154 lines
3.2 KiB
Plaintext
154 lines
3.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import absolute_import\n",
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"\n",
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"import json\n",
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"import os\n",
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"import tempfile\n",
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"import shutil\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from shapely.geometry import Point, Polygon\n",
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"import fiona\n",
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"\n",
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"import geopandas\n",
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"from geopandas import GeoDataFrame, read_file, GeoSeries\n",
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"#from geopandas.geodataframe import points_from_xy\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# NEW GeoDataFrame via read_file()\n",
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"\n",
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"tempdir = tempfile.mkdtemp()\n",
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"\n",
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"nybb_filename = geopandas.datasets.get_path('nybb')\n",
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"df_nybb = read_file(nybb_filename)\n",
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"df_nybb\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# NEW GeoDataFrame via list\n",
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"\n",
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"nybb_filename = geopandas.datasets.get_path('nybb')\n",
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"with fiona.open(nybb_filename) as f:\n",
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" features = list(f)\n",
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" crs = f.crs\n",
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"\n",
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"df = GeoDataFrame.from_features(features, crs=crs)\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# NEW GeoDataFrame via dict\n",
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"\n",
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"crs = {'init': 'epsg:4326'}\n",
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"N = 10\n",
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"\n",
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"df2_synthetic = GeoDataFrame([\n",
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" {'geometry': Point(x, y), 'value1': x + y, 'value2': x * y}\n",
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" for x, y in zip(range(N), range(N))], crs=crs)\n",
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"df2_synthetic\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df2_synthetic.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df2_synthetic.to_json()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"## Filter to subset\n",
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"## [ EXPR ] returns a Dataframe of Boolean\n",
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"## df[ EXPR ] returns the subset as a new Dataframe\n",
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"\n",
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"df_nybb_bs= df_nybb[ df_nybb['BoroName'].str.contains('B') ]\n",
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"df_nybb_bs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"## show all defined variables\n",
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"%whos"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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