PlotlyBasics_01.ipynb

plotlybasics_01.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-wvNeU7YZ14z"
      },
      "source": [
        "# **PLOTLY / PYTHON BASICS**"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "DYfxWX5LaEt7"
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      "source": [
        "## DATAFRAMES"
      ]
    },
    {
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        "id": "CWB8Bu26aKcT"
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      "source": [
        "### **IMPORTING LIBRARIES**\n",
        "\n",
        "Pandas allows for data manipulation \n",
        "\n",
        "Numpy makes possible the generation of random numbers and strings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MG79OD3nuhxq"
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      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "from numpy import random \n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### **GENERATING A RANDOM SERIES**\n",
        "\n",
        "First we establish a random seed. \n",
        "We then define the name of our columns. \n",
        "Then we declare that the variable **X** is equal to a random number between **0** and **1200**.\n",
        "We also say that we will need an array that is 1 x 4. \n",
        "**Why 4 x 4?**\n",
        "We will create *one line* of random numbers, for each column that we defined. \n",
        "Depending on your needs you can change this. \n",
        "\n",
        "Just make sure that the number of columns you have defined (in this case **4**) is equal to the number you include in the second variable of *size*\n",
        "\n",
        "If you print the variable **X** you wil see that we have a series of four random numbers\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "upolgOv_vPGg",
        "outputId": "fdd8fb87-793c-447b-e97b-5df3659f0ea2"
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      "source": [
        "np.random.seed(1); cols = [\"Jonh\",\"Peter\",\"Mary\",\"Annabelle\"]\n",
        "X = np.random.randint(1200,size=(1,4))\n",
        "print(X)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1061  235 1096  905]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmfZja5Sig5A"
      },
      "source": [
        "### **CREATING A DATAFRAME**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9bTzKDm15C9"
      },
      "source": [
        "**data** is the name of the dataframe. It can be anyting you want, but keep it simple as you will use this variable throughout your script. \n",
        "\n",
        "We then call pandas (**pd**) function to build a dataframe, with the series from **X** and for the columns we defined above. \n",
        "\n",
        "We are also adding a column named ìd` that related to our index. This will come handy later. \n",
        "\n",
        "When you print **data** you see the result\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
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          "height": 80
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        "id": "Lel_4pSORZma",
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      "source": [
        "data=pd.DataFrame(X, columns=cols)\n",
        "data[\"id\"] = data.index + 1\n",
        "data\n"
      ],
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Jonh</th>\n",
              "      <th>Peter</th>\n",
              "      <th>Mary</th>\n",
              "      <th>Annabelle</th>\n",
              "      <th>id</th>\n",
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              "      <th>0</th>\n",
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              "   Jonh  Peter  Mary  Annabelle  id\n",
              "0  1061    235  1096        905   1"
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      "cell_type": "markdown",
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      "source": [
        "### TREATING DATA"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "One of the most important things when dealing with large datasets is to understand the difference between a **wide** format and a **long format**.\n",
        "Our dataframe **data** is at the moment in **wide** format and we won't be able to do much with it. \n",
        "\n",
        "For that we need to transform it into a long format, where the columns will repeat for all the different values. \n",
        "\n",
        "First we will create a new dataframe based on a series with more than one value per column "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJcVdnhc71g7"
      },
      "source": [
        "We start by generating a series with 4 columns and with 10 rows.\n"
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
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        "id": "fyoXS7Gr7u81",
        "outputId": "1ef78327-17f4-4563-88df-f1fb6acaa1b0"
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      "source": [
        "np.random.seed(1); cols = [\"Jonh\",\"Peter\",\"Mary\",\"Annabelle\"]\n",
        "X1 = np.random.randint(1200,size=(11,4))\n",
        "print(X1)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1061  235 1096  905]\n",
            " [ 715  847  960  144]\n",
            " [ 129  749  508  357]\n",
            " [ 914  468  907  252]\n",
            " [ 668  398  562  580]\n",
            " [1001  753 1110  141]\n",
            " [1031  319  829  513]\n",
            " [ 316  209  728  627]\n",
            " [ 431  633  456  542]\n",
            " [1095  515  964  792]\n",
            " [ 497 1067 1050  621]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAog-lQD8QOz"
      },
      "source": [
        "We then create a new dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gcz5178U8Ku7",
        "outputId": "0f8be373-8575-4831-88af-5585dd186441"
      },
      "source": [
        "data1=pd.DataFrame(X1,columns=cols)\n",
        "data1[\"id\"] = data1.index + 1\n",
        "print(data1)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    Jonh  Peter  Mary  Annabelle  id\n",
            "0   1061    235  1096        905   1\n",
            "1    715    847   960        144   2\n",
            "2    129    749   508        357   3\n",
            "3    914    468   907        252   4\n",
            "4    668    398   562        580   5\n",
            "5   1001    753  1110        141   6\n",
            "6   1031    319   829        513   7\n",
            "7    316    209   728        627   8\n",
            "8    431    633   456        542   9\n",
            "9   1095    515   964        792  10\n",
            "10   497   1067  1050        621  11\n"
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      "cell_type": "markdown",
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      "source": [
        "Now for every column we have 10 random values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJ2f60mw-bRZ"
      },
      "source": [
        "For you to understand the importance of **wide** versus **long** format, let's try and plot a **line graph** using this wide dataframe\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mv670GQg-fSA"
      },
      "source": [
        "First we import Plotly Express, which is a graph library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0H765k9h-kEm"
      },
      "source": [
        "import plotly.express as px "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_mLbNrU-mgf"
      },
      "source": [
        "We then try to plot a graph from our dataframe **data1**\n",
        "\n",
        "A basic *line graph* needs an **x** and a **y*** variable which we don't have in our current dataframe. What we have are 4 columns that we can correlate, with the following result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "Nu5dzXhB-aoa",
        "outputId": "3ee010b9-c81e-4533-eb90-1481a0badeb7"
      },
      "source": [
        "fig=px.line(data1,x='Jonh', y='Mary')\n",
        "fig.show()"
      ],
      "execution_count": 8,
      "outputs": [
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              "                        {\"responsive\": true}\n",
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              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "var outputEl = gd.closest('.output');\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "                        })\n",
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              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOv1zXuB_8MH"
      },
      "source": [
        "**If you are into abstract visualizations then read no further because you can have lots of fun just by playing around with those ;)**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OoXez7sSALvZ"
      },
      "source": [
        "**If you want to make proper graphics let's continue:**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4q12sU29iVO"
      },
      "source": [
        "We going to turn the dataframe from **wide** to **long** format by using Pandas \"melt\" funtion. \n",
        "\n",
        "`pd.melt` works by assigning the dataframe you want to modify, and then telling what the name of your new two columns will be. \n",
        "\n",
        "There are more options to this but we will start by the most simple example:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hFC0H3-cUd_S",
        "outputId": "38875b90-0f04-48a6-f909-e7078332a4c4"
      },
      "source": [
        "data1_long = pd.melt(data1,id_vars='id', var_name='Family Members',value_name='Hugs')\n",
        "data1_long"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Family Members</th>\n",
              "      <th>Hugs</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Jonh</td>\n",
              "      <td>1061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
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              "      <td>715</td>\n",
              "    </tr>\n",
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              "      <td>Jonh</td>\n",
              "      <td>129</td>\n",
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              "      <td>4</td>\n",
              "      <td>Jonh</td>\n",
              "      <td>914</td>\n",
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              "    <tr>\n",
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              "      <td>5</td>\n",
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              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Jonh</td>\n",
              "      <td>1001</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Jonh</td>\n",
              "      <td>1031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Jonh</td>\n",
              "      <td>316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Jonh</td>\n",
              "      <td>431</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Jonh</td>\n",
              "      <td>1095</td>\n",
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              "      <th>10</th>\n",
              "      <td>11</td>\n",
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              "      <td>497</td>\n",
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              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>Peter</td>\n",
              "      <td>235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2</td>\n",
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              "      <td>847</td>\n",
              "    </tr>\n",
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              "      <th>13</th>\n",
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              "      <td>749</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>4</td>\n",
              "      <td>Peter</td>\n",
              "      <td>468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5</td>\n",
              "      <td>Peter</td>\n",
              "      <td>398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>6</td>\n",
              "      <td>Peter</td>\n",
              "      <td>753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>7</td>\n",
              "      <td>Peter</td>\n",
              "      <td>319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>8</td>\n",
              "      <td>Peter</td>\n",
              "      <td>209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>9</td>\n",
              "      <td>Peter</td>\n",
              "      <td>633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>10</td>\n",
              "      <td>Peter</td>\n",
              "      <td>515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>11</td>\n",
              "      <td>Peter</td>\n",
              "      <td>1067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1</td>\n",
              "      <td>Mary</td>\n",
              "      <td>1096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2</td>\n",
              "      <td>Mary</td>\n",
              "      <td>960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>3</td>\n",
              "      <td>Mary</td>\n",
              "      <td>508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>4</td>\n",
              "      <td>Mary</td>\n",
              "      <td>907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5</td>\n",
              "      <td>Mary</td>\n",
              "      <td>562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>6</td>\n",
              "      <td>Mary</td>\n",
              "      <td>1110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>7</td>\n",
              "      <td>Mary</td>\n",
              "      <td>829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>8</td>\n",
              "      <td>Mary</td>\n",
              "      <td>728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>9</td>\n",
              "      <td>Mary</td>\n",
              "      <td>456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>10</td>\n",
              "      <td>Mary</td>\n",
              "      <td>964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>11</td>\n",
              "      <td>Mary</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>3</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>4</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>5</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>6</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>7</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>8</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>9</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>10</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>11</td>\n",
              "      <td>Annabelle</td>\n",
              "      <td>621</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    id Family Members  Hugs\n",
              "0    1           Jonh  1061\n",
              "1    2           Jonh   715\n",
              "2    3           Jonh   129\n",
              "3    4           Jonh   914\n",
              "4    5           Jonh   668\n",
              "5    6           Jonh  1001\n",
              "6    7           Jonh  1031\n",
              "7    8           Jonh   316\n",
              "8    9           Jonh   431\n",
              "9   10           Jonh  1095\n",
              "10  11           Jonh   497\n",
              "11   1          Peter   235\n",
              "12   2          Peter   847\n",
              "13   3          Peter   749\n",
              "14   4          Peter   468\n",
              "15   5          Peter   398\n",
              "16   6          Peter   753\n",
              "17   7          Peter   319\n",
              "18   8          Peter   209\n",
              "19   9          Peter   633\n",
              "20  10          Peter   515\n",
              "21  11          Peter  1067\n",
              "22   1           Mary  1096\n",
              "23   2           Mary   960\n",
              "24   3           Mary   508\n",
              "25   4           Mary   907\n",
              "26   5           Mary   562\n",
              "27   6           Mary  1110\n",
              "28   7           Mary   829\n",
              "29   8           Mary   728\n",
              "30   9           Mary   456\n",
              "31  10           Mary   964\n",
              "32  11           Mary  1050\n",
              "33   1      Annabelle   905\n",
              "34   2      Annabelle   144\n",
              "35   3      Annabelle   357\n",
              "36   4      Annabelle   252\n",
              "37   5      Annabelle   580\n",
              "38   6      Annabelle   141\n",
              "39   7      Annabelle   513\n",
              "40   8      Annabelle   627\n",
              "41   9      Annabelle   542\n",
              "42  10      Annabelle   792\n",
              "43  11      Annabelle   621"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JO9gGBzHA1BG"
      },
      "source": [
        "**So what happened here? **\n",
        "\n",
        "`pandas.melt` grabbed our **wide** dataframe and turned it into a **long** dataframe. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVoDU5avBghA"
      },
      "source": [
        "## CREATING A SIMPLE LINE GRAPH"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTSG8DWCBoKA"
      },
      "source": [
        "Now that we have a **long** format dataframe we can try and plot the some line graph that we made earlier.\n",
        "\n",
        "The difference now is that we have to tell plotly that our **x** is `id` and our **y** is `value` \n",
        "\n",
        "By keeping the column `id` we now have a numeric increment that will allow to have an **x** axis that allows for all the values to be plotted. \n",
        "\n",
        "Think of our `id`as a column that has dates in it and the lines show variations over a time period. \n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ier6JzBbByx8",
        "outputId": "93a738d6-bac4-4f40-d8cd-114a7e73f6b7"
      },
      "source": [
        "fig=px.line(data1_long,x='id', y='Hugs')\n",
        "fig.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
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      "metadata": {
        "id": "mJWjAABlKR7U"
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      "source": [
        "However these are not the results we were looking for. All lines of the plot are there but we can't really tell which ones are which. For that we will use the `color` variable that basically tells plotly to assign a different color to each different variable we have on our dataframe"
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    {
      "cell_type": "code",
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        "id": "C2SCZlWfLIaY",
        "outputId": "b1cc4a99-5989-432e-a4c0-cfdbe65f530d"
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      "source": [
        "fig=px.line(data1_long,x='id', y='Hugs', color='Family Members')\n",
        "fig.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Xk_H3QAOYUp"
      },
      "source": [
        "### MAKING IT PRETTY"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JegZMnc0Ohiw"
      },
      "source": [
        "Data visualization has to be appealing and this is something very important to keep in mind. In this section we will deal with that aspect based on the line graph we have made above"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9BWpMnvPYZ3"
      },
      "source": [
        "First let's get rid of that greyish blue background by using one of plotly's internal `template`s. \n",
        "\n",
        "We also add a title to our graph. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "mgp4AU4jPdjR",
        "outputId": "17eecb8a-446f-4a15-c258-e128db854c45"
      },
      "source": [
        "fig=px.line(data1_long,x='id', y='value', color='name', template='plotly_white', title='Such a pretty line graph')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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        "Now we are going to transform those straight lines in something more fluid using the `line_shape` option"
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        "fig=px.line(data1_long,x='id', y='value', color='name', template='plotly_white', title='Such a pretty line graph',\n",
        "            line_shape='spline')\n",
        "\n",
        "fig.show()"
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      "execution_count": null,
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