Matplotlib_Part6_Histograms.ipynb

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      "name": "Matplotlib_Part6_Histograms.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/bgswaroop/python-matplotlib/blob/master/Matplotlib_Part6_Histograms.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "cell_type": "markdown",
      "metadata": {
        "id": "pR0JFPIOUKbU",
        "colab_type": "text"
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      "source": [
        "##Part 6 : Histograms\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Histogram allows us to group data in to bins instead of plotting them directly.\n",
        "\n",
        "This notebook is a continuation from Parts 1-5. \n",
        "\n",
        "*    Part1 - Introduction with line plots\n",
        "*    Part2 - Bar plots\n",
        "*    Part3 - Pie plots\n",
        "*    Part4 - Stacked plots\n",
        "*    Part5 - Fill betweens\n",
        "\n",
        "Lets write some quick starter code to understand histograms !\n",
        "\n",
        "\n",
        "Let us begin by downloading a data file.\n",
        "The data file contains developers age of all the different respondents for our survey.\n",
        "\n",
        "\n",
        "The following code distributes our data into 5 different uniformly spaced bins.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKbB_a_K8Z1u",
        "colab_type": "code",
        "outputId": "fd32d3b5-ed92-400f-b83f-add13b70ef69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "'https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv' \\\n",
        "-O '/tmp/part6_data.csv'\n",
        "\n",
        "ages = [18, 19, 21, 25, 26, 26, 30, 32, 38, 45, 55]\n",
        "plt.hist(ages, bins = 5)\n",
        "\n",
        "plt.title('Ages of Respondents')\n",
        "plt.xlabel('Ages')\n",
        "plt.ylabel('Total Respondents')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-03 16:40:45--  https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 702942 (686K) [text/plain]\n",
            "Saving to: ‘/tmp/part6_data.csv’\n",
            "\n",
            "\r/tmp/part6_data.csv   0%[                    ]       0  --.-KB/s               \r/tmp/part6_data.csv 100%[===================>] 686.47K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2019-10-03 16:40:45 (18.4 MB/s) - ‘/tmp/part6_data.csv’ saved [702942/702942]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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5qtrk5s2b2Lx5s9S0gwcP4uzZs7Czs5MMw65Xrx6GDRuGwsJCLFiwQKrtHz58iFWrVkEg\nEGD06NHVOXQZ5YNbFi1aJHUsIpEIy5cvl1leS0sLfn5+ePz4MaZPn47CwkKZZXJycmR6z9XVqFEj\nAMD9+/dl5r148aLCgBGLxZKepDyfR6oeXgNSYykpKbhx4wbs7e0rvCBcbsSIEVi0aBGio6OxePFi\n6Ovr49tvv0VsbCwOHDiAmzdvolevXnj27Bn2798PFxcXxMbGylz4XrZsGTIyMrB06VLs2bMHLi4u\nMDExQVZWFtLT03Hu3DksWbJEcp1kzJgx0NPTg6OjIywtLSEQCHD+/HmcPn0aNjY2GDRo0BuPcfDg\nwYiLi8PevXvRuXNnDBw4UHIfUHp6Orp3745Jkya9XUPKwcnJCX379kV8fDy2bdsmGZU2atQoXLp0\nCRs2bEC7du3Qu3dvWFpa4unTp7h79y5OnTqFnj17IiIiAgCwevVqnDhxAl26dIGVlRUaNGiA9PR0\nxMfHQ0dHp8IRiL1798bcuXNx9OhRtGrVSnIfkI6ODkJDQ6VGu82fPx+nT5/Gtm3bcPnyZfTo0UNy\nH1Bubi5mzpz51o9nGjJkCPbt24cjR46gS5cuGDhwIEpKSnDo0CG0a9cON2/elFlnxowZuH79OrZt\n24aEhAS4urrCzMwM//zzD27duoXU1FTJzbk1ZWdnB3Nzc5w+fRo+Pj5o1qwZNDU10b9/f5ibm6N/\n//6wtrZG+/btYWFhgZKSEiQnJ+PKlStwcnKS65okVQ8DSI2V92je1ANo0qQJBgwYgAMHDiA6Ohpj\nx46Fjo4ODh06hB9++AExMTFYt24drKysEBAQIAmg8mtF5Ro0aIDDhw9j+/bt2Lt3Lw4fPoyioiIY\nGRnBysoK8+fPh5eXl2T5oKAg/P7777hy5QqOHTsGLS0tmJubIzAwEL6+vpVeYP63X3/9FS4uLti+\nfTu2b9+OsrIyNGvWDAsXLoSfn1+Fw2qVYe7cuUhISEBISAhGjhwpGRixbNkyuLu7Y/PmzUhOTkZu\nbi4aNmwIU1NTTJgwAUOGDJFsY8KECTA0NMT58+dx5swZlJSUoGnTphg+fDgmT55c4Wi48tN0S5Ys\nwYYNGwAAPXv2xLx582ROpwmFQsTHx2P16tWIiYnB2rVrUb9+fbRt2xa+vr7w8PB463YQCAQIDw/H\nqlWrEBERgY0bN8LExAQjR47EzJkzJYMdXqelpYVt27YhOjoaO3fuRGJiomRghoWFBQICAjBs2LC3\nqktTUxM7duxAUFAQ4uPj8ezZM4jFYpiamqJFixZYuHAhTp48iXPnzuHIkSPQ0dGBlZUVFi9ejHHj\nxtXa56guEYhEItlzIERVCA8Px7fffotVq1ZJRs5R7du5cyf8/f0RGBhY4Y2VRO86XgOiSpU/tPN1\n9+7dQ0hICLS0tNCvXz8VVEVE6oJ9SqrU+PHj8fz5c7Rr1w4NGzbE3bt3ER8fj8LCQsyfPx9NmzZV\ndYlE9B5jAFGlvL29sXv3bsTExCAvLw96enro2LEjfHx8FHKtgIjqNl4DIiIileA1ICIiUgkGEBER\nqQQDiIiIVIIBVE3Vfb8I1RzbunawnWsH21kWA4iIiFSCAURERCrxzgTQypUrIRQKMWPGDFWXQkRE\nteCdCKBz585h69ataNWqlapLISKiWqLyAHr69Cl8fHywZs0aCIVCVZdDRES1ROUBNHXqVHh6esLV\n1VXVpRARUS1S6bPgwsPDkZGRIXmHiTxqOpTRKVlRbzPUBZIzFbQtqpry2/rcJ7Jv36yLOES4dtS1\ndrazs6tyvsoCKC0tDQsXLkRcXBzq1asn93pvOqBKMTSoAjX+PKmRtLQ0tkMtYDvLUlkAnT17Fjk5\nOejcubNkWmlpKU6dOoUtW7bgwYMHqF+/vqrKIyIiJVNZAA0cOBDt27eXmubv749mzZohICAA2tra\nKqqMiIhqg8oCSCgUyox609XVhaGhIRwcHFRUFRER1RaVj4IjIqK66Z16I2psbKyqSyAiolrCHhAR\nEakEA4iIiFSCAURERCrBACIiIpVgABERkUowgIiISCXeOoBEIhFu376tgFKIiKgukTuAIiIiMHXq\nVKlp8+bNg62tLTp06AA3NzeIRCKFF0hEROpJ7gD69ddfIRAIJN+fOnUKa9asgYeHB2bOnInr169j\nxYoVSimSiIjUj9xPQrh9+zZGjx4t+X7//v1o2rQpNm/eDE1NTRQWFuLw4cNYtGiRUgolIiL1IncP\nqKSkROoJ1b///jvc3NygqakJ4NV7VR4+fKj4ComISC3JHUBWVlZISUkBAFy+fBkZGRno3bu3ZH52\ndjb09fUVXyEREakluU/BffHFF5gzZw7S09Nx9+5dNG3aFH369JHMP3PmDOzt7ZVSJBERqR+5A+jr\nr7+GlpYWEhISYGNjg4CAAOjq6gIAcnNzcffuXUyYMEFphRIRkXoRiEQisaqLqA3CsExVl0DvINE4\nM1WXoHJpaWmws7NTdRlqj+0sS+5rQJ06dUJ8fHyl8xMTE9GpUyeFFEVEROpP7gC6ceMG8vLyKp3/\n7NkzpKWlKaQoIiJSf9V6FM/rN6L+W0ZGBkfBERGR3KochBAZGYm9e/dKvl+9ejX27Nkjs5xIJML/\n/vc/uLm5Kb5CIiJSS1UGUG5uLm7evAngVe/n8ePHKCgokFpGIBBAV1cXI0eOxNy5c5VXKRERqZUq\nA8jX1xe+vr4AgBYtWmD58uX47LPPaqUwIiJSb3LfB/T3338rsw4iIqpj5A6gci9evEBmZiZEIhHE\nYtlbiDp27KiQwoiISL3JHUAikQhz5sxBdHQ0SkpKZOaLxWIIBAI8efJEoQUSEZF6kjuApk6dikOH\nDmHcuHHo0qULhEKhMusiIiI1J3cAHT16FH5+fliyZIky6yEiojpC7htR69evD1tbW2XWQkREdYjc\nATR48GAcOXJEmbUQEVEdIvcpOG9vb0ybNg3Dhw/HmDFjYG5uLnkb6utat26t0AKJiEg9yR1A7u7u\nAIArV64gISGh0uU4Co6IiOQhdwCtWLGiyoeREhERVYfcATR+/Hhl1kFERHVMtV7HUO7+/fu4ePEi\n8vPzFV0PERHVEdUKoIMHD6J9+/Zo27YtevXqhfPnzwMAcnJy4OLigpiYGKUUSURE6kfuAPrtt98w\nbtw4NG3aFHPnzpV6Dlzjxo1hYWGBiIgIpRRJRETqR+4ACgkJQdeuXSVB9G9OTk64evWqQosjIiL1\nJXcA/fnnnxg0aFCl842NjZGdnS33jjdu3AgXFxdYWFjAwsICffr0QXx8vNzrExHR+03uANLR0UFh\nYWGl82/fvg1DQ0O5d2xqaooFCxYgKSkJx48fh6urK0aNGsVeFBFRHSF3AH3yySfYtWsXSktLZeZl\nZ2dj27Zt6Nmzp9w7HjhwIPr06QNbW1s0b94c8+bNg76+Ps6dOyf3NoiI6P0ldwD95z//wf3799G7\nd29s374dAoEASUlJCA4OhouLC0pLSxEYGFijIkpLSxEdHY2CggI4OzvXaBtERPR+EYhEItnXmlbi\n6tWrmDFjBlJTU6WmOzs7Y9WqVXBwcKjWzq9duwZ3d3cUFRVBT08PGzduRN++fatcJy0trVr7KOeU\nrFuj9Ui9nfuk8tPKRPR27OzsqpxfrQAq9/jxY6Snp6OsrAw2NjYwMzOrUXHFxcW4f/8+8vLycPDg\nQYSHh+Pw4cPVDjJ5CMMyFb5Nev+JxtXss6tO0tLS3viLgt4e21mW3I/ieZ2xsTGMjY3feufa2tqS\ndwy1a9cOFy5cwNq1a7FmzZq33jYREb3bKg2g/fv312iDXl5eNS6mrKwMxcXFNV6fiIjeH5UGUEUP\nH339adjlT0L49xOy5Q2goKAguLu7w8zMDPn5+YiKikJycjIiIyPlWp+IiN5vlQbQ2bNnpb7Pz8/H\nN998A319fYwfPx7NmzcH8Oq85pYtW1BYWIjQ0FC5d5yVlYWJEyfi8ePHMDAwQKtWrRAVFYXevXvX\n8FCIiOh9IvcghClTpuD27ds4cOAANDSkR2+XlpZi0KBBsLGxwc8//6yUQt8WByFQRTgIgRfHawvb\nWZbc9wEdOnQIHh4eMuEDAJqamvDw8MChQ4cUWhwREakvuQOotLQU6enplc5PT0+v8CkJREREFZE7\ngPr164fNmzdj48aNePHihWT6ixcvsGHDBmzZsgX9+vVTSpFERKR+5L4PaOnSpcjIyMDMmTPx/fff\no2nTpgCAhw8foqioCB06dMDSpUuVVigREakXuQPI0NAQCQkJOHDgABITE3Hv3j0AQKdOneDu7g5P\nT88Krw8RERFVpFpPQtDQ0MDnn3+Ozz//XFn1EBFRHcEuCxERqUS1ekARERHYvn077ty5A5FIJHka\nQjmBQIAHDx4otEAiIlJPcgfQokWLsGrVKrRo0QJ9+/aFUChUZl1ERKTm5A6gbdu2oX///ti5c6cy\n6yEiojpC7mtAz58/h5ubmzJrISKiOkTuAOratSsuX76szFqIiKgOkTuAVqxYgVOnTiE0NBRPnz5V\nZk1ERFQHyP00bCsrK5SWlqKwsBAAoK+vL3PjqUAgwK1btxRfpQLwadhUET4Nm09pri1sZ1lyD0Lo\n3bu3zMvniIiIakruANqyZYsy6yAiojqGT0IgIiKVqNaTEPLy8hAaGoqEhATcvXsXAGBpaYl+/frB\n398fBgYGSimSiIjUj9w9oIcPH8LV1RXLly9HSUkJunfvju7du6OkpATLli1D9+7d8ejRI2XWSkRE\nakTuHtD8+fORnZ2NqKgo9O7dW2re8ePHMWbMGAQFBWH9+vUKL5KIiNSP3D2gY8eOwc/PTyZ8AKBn\nz56YOHEiEhMTFVocERGpL7kDqLCwEMbGxpXONzExkdwjRERE9CZyB5C9vT2io6NRUlIiM+/ly5eI\njo6Gvb29QosjIiL1Jfc1oClTpmDChAlwc3PDhAkT0Lx5cwCv7u7dsmULLl++jM2bNyutUCIiUi9y\nB9DgwYNRUFCAoKAgTJkyRfJUBLFYDENDQ/z000/w8vJSWqFERKReqnUf0BdffIFhw4bhzJkzuH//\nPgDAwsICzs7OqF+/vlIKJCIi9VStAAKA+vXrw9XVVRm1EBFRHVKtACorK0N0dDTi4+OlnoTQt29f\nDB48WObp2ERERJWROzGePHmCPn36wNfXF3FxccjLy0NeXh7i4uLg6+sLNzc35OTkKLNWIiJSI3IH\nUGBgIC5duoSQkBBkZGQgNTUVqampyMjIwLJly3DlyhXMmjVLmbUSEZEakfsUXHx8PCZOnIivvvpK\narq2tjYmTJiA9PR0REREKLxAIiJST3L3gLS0tGBjY1Pp/GbNmkFLq9pjGoiIqI6SO4A8PDywf/9+\nlJaWysx7+fIl9u3bh0GDBim0OCIiUl9yd1lGjBiBgIAAuLu7Y/z48bC1tQUA3Lx5E2FhYXjx4gWG\nDx+Oq1evSq3XunVrxVZMRERqQe4A6tevn+T///e//0nNE4vFMsuUe/LkSU1rIyIiNSZ3AK1YsULy\n+B0iIqK3JXcAjR8/XqE7XrlyJQ4dOoT09HRoa2vD0dER8+fPh4ODg0L3Q0RE76a3fnTBpUuXcOLE\nCRQVFVVrveTkZHz11VeIj49HTEwMtLS0MGjQIOTm5r5tSURE9B6Quwe0evVqpKamYteuXZJpX375\nJWJiYgC8eijpb7/9BjMzM7m2t2/fPqnvf/31V1haWiI1NRX9+/eXtywiInpPyd0D2r17N6ytrSXf\nJyQk4ODBg/Dz88OaNWuQn5+PZcuW1biQ/Px8lJWVQSgU1ngbRET0/pC7B5SZmSn1xtOYmBjY2Njg\nhx9+AADcuXNHqndUXbNmzUKbNm3g7Oxc5XJpaWk13INuDdcjdVbzz5N6YTvUjrrWznZ2dlXOlzuA\nyodal/vjjz+kTpWZm5vj8ePH1SzvlTlz5iA1NRVxcXHQ1NSsctk3HVClkjNrth6ptRp/ntRIWloa\n26EWsJ1lyX0KrlmzZjhy5AgAICkpCQ8ePICbm5tk/oMHD2p0+mz27NmIjo5GTEyM1Ck+IiJSb3L3\ngCZNmgRfX1/Y29sjLy8PzZs3R69evSTzT548We2nHgQGBmL//v04dOiQ1Ok9IiJSf3IH0NChQ9Gw\nYUMkJCTAwMAAEydORL169QAAubm50NHRwciRI+Xe8fTp07Fnzx7s2LEDQqEQWVlZAAA9PT3o6+tX\n8zCIiOh9IxCJROI3L6Z4lZ1SaRauAAAS3ElEQVSuCwwMxOzZsxW/vzBeAyJZonHy3Tagznhtonaw\nnWVV+/0J9+7dQ0pKCrKzs+Hl5QVzc3O8fPkSubm5MDQ0lPuVDCKRqNrFEhGR+qjWKLjAwEBs2bIF\npaWlEAgEaNu2LczNzVFYWIgOHTpg1qxZ8Pf3V2a9RESkJuQeBbd69Wps2rQJ3333HQ4dOiQ1LNvA\nwACffvopDh8+rJQiiYhI/cgdQNu2bcOIESMwd+7cCh8Y2qpVK6Snpyu0OCIiUl9yB1BmZmaVTynQ\n09PDs2fPFFIUERGpP7kDyMjICJmZlY8ku3TpktwPIiUiIpI7gAYMGICwsDDcuXNHZl5SUhIiIiIw\naNAghRZHRETqS+4AmjNnDpo0aYJu3brB398fAoEAv/zyCwYOHAgvLy+0aNECAQEByqyViIjUiNwB\nJBQKcfToUfj6+uLmzZsQCAQ4duwYHjx4gO+++w5xcXHQ09NTZq1ERKRGqnUjqp6eHubOnYu5c+cC\neHVvkEAgUEphRESk3t7qldyvh49YLEZUVNRbF0RERHXDWwUQABQXFyMsLAwdOnTAxIkTFVETERHV\nAW8MoMjISPTo0QNWVlb4+OOPMX/+fJSUlAAAtm7dio8//hgBAQGoV68eVq1apfSCiYhIPVR5DSg6\nOhq+vr744IMPYGNjg0ePHiE0NBQFBQXIy8vD3r170blzZ6xcuVLq7ahERERvUmUArV+/Hra2tvjt\nt99gYmKCkpIS+Pr6IiwsDPr6+ti5cycGDBhQW7USEZEaqfIU3J9//okvv/wSJiYmAIB69eohICAA\nZWVlmD59OsOHiIhqrMoAKigowIcffig1zdTUFMCrh48SERHV1BsHIVR2n4+8L54jIiKqyBtTZO3a\ntThw4IDk+5KSEggEAgQHB2PDhg1SywoEAmzfvl3xVRIRkdqpMoDKn4D976dgGxkZISMjAxkZGVLT\n+VQEIiKSV5UBdOPGjdqqg4iI6pi3fhICERFRTTCAiIhIJRhARESkEgwgIiJSCQYQERGpBAOIiIhU\nggFEREQqUel9QKtXr672xgQCAaZMmfJWBRERUd1QaQAFBQVVe2MMICIiklelAfTo0aParIOIiOqY\nSgOofv36tVkHERHVMRyEQEREKlGtl/qkp6djw4YNuHTpEvLy8lBWViY1XyAQIDU1VaEFEhGRepK7\nB3Tu3Dm4uroiKioKurq6+Ouvv9C4cWPo6Ojgxo0b0NTU5FtSiYhIbnIH0OLFi2FiYoL//ve/2LRp\nEwAgMDAQf/zxBw4ePIjMzEyMHTtWaYUSEZF6kTuALly4gLFjx6JRo0bQ0Hi1mlgsBgC4urpi9OjR\nWLRokXKqJCIitSN3AInFYjRq1AgAoKurCwB48uSJZH6LFi1w/fr1au08JSUFw4cPx0cffQShUIid\nO3dWa30iInp/yR1AlpaWuHPnDoBXQ7QtLCxw4sQJyfyzZ89CKBRWa+cFBQVwcHDAjz/+CB0dnWqt\nS0RE7ze5R8H16NEDBw4cwLx58wAAo0ePRnBwMB48eACxWIxjx47B39+/Wjt3d3eHu7s7AGDSpEnV\nWpeIiN5vcgfQtGnT4OHhgeLiYmhra2P69OkoLi5GTEwMNDU1MXXqVAQGBiqzViIiUiMCkUgkVnUR\nAGBmZoZly5Zh1KhRVS6XlpZWo+07JevWaD0iorrq3CeFb7W+nZ1dlfPl7gF5e3tjypQp6NatW4Xz\nU1JS8NNPP2Hv3r3Vq7Ca3nRAlUrOVGwhRERqrsa/b+Uk9yCEo0ePVvmA0qysLBw7dkwhRRERkfpT\n2LPgHj58yJFsREQktypPwcXHxyMhIUHy/c6dOyt81ptIJEJiYiLat29frZ3n5+cjIyMDAFBWVob7\n9+/j8uXLMDQ0hIWFRbW2RURE75cqByEsW7YMy5YtA/AqIAQCAQQCgfQGBALo6uqiffv2WLZsGezt\n7eXe+cmTJ/HZZ5/JTB8xYgTWrVsn93bkIQzjNSAiouoQjTNT6vblHgVnaGiIDRs2wNvbW6kFKQsD\niIioepQdQHKPgnv06BG0tbWVWQsREdUhcgdQ+RtSb926hYSEBNy9exfAq0f0uLu7w8bGRjkVEhGR\nWqrWjahz5szBr7/+KvMiOg0NDfj5+WHJkiUKL1BReAqOiKh6lH0KTu5h2KGhoVi3bh28vLxw/Phx\nZGZmIjMzE8ePH8fnn3+OdevW4ZdfflFmrUREpEbk7gE5OTnBzs4OERERFc4fOXIk0tLScO7cOYUW\nqCjsARERVc870wO6e/cuevfuXen83r17S64LERERvYncAdSkSRNcu3at0vnXrl1DkyZNFFIUERGp\nP7kDyMPDA+Hh4QgNDcXz588l058/f47Q0FCEh4fD09NTKUUSEZH6kfsaUH5+PoYOHYrTp09DW1sb\npqamAIAHDx6guLgYLi4uiIyMhJ6enlILrileAyIiqp535kkIACAWi3HgwAEkJCTg3r17AAALCwv0\n69cPHh4eMo/peZcwgIiIqkelAbRr1y64uLjAyspKqUXUBgYQEVH1qHQUnL+/P86ePavUAoiIqG6q\nMoDE4nfibd1ERKSGFPZCOiIioup4YwC9ywMLiIjo/VXlIARDQ0Noa2tDQ0O+jpJAIMCDBw8UVpwi\ncRACEVH1qPx9QB07doS1tbVSiyAiorrnjQE0bty49/YtqERE9O7iIAQiIlIJBhAREakEA4iIiFSi\nymtAubm5tVUHERHVMewBERGRSjCAiIhIJRhARESkEgwgIiJSCQYQERGpBAOIiIhUggFEREQqwQAi\nIiKVYAAREZFKMICIiEglGEBERKQSDCAiIlIJBhAREakEA4iIiFRC5QG0adMmtG3bFiYmJujevTtO\nnTql6pKIiKgWqDSA9u3bh1mzZmHatGk4ceIEnJ2d4e3tjXv37qmyLCIiqgUqDaBffvkFI0eOxNix\nY9GiRQuEhITAxMQEW7ZsUWVZRERUC6p8I6oyFRcX4+LFi/jmm2+kpvfq1QtnzpxR+P5E48wUvk0i\nIqo5lfWAcnJyUFpaCiMjI6npRkZGePz4sYqqIiKi2qLyQQhERFQ3qSyAGjduDE1NTWRnZ0tNz87O\nhrGxsYqqIiKi2qKyANLW1ka7du1w/PhxqenHjx9Hp06dVFQVERHVFpUNQgAAf39/+Pr6omPHjujU\nqRO2bNmCR48eYdy4caosi4iIaoFKrwF9/vnnCA4ORkhICLp164bU1FRERkbC0tJSlWVh5cqV6Nmz\nJywsLNCsWTMMGzYM169fl1pGLBYjODgYLVu2xIcffoiBAwfizz//VFHF76eNGzfCxcUFFhYWsLCw\nQJ8+fRAfHy+ZzzZWjpUrV0IoFGLGjBmSaWzrtxccHAyhUCj1ZW9vL5nPNpal8kEIEyZMwJUrV/D4\n8WMkJSWha9euqi4JycnJ+OqrrxAfH4+YmBhoaWlh0KBByM3NlSyzevVq/PLLL1i6dCl+//13GBkZ\nwcvLC8+ePVNh5e8XU1NTLFiwAElJSTh+/DhcXV0xatQoXL16FQDbWBnOnTuHrVu3olWrVlLT2daK\nYWdnh7///lvy9fqTXdjGsgQikUis6iLedfn5+bC0tMTOnTvRv39/iMVitGzZEj4+Ppg+fToA4Pnz\n57Czs8OiRYt4CvEtWFtbY/78+fjyyy/Zxgr29OlTdO/eHT///DOWLl0KBwcHhISE8POsIMHBwYiJ\nicHp06dl5rGNK6byHtD7ID8/H2VlZRAKhQCAO3fuICsrC7169ZIso6OjAxcXF6XcRFsXlJaWIjo6\nGgUFBXB2dmYbK8HUqVPh6ekJV1dXqelsa8W5ffs2WrZsibZt22L8+PG4ffs2ALZxZVQ6COF9MWvW\nLLRp0wbOzs4AgKysLACo8Cbahw8f1np977Nr167B3d0dRUVF0NPTw44dO9CqVSvJP0q2sWKEh4cj\nIyMDGzZskJnHz7NiODo6Yu3atbCzs8M///yDkJAQuLu7IzU1lW1cCQbQG8yZMwepqamIi4uDpqam\nqstRO3Z2djh58iTy8vJw8OBBfP311zh8+LCqy1IraWlpWLhwIeLi4lCvXj1Vl6O2+vTpI/W9o6Mj\n2rVrh4iICDg5OamoqncbT8FVYfbs2YiOjkZMTAysra0l001MTACAN9EqgLa2NmxtbdGuXTvMnz8f\nbdq0wdq1a9nGCnT27Fnk5OSgc+fOaNy4MRo3boyUlBRs2rQJjRs3RqNGjQCwrRVNX18fLVu2REZG\nBj/PlWAAVSIwMFASPq8PpQQAKysrmJiYSN1EW1RUhNOnT/Mm2rdUVlaG4uJitrECDRw4EKdOncLJ\nkyclX+3bt8fgwYNx8uRJNG/enG2tBEVFRUhLS4OJiQk/z5XgKbgKTJ8+HXv27MGOHTsgFAol52/1\n9PSgr68PgUCAr7/+GitXroSdnR2aN2+O5cuXQ09PD0OGDFFx9e+PoKAguLu7w8zMDPn5+YiKikJy\ncjIiIyPZxgpUfk/K63R1dWFoaAgHBwcAYFsrwH/+8x/069cP5ubmkmtAhYWFGDFiBD/PlWAAVWDT\npk0AAE9PT6npgYGBmD17NgDg22+/xfPnzzFjxgyIRCJ07NgR+/btQ4MGDWq93vdVVlYWJk6ciMeP\nH8PAwACtWrVCVFQUevfuDYBtXJvY1m/vwYMHmDBhAnJyctCkSRM4OjoiMTFRcmM921gW7wMiIiKV\n4DUgIiJSCQYQERGpBAOIiIhUggFEREQqwQAiIiKVYAAREZFKMICIiEglGEBEb2n79u0QCoVwdHRU\ndSlE7xUGENFbKn+NfHp6Oi5cuKDqcojeGwwgoreQmZmJlJQUBAUFwczMDHv27FF1SUTvDQYQ0VuI\nioqCrq4u+vfvDy8vL+zfvx+lpaVSyzx//hwzZ86Era0tzM3NMXz4cGRmZkIoFCI4OFhq2UePHuGb\nb76Bvb09jI2N4ezsjM2bN8vsd9OmTXBxcYGpqSksLS3xySefICwsTKnHSqRofBgp0VvYs2cPBgwY\nAB0dHQwZMgRr1qzB8ePH4ebmJllm0qRJ2L9/P4YOHQpnZ2ekpKRg6NChMtvKzs6Gm5sbysrK8NVX\nX8HIyAhJSUmYNm0anjx5ghkzZgAAtm3bhunTp8PT0xM+Pj4oKSnBX3/9hTNnzmDcuHG1duxEb4sB\nRFRDV69exfXr1/H9998DANq1a4dmzZohMjJSEkAXL17E/v374ePjg5CQEADAhAkTMGnSJFy7dk1q\ne4sXL0ZxcTFOnTqFJk2aAADGjx+PKVOmYOXKlfDx8YFQKER8fDw++ugjhIeH1+LREikeT8ER1VBk\nZCQMDQ0lr48AgMGDByM2NhYFBQUAgGPHjgF4FTqvmzhxotT3YrEYBw8ehLu7OwQCAXJyciRfvXr1\nwvPnz3H+/HkAgIGBATIzMznggd577AER1UBZWRmio6PRtWtX3Lt3TzK9Y8eOKCgoQGxsLIYOHYp7\n9+5BIBDAxsZGan1bW1up7//55x+IRCLs2LEDO3bsqHCf5a9znjp1Kk6cOIFevXrB2toaPXv2hJeX\nF1xdXRV8lETKxQAiqoGTJ08iMzMTmZmZOHz4sMz8yMjICq/zVKasrAwAMGTIEIwePbrCZVq2bAkA\naNGiBc6dO4fExEQcO3YM8fHxCAsLw4QJE7B8+fIaHA2RajCAiGogMjISjRs3xsqVK2XmHTt2DBER\nEcjOzoaFhQXEYjFu3bqFFi1aSJbJyMiQWqdJkyZo0KABXr58iR49erxx/7q6uvD09ISnpydevnyJ\nSZMmYdOmTQgICICpqelbHx9RbeA1IKJqKioqwqFDh9CnTx9JCLz+NXnyZLx8+RLR0dGS60Plr3kv\nt2HDBqnvNTU14eHhgdjYWFy5ckVmn//884/k/588eSI1T0tLCw4ODgCAp0+fKuQYiWoDe0BE1XTk\nyBHk5eWhf//+Fc63t7eXjIbz8/ODh4cHNm7ciLy8PDg5OSElJQXp6ekAAIFAIFkvKCgIycnJcHd3\nxxdffIGPPvoIIpEIV65cweHDh5GVlQUA8PLygpGRETp37gxjY2PcunULGzZsQKtWraR6WUTvOgYQ\nUTXt2bMH2tra6NWrV6XLDBgwAKGhoUhPT8f69ethYmKCqKgoxMbGonv37ggLC4OjoyM++OADyTpG\nRkY4duwYli1bhtjYWGzZsgWGhoawt7fH4sWLJcuNGzcOe/fuxbp16/Ds2TN8+OGHGDVqFGbMmAEN\nDZ7UoPeHQCQSiVVdBFFdc/nyZbi6umLDhg3VGqxApE745xKRkj1//lxm2rp166ChoQEXFxcVVET0\nbuApOCIlW716NS5evIhu3bpBS0sLR48eRWJiIr788kuYm5urujwileEpOCIlO378OJYuXYq//voL\nBQUFMDc3x7BhwzB9+nRoafFvQKq7GEBERKQSvAZEREQqwQAiIiKVYAAREZFKMICIiEglGEBERKQS\nDCAiIlKJ/wdGEOxxBm47qQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7qruxvBo9PiM",
        "colab_type": "text"
      },
      "source": [
        "By looking at the plot we cant immediately tell, how many bins are present. So, lets add edge color to make this plot more clear.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7SMFubko8tCE",
        "colab_type": "code",
        "outputId": "7484c30e-2a66-4fa4-f9b4-2ae1f4f8f91f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "'https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv' \\\n",
        "-O '/tmp/part6_data.csv'\n",
        "\n",
        "ages = [18, 19, 21, 25, 26, 26, 30, 32, 38, 45, 55]\n",
        "plt.hist(ages, bins = 5, edgecolor='black')\n",
        "\n",
        "plt.title('Ages of Respondents')\n",
        "plt.xlabel('Ages')\n",
        "plt.ylabel('Total Respondents')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-03 16:44:18--  https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 702942 (686K) [text/plain]\n",
            "Saving to: ‘/tmp/part6_data.csv’\n",
            "\n",
            "\r/tmp/part6_data.csv   0%[                    ]       0  --.-KB/s               \r/tmp/part6_data.csv 100%[===================>] 686.47K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2019-10-03 16:44:18 (17.9 MB/s) - ‘/tmp/part6_data.csv’ saved [702942/702942]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XKSIi1sfsI6Ddu3fTu3fvVOED0LBhQ3r27MnOnTuztDgREbFeZgdQXFwcrq6u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\nIhRAIiJiEQogERGxCAWQiIhYxP8D6FbN8eoqMa4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xaS3zDwR9n_o",
        "colab_type": "text"
      },
      "source": [
        "We could alternatively specify our own bins"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykKiLIn69Uyl",
        "colab_type": "code",
        "outputId": "50420ba8-1a5f-4903-a32f-173844081c62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "'https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv' \\\n",
        "-O '/tmp/part6_data.csv'\n",
        "\n",
        "ages = [18, 19, 21, 25, 26, 26, 30, 32, 38, 45, 55]\n",
        "bins = [10, 20, 30, 40, 50, 60]\n",
        "# For the above arrangement of bins all values below 10 and above 60 will be ignored.\n",
        "\n",
        "plt.hist(ages, bins = bins, edgecolor='black')\n",
        "\n",
        "plt.title('Ages of Respondents')\n",
        "plt.xlabel('Ages')\n",
        "plt.ylabel('Total Respondents')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-03 16:49:10--  https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 702942 (686K) [text/plain]\n",
            "Saving to: ‘/tmp/part6_data.csv’\n",
            "\n",
            "\r/tmp/part6_data.csv   0%[                    ]       0  --.-KB/s               \r/tmp/part6_data.csv 100%[===================>] 686.47K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2019-10-03 16:49:11 (17.7 MB/s) - ‘/tmp/part6_data.csv’ saved [702942/702942]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8eRObN2+WmnbgwAGcO3cOLVu2lAzDrlWrFkaOHIm8vDwsWLBAqu0fPnyIlStXQiAQYNy4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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVoX3xcXXIGK",
        "colab_type": "text"
      },
      "source": [
        "Right now we are filling between zero and the py_salaries.\n",
        "\n",
        "But, we could use some additional conditions !"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCVAQKZcXEf-",
        "colab_type": "code",
        "outputId": "05b9d3a1-de57-4283-f339-506fbd5c51b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "'https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv' \\\n",
        "-O '/tmp/part6_data.csv'\n",
        "\n",
        "data = pd.read_csv('/tmp/part6_data.csv')\n",
        "ids = data['Responder_id']\n",
        "ages = data['Age']\n",
        "\n",
        "bins = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
        "# For the above arrangement of bins all values below 20 and above 60 will be ignored.\n",
        "\n",
        "plt.hist(ages, bins = bins, edgecolor='black')\n",
        "\n",
        "plt.title('Ages of Respondents')\n",
        "plt.xlabel('Ages')\n",
        "plt.ylabel('Total Respondents')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-03 16:50:16--  https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 702942 (686K) [text/plain]\n",
            "Saving to: ‘/tmp/part6_data.csv’\n",
            "\n",
            "\r/tmp/part6_data.csv   0%[                    ]       0  --.-KB/s               \r/tmp/part6_data.csv 100%[===================>] 686.47K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2019-10-03 16:50:16 (17.0 MB/s) - ‘/tmp/part6_data.csv’ saved [702942/702942]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6+Llz5+jevTuZmZkYGRmxYcMGmjZtqvzyNzU11apvampKYmIiALdu3cLExERr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\nQgi9+D94hqaLkSq/1wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cCd4Eo9AX5yT",
        "colab_type": "text"
      },
      "source": [
        "In the above plot we actually have some values that are belonging to bins 70, 80 and 90. Visually we dont see them because of scale. \n",
        "\n",
        "When we have such a data, it always better to use a logarithmic scale "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "envoTJpFXxgJ",
        "colab_type": "code",
        "outputId": "837ad553-6f17-4542-b7a3-e2cae774cf83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "'https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv' \\\n",
        "-O '/tmp/part6_data.csv'\n",
        "\n",
        "data = pd.read_csv('/tmp/part6_data.csv')\n",
        "ids = data['Responder_id']\n",
        "ages = data['Age']\n",
        "\n",
        "bins = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
        "# For the above arrangement of bins all values below 20 and above 60 will be ignored.\n",
        "\n",
        "plt.hist(ages, bins = bins, edgecolor='black', log=True)\n",
        "\n",
        "plt.title('Ages of Respondents')\n",
        "plt.xlabel('Ages')\n",
        "plt.ylabel('Total Respondents')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-03 16:53:32--  https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 702942 (686K) [text/plain]\n",
            "Saving to: ‘/tmp/part6_data.csv’\n",
            "\n",
            "\r/tmp/part6_data.csv   0%[                    ]       0  --.-KB/s               \r/tmp/part6_data.csv 100%[===================>] 686.47K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2019-10-03 16:53:32 (18.1 MB/s) - ‘/tmp/part6_data.csv’ saved [702942/702942]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Fi1EoFAwdOjQ/m55D9uCWWbNmqW2LUqlkwYIFOebX1dXF3d2dhw8fMmHCBJKTk3PMExcX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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWTBlv5FZMFy",
        "colab_type": "text"
      },
      "source": [
        "Now lets say that the median age of all our respondents is 29"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PAx6FqcYs6K",
        "colab_type": "code",
        "outputId": "eec01227-2aaa-49c0-aef2-fc1e001c18da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "'https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv' \\\n",
        "-O '/tmp/part6_data.csv'\n",
        "\n",
        "data = pd.read_csv('/tmp/part6_data.csv')\n",
        "ids = data['Responder_id']\n",
        "ages = data['Age']\n",
        "\n",
        "bins = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
        "# For the above arrangement of bins all values below 20 and above 60 will be ignored.\n",
        "\n",
        "plt.hist(ages, bins = bins, edgecolor='black', log=True)\n",
        "\n",
        "median_age = 29\n",
        "color = '#fc4f30'\n",
        "\n",
        "# Lets plot a vertical line with this median\n",
        "# axis vertical line\n",
        "plt.axvline(median_age, color=color, linewidth=2, label='Age Median')\n",
        "\n",
        "plt.legend()\n",
        "plt.title('Ages of Respondents')\n",
        "plt.xlabel('Ages')\n",
        "plt.ylabel('Total Respondents')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-03 16:58:11--  https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part6_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 702942 (686K) [text/plain]\n",
            "Saving to: ‘/tmp/part6_data.csv’\n",
            "\n",
            "\r/tmp/part6_data.csv   0%[                    ]       0  --.-KB/s               \r/tmp/part6_data.csv 100%[===================>] 686.47K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2019-10-03 16:58:11 (18.1 MB/s) - ‘/tmp/part6_data.csv’ saved [702942/702942]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zMH/+fFq2bKmVfQshhMibHBOQm5sbbm5uwKulv3/44Qd69uxZKIFp6ubNm1y/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\nkAQkhBBCKyQBCSGE0Ir/A9WQW0Q+lyoxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jfUKZVRZ0Ud",
        "colab_type": "text"
      },
      "source": [
        "We could also fill between the area between two line plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BBdKR9xBZisX",
        "colab_type": "code",
        "outputId": "ce0ac7e6-3044-4a69-e9ae-321121be5e0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        }
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "'https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part5_data.csv' \\\n",
        "-O '/tmp/part5_data.csv'\n",
        "\n",
        "data = pd.read_csv('/tmp/part5_data.csv')\n",
        "ages = data['Age']\n",
        "dev_salaries = data['All_Devs']\n",
        "py_salaries = data['Python']\n",
        "js_salaries = data['JavaScript']\n",
        "\n",
        "plt.plot(ages, dev_salaries, color='#444444',\n",
        "         linestyle='--', label='All Devs')\n",
        "\n",
        "plt.plot(ages, py_salaries, label='Python')\n",
        "\n",
        "overall_median = 57287\n",
        "plt.fill_between(ages, py_salaries, dev_salaries, \n",
        "                 where=(py_salaries > dev_salaries), \n",
        "                 interpolate=True, alpha=0.25)\n",
        "\n",
        "plt.fill_between(ages, py_salaries, dev_salaries, \n",
        "                 where=(py_salaries <= dev_salaries), \n",
        "                 interpolate=True, alpha=0.25)\n",
        "plt.legend()\n",
        "\n",
        "plt.title('Median Salary (USD) by Age')\n",
        "plt.xlabel('Ages')\n",
        "plt.ylabel('Median Salary (USD)')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-03 16:16:25--  https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part5_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 850 [text/plain]\n",
            "Saving to: ‘/tmp/part5_data.csv’\n",
            "\n",
            "\r/tmp/part5_data.csv   0%[                    ]       0  --.-KB/s               \r/tmp/part5_data.csv 100%[===================>]     850  --.-KB/s    in 0s      \n",
            "\n",
            "2019-10-03 16:16:25 (286 MB/s) - ‘/tmp/part5_data.csv’ saved [850/850]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9HD+0n7jD+0g8mUBAYB3e/+QLMnLyeP6pZ0k7n0ZoRGPzqwl16oUWPqNksVhI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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLJU-7XnakKj",
        "colab_type": "text"
      },
      "source": [
        "We can also have labels for fill between"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eE3XZPGaaH04",
        "colab_type": "code",
        "outputId": "f3f82ff7-a1cd-4ea0-ec0e-bf05faaeabef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        }
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "'https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part5_data.csv' \\\n",
        "-O '/tmp/part5_data.csv'\n",
        "\n",
        "data = pd.read_csv('/tmp/part5_data.csv')\n",
        "ages = data['Age']\n",
        "dev_salaries = data['All_Devs']\n",
        "py_salaries = data['Python']\n",
        "js_salaries = data['JavaScript']\n",
        "\n",
        "plt.plot(ages, dev_salaries, color='#444444',\n",
        "         linestyle='--', label='All Devs')\n",
        "\n",
        "plt.plot(ages, py_salaries, label='Python')\n",
        "\n",
        "overall_median = 57287\n",
        "plt.fill_between(ages, py_salaries, dev_salaries, \n",
        "                 where=(py_salaries > dev_salaries), \n",
        "                 interpolate=True, alpha=0.25, label='above avg')\n",
        "\n",
        "plt.fill_between(ages, py_salaries, dev_salaries, \n",
        "                 where=(py_salaries <= dev_salaries), \n",
        "                 interpolate=True, alpha=0.25, label='below avg')\n",
        "plt.legend()\n",
        "\n",
        "plt.title('Median Salary (USD) by Age')\n",
        "plt.xlabel('Ages')\n",
        "plt.ylabel('Median Salary (USD)')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-03 16:19:30--  https://raw.githubusercontent.com/bgswaroop/python-matplotlib/master/data/part5_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 850 [text/plain]\n",
            "Saving to: ‘/tmp/part5_data.csv’\n",
            "\n",
            "\r/tmp/part5_data.csv   0%[                    ]       0  --.-KB/s               \r/tmp/part5_data.csv 100%[===================>]     850  --.-KB/s    in 0s      \n",
            "\n",
            "2019-10-03 16:19:30 (77.6 MB/s) - ‘/tmp/part5_data.csv’ saved [850/850]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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BJ36IwNFeePWehgT65n+/yRY3ZwceuTOQD5Yf5su1xxjVoTazt0YR5u9JaHVP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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AG8BIgKma0_n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}