| 136 | 136 |
"metadata": {},
|
| 137 | 137 |
"outputs": [],
|
| 138 | 138 |
"source": [
|
| 139 | |
"# pandas Python Data Analysis Library\n",
|
|
139 |
"# pandas 即 Python Data Analysis Library\n",
|
| 140 | 140 |
"import pandas as pd\n",
|
| 141 | 141 |
"import os\n",
|
| 142 | 142 |
"\n",
|
|
| 473 | 473 |
},
|
| 474 | 474 |
"source": [
|
| 475 | 475 |
"### 格式转换\n",
|
| 476 | |
"将6行10列数据转换为1行60列,并保存在列表`yes_no_data`中\n",
|
|
476 |
"将6行10列表格数据转换为1行60列数据,保存在列表`yes_no_data`中\n",
|
| 477 | 477 |
"\n",
|
| 478 | 478 |
"同时将tag(y值)保存在列表`yes_no`中,县(市)改区 = '`T`', 非县(市)改区 = '`F`' \n"
|
| 479 | 479 |
]
|
|
| 1262 | 1262 |
},
|
| 1263 | 1263 |
{
|
| 1264 | 1264 |
"cell_type": "code",
|
| 1265 | |
"execution_count": null,
|
|
1265 |
"execution_count": 1,
|
| 1266 | 1266 |
"metadata": {},
|
| 1267 | 1267 |
"outputs": [],
|
| 1268 | 1268 |
"source": [
|
| 1269 | 1269 |
"import pandas as pd\n",
|
|
1270 |
"import joblib\n",
|
| 1270 | 1271 |
"def handle(conf):\n",
|
| 1271 | 1272 |
" \"\"\"\n",
|
| 1272 | 1273 |
" 该方法是部署之后,其他人调用你的服务时候的处理方法。\n",
|
|
| 1308 | 1309 |
},
|
| 1309 | 1310 |
{
|
| 1310 | 1311 |
"cell_type": "code",
|
| 1311 | |
"execution_count": 73,
|
| 1312 | |
"metadata": {},
|
| 1313 | |
"outputs": [
|
| 1314 | |
{
|
| 1315 | |
"name": "stdout",
|
| 1316 | |
"output_type": "stream",
|
| 1317 | |
"text": [
|
| 1318 | |
" a b c\n",
|
| 1319 | |
"0 1 2 3\n",
|
| 1320 | |
" b c c\n",
|
| 1321 | |
"0 2 3 3\n"
|
| 1322 | |
]
|
| 1323 | |
}
|
| 1324 | |
],
|
| 1325 | |
"source": [
|
| 1326 | |
"df = pd.DataFrame({'a':1,'b':2,'c':3}, index=[0])\n",
|
| 1327 | |
"print(df)\n",
|
| 1328 | |
"df = df[['b','c','c']]\n",
|
| 1329 | |
"print(df)"
|
| 1330 | |
]
|
| 1331 | |
},
|
| 1332 | |
{
|
| 1333 | |
"cell_type": "code",
|
| 1334 | 1312 |
"execution_count": null,
|
| 1335 | 1313 |
"metadata": {},
|
| 1336 | 1314 |
"outputs": [],
|