119 lines
3.0 KiB
Plaintext
119 lines
3.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"from great_schools import get_nearby_schools\n",
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"from secret import get_key"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Shaun and Daniela's Boston Public School Analysis\n",
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"#### 2021.04.10"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Fetch the API key from the local filesystem."
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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": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"# get the API key\n",
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"api_key_file = '../keys/api.key'\n",
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"api_key = get_key(api_key_file)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Use the `nearby_schools` API endpoint to grab raw data of all schools within the maximum radius"
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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": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Some columns will dropped immediately as pre-processing.\n",
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"drops = [\n",
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" 'nces-id',\n",
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" 'school-summary',\n",
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" 'street',\n",
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" 'fipscounty',\n",
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" 'phone',\n",
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" 'fax',\n",
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" 'web-site',\n",
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" 'overview-url',\n",
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" 'rating-description',\n",
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"]\n",
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"\n",
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"# Grab data for Boston.\n",
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"refresh = False\n",
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"boston_nearby_schools_file = '../data/nearby_schools/boston.csv'\n",
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"if refresh:\n",
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" boston_schools = get_nearby_schools(api_key,\"42.3\",\"-71.2\",\"50\")\n",
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" boston_df = pd.DataFrame.from_dict(boston_schools)\n",
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" boston_df.drop(columns=drops,inplace=True)\n",
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" boston_df.to_csv(boston_nearby_schools_file)\n",
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"else:\n",
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" boston_df = pd.read_csv(boston_nearby_schools_file)\n",
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"\n",
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"# Grab data for Buffalo.\n",
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"refresh = False\n",
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"buffalo_nearby_schools_file = '../data/nearby_schools/buffalo.csv'\n",
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"if refresh:\n",
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" buffalo_schools = get_nearby_schools(api_key,\"42.9625\",\"-78.7425\",\"50\")\n",
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" buffalo_df = pd.DataFrame.from_dict(buffalo_schools)\n",
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" buffalo_df.drop(columns=drops,inplace=True)\n",
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" buffalo_df.to_csv(buffalo_nearby_schools_file)\n",
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"else:\n",
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" buffalo_df = pd.read_csv(buffalo_nearby_schools_file)"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "4fc861b332db140b7b363b167627eee6a3238262e7c99e0237067fec0875fee7"
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},
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"kernelspec": {
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"display_name": "Python 3.8.10 ('venv': venv)",
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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": "3.8.10"
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},
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"orig_nbformat": 4
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"nbformat": 4,
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}
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