Converted analysis format to jupyter notebook.

main/analysis.ipynb

@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "from great_schools import get_nearby_schools\n",
+    "from secret import get_key"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shaun and Daniela's Boston Public School Analysis\n",
+    "#### 2021.04.10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fetch the API key from the local filesystem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the API key\n",
+    "api_key_file = '../keys/api.key'\n",
+    "api_key = get_key(api_key_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the `nearby_schools` API endpoint to grab raw data of all schools within the maximum radius"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Some columns will dropped immediately as pre-processing.\n",
+    "drops = [\n",
+    "    'nces-id',\n",
+    "    'school-summary',\n",
+    "    'street',\n",
+    "    'fipscounty',\n",
+    "    'phone',\n",
+    "    'fax',\n",
+    "    'web-site',\n",
+    "    'overview-url',\n",
+    "    'rating-description',\n",
+    "]\n",
+    "\n",
+    "# Grab data for Boston.\n",
+    "refresh = False\n",
+    "boston_nearby_schools_file = '../data/nearby_schools/boston.csv'\n",
+    "if refresh:\n",
+    "    boston_schools = get_nearby_schools(api_key,\"42.3\",\"-71.2\",\"50\")\n",
+    "    boston_df = pd.DataFrame.from_dict(boston_schools)\n",
+    "    boston_df.drop(columns=drops,inplace=True)\n",
+    "    boston_df.to_csv(boston_nearby_schools_file)\n",
+    "else:\n",
+    "    boston_df = pd.read_csv(boston_nearby_schools_file)\n",
+    "\n",
+    "# Grab data for Buffalo.\n",
+    "refresh = False\n",
+    "buffalo_nearby_schools_file = '../data/nearby_schools/buffalo.csv'\n",
+    "if refresh:\n",
+    "    buffalo_schools = get_nearby_schools(api_key,\"42.9625\",\"-78.7425\",\"50\")\n",
+    "    buffalo_df = pd.DataFrame.from_dict(buffalo_schools)\n",
+    "    buffalo_df.drop(columns=drops,inplace=True)\n",
+    "    buffalo_df.to_csv(buffalo_nearby_schools_file)\n",
+    "else:\n",
+    "    buffalo_df = pd.read_csv(buffalo_nearby_schools_file)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "4fc861b332db140b7b363b167627eee6a3238262e7c99e0237067fec0875fee7"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.10 ('venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

main/run.py

@@ -1,27 +0,0 @@
-#!/usr/bin/env python3
-
-from secret import get_key
-from great_schools import get_nearby_schools
-
-import numpy as np
-import pandas as pd
-
-# Get secret.
-api_key_file = '../keys/api.key'
-api_key = get_key(api_key_file)
-
-# Grab data for Boston.
-refresh = False
-if refresh:
-    boston_nearby_schools_file = '../data/nearby_schools/boston.csv'
-    boston_schools = get_nearby_schools(api_key,"42.3","-71.2","50")
-    boston_df = pd.DataFrame.from_dict(boston_schools)
-    boston_df.to_csv(boston_nearby_schools_file)
-
-# Grab data for Buffalo.
-refresh = True
-if refresh:
-    buffalo_nearby_schools_file = '../data/nearby_schools/buffalo.csv'
-    buffalo_schools = get_nearby_schools(api_key,"42.9625","-78.7425","50")
-    buffalo_df = pd.DataFrame.from_dict(buffalo_schools)
-    buffalo_df.to_csv(buffalo_nearby_schools_file)