PyBer_ride_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c540360d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4fe15612",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c1d8da0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2294e384",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "821a147a",
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in csv\n",
    "\n",
    "pyber_ride_df = pd.read_csv(\"Resources/PyBer_ride_data.csv\")\n",
    "pyber_ride_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "06b8d1da",
   "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 ($USD)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "efe92d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set x-axis and tick locations.\n",
    "\n",
    "x_axis = np.arange(len(pyber_ride_df))\n",
    "tick_locations = [value for value in x_axis]\n",
    "\n",
    "# Plot the data.\n",
    "pyber_ride_df.plot(x=\"Month\", y=\"Avg. Fare ($USD)\")\n",
    "plt.xticks(tick_locations, pyber_ride_df[\"Month\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "65a777c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "791723f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.208367917035753"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the standard deviation of the values in the y-axis.\n",
    "\n",
    "stdev = statistics.stdev(pyber_ride_df[\"Avg. Fare ($USD)\"])\n",
    "stdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c3af96a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyber_ride_df.plot.bar(x=\"Month\", y=\"Avg. Fare ($USD)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f8b352a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "pyber_ride_df.plot(x=\"Month\", y=\"Avg. Fare ($USD)\", kind='bar', yerr=stdev, capsize=3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5436cee1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonData2] *",
   "language": "python",
   "name": "conda-env-PythonData2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}