|
81 | 81 | },
|
82 | 82 | {
|
83 | 83 | "cell_type": "code",
|
84 |
| - "execution_count": 1, |
| 84 | + "execution_count": 3, |
85 | 85 | "metadata": {},
|
86 | 86 | "outputs": [],
|
87 | 87 | "source": [
|
88 |
| - "import re\n", |
89 |
| - "import os\n", |
90 | 88 | "import pandas as pd\n",
|
91 | 89 | "import zipfile,unicodedata\n",
|
92 | 90 | "from itertools import repeat\n",
|
93 | 91 | "from pathlib import Path\n",
|
94 |
| - "from datetime import datetime\n", |
95 |
| - "\n", |
96 | 92 | "from arcgis.gis import GIS\n",
|
97 | 93 | "from arcgis.learn import prepare_data\n",
|
98 | 94 | "from arcgis.learn.text import EntityRecognizer\n",
|
99 |
| - "from arcgis.geocoding import batch_geocode" |
| 95 | + "from arcgis.geocoding import batch_geocode\n", |
| 96 | + "import re\n", |
| 97 | + "import os\n", |
| 98 | + "import datetime" |
100 | 99 | ]
|
101 | 100 | },
|
102 | 101 | {
|
|
1572 | 1571 | "outputs": [],
|
1573 | 1572 | "source": [
|
1574 | 1573 | "# This will take few minutes to run\n",
|
1575 |
| - "madison_crime_layer = publish_to_feature(results, gis, layer_title='Madison_Crime' + str(datetime.now().microsecond), \n", |
| 1574 | + "madison_crime_layer = publish_to_feature(results, gis, layer_title='Madison_Crime' + str(datetime.datetime.now().microsecond), \n", |
1576 | 1575 | " tags='nlp,madison,crime', city='Madison', \n",
|
1577 | 1576 | " region='WI', address_col='Address')"
|
1578 | 1577 | ]
|
|
1695 | 1694 | " {\"xmin\":-10091700.007046243,\"ymin\":5225939.095608932,\n",
|
1696 | 1695 | " \"xmax\":-9731528.729766665,\"ymax\":5422840.88047145,\n",
|
1697 | 1696 | " \"spatialReference\":{\"wkid\":102100,\"latestWkid\":3857}}},\n",
|
1698 |
| - " output_name=\"crime_hotspots_madison\" + str(datetime.now().microsecond))" |
| 1697 | + " output_name=\"crime_hotspots_madison\" + str(datetime.datetime.now().microsecond))" |
1699 | 1698 | ]
|
1700 | 1699 | },
|
1701 | 1700 | {
|
|
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