analysis-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas_gbq\n",
    "\n",
    "from matplotlib import pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from project import project_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Seaborn Theme Settings ##\n",
    "\n",
    "sns.set_style('dark')\n",
    "sns.set_palette('mako')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_name\n",
    "\n",
    "def df_query(sql):\n",
    "    df = pandas_gbq.read_gbq(sql, project_id = project_name)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "SELECT\n",
    "  CASE WHEN trip_duration_min BETWEEN 0 AND 30 THEN '0 - 30'\n",
    "    WHEN trip_duration_min BETWEEN 31 AND 60 THEN '30 - 60'\n",
    "    WHEN trip_duration_min BETWEEN 61 AND 90 THEN '60 - 90'\n",
    "    ELSE '> 90' END AS trip_bin,\n",
    "  COUNT(*) AS trips\n",
    "FROM chatt_bike_share.bike_trips\n",
    "GROUP BY 1\n",
    "ORDER BY 1;\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading: 100%|\u001b[32m██████████\u001b[0m|\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_bin</th>\n",
       "      <th>trips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 - 30</td>\n",
       "      <td>346115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30 - 60</td>\n",
       "      <td>203634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60 - 90</td>\n",
       "      <td>37455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&gt; 90</td>\n",
       "      <td>36265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trip_bin   trips\n",
       "0   0 - 30  346115\n",
       "1  30 - 60  203634\n",
       "2  60 - 90   37455\n",
       "3     > 90   36265"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trip_duration_bins = df_query(query)\n",
    "\n",
    "trip_duration_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(\n",
    "    data = trip_duration_bins,\n",
    "    x = 'trip_bin',\n",
    "    y = 'trips'\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}