PyBer_ride_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "40b57b6f",
   "metadata": {},
   "outputs": [
    {
     "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>Month</th>\n",
       "      <th>Avg. Fare ($USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>10.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>23.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>39.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>April</td>\n",
       "      <td>35.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>32.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>June</td>\n",
       "      <td>27.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>July</td>\n",
       "      <td>43.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>10.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sept</td>\n",
       "      <td>11.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>27.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>20.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>20.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Avg. Fare ($USD)\n",
       "0     Jan             10.02\n",
       "1     Feb             23.24\n",
       "2     Mar             39.20\n",
       "3   April             35.42\n",
       "4     May             32.34\n",
       "5    June             27.04\n",
       "6    July             43.82\n",
       "7     Aug             10.56\n",
       "8    Sept             11.85\n",
       "9     Oct             27.90\n",
       "10    Nov             20.71\n",
       "11    Dec             20.09"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "#dependancies\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statistics\n",
    "\n",
    "# load csv data\n",
    "pyber_ride_df = pd.read_csv(\"Resources/PyBer_ride_data.csv\")\n",
    "pyber_ride_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3cbc612d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyber_ride_df.plot(x=\"Month\", y=\"Avg. Fare ($USD)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "795324a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set x-axis and tick locations.\n",
    "x_axis = np.arange(len(pyber_ride_df))\n",
    "tick_locations = [value for value in x_axis]\n",
    "# Plot the data.\n",
    "pyber_ride_df.plot(x=\"Month\", y=\"Avg. Fare ($USD)\", kind=\"bar\")\n",
    "plt.xticks(tick_locations, pyber_ride_df[\"Month\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6928429e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stdev = statistics.stdev(pyber_ride_df[\"Avg. Fare ($USD)\"])\n",
    "pyber_ride_df.plot.bar(x=\"Month\", y=\"Avg. Fare ($USD)\", color='skyblue', yerr=stdev, capsize=3)\n",
    "plt.yticks(np.arange(0, 51, step=5.0))\n",
    "plt.xticks(rotation='horizontal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3f9bd83",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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