|
| 3751 |
+ "text/plain": [ |
|
| 3752 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율 \\\n", |
|
| 3753 |
+ "구별 \n", |
|
| 3754 |
+ "중구 2447 217.792208 131787 122499 9288 24392 7.047736 18.508654 \n", |
|
| 3755 |
+ "성동구 3829 218.552413 292672 285990 6682 46380 2.283102 15.847092 \n", |
|
| 3756 |
+ "강남구 6871 136.523236 537800 533042 4758 78226 0.884716 14.545556 \n", |
|
| 3757 |
+ "종로구 1715 47.844828 153789 144683 9106 27818 5.921100 18.088420 \n", |
|
| 3758 |
+ "용산구 2611 165.615463 237285 222953 14332 39070 6.039994 16.465432 \n", |
|
| 3759 |
+ "\n", |
|
| 3760 |
+ " CCTV비율 \n", |
|
| 3761 |
+ "구별 \n", |
|
| 3762 |
+ "중구 1.856784 \n", |
|
| 3763 |
+ "성동구 1.308291 \n", |
|
| 3764 |
+ "강남구 1.277612 \n", |
|
| 3765 |
+ "종로구 1.115164 \n", |
|
| 3766 |
+ "용산구 1.100365 " |
|
| 3767 |
+ ] |
|
| 3768 |
+ }, |
|
| 3769 |
+ "execution_count": 114, |
|
| 3770 |
+ "metadata": {},
|
|
| 3771 |
+ "output_type": "execute_result" |
|
| 3772 |
+ } |
|
| 3773 |
+ ], |
|
| 3774 |
+ "source": [ |
|
| 3775 |
+ "dataset.sort_values(by='CCTV비율', ascending=False).head()" |
|
| 3776 |
+ ] |
|
| 3777 |
+ }, |
|
| 3778 |
+ {
|
|
| 3779 |
+ "cell_type": "code", |
|
| 3780 |
+ "execution_count": 115, |
|
| 3781 |
+ "id": "19fe7dae", |
|
| 3782 |
+ "metadata": {},
|
|
| 3783 |
+ "outputs": [ |
|
| 3784 |
+ {
|
|
| 3785 |
+ "data": {
|
|
| 3786 |
+ "text/plain": [ |
|
| 3787 |
+ "<AxesSubplot:ylabel='구별'>" |
|
| 3788 |
+ ] |
|
| 3789 |
+ }, |
|
| 3790 |
+ "execution_count": 115, |
|
| 3791 |
+ "metadata": {},
|
|
| 3792 |
+ "output_type": "execute_result" |
|
| 3793 |
+ }, |
|
| 3794 |
+ {
|
|
| 3795 |
+ "data": {
|
|
| 3796 |
+ "image/png": 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\n", |
|
| 3797 |
+ "text/plain": [ |
|
| 3798 |
+ "<Figure size 720x720 with 1 Axes>" |
|
| 3799 |
+ ] |
|
| 3800 |
+ }, |
|
| 3801 |
+ "metadata": {
|
|
| 3802 |
+ "needs_background": "light" |
|
| 3803 |
+ }, |
|
| 3804 |
+ "output_type": "display_data" |
|
| 3805 |
+ } |
|
| 3806 |
+ ], |
|
| 3807 |
+ "source": [ |
|
| 3808 |
+ "dataset['CCTV비율'].sort_values().plot(kind='barh', grid=True, figsize=(10, 10))" |
|
| 3809 |
+ ] |
|
| 3810 |
+ }, |
|
| 3811 |
+ {
|
|
| 3812 |
+ "cell_type": "code", |
|
| 3813 |
+ "execution_count": 116, |
|
| 3814 |
+ "id": "b0b97c9f", |
|
| 3815 |
+ "metadata": {},
|
|
| 3816 |
+ "outputs": [ |
|
| 3817 |
+ {
|
|
| 3818 |
+ "data": {
|
|
| 3819 |
+ "text/plain": [ |
|
| 3820 |
+ "<function matplotlib.pyplot.show(close=None, block=None)>" |
|
| 3821 |
+ ] |
|
| 3822 |
+ }, |
|
| 3823 |
+ "execution_count": 116, |
|
| 3824 |
+ "metadata": {},
|
|
| 3825 |
+ "output_type": "execute_result" |
|
| 3826 |
+ }, |
|
| 3827 |
+ {
|
|
| 3828 |
+ "data": {
|
|
| 3829 |
+ "image/png": "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\n", |
|
| 3830 |
+ "text/plain": [ |
|
| 3831 |
+ "<Figure size 432x360 with 1 Axes>" |
|
| 3832 |
+ ] |
|
| 3833 |
+ }, |
|
| 3834 |
+ "metadata": {
|
|
| 3835 |
+ "needs_background": "light" |
|
| 3836 |
+ }, |
|
| 3837 |
+ "output_type": "display_data" |
|
| 3838 |
+ } |
|
| 3839 |
+ ], |
|
| 3840 |
+ "source": [ |
|
| 3841 |
+ "plt.figure(figsize=(6, 5))\n", |
|
| 3842 |
+ "plt.scatter(dataset['인구수'], dataset['총계'])\n", |
|
| 3843 |
+ "plt.grid\n", |
|
| 3844 |
+ "plt.xlabel('인구수')\n",
|
|
| 3845 |
+ "plt.ylabel('CCTV수')\n",
|
|
| 3846 |
+ "plt.show" |
|
| 3847 |
+ ] |
|
| 3848 |
+ }, |
|
| 3849 |
+ {
|
|
| 3850 |
+ "cell_type": "code", |
|
| 3851 |
+ "execution_count": 117, |
|
| 3852 |
+ "id": "eb925fdf", |
|
| 3853 |
+ "metadata": {},
|
|
| 3854 |
+ "outputs": [], |
|
| 3855 |
+ "source": [ |
|
| 3856 |
+ "x = np.arange(0, 10, 0.01)\n", |
|
| 3857 |
+ "y = 3 * x + 5" |
|
| 3858 |
+ ] |
|
| 3859 |
+ }, |
|
| 3860 |
+ {
|
|
| 3861 |
+ "cell_type": "code", |
|
| 3862 |
+ "execution_count": 118, |
|
| 3863 |
+ "id": "86626d1a", |
|
| 3864 |
+ "metadata": {},
|
|
| 3865 |
+ "outputs": [ |
|
| 3866 |
+ {
|
|
| 3867 |
+ "data": {
|
|
| 3868 |
+ "image/png": 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\n", |
|
| 3869 |
+ "text/plain": [ |
|
| 3870 |
+ "<Figure size 864x576 with 1 Axes>" |
|
| 3871 |
+ ] |
|
| 3872 |
+ }, |
|
| 3873 |
+ "metadata": {
|
|
| 3874 |
+ "needs_background": "light" |
|
| 3875 |
+ }, |
|
| 3876 |
+ "output_type": "display_data" |
|
| 3877 |
+ } |
|
| 3878 |
+ ], |
|
| 3879 |
+ "source": [ |
|
| 3880 |
+ "plt.figure(figsize=(12, 8))\n", |
|
| 3881 |
+ "plt.plot(x, y)\n", |
|
| 3882 |
+ "plt.grid()\n", |
|
| 3883 |
+ "plt.show()" |
|
| 3884 |
+ ] |
|
| 3885 |
+ }, |
|
| 3886 |
+ {
|
|
| 3887 |
+ "cell_type": "code", |
|
| 3888 |
+ "execution_count": 119, |
|
| 3889 |
+ "id": "6dbb15a9", |
|
| 3890 |
+ "metadata": {},
|
|
| 3891 |
+ "outputs": [], |
|
| 3892 |
+ "source": [ |
|
| 3893 |
+ "y_noise = y + np.random.randn(len(y))" |
|
| 3894 |
+ ] |
|
| 3895 |
+ }, |
|
| 3896 |
+ {
|
|
| 3897 |
+ "cell_type": "code", |
|
| 3898 |
+ "execution_count": 120, |
|
| 3899 |
+ "id": "cd3d2055", |
|
| 3900 |
+ "metadata": {},
|
|
| 3901 |
+ "outputs": [ |
|
| 3902 |
+ {
|
|
| 3903 |
+ "data": {
|
|
| 3904 |
+ "image/png": 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\n", |
|
| 3905 |
+ "text/plain": [ |
|
| 3906 |
+ "<Figure size 720x576 with 1 Axes>" |
|
| 3907 |
+ ] |
|
| 3908 |
+ }, |
|
| 3909 |
+ "metadata": {
|
|
| 3910 |
+ "needs_background": "light" |
|
| 3911 |
+ }, |
|
| 3912 |
+ "output_type": "display_data" |
|
| 3913 |
+ } |
|
| 3914 |
+ ], |
|
| 3915 |
+ "source": [ |
|
| 3916 |
+ "plt.figure(figsize=(10, 8))\n", |
|
| 3917 |
+ "plt.plot(x, y_noise)\n", |
|
| 3918 |
+ "plt.plot(x, y)\n", |
|
| 3919 |
+ "plt.grid()\n", |
|
| 3920 |
+ "plt.show()" |
|
| 3921 |
+ ] |
|
| 3922 |
+ }, |
|
| 3923 |
+ {
|
|
| 3924 |
+ "cell_type": "code", |
|
| 3925 |
+ "execution_count": 121, |
|
| 3926 |
+ "id": "e4546543", |
|
| 3927 |
+ "metadata": {},
|
|
| 3928 |
+ "outputs": [], |
|
| 3929 |
+ "source": [ |
|
| 3930 |
+ "fp = np.polyfit(x, y_noise, 1)" |
|
| 3931 |
+ ] |
|
| 3932 |
+ }, |
|
| 3933 |
+ {
|
|
| 3934 |
+ "cell_type": "code", |
|
| 3935 |
+ "execution_count": 122, |
|
| 3936 |
+ "id": "6afbc178", |
|
| 3937 |
+ "metadata": {},
|
|
| 3938 |
+ "outputs": [ |
|
| 3939 |
+ {
|
|
| 3940 |
+ "data": {
|
|
| 3941 |
+ "text/plain": [ |
|
| 3942 |
+ "array([2.99226346, 4.97267413])" |
|
| 3943 |
+ ] |
|
| 3944 |
+ }, |
|
| 3945 |
+ "execution_count": 122, |
|
| 3946 |
+ "metadata": {},
|
|
| 3947 |
+ "output_type": "execute_result" |
|
| 3948 |
+ } |
|
| 3949 |
+ ], |
|
| 3950 |
+ "source": [ |
|
| 3951 |
+ "fp" |
|
| 3952 |
+ ] |
|
| 3953 |
+ }, |
|
| 3954 |
+ {
|
|
| 3955 |
+ "cell_type": "code", |
|
| 3956 |
+ "execution_count": 123, |
|
| 3957 |
+ "id": "9196fa60", |
|
| 3958 |
+ "metadata": {},
|
|
| 3959 |
+ "outputs": [], |
|
| 3960 |
+ "source": [ |
|
| 3961 |
+ "f = np.poly1d(fp)" |
|
| 3962 |
+ ] |
|
| 3963 |
+ }, |
|
| 3964 |
+ {
|
|
| 3965 |
+ "cell_type": "code", |
|
| 3966 |
+ "execution_count": 124, |
|
| 3967 |
+ "id": "06d84f35", |
|
| 3968 |
+ "metadata": {},
|
|
| 3969 |
+ "outputs": [ |
|
| 3970 |
+ {
|
|
| 3971 |
+ "data": {
|
|
| 3972 |
+ "text/plain": [ |
|
| 3973 |
+ "poly1d([2.99226346, 4.97267413])" |
|
| 3974 |
+ ] |
|
| 3975 |
+ }, |
|
| 3976 |
+ "execution_count": 124, |
|
| 3977 |
+ "metadata": {},
|
|
| 3978 |
+ "output_type": "execute_result" |
|
| 3979 |
+ } |
|
| 3980 |
+ ], |
|
| 3981 |
+ "source": [ |
|
| 3982 |
+ "f" |
|
| 3983 |
+ ] |
|
| 3984 |
+ }, |
|
| 3985 |
+ {
|
|
| 3986 |
+ "cell_type": "code", |
|
| 3987 |
+ "execution_count": 125, |
|
| 3988 |
+ "id": "c5904106", |
|
| 3989 |
+ "metadata": {},
|
|
| 3990 |
+ "outputs": [ |
|
| 3991 |
+ {
|
|
| 3992 |
+ "data": {
|
|
| 3993 |
+ "text/plain": [ |
|
| 3994 |
+ "poly1d([2, 0])" |
|
| 3995 |
+ ] |
|
| 3996 |
+ }, |
|
| 3997 |
+ "execution_count": 125, |
|
| 3998 |
+ "metadata": {},
|
|
| 3999 |
+ "output_type": "execute_result" |
|
| 4000 |
+ } |
|
| 4001 |
+ ], |
|
| 4002 |
+ "source": [ |
|
| 4003 |
+ "np.poly1d([1, 1]) + np.poly1d([1, -1])" |
|
| 4004 |
+ ] |
|
| 4005 |
+ }, |
|
| 4006 |
+ {
|
|
| 4007 |
+ "cell_type": "code", |
|
| 4008 |
+ "execution_count": 126, |
|
| 4009 |
+ "id": "54d80056", |
|
| 4010 |
+ "metadata": {},
|
|
| 4011 |
+ "outputs": [ |
|
| 4012 |
+ {
|
|
| 4013 |
+ "data": {
|
|
| 4014 |
+ "text/plain": [ |
|
| 4015 |
+ "poly1d([4, 3])" |
|
| 4016 |
+ ] |
|
| 4017 |
+ }, |
|
| 4018 |
+ "execution_count": 126, |
|
| 4019 |
+ "metadata": {},
|
|
| 4020 |
+ "output_type": "execute_result" |
|
| 4021 |
+ } |
|
| 4022 |
+ ], |
|
| 4023 |
+ "source": [ |
|
| 4024 |
+ "np.poly1d([1, 1]) + np.poly1d([3, 2])" |
|
| 4025 |
+ ] |
|
| 4026 |
+ }, |
|
| 4027 |
+ {
|
|
| 4028 |
+ "cell_type": "code", |
|
| 4029 |
+ "execution_count": 127, |
|
| 4030 |
+ "id": "fe5ae5f7", |
|
| 4031 |
+ "metadata": {
|
|
| 4032 |
+ "scrolled": true |
|
| 4033 |
+ }, |
|
| 4034 |
+ "outputs": [ |
|
| 4035 |
+ {
|
|
| 4036 |
+ "data": {
|
|
| 4037 |
+ "image/png": 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\n", 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+ "text/plain": [ |
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+ "<Figure size 720x432 with 1 Axes>" |
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+ ] |
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+ }, |
|
| 4042 |
+ "metadata": {
|
|
| 4043 |
+ "needs_background": "light" |
|
| 4044 |
+ }, |
|
| 4045 |
+ "output_type": "display_data" |
|
| 4046 |
+ } |
|
| 4047 |
+ ], |
|
| 4048 |
+ "source": [ |
|
| 4049 |
+ "plt.figure(figsize=(10, 6))\n", |
|
| 4050 |
+ "plt.plot(x, y_noise, label='실제값', c='g')\n", |
|
| 4051 |
+ "plt.plot(x, y, label='원본', c='y', lw=5)\n", |
|
| 4052 |
+ "plt.plot(x, f(x), lw=2, c=\"tomato\", ls='dashed', label='예측값')\n", |
|
| 4053 |
+ "plt.grid()\n", |
|
| 4054 |
+ "plt.show()" |
|
| 4055 |
+ ] |
|
| 4056 |
+ }, |
|
| 4057 |
+ {
|
|
| 4058 |
+ "cell_type": "code", |
|
| 4059 |
+ "execution_count": 128, |
|
| 4060 |
+ "id": "bb3f33e5", |
|
| 4061 |
+ "metadata": {},
|
|
| 4062 |
+ "outputs": [ |
|
| 4063 |
+ {
|
|
| 4064 |
+ "data": {
|
|
| 4065 |
+ "text/plain": [ |
|
| 4066 |
+ "(poly1d([ 1., -1.]), poly1d([1.]))" |
|
| 4067 |
+ ] |
|
| 4068 |
+ }, |
|
| 4069 |
+ "execution_count": 128, |
|
| 4070 |
+ "metadata": {},
|
|
| 4071 |
+ "output_type": "execute_result" |
|
| 4072 |
+ } |
|
| 4073 |
+ ], |
|
| 4074 |
+ "source": [ |
|
| 4075 |
+ "np.poly1d([1, -2, 2]) / np.poly1d([1, -1])" |
|
| 4076 |
+ ] |
|
| 4077 |
+ }, |
|
| 4078 |
+ {
|
|
| 4079 |
+ "cell_type": "code", |
|
| 4080 |
+ "execution_count": 129, |
|
| 4081 |
+ "id": "4c752ad0", |
|
| 4082 |
+ "metadata": {},
|
|
| 4083 |
+ "outputs": [], |
|
| 4084 |
+ "source": [ |
|
| 4085 |
+ "x = np.arange(-10, 10, 0.01)\n", |
|
| 4086 |
+ "y = np.square(x-1)+1\n", |
|
| 4087 |
+ "y_noise = y + np.random.randn(len(y))*10\n", |
|
| 4088 |
+ "fp = np.polyfit(x, y_noise, 2)\n", |
|
| 4089 |
+ "f = np.poly1d(fp)" |
|
| 4090 |
+ ] |
|
| 4091 |
+ }, |
|
| 4092 |
+ {
|
|
| 4093 |
+ "cell_type": "code", |
|
| 4094 |
+ "execution_count": 130, |
|
| 4095 |
+ "id": "da3b157b", |
|
| 4096 |
+ "metadata": {},
|
|
| 4097 |
+ "outputs": [ |
|
| 4098 |
+ {
|
|
| 4099 |
+ "name": "stderr", |
|
| 4100 |
+ "output_type": "stream", |
|
| 4101 |
+ "text": [ |
|
| 4102 |
+ "C:\\Users\\pc\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 8722 missing from current font.\n", |
|
| 4103 |
+ " font.set_text(s, 0.0, flags=flags)\n", |
|
| 4104 |
+ "C:\\Users\\pc\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 8722 missing from current font.\n", |
|
| 4105 |
+ " font.set_text(s, 0, flags=flags)\n" |
|
| 4106 |
+ ] |
|
| 4107 |
+ }, |
|
| 4108 |
+ {
|
|
| 4109 |
+ "data": {
|
|
| 4110 |
+ "image/png": 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\n", |
|
| 4111 |
+ "text/plain": [ |
|
| 4112 |
+ "<Figure size 720x432 with 1 Axes>" |
|
| 4113 |
+ ] |
|
| 4114 |
+ }, |
|
| 4115 |
+ "metadata": {
|
|
| 4116 |
+ "needs_background": "light" |
|
| 4117 |
+ }, |
|
| 4118 |
+ "output_type": "display_data" |
|
| 4119 |
+ } |
|
| 4120 |
+ ], |
|
| 4121 |
+ "source": [ |
|
| 4122 |
+ "plt.figure(figsize=(10, 6))\n", |
|
| 4123 |
+ "plt.plot(x, y_noise, label='노이즈', c='g')\n", |
|
| 4124 |
+ "plt.plot(x, y, label='원본', c='y', lw=5)\n", |
|
| 4125 |
+ "plt.plot(x, f(x), lw=2, c=\"tomato\", ls='dashed', label='예측')\n", |
|
| 4126 |
+ "plt.grid()\n", |
|
| 4127 |
+ "plt.show()" |
|
| 4128 |
+ ] |
|
| 4129 |
+ }, |
|
| 4130 |
+ {
|
|
| 4131 |
+ "cell_type": "code", |
|
| 4132 |
+ "execution_count": 131, |
|
| 4133 |
+ "id": "a8047c66", |
|
| 4134 |
+ "metadata": {},
|
|
| 4135 |
+ "outputs": [], |
|
| 4136 |
+ "source": [ |
|
| 4137 |
+ "fp1 = np.polyfit(dataset['인구수'], dataset['총계'], 1)" |
|
| 4138 |
+ ] |
|
| 4139 |
+ }, |
|
| 4140 |
+ {
|
|
| 4141 |
+ "cell_type": "code", |
|
| 4142 |
+ "execution_count": 132, |
|
| 4143 |
+ "id": "a9fd1771", |
|
| 4144 |
+ "metadata": {},
|
|
| 4145 |
+ "outputs": [], |
|
| 4146 |
+ "source": [ |
|
| 4147 |
+ "f1 = np.poly1d(fp1)" |
|
| 4148 |
+ ] |
|
| 4149 |
+ }, |
|
| 4150 |
+ {
|
|
| 4151 |
+ "cell_type": "code", |
|
| 4152 |
+ "execution_count": 133, |
|
| 4153 |
+ "id": "47b54d04", |
|
| 4154 |
+ "metadata": {},
|
|
| 4155 |
+ "outputs": [], |
|
| 4156 |
+ "source": [ |
|
| 4157 |
+ "x = np.linspace(100000, 700000, 100)" |
|
| 4158 |
+ ] |
|
| 4159 |
+ }, |
|
| 4160 |
+ {
|
|
| 4161 |
+ "cell_type": "code", |
|
| 4162 |
+ "execution_count": 66, |
|
| 4163 |
+ "id": "8d74eda4", |
|
| 4164 |
+ "metadata": {},
|
|
| 4165 |
+ "outputs": [ |
|
| 4166 |
+ {
|
|
| 4167 |
+ "data": {
|
|
| 4168 |
+ "image/png": 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\n", 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+ "text/plain": [ |
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+ }, |
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+ "metadata": {
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+ "output_type": "display_data" |
|
| 4177 |
+ } |
|
| 4178 |
+ ], |
|
| 4179 |
+ "source": [ |
|
| 4180 |
+ "plt.figure(figsize=(10, 10))\n", |
|
| 4181 |
+ "plt.scatter(dataset['인구수'], dataset['총계'], s=50)\n", |
|
| 4182 |
+ "plt.plot(x, f1(x), ls='dashed', lw=3, color='tomato')\n", |
|
| 4183 |
+ "plt.xlabel('인구수')\n",
|
|
| 4184 |
+ "plt.xlabel('CCTV수')\n",
|
|
| 4185 |
+ "plt.grid()\n", |
|
| 4186 |
+ "plt.show()" |
|
| 4187 |
+ ] |
|
| 4188 |
+ }, |
|
| 4189 |
+ {
|
|
| 4190 |
+ "cell_type": "code", |
|
| 4191 |
+ "execution_count": 136, |
|
| 4192 |
+ "id": "e37d7af9", |
|
| 4193 |
+ "metadata": {},
|
|
| 4194 |
+ "outputs": [], |
|
| 4195 |
+ "source": [ |
|
| 4196 |
+ "dataset['오차'] = np.abs(dataset['총계'] - f1(dataset['인구수']))" |
|
| 4197 |
+ ] |
|
| 4198 |
+ }, |
|
| 4199 |
+ {
|
|
| 4200 |
+ "cell_type": "code", |
|
| 4201 |
+ "execution_count": 137, |
|
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+ "id": "da464baa", |
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+ "metadata": {},
|
|
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| 4208 |
+ "<div>\n", |
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+ " vertical-align: middle;\n", |
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| 4215 |
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| 4218 |
+ " .dataframe thead th {\n",
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|
| 4219 |
+ " text-align: right;\n", |
|
| 4220 |
+ " }\n", |
|
| 4221 |
+ "</style>\n", |
|
| 4222 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 4223 |
+ " <thead>\n", |
|
| 4224 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 4225 |
+ " <th></th>\n", |
|
| 4226 |
+ " <th>총계</th>\n", |
|
| 4227 |
+ " <th>최근증가율</th>\n", |
|
| 4228 |
+ " <th>인구수</th>\n", |
|
| 4229 |
+ " <th>한국인</th>\n", |
|
| 4230 |
+ " <th>외국인</th>\n", |
|
| 4231 |
+ " <th>고령자</th>\n", |
|
| 4232 |
+ " <th>외국인비율</th>\n", |
|
| 4233 |
+ " <th>고령자비율</th>\n", |
|
| 4234 |
+ " <th>CCTV비율</th>\n", |
|
| 4235 |
+ " <th>오차</th>\n", |
|
| 4236 |
+ " </tr>\n", |
|
| 4237 |
+ " <tr>\n", |
|
| 4238 |
+ " <th>구별</th>\n", |
|
| 4239 |
+ " <th></th>\n", |
|
| 4240 |
+ " <th></th>\n", |
|
| 4241 |
+ " <th></th>\n", |
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| 4242 |
+ " <th></th>\n", |
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| 4243 |
+ " <th></th>\n", |
|
| 4244 |
+ " <th></th>\n", |
|
| 4245 |
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|
| 4246 |
+ " <th></th>\n", |
|
| 4247 |
+ " <th></th>\n", |
|
| 4248 |
+ " <th></th>\n", |
|
| 4249 |
+ " </tr>\n", |
|
| 4250 |
+ " </thead>\n", |
|
| 4251 |
+ " <tbody>\n", |
|
| 4252 |
+ " <tr>\n", |
|
| 4253 |
+ " <th>종로구</th>\n", |
|
| 4254 |
+ " <td>1715</td>\n", |
|
| 4255 |
+ " <td>47.844828</td>\n", |
|
| 4256 |
+ " <td>153789</td>\n", |
|
| 4257 |
+ " <td>144683</td>\n", |
|
| 4258 |
+ " <td>9106</td>\n", |
|
| 4259 |
+ " <td>27818</td>\n", |
|
| 4260 |
+ " <td>5.921100</td>\n", |
|
| 4261 |
+ " <td>18.088420</td>\n", |
|
| 4262 |
+ " <td>1.115164</td>\n", |
|
| 4263 |
+ " <td>664.540101</td>\n", |
|
| 4264 |
+ " </tr>\n", |
|
| 4265 |
+ " <tr>\n", |
|
| 4266 |
+ " <th>중구</th>\n", |
|
| 4267 |
+ " <td>2447</td>\n", |
|
| 4268 |
+ " <td>217.792208</td>\n", |
|
| 4269 |
+ " <td>131787</td>\n", |
|
| 4270 |
+ " <td>122499</td>\n", |
|
| 4271 |
+ " <td>9288</td>\n", |
|
| 4272 |
+ " <td>24392</td>\n", |
|
| 4273 |
+ " <td>7.047736</td>\n", |
|
| 4274 |
+ " <td>18.508654</td>\n", |
|
| 4275 |
+ " <td>1.856784</td>\n", |
|
| 4276 |
+ " <td>157.347513</td>\n", |
|
| 4277 |
+ " </tr>\n", |
|
| 4278 |
+ " <tr>\n", |
|
| 4279 |
+ " <th>용산구</th>\n", |
|
| 4280 |
+ " <td>2611</td>\n", |
|
| 4281 |
+ " <td>165.615463</td>\n", |
|
| 4282 |
+ " <td>237285</td>\n", |
|
| 4283 |
+ " <td>222953</td>\n", |
|
| 4284 |
+ " <td>14332</td>\n", |
|
| 4285 |
+ " <td>39070</td>\n", |
|
| 4286 |
+ " <td>6.039994</td>\n", |
|
| 4287 |
+ " <td>16.465432</td>\n", |
|
| 4288 |
+ " <td>1.100365</td>\n", |
|
| 4289 |
+ " <td>109.657101</td>\n", |
|
| 4290 |
+ " </tr>\n", |
|
| 4291 |
+ " <tr>\n", |
|
| 4292 |
+ " <th>성동구</th>\n", |
|
| 4293 |
+ " <td>3829</td>\n", |
|
| 4294 |
+ " <td>218.552413</td>\n", |
|
| 4295 |
+ " <td>292672</td>\n", |
|
| 4296 |
+ " <td>285990</td>\n", |
|
| 4297 |
+ " <td>6682</td>\n", |
|
| 4298 |
+ " <td>46380</td>\n", |
|
| 4299 |
+ " <td>2.283102</td>\n", |
|
| 4300 |
+ " <td>15.847092</td>\n", |
|
| 4301 |
+ " <td>1.308291</td>\n", |
|
| 4302 |
+ " <td>882.063229</td>\n", |
|
| 4303 |
+ " </tr>\n", |
|
| 4304 |
+ " <tr>\n", |
|
| 4305 |
+ " <th>광진구</th>\n", |
|
| 4306 |
+ " <td>3211</td>\n", |
|
| 4307 |
+ " <td>506.994329</td>\n", |
|
| 4308 |
+ " <td>352627</td>\n", |
|
| 4309 |
+ " <td>339996</td>\n", |
|
| 4310 |
+ " <td>12631</td>\n", |
|
| 4311 |
+ " <td>51723</td>\n", |
|
| 4312 |
+ " <td>3.581972</td>\n", |
|
| 4313 |
+ " <td>14.667907</td>\n", |
|
| 4314 |
+ " <td>0.910594</td>\n", |
|
| 4315 |
+ " <td>19.121319</td>\n", |
|
| 4316 |
+ " </tr>\n", |
|
| 4317 |
+ " </tbody>\n", |
|
| 4318 |
+ "</table>\n", |
|
| 4319 |
+ "</div>" |
|
| 4320 |
+ ], |
|
| 4321 |
+ "text/plain": [ |
|
| 4322 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율 \\\n", |
|
| 4323 |
+ "구별 \n", |
|
| 4324 |
+ "종로구 1715 47.844828 153789 144683 9106 27818 5.921100 18.088420 \n", |
|
| 4325 |
+ "중구 2447 217.792208 131787 122499 9288 24392 7.047736 18.508654 \n", |
|
| 4326 |
+ "용산구 2611 165.615463 237285 222953 14332 39070 6.039994 16.465432 \n", |
|
| 4327 |
+ "성동구 3829 218.552413 292672 285990 6682 46380 2.283102 15.847092 \n", |
|
| 4328 |
+ "광진구 3211 506.994329 352627 339996 12631 51723 3.581972 14.667907 \n", |
|
| 4329 |
+ "\n", |
|
| 4330 |
+ " CCTV비율 오차 \n", |
|
| 4331 |
+ "구별 \n", |
|
| 4332 |
+ "종로구 1.115164 664.540101 \n", |
|
| 4333 |
+ "중구 1.856784 157.347513 \n", |
|
| 4334 |
+ "용산구 1.100365 109.657101 \n", |
|
| 4335 |
+ "성동구 1.308291 882.063229 \n", |
|
| 4336 |
+ "광진구 0.910594 19.121319 " |
|
| 4337 |
+ ] |
|
| 4338 |
+ }, |
|
| 4339 |
+ "execution_count": 137, |
|
| 4340 |
+ "metadata": {},
|
|
| 4341 |
+ "output_type": "execute_result" |
|
| 4342 |
+ } |
|
| 4343 |
+ ], |
|
| 4344 |
+ "source": [ |
|
| 4345 |
+ "dataset.head()" |
|
| 4346 |
+ ] |
|
| 4347 |
+ }, |
|
| 4348 |
+ {
|
|
| 4349 |
+ "cell_type": "code", |
|
| 4350 |
+ "execution_count": 147, |
|
| 4351 |
+ "id": "bafc7c01", |
|
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+ "metadata": {
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|
| 4353 |
+ "scrolled": true |
|
| 4354 |
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|
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| 4356 |
+ {
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|
| 4366 |
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|
| 4367 |
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|
| 4368 |
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|
| 4369 |
+ " .dataframe thead th {\n",
|
|
| 4370 |
+ " text-align: right;\n", |
|
| 4371 |
+ " }\n", |
|
| 4372 |
+ "</style>\n", |
|
| 4373 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 4374 |
+ " <thead>\n", |
|
| 4375 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 4376 |
+ " <th></th>\n", |
|
| 4377 |
+ " <th>총계</th>\n", |
|
| 4378 |
+ " <th>최근증가율</th>\n", |
|
| 4379 |
+ " <th>인구수</th>\n", |
|
| 4380 |
+ " <th>한국인</th>\n", |
|
| 4381 |
+ " <th>외국인</th>\n", |
|
| 4382 |
+ " <th>고령자</th>\n", |
|
| 4383 |
+ " <th>외국인비율</th>\n", |
|
| 4384 |
+ " <th>고령자비율</th>\n", |
|
| 4385 |
+ " <th>CCTV비율</th>\n", |
|
| 4386 |
+ " <th>오차</th>\n", |
|
| 4387 |
+ " </tr>\n", |
|
| 4388 |
+ " <tr>\n", |
|
| 4389 |
+ " <th>구별</th>\n", |
|
| 4390 |
+ " <th></th>\n", |
|
| 4391 |
+ " <th></th>\n", |
|
| 4392 |
+ " <th></th>\n", |
|
| 4393 |
+ " <th></th>\n", |
|
| 4394 |
+ " <th></th>\n", |
|
| 4395 |
+ " <th></th>\n", |
|
| 4396 |
+ " <th></th>\n", |
|
| 4397 |
+ " <th></th>\n", |
|
| 4398 |
+ " <th></th>\n", |
|
| 4399 |
+ " <th></th>\n", |
|
| 4400 |
+ " </tr>\n", |
|
| 4401 |
+ " </thead>\n", |
|
| 4402 |
+ " <tbody>\n", |
|
| 4403 |
+ " <tr>\n", |
|
| 4404 |
+ " <th>강남구</th>\n", |
|
| 4405 |
+ " <td>6871</td>\n", |
|
| 4406 |
+ " <td>136.523236</td>\n", |
|
| 4407 |
+ " <td>537800</td>\n", |
|
| 4408 |
+ " <td>533042</td>\n", |
|
| 4409 |
+ " <td>4758</td>\n", |
|
| 4410 |
+ " <td>78226</td>\n", |
|
| 4411 |
+ " <td>0.884716</td>\n", |
|
| 4412 |
+ " <td>14.545556</td>\n", |
|
| 4413 |
+ " <td>1.277612</td>\n", |
|
| 4414 |
+ " <td>2922.610130</td>\n", |
|
| 4415 |
+ " </tr>\n", |
|
| 4416 |
+ " <tr>\n", |
|
| 4417 |
+ " <th>송파구</th>\n", |
|
| 4418 |
+ " <td>2897</td>\n", |
|
| 4419 |
+ " <td>336.295181</td>\n", |
|
| 4420 |
+ " <td>663965</td>\n", |
|
| 4421 |
+ " <td>658338</td>\n", |
|
| 4422 |
+ " <td>5627</td>\n", |
|
| 4423 |
+ " <td>97691</td>\n", |
|
| 4424 |
+ " <td>0.847484</td>\n", |
|
| 4425 |
+ " <td>14.713276</td>\n", |
|
| 4426 |
+ " <td>0.436318</td>\n", |
|
| 4427 |
+ " <td>1566.828051</td>\n", |
|
| 4428 |
+ " </tr>\n", |
|
| 4429 |
+ " <tr>\n", |
|
| 4430 |
+ " <th>강서구</th>\n", |
|
| 4431 |
+ " <td>2744</td>\n", |
|
| 4432 |
+ " <td>223.203769</td>\n", |
|
| 4433 |
+ " <td>579768</td>\n", |
|
| 4434 |
+ " <td>574315</td>\n", |
|
| 4435 |
+ " <td>5453</td>\n", |
|
| 4436 |
+ " <td>92558</td>\n", |
|
| 4437 |
+ " <td>0.940549</td>\n", |
|
| 4438 |
+ " <td>15.964662</td>\n", |
|
| 4439 |
+ " <td>0.473293</td>\n", |
|
| 4440 |
+ " <td>1375.847165</td>\n", |
|
| 4441 |
+ " </tr>\n", |
|
| 4442 |
+ " <tr>\n", |
|
| 4443 |
+ " <th>노원구</th>\n", |
|
| 4444 |
+ " <td>2492</td>\n", |
|
| 4445 |
+ " <td>112.446718</td>\n", |
|
| 4446 |
+ " <td>514946</td>\n", |
|
| 4447 |
+ " <td>510956</td>\n", |
|
| 4448 |
+ " <td>3990</td>\n", |
|
| 4449 |
+ " <td>88345</td>\n", |
|
| 4450 |
+ " <td>0.774839</td>\n", |
|
| 4451 |
+ " <td>17.156168</td>\n", |
|
| 4452 |
+ " <td>0.483934</td>\n", |
|
| 4453 |
+ " <td>1363.021470</td>\n", |
|
| 4454 |
+ " </tr>\n", |
|
| 4455 |
+ " <tr>\n", |
|
| 4456 |
+ " <th>관악구</th>\n", |
|
| 4457 |
+ " <td>5149</td>\n", |
|
| 4458 |
+ " <td>96.152381</td>\n", |
|
| 4459 |
+ " <td>499449</td>\n", |
|
| 4460 |
+ " <td>485699</td>\n", |
|
| 4461 |
+ " <td>13750</td>\n", |
|
| 4462 |
+ " <td>79871</td>\n", |
|
| 4463 |
+ " <td>2.753034</td>\n", |
|
| 4464 |
+ " <td>15.991823</td>\n", |
|
| 4465 |
+ " <td>1.030936</td>\n", |
|
| 4466 |
+ " <td>1357.290427</td>\n", |
|
| 4467 |
+ " </tr>\n", |
|
| 4468 |
+ " </tbody>\n", |
|
| 4469 |
+ "</table>\n", |
|
| 4470 |
+ "</div>" |
|
| 4471 |
+ ], |
|
| 4472 |
+ "text/plain": [ |
|
| 4473 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율 \\\n", |
|
| 4474 |
+ "구별 \n", |
|
| 4475 |
+ "강남구 6871 136.523236 537800 533042 4758 78226 0.884716 14.545556 \n", |
|
| 4476 |
+ "송파구 2897 336.295181 663965 658338 5627 97691 0.847484 14.713276 \n", |
|
| 4477 |
+ "강서구 2744 223.203769 579768 574315 5453 92558 0.940549 15.964662 \n", |
|
| 4478 |
+ "노원구 2492 112.446718 514946 510956 3990 88345 0.774839 17.156168 \n", |
|
| 4479 |
+ "관악구 5149 96.152381 499449 485699 13750 79871 2.753034 15.991823 \n", |
|
| 4480 |
+ "\n", |
|
| 4481 |
+ " CCTV비율 오차 \n", |
|
| 4482 |
+ "구별 \n", |
|
| 4483 |
+ "강남구 1.277612 2922.610130 \n", |
|
| 4484 |
+ "송파구 0.436318 1566.828051 \n", |
|
| 4485 |
+ "강서구 0.473293 1375.847165 \n", |
|
| 4486 |
+ "노원구 0.483934 1363.021470 \n", |
|
| 4487 |
+ "관악구 1.030936 1357.290427 " |
|
| 4488 |
+ ] |
|
| 4489 |
+ }, |
|
| 4490 |
+ "execution_count": 147, |
|
| 4491 |
+ "metadata": {},
|
|
| 4492 |
+ "output_type": "execute_result" |
|
| 4493 |
+ } |
|
| 4494 |
+ ], |
|
| 4495 |
+ "source": [ |
|
| 4496 |
+ "df_sort = dataset.sort_values(by='오차', ascending=False)\n", |
|
| 4497 |
+ "df_sort.head()" |
|
| 4498 |
+ ] |
|
| 4499 |
+ }, |
|
| 4500 |
+ {
|
|
| 4501 |
+ "cell_type": "code", |
|
| 4502 |
+ "execution_count": 146, |
|
| 4503 |
+ "id": "3fd155d4", |
|
| 4504 |
+ "metadata": {},
|
|
| 4505 |
+ "outputs": [ |
|
| 4506 |
+ {
|
|
| 4507 |
+ "data": {
|
|
| 4508 |
+ "image/png": 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\n", |
|
| 4509 |
+ "text/plain": [ |
|
| 4510 |
+ "<Figure size 1008x720 with 2 Axes>" |
|
| 4511 |
+ ] |
|
| 4512 |
+ }, |
|
| 4513 |
+ "metadata": {
|
|
| 4514 |
+ "needs_background": "light" |
|
| 4515 |
+ }, |
|
| 4516 |
+ "output_type": "display_data" |
|
| 4517 |
+ } |
|
| 4518 |
+ ], |
|
| 4519 |
+ "source": [ |
|
| 4520 |
+ "plt.figure(figsize=(14, 10))\n", |
|
| 4521 |
+ "plt.scatter(dataset['인구수'], dataset['총계'], c=dataset['오차'], s=50)\n", |
|
| 4522 |
+ "plt.plot(x, f1(x), ls='dashed', lw=3, color='tomato')\n", |
|
| 4523 |
+ "\n", |
|
| 4524 |
+ "for n in range(20):\n", |
|
| 4525 |
+ " plt.text(df_sort['인구수'][n]*1.02, df_sort['총계'][n]*0.98, df_sort.index[n], fontsize=15)\n", |
|
| 4526 |
+ "\n", |
|
| 4527 |
+ "plt.xlabel('인구수')\n",
|
|
| 4528 |
+ "plt.ylabel('인구수 당 CCTV 비율')\n",
|
|
| 4529 |
+ "plt.colorbar()\n", |
|
| 4530 |
+ "plt.grid()\n", |
|
| 4531 |
+ "plt.show()" |
|
| 4532 |
+ ] |
|
| 1549 | 4533 |
} |
| 1550 | 4534 |
], |
| 1551 | 4535 |
"metadata": {
|
+++ 20220530/NanumGothic.ttf
| Binary file is not shown |
--- 20220530/cctv.ipynb
+++ 20220530/cctv.ipynb
... | ... | @@ -2,7 +2,7 @@ |
| 2 | 2 |
"cells": [ |
| 3 | 3 |
{
|
| 4 | 4 |
"cell_type": "code", |
| 5 |
- "execution_count": 16, |
|
| 5 |
+ "execution_count": 73, |
|
| 6 | 6 |
"id": "55e4cb48", |
| 7 | 7 |
"metadata": {},
|
| 8 | 8 |
"outputs": [], |
... | ... | @@ -14,8 +14,8 @@ |
| 14 | 14 |
}, |
| 15 | 15 |
{
|
| 16 | 16 |
"cell_type": "code", |
| 17 |
- "execution_count": 37, |
|
| 18 |
- "id": "0dcd7237", |
|
| 17 |
+ "execution_count": 74, |
|
| 18 |
+ "id": "482ba70c", |
|
| 19 | 19 |
"metadata": {
|
| 20 | 20 |
"scrolled": true |
| 21 | 21 |
}, |
... | ... | @@ -168,7 +168,7 @@ |
| 168 | 168 |
"성동구 490 472 283 " |
| 169 | 169 |
] |
| 170 | 170 |
}, |
| 171 |
- "execution_count": 37, |
|
| 171 |
+ "execution_count": 74, |
|
| 172 | 172 |
"metadata": {},
|
| 173 | 173 |
"output_type": "execute_result" |
| 174 | 174 |
} |
... | ... | @@ -180,8 +180,8 @@ |
| 180 | 180 |
}, |
| 181 | 181 |
{
|
| 182 | 182 |
"cell_type": "code", |
| 183 |
- "execution_count": 38, |
|
| 184 |
- "id": "553f652c", |
|
| 183 |
+ "execution_count": 75, |
|
| 184 |
+ "id": "bcf14fbe", |
|
| 185 | 185 |
"metadata": {
|
| 186 | 186 |
"scrolled": true |
| 187 | 187 |
}, |
... | ... | @@ -334,7 +334,7 @@ |
| 334 | 334 |
"광진구 175 655 " |
| 335 | 335 |
] |
| 336 | 336 |
}, |
| 337 |
- "execution_count": 38, |
|
| 337 |
+ "execution_count": 75, |
|
| 338 | 338 |
"metadata": {},
|
| 339 | 339 |
"output_type": "execute_result" |
| 340 | 340 |
} |
... | ... | @@ -346,8 +346,8 @@ |
| 346 | 346 |
}, |
| 347 | 347 |
{
|
| 348 | 348 |
"cell_type": "code", |
| 349 |
- "execution_count": 39, |
|
| 350 |
- "id": "783465f1", |
|
| 349 |
+ "execution_count": 76, |
|
| 350 |
+ "id": "527eb8ab", |
|
| 351 | 351 |
"metadata": {},
|
| 352 | 352 |
"outputs": [ |
| 353 | 353 |
{
|
... | ... | @@ -487,7 +487,7 @@ |
| 487 | 487 |
"4 712 175 655 " |
| 488 | 488 |
] |
| 489 | 489 |
}, |
| 490 |
- "execution_count": 39, |
|
| 490 |
+ "execution_count": 76, |
|
| 491 | 491 |
"metadata": {},
|
| 492 | 492 |
"output_type": "execute_result" |
| 493 | 493 |
} |
... | ... | @@ -499,8 +499,8 @@ |
| 499 | 499 |
}, |
| 500 | 500 |
{
|
| 501 | 501 |
"cell_type": "code", |
| 502 |
- "execution_count": 40, |
|
| 503 |
- "id": "1cedd174", |
|
| 502 |
+ "execution_count": 77, |
|
| 503 |
+ "id": "dd403320", |
|
| 504 | 504 |
"metadata": {},
|
| 505 | 505 |
"outputs": [ |
| 506 | 506 |
{
|
... | ... | @@ -640,7 +640,7 @@ |
| 640 | 640 |
"4 712 175 655 " |
| 641 | 641 |
] |
| 642 | 642 |
}, |
| 643 |
- "execution_count": 40, |
|
| 643 |
+ "execution_count": 77, |
|
| 644 | 644 |
"metadata": {},
|
| 645 | 645 |
"output_type": "execute_result" |
| 646 | 646 |
} |
... | ... | @@ -652,8 +652,8 @@ |
| 652 | 652 |
}, |
| 653 | 653 |
{
|
| 654 | 654 |
"cell_type": "code", |
| 655 |
- "execution_count": 41, |
|
| 656 |
- "id": "db484ef5", |
|
| 655 |
+ "execution_count": 78, |
|
| 656 |
+ "id": "4c9e2cea", |
|
| 657 | 657 |
"metadata": {
|
| 658 | 658 |
"scrolled": true |
| 659 | 659 |
}, |
... | ... | @@ -740,7 +740,7 @@ |
| 740 | 740 |
"4 성동구 292672 285990 6682 46380" |
| 741 | 741 |
] |
| 742 | 742 |
}, |
| 743 |
- "execution_count": 41, |
|
| 743 |
+ "execution_count": 78, |
|
| 744 | 744 |
"metadata": {},
|
| 745 | 745 |
"output_type": "execute_result" |
| 746 | 746 |
} |
... | ... | @@ -752,8 +752,8 @@ |
| 752 | 752 |
}, |
| 753 | 753 |
{
|
| 754 | 754 |
"cell_type": "code", |
| 755 |
- "execution_count": 42, |
|
| 756 |
- "id": "3d54312e", |
|
| 755 |
+ "execution_count": 79, |
|
| 756 |
+ "id": "1f6c133e", |
|
| 757 | 757 |
"metadata": {},
|
| 758 | 758 |
"outputs": [], |
| 759 | 759 |
"source": [ |
... | ... | @@ -762,8 +762,8 @@ |
| 762 | 762 |
}, |
| 763 | 763 |
{
|
| 764 | 764 |
"cell_type": "code", |
| 765 |
- "execution_count": 43, |
|
| 766 |
- "id": "b055fd00", |
|
| 765 |
+ "execution_count": 80, |
|
| 766 |
+ "id": "f68a3330", |
|
| 767 | 767 |
"metadata": {},
|
| 768 | 768 |
"outputs": [ |
| 769 | 769 |
{
|
... | ... | @@ -848,7 +848,7 @@ |
| 848 | 848 |
"4 성동구 292672 285990 6682 46380" |
| 849 | 849 |
] |
| 850 | 850 |
}, |
| 851 |
- "execution_count": 43, |
|
| 851 |
+ "execution_count": 80, |
|
| 852 | 852 |
"metadata": {},
|
| 853 | 853 |
"output_type": "execute_result" |
| 854 | 854 |
} |
... | ... | @@ -859,8 +859,8 @@ |
| 859 | 859 |
}, |
| 860 | 860 |
{
|
| 861 | 861 |
"cell_type": "code", |
| 862 |
- "execution_count": 44, |
|
| 863 |
- "id": "1155ded1", |
|
| 862 |
+ "execution_count": 81, |
|
| 863 |
+ "id": "5877cc50", |
|
| 864 | 864 |
"metadata": {},
|
| 865 | 865 |
"outputs": [ |
| 866 | 866 |
{
|
... | ... | @@ -1000,7 +1000,7 @@ |
| 1000 | 1000 |
"11 1057 288 471 " |
| 1001 | 1001 |
] |
| 1002 | 1002 |
}, |
| 1003 |
- "execution_count": 44, |
|
| 1003 |
+ "execution_count": 81, |
|
| 1004 | 1004 |
"metadata": {},
|
| 1005 | 1005 |
"output_type": "execute_result" |
| 1006 | 1006 |
} |
... | ... | @@ -1011,7 +1011,7 @@ |
| 1011 | 1011 |
}, |
| 1012 | 1012 |
{
|
| 1013 | 1013 |
"cell_type": "markdown", |
| 1014 |
- "id": "c19ff9a5", |
|
| 1014 |
+ "id": "b727130c", |
|
| 1015 | 1015 |
"metadata": {},
|
| 1016 | 1016 |
"source": [ |
| 1017 | 1017 |
"# 결측치 확인" |
... | ... | @@ -1019,8 +1019,8 @@ |
| 1019 | 1019 |
}, |
| 1020 | 1020 |
{
|
| 1021 | 1021 |
"cell_type": "code", |
| 1022 |
- "execution_count": 45, |
|
| 1023 |
- "id": "f5ae607d", |
|
| 1022 |
+ "execution_count": 82, |
|
| 1023 |
+ "id": "8b3b55cd", |
|
| 1024 | 1024 |
"metadata": {
|
| 1025 | 1025 |
"scrolled": true |
| 1026 | 1026 |
}, |
... | ... | @@ -1162,7 +1162,7 @@ |
| 1162 | 1162 |
"4 False False False " |
| 1163 | 1163 |
] |
| 1164 | 1164 |
}, |
| 1165 |
- "execution_count": 45, |
|
| 1165 |
+ "execution_count": 82, |
|
| 1166 | 1166 |
"metadata": {},
|
| 1167 | 1167 |
"output_type": "execute_result" |
| 1168 | 1168 |
} |
... | ... | @@ -1173,7 +1173,7 @@ |
| 1173 | 1173 |
}, |
| 1174 | 1174 |
{
|
| 1175 | 1175 |
"cell_type": "markdown", |
| 1176 |
- "id": "8870c6b9", |
|
| 1176 |
+ "id": "b467ed59", |
|
| 1177 | 1177 |
"metadata": {},
|
| 1178 | 1178 |
"source": [ |
| 1179 | 1179 |
"# 결측치 -> 0" |
... | ... | @@ -1181,8 +1181,8 @@ |
| 1181 | 1181 |
}, |
| 1182 | 1182 |
{
|
| 1183 | 1183 |
"cell_type": "code", |
| 1184 |
- "execution_count": 46, |
|
| 1185 |
- "id": "0a55bfc0", |
|
| 1184 |
+ "execution_count": 83, |
|
| 1185 |
+ "id": "88f4cd02", |
|
| 1186 | 1186 |
"metadata": {},
|
| 1187 | 1187 |
"outputs": [], |
| 1188 | 1188 |
"source": [ |
... | ... | @@ -1191,8 +1191,8 @@ |
| 1191 | 1191 |
}, |
| 1192 | 1192 |
{
|
| 1193 | 1193 |
"cell_type": "code", |
| 1194 |
- "execution_count": 47, |
|
| 1195 |
- "id": "2d93fa68", |
|
| 1194 |
+ "execution_count": 84, |
|
| 1195 |
+ "id": "c9f416a2", |
|
| 1196 | 1196 |
"metadata": {},
|
| 1197 | 1197 |
"outputs": [ |
| 1198 | 1198 |
{
|
... | ... | @@ -1332,7 +1332,7 @@ |
| 1332 | 1332 |
"4 712 175 655 " |
| 1333 | 1333 |
] |
| 1334 | 1334 |
}, |
| 1335 |
- "execution_count": 47, |
|
| 1335 |
+ "execution_count": 84, |
|
| 1336 | 1336 |
"metadata": {},
|
| 1337 | 1337 |
"output_type": "execute_result" |
| 1338 | 1338 |
} |
... | ... | @@ -1343,8 +1343,8 @@ |
| 1343 | 1343 |
}, |
| 1344 | 1344 |
{
|
| 1345 | 1345 |
"cell_type": "code", |
| 1346 |
- "execution_count": 48, |
|
| 1347 |
- "id": "154242e7", |
|
| 1346 |
+ "execution_count": 85, |
|
| 1347 |
+ "id": "c359a27e", |
|
| 1348 | 1348 |
"metadata": {},
|
| 1349 | 1349 |
"outputs": [], |
| 1350 | 1350 |
"source": [ |
... | ... | @@ -1357,8 +1357,8 @@ |
| 1357 | 1357 |
}, |
| 1358 | 1358 |
{
|
| 1359 | 1359 |
"cell_type": "code", |
| 1360 |
- "execution_count": 49, |
|
| 1361 |
- "id": "326eedd9", |
|
| 1360 |
+ "execution_count": 86, |
|
| 1361 |
+ "id": "337c268e", |
|
| 1362 | 1362 |
"metadata": {
|
| 1363 | 1363 |
"scrolled": true |
| 1364 | 1364 |
}, |
... | ... | @@ -1369,8 +1369,8 @@ |
| 1369 | 1369 |
}, |
| 1370 | 1370 |
{
|
| 1371 | 1371 |
"cell_type": "code", |
| 1372 |
- "execution_count": 50, |
|
| 1373 |
- "id": "ae114acf", |
|
| 1372 |
+ "execution_count": 87, |
|
| 1373 |
+ "id": "7d40ddf3", |
|
| 1374 | 1374 |
"metadata": {},
|
| 1375 | 1375 |
"outputs": [ |
| 1376 | 1376 |
{
|
... | ... | @@ -1516,7 +1516,7 @@ |
| 1516 | 1516 |
"4 712 175 655 506.994329 " |
| 1517 | 1517 |
] |
| 1518 | 1518 |
}, |
| 1519 |
- "execution_count": 50, |
|
| 1519 |
+ "execution_count": 87, |
|
| 1520 | 1520 |
"metadata": {},
|
| 1521 | 1521 |
"output_type": "execute_result" |
| 1522 | 1522 |
} |
... | ... | @@ -1527,8 +1527,8 @@ |
| 1527 | 1527 |
}, |
| 1528 | 1528 |
{
|
| 1529 | 1529 |
"cell_type": "code", |
| 1530 |
- "execution_count": 51, |
|
| 1531 |
- "id": "989c4761", |
|
| 1530 |
+ "execution_count": 88, |
|
| 1531 |
+ "id": "d186a399", |
|
| 1532 | 1532 |
"metadata": {},
|
| 1533 | 1533 |
"outputs": [], |
| 1534 | 1534 |
"source": [ |
... | ... | @@ -1537,8 +1537,8 @@ |
| 1537 | 1537 |
}, |
| 1538 | 1538 |
{
|
| 1539 | 1539 |
"cell_type": "code", |
| 1540 |
- "execution_count": 52, |
|
| 1541 |
- "id": "dd336bb1", |
|
| 1540 |
+ "execution_count": 89, |
|
| 1541 |
+ "id": "66ba0c27", |
|
| 1542 | 1542 |
"metadata": {},
|
| 1543 | 1543 |
"outputs": [ |
| 1544 | 1544 |
{
|
... | ... | @@ -1623,7 +1623,7 @@ |
| 1623 | 1623 |
"5 광진구 352627 339996 12631 51723" |
| 1624 | 1624 |
] |
| 1625 | 1625 |
}, |
| 1626 |
- "execution_count": 52, |
|
| 1626 |
+ "execution_count": 89, |
|
| 1627 | 1627 |
"metadata": {},
|
| 1628 | 1628 |
"output_type": "execute_result" |
| 1629 | 1629 |
} |
... | ... | @@ -1634,8 +1634,8 @@ |
| 1634 | 1634 |
}, |
| 1635 | 1635 |
{
|
| 1636 | 1636 |
"cell_type": "code", |
| 1637 |
- "execution_count": 53, |
|
| 1638 |
- "id": "90558ebd", |
|
| 1637 |
+ "execution_count": 90, |
|
| 1638 |
+ "id": "54dfd1a4", |
|
| 1639 | 1639 |
"metadata": {},
|
| 1640 | 1640 |
"outputs": [], |
| 1641 | 1641 |
"source": [ |
... | ... | @@ -1644,8 +1644,8 @@ |
| 1644 | 1644 |
}, |
| 1645 | 1645 |
{
|
| 1646 | 1646 |
"cell_type": "code", |
| 1647 |
- "execution_count": 54, |
|
| 1648 |
- "id": "d6567617", |
|
| 1647 |
+ "execution_count": 91, |
|
| 1648 |
+ "id": "7c6cffe1", |
|
| 1649 | 1649 |
"metadata": {},
|
| 1650 | 1650 |
"outputs": [ |
| 1651 | 1651 |
{
|
... | ... | @@ -1736,7 +1736,7 @@ |
| 1736 | 1736 |
"4 5 광진구 352627 339996 12631 51723" |
| 1737 | 1737 |
] |
| 1738 | 1738 |
}, |
| 1739 |
- "execution_count": 54, |
|
| 1739 |
+ "execution_count": 91, |
|
| 1740 | 1740 |
"metadata": {},
|
| 1741 | 1741 |
"output_type": "execute_result" |
| 1742 | 1742 |
} |
... | ... | @@ -1747,8 +1747,8 @@ |
| 1747 | 1747 |
}, |
| 1748 | 1748 |
{
|
| 1749 | 1749 |
"cell_type": "code", |
| 1750 |
- "execution_count": 55, |
|
| 1751 |
- "id": "3b2078b6", |
|
| 1750 |
+ "execution_count": 92, |
|
| 1751 |
+ "id": "73582211", |
|
| 1752 | 1752 |
"metadata": {},
|
| 1753 | 1753 |
"outputs": [], |
| 1754 | 1754 |
"source": [ |
... | ... | @@ -1757,8 +1757,8 @@ |
| 1757 | 1757 |
}, |
| 1758 | 1758 |
{
|
| 1759 | 1759 |
"cell_type": "code", |
| 1760 |
- "execution_count": 56, |
|
| 1761 |
- "id": "5df44162", |
|
| 1760 |
+ "execution_count": 93, |
|
| 1761 |
+ "id": "ffd03a3c", |
|
| 1762 | 1762 |
"metadata": {},
|
| 1763 | 1763 |
"outputs": [ |
| 1764 | 1764 |
{
|
... | ... | @@ -1843,7 +1843,7 @@ |
| 1843 | 1843 |
"4 광진구 352627 339996 12631 51723" |
| 1844 | 1844 |
] |
| 1845 | 1845 |
}, |
| 1846 |
- "execution_count": 56, |
|
| 1846 |
+ "execution_count": 93, |
|
| 1847 | 1847 |
"metadata": {},
|
| 1848 | 1848 |
"output_type": "execute_result" |
| 1849 | 1849 |
} |
... | ... | @@ -1854,8 +1854,8 @@ |
| 1854 | 1854 |
}, |
| 1855 | 1855 |
{
|
| 1856 | 1856 |
"cell_type": "code", |
| 1857 |
- "execution_count": 61, |
|
| 1858 |
- "id": "5eeb7eef", |
|
| 1857 |
+ "execution_count": 94, |
|
| 1858 |
+ "id": "35bbd689", |
|
| 1859 | 1859 |
"metadata": {},
|
| 1860 | 1860 |
"outputs": [], |
| 1861 | 1861 |
"source": [ |
... | ... | @@ -1865,8 +1865,8 @@ |
| 1865 | 1865 |
}, |
| 1866 | 1866 |
{
|
| 1867 | 1867 |
"cell_type": "code", |
| 1868 |
- "execution_count": 62, |
|
| 1869 |
- "id": "68762fc0", |
|
| 1868 |
+ "execution_count": 95, |
|
| 1869 |
+ "id": "ff87d5a4", |
|
| 1870 | 1870 |
"metadata": {},
|
| 1871 | 1871 |
"outputs": [ |
| 1872 | 1872 |
{
|
... | ... | @@ -1963,7 +1963,7 @@ |
| 1963 | 1963 |
"4 광진구 352627 339996 12631 51723 3.581972 14.667907" |
| 1964 | 1964 |
] |
| 1965 | 1965 |
}, |
| 1966 |
- "execution_count": 62, |
|
| 1966 |
+ "execution_count": 95, |
|
| 1967 | 1967 |
"metadata": {},
|
| 1968 | 1968 |
"output_type": "execute_result" |
| 1969 | 1969 |
} |
... | ... | @@ -1974,8 +1974,8 @@ |
| 1974 | 1974 |
}, |
| 1975 | 1975 |
{
|
| 1976 | 1976 |
"cell_type": "code", |
| 1977 |
- "execution_count": 64, |
|
| 1978 |
- "id": "e185b622", |
|
| 1977 |
+ "execution_count": 96, |
|
| 1978 |
+ "id": "619fdca3", |
|
| 1979 | 1979 |
"metadata": {
|
| 1980 | 1980 |
"scrolled": true |
| 1981 | 1981 |
}, |
... | ... | @@ -2074,7 +2074,7 @@ |
| 2074 | 2074 |
"20 관악구 499449 485699 13750 79871 2.753034 15.991823" |
| 2075 | 2075 |
] |
| 2076 | 2076 |
}, |
| 2077 |
- "execution_count": 64, |
|
| 2077 |
+ "execution_count": 96, |
|
| 2078 | 2078 |
"metadata": {},
|
| 2079 | 2079 |
"output_type": "execute_result" |
| 2080 | 2080 |
} |
... | ... | @@ -2085,8 +2085,8 @@ |
| 2085 | 2085 |
}, |
| 2086 | 2086 |
{
|
| 2087 | 2087 |
"cell_type": "code", |
| 2088 |
- "execution_count": 68, |
|
| 2089 |
- "id": "42fe3110", |
|
| 2088 |
+ "execution_count": 97, |
|
| 2089 |
+ "id": "0484041d", |
|
| 2090 | 2090 |
"metadata": {},
|
| 2091 | 2091 |
"outputs": [ |
| 2092 | 2092 |
{
|
... | ... | @@ -2183,7 +2183,7 @@ |
| 2183 | 2183 |
"20 관악구 499449 485699 13750 79871 2.753034 15.991823" |
| 2184 | 2184 |
] |
| 2185 | 2185 |
}, |
| 2186 |
- "execution_count": 68, |
|
| 2186 |
+ "execution_count": 97, |
|
| 2187 | 2187 |
"metadata": {},
|
| 2188 | 2188 |
"output_type": "execute_result" |
| 2189 | 2189 |
} |
... | ... | @@ -2194,8 +2194,8 @@ |
| 2194 | 2194 |
}, |
| 2195 | 2195 |
{
|
| 2196 | 2196 |
"cell_type": "code", |
| 2197 |
- "execution_count": 83, |
|
| 2198 |
- "id": "0c7a1da6", |
|
| 2197 |
+ "execution_count": 98, |
|
| 2198 |
+ "id": "fe65b767", |
|
| 2199 | 2199 |
"metadata": {
|
| 2200 | 2200 |
"scrolled": true |
| 2201 | 2201 |
}, |
... | ... | @@ -2386,7 +2386,7 @@ |
| 2386 | 2386 |
"4 14.667907 " |
| 2387 | 2387 |
] |
| 2388 | 2388 |
}, |
| 2389 |
- "execution_count": 83, |
|
| 2389 |
+ "execution_count": 98, |
|
| 2390 | 2390 |
"metadata": {},
|
| 2391 | 2391 |
"output_type": "execute_result" |
| 2392 | 2392 |
} |
... | ... | @@ -2398,8 +2398,8 @@ |
| 2398 | 2398 |
}, |
| 2399 | 2399 |
{
|
| 2400 | 2400 |
"cell_type": "code", |
| 2401 |
- "execution_count": 84, |
|
| 2402 |
- "id": "9d8d3ade", |
|
| 2401 |
+ "execution_count": 99, |
|
| 2402 |
+ "id": "d300cb33", |
|
| 2403 | 2403 |
"metadata": {},
|
| 2404 | 2404 |
"outputs": [], |
| 2405 | 2405 |
"source": [ |
... | ... | @@ -2418,8 +2418,8 @@ |
| 2418 | 2418 |
}, |
| 2419 | 2419 |
{
|
| 2420 | 2420 |
"cell_type": "code", |
| 2421 |
- "execution_count": 85, |
|
| 2422 |
- "id": "08238cb5", |
|
| 2421 |
+ "execution_count": 100, |
|
| 2422 |
+ "id": "30b88050", |
|
| 2423 | 2423 |
"metadata": {},
|
| 2424 | 2424 |
"outputs": [ |
| 2425 | 2425 |
{
|
... | ... | @@ -2528,7 +2528,7 @@ |
| 2528 | 2528 |
"4 광진구 3211 506.994329 352627 339996 12631 51723 3.581972 14.667907" |
| 2529 | 2529 |
] |
| 2530 | 2530 |
}, |
| 2531 |
- "execution_count": 85, |
|
| 2531 |
+ "execution_count": 100, |
|
| 2532 | 2532 |
"metadata": {},
|
| 2533 | 2533 |
"output_type": "execute_result" |
| 2534 | 2534 |
} |
... | ... | @@ -2539,8 +2539,8 @@ |
| 2539 | 2539 |
}, |
| 2540 | 2540 |
{
|
| 2541 | 2541 |
"cell_type": "code", |
| 2542 |
- "execution_count": 86, |
|
| 2543 |
- "id": "4f6a862c", |
|
| 2542 |
+ "execution_count": 101, |
|
| 2543 |
+ "id": "460ad616", |
|
| 2544 | 2544 |
"metadata": {},
|
| 2545 | 2545 |
"outputs": [], |
| 2546 | 2546 |
"source": [ |
... | ... | @@ -2549,8 +2549,8 @@ |
| 2549 | 2549 |
}, |
| 2550 | 2550 |
{
|
| 2551 | 2551 |
"cell_type": "code", |
| 2552 |
- "execution_count": 87, |
|
| 2553 |
- "id": "e010a71c", |
|
| 2552 |
+ "execution_count": 102, |
|
| 2553 |
+ "id": "150a3b3f", |
|
| 2554 | 2554 |
"metadata": {},
|
| 2555 | 2555 |
"outputs": [ |
| 2556 | 2556 |
{
|
... | ... | @@ -2665,7 +2665,7 @@ |
| 2665 | 2665 |
"광진구 3211 506.994329 352627 339996 12631 51723 3.581972 14.667907" |
| 2666 | 2666 |
] |
| 2667 | 2667 |
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| 2669 | 2669 |
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| 2671 | 2671 |
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... | ... | @@ -2676,27 +2676,1860 @@ |
| 2676 | 2676 |
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| 2677 | 2677 |
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"cell_type": "code", |
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- "id": "981d8131", |
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- "outputs": [], |
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- "source": [] |
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+ "np.corrcoef(dataset['외국인비율'], dataset['총계'])" |
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+ ] |
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+ "np.corrcoef(dataset['인구수'], dataset['총계'])" |
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+ ] |
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+ }, |
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+ " <thead>\n", |
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+ " <tr style=\"text-align: right;\">\n", |
|
| 2769 |
+ " <th></th>\n", |
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| 2770 |
+ " <th>총계</th>\n", |
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| 2771 |
+ " <th>최근증가율</th>\n", |
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| 2772 |
+ " <th>인구수</th>\n", |
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| 2773 |
+ " <th>한국인</th>\n", |
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+ " <th>외국인</th>\n", |
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+ " <th>고령자</th>\n", |
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| 2776 |
+ " <th>외국인비율</th>\n", |
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| 2777 |
+ " <th>고령자비율</th>\n", |
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| 2778 |
+ " </tr>\n", |
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| 2779 |
+ " <tr>\n", |
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| 2780 |
+ " <th>구별</th>\n", |
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| 2782 |
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| 2783 |
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| 2793 |
+ " <th>강남구</th>\n", |
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| 2794 |
+ " <td>6871</td>\n", |
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| 2795 |
+ " <td>136.523236</td>\n", |
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| 2796 |
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| 2797 |
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| 2799 |
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+ " </tr>\n", |
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| 2804 |
+ " <th>관악구</th>\n", |
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| 2805 |
+ " <td>5149</td>\n", |
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| 2806 |
+ " <td>96.152381</td>\n", |
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| 2807 |
+ " <td>499449</td>\n", |
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| 2811 |
+ " <td>2.753034</td>\n", |
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| 2812 |
+ " <td>15.991823</td>\n", |
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| 2813 |
+ " </tr>\n", |
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| 2814 |
+ " <tr>\n", |
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| 2815 |
+ " <th>구로구</th>\n", |
|
| 2816 |
+ " <td>4608</td>\n", |
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| 2817 |
+ " <td>109.645132</td>\n", |
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| 2818 |
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| 2819 |
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| 2820 |
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| 2821 |
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| 2822 |
+ " <td>5.795618</td>\n", |
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| 2823 |
+ " <td>17.240593</td>\n", |
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| 2824 |
+ " </tr>\n", |
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| 2825 |
+ " <tr>\n", |
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| 2826 |
+ " <th>성북구</th>\n", |
|
| 2827 |
+ " <td>4602</td>\n", |
|
| 2828 |
+ " <td>212.211669</td>\n", |
|
| 2829 |
+ " <td>440142</td>\n", |
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| 2830 |
+ " <td>430528</td>\n", |
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| 2831 |
+ " <td>9614</td>\n", |
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| 2832 |
+ " <td>74709</td>\n", |
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| 2833 |
+ " <td>2.184295</td>\n", |
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| 2834 |
+ " <td>16.973840</td>\n", |
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| 2835 |
+ " </tr>\n", |
|
| 2836 |
+ " <tr>\n", |
|
| 2837 |
+ " <th>은평구</th>\n", |
|
| 2838 |
+ " <td>4131</td>\n", |
|
| 2839 |
+ " <td>223.492561</td>\n", |
|
| 2840 |
+ " <td>477173</td>\n", |
|
| 2841 |
+ " <td>473307</td>\n", |
|
| 2842 |
+ " <td>3866</td>\n", |
|
| 2843 |
+ " <td>87241</td>\n", |
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| 2844 |
+ " <td>0.810188</td>\n", |
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| 2845 |
+ " <td>18.282887</td>\n", |
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| 2846 |
+ " </tr>\n", |
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| 2848 |
+ "</table>\n", |
|
| 2849 |
+ "</div>" |
|
| 2850 |
+ ], |
|
| 2851 |
+ "text/plain": [ |
|
| 2852 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 2853 |
+ "구별 \n", |
|
| 2854 |
+ "강남구 6871 136.523236 537800 533042 4758 78226 0.884716 14.545556\n", |
|
| 2855 |
+ "관악구 5149 96.152381 499449 485699 13750 79871 2.753034 15.991823\n", |
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| 2856 |
+ "구로구 4608 109.645132 421163 396754 24409 72611 5.795618 17.240593\n", |
|
| 2857 |
+ "성북구 4602 212.211669 440142 430528 9614 74709 2.184295 16.973840\n", |
|
| 2858 |
+ "은평구 4131 223.492561 477173 473307 3866 87241 0.810188 18.282887" |
|
| 2859 |
+ ] |
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| 2860 |
+ }, |
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| 2861 |
+ "execution_count": 106, |
|
| 2862 |
+ "metadata": {},
|
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| 2863 |
+ "output_type": "execute_result" |
|
| 2864 |
+ } |
|
| 2865 |
+ ], |
|
| 2866 |
+ "source": [ |
|
| 2867 |
+ "dataset.sort_values(by=\"총계\", ascending=False).head()" |
|
| 2868 |
+ ] |
|
| 2869 |
+ }, |
|
| 2870 |
+ {
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+ "execution_count": 107, |
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+ "id": "ce28effb", |
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+ "metadata": {},
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+ "outputs": [ |
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| 2876 |
+ {
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| 2877 |
+ "data": {
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| 2878 |
+ "text/html": [ |
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+ "<div>\n", |
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+ "<style scoped>\n", |
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n", |
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+ " }\n", |
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+ "\n", |
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n", |
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+ " }\n", |
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+ "\n", |
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| 2889 |
+ " .dataframe thead th {\n",
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| 2890 |
+ " text-align: right;\n", |
|
| 2891 |
+ " }\n", |
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| 2892 |
+ "</style>\n", |
|
| 2893 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 2894 |
+ " <thead>\n", |
|
| 2895 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 2896 |
+ " <th></th>\n", |
|
| 2897 |
+ " <th>총계</th>\n", |
|
| 2898 |
+ " <th>최근증가율</th>\n", |
|
| 2899 |
+ " <th>인구수</th>\n", |
|
| 2900 |
+ " <th>한국인</th>\n", |
|
| 2901 |
+ " <th>외국인</th>\n", |
|
| 2902 |
+ " <th>고령자</th>\n", |
|
| 2903 |
+ " <th>외국인비율</th>\n", |
|
| 2904 |
+ " <th>고령자비율</th>\n", |
|
| 2905 |
+ " </tr>\n", |
|
| 2906 |
+ " <tr>\n", |
|
| 2907 |
+ " <th>구별</th>\n", |
|
| 2908 |
+ " <th></th>\n", |
|
| 2909 |
+ " <th></th>\n", |
|
| 2910 |
+ " <th></th>\n", |
|
| 2911 |
+ " <th></th>\n", |
|
| 2912 |
+ " <th></th>\n", |
|
| 2913 |
+ " <th></th>\n", |
|
| 2914 |
+ " <th></th>\n", |
|
| 2915 |
+ " <th></th>\n", |
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| 2916 |
+ " </tr>\n", |
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| 2917 |
+ " </thead>\n", |
|
| 2918 |
+ " <tbody>\n", |
|
| 2919 |
+ " <tr>\n", |
|
| 2920 |
+ " <th>송파구</th>\n", |
|
| 2921 |
+ " <td>2897</td>\n", |
|
| 2922 |
+ " <td>336.295181</td>\n", |
|
| 2923 |
+ " <td>663965</td>\n", |
|
| 2924 |
+ " <td>658338</td>\n", |
|
| 2925 |
+ " <td>5627</td>\n", |
|
| 2926 |
+ " <td>97691</td>\n", |
|
| 2927 |
+ " <td>0.847484</td>\n", |
|
| 2928 |
+ " <td>14.713276</td>\n", |
|
| 2929 |
+ " </tr>\n", |
|
| 2930 |
+ " <tr>\n", |
|
| 2931 |
+ " <th>강서구</th>\n", |
|
| 2932 |
+ " <td>2744</td>\n", |
|
| 2933 |
+ " <td>223.203769</td>\n", |
|
| 2934 |
+ " <td>579768</td>\n", |
|
| 2935 |
+ " <td>574315</td>\n", |
|
| 2936 |
+ " <td>5453</td>\n", |
|
| 2937 |
+ " <td>92558</td>\n", |
|
| 2938 |
+ " <td>0.940549</td>\n", |
|
| 2939 |
+ " <td>15.964662</td>\n", |
|
| 2940 |
+ " </tr>\n", |
|
| 2941 |
+ " <tr>\n", |
|
| 2942 |
+ " <th>강남구</th>\n", |
|
| 2943 |
+ " <td>6871</td>\n", |
|
| 2944 |
+ " <td>136.523236</td>\n", |
|
| 2945 |
+ " <td>537800</td>\n", |
|
| 2946 |
+ " <td>533042</td>\n", |
|
| 2947 |
+ " <td>4758</td>\n", |
|
| 2948 |
+ " <td>78226</td>\n", |
|
| 2949 |
+ " <td>0.884716</td>\n", |
|
| 2950 |
+ " <td>14.545556</td>\n", |
|
| 2951 |
+ " </tr>\n", |
|
| 2952 |
+ " <tr>\n", |
|
| 2953 |
+ " <th>노원구</th>\n", |
|
| 2954 |
+ " <td>2492</td>\n", |
|
| 2955 |
+ " <td>112.446718</td>\n", |
|
| 2956 |
+ " <td>514946</td>\n", |
|
| 2957 |
+ " <td>510956</td>\n", |
|
| 2958 |
+ " <td>3990</td>\n", |
|
| 2959 |
+ " <td>88345</td>\n", |
|
| 2960 |
+ " <td>0.774839</td>\n", |
|
| 2961 |
+ " <td>17.156168</td>\n", |
|
| 2962 |
+ " </tr>\n", |
|
| 2963 |
+ " <tr>\n", |
|
| 2964 |
+ " <th>관악구</th>\n", |
|
| 2965 |
+ " <td>5149</td>\n", |
|
| 2966 |
+ " <td>96.152381</td>\n", |
|
| 2967 |
+ " <td>499449</td>\n", |
|
| 2968 |
+ " <td>485699</td>\n", |
|
| 2969 |
+ " <td>13750</td>\n", |
|
| 2970 |
+ " <td>79871</td>\n", |
|
| 2971 |
+ " <td>2.753034</td>\n", |
|
| 2972 |
+ " <td>15.991823</td>\n", |
|
| 2973 |
+ " </tr>\n", |
|
| 2974 |
+ " </tbody>\n", |
|
| 2975 |
+ "</table>\n", |
|
| 2976 |
+ "</div>" |
|
| 2977 |
+ ], |
|
| 2978 |
+ "text/plain": [ |
|
| 2979 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 2980 |
+ "구별 \n", |
|
| 2981 |
+ "송파구 2897 336.295181 663965 658338 5627 97691 0.847484 14.713276\n", |
|
| 2982 |
+ "강서구 2744 223.203769 579768 574315 5453 92558 0.940549 15.964662\n", |
|
| 2983 |
+ "강남구 6871 136.523236 537800 533042 4758 78226 0.884716 14.545556\n", |
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| 2984 |
+ "노원구 2492 112.446718 514946 510956 3990 88345 0.774839 17.156168\n", |
|
| 2985 |
+ "관악구 5149 96.152381 499449 485699 13750 79871 2.753034 15.991823" |
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| 2986 |
+ ] |
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| 2987 |
+ }, |
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+ "execution_count": 107, |
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| 2989 |
+ "metadata": {},
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| 2990 |
+ "output_type": "execute_result" |
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| 2991 |
+ } |
|
| 2992 |
+ ], |
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| 2993 |
+ "source": [ |
|
| 2994 |
+ "dataset.sort_values(by=\"인구수\", ascending=False).head(5)" |
|
| 2995 |
+ ] |
|
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+ }, |
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+ {
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+ "cell_type": "code", |
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+ "execution_count": 108, |
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+ "id": "ba6ceb68", |
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+ "metadata": {
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+ "scrolled": true |
|
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+ }, |
|
| 3004 |
+ "outputs": [], |
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| 3005 |
+ "source": [ |
|
| 3006 |
+ "import matplotlib.font_manager as fm\n", |
|
| 3007 |
+ "font_name = fm.FontProperties(fname=\"C:/Users/pc/AppData/Local/Microsoft/Windows/Fonts/NanumGothic.ttf\").get_name()\n", |
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+ "plt.rc('font', family=font_name)"
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+ ] |
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+ }, |
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+ {
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| 3012 |
+ "cell_type": "code", |
|
| 3013 |
+ "execution_count": 109, |
|
| 3014 |
+ "id": "e49b7cdc", |
|
| 3015 |
+ "metadata": {},
|
|
| 3016 |
+ "outputs": [ |
|
| 3017 |
+ {
|
|
| 3018 |
+ "data": {
|
|
| 3019 |
+ "text/plain": [ |
|
| 3020 |
+ "<AxesSubplot:ylabel='구별'>" |
|
| 3021 |
+ ] |
|
| 3022 |
+ }, |
|
| 3023 |
+ "execution_count": 109, |
|
| 3024 |
+ "metadata": {},
|
|
| 3025 |
+ "output_type": "execute_result" |
|
| 3026 |
+ }, |
|
| 3027 |
+ {
|
|
| 3028 |
+ "data": {
|
|
| 3029 |
+ "image/png": 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\n", |
|
| 3030 |
+ "text/plain": [ |
|
| 3031 |
+ "<Figure size 720x720 with 1 Axes>" |
|
| 3032 |
+ ] |
|
| 3033 |
+ }, |
|
| 3034 |
+ "metadata": {
|
|
| 3035 |
+ "needs_background": "light" |
|
| 3036 |
+ }, |
|
| 3037 |
+ "output_type": "display_data" |
|
| 3038 |
+ } |
|
| 3039 |
+ ], |
|
| 3040 |
+ "source": [ |
|
| 3041 |
+ "dataset['총계'].plot(kind='barh', grid=True, figsize=(10, 10))" |
|
| 3042 |
+ ] |
|
| 3043 |
+ }, |
|
| 3044 |
+ {
|
|
| 3045 |
+ "cell_type": "code", |
|
| 3046 |
+ "execution_count": 110, |
|
| 3047 |
+ "id": "7108c4f0", |
|
| 3048 |
+ "metadata": {},
|
|
| 3049 |
+ "outputs": [ |
|
| 3050 |
+ {
|
|
| 3051 |
+ "data": {
|
|
| 3052 |
+ "text/plain": [ |
|
| 3053 |
+ "<AxesSubplot:ylabel='구별'>" |
|
| 3054 |
+ ] |
|
| 3055 |
+ }, |
|
| 3056 |
+ "execution_count": 110, |
|
| 3057 |
+ "metadata": {},
|
|
| 3058 |
+ "output_type": "execute_result" |
|
| 3059 |
+ }, |
|
| 3060 |
+ {
|
|
| 3061 |
+ "data": {
|
|
| 3062 |
+ "image/png": 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\n", 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+ "<Figure size 720x720 with 1 Axes>" |
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+ }, |
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+ "metadata": {
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+ "needs_background": "light" |
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+ "output_type": "display_data" |
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+ } |
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+ ], |
|
| 3073 |
+ "source": [ |
|
| 3074 |
+ "dataset['총계'].sort_values().plot(kind='barh', grid=True, figsize=(10, 10))" |
|
| 3075 |
+ ] |
|
| 3076 |
+ }, |
|
| 3077 |
+ {
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+ "cell_type": "code", |
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| 3079 |
+ "execution_count": 111, |
|
| 3080 |
+ "id": "20db3ebf", |
|
| 3081 |
+ "metadata": {},
|
|
| 3082 |
+ "outputs": [ |
|
| 3083 |
+ {
|
|
| 3084 |
+ "data": {
|
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| 3085 |
+ "text/html": [ |
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| 3086 |
+ "<div>\n", |
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+ "<style scoped>\n", |
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+ " .dataframe tbody tr th:only-of-type {\n",
|
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+ " vertical-align: middle;\n", |
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+ " }\n", |
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+ "\n", |
|
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+ " .dataframe tbody tr th {\n",
|
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+ " vertical-align: top;\n", |
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+ " }\n", |
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+ "\n", |
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+ " .dataframe thead th {\n",
|
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+ " text-align: right;\n", |
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+ " }\n", |
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+ "</style>\n", |
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+ " <thead>\n", |
|
| 3102 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 3103 |
+ " <th></th>\n", |
|
| 3104 |
+ " <th>총계</th>\n", |
|
| 3105 |
+ " <th>최근증가율</th>\n", |
|
| 3106 |
+ " <th>인구수</th>\n", |
|
| 3107 |
+ " <th>한국인</th>\n", |
|
| 3108 |
+ " <th>외국인</th>\n", |
|
| 3109 |
+ " <th>고령자</th>\n", |
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| 3110 |
+ " <th>외국인비율</th>\n", |
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| 3111 |
+ " <th>고령자비율</th>\n", |
|
| 3112 |
+ " </tr>\n", |
|
| 3113 |
+ " <tr>\n", |
|
| 3114 |
+ " <th>구별</th>\n", |
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| 3115 |
+ " <th></th>\n", |
|
| 3116 |
+ " <th></th>\n", |
|
| 3117 |
+ " <th></th>\n", |
|
| 3118 |
+ " <th></th>\n", |
|
| 3119 |
+ " <th></th>\n", |
|
| 3120 |
+ " <th></th>\n", |
|
| 3121 |
+ " <th></th>\n", |
|
| 3122 |
+ " <th></th>\n", |
|
| 3123 |
+ " </tr>\n", |
|
| 3124 |
+ " </thead>\n", |
|
| 3125 |
+ " <tbody>\n", |
|
| 3126 |
+ " <tr>\n", |
|
| 3127 |
+ " <th>종로구</th>\n", |
|
| 3128 |
+ " <td>1715</td>\n", |
|
| 3129 |
+ " <td>47.844828</td>\n", |
|
| 3130 |
+ " <td>153789</td>\n", |
|
| 3131 |
+ " <td>144683</td>\n", |
|
| 3132 |
+ " <td>9106</td>\n", |
|
| 3133 |
+ " <td>27818</td>\n", |
|
| 3134 |
+ " <td>5.921100</td>\n", |
|
| 3135 |
+ " <td>18.088420</td>\n", |
|
| 3136 |
+ " </tr>\n", |
|
| 3137 |
+ " <tr>\n", |
|
| 3138 |
+ " <th>중구</th>\n", |
|
| 3139 |
+ " <td>2447</td>\n", |
|
| 3140 |
+ " <td>217.792208</td>\n", |
|
| 3141 |
+ " <td>131787</td>\n", |
|
| 3142 |
+ " <td>122499</td>\n", |
|
| 3143 |
+ " <td>9288</td>\n", |
|
| 3144 |
+ " <td>24392</td>\n", |
|
| 3145 |
+ " <td>7.047736</td>\n", |
|
| 3146 |
+ " <td>18.508654</td>\n", |
|
| 3147 |
+ " </tr>\n", |
|
| 3148 |
+ " <tr>\n", |
|
| 3149 |
+ " <th>용산구</th>\n", |
|
| 3150 |
+ " <td>2611</td>\n", |
|
| 3151 |
+ " <td>165.615463</td>\n", |
|
| 3152 |
+ " <td>237285</td>\n", |
|
| 3153 |
+ " <td>222953</td>\n", |
|
| 3154 |
+ " <td>14332</td>\n", |
|
| 3155 |
+ " <td>39070</td>\n", |
|
| 3156 |
+ " <td>6.039994</td>\n", |
|
| 3157 |
+ " <td>16.465432</td>\n", |
|
| 3158 |
+ " </tr>\n", |
|
| 3159 |
+ " <tr>\n", |
|
| 3160 |
+ " <th>성동구</th>\n", |
|
| 3161 |
+ " <td>3829</td>\n", |
|
| 3162 |
+ " <td>218.552413</td>\n", |
|
| 3163 |
+ " <td>292672</td>\n", |
|
| 3164 |
+ " <td>285990</td>\n", |
|
| 3165 |
+ " <td>6682</td>\n", |
|
| 3166 |
+ " <td>46380</td>\n", |
|
| 3167 |
+ " <td>2.283102</td>\n", |
|
| 3168 |
+ " <td>15.847092</td>\n", |
|
| 3169 |
+ " </tr>\n", |
|
| 3170 |
+ " <tr>\n", |
|
| 3171 |
+ " <th>광진구</th>\n", |
|
| 3172 |
+ " <td>3211</td>\n", |
|
| 3173 |
+ " <td>506.994329</td>\n", |
|
| 3174 |
+ " <td>352627</td>\n", |
|
| 3175 |
+ " <td>339996</td>\n", |
|
| 3176 |
+ " <td>12631</td>\n", |
|
| 3177 |
+ " <td>51723</td>\n", |
|
| 3178 |
+ " <td>3.581972</td>\n", |
|
| 3179 |
+ " <td>14.667907</td>\n", |
|
| 3180 |
+ " </tr>\n", |
|
| 3181 |
+ " </tbody>\n", |
|
| 3182 |
+ "</table>\n", |
|
| 3183 |
+ "</div>" |
|
| 3184 |
+ ], |
|
| 3185 |
+ "text/plain": [ |
|
| 3186 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n", |
|
| 3187 |
+ "구별 \n", |
|
| 3188 |
+ "종로구 1715 47.844828 153789 144683 9106 27818 5.921100 18.088420\n", |
|
| 3189 |
+ "중구 2447 217.792208 131787 122499 9288 24392 7.047736 18.508654\n", |
|
| 3190 |
+ "용산구 2611 165.615463 237285 222953 14332 39070 6.039994 16.465432\n", |
|
| 3191 |
+ "성동구 3829 218.552413 292672 285990 6682 46380 2.283102 15.847092\n", |
|
| 3192 |
+ "광진구 3211 506.994329 352627 339996 12631 51723 3.581972 14.667907" |
|
| 3193 |
+ ] |
|
| 3194 |
+ }, |
|
| 3195 |
+ "execution_count": 111, |
|
| 3196 |
+ "metadata": {},
|
|
| 3197 |
+ "output_type": "execute_result" |
|
| 3198 |
+ } |
|
| 3199 |
+ ], |
|
| 3200 |
+ "source": [ |
|
| 3201 |
+ "dataset.head()" |
|
| 3202 |
+ ] |
|
| 3203 |
+ }, |
|
| 3204 |
+ {
|
|
| 3205 |
+ "cell_type": "code", |
|
| 3206 |
+ "execution_count": 112, |
|
| 3207 |
+ "id": "da5811c6", |
|
| 2697 | 3208 |
"metadata": {},
|
| 2698 | 3209 |
"outputs": [], |
| 2699 |
- "source": [] |
|
| 3210 |
+ "source": [ |
|
| 3211 |
+ "dataset['CCTV비율'] = dataset['총계']/dataset['인구수'] * 100" |
|
| 3212 |
+ ] |
|
| 3213 |
+ }, |
|
| 3214 |
+ {
|
|
| 3215 |
+ "cell_type": "code", |
|
| 3216 |
+ "execution_count": 113, |
|
| 3217 |
+ "id": "350fa4e9", |
|
| 3218 |
+ "metadata": {},
|
|
| 3219 |
+ "outputs": [ |
|
| 3220 |
+ {
|
|
| 3221 |
+ "data": {
|
|
| 3222 |
+ "text/html": [ |
|
| 3223 |
+ "<div>\n", |
|
| 3224 |
+ "<style scoped>\n", |
|
| 3225 |
+ " .dataframe tbody tr th:only-of-type {\n",
|
|
| 3226 |
+ " vertical-align: middle;\n", |
|
| 3227 |
+ " }\n", |
|
| 3228 |
+ "\n", |
|
| 3229 |
+ " .dataframe tbody tr th {\n",
|
|
| 3230 |
+ " vertical-align: top;\n", |
|
| 3231 |
+ " }\n", |
|
| 3232 |
+ "\n", |
|
| 3233 |
+ " .dataframe thead th {\n",
|
|
| 3234 |
+ " text-align: right;\n", |
|
| 3235 |
+ " }\n", |
|
| 3236 |
+ "</style>\n", |
|
| 3237 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 3238 |
+ " <thead>\n", |
|
| 3239 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 3240 |
+ " <th></th>\n", |
|
| 3241 |
+ " <th>총계</th>\n", |
|
| 3242 |
+ " <th>최근증가율</th>\n", |
|
| 3243 |
+ " <th>인구수</th>\n", |
|
| 3244 |
+ " <th>한국인</th>\n", |
|
| 3245 |
+ " <th>외국인</th>\n", |
|
| 3246 |
+ " <th>고령자</th>\n", |
|
| 3247 |
+ " <th>외국인비율</th>\n", |
|
| 3248 |
+ " <th>고령자비율</th>\n", |
|
| 3249 |
+ " <th>CCTV비율</th>\n", |
|
| 3250 |
+ " </tr>\n", |
|
| 3251 |
+ " <tr>\n", |
|
| 3252 |
+ " <th>구별</th>\n", |
|
| 3253 |
+ " <th></th>\n", |
|
| 3254 |
+ " <th></th>\n", |
|
| 3255 |
+ " <th></th>\n", |
|
| 3256 |
+ " <th></th>\n", |
|
| 3257 |
+ " <th></th>\n", |
|
| 3258 |
+ " <th></th>\n", |
|
| 3259 |
+ " <th></th>\n", |
|
| 3260 |
+ " <th></th>\n", |
|
| 3261 |
+ " <th></th>\n", |
|
| 3262 |
+ " </tr>\n", |
|
| 3263 |
+ " </thead>\n", |
|
| 3264 |
+ " <tbody>\n", |
|
| 3265 |
+ " <tr>\n", |
|
| 3266 |
+ " <th>종로구</th>\n", |
|
| 3267 |
+ " <td>1715</td>\n", |
|
| 3268 |
+ " <td>47.844828</td>\n", |
|
| 3269 |
+ " <td>153789</td>\n", |
|
| 3270 |
+ " <td>144683</td>\n", |
|
| 3271 |
+ " <td>9106</td>\n", |
|
| 3272 |
+ " <td>27818</td>\n", |
|
| 3273 |
+ " <td>5.921100</td>\n", |
|
| 3274 |
+ " <td>18.088420</td>\n", |
|
| 3275 |
+ " <td>1.115164</td>\n", |
|
| 3276 |
+ " </tr>\n", |
|
| 3277 |
+ " <tr>\n", |
|
| 3278 |
+ " <th>중구</th>\n", |
|
| 3279 |
+ " <td>2447</td>\n", |
|
| 3280 |
+ " <td>217.792208</td>\n", |
|
| 3281 |
+ " <td>131787</td>\n", |
|
| 3282 |
+ " <td>122499</td>\n", |
|
| 3283 |
+ " <td>9288</td>\n", |
|
| 3284 |
+ " <td>24392</td>\n", |
|
| 3285 |
+ " <td>7.047736</td>\n", |
|
| 3286 |
+ " <td>18.508654</td>\n", |
|
| 3287 |
+ " <td>1.856784</td>\n", |
|
| 3288 |
+ " </tr>\n", |
|
| 3289 |
+ " <tr>\n", |
|
| 3290 |
+ " <th>용산구</th>\n", |
|
| 3291 |
+ " <td>2611</td>\n", |
|
| 3292 |
+ " <td>165.615463</td>\n", |
|
| 3293 |
+ " <td>237285</td>\n", |
|
| 3294 |
+ " <td>222953</td>\n", |
|
| 3295 |
+ " <td>14332</td>\n", |
|
| 3296 |
+ " <td>39070</td>\n", |
|
| 3297 |
+ " <td>6.039994</td>\n", |
|
| 3298 |
+ " <td>16.465432</td>\n", |
|
| 3299 |
+ " <td>1.100365</td>\n", |
|
| 3300 |
+ " </tr>\n", |
|
| 3301 |
+ " <tr>\n", |
|
| 3302 |
+ " <th>성동구</th>\n", |
|
| 3303 |
+ " <td>3829</td>\n", |
|
| 3304 |
+ " <td>218.552413</td>\n", |
|
| 3305 |
+ " <td>292672</td>\n", |
|
| 3306 |
+ " <td>285990</td>\n", |
|
| 3307 |
+ " <td>6682</td>\n", |
|
| 3308 |
+ " <td>46380</td>\n", |
|
| 3309 |
+ " <td>2.283102</td>\n", |
|
| 3310 |
+ " <td>15.847092</td>\n", |
|
| 3311 |
+ " <td>1.308291</td>\n", |
|
| 3312 |
+ " </tr>\n", |
|
| 3313 |
+ " <tr>\n", |
|
| 3314 |
+ " <th>광진구</th>\n", |
|
| 3315 |
+ " <td>3211</td>\n", |
|
| 3316 |
+ " <td>506.994329</td>\n", |
|
| 3317 |
+ " <td>352627</td>\n", |
|
| 3318 |
+ " <td>339996</td>\n", |
|
| 3319 |
+ " <td>12631</td>\n", |
|
| 3320 |
+ " <td>51723</td>\n", |
|
| 3321 |
+ " <td>3.581972</td>\n", |
|
| 3322 |
+ " <td>14.667907</td>\n", |
|
| 3323 |
+ " <td>0.910594</td>\n", |
|
| 3324 |
+ " </tr>\n", |
|
| 3325 |
+ " <tr>\n", |
|
| 3326 |
+ " <th>동대문구</th>\n", |
|
| 3327 |
+ " <td>2628</td>\n", |
|
| 3328 |
+ " <td>175.760756</td>\n", |
|
| 3329 |
+ " <td>352006</td>\n", |
|
| 3330 |
+ " <td>337400</td>\n", |
|
| 3331 |
+ " <td>14606</td>\n", |
|
| 3332 |
+ " <td>62211</td>\n", |
|
| 3333 |
+ " <td>4.149361</td>\n", |
|
| 3334 |
+ " <td>17.673278</td>\n", |
|
| 3335 |
+ " <td>0.746578</td>\n", |
|
| 3336 |
+ " </tr>\n", |
|
| 3337 |
+ " <tr>\n", |
|
| 3338 |
+ " <th>중랑구</th>\n", |
|
| 3339 |
+ " <td>3737</td>\n", |
|
| 3340 |
+ " <td>281.716037</td>\n", |
|
| 3341 |
+ " <td>391885</td>\n", |
|
| 3342 |
+ " <td>387350</td>\n", |
|
| 3343 |
+ " <td>4535</td>\n", |
|
| 3344 |
+ " <td>71682</td>\n", |
|
| 3345 |
+ " <td>1.157227</td>\n", |
|
| 3346 |
+ " <td>18.291591</td>\n", |
|
| 3347 |
+ " <td>0.953596</td>\n", |
|
| 3348 |
+ " </tr>\n", |
|
| 3349 |
+ " <tr>\n", |
|
| 3350 |
+ " <th>성북구</th>\n", |
|
| 3351 |
+ " <td>4602</td>\n", |
|
| 3352 |
+ " <td>212.211669</td>\n", |
|
| 3353 |
+ " <td>440142</td>\n", |
|
| 3354 |
+ " <td>430528</td>\n", |
|
| 3355 |
+ " <td>9614</td>\n", |
|
| 3356 |
+ " <td>74709</td>\n", |
|
| 3357 |
+ " <td>2.184295</td>\n", |
|
| 3358 |
+ " <td>16.973840</td>\n", |
|
| 3359 |
+ " <td>1.045572</td>\n", |
|
| 3360 |
+ " </tr>\n", |
|
| 3361 |
+ " <tr>\n", |
|
| 3362 |
+ " <th>강북구</th>\n", |
|
| 3363 |
+ " <td>3090</td>\n", |
|
| 3364 |
+ " <td>563.090129</td>\n", |
|
| 3365 |
+ " <td>302563</td>\n", |
|
| 3366 |
+ " <td>299182</td>\n", |
|
| 3367 |
+ " <td>3381</td>\n", |
|
| 3368 |
+ " <td>64333</td>\n", |
|
| 3369 |
+ " <td>1.117453</td>\n", |
|
| 3370 |
+ " <td>21.262679</td>\n", |
|
| 3371 |
+ " <td>1.021275</td>\n", |
|
| 3372 |
+ " </tr>\n", |
|
| 3373 |
+ " <tr>\n", |
|
| 3374 |
+ " <th>도봉구</th>\n", |
|
| 3375 |
+ " <td>1930</td>\n", |
|
| 3376 |
+ " <td>226.013514</td>\n", |
|
| 3377 |
+ " <td>319373</td>\n", |
|
| 3378 |
+ " <td>317366</td>\n", |
|
| 3379 |
+ " <td>2007</td>\n", |
|
| 3380 |
+ " <td>64160</td>\n", |
|
| 3381 |
+ " <td>0.628419</td>\n", |
|
| 3382 |
+ " <td>20.089363</td>\n", |
|
| 3383 |
+ " <td>0.604309</td>\n", |
|
| 3384 |
+ " </tr>\n", |
|
| 3385 |
+ " <tr>\n", |
|
| 3386 |
+ " <th>노원구</th>\n", |
|
| 3387 |
+ " <td>2492</td>\n", |
|
| 3388 |
+ " <td>112.446718</td>\n", |
|
| 3389 |
+ " <td>514946</td>\n", |
|
| 3390 |
+ " <td>510956</td>\n", |
|
| 3391 |
+ " <td>3990</td>\n", |
|
| 3392 |
+ " <td>88345</td>\n", |
|
| 3393 |
+ " <td>0.774839</td>\n", |
|
| 3394 |
+ " <td>17.156168</td>\n", |
|
| 3395 |
+ " <td>0.483934</td>\n", |
|
| 3396 |
+ " </tr>\n", |
|
| 3397 |
+ " <tr>\n", |
|
| 3398 |
+ " <th>은평구</th>\n", |
|
| 3399 |
+ " <td>4131</td>\n", |
|
| 3400 |
+ " <td>223.492561</td>\n", |
|
| 3401 |
+ " <td>477173</td>\n", |
|
| 3402 |
+ " <td>473307</td>\n", |
|
| 3403 |
+ " <td>3866</td>\n", |
|
| 3404 |
+ " <td>87241</td>\n", |
|
| 3405 |
+ " <td>0.810188</td>\n", |
|
| 3406 |
+ " <td>18.282887</td>\n", |
|
| 3407 |
+ " <td>0.865724</td>\n", |
|
| 3408 |
+ " </tr>\n", |
|
| 3409 |
+ " <tr>\n", |
|
| 3410 |
+ " <th>서대문구</th>\n", |
|
| 3411 |
+ " <td>3055</td>\n", |
|
| 3412 |
+ " <td>90.224159</td>\n", |
|
| 3413 |
+ " <td>315659</td>\n", |
|
| 3414 |
+ " <td>304819</td>\n", |
|
| 3415 |
+ " <td>10840</td>\n", |
|
| 3416 |
+ " <td>54268</td>\n", |
|
| 3417 |
+ " <td>3.434086</td>\n", |
|
| 3418 |
+ " <td>17.191970</td>\n", |
|
| 3419 |
+ " <td>0.967817</td>\n", |
|
| 3420 |
+ " </tr>\n", |
|
| 3421 |
+ " <tr>\n", |
|
| 3422 |
+ " <th>마포구</th>\n", |
|
| 3423 |
+ " <td>2496</td>\n", |
|
| 3424 |
+ " <td>191.248541</td>\n", |
|
| 3425 |
+ " <td>378686</td>\n", |
|
| 3426 |
+ " <td>368905</td>\n", |
|
| 3427 |
+ " <td>9781</td>\n", |
|
| 3428 |
+ " <td>54582</td>\n", |
|
| 3429 |
+ " <td>2.582879</td>\n", |
|
| 3430 |
+ " <td>14.413525</td>\n", |
|
| 3431 |
+ " <td>0.659121</td>\n", |
|
| 3432 |
+ " </tr>\n", |
|
| 3433 |
+ " <tr>\n", |
|
| 3434 |
+ " <th>양천구</th>\n", |
|
| 3435 |
+ " <td>3650</td>\n", |
|
| 3436 |
+ " <td>202.402651</td>\n", |
|
| 3437 |
+ " <td>450487</td>\n", |
|
| 3438 |
+ " <td>447302</td>\n", |
|
| 3439 |
+ " <td>3185</td>\n", |
|
| 3440 |
+ " <td>68627</td>\n", |
|
| 3441 |
+ " <td>0.707013</td>\n", |
|
| 3442 |
+ " <td>15.233958</td>\n", |
|
| 3443 |
+ " <td>0.810234</td>\n", |
|
| 3444 |
+ " </tr>\n", |
|
| 3445 |
+ " <tr>\n", |
|
| 3446 |
+ " <th>강서구</th>\n", |
|
| 3447 |
+ " <td>2744</td>\n", |
|
| 3448 |
+ " <td>223.203769</td>\n", |
|
| 3449 |
+ " <td>579768</td>\n", |
|
| 3450 |
+ " <td>574315</td>\n", |
|
| 3451 |
+ " <td>5453</td>\n", |
|
| 3452 |
+ " <td>92558</td>\n", |
|
| 3453 |
+ " <td>0.940549</td>\n", |
|
| 3454 |
+ " <td>15.964662</td>\n", |
|
| 3455 |
+ " <td>0.473293</td>\n", |
|
| 3456 |
+ " </tr>\n", |
|
| 3457 |
+ " <tr>\n", |
|
| 3458 |
+ " <th>구로구</th>\n", |
|
| 3459 |
+ " <td>4608</td>\n", |
|
| 3460 |
+ " <td>109.645132</td>\n", |
|
| 3461 |
+ " <td>421163</td>\n", |
|
| 3462 |
+ " <td>396754</td>\n", |
|
| 3463 |
+ " <td>24409</td>\n", |
|
| 3464 |
+ " <td>72611</td>\n", |
|
| 3465 |
+ " <td>5.795618</td>\n", |
|
| 3466 |
+ " <td>17.240593</td>\n", |
|
| 3467 |
+ " <td>1.094113</td>\n", |
|
| 3468 |
+ " </tr>\n", |
|
| 3469 |
+ " <tr>\n", |
|
| 3470 |
+ " <th>금천구</th>\n", |
|
| 3471 |
+ " <td>2411</td>\n", |
|
| 3472 |
+ " <td>222.326203</td>\n", |
|
| 3473 |
+ " <td>244891</td>\n", |
|
| 3474 |
+ " <td>230811</td>\n", |
|
| 3475 |
+ " <td>14080</td>\n", |
|
| 3476 |
+ " <td>41041</td>\n", |
|
| 3477 |
+ " <td>5.749497</td>\n", |
|
| 3478 |
+ " <td>16.758885</td>\n", |
|
| 3479 |
+ " <td>0.984520</td>\n", |
|
| 3480 |
+ " </tr>\n", |
|
| 3481 |
+ " <tr>\n", |
|
| 3482 |
+ " <th>영등포구</th>\n", |
|
| 3483 |
+ " <td>4056</td>\n", |
|
| 3484 |
+ " <td>165.792923</td>\n", |
|
| 3485 |
+ " <td>400908</td>\n", |
|
| 3486 |
+ " <td>376837</td>\n", |
|
| 3487 |
+ " <td>24071</td>\n", |
|
| 3488 |
+ " <td>62516</td>\n", |
|
| 3489 |
+ " <td>6.004121</td>\n", |
|
| 3490 |
+ " <td>15.593603</td>\n", |
|
| 3491 |
+ " <td>1.011703</td>\n", |
|
| 3492 |
+ " </tr>\n", |
|
| 3493 |
+ " <tr>\n", |
|
| 3494 |
+ " <th>동작구</th>\n", |
|
| 3495 |
+ " <td>2306</td>\n", |
|
| 3496 |
+ " <td>136.755647</td>\n", |
|
| 3497 |
+ " <td>394364</td>\n", |
|
| 3498 |
+ " <td>385483</td>\n", |
|
| 3499 |
+ " <td>8881</td>\n", |
|
| 3500 |
+ " <td>66613</td>\n", |
|
| 3501 |
+ " <td>2.251980</td>\n", |
|
| 3502 |
+ " <td>16.891248</td>\n", |
|
| 3503 |
+ " <td>0.584739</td>\n", |
|
| 3504 |
+ " </tr>\n", |
|
| 3505 |
+ " <tr>\n", |
|
| 3506 |
+ " <th>관악구</th>\n", |
|
| 3507 |
+ " <td>5149</td>\n", |
|
| 3508 |
+ " <td>96.152381</td>\n", |
|
| 3509 |
+ " <td>499449</td>\n", |
|
| 3510 |
+ " <td>485699</td>\n", |
|
| 3511 |
+ " <td>13750</td>\n", |
|
| 3512 |
+ " <td>79871</td>\n", |
|
| 3513 |
+ " <td>2.753034</td>\n", |
|
| 3514 |
+ " <td>15.991823</td>\n", |
|
| 3515 |
+ " <td>1.030936</td>\n", |
|
| 3516 |
+ " </tr>\n", |
|
| 3517 |
+ " <tr>\n", |
|
| 3518 |
+ " <th>서초구</th>\n", |
|
| 3519 |
+ " <td>4082</td>\n", |
|
| 3520 |
+ " <td>214.969136</td>\n", |
|
| 3521 |
+ " <td>416167</td>\n", |
|
| 3522 |
+ " <td>412279</td>\n", |
|
| 3523 |
+ " <td>3888</td>\n", |
|
| 3524 |
+ " <td>60678</td>\n", |
|
| 3525 |
+ " <td>0.934240</td>\n", |
|
| 3526 |
+ " <td>14.580205</td>\n", |
|
| 3527 |
+ " <td>0.980856</td>\n", |
|
| 3528 |
+ " </tr>\n", |
|
| 3529 |
+ " <tr>\n", |
|
| 3530 |
+ " <th>강남구</th>\n", |
|
| 3531 |
+ " <td>6871</td>\n", |
|
| 3532 |
+ " <td>136.523236</td>\n", |
|
| 3533 |
+ " <td>537800</td>\n", |
|
| 3534 |
+ " <td>533042</td>\n", |
|
| 3535 |
+ " <td>4758</td>\n", |
|
| 3536 |
+ " <td>78226</td>\n", |
|
| 3537 |
+ " <td>0.884716</td>\n", |
|
| 3538 |
+ " <td>14.545556</td>\n", |
|
| 3539 |
+ " <td>1.277612</td>\n", |
|
| 3540 |
+ " </tr>\n", |
|
| 3541 |
+ " <tr>\n", |
|
| 3542 |
+ " <th>송파구</th>\n", |
|
| 3543 |
+ " <td>2897</td>\n", |
|
| 3544 |
+ " <td>336.295181</td>\n", |
|
| 3545 |
+ " <td>663965</td>\n", |
|
| 3546 |
+ " <td>658338</td>\n", |
|
| 3547 |
+ " <td>5627</td>\n", |
|
| 3548 |
+ " <td>97691</td>\n", |
|
| 3549 |
+ " <td>0.847484</td>\n", |
|
| 3550 |
+ " <td>14.713276</td>\n", |
|
| 3551 |
+ " <td>0.436318</td>\n", |
|
| 3552 |
+ " </tr>\n", |
|
| 3553 |
+ " <tr>\n", |
|
| 3554 |
+ " <th>강동구</th>\n", |
|
| 3555 |
+ " <td>2809</td>\n", |
|
| 3556 |
+ " <td>203.020496</td>\n", |
|
| 3557 |
+ " <td>466472</td>\n", |
|
| 3558 |
+ " <td>462664</td>\n", |
|
| 3559 |
+ " <td>3808</td>\n", |
|
| 3560 |
+ " <td>74070</td>\n", |
|
| 3561 |
+ " <td>0.816341</td>\n", |
|
| 3562 |
+ " <td>15.878767</td>\n", |
|
| 3563 |
+ " <td>0.602180</td>\n", |
|
| 3564 |
+ " </tr>\n", |
|
| 3565 |
+ " </tbody>\n", |
|
| 3566 |
+ "</table>\n", |
|
| 3567 |
+ "</div>" |
|
| 3568 |
+ ], |
|
| 3569 |
+ "text/plain": [ |
|
| 3570 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율 \\\n", |
|
| 3571 |
+ "구별 \n", |
|
| 3572 |
+ "종로구 1715 47.844828 153789 144683 9106 27818 5.921100 18.088420 \n", |
|
| 3573 |
+ "중구 2447 217.792208 131787 122499 9288 24392 7.047736 18.508654 \n", |
|
| 3574 |
+ "용산구 2611 165.615463 237285 222953 14332 39070 6.039994 16.465432 \n", |
|
| 3575 |
+ "성동구 3829 218.552413 292672 285990 6682 46380 2.283102 15.847092 \n", |
|
| 3576 |
+ "광진구 3211 506.994329 352627 339996 12631 51723 3.581972 14.667907 \n", |
|
| 3577 |
+ "동대문구 2628 175.760756 352006 337400 14606 62211 4.149361 17.673278 \n", |
|
| 3578 |
+ "중랑구 3737 281.716037 391885 387350 4535 71682 1.157227 18.291591 \n", |
|
| 3579 |
+ "성북구 4602 212.211669 440142 430528 9614 74709 2.184295 16.973840 \n", |
|
| 3580 |
+ "강북구 3090 563.090129 302563 299182 3381 64333 1.117453 21.262679 \n", |
|
| 3581 |
+ "도봉구 1930 226.013514 319373 317366 2007 64160 0.628419 20.089363 \n", |
|
| 3582 |
+ "노원구 2492 112.446718 514946 510956 3990 88345 0.774839 17.156168 \n", |
|
| 3583 |
+ "은평구 4131 223.492561 477173 473307 3866 87241 0.810188 18.282887 \n", |
|
| 3584 |
+ "서대문구 3055 90.224159 315659 304819 10840 54268 3.434086 17.191970 \n", |
|
| 3585 |
+ "마포구 2496 191.248541 378686 368905 9781 54582 2.582879 14.413525 \n", |
|
| 3586 |
+ "양천구 3650 202.402651 450487 447302 3185 68627 0.707013 15.233958 \n", |
|
| 3587 |
+ "강서구 2744 223.203769 579768 574315 5453 92558 0.940549 15.964662 \n", |
|
| 3588 |
+ "구로구 4608 109.645132 421163 396754 24409 72611 5.795618 17.240593 \n", |
|
| 3589 |
+ "금천구 2411 222.326203 244891 230811 14080 41041 5.749497 16.758885 \n", |
|
| 3590 |
+ "영등포구 4056 165.792923 400908 376837 24071 62516 6.004121 15.593603 \n", |
|
| 3591 |
+ "동작구 2306 136.755647 394364 385483 8881 66613 2.251980 16.891248 \n", |
|
| 3592 |
+ "관악구 5149 96.152381 499449 485699 13750 79871 2.753034 15.991823 \n", |
|
| 3593 |
+ "서초구 4082 214.969136 416167 412279 3888 60678 0.934240 14.580205 \n", |
|
| 3594 |
+ "강남구 6871 136.523236 537800 533042 4758 78226 0.884716 14.545556 \n", |
|
| 3595 |
+ "송파구 2897 336.295181 663965 658338 5627 97691 0.847484 14.713276 \n", |
|
| 3596 |
+ "강동구 2809 203.020496 466472 462664 3808 74070 0.816341 15.878767 \n", |
|
| 3597 |
+ "\n", |
|
| 3598 |
+ " CCTV비율 \n", |
|
| 3599 |
+ "구별 \n", |
|
| 3600 |
+ "종로구 1.115164 \n", |
|
| 3601 |
+ "중구 1.856784 \n", |
|
| 3602 |
+ "용산구 1.100365 \n", |
|
| 3603 |
+ "성동구 1.308291 \n", |
|
| 3604 |
+ "광진구 0.910594 \n", |
|
| 3605 |
+ "동대문구 0.746578 \n", |
|
| 3606 |
+ "중랑구 0.953596 \n", |
|
| 3607 |
+ "성북구 1.045572 \n", |
|
| 3608 |
+ "강북구 1.021275 \n", |
|
| 3609 |
+ "도봉구 0.604309 \n", |
|
| 3610 |
+ "노원구 0.483934 \n", |
|
| 3611 |
+ "은평구 0.865724 \n", |
|
| 3612 |
+ "서대문구 0.967817 \n", |
|
| 3613 |
+ "마포구 0.659121 \n", |
|
| 3614 |
+ "양천구 0.810234 \n", |
|
| 3615 |
+ "강서구 0.473293 \n", |
|
| 3616 |
+ "구로구 1.094113 \n", |
|
| 3617 |
+ "금천구 0.984520 \n", |
|
| 3618 |
+ "영등포구 1.011703 \n", |
|
| 3619 |
+ "동작구 0.584739 \n", |
|
| 3620 |
+ "관악구 1.030936 \n", |
|
| 3621 |
+ "서초구 0.980856 \n", |
|
| 3622 |
+ "강남구 1.277612 \n", |
|
| 3623 |
+ "송파구 0.436318 \n", |
|
| 3624 |
+ "강동구 0.602180 " |
|
| 3625 |
+ ] |
|
| 3626 |
+ }, |
|
| 3627 |
+ "execution_count": 113, |
|
| 3628 |
+ "metadata": {},
|
|
| 3629 |
+ "output_type": "execute_result" |
|
| 3630 |
+ } |
|
| 3631 |
+ ], |
|
| 3632 |
+ "source": [ |
|
| 3633 |
+ "dataset" |
|
| 3634 |
+ ] |
|
| 3635 |
+ }, |
|
| 3636 |
+ {
|
|
| 3637 |
+ "cell_type": "code", |
|
| 3638 |
+ "execution_count": 114, |
|
| 3639 |
+ "id": "2050dc32", |
|
| 3640 |
+ "metadata": {},
|
|
| 3641 |
+ "outputs": [ |
|
| 3642 |
+ {
|
|
| 3643 |
+ "data": {
|
|
| 3644 |
+ "text/html": [ |
|
| 3645 |
+ "<div>\n", |
|
| 3646 |
+ "<style scoped>\n", |
|
| 3647 |
+ " .dataframe tbody tr th:only-of-type {\n",
|
|
| 3648 |
+ " vertical-align: middle;\n", |
|
| 3649 |
+ " }\n", |
|
| 3650 |
+ "\n", |
|
| 3651 |
+ " .dataframe tbody tr th {\n",
|
|
| 3652 |
+ " vertical-align: top;\n", |
|
| 3653 |
+ " }\n", |
|
| 3654 |
+ "\n", |
|
| 3655 |
+ " .dataframe thead th {\n",
|
|
| 3656 |
+ " text-align: right;\n", |
|
| 3657 |
+ " }\n", |
|
| 3658 |
+ "</style>\n", |
|
| 3659 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 3660 |
+ " <thead>\n", |
|
| 3661 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 3662 |
+ " <th></th>\n", |
|
| 3663 |
+ " <th>총계</th>\n", |
|
| 3664 |
+ " <th>최근증가율</th>\n", |
|
| 3665 |
+ " <th>인구수</th>\n", |
|
| 3666 |
+ " <th>한국인</th>\n", |
|
| 3667 |
+ " <th>외국인</th>\n", |
|
| 3668 |
+ " <th>고령자</th>\n", |
|
| 3669 |
+ " <th>외국인비율</th>\n", |
|
| 3670 |
+ " <th>고령자비율</th>\n", |
|
| 3671 |
+ " <th>CCTV비율</th>\n", |
|
| 3672 |
+ " </tr>\n", |
|
| 3673 |
+ " <tr>\n", |
|
| 3674 |
+ " <th>구별</th>\n", |
|
| 3675 |
+ " <th></th>\n", |
|
| 3676 |
+ " <th></th>\n", |
|
| 3677 |
+ " <th></th>\n", |
|
| 3678 |
+ " <th></th>\n", |
|
| 3679 |
+ " <th></th>\n", |
|
| 3680 |
+ " <th></th>\n", |
|
| 3681 |
+ " <th></th>\n", |
|
| 3682 |
+ " <th></th>\n", |
|
| 3683 |
+ " <th></th>\n", |
|
| 3684 |
+ " </tr>\n", |
|
| 3685 |
+ " </thead>\n", |
|
| 3686 |
+ " <tbody>\n", |
|
| 3687 |
+ " <tr>\n", |
|
| 3688 |
+ " <th>중구</th>\n", |
|
| 3689 |
+ " <td>2447</td>\n", |
|
| 3690 |
+ " <td>217.792208</td>\n", |
|
| 3691 |
+ " <td>131787</td>\n", |
|
| 3692 |
+ " <td>122499</td>\n", |
|
| 3693 |
+ " <td>9288</td>\n", |
|
| 3694 |
+ " <td>24392</td>\n", |
|
| 3695 |
+ " <td>7.047736</td>\n", |
|
| 3696 |
+ " <td>18.508654</td>\n", |
|
| 3697 |
+ " <td>1.856784</td>\n", |
|
| 3698 |
+ " </tr>\n", |
|
| 3699 |
+ " <tr>\n", |
|
| 3700 |
+ " <th>성동구</th>\n", |
|
| 3701 |
+ " <td>3829</td>\n", |
|
| 3702 |
+ " <td>218.552413</td>\n", |
|
| 3703 |
+ " <td>292672</td>\n", |
|
| 3704 |
+ " <td>285990</td>\n", |
|
| 3705 |
+ " <td>6682</td>\n", |
|
| 3706 |
+ " <td>46380</td>\n", |
|
| 3707 |
+ " <td>2.283102</td>\n", |
|
| 3708 |
+ " <td>15.847092</td>\n", |
|
| 3709 |
+ " <td>1.308291</td>\n", |
|
| 3710 |
+ " </tr>\n", |
|
| 3711 |
+ " <tr>\n", |
|
| 3712 |
+ " <th>강남구</th>\n", |
|
| 3713 |
+ " <td>6871</td>\n", |
|
| 3714 |
+ " <td>136.523236</td>\n", |
|
| 3715 |
+ " <td>537800</td>\n", |
|
| 3716 |
+ " <td>533042</td>\n", |
|
| 3717 |
+ " <td>4758</td>\n", |
|
| 3718 |
+ " <td>78226</td>\n", |
|
| 3719 |
+ " <td>0.884716</td>\n", |
|
| 3720 |
+ " <td>14.545556</td>\n", |
|
| 3721 |
+ " <td>1.277612</td>\n", |
|
| 3722 |
+ " </tr>\n", |
|
| 3723 |
+ " <tr>\n", |
|
| 3724 |
+ " <th>종로구</th>\n", |
|
| 3725 |
+ " <td>1715</td>\n", |
|
| 3726 |
+ " <td>47.844828</td>\n", |
|
| 3727 |
+ " <td>153789</td>\n", |
|
| 3728 |
+ " <td>144683</td>\n", |
|
| 3729 |
+ " <td>9106</td>\n", |
|
| 3730 |
+ " <td>27818</td>\n", |
|
| 3731 |
+ " <td>5.921100</td>\n", |
|
| 3732 |
+ " <td>18.088420</td>\n", |
|
| 3733 |
+ " <td>1.115164</td>\n", |
|
| 3734 |
+ " </tr>\n", |
|
| 3735 |
+ " <tr>\n", |
|
| 3736 |
+ " <th>용산구</th>\n", |
|
| 3737 |
+ " <td>2611</td>\n", |
|
| 3738 |
+ " <td>165.615463</td>\n", |
|
| 3739 |
+ " <td>237285</td>\n", |
|
| 3740 |
+ " <td>222953</td>\n", |
|
| 3741 |
+ " <td>14332</td>\n", |
|
| 3742 |
+ " <td>39070</td>\n", |
|
| 3743 |
+ " <td>6.039994</td>\n", |
|
| 3744 |
+ " <td>16.465432</td>\n", |
|
| 3745 |
+ " <td>1.100365</td>\n", |
|
| 3746 |
+ " </tr>\n", |
|
| 3747 |
+ " </tbody>\n", |
|
| 3748 |
+ "</table>\n", |
|
| 3749 |
+ "</div>" |
|
| 3750 |
+ ], |
|
| 3751 |
+ "text/plain": [ |
|
| 3752 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율 \\\n", |
|
| 3753 |
+ "구별 \n", |
|
| 3754 |
+ "중구 2447 217.792208 131787 122499 9288 24392 7.047736 18.508654 \n", |
|
| 3755 |
+ "성동구 3829 218.552413 292672 285990 6682 46380 2.283102 15.847092 \n", |
|
| 3756 |
+ "강남구 6871 136.523236 537800 533042 4758 78226 0.884716 14.545556 \n", |
|
| 3757 |
+ "종로구 1715 47.844828 153789 144683 9106 27818 5.921100 18.088420 \n", |
|
| 3758 |
+ "용산구 2611 165.615463 237285 222953 14332 39070 6.039994 16.465432 \n", |
|
| 3759 |
+ "\n", |
|
| 3760 |
+ " CCTV비율 \n", |
|
| 3761 |
+ "구별 \n", |
|
| 3762 |
+ "중구 1.856784 \n", |
|
| 3763 |
+ "성동구 1.308291 \n", |
|
| 3764 |
+ "강남구 1.277612 \n", |
|
| 3765 |
+ "종로구 1.115164 \n", |
|
| 3766 |
+ "용산구 1.100365 " |
|
| 3767 |
+ ] |
|
| 3768 |
+ }, |
|
| 3769 |
+ "execution_count": 114, |
|
| 3770 |
+ "metadata": {},
|
|
| 3771 |
+ "output_type": "execute_result" |
|
| 3772 |
+ } |
|
| 3773 |
+ ], |
|
| 3774 |
+ "source": [ |
|
| 3775 |
+ "dataset.sort_values(by='CCTV비율', ascending=False).head()" |
|
| 3776 |
+ ] |
|
| 3777 |
+ }, |
|
| 3778 |
+ {
|
|
| 3779 |
+ "cell_type": "code", |
|
| 3780 |
+ "execution_count": 115, |
|
| 3781 |
+ "id": "19fe7dae", |
|
| 3782 |
+ "metadata": {},
|
|
| 3783 |
+ "outputs": [ |
|
| 3784 |
+ {
|
|
| 3785 |
+ "data": {
|
|
| 3786 |
+ "text/plain": [ |
|
| 3787 |
+ "<AxesSubplot:ylabel='구별'>" |
|
| 3788 |
+ ] |
|
| 3789 |
+ }, |
|
| 3790 |
+ "execution_count": 115, |
|
| 3791 |
+ "metadata": {},
|
|
| 3792 |
+ "output_type": "execute_result" |
|
| 3793 |
+ }, |
|
| 3794 |
+ {
|
|
| 3795 |
+ "data": {
|
|
| 3796 |
+ "image/png": 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\n", |
|
| 3797 |
+ "text/plain": [ |
|
| 3798 |
+ "<Figure size 720x720 with 1 Axes>" |
|
| 3799 |
+ ] |
|
| 3800 |
+ }, |
|
| 3801 |
+ "metadata": {
|
|
| 3802 |
+ "needs_background": "light" |
|
| 3803 |
+ }, |
|
| 3804 |
+ "output_type": "display_data" |
|
| 3805 |
+ } |
|
| 3806 |
+ ], |
|
| 3807 |
+ "source": [ |
|
| 3808 |
+ "dataset['CCTV비율'].sort_values().plot(kind='barh', grid=True, figsize=(10, 10))" |
|
| 3809 |
+ ] |
|
| 3810 |
+ }, |
|
| 3811 |
+ {
|
|
| 3812 |
+ "cell_type": "code", |
|
| 3813 |
+ "execution_count": 116, |
|
| 3814 |
+ "id": "b0b97c9f", |
|
| 3815 |
+ "metadata": {},
|
|
| 3816 |
+ "outputs": [ |
|
| 3817 |
+ {
|
|
| 3818 |
+ "data": {
|
|
| 3819 |
+ "text/plain": [ |
|
| 3820 |
+ "<function matplotlib.pyplot.show(close=None, block=None)>" |
|
| 3821 |
+ ] |
|
| 3822 |
+ }, |
|
| 3823 |
+ "execution_count": 116, |
|
| 3824 |
+ "metadata": {},
|
|
| 3825 |
+ "output_type": "execute_result" |
|
| 3826 |
+ }, |
|
| 3827 |
+ {
|
|
| 3828 |
+ "data": {
|
|
| 3829 |
+ "image/png": "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\n", |
|
| 3830 |
+ "text/plain": [ |
|
| 3831 |
+ "<Figure size 432x360 with 1 Axes>" |
|
| 3832 |
+ ] |
|
| 3833 |
+ }, |
|
| 3834 |
+ "metadata": {
|
|
| 3835 |
+ "needs_background": "light" |
|
| 3836 |
+ }, |
|
| 3837 |
+ "output_type": "display_data" |
|
| 3838 |
+ } |
|
| 3839 |
+ ], |
|
| 3840 |
+ "source": [ |
|
| 3841 |
+ "plt.figure(figsize=(6, 5))\n", |
|
| 3842 |
+ "plt.scatter(dataset['인구수'], dataset['총계'])\n", |
|
| 3843 |
+ "plt.grid\n", |
|
| 3844 |
+ "plt.xlabel('인구수')\n",
|
|
| 3845 |
+ "plt.ylabel('CCTV수')\n",
|
|
| 3846 |
+ "plt.show" |
|
| 3847 |
+ ] |
|
| 3848 |
+ }, |
|
| 3849 |
+ {
|
|
| 3850 |
+ "cell_type": "code", |
|
| 3851 |
+ "execution_count": 117, |
|
| 3852 |
+ "id": "eb925fdf", |
|
| 3853 |
+ "metadata": {},
|
|
| 3854 |
+ "outputs": [], |
|
| 3855 |
+ "source": [ |
|
| 3856 |
+ "x = np.arange(0, 10, 0.01)\n", |
|
| 3857 |
+ "y = 3 * x + 5" |
|
| 3858 |
+ ] |
|
| 3859 |
+ }, |
|
| 3860 |
+ {
|
|
| 3861 |
+ "cell_type": "code", |
|
| 3862 |
+ "execution_count": 118, |
|
| 3863 |
+ "id": "86626d1a", |
|
| 3864 |
+ "metadata": {},
|
|
| 3865 |
+ "outputs": [ |
|
| 3866 |
+ {
|
|
| 3867 |
+ "data": {
|
|
| 3868 |
+ "image/png": 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\n", |
|
| 3869 |
+ "text/plain": [ |
|
| 3870 |
+ "<Figure size 864x576 with 1 Axes>" |
|
| 3871 |
+ ] |
|
| 3872 |
+ }, |
|
| 3873 |
+ "metadata": {
|
|
| 3874 |
+ "needs_background": "light" |
|
| 3875 |
+ }, |
|
| 3876 |
+ "output_type": "display_data" |
|
| 3877 |
+ } |
|
| 3878 |
+ ], |
|
| 3879 |
+ "source": [ |
|
| 3880 |
+ "plt.figure(figsize=(12, 8))\n", |
|
| 3881 |
+ "plt.plot(x, y)\n", |
|
| 3882 |
+ "plt.grid()\n", |
|
| 3883 |
+ "plt.show()" |
|
| 3884 |
+ ] |
|
| 3885 |
+ }, |
|
| 3886 |
+ {
|
|
| 3887 |
+ "cell_type": "code", |
|
| 3888 |
+ "execution_count": 119, |
|
| 3889 |
+ "id": "6dbb15a9", |
|
| 3890 |
+ "metadata": {},
|
|
| 3891 |
+ "outputs": [], |
|
| 3892 |
+ "source": [ |
|
| 3893 |
+ "y_noise = y + np.random.randn(len(y))" |
|
| 3894 |
+ ] |
|
| 3895 |
+ }, |
|
| 3896 |
+ {
|
|
| 3897 |
+ "cell_type": "code", |
|
| 3898 |
+ "execution_count": 120, |
|
| 3899 |
+ "id": "cd3d2055", |
|
| 3900 |
+ "metadata": {},
|
|
| 3901 |
+ "outputs": [ |
|
| 3902 |
+ {
|
|
| 3903 |
+ "data": {
|
|
| 3904 |
+ "image/png": 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\n", |
|
| 3905 |
+ "text/plain": [ |
|
| 3906 |
+ "<Figure size 720x576 with 1 Axes>" |
|
| 3907 |
+ ] |
|
| 3908 |
+ }, |
|
| 3909 |
+ "metadata": {
|
|
| 3910 |
+ "needs_background": "light" |
|
| 3911 |
+ }, |
|
| 3912 |
+ "output_type": "display_data" |
|
| 3913 |
+ } |
|
| 3914 |
+ ], |
|
| 3915 |
+ "source": [ |
|
| 3916 |
+ "plt.figure(figsize=(10, 8))\n", |
|
| 3917 |
+ "plt.plot(x, y_noise)\n", |
|
| 3918 |
+ "plt.plot(x, y)\n", |
|
| 3919 |
+ "plt.grid()\n", |
|
| 3920 |
+ "plt.show()" |
|
| 3921 |
+ ] |
|
| 3922 |
+ }, |
|
| 3923 |
+ {
|
|
| 3924 |
+ "cell_type": "code", |
|
| 3925 |
+ "execution_count": 121, |
|
| 3926 |
+ "id": "e4546543", |
|
| 3927 |
+ "metadata": {},
|
|
| 3928 |
+ "outputs": [], |
|
| 3929 |
+ "source": [ |
|
| 3930 |
+ "fp = np.polyfit(x, y_noise, 1)" |
|
| 3931 |
+ ] |
|
| 3932 |
+ }, |
|
| 3933 |
+ {
|
|
| 3934 |
+ "cell_type": "code", |
|
| 3935 |
+ "execution_count": 122, |
|
| 3936 |
+ "id": "6afbc178", |
|
| 3937 |
+ "metadata": {},
|
|
| 3938 |
+ "outputs": [ |
|
| 3939 |
+ {
|
|
| 3940 |
+ "data": {
|
|
| 3941 |
+ "text/plain": [ |
|
| 3942 |
+ "array([2.99226346, 4.97267413])" |
|
| 3943 |
+ ] |
|
| 3944 |
+ }, |
|
| 3945 |
+ "execution_count": 122, |
|
| 3946 |
+ "metadata": {},
|
|
| 3947 |
+ "output_type": "execute_result" |
|
| 3948 |
+ } |
|
| 3949 |
+ ], |
|
| 3950 |
+ "source": [ |
|
| 3951 |
+ "fp" |
|
| 3952 |
+ ] |
|
| 3953 |
+ }, |
|
| 3954 |
+ {
|
|
| 3955 |
+ "cell_type": "code", |
|
| 3956 |
+ "execution_count": 123, |
|
| 3957 |
+ "id": "9196fa60", |
|
| 3958 |
+ "metadata": {},
|
|
| 3959 |
+ "outputs": [], |
|
| 3960 |
+ "source": [ |
|
| 3961 |
+ "f = np.poly1d(fp)" |
|
| 3962 |
+ ] |
|
| 3963 |
+ }, |
|
| 3964 |
+ {
|
|
| 3965 |
+ "cell_type": "code", |
|
| 3966 |
+ "execution_count": 124, |
|
| 3967 |
+ "id": "06d84f35", |
|
| 3968 |
+ "metadata": {},
|
|
| 3969 |
+ "outputs": [ |
|
| 3970 |
+ {
|
|
| 3971 |
+ "data": {
|
|
| 3972 |
+ "text/plain": [ |
|
| 3973 |
+ "poly1d([2.99226346, 4.97267413])" |
|
| 3974 |
+ ] |
|
| 3975 |
+ }, |
|
| 3976 |
+ "execution_count": 124, |
|
| 3977 |
+ "metadata": {},
|
|
| 3978 |
+ "output_type": "execute_result" |
|
| 3979 |
+ } |
|
| 3980 |
+ ], |
|
| 3981 |
+ "source": [ |
|
| 3982 |
+ "f" |
|
| 3983 |
+ ] |
|
| 3984 |
+ }, |
|
| 3985 |
+ {
|
|
| 3986 |
+ "cell_type": "code", |
|
| 3987 |
+ "execution_count": 125, |
|
| 3988 |
+ "id": "c5904106", |
|
| 3989 |
+ "metadata": {},
|
|
| 3990 |
+ "outputs": [ |
|
| 3991 |
+ {
|
|
| 3992 |
+ "data": {
|
|
| 3993 |
+ "text/plain": [ |
|
| 3994 |
+ "poly1d([2, 0])" |
|
| 3995 |
+ ] |
|
| 3996 |
+ }, |
|
| 3997 |
+ "execution_count": 125, |
|
| 3998 |
+ "metadata": {},
|
|
| 3999 |
+ "output_type": "execute_result" |
|
| 4000 |
+ } |
|
| 4001 |
+ ], |
|
| 4002 |
+ "source": [ |
|
| 4003 |
+ "np.poly1d([1, 1]) + np.poly1d([1, -1])" |
|
| 4004 |
+ ] |
|
| 4005 |
+ }, |
|
| 4006 |
+ {
|
|
| 4007 |
+ "cell_type": "code", |
|
| 4008 |
+ "execution_count": 126, |
|
| 4009 |
+ "id": "54d80056", |
|
| 4010 |
+ "metadata": {},
|
|
| 4011 |
+ "outputs": [ |
|
| 4012 |
+ {
|
|
| 4013 |
+ "data": {
|
|
| 4014 |
+ "text/plain": [ |
|
| 4015 |
+ "poly1d([4, 3])" |
|
| 4016 |
+ ] |
|
| 4017 |
+ }, |
|
| 4018 |
+ "execution_count": 126, |
|
| 4019 |
+ "metadata": {},
|
|
| 4020 |
+ "output_type": "execute_result" |
|
| 4021 |
+ } |
|
| 4022 |
+ ], |
|
| 4023 |
+ "source": [ |
|
| 4024 |
+ "np.poly1d([1, 1]) + np.poly1d([3, 2])" |
|
| 4025 |
+ ] |
|
| 4026 |
+ }, |
|
| 4027 |
+ {
|
|
| 4028 |
+ "cell_type": "code", |
|
| 4029 |
+ "execution_count": 127, |
|
| 4030 |
+ "id": "fe5ae5f7", |
|
| 4031 |
+ "metadata": {
|
|
| 4032 |
+ "scrolled": true |
|
| 4033 |
+ }, |
|
| 4034 |
+ "outputs": [ |
|
| 4035 |
+ {
|
|
| 4036 |
+ "data": {
|
|
| 4037 |
+ "image/png": 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\n", 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+ "text/plain": [ |
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+ "<Figure size 720x432 with 1 Axes>" |
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+ ] |
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+ }, |
|
| 4042 |
+ "metadata": {
|
|
| 4043 |
+ "needs_background": "light" |
|
| 4044 |
+ }, |
|
| 4045 |
+ "output_type": "display_data" |
|
| 4046 |
+ } |
|
| 4047 |
+ ], |
|
| 4048 |
+ "source": [ |
|
| 4049 |
+ "plt.figure(figsize=(10, 6))\n", |
|
| 4050 |
+ "plt.plot(x, y_noise, label='실제값', c='g')\n", |
|
| 4051 |
+ "plt.plot(x, y, label='원본', c='y', lw=5)\n", |
|
| 4052 |
+ "plt.plot(x, f(x), lw=2, c=\"tomato\", ls='dashed', label='예측값')\n", |
|
| 4053 |
+ "plt.grid()\n", |
|
| 4054 |
+ "plt.show()" |
|
| 4055 |
+ ] |
|
| 4056 |
+ }, |
|
| 4057 |
+ {
|
|
| 4058 |
+ "cell_type": "code", |
|
| 4059 |
+ "execution_count": 128, |
|
| 4060 |
+ "id": "bb3f33e5", |
|
| 4061 |
+ "metadata": {},
|
|
| 4062 |
+ "outputs": [ |
|
| 4063 |
+ {
|
|
| 4064 |
+ "data": {
|
|
| 4065 |
+ "text/plain": [ |
|
| 4066 |
+ "(poly1d([ 1., -1.]), poly1d([1.]))" |
|
| 4067 |
+ ] |
|
| 4068 |
+ }, |
|
| 4069 |
+ "execution_count": 128, |
|
| 4070 |
+ "metadata": {},
|
|
| 4071 |
+ "output_type": "execute_result" |
|
| 4072 |
+ } |
|
| 4073 |
+ ], |
|
| 4074 |
+ "source": [ |
|
| 4075 |
+ "np.poly1d([1, -2, 2]) / np.poly1d([1, -1])" |
|
| 4076 |
+ ] |
|
| 4077 |
+ }, |
|
| 4078 |
+ {
|
|
| 4079 |
+ "cell_type": "code", |
|
| 4080 |
+ "execution_count": 129, |
|
| 4081 |
+ "id": "4c752ad0", |
|
| 4082 |
+ "metadata": {},
|
|
| 4083 |
+ "outputs": [], |
|
| 4084 |
+ "source": [ |
|
| 4085 |
+ "x = np.arange(-10, 10, 0.01)\n", |
|
| 4086 |
+ "y = np.square(x-1)+1\n", |
|
| 4087 |
+ "y_noise = y + np.random.randn(len(y))*10\n", |
|
| 4088 |
+ "fp = np.polyfit(x, y_noise, 2)\n", |
|
| 4089 |
+ "f = np.poly1d(fp)" |
|
| 4090 |
+ ] |
|
| 4091 |
+ }, |
|
| 4092 |
+ {
|
|
| 4093 |
+ "cell_type": "code", |
|
| 4094 |
+ "execution_count": 130, |
|
| 4095 |
+ "id": "da3b157b", |
|
| 4096 |
+ "metadata": {},
|
|
| 4097 |
+ "outputs": [ |
|
| 4098 |
+ {
|
|
| 4099 |
+ "name": "stderr", |
|
| 4100 |
+ "output_type": "stream", |
|
| 4101 |
+ "text": [ |
|
| 4102 |
+ "C:\\Users\\pc\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 8722 missing from current font.\n", |
|
| 4103 |
+ " font.set_text(s, 0.0, flags=flags)\n", |
|
| 4104 |
+ "C:\\Users\\pc\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 8722 missing from current font.\n", |
|
| 4105 |
+ " font.set_text(s, 0, flags=flags)\n" |
|
| 4106 |
+ ] |
|
| 4107 |
+ }, |
|
| 4108 |
+ {
|
|
| 4109 |
+ "data": {
|
|
| 4110 |
+ "image/png": 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\n", |
|
| 4111 |
+ "text/plain": [ |
|
| 4112 |
+ "<Figure size 720x432 with 1 Axes>" |
|
| 4113 |
+ ] |
|
| 4114 |
+ }, |
|
| 4115 |
+ "metadata": {
|
|
| 4116 |
+ "needs_background": "light" |
|
| 4117 |
+ }, |
|
| 4118 |
+ "output_type": "display_data" |
|
| 4119 |
+ } |
|
| 4120 |
+ ], |
|
| 4121 |
+ "source": [ |
|
| 4122 |
+ "plt.figure(figsize=(10, 6))\n", |
|
| 4123 |
+ "plt.plot(x, y_noise, label='노이즈', c='g')\n", |
|
| 4124 |
+ "plt.plot(x, y, label='원본', c='y', lw=5)\n", |
|
| 4125 |
+ "plt.plot(x, f(x), lw=2, c=\"tomato\", ls='dashed', label='예측')\n", |
|
| 4126 |
+ "plt.grid()\n", |
|
| 4127 |
+ "plt.show()" |
|
| 4128 |
+ ] |
|
| 4129 |
+ }, |
|
| 4130 |
+ {
|
|
| 4131 |
+ "cell_type": "code", |
|
| 4132 |
+ "execution_count": 131, |
|
| 4133 |
+ "id": "a8047c66", |
|
| 4134 |
+ "metadata": {},
|
|
| 4135 |
+ "outputs": [], |
|
| 4136 |
+ "source": [ |
|
| 4137 |
+ "fp1 = np.polyfit(dataset['인구수'], dataset['총계'], 1)" |
|
| 4138 |
+ ] |
|
| 4139 |
+ }, |
|
| 4140 |
+ {
|
|
| 4141 |
+ "cell_type": "code", |
|
| 4142 |
+ "execution_count": 132, |
|
| 4143 |
+ "id": "a9fd1771", |
|
| 4144 |
+ "metadata": {},
|
|
| 4145 |
+ "outputs": [], |
|
| 4146 |
+ "source": [ |
|
| 4147 |
+ "f1 = np.poly1d(fp1)" |
|
| 4148 |
+ ] |
|
| 4149 |
+ }, |
|
| 4150 |
+ {
|
|
| 4151 |
+ "cell_type": "code", |
|
| 4152 |
+ "execution_count": 133, |
|
| 4153 |
+ "id": "47b54d04", |
|
| 4154 |
+ "metadata": {},
|
|
| 4155 |
+ "outputs": [], |
|
| 4156 |
+ "source": [ |
|
| 4157 |
+ "x = np.linspace(100000, 700000, 100)" |
|
| 4158 |
+ ] |
|
| 4159 |
+ }, |
|
| 4160 |
+ {
|
|
| 4161 |
+ "cell_type": "code", |
|
| 4162 |
+ "execution_count": 66, |
|
| 4163 |
+ "id": "8d74eda4", |
|
| 4164 |
+ "metadata": {},
|
|
| 4165 |
+ "outputs": [ |
|
| 4166 |
+ {
|
|
| 4167 |
+ "data": {
|
|
| 4168 |
+ "image/png": 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\n", 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+ "text/plain": [ |
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+ }, |
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+ "metadata": {
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+ "output_type": "display_data" |
|
| 4177 |
+ } |
|
| 4178 |
+ ], |
|
| 4179 |
+ "source": [ |
|
| 4180 |
+ "plt.figure(figsize=(10, 10))\n", |
|
| 4181 |
+ "plt.scatter(dataset['인구수'], dataset['총계'], s=50)\n", |
|
| 4182 |
+ "plt.plot(x, f1(x), ls='dashed', lw=3, color='tomato')\n", |
|
| 4183 |
+ "plt.xlabel('인구수')\n",
|
|
| 4184 |
+ "plt.xlabel('CCTV수')\n",
|
|
| 4185 |
+ "plt.grid()\n", |
|
| 4186 |
+ "plt.show()" |
|
| 4187 |
+ ] |
|
| 4188 |
+ }, |
|
| 4189 |
+ {
|
|
| 4190 |
+ "cell_type": "code", |
|
| 4191 |
+ "execution_count": 136, |
|
| 4192 |
+ "id": "e37d7af9", |
|
| 4193 |
+ "metadata": {},
|
|
| 4194 |
+ "outputs": [], |
|
| 4195 |
+ "source": [ |
|
| 4196 |
+ "dataset['오차'] = np.abs(dataset['총계'] - f1(dataset['인구수']))" |
|
| 4197 |
+ ] |
|
| 4198 |
+ }, |
|
| 4199 |
+ {
|
|
| 4200 |
+ "cell_type": "code", |
|
| 4201 |
+ "execution_count": 137, |
|
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+ "id": "da464baa", |
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+ "metadata": {},
|
|
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| 4208 |
+ "<div>\n", |
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+ " vertical-align: middle;\n", |
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| 4215 |
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| 4218 |
+ " .dataframe thead th {\n",
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|
| 4219 |
+ " text-align: right;\n", |
|
| 4220 |
+ " }\n", |
|
| 4221 |
+ "</style>\n", |
|
| 4222 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 4223 |
+ " <thead>\n", |
|
| 4224 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 4225 |
+ " <th></th>\n", |
|
| 4226 |
+ " <th>총계</th>\n", |
|
| 4227 |
+ " <th>최근증가율</th>\n", |
|
| 4228 |
+ " <th>인구수</th>\n", |
|
| 4229 |
+ " <th>한국인</th>\n", |
|
| 4230 |
+ " <th>외국인</th>\n", |
|
| 4231 |
+ " <th>고령자</th>\n", |
|
| 4232 |
+ " <th>외국인비율</th>\n", |
|
| 4233 |
+ " <th>고령자비율</th>\n", |
|
| 4234 |
+ " <th>CCTV비율</th>\n", |
|
| 4235 |
+ " <th>오차</th>\n", |
|
| 4236 |
+ " </tr>\n", |
|
| 4237 |
+ " <tr>\n", |
|
| 4238 |
+ " <th>구별</th>\n", |
|
| 4239 |
+ " <th></th>\n", |
|
| 4240 |
+ " <th></th>\n", |
|
| 4241 |
+ " <th></th>\n", |
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| 4242 |
+ " <th></th>\n", |
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| 4243 |
+ " <th></th>\n", |
|
| 4244 |
+ " <th></th>\n", |
|
| 4245 |
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|
| 4246 |
+ " <th></th>\n", |
|
| 4247 |
+ " <th></th>\n", |
|
| 4248 |
+ " <th></th>\n", |
|
| 4249 |
+ " </tr>\n", |
|
| 4250 |
+ " </thead>\n", |
|
| 4251 |
+ " <tbody>\n", |
|
| 4252 |
+ " <tr>\n", |
|
| 4253 |
+ " <th>종로구</th>\n", |
|
| 4254 |
+ " <td>1715</td>\n", |
|
| 4255 |
+ " <td>47.844828</td>\n", |
|
| 4256 |
+ " <td>153789</td>\n", |
|
| 4257 |
+ " <td>144683</td>\n", |
|
| 4258 |
+ " <td>9106</td>\n", |
|
| 4259 |
+ " <td>27818</td>\n", |
|
| 4260 |
+ " <td>5.921100</td>\n", |
|
| 4261 |
+ " <td>18.088420</td>\n", |
|
| 4262 |
+ " <td>1.115164</td>\n", |
|
| 4263 |
+ " <td>664.540101</td>\n", |
|
| 4264 |
+ " </tr>\n", |
|
| 4265 |
+ " <tr>\n", |
|
| 4266 |
+ " <th>중구</th>\n", |
|
| 4267 |
+ " <td>2447</td>\n", |
|
| 4268 |
+ " <td>217.792208</td>\n", |
|
| 4269 |
+ " <td>131787</td>\n", |
|
| 4270 |
+ " <td>122499</td>\n", |
|
| 4271 |
+ " <td>9288</td>\n", |
|
| 4272 |
+ " <td>24392</td>\n", |
|
| 4273 |
+ " <td>7.047736</td>\n", |
|
| 4274 |
+ " <td>18.508654</td>\n", |
|
| 4275 |
+ " <td>1.856784</td>\n", |
|
| 4276 |
+ " <td>157.347513</td>\n", |
|
| 4277 |
+ " </tr>\n", |
|
| 4278 |
+ " <tr>\n", |
|
| 4279 |
+ " <th>용산구</th>\n", |
|
| 4280 |
+ " <td>2611</td>\n", |
|
| 4281 |
+ " <td>165.615463</td>\n", |
|
| 4282 |
+ " <td>237285</td>\n", |
|
| 4283 |
+ " <td>222953</td>\n", |
|
| 4284 |
+ " <td>14332</td>\n", |
|
| 4285 |
+ " <td>39070</td>\n", |
|
| 4286 |
+ " <td>6.039994</td>\n", |
|
| 4287 |
+ " <td>16.465432</td>\n", |
|
| 4288 |
+ " <td>1.100365</td>\n", |
|
| 4289 |
+ " <td>109.657101</td>\n", |
|
| 4290 |
+ " </tr>\n", |
|
| 4291 |
+ " <tr>\n", |
|
| 4292 |
+ " <th>성동구</th>\n", |
|
| 4293 |
+ " <td>3829</td>\n", |
|
| 4294 |
+ " <td>218.552413</td>\n", |
|
| 4295 |
+ " <td>292672</td>\n", |
|
| 4296 |
+ " <td>285990</td>\n", |
|
| 4297 |
+ " <td>6682</td>\n", |
|
| 4298 |
+ " <td>46380</td>\n", |
|
| 4299 |
+ " <td>2.283102</td>\n", |
|
| 4300 |
+ " <td>15.847092</td>\n", |
|
| 4301 |
+ " <td>1.308291</td>\n", |
|
| 4302 |
+ " <td>882.063229</td>\n", |
|
| 4303 |
+ " </tr>\n", |
|
| 4304 |
+ " <tr>\n", |
|
| 4305 |
+ " <th>광진구</th>\n", |
|
| 4306 |
+ " <td>3211</td>\n", |
|
| 4307 |
+ " <td>506.994329</td>\n", |
|
| 4308 |
+ " <td>352627</td>\n", |
|
| 4309 |
+ " <td>339996</td>\n", |
|
| 4310 |
+ " <td>12631</td>\n", |
|
| 4311 |
+ " <td>51723</td>\n", |
|
| 4312 |
+ " <td>3.581972</td>\n", |
|
| 4313 |
+ " <td>14.667907</td>\n", |
|
| 4314 |
+ " <td>0.910594</td>\n", |
|
| 4315 |
+ " <td>19.121319</td>\n", |
|
| 4316 |
+ " </tr>\n", |
|
| 4317 |
+ " </tbody>\n", |
|
| 4318 |
+ "</table>\n", |
|
| 4319 |
+ "</div>" |
|
| 4320 |
+ ], |
|
| 4321 |
+ "text/plain": [ |
|
| 4322 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율 \\\n", |
|
| 4323 |
+ "구별 \n", |
|
| 4324 |
+ "종로구 1715 47.844828 153789 144683 9106 27818 5.921100 18.088420 \n", |
|
| 4325 |
+ "중구 2447 217.792208 131787 122499 9288 24392 7.047736 18.508654 \n", |
|
| 4326 |
+ "용산구 2611 165.615463 237285 222953 14332 39070 6.039994 16.465432 \n", |
|
| 4327 |
+ "성동구 3829 218.552413 292672 285990 6682 46380 2.283102 15.847092 \n", |
|
| 4328 |
+ "광진구 3211 506.994329 352627 339996 12631 51723 3.581972 14.667907 \n", |
|
| 4329 |
+ "\n", |
|
| 4330 |
+ " CCTV비율 오차 \n", |
|
| 4331 |
+ "구별 \n", |
|
| 4332 |
+ "종로구 1.115164 664.540101 \n", |
|
| 4333 |
+ "중구 1.856784 157.347513 \n", |
|
| 4334 |
+ "용산구 1.100365 109.657101 \n", |
|
| 4335 |
+ "성동구 1.308291 882.063229 \n", |
|
| 4336 |
+ "광진구 0.910594 19.121319 " |
|
| 4337 |
+ ] |
|
| 4338 |
+ }, |
|
| 4339 |
+ "execution_count": 137, |
|
| 4340 |
+ "metadata": {},
|
|
| 4341 |
+ "output_type": "execute_result" |
|
| 4342 |
+ } |
|
| 4343 |
+ ], |
|
| 4344 |
+ "source": [ |
|
| 4345 |
+ "dataset.head()" |
|
| 4346 |
+ ] |
|
| 4347 |
+ }, |
|
| 4348 |
+ {
|
|
| 4349 |
+ "cell_type": "code", |
|
| 4350 |
+ "execution_count": 147, |
|
| 4351 |
+ "id": "bafc7c01", |
|
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+ "metadata": {
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|
| 4353 |
+ "scrolled": true |
|
| 4354 |
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|
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| 4356 |
+ {
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|
| 4366 |
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|
| 4367 |
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|
| 4368 |
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|
| 4369 |
+ " .dataframe thead th {\n",
|
|
| 4370 |
+ " text-align: right;\n", |
|
| 4371 |
+ " }\n", |
|
| 4372 |
+ "</style>\n", |
|
| 4373 |
+ "<table border=\"1\" class=\"dataframe\">\n", |
|
| 4374 |
+ " <thead>\n", |
|
| 4375 |
+ " <tr style=\"text-align: right;\">\n", |
|
| 4376 |
+ " <th></th>\n", |
|
| 4377 |
+ " <th>총계</th>\n", |
|
| 4378 |
+ " <th>최근증가율</th>\n", |
|
| 4379 |
+ " <th>인구수</th>\n", |
|
| 4380 |
+ " <th>한국인</th>\n", |
|
| 4381 |
+ " <th>외국인</th>\n", |
|
| 4382 |
+ " <th>고령자</th>\n", |
|
| 4383 |
+ " <th>외국인비율</th>\n", |
|
| 4384 |
+ " <th>고령자비율</th>\n", |
|
| 4385 |
+ " <th>CCTV비율</th>\n", |
|
| 4386 |
+ " <th>오차</th>\n", |
|
| 4387 |
+ " </tr>\n", |
|
| 4388 |
+ " <tr>\n", |
|
| 4389 |
+ " <th>구별</th>\n", |
|
| 4390 |
+ " <th></th>\n", |
|
| 4391 |
+ " <th></th>\n", |
|
| 4392 |
+ " <th></th>\n", |
|
| 4393 |
+ " <th></th>\n", |
|
| 4394 |
+ " <th></th>\n", |
|
| 4395 |
+ " <th></th>\n", |
|
| 4396 |
+ " <th></th>\n", |
|
| 4397 |
+ " <th></th>\n", |
|
| 4398 |
+ " <th></th>\n", |
|
| 4399 |
+ " <th></th>\n", |
|
| 4400 |
+ " </tr>\n", |
|
| 4401 |
+ " </thead>\n", |
|
| 4402 |
+ " <tbody>\n", |
|
| 4403 |
+ " <tr>\n", |
|
| 4404 |
+ " <th>강남구</th>\n", |
|
| 4405 |
+ " <td>6871</td>\n", |
|
| 4406 |
+ " <td>136.523236</td>\n", |
|
| 4407 |
+ " <td>537800</td>\n", |
|
| 4408 |
+ " <td>533042</td>\n", |
|
| 4409 |
+ " <td>4758</td>\n", |
|
| 4410 |
+ " <td>78226</td>\n", |
|
| 4411 |
+ " <td>0.884716</td>\n", |
|
| 4412 |
+ " <td>14.545556</td>\n", |
|
| 4413 |
+ " <td>1.277612</td>\n", |
|
| 4414 |
+ " <td>2922.610130</td>\n", |
|
| 4415 |
+ " </tr>\n", |
|
| 4416 |
+ " <tr>\n", |
|
| 4417 |
+ " <th>송파구</th>\n", |
|
| 4418 |
+ " <td>2897</td>\n", |
|
| 4419 |
+ " <td>336.295181</td>\n", |
|
| 4420 |
+ " <td>663965</td>\n", |
|
| 4421 |
+ " <td>658338</td>\n", |
|
| 4422 |
+ " <td>5627</td>\n", |
|
| 4423 |
+ " <td>97691</td>\n", |
|
| 4424 |
+ " <td>0.847484</td>\n", |
|
| 4425 |
+ " <td>14.713276</td>\n", |
|
| 4426 |
+ " <td>0.436318</td>\n", |
|
| 4427 |
+ " <td>1566.828051</td>\n", |
|
| 4428 |
+ " </tr>\n", |
|
| 4429 |
+ " <tr>\n", |
|
| 4430 |
+ " <th>강서구</th>\n", |
|
| 4431 |
+ " <td>2744</td>\n", |
|
| 4432 |
+ " <td>223.203769</td>\n", |
|
| 4433 |
+ " <td>579768</td>\n", |
|
| 4434 |
+ " <td>574315</td>\n", |
|
| 4435 |
+ " <td>5453</td>\n", |
|
| 4436 |
+ " <td>92558</td>\n", |
|
| 4437 |
+ " <td>0.940549</td>\n", |
|
| 4438 |
+ " <td>15.964662</td>\n", |
|
| 4439 |
+ " <td>0.473293</td>\n", |
|
| 4440 |
+ " <td>1375.847165</td>\n", |
|
| 4441 |
+ " </tr>\n", |
|
| 4442 |
+ " <tr>\n", |
|
| 4443 |
+ " <th>노원구</th>\n", |
|
| 4444 |
+ " <td>2492</td>\n", |
|
| 4445 |
+ " <td>112.446718</td>\n", |
|
| 4446 |
+ " <td>514946</td>\n", |
|
| 4447 |
+ " <td>510956</td>\n", |
|
| 4448 |
+ " <td>3990</td>\n", |
|
| 4449 |
+ " <td>88345</td>\n", |
|
| 4450 |
+ " <td>0.774839</td>\n", |
|
| 4451 |
+ " <td>17.156168</td>\n", |
|
| 4452 |
+ " <td>0.483934</td>\n", |
|
| 4453 |
+ " <td>1363.021470</td>\n", |
|
| 4454 |
+ " </tr>\n", |
|
| 4455 |
+ " <tr>\n", |
|
| 4456 |
+ " <th>관악구</th>\n", |
|
| 4457 |
+ " <td>5149</td>\n", |
|
| 4458 |
+ " <td>96.152381</td>\n", |
|
| 4459 |
+ " <td>499449</td>\n", |
|
| 4460 |
+ " <td>485699</td>\n", |
|
| 4461 |
+ " <td>13750</td>\n", |
|
| 4462 |
+ " <td>79871</td>\n", |
|
| 4463 |
+ " <td>2.753034</td>\n", |
|
| 4464 |
+ " <td>15.991823</td>\n", |
|
| 4465 |
+ " <td>1.030936</td>\n", |
|
| 4466 |
+ " <td>1357.290427</td>\n", |
|
| 4467 |
+ " </tr>\n", |
|
| 4468 |
+ " </tbody>\n", |
|
| 4469 |
+ "</table>\n", |
|
| 4470 |
+ "</div>" |
|
| 4471 |
+ ], |
|
| 4472 |
+ "text/plain": [ |
|
| 4473 |
+ " 총계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율 \\\n", |
|
| 4474 |
+ "구별 \n", |
|
| 4475 |
+ "강남구 6871 136.523236 537800 533042 4758 78226 0.884716 14.545556 \n", |
|
| 4476 |
+ "송파구 2897 336.295181 663965 658338 5627 97691 0.847484 14.713276 \n", |
|
| 4477 |
+ "강서구 2744 223.203769 579768 574315 5453 92558 0.940549 15.964662 \n", |
|
| 4478 |
+ "노원구 2492 112.446718 514946 510956 3990 88345 0.774839 17.156168 \n", |
|
| 4479 |
+ "관악구 5149 96.152381 499449 485699 13750 79871 2.753034 15.991823 \n", |
|
| 4480 |
+ "\n", |
|
| 4481 |
+ " CCTV비율 오차 \n", |
|
| 4482 |
+ "구별 \n", |
|
| 4483 |
+ "강남구 1.277612 2922.610130 \n", |
|
| 4484 |
+ "송파구 0.436318 1566.828051 \n", |
|
| 4485 |
+ "강서구 0.473293 1375.847165 \n", |
|
| 4486 |
+ "노원구 0.483934 1363.021470 \n", |
|
| 4487 |
+ "관악구 1.030936 1357.290427 " |
|
| 4488 |
+ ] |
|
| 4489 |
+ }, |
|
| 4490 |
+ "execution_count": 147, |
|
| 4491 |
+ "metadata": {},
|
|
| 4492 |
+ "output_type": "execute_result" |
|
| 4493 |
+ } |
|
| 4494 |
+ ], |
|
| 4495 |
+ "source": [ |
|
| 4496 |
+ "df_sort = dataset.sort_values(by='오차', ascending=False)\n", |
|
| 4497 |
+ "df_sort.head()" |
|
| 4498 |
+ ] |
|
| 4499 |
+ }, |
|
| 4500 |
+ {
|
|
| 4501 |
+ "cell_type": "code", |
|
| 4502 |
+ "execution_count": 146, |
|
| 4503 |
+ "id": "3fd155d4", |
|
| 4504 |
+ "metadata": {},
|
|
| 4505 |
+ "outputs": [ |
|
| 4506 |
+ {
|
|
| 4507 |
+ "data": {
|
|
| 4508 |
+ "image/png": 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\n", |
|
| 4509 |
+ "text/plain": [ |
|
| 4510 |
+ "<Figure size 1008x720 with 2 Axes>" |
|
| 4511 |
+ ] |
|
| 4512 |
+ }, |
|
| 4513 |
+ "metadata": {
|
|
| 4514 |
+ "needs_background": "light" |
|
| 4515 |
+ }, |
|
| 4516 |
+ "output_type": "display_data" |
|
| 4517 |
+ } |
|
| 4518 |
+ ], |
|
| 4519 |
+ "source": [ |
|
| 4520 |
+ "plt.figure(figsize=(14, 10))\n", |
|
| 4521 |
+ "plt.scatter(dataset['인구수'], dataset['총계'], c=dataset['오차'], s=50)\n", |
|
| 4522 |
+ "plt.plot(x, f1(x), ls='dashed', lw=3, color='tomato')\n", |
|
| 4523 |
+ "\n", |
|
| 4524 |
+ "for n in range(20):\n", |
|
| 4525 |
+ " plt.text(df_sort['인구수'][n]*1.02, df_sort['총계'][n]*0.98, df_sort.index[n], fontsize=15)\n", |
|
| 4526 |
+ "\n", |
|
| 4527 |
+ "plt.xlabel('인구수')\n",
|
|
| 4528 |
+ "plt.ylabel('인구수 당 CCTV 비율')\n",
|
|
| 4529 |
+ "plt.colorbar()\n", |
|
| 4530 |
+ "plt.grid()\n", |
|
| 4531 |
+ "plt.show()" |
|
| 4532 |
+ ] |
|
| 2700 | 4533 |
} |
| 2701 | 4534 |
], |
| 2702 | 4535 |
"metadata": {
|
+++ 20220613/.ipynb_checkpoints/cctv-sample-checkpoint.ipynb
... | ... | @@ -0,0 +1,558 @@ |
| 1 | +{ | |
| 2 | + "cells": [ | |
| 3 | + { | |
| 4 | + "cell_type": "code", | |
| 5 | + "execution_count": null, | |
| 6 | + "id": "fec63b3f-eb21-40d6-a807-c270fc17275d", | |
| 7 | + "metadata": {}, | |
| 8 | + "outputs": [], | |
| 9 | + "source": [ | |
| 10 | + "import pandas as pd\n", | |
| 11 | + "import numpy as np\n", | |
| 12 | + "from matplotlib import pyplot as plt" | |
| 13 | + ] | |
| 14 | + }, | |
| 15 | + { | |
| 16 | + "cell_type": "code", | |
| 17 | + "execution_count": null, | |
| 18 | + "id": "d4a399c5-d032-447d-9a5a-32fcef23fbcd", | |
| 19 | + "metadata": {}, | |
| 20 | + "outputs": [], | |
| 21 | + "source": [ | |
| 22 | + "cctvdata = pd.read_csv('seoul_cctv_202112.csv', encoding='cp949', skiprows=1, index_col='구분', thousands=',')" | |
| 23 | + ] | |
| 24 | + }, | |
| 25 | + { | |
| 26 | + "cell_type": "code", | |
| 27 | + "execution_count": null, | |
| 28 | + "id": "bc74eccf-fd7e-41f2-b230-d0fa05c03ceb", | |
| 29 | + "metadata": {}, | |
| 30 | + "outputs": [], | |
| 31 | + "source": [ | |
| 32 | + "cctvdata.head()" | |
| 33 | + ] | |
| 34 | + }, | |
| 35 | + { | |
| 36 | + "cell_type": "code", | |
| 37 | + "execution_count": null, | |
| 38 | + "id": "3971fdf8-c8ed-49c5-9814-ae70399632db", | |
| 39 | + "metadata": {}, | |
| 40 | + "outputs": [], | |
| 41 | + "source": [ | |
| 42 | + "cctvdata.drop('계', inplace=True)" | |
| 43 | + ] | |
| 44 | + }, | |
| 45 | + { | |
| 46 | + "cell_type": "code", | |
| 47 | + "execution_count": null, | |
| 48 | + "id": "c99dc712-7607-4f50-a884-1eedb1af8deb", | |
| 49 | + "metadata": {}, | |
| 50 | + "outputs": [], | |
| 51 | + "source": [ | |
| 52 | + "cctvdata.head()" | |
| 53 | + ] | |
| 54 | + }, | |
| 55 | + { | |
| 56 | + "cell_type": "code", | |
| 57 | + "execution_count": null, | |
| 58 | + "id": "8dd5215b-cf27-4fa2-b6a4-057354cb4981", | |
| 59 | + "metadata": {}, | |
| 60 | + "outputs": [], | |
| 61 | + "source": [ | |
| 62 | + "cctvdata.reset_index(inplace=True)" | |
| 63 | + ] | |
| 64 | + }, | |
| 65 | + { | |
| 66 | + "cell_type": "code", | |
| 67 | + "execution_count": null, | |
| 68 | + "id": "f56b5283-5eb6-42c4-b77f-918cf64ab218", | |
| 69 | + "metadata": {}, | |
| 70 | + "outputs": [], | |
| 71 | + "source": [ | |
| 72 | + "cctvdata.head()" | |
| 73 | + ] | |
| 74 | + }, | |
| 75 | + { | |
| 76 | + "cell_type": "code", | |
| 77 | + "execution_count": null, | |
| 78 | + "id": "70df1a3f-2390-41f7-86d6-ffad481455cc", | |
| 79 | + "metadata": {}, | |
| 80 | + "outputs": [], | |
| 81 | + "source": [ | |
| 82 | + "cctvdata.rename(columns={'구분':'구별'}, inplace=True)" | |
| 83 | + ] | |
| 84 | + }, | |
| 85 | + { | |
| 86 | + "cell_type": "code", | |
| 87 | + "execution_count": null, | |
| 88 | + "id": "aa20ad9a-faee-4b83-a544-666e95af9e26", | |
| 89 | + "metadata": {}, | |
| 90 | + "outputs": [], | |
| 91 | + "source": [ | |
| 92 | + "cctvdata.head()" | |
| 93 | + ] | |
| 94 | + }, | |
| 95 | + { | |
| 96 | + "cell_type": "code", | |
| 97 | + "execution_count": null, | |
| 98 | + "id": "0f2bb5c0-7f0a-41fa-acb7-be01d36aeef8", | |
| 99 | + "metadata": {}, | |
| 100 | + "outputs": [], | |
| 101 | + "source": [ | |
| 102 | + "popdata = pd.read_excel('seoul_population.xls', header=2, usecols='B, D, G, J, N')" | |
| 103 | + ] | |
| 104 | + }, | |
| 105 | + { | |
| 106 | + "cell_type": "code", | |
| 107 | + "execution_count": null, | |
| 108 | + "id": "dbaa6931-3a4e-4cbd-9549-8e586c919c2f", | |
| 109 | + "metadata": {}, | |
| 110 | + "outputs": [], | |
| 111 | + "source": [ | |
| 112 | + "popdata.head()" | |
| 113 | + ] | |
| 114 | + }, | |
| 115 | + { | |
| 116 | + "cell_type": "code", | |
| 117 | + "execution_count": null, | |
| 118 | + "id": "3fbf8d68-0787-4d33-9b4b-49fec65eaaad", | |
| 119 | + "metadata": {}, | |
| 120 | + "outputs": [], | |
| 121 | + "source": [ | |
| 122 | + "popdata.rename(columns={'자치구':'구별',\n", | |
| 123 | + " '계':'인구수',\n", | |
| 124 | + " '계.1':'한국인',\n", | |
| 125 | + " '계.2':'외국인',\n", | |
| 126 | + " '65세이상고령자':'고령자'}, inplace=True)" | |
| 127 | + ] | |
| 128 | + }, | |
| 129 | + { | |
| 130 | + "cell_type": "code", | |
| 131 | + "execution_count": null, | |
| 132 | + "id": "97530269-056e-4ef9-88e6-357e7c0d9ef6", | |
| 133 | + "metadata": {}, | |
| 134 | + "outputs": [], | |
| 135 | + "source": [ | |
| 136 | + "popdata.head()" | |
| 137 | + ] | |
| 138 | + }, | |
| 139 | + { | |
| 140 | + "cell_type": "code", | |
| 141 | + "execution_count": null, | |
| 142 | + "id": "ca429d15-1806-4357-9845-dbc61827ef3b", | |
| 143 | + "metadata": {}, | |
| 144 | + "outputs": [], | |
| 145 | + "source": [ | |
| 146 | + "cctvdata.sort_values(by='총계', ascending=False).head()" | |
| 147 | + ] | |
| 148 | + }, | |
| 149 | + { | |
| 150 | + "cell_type": "code", | |
| 151 | + "execution_count": null, | |
| 152 | + "id": "ebca079a-fbf9-4dbd-912e-a2cef7f6022f", | |
| 153 | + "metadata": {}, | |
| 154 | + "outputs": [], | |
| 155 | + "source": [ | |
| 156 | + "cctvdata.isnull()" | |
| 157 | + ] | |
| 158 | + }, | |
| 159 | + { | |
| 160 | + "cell_type": "code", | |
| 161 | + "execution_count": null, | |
| 162 | + "id": "93420311-e679-4a0a-a439-a72bc3a9e679", | |
| 163 | + "metadata": {}, | |
| 164 | + "outputs": [], | |
| 165 | + "source": [ | |
| 166 | + "# 결측치 처리 : 0으로 치환\n", | |
| 167 | + "cctvdata = cctvdata.fillna(0)" | |
| 168 | + ] | |
| 169 | + }, | |
| 170 | + { | |
| 171 | + "cell_type": "code", | |
| 172 | + "execution_count": null, | |
| 173 | + "id": "d095117c-c01d-4ec5-b612-59cba46abe3d", | |
| 174 | + "metadata": {}, | |
| 175 | + "outputs": [], | |
| 176 | + "source": [ | |
| 177 | + "cctvdata.head()" | |
| 178 | + ] | |
| 179 | + }, | |
| 180 | + { | |
| 181 | + "cell_type": "code", | |
| 182 | + "execution_count": null, | |
| 183 | + "id": "c881dfa5-19ba-40cc-a689-74c5ec5eee01", | |
| 184 | + "metadata": {}, | |
| 185 | + "outputs": [], | |
| 186 | + "source": [ | |
| 187 | + "# str to int\n", | |
| 188 | + "for col in cctvdata.columns:\n", | |
| 189 | + " if col=='구별':\n", | |
| 190 | + " continue\n", | |
| 191 | + " else:\n", | |
| 192 | + " cctvdata[col] = pd.to_numeric(cctvdata[col])" | |
| 193 | + ] | |
| 194 | + }, | |
| 195 | + { | |
| 196 | + "cell_type": "code", | |
| 197 | + "execution_count": null, | |
| 198 | + "id": "e691bc55-8b03-44fc-b7f3-316f5c4c3736", | |
| 199 | + "metadata": {}, | |
| 200 | + "outputs": [], | |
| 201 | + "source": [ | |
| 202 | + "cctvdata.head()" | |
| 203 | + ] | |
| 204 | + }, | |
| 205 | + { | |
| 206 | + "cell_type": "code", | |
| 207 | + "execution_count": null, | |
| 208 | + "id": "c07d9499-a67f-47c9-b979-95de42539a36", | |
| 209 | + "metadata": {}, | |
| 210 | + "outputs": [], | |
| 211 | + "source": [ | |
| 212 | + "cctvdata['최근증가율'] = (cctvdata['2021년']+cctvdata['2020년']+cctvdata['2019년']+cctvdata['2018년']+cctvdata['2017년'])*100/(cctvdata['2016년']+cctvdata['2015년']+cctvdata['2014년']+cctvdata['2013년']+cctvdata['2012년']+cctvdata['2012년 이전'])" | |
| 213 | + ] | |
| 214 | + }, | |
| 215 | + { | |
| 216 | + "cell_type": "code", | |
| 217 | + "execution_count": null, | |
| 218 | + "id": "ad2b0de9-7b4f-43be-baa7-05a4c2850a1f", | |
| 219 | + "metadata": {}, | |
| 220 | + "outputs": [], | |
| 221 | + "source": [ | |
| 222 | + "cctvdata.head()" | |
| 223 | + ] | |
| 224 | + }, | |
| 225 | + { | |
| 226 | + "cell_type": "code", | |
| 227 | + "execution_count": null, | |
| 228 | + "id": "0f9a5203-2ef2-40ae-b82e-bf4e3cf5576c", | |
| 229 | + "metadata": {}, | |
| 230 | + "outputs": [], | |
| 231 | + "source": [ | |
| 232 | + "popdata.head()" | |
| 233 | + ] | |
| 234 | + }, | |
| 235 | + { | |
| 236 | + "cell_type": "code", | |
| 237 | + "execution_count": null, | |
| 238 | + "id": "df89ddcd-6605-4c31-8a3f-7c0be71ae931", | |
| 239 | + "metadata": {}, | |
| 240 | + "outputs": [], | |
| 241 | + "source": [ | |
| 242 | + "popdata.drop(0, inplace=True)" | |
| 243 | + ] | |
| 244 | + }, | |
| 245 | + { | |
| 246 | + "cell_type": "code", | |
| 247 | + "execution_count": null, | |
| 248 | + "id": "aae35dc3-378d-4a7c-b1d1-6b3b4f118896", | |
| 249 | + "metadata": {}, | |
| 250 | + "outputs": [], | |
| 251 | + "source": [ | |
| 252 | + "popdata.head()" | |
| 253 | + ] | |
| 254 | + }, | |
| 255 | + { | |
| 256 | + "cell_type": "code", | |
| 257 | + "execution_count": null, | |
| 258 | + "id": "ecad3014-faab-4ea0-8baa-33064f576311", | |
| 259 | + "metadata": {}, | |
| 260 | + "outputs": [], | |
| 261 | + "source": [ | |
| 262 | + "popdata.reset_index(inplace=True)" | |
| 263 | + ] | |
| 264 | + }, | |
| 265 | + { | |
| 266 | + "cell_type": "code", | |
| 267 | + "execution_count": null, | |
| 268 | + "id": "7266b86f-21d4-4e15-8cf4-0becc3caf300", | |
| 269 | + "metadata": {}, | |
| 270 | + "outputs": [], | |
| 271 | + "source": [ | |
| 272 | + "popdata.head()" | |
| 273 | + ] | |
| 274 | + }, | |
| 275 | + { | |
| 276 | + "cell_type": "code", | |
| 277 | + "execution_count": null, | |
| 278 | + "id": "6d0a4c48-f203-41ba-a37b-ca740929e875", | |
| 279 | + "metadata": {}, | |
| 280 | + "outputs": [], | |
| 281 | + "source": [ | |
| 282 | + "del popdata['index']" | |
| 283 | + ] | |
| 284 | + }, | |
| 285 | + { | |
| 286 | + "cell_type": "code", | |
| 287 | + "execution_count": null, | |
| 288 | + "id": "16504402-add5-493b-b613-6a7dff1ffe76", | |
| 289 | + "metadata": {}, | |
| 290 | + "outputs": [], | |
| 291 | + "source": [ | |
| 292 | + "popdata.head()" | |
| 293 | + ] | |
| 294 | + }, | |
| 295 | + { | |
| 296 | + "cell_type": "code", | |
| 297 | + "execution_count": null, | |
| 298 | + "id": "31efa200-fa9e-4aa6-aeca-5087e59ce1d4", | |
| 299 | + "metadata": {}, | |
| 300 | + "outputs": [], | |
| 301 | + "source": [ | |
| 302 | + "popdata['외국인비율'] = popdata['외국인'] / popdata['인구수'] * 100\n", | |
| 303 | + "popdata['고령자비율'] = popdata['고령자'] / popdata['인구수'] * 100" | |
| 304 | + ] | |
| 305 | + }, | |
| 306 | + { | |
| 307 | + "cell_type": "code", | |
| 308 | + "execution_count": null, | |
| 309 | + "id": "4ddf9a5a-d6c4-48b8-b0c2-eb1d805b337b", | |
| 310 | + "metadata": {}, | |
| 311 | + "outputs": [], | |
| 312 | + "source": [ | |
| 313 | + "popdata.head()" | |
| 314 | + ] | |
| 315 | + }, | |
| 316 | + { | |
| 317 | + "cell_type": "code", | |
| 318 | + "execution_count": null, | |
| 319 | + "id": "1a81fa14-f6d8-400e-81a0-fe82151135e0", | |
| 320 | + "metadata": {}, | |
| 321 | + "outputs": [], | |
| 322 | + "source": [ | |
| 323 | + "popdata.sort_values(by='인구수', ascending=False)" | |
| 324 | + ] | |
| 325 | + }, | |
| 326 | + { | |
| 327 | + "cell_type": "code", | |
| 328 | + "execution_count": null, | |
| 329 | + "id": "715da3d8-1c72-46d1-b5ed-8e2edd7a644d", | |
| 330 | + "metadata": {}, | |
| 331 | + "outputs": [], | |
| 332 | + "source": [ | |
| 333 | + "popdata.sort_values(by='고령자', ascending=False)" | |
| 334 | + ] | |
| 335 | + }, | |
| 336 | + { | |
| 337 | + "cell_type": "code", | |
| 338 | + "execution_count": null, | |
| 339 | + "id": "a09e0777-9db8-44c3-ae46-5e493a77f3a1", | |
| 340 | + "metadata": {}, | |
| 341 | + "outputs": [], | |
| 342 | + "source": [ | |
| 343 | + "popdata.sort_values(by='고령자비율', ascending=False)" | |
| 344 | + ] | |
| 345 | + }, | |
| 346 | + { | |
| 347 | + "cell_type": "code", | |
| 348 | + "execution_count": null, | |
| 349 | + "id": "2fa82489-56c5-4b1e-b649-3f66824424f5", | |
| 350 | + "metadata": {}, | |
| 351 | + "outputs": [], | |
| 352 | + "source": [ | |
| 353 | + "popdata.sort_values(by='외국인비율', ascending=False)" | |
| 354 | + ] | |
| 355 | + }, | |
| 356 | + { | |
| 357 | + "cell_type": "code", | |
| 358 | + "execution_count": null, | |
| 359 | + "id": "e5850abc-83de-4730-af10-934257d2ef0a", | |
| 360 | + "metadata": {}, | |
| 361 | + "outputs": [], | |
| 362 | + "source": [ | |
| 363 | + "dataset = pd.merge(cctvdata, popdata, on='구별')" | |
| 364 | + ] | |
| 365 | + }, | |
| 366 | + { | |
| 367 | + "cell_type": "code", | |
| 368 | + "execution_count": null, | |
| 369 | + "id": "1c87d68b-8db7-48e2-b682-ed4f2ac075a3", | |
| 370 | + "metadata": {}, | |
| 371 | + "outputs": [], | |
| 372 | + "source": [ | |
| 373 | + "dataset.head()" | |
| 374 | + ] | |
| 375 | + }, | |
| 376 | + { | |
| 377 | + "cell_type": "code", | |
| 378 | + "execution_count": null, | |
| 379 | + "id": "f61e4c23-714c-4063-b9a3-1b93b5e2fd01", | |
| 380 | + "metadata": {}, | |
| 381 | + "outputs": [], | |
| 382 | + "source": [ | |
| 383 | + "del dataset['2012년 이전']\n", | |
| 384 | + "del dataset['2012년']\n", | |
| 385 | + "del dataset['2013년']\n", | |
| 386 | + "del dataset['2014년']\n", | |
| 387 | + "del dataset['2015년']\n", | |
| 388 | + "del dataset['2016년']\n", | |
| 389 | + "del dataset['2017년']\n", | |
| 390 | + "del dataset['2018년']\n", | |
| 391 | + "del dataset['2019년']\n", | |
| 392 | + "del dataset['2020년']\n", | |
| 393 | + "del dataset['2021년']" | |
| 394 | + ] | |
| 395 | + }, | |
| 396 | + { | |
| 397 | + "cell_type": "code", | |
| 398 | + "execution_count": null, | |
| 399 | + "id": "b691df20-6b18-49ea-bf10-1f2115f6838e", | |
| 400 | + "metadata": {}, | |
| 401 | + "outputs": [], | |
| 402 | + "source": [ | |
| 403 | + "dataset.head()" | |
| 404 | + ] | |
| 405 | + }, | |
| 406 | + { | |
| 407 | + "cell_type": "code", | |
| 408 | + "execution_count": null, | |
| 409 | + "id": "c0e8211a-a1aa-4bab-8b05-d8edfa6a99bf", | |
| 410 | + "metadata": {}, | |
| 411 | + "outputs": [], | |
| 412 | + "source": [ | |
| 413 | + "dataset.set_index('구별', inplace=True)" | |
| 414 | + ] | |
| 415 | + }, | |
| 416 | + { | |
| 417 | + "cell_type": "code", | |
| 418 | + "execution_count": null, | |
| 419 | + "id": "122789c2-3c24-4c24-a4b5-76b86cee7049", | |
| 420 | + "metadata": {}, | |
| 421 | + "outputs": [], | |
| 422 | + "source": [ | |
| 423 | + "dataset.head()" | |
| 424 | + ] | |
| 425 | + }, | |
| 426 | + { | |
| 427 | + "cell_type": "code", | |
| 428 | + "execution_count": null, | |
| 429 | + "id": "c1e9fbaa-0e12-4cbb-8575-b025a9e148d9", | |
| 430 | + "metadata": {}, | |
| 431 | + "outputs": [], | |
| 432 | + "source": [ | |
| 433 | + "np.corrcoef(dataset['고령자비율'], dataset['총계'])" | |
| 434 | + ] | |
| 435 | + }, | |
| 436 | + { | |
| 437 | + "cell_type": "code", | |
| 438 | + "execution_count": null, | |
| 439 | + "id": "60d499df-ecf3-4e31-bdac-afb1a3d053bb", | |
| 440 | + "metadata": {}, | |
| 441 | + "outputs": [], | |
| 442 | + "source": [ | |
| 443 | + "np.corrcoef(dataset['외국인비율'], dataset['총계'])" | |
| 444 | + ] | |
| 445 | + }, | |
| 446 | + { | |
| 447 | + "cell_type": "code", | |
| 448 | + "execution_count": null, | |
| 449 | + "id": "52e88ee7-58a6-4265-97d4-21971b8efb0c", | |
| 450 | + "metadata": {}, | |
| 451 | + "outputs": [], | |
| 452 | + "source": [ | |
| 453 | + "np.corrcoef(dataset['인구수'], dataset['총계'])" | |
| 454 | + ] | |
| 455 | + }, | |
| 456 | + { | |
| 457 | + "cell_type": "code", | |
| 458 | + "execution_count": null, | |
| 459 | + "id": "27605558-2948-4bcb-8c58-f982726c2449", | |
| 460 | + "metadata": {}, | |
| 461 | + "outputs": [], | |
| 462 | + "source": [ | |
| 463 | + "dataset.sort_values(by='총계', ascending=False).head()" | |
| 464 | + ] | |
| 465 | + }, | |
| 466 | + { | |
| 467 | + "cell_type": "code", | |
| 468 | + "execution_count": null, | |
| 469 | + "id": "0c008ad5-f012-48ce-8dec-cec36af57aee", | |
| 470 | + "metadata": {}, | |
| 471 | + "outputs": [], | |
| 472 | + "source": [ | |
| 473 | + "dataset.sort_values(by='인구수', ascending=False).head()" | |
| 474 | + ] | |
| 475 | + }, | |
| 476 | + { | |
| 477 | + "cell_type": "code", | |
| 478 | + "execution_count": null, | |
| 479 | + "id": "376b9ec4-313e-4ef8-9f1b-6581dd85bcdb", | |
| 480 | + "metadata": {}, | |
| 481 | + "outputs": [], | |
| 482 | + "source": [ | |
| 483 | + "plt.rc('font', family='NanumGothic')" | |
| 484 | + ] | |
| 485 | + }, | |
| 486 | + { | |
| 487 | + "cell_type": "code", | |
| 488 | + "execution_count": null, | |
| 489 | + "id": "1bdab3f2-d0e7-459a-9d2b-d254b97af2c3", | |
| 490 | + "metadata": {}, | |
| 491 | + "outputs": [], | |
| 492 | + "source": [ | |
| 493 | + "dataset['총계'].plot(kind='barh', grid=True, figsize=(10, 10))" | |
| 494 | + ] | |
| 495 | + }, | |
| 496 | + { | |
| 497 | + "cell_type": "code", | |
| 498 | + "execution_count": null, | |
| 499 | + "id": "958c14ef-f009-4771-82ac-352563801055", | |
| 500 | + "metadata": {}, | |
| 501 | + "outputs": [], | |
| 502 | + "source": [ | |
| 503 | + "dataset['총계'].sort_values().plot(kind='barh', grid=True, figsize=(10, 10))" | |
| 504 | + ] | |
| 505 | + }, | |
| 506 | + { | |
| 507 | + "cell_type": "code", | |
| 508 | + "execution_count": null, | |
| 509 | + "id": "0affbf15-4bde-47d0-85f3-52e10a58d09b", | |
| 510 | + "metadata": {}, | |
| 511 | + "outputs": [], | |
| 512 | + "source": [ | |
| 513 | + "dataset.head()" | |
| 514 | + ] | |
| 515 | + }, | |
| 516 | + { | |
| 517 | + "cell_type": "code", | |
| 518 | + "execution_count": null, | |
| 519 | + "id": "9e340e4a-8307-4c6e-b327-ace8b01b85c0", | |
| 520 | + "metadata": {}, | |
| 521 | + "outputs": [], | |
| 522 | + "source": [ | |
| 523 | + "dataset['CCTV비율'] = dataset['총계'] / dataset['인구수'] * 100" | |
| 524 | + ] | |
| 525 | + }, | |
| 526 | + { | |
| 527 | + "cell_type": "code", | |
| 528 | + "execution_count": null, | |
| 529 | + "id": "d5f16688-4554-44f9-a70f-a55c61f6e34b", | |
| 530 | + "metadata": {}, | |
| 531 | + "outputs": [], | |
| 532 | + "source": [ | |
| 533 | + "dataset.head()" | |
| 534 | + ] | |
| 535 | + } | |
| 536 | + ], | |
| 537 | + "metadata": { | |
| 538 | + "kernelspec": { | |
| 539 | + "display_name": "Python 3 (ipykernel)", | |
| 540 | + "language": "python", | |
| 541 | + "name": "python3" | |
| 542 | + }, | |
| 543 | + "language_info": { | |
| 544 | + "codemirror_mode": { | |
| 545 | + "name": "ipython", | |
| 546 | + "version": 3 | |
| 547 | + }, | |
| 548 | + "file_extension": ".py", | |
| 549 | + "mimetype": "text/x-python", | |
| 550 | + "name": "python", | |
| 551 | + "nbconvert_exporter": "python", | |
| 552 | + "pygments_lexer": "ipython3", | |
| 553 | + "version": "3.9.7" | |
| 554 | + } | |
| 555 | + }, | |
| 556 | + "nbformat": 4, | |
| 557 | + "nbformat_minor": 5 | |
| 558 | +} |
+++ 20220613/cctv-sample.ipynb
... | ... | @@ -0,0 +1,558 @@ |
| 1 | +{ | |
| 2 | + "cells": [ | |
| 3 | + { | |
| 4 | + "cell_type": "code", | |
| 5 | + "execution_count": null, | |
| 6 | + "id": "fec63b3f-eb21-40d6-a807-c270fc17275d", | |
| 7 | + "metadata": {}, | |
| 8 | + "outputs": [], | |
| 9 | + "source": [ | |
| 10 | + "import pandas as pd\n", | |
| 11 | + "import numpy as np\n", | |
| 12 | + "from matplotlib import pyplot as plt" | |
| 13 | + ] | |
| 14 | + }, | |
| 15 | + { | |
| 16 | + "cell_type": "code", | |
| 17 | + "execution_count": null, | |
| 18 | + "id": "d4a399c5-d032-447d-9a5a-32fcef23fbcd", | |
| 19 | + "metadata": {}, | |
| 20 | + "outputs": [], | |
| 21 | + "source": [ | |
| 22 | + "cctvdata = pd.read_csv('seoul_cctv_202112.csv', encoding='cp949', skiprows=1, index_col='구분', thousands=',')" | |
| 23 | + ] | |
| 24 | + }, | |
| 25 | + { | |
| 26 | + "cell_type": "code", | |
| 27 | + "execution_count": null, | |
| 28 | + "id": "bc74eccf-fd7e-41f2-b230-d0fa05c03ceb", | |
| 29 | + "metadata": {}, | |
| 30 | + "outputs": [], | |
| 31 | + "source": [ | |
| 32 | + "cctvdata.head()" | |
| 33 | + ] | |
| 34 | + }, | |
| 35 | + { | |
| 36 | + "cell_type": "code", | |
| 37 | + "execution_count": null, | |
| 38 | + "id": "3971fdf8-c8ed-49c5-9814-ae70399632db", | |
| 39 | + "metadata": {}, | |
| 40 | + "outputs": [], | |
| 41 | + "source": [ | |
| 42 | + "cctvdata.drop('계', inplace=True)" | |
| 43 | + ] | |
| 44 | + }, | |
| 45 | + { | |
| 46 | + "cell_type": "code", | |
| 47 | + "execution_count": null, | |
| 48 | + "id": "c99dc712-7607-4f50-a884-1eedb1af8deb", | |
| 49 | + "metadata": {}, | |
| 50 | + "outputs": [], | |
| 51 | + "source": [ | |
| 52 | + "cctvdata.head()" | |
| 53 | + ] | |
| 54 | + }, | |
| 55 | + { | |
| 56 | + "cell_type": "code", | |
| 57 | + "execution_count": null, | |
| 58 | + "id": "8dd5215b-cf27-4fa2-b6a4-057354cb4981", | |
| 59 | + "metadata": {}, | |
| 60 | + "outputs": [], | |
| 61 | + "source": [ | |
| 62 | + "cctvdata.reset_index(inplace=True)" | |
| 63 | + ] | |
| 64 | + }, | |
| 65 | + { | |
| 66 | + "cell_type": "code", | |
| 67 | + "execution_count": null, | |
| 68 | + "id": "f56b5283-5eb6-42c4-b77f-918cf64ab218", | |
| 69 | + "metadata": {}, | |
| 70 | + "outputs": [], | |
| 71 | + "source": [ | |
| 72 | + "cctvdata.head()" | |
| 73 | + ] | |
| 74 | + }, | |
| 75 | + { | |
| 76 | + "cell_type": "code", | |
| 77 | + "execution_count": null, | |
| 78 | + "id": "70df1a3f-2390-41f7-86d6-ffad481455cc", | |
| 79 | + "metadata": {}, | |
| 80 | + "outputs": [], | |
| 81 | + "source": [ | |
| 82 | + "cctvdata.rename(columns={'구분':'구별'}, inplace=True)" | |
| 83 | + ] | |
| 84 | + }, | |
| 85 | + { | |
| 86 | + "cell_type": "code", | |
| 87 | + "execution_count": null, | |
| 88 | + "id": "aa20ad9a-faee-4b83-a544-666e95af9e26", | |
| 89 | + "metadata": {}, | |
| 90 | + "outputs": [], | |
| 91 | + "source": [ | |
| 92 | + "cctvdata.head()" | |
| 93 | + ] | |
| 94 | + }, | |
| 95 | + { | |
| 96 | + "cell_type": "code", | |
| 97 | + "execution_count": null, | |
| 98 | + "id": "0f2bb5c0-7f0a-41fa-acb7-be01d36aeef8", | |
| 99 | + "metadata": {}, | |
| 100 | + "outputs": [], | |
| 101 | + "source": [ | |
| 102 | + "popdata = pd.read_excel('seoul_population.xls', header=2, usecols='B, D, G, J, N')" | |
| 103 | + ] | |
| 104 | + }, | |
| 105 | + { | |
| 106 | + "cell_type": "code", | |
| 107 | + "execution_count": null, | |
| 108 | + "id": "dbaa6931-3a4e-4cbd-9549-8e586c919c2f", | |
| 109 | + "metadata": {}, | |
| 110 | + "outputs": [], | |
| 111 | + "source": [ | |
| 112 | + "popdata.head()" | |
| 113 | + ] | |
| 114 | + }, | |
| 115 | + { | |
| 116 | + "cell_type": "code", | |
| 117 | + "execution_count": null, | |
| 118 | + "id": "3fbf8d68-0787-4d33-9b4b-49fec65eaaad", | |
| 119 | + "metadata": {}, | |
| 120 | + "outputs": [], | |
| 121 | + "source": [ | |
| 122 | + "popdata.rename(columns={'자치구':'구별',\n", | |
| 123 | + " '계':'인구수',\n", | |
| 124 | + " '계.1':'한국인',\n", | |
| 125 | + " '계.2':'외국인',\n", | |
| 126 | + " '65세이상고령자':'고령자'}, inplace=True)" | |
| 127 | + ] | |
| 128 | + }, | |
| 129 | + { | |
| 130 | + "cell_type": "code", | |
| 131 | + "execution_count": null, | |
| 132 | + "id": "97530269-056e-4ef9-88e6-357e7c0d9ef6", | |
| 133 | + "metadata": {}, | |
| 134 | + "outputs": [], | |
| 135 | + "source": [ | |
| 136 | + "popdata.head()" | |
| 137 | + ] | |
| 138 | + }, | |
| 139 | + { | |
| 140 | + "cell_type": "code", | |
| 141 | + "execution_count": null, | |
| 142 | + "id": "ca429d15-1806-4357-9845-dbc61827ef3b", | |
| 143 | + "metadata": {}, | |
| 144 | + "outputs": [], | |
| 145 | + "source": [ | |
| 146 | + "cctvdata.sort_values(by='총계', ascending=False).head()" | |
| 147 | + ] | |
| 148 | + }, | |
| 149 | + { | |
| 150 | + "cell_type": "code", | |
| 151 | + "execution_count": null, | |
| 152 | + "id": "ebca079a-fbf9-4dbd-912e-a2cef7f6022f", | |
| 153 | + "metadata": {}, | |
| 154 | + "outputs": [], | |
| 155 | + "source": [ | |
| 156 | + "cctvdata.isnull()" | |
| 157 | + ] | |
| 158 | + }, | |
| 159 | + { | |
| 160 | + "cell_type": "code", | |
| 161 | + "execution_count": null, | |
| 162 | + "id": "93420311-e679-4a0a-a439-a72bc3a9e679", | |
| 163 | + "metadata": {}, | |
| 164 | + "outputs": [], | |
| 165 | + "source": [ | |
| 166 | + "# 결측치 처리 : 0으로 치환\n", | |
| 167 | + "cctvdata = cctvdata.fillna(0)" | |
| 168 | + ] | |
| 169 | + }, | |
| 170 | + { | |
| 171 | + "cell_type": "code", | |
| 172 | + "execution_count": null, | |
| 173 | + "id": "d095117c-c01d-4ec5-b612-59cba46abe3d", | |
| 174 | + "metadata": {}, | |
| 175 | + "outputs": [], | |
| 176 | + "source": [ | |
| 177 | + "cctvdata.head()" | |
| 178 | + ] | |
| 179 | + }, | |
| 180 | + { | |
| 181 | + "cell_type": "code", | |
| 182 | + "execution_count": null, | |
| 183 | + "id": "c881dfa5-19ba-40cc-a689-74c5ec5eee01", | |
| 184 | + "metadata": {}, | |
| 185 | + "outputs": [], | |
| 186 | + "source": [ | |
| 187 | + "# str to int\n", | |
| 188 | + "for col in cctvdata.columns:\n", | |
| 189 | + " if col=='구별':\n", | |
| 190 | + " continue\n", | |
| 191 | + " else:\n", | |
| 192 | + " cctvdata[col] = pd.to_numeric(cctvdata[col])" | |
| 193 | + ] | |
| 194 | + }, | |
| 195 | + { | |
| 196 | + "cell_type": "code", | |
| 197 | + "execution_count": null, | |
| 198 | + "id": "e691bc55-8b03-44fc-b7f3-316f5c4c3736", | |
| 199 | + "metadata": {}, | |
| 200 | + "outputs": [], | |
| 201 | + "source": [ | |
| 202 | + "cctvdata.head()" | |
| 203 | + ] | |
| 204 | + }, | |
| 205 | + { | |
| 206 | + "cell_type": "code", | |
| 207 | + "execution_count": null, | |
| 208 | + "id": "c07d9499-a67f-47c9-b979-95de42539a36", | |
| 209 | + "metadata": {}, | |
| 210 | + "outputs": [], | |
| 211 | + "source": [ | |
| 212 | + "cctvdata['최근증가율'] = (cctvdata['2021년']+cctvdata['2020년']+cctvdata['2019년']+cctvdata['2018년']+cctvdata['2017년'])*100/(cctvdata['2016년']+cctvdata['2015년']+cctvdata['2014년']+cctvdata['2013년']+cctvdata['2012년']+cctvdata['2012년 이전'])" | |
| 213 | + ] | |
| 214 | + }, | |
| 215 | + { | |
| 216 | + "cell_type": "code", | |
| 217 | + "execution_count": null, | |
| 218 | + "id": "ad2b0de9-7b4f-43be-baa7-05a4c2850a1f", | |
| 219 | + "metadata": {}, | |
| 220 | + "outputs": [], | |
| 221 | + "source": [ | |
| 222 | + "cctvdata.head()" | |
| 223 | + ] | |
| 224 | + }, | |
| 225 | + { | |
| 226 | + "cell_type": "code", | |
| 227 | + "execution_count": null, | |
| 228 | + "id": "0f9a5203-2ef2-40ae-b82e-bf4e3cf5576c", | |
| 229 | + "metadata": {}, | |
| 230 | + "outputs": [], | |
| 231 | + "source": [ | |
| 232 | + "popdata.head()" | |
| 233 | + ] | |
| 234 | + }, | |
| 235 | + { | |
| 236 | + "cell_type": "code", | |
| 237 | + "execution_count": null, | |
| 238 | + "id": "df89ddcd-6605-4c31-8a3f-7c0be71ae931", | |
| 239 | + "metadata": {}, | |
| 240 | + "outputs": [], | |
| 241 | + "source": [ | |
| 242 | + "popdata.drop(0, inplace=True)" | |
| 243 | + ] | |
| 244 | + }, | |
| 245 | + { | |
| 246 | + "cell_type": "code", | |
| 247 | + "execution_count": null, | |
| 248 | + "id": "aae35dc3-378d-4a7c-b1d1-6b3b4f118896", | |
| 249 | + "metadata": {}, | |
| 250 | + "outputs": [], | |
| 251 | + "source": [ | |
| 252 | + "popdata.head()" | |
| 253 | + ] | |
| 254 | + }, | |
| 255 | + { | |
| 256 | + "cell_type": "code", | |
| 257 | + "execution_count": null, | |
| 258 | + "id": "ecad3014-faab-4ea0-8baa-33064f576311", | |
| 259 | + "metadata": {}, | |
| 260 | + "outputs": [], | |
| 261 | + "source": [ | |
| 262 | + "popdata.reset_index(inplace=True)" | |
| 263 | + ] | |
| 264 | + }, | |
| 265 | + { | |
| 266 | + "cell_type": "code", | |
| 267 | + "execution_count": null, | |
| 268 | + "id": "7266b86f-21d4-4e15-8cf4-0becc3caf300", | |
| 269 | + "metadata": {}, | |
| 270 | + "outputs": [], | |
| 271 | + "source": [ | |
| 272 | + "popdata.head()" | |
| 273 | + ] | |
| 274 | + }, | |
| 275 | + { | |
| 276 | + "cell_type": "code", | |
| 277 | + "execution_count": null, | |
| 278 | + "id": "6d0a4c48-f203-41ba-a37b-ca740929e875", | |
| 279 | + "metadata": {}, | |
| 280 | + "outputs": [], | |
| 281 | + "source": [ | |
| 282 | + "del popdata['index']" | |
| 283 | + ] | |
| 284 | + }, | |
| 285 | + { | |
| 286 | + "cell_type": "code", | |
| 287 | + "execution_count": null, | |
| 288 | + "id": "16504402-add5-493b-b613-6a7dff1ffe76", | |
| 289 | + "metadata": {}, | |
| 290 | + "outputs": [], | |
| 291 | + "source": [ | |
| 292 | + "popdata.head()" | |
| 293 | + ] | |
| 294 | + }, | |
| 295 | + { | |
| 296 | + "cell_type": "code", | |
| 297 | + "execution_count": null, | |
| 298 | + "id": "31efa200-fa9e-4aa6-aeca-5087e59ce1d4", | |
| 299 | + "metadata": {}, | |
| 300 | + "outputs": [], | |
| 301 | + "source": [ | |
| 302 | + "popdata['외국인비율'] = popdata['외국인'] / popdata['인구수'] * 100\n", | |
| 303 | + "popdata['고령자비율'] = popdata['고령자'] / popdata['인구수'] * 100" | |
| 304 | + ] | |
| 305 | + }, | |
| 306 | + { | |
| 307 | + "cell_type": "code", | |
| 308 | + "execution_count": null, | |
| 309 | + "id": "4ddf9a5a-d6c4-48b8-b0c2-eb1d805b337b", | |
| 310 | + "metadata": {}, | |
| 311 | + "outputs": [], | |
| 312 | + "source": [ | |
| 313 | + "popdata.head()" | |
| 314 | + ] | |
| 315 | + }, | |
| 316 | + { | |
| 317 | + "cell_type": "code", | |
| 318 | + "execution_count": null, | |
| 319 | + "id": "1a81fa14-f6d8-400e-81a0-fe82151135e0", | |
| 320 | + "metadata": {}, | |
| 321 | + "outputs": [], | |
| 322 | + "source": [ | |
| 323 | + "popdata.sort_values(by='인구수', ascending=False)" | |
| 324 | + ] | |
| 325 | + }, | |
| 326 | + { | |
| 327 | + "cell_type": "code", | |
| 328 | + "execution_count": null, | |
| 329 | + "id": "715da3d8-1c72-46d1-b5ed-8e2edd7a644d", | |
| 330 | + "metadata": {}, | |
| 331 | + "outputs": [], | |
| 332 | + "source": [ | |
| 333 | + "popdata.sort_values(by='고령자', ascending=False)" | |
| 334 | + ] | |
| 335 | + }, | |
| 336 | + { | |
| 337 | + "cell_type": "code", | |
| 338 | + "execution_count": null, | |
| 339 | + "id": "a09e0777-9db8-44c3-ae46-5e493a77f3a1", | |
| 340 | + "metadata": {}, | |
| 341 | + "outputs": [], | |
| 342 | + "source": [ | |
| 343 | + "popdata.sort_values(by='고령자비율', ascending=False)" | |
| 344 | + ] | |
| 345 | + }, | |
| 346 | + { | |
| 347 | + "cell_type": "code", | |
| 348 | + "execution_count": null, | |
| 349 | + "id": "2fa82489-56c5-4b1e-b649-3f66824424f5", | |
| 350 | + "metadata": {}, | |
| 351 | + "outputs": [], | |
| 352 | + "source": [ | |
| 353 | + "popdata.sort_values(by='외국인비율', ascending=False)" | |
| 354 | + ] | |
| 355 | + }, | |
| 356 | + { | |
| 357 | + "cell_type": "code", | |
| 358 | + "execution_count": null, | |
| 359 | + "id": "e5850abc-83de-4730-af10-934257d2ef0a", | |
| 360 | + "metadata": {}, | |
| 361 | + "outputs": [], | |
| 362 | + "source": [ | |
| 363 | + "dataset = pd.merge(cctvdata, popdata, on='구별')" | |
| 364 | + ] | |
| 365 | + }, | |
| 366 | + { | |
| 367 | + "cell_type": "code", | |
| 368 | + "execution_count": null, | |
| 369 | + "id": "1c87d68b-8db7-48e2-b682-ed4f2ac075a3", | |
| 370 | + "metadata": {}, | |
| 371 | + "outputs": [], | |
| 372 | + "source": [ | |
| 373 | + "dataset.head()" | |
| 374 | + ] | |
| 375 | + }, | |
| 376 | + { | |
| 377 | + "cell_type": "code", | |
| 378 | + "execution_count": null, | |
| 379 | + "id": "f61e4c23-714c-4063-b9a3-1b93b5e2fd01", | |
| 380 | + "metadata": {}, | |
| 381 | + "outputs": [], | |
| 382 | + "source": [ | |
| 383 | + "del dataset['2012년 이전']\n", | |
| 384 | + "del dataset['2012년']\n", | |
| 385 | + "del dataset['2013년']\n", | |
| 386 | + "del dataset['2014년']\n", | |
| 387 | + "del dataset['2015년']\n", | |
| 388 | + "del dataset['2016년']\n", | |
| 389 | + "del dataset['2017년']\n", | |
| 390 | + "del dataset['2018년']\n", | |
| 391 | + "del dataset['2019년']\n", | |
| 392 | + "del dataset['2020년']\n", | |
| 393 | + "del dataset['2021년']" | |
| 394 | + ] | |
| 395 | + }, | |
| 396 | + { | |
| 397 | + "cell_type": "code", | |
| 398 | + "execution_count": null, | |
| 399 | + "id": "b691df20-6b18-49ea-bf10-1f2115f6838e", | |
| 400 | + "metadata": {}, | |
| 401 | + "outputs": [], | |
| 402 | + "source": [ | |
| 403 | + "dataset.head()" | |
| 404 | + ] | |
| 405 | + }, | |
| 406 | + { | |
| 407 | + "cell_type": "code", | |
| 408 | + "execution_count": null, | |
| 409 | + "id": "c0e8211a-a1aa-4bab-8b05-d8edfa6a99bf", | |
| 410 | + "metadata": {}, | |
| 411 | + "outputs": [], | |
| 412 | + "source": [ | |
| 413 | + "dataset.set_index('구별', inplace=True)" | |
| 414 | + ] | |
| 415 | + }, | |
| 416 | + { | |
| 417 | + "cell_type": "code", | |
| 418 | + "execution_count": null, | |
| 419 | + "id": "122789c2-3c24-4c24-a4b5-76b86cee7049", | |
| 420 | + "metadata": {}, | |
| 421 | + "outputs": [], | |
| 422 | + "source": [ | |
| 423 | + "dataset.head()" | |
| 424 | + ] | |
| 425 | + }, | |
| 426 | + { | |
| 427 | + "cell_type": "code", | |
| 428 | + "execution_count": null, | |
| 429 | + "id": "c1e9fbaa-0e12-4cbb-8575-b025a9e148d9", | |
| 430 | + "metadata": {}, | |
| 431 | + "outputs": [], | |
| 432 | + "source": [ | |
| 433 | + "np.corrcoef(dataset['고령자비율'], dataset['총계'])" | |
| 434 | + ] | |
| 435 | + }, | |
| 436 | + { | |
| 437 | + "cell_type": "code", | |
| 438 | + "execution_count": null, | |
| 439 | + "id": "60d499df-ecf3-4e31-bdac-afb1a3d053bb", | |
| 440 | + "metadata": {}, | |
| 441 | + "outputs": [], | |
| 442 | + "source": [ | |
| 443 | + "np.corrcoef(dataset['외국인비율'], dataset['총계'])" | |
| 444 | + ] | |
| 445 | + }, | |
| 446 | + { | |
| 447 | + "cell_type": "code", | |
| 448 | + "execution_count": null, | |
| 449 | + "id": "52e88ee7-58a6-4265-97d4-21971b8efb0c", | |
| 450 | + "metadata": {}, | |
| 451 | + "outputs": [], | |
| 452 | + "source": [ | |
| 453 | + "np.corrcoef(dataset['인구수'], dataset['총계'])" | |
| 454 | + ] | |
| 455 | + }, | |
| 456 | + { | |
| 457 | + "cell_type": "code", | |
| 458 | + "execution_count": null, | |
| 459 | + "id": "27605558-2948-4bcb-8c58-f982726c2449", | |
| 460 | + "metadata": {}, | |
| 461 | + "outputs": [], | |
| 462 | + "source": [ | |
| 463 | + "dataset.sort_values(by='총계', ascending=False).head()" | |
| 464 | + ] | |
| 465 | + }, | |
| 466 | + { | |
| 467 | + "cell_type": "code", | |
| 468 | + "execution_count": null, | |
| 469 | + "id": "0c008ad5-f012-48ce-8dec-cec36af57aee", | |
| 470 | + "metadata": {}, | |
| 471 | + "outputs": [], | |
| 472 | + "source": [ | |
| 473 | + "dataset.sort_values(by='인구수', ascending=False).head()" | |
| 474 | + ] | |
| 475 | + }, | |
| 476 | + { | |
| 477 | + "cell_type": "code", | |
| 478 | + "execution_count": null, | |
| 479 | + "id": "376b9ec4-313e-4ef8-9f1b-6581dd85bcdb", | |
| 480 | + "metadata": {}, | |
| 481 | + "outputs": [], | |
| 482 | + "source": [ | |
| 483 | + "plt.rc('font', family='NanumGothic')" | |
| 484 | + ] | |
| 485 | + }, | |
| 486 | + { | |
| 487 | + "cell_type": "code", | |
| 488 | + "execution_count": null, | |
| 489 | + "id": "1bdab3f2-d0e7-459a-9d2b-d254b97af2c3", | |
| 490 | + "metadata": {}, | |
| 491 | + "outputs": [], | |
| 492 | + "source": [ | |
| 493 | + "dataset['총계'].plot(kind='barh', grid=True, figsize=(10, 10))" | |
| 494 | + ] | |
| 495 | + }, | |
| 496 | + { | |
| 497 | + "cell_type": "code", | |
| 498 | + "execution_count": null, | |
| 499 | + "id": "958c14ef-f009-4771-82ac-352563801055", | |
| 500 | + "metadata": {}, | |
| 501 | + "outputs": [], | |
| 502 | + "source": [ | |
| 503 | + "dataset['총계'].sort_values().plot(kind='barh', grid=True, figsize=(10, 10))" | |
| 504 | + ] | |
| 505 | + }, | |
| 506 | + { | |
| 507 | + "cell_type": "code", | |
| 508 | + "execution_count": null, | |
| 509 | + "id": "0affbf15-4bde-47d0-85f3-52e10a58d09b", | |
| 510 | + "metadata": {}, | |
| 511 | + "outputs": [], | |
| 512 | + "source": [ | |
| 513 | + "dataset.head()" | |
| 514 | + ] | |
| 515 | + }, | |
| 516 | + { | |
| 517 | + "cell_type": "code", | |
| 518 | + "execution_count": null, | |
| 519 | + "id": "9e340e4a-8307-4c6e-b327-ace8b01b85c0", | |
| 520 | + "metadata": {}, | |
| 521 | + "outputs": [], | |
| 522 | + "source": [ | |
| 523 | + "dataset['CCTV비율'] = dataset['총계'] / dataset['인구수'] * 100" | |
| 524 | + ] | |
| 525 | + }, | |
| 526 | + { | |
| 527 | + "cell_type": "code", | |
| 528 | + "execution_count": null, | |
| 529 | + "id": "d5f16688-4554-44f9-a70f-a55c61f6e34b", | |
| 530 | + "metadata": {}, | |
| 531 | + "outputs": [], | |
| 532 | + "source": [ | |
| 533 | + "dataset.head()" | |
| 534 | + ] | |
| 535 | + } | |
| 536 | + ], | |
| 537 | + "metadata": { | |
| 538 | + "kernelspec": { | |
| 539 | + "display_name": "Python 3 (ipykernel)", | |
| 540 | + "language": "python", | |
| 541 | + "name": "python3" | |
| 542 | + }, | |
| 543 | + "language_info": { | |
| 544 | + "codemirror_mode": { | |
| 545 | + "name": "ipython", | |
| 546 | + "version": 3 | |
| 547 | + }, | |
| 548 | + "file_extension": ".py", | |
| 549 | + "mimetype": "text/x-python", | |
| 550 | + "name": "python", | |
| 551 | + "nbconvert_exporter": "python", | |
| 552 | + "pygments_lexer": "ipython3", | |
| 553 | + "version": "3.9.7" | |
| 554 | + } | |
| 555 | + }, | |
| 556 | + "nbformat": 4, | |
| 557 | + "nbformat_minor": 5 | |
| 558 | +} |
+++ 20220613/seoul_cctv_202112.csv
... | ... | @@ -0,0 +1,28 @@ |
| 1 | +"¡Ø 2021.12.31. ÇöȲÀ» ±âÁØÀ¸·Î °¢ ¿¬µµº° ¼³Ä¡µÈ CCTV ¼ö·®. ±³Ã¼(ÀúÈÁú±³Ã¼, ¼º´É°³¼±)´Â ÃÖÃʼ³Ä¡¿¬µµ°¡ ¾Æ´Ñ ±³Ã¼³âµµ ¼ö·®¿¡ Æ÷ÇÔ",,,,,,,,,,,, | |
| 2 | +±¸ºÐ,ÃÑ°è,2012³â ÀÌÀü,2012³â,2013³â,2014³â,2015³â,2016³â,2017³â,2018³â,2019³â,2020³â,2021³â | |
| 3 | +°è,"83,557","4,812","1,851","3,434","4,295","6,840","8,708","11,572","10,627","12,267","11,247","7,904" | |
| 4 | +Á¾·Î±¸,"1,715",815,,,195,150,0,261,85,9,200,0 | |
| 5 | +Áß±¸,"2,447",16,114,87,77,236,240,372,386,155,361,403 | |
| 6 | +¿ë»ê±¸,"2,611",34,71,234,125,221,298,351,125,307,617,228 | |
| 7 | +¼ºµ¿±¸,"3,829",163,144,208,107,325,255,967,415,490,472,283 | |
| 8 | +±¤Áø±¸,"3,211",35,57,100,187,98,52,675,465,712,175,655 | |
| 9 | +µ¿´ë¹®±¸,"2,628",4,0,14,16,115,804,814,201,218,223,219 | |
| 10 | +Á߶û±¸,"3,737",346,21,253,72,132,155,153,174,1049,934,448 | |
| 11 | +¼ººÏ±¸,"4,602",81,78,170,229,322,594,890,867,714,253,404 | |
| 12 | +°ºÏ±¸,"3,090",8,0,22,61,124,251,29,391,1078,656,470 | |
| 13 | +µµºÀ±¸,"1,930",128,22,2,145,172,123,129,222,210,184,593 | |
| 14 | +³ë¿ø±¸,"2,492",0,87,164,77,514,331,175,216,320,387,221 | |
| 15 | +ÀºÆò±¸,"4,131",14,3,44,332,329,555,403,635,1057,288,471 | |
| 16 | +¼´ë¹®±¸,"3,055",730,253,155,108,137,223,356,237,343,397,116 | |
| 17 | +¸¶Æ÷±¸,"2,496",23,19,40,112,185,478,365,373,494,299,108 | |
| 18 | +¾çõ±¸,"3,650",71,174,158,134,246,424,577,837,312,346,371 | |
| 19 | +°¼±¸,"2,744",54,122,104,202,199,168,506,259,457,356,317 | |
| 20 | +±¸·Î±¸,"4,608",852,216,349,187,268,326,540,488,434,415,533 | |
| 21 | +±Ýõ±¸,"2,411",0,0,174,80,361,133,196,539,367,513,48 | |
| 22 | +¿µµîÆ÷±¸,"4,056",572,136,238,123,209,248,311,658,65,1213,283 | |
| 23 | +µ¿ÀÛ±¸,"2,306",41,24,25,503,128,253,271,300,322,419,20 | |
| 24 | +°ü¾Ç±¸,"5,149",440,84,431,439,609,622,688,674,595,331,236 | |
| 25 | +¼Ãʱ¸,"4,082",8,73,79,70,562,504,1041,417,339,433,556 | |
| 26 | +°³²±¸,"6,871",69,67,66,580,830,1293,988,745,791,926,516 | |
| 27 | +¼ÛÆı¸,"2,897",40,59,90,85,214,176,241,541,1073,235,143 | |
| 28 | +°µ¿±¸,"2,809",268,27,227,49,154,202,273,377,356,614,262 |
+++ 20220613/seoul_population.xls
| Binary file is not shown |
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