PyBer_ride_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%` not found.\n"
     ]
    }
   ],
   "source": [
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load into dataframe\n",
    "pyber_ride_df = pd.read_csv(\"Resources/PyBer_ride_data.csv\") \n",
    "pyber_ride_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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(x=\"Month\", y=\"Avg. Fare ($USD)\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set x-axis and tick locations.\n",
    "x_axis = np.arange(len(pyber_ride_df))\n",
    "tick_locations = [value for value in x_axis]\n",
    "# Plot the data.\n",
    "pyber_ride_df.plot(x=\"Month\", y=\"Avg. Fare ($USD)\")\n",
    "plt.xticks(tick_locations, pyber_ride_df[\"Month\"])\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAETCAYAAAAveV3LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAdM0lEQVR4nO3dfZxWdZ3/8debG4VERWBgVVLURdREgR0yN03IO1o1FTLQjXgsBpWauu66y1q/Qv1V1nqz/sraMFAkQ03LMNPCG0wsRUREVlAL0Vh9xI3mPcrN5/fHOTNcDgMzzJxzMd/h/Xw8eMx1neua8/nODNf7fM/3fM85igjMzCw9HbZ3A8zMrGUc4GZmiXKAm5klygFuZpYoB7iZWaI6VbNYr169ol+/ftUsaWaWvCeeeGJ1RNQ0XF7VAO/Xrx/z58+vZkkzs+RJerGx5R5CMTNLlAPczCxRDnAzs0RVdQzczFpn3bp1rFixgrVr127vplgJunTpQt++fencuXOz3u8AN0vIihUr2HXXXenXrx+StndzrEARwZo1a1ixYgX77bdfs77HQyhmCVm7di09e/Z0eLdDkujZs+c27V05wM0S4/Buv7b1b+sANzNLlMfAzRJ2xZOrC13fpMG9mvW+X/ziF4wcOZIlS5Zw0EEHFdqG5cuXc/DBBzNgwID6ZfPmzWOnnXYqrMa7777LiBEjeOCBB+jYsSMAc+bMAWDYsGH17xs2bBhXXnkltbW19W07+eSTWbx4Me+88w4TJkxg0aJFRATdu3fn3nvvpVu3bnTs2JGBAweybt06OnXqxLhx47jwwgvp0KEDTz/9NFdddRU33nhjq38OB7hZE1oaks0NwxTNnDmTo446iltuuYXJkycXvv4DDjiAhQsXbtP3bNiwoT6MmzJt2jRGjhxZ//6vf/3rzJgxA0n079+fu+++m06dth6P1157LX369OHpp58G4Nlnn62fPdK1a9f69q9cuZKzzjqL119/nUsvvZSBAweyYsUKXnrpJfbZZ59t+hkb8hCKmW2Tt956i0ceeYSpU6dyyy23AHDPPffw2c9+tv49c+bM4ZRTTgFg6tSpHHjggQwbNowJEyZw3nnnbXPN5cuXc/TRRzNkyBCGDBnC73//+/o6w4cP56yzzmLgwIFs2LCBiy++mKFDh3LYYYfxox/9qNH13XzzzZx66qkALFmyhNtvv51rr72WadOmcfXVV9OhQ9PR+Morr7D33nvXPx8wYAA777zzZu/r3bs3U6ZM4fvf/z51d0A75ZRT6n93reEAN7NtcueddzJixAgOPPBAevTowYIFCzj++ON59NFHefvttwG49dZbGT16NC+//DKXX345jz76KLNnz2bp0qXNqvGnP/2JQYMGMWjQIM4991x69+7N7NmzWbBgAbfeeivnn39+/XvnzZvHN7/5TZ555hmmTp3K7rvvzuOPP87jjz/O9ddfzwsvvPCBdb///vssW7aMugvrderUiTfffJN33nkHgI985CPNCvDx48fzne98hyOPPJKvfe1rPP/881t87/7778/GjRtZuXIlALW1tTz88MPN+l1sjQPczLbJzJkzGTNmDABjxoxh5syZdOrUiREjRnDXXXexfv167r77bk499VTmzZvHMcccQ48ePejcuTNnnHFGs2rUDaEsXLiQ6667jnXr1jFhwgQGDhzIGWecwTPPPFP/3o9+9KP186Z/+9vfctNNNzFo0CCOOOII1qxZs1mwrl69mu7du9c/79+/P5MnT+ayyy5j4sSJXH755WzcuBFofFZI3bJBgwaxbNkyLr74Yl599VWGDh3KkiVLtvgzVd5/uHfv3rz88svN+l1sjcfAzazZ1qxZwwMPPMDixYuRxIYNG5DEd7/7XUaPHs11111Hjx49GDp0KLvuuitF3TT9mmuuoU+fPjz11FNs3LiRLl261L+2yy671D+OCL73ve9x4oknbnFdXbt23Wyu9dlnn80BBxzA6tWrmTJlCjfffDNjx46lZ8+evPbaa/Xve/XVV+nVa9OxjW7dujFy5EhGjhxJhw4d+PWvf83BBx+8Wc1ly5bRsWNHevfuDWTz+bt27brtv4gG3AM3s2a7/fbb+fznP8+LL77I8uXL+fOf/8x+++3H3LlzGTZsGAsWLOD6669n9OjRQNY7fuihh3jttddYv349d9xxR4vqvv766+y555506NCBGTNmsGHDhkbfd+KJJ/LDH/6QdevWAfDcc8/VD+vU2WOPPdiwYUN9iK9cuZLVq7MD1b169WL//ffnzTffBLJZKD/5yU/qN0TTp09n+PDhADzyyCP14f7+++/zzDPPsO+++27WplWrVvGlL32J8847r773/txzz3HooYe26HdRyT1ws4RVe6bLzJkzmTRp0geWjRo1ip/+9KccffTRnHzyydx4441Mnz4dgL333ptLLrmEI444gr322otDDjmE3XffHYBZs2Yxf/58LrvssibrnnPOOYwaNYqf/exnDB8+/AO97kpf+MIXWL58OUOGDCEiqKmp4c4779zsfSeccAJz587luOOO44033mDixImsWrWKtWvXMmDAAK688koAJk6cyNKlSzn88MORRG1tLd/+9reBbJz+y1/+MhHBxo0bOemkkxg1ahSQTVMcNGhQ/TTCsWPHctFFF9XXf/DBBznppJOa/LmboqJ2cZqjtrY2fEMHS01bmka4ZMmSRnfR27K33nqLbt26sX79ek4//XTGjx/P6aefvl3b9OSTT3L11VczY8aM+mWNzQMvw3vvvccxxxzD3LlzG52q2NjfWNITEVHb8L3ugZtZqSZPnsx9993H2rVrOeGEEzjttNO2d5MYPHgww4cP/8Dc8Wrd7vGll17iiiuuaHKeeXM4wM2sVHXDEW3N+PHjP/C8WgHev39/+vfvX8i6fBDTLDHVHPa06trWv60D3CwhXbp0Yc2aNQ7xdqjueuCVUySb4iEUs4T07duXFStWsGrVqu3dFCtB3R15mssBbpaQzp07N/tuLdb+eQjFzCxRzQ5wSR0lPSnpV/nzHpJmS3o+/7pHec00M7OGtqUHfgFQeaWWScD9EdEfuD9/bmZmVdKsAJfUFzgJ+HHF4lOB6fnj6cD2n51vZrYDaW4P/L+AfwM2VizrExGvAORfezf2jZImSpovab6PnJuZFafJAJd0MrAyIp5oSYGImBIRtRFRW1NT05JVmJlZI5ozjfDjwKcl/QPQBdhN0k+Av0jaMyJekbQnsLLMhpqZ2Qc12QOPiP+IiL4R0Q8YAzwQEZ8DZgHj8reNA35ZWivNzGwzrTmR5wrgNklnAy8BzbtX0g6qLV2S1Mzah20K8IiYA8zJH68Bji2+SWZm1hw+E9PMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS1ZobOlgb5htImLV/7oGbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mlihfTtYK0ZLL1/rStWat4x64mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJarJAJfURdI8SU9J+h9Jl+bLe0iaLen5/Ose5TfXzMzqNKcH/h7wyYg4HBgEjJD0MWAScH9E9Afuz5+bmVmVNBngkXkrf9o5/xfAqcD0fPl04LRSWmhmZo1q1hi4pI6SFgIrgdkR8RjQJyJeAci/9t7C906UNF/S/FWrVhXVbjOzHV6zAjwiNkTEIKAv8FFJhza3QERMiYjaiKitqalpaTvNzKyBbZqFEhF/BeYAI4C/SNoTIP+6svDWmZnZFjVnFkqNpO75467AccBSYBYwLn/bOOCXZTXSzMw215zLye4JTJfUkSzwb4uIX0n6A3CbpLOBl4AzSmynmZk10GSAR8QiYHAjy9cAx5bRKDMza5rPxDQzS5QD3MwsUQ5wM7NEOcDNzBLlmxqbWVW15AbY4JtgN8Y9cDOzRDnAzcwS5QA3M0uUx8AtOR5DNcu4B25mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mlqgd9o48vquLmaXOPXAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLVJPXQpH0YeAm4G+AjcCUiLhWUg/gVqAfsBz4bES8Vl5Tzcy2XXu+7lFzeuDrgX+JiIOBjwHnSjoEmATcHxH9gfvz52ZmViVNBnhEvBIRC/LHbwJLgL2BU4Hp+dumA6eV1UgzM9vcNl1OVlI/YDDwGNAnIl6BLOQl9d7C90wEJgLss88+rWmrmVmbV80hm2YfxJTUDbgDuDAi3mju90XElIiojYjampqabW6gmZk1rlkBLqkzWXjfHBE/zxf/RdKe+et7AivLaaKZmTWmyQCXJGAqsCQirq54aRYwLn88Dvhl8c0zM7Mtac4Y+MeBscDTkhbmyy4BrgBuk3Q28BJwRjlNNDOzxjQZ4BExF9AWXj622OaYmVlz+UxMM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEddreDTCz7euKJ1e36PsmDe5VcEtsW7kHbmaWKAe4mVmiHOBmZolygJuZJarJAJc0TdJKSYsrlvWQNFvS8/nXPcptppmZNdScWSg3At8HbqpYNgm4PyKukDQpf/7vxTfPbMfjWSHWXE32wCPid8CrDRafCkzPH08HTiu4XWZm1oSWzgPvExGvAETEK5J6t7Yh7nWYmW2b0g9iSpooab6k+atWrSq7nJnZDqOlAf4XSXsC5F9XbumNETElImojorampqaF5czMrKGWBvgsYFz+eBzwy2KaY2ZmzdWcaYQzgT8AAyStkHQ2cAVwvKTngePz52ZmVkVNHsSMiDO38NKxBbfFzMy2gc/ENDNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0S1KsAljZD0rKQ/SppUVKPMzKxpLQ5wSR2B64BPAYcAZ0o6pKiGmZnZ1rWmB/5R4I8RsSwi3gduAU4tpllmZtYURUTLvlH6DDAiIr6QPx8LHBER5zV430RgYv50APBsC8r1Ala3qKEt43rp1mvPP5vr7bj19o2ImoYLO7WiIWpk2WZbg4iYAkxpRR0kzY+I2tasw/V2jHrt+WdzPddrqDVDKCuAD1c87wu83LrmmJlZc7UmwB8H+kvaT9JOwBhgVjHNMjOzprR4CCUi1ks6D/gN0BGYFhH/U1jLPqhVQzCut0PVa88/m+u53ge0+CCmmZltXz4T08wsUQ5wM7NEOcAteZK6ShqwvdthVm1tLsAl7SzpLEmXSPp63b8q1t9N0q7VqtceSTq0irVOARYC9+bPB0kqbTaUpCslfaSs9bcFkj7enGWpyi8D0i60uQAHfkl2Sv564O2Kf6WSVCvpaWARsFjSU5L+rqRaHSXdV8a6G9TpsbV/JZb+b0nzJJ0jqXuJdQAmk13W4a8AEbEQ6FdivaXAFEmPSfqSpN1LrIWkuyTNavBvhqQLJHUpqez3mrmsEJLub86yAr0gaYqkYyU1dkJioSR9q/JzIGkPSf+3iHW35kzMsvSNiBHboe404JyIeBhA0lHADcBhRReKiA2S3pG0e0S8XvT6KzxBdnbsls6a3b+MohFxlKT+wHhgvqR5wA0RMbuEcusj4vUqfA4BiIgfAz/Oh2z+CVgk6RHg+oh4sISSy4AaYGb+fDTwF+BA4HpgbFGFJB0J/D1QI+miipd2I5sqXKh8A/QhoJekPdj0/3Q3YK+i61UYAJwCnAtMlfQr4JaImFtSvU9FxCV1TyLiNUn/AHyttStuiwH+e0kDI+LpKtd9sy68ASJirqQ3S6y3Fnha0mwq9jAi4vyiCkTEfkWtqwW1n5f0NWA+8P+AwXlv55KI+HmBpRZLOgvomG80zgd+X+D6N5Pvgh+U/1sNPAVcJOmLETGm4HKDI+ITFc/vkvS7iPiEpKLPu9gJ6EaWC5XDiG8Anym4FsAXgQvJwvoJNgX4G2RXOi1FRLwL3Abclm84rgUeooSNVK6jpJ0j4j3IjtkAOxex4jY3D1zSM8DfAi8A75H9USMiCu8J5/WG5A/HkvUGZpL1TkcDr0XEV0uqO66x5RExvcAaB0XE0oqfsWGtBUXValD3MLLe6UnAbGBqRCyQtBfwh4jYt8BaHwK+CpxA9n/lN8DlEbG2qBoN6l0NfBq4n+znmlfx2rMRUejBVElLgBMj4qX8+T7AvRFxiKQnI2JwkfXyGvtGxIuSdiP77JXZkUHSVyKitCGaLdQ8huwz/imys8pvjYg7Sqr1b2T/Z24gy5bxwKyI+G6r190GA7zRD3dEvFhSva3t9kZEfLKMutUgaUpETNzCz1jazybpd2S797fnvZ3K18ZGxIwy6laDpPFku9vvNPJa4UNi+a72fwN/IttA7QecA8wBJkTEfxVZL69ZSxY2db3w14HxEfFE0bXyeucCN0fEX/PnewBnRsQPSqr3AtmB79vIgrQax9hGAMeR/Q1/GxG/KWS9bS3A60jqDdQfpKnrgbQX+e7+t8luhlH5cxY6Li2pA3BkRDxS5HrbCkkHAv9KduCyfkiw6I3TlvZiKuqVsjeT196ZbLhGwNKy9i4q6i0Czm1wPOgHJe4FL4yIQQ2WlbJ3ka97t4h4o4x1b6XmvkD/iLgv32vsWMSeTZsbA5f0aeAqsnGxlcC+wBKg1KlbkvoA3wL2iohPKbu70JERMbWkkjcA3wCuAYaTDTkUfiQuIjZKuhI4suh1b0m1Nk65n5H1UH8MbChh/XWu2sprAZS1N/P5BosOk0RE3FRGvVy1jwd1kKTIe5P5MYadSqy3m6TpwMfJ/nZzgQsiYkUZxSRNILsnQg/gAGBvsv+zx7Z63W2tBy7pKbIPw30RMVjScLLdqYlNfGtr695DFqpfjYjDJXUCnoyIgSXVeyIi/k7S03U1JD0cEUeXUOtSsumRP48q/MElzWXTxukU8o1TRHyjhFpPREQp0z3bAkmVY8NdyD70CyKijIOKdTWvoZHjQcAdUPzeRt7B2Jcs1AL4MvBSRPxLkXUq6s0GfgrUDeV9DvjHiDi+pHoLyaa6Pla3V1H5uW+NNtcDB9ZFxBpJHSR1iIgHJX2nCnV7RcRtkv4D6q+2WGaPbm0+vPG8sqs6/i/Qu6RaFwG7ABskvcumA8O7lVSva0Tcn/eqXgQmS3qYLNSLdpekc4BfkB30BiAiXi2hVmM94rp6pfSII+IrDervzqbgKUvdcEbDv9ffU87exv8BJgBfIh8jBsra8wWoiYgbKp7fKOnCEuu9FxHv1011zTuHhXSk2mKA/1VSN+Bh4GZJK8lO6inb25J6kv9iJX2M7OBNWS4k6+WcD1xO9qFodGZKa0VEtc8srebGqe53dnHFstLmuANDKx7X94iBMoc0Kr1DNge8NBExvMz118mD7Ftke2h/JgvvD5PNQOtAeUNiqyV9jk1z688E1pRUC+AhSZcAXSUdT3YQ+q4iVtwWh1A+RDZHWmS7NruRHaEupUdVUXcI2dlmhwKLyU6e+ExELCqzbrVIGgkcRRZuD0fEnSXWGkp23KI72cZpd+C7EfFoWTW3l7oecUR8uqT138Wm3lpH4GDgtoiYVEa9vGZVjgflQzW7Av9cd0BP2WUsrgLejYgLiqxXUXcf4Ptkx4WC7LyBC0qc6dYBOJsPTnX9cRHDmW0mwPODJA0bU3dQby3ZNKqvRkShp9hK2qdijm0nsrO0BDwbEeuKrJXX2Op1OsoIAkk/IJtbX3k2358i4tyia1VbtYc0GqnfGVgUEQeXtP5jKp6uJ/u/eWaZf7tqHQ+S9DxwYMMgyw9iLo2I/kXW254k1QBExKoi19tmhlC2tpuf/0EPBW7OvxbpTqBuititETGq4PU3dCTZ7uJM4DFKmHnSiGOAQyuO8k8HCj/TdXtsnKjykEYjPeJDyOYTlyIiHpI0CDgL+CzZ8EIpJ5xUqNbxoGisFxrZpSYK71lq6xfFi4i4vOB6IjuOcB7Z51z57/F7EXFZETXaTIBvTURsAJ5qcES+KJUBWta4aaW/AY4nG3c7C7gbmBnl3Y4O4FlgH6BuF/HDZLNSilb1jdN2OMh3JZsCfD3wYkT8b9FF8vntY9g0Pnsr2R5zNcanq3U86BlJn2+4t5SPTy8toV5jJ+zsQja80ZNsuK9IF5JNVRwaES8ASNof+KGkf46Ia1pboM0MoWwvkhZExJCGj6tUe2eyD+h/ApdFSacTS3qIrKdad9r3UOAPZAfECusZ53tKdRunw6jOxqlhG0oZ0qgY4mu4UQqy2S+FDvFJ2kh2IP/siPhjvmxZSXPpG9auyvEgSXsDPwfeZdOF14YCXYHTy9gwVtTeFbiALLxvA66KiJUF13gSOD4iVjdYXkN2NmarT1RKogdessMlvUH2weyaP4YSp9rlwX0SWdD1I7vYU5EXeGqoctdRZAczzyQ7Gl6YfE/pXuDeio3THEllbpyqMqSxHYb4RpH1wB+UdC9wCyXv0eQHn/8c2XVrjiG72NQosml9hZ/kkgf0EZI+SXainoB7ij7OVUnZZZQvAv4RmA4MiYjXSirXuWF4QzYOnnc0Wm2H74FXWz7+fChwD9k1NRZXqW7DcdSflxGqjWycZgHTyupN5UFT+pBGM9vyxYj4UcHr3AU4jez3+Umy0PlFRPy2yDp5rQXAcRHxqqRPkG00vkI2L/zgMk8eqgZJ/wmMJLsz/HUR8VbJ9ba4R1/U3r4DvMryXeO6sbjKX37hPf4tjKP+axR4NcAG9aq2car2kEZbkPcezwBGRwkXIpP0VEQcnj++DlgVEZPz55tdryQ1+WfvPbINfamfvbzeBhofdxfQJSJa3Qt3gLdj1R5HrebGqYl21A9pRETVbu+WOkmLgUH5rJOlwMSI+F3da/5dtj0eA2/fqjqOGhFt4hZ9Jc9aas9mkp01uJrswGLd1Qj/lnLPSrYWcg98B1DNcVRLWz5lcE+yWRJv58sOBLpFiZfMtZZxgO9gyh5HNbPqcYCbmSWqTYxZmpnZtnOAm5klygFu7YqkkDSj4nknSask/aqF6+uu7IYRdc+HtXRdZkVzgFt78zZwqKSu+fPjyW4o0VLdKfiSA2ZFcYBbe3QP2en8kE2drLsOOpJ6SLpT0iJJj0o6LF8+WdI0SXMkLZN0fv4tVwAHSFqYn4oN0E3S7ZKWSro5v2yoWdU5wK09ugUYI6kL2VURH6t47VKymxMcBlzCB68bfhBwItkNaL+RX3BoEtnNLwZFRN1t2waTXSr0ELJLEH+8zB/GbEsc4Nbu5Jc97UfW+/51g5ePIr9eeEQ8APTMryEOcHdEvJdfQW4l0GcLJeZFxIqI2AgszGuZVZ1Ppbf2ahbZzReGkV2sv05jwx11J0O8V7FsA1v+fDT3fWalcg/c2qtpZDfJaHjruN+RXQsaScOA1RHxBlv2JtmNd83aHPccrF2KiBXAtY28NBm4QdIisjsSjWtiPWskPZJfqe8esrsMmbUJPpXezCxRHkIxM0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRP1/qMjDIJmOLIUAAAAASUVORK5CYII=\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\", color=\"skyblue\")  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}