Seerfest 2020 - Automating Insights.ipynb

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  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Automate GSC Insights - Seerfest 2020",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BritneyMuller/colab-notebooks/blob/master/Seerfest%202020%20-%20Automating%20Insights.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "dOGPtWU6fAKR"
      },
      "source": [
        "#@title Import Libraries\n",
        "variable_name = \"\"\n",
        "#@Import Libraries\n",
        "import csv\n",
        "import json\n",
        "import requests\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use(\"ggplot\")\n",
        "import re\n",
        "from IPython.display import display"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "TSOQ1M2kO1F_",
        "outputId": "a7e36072-ea90-4751-90a3-ec6e1dab0df2",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 38
        }
      },
      "source": [
        "#@title Upload GSC File\n",
        "#@test\n",
        "#@Upload GSC File\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1db1b13f-619b-4092-8094-d5778b234696\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1db1b13f-619b-4092-8094-d5778b234696\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvhbitJsgn3A"
      },
      "source": [
        "#Upload data as \"df\" (dataframe)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bwFFew5O4_y"
      },
      "source": [
        "df = pd.read_csv(\"your-csv-name.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1d2HJG0gt4P"
      },
      "source": [
        "#Look at the first 5 lines (this includes the header)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PAuO2kzLe5QU",
        "outputId": "4dd5e9dc-0b61-49cc-e344-6ae510f4382e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Query</th>\n",
              "      <th>Clicks</th>\n",
              "      <th>Average Position</th>\n",
              "      <th>Site CTR</th>\n",
              "      <th>Impressions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>seer interactive</td>\n",
              "      <td>3444</td>\n",
              "      <td>1.407144</td>\n",
              "      <td>0.597191</td>\n",
              "      <td>5767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>google data studio</td>\n",
              "      <td>1352</td>\n",
              "      <td>8.237460</td>\n",
              "      <td>0.003478</td>\n",
              "      <td>388735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>johari window exercise</td>\n",
              "      <td>992</td>\n",
              "      <td>1.128184</td>\n",
              "      <td>0.414196</td>\n",
              "      <td>2395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>keywords everywhere</td>\n",
              "      <td>985</td>\n",
              "      <td>6.454694</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>153930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>google discovery</td>\n",
              "      <td>910</td>\n",
              "      <td>3.242859</td>\n",
              "      <td>0.060449</td>\n",
              "      <td>15054</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Query  Clicks  Average Position  Site CTR  Impressions\n",
              "0        seer interactive    3444          1.407144  0.597191         5767\n",
              "1      google data studio    1352          8.237460  0.003478       388735\n",
              "2  johari window exercise     992          1.128184  0.414196         2395\n",
              "3     keywords everywhere     985          6.454694  0.006399       153930\n",
              "4        google discovery     910          3.242859  0.060449        15054"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EUNZpQBtXdN8"
      },
      "source": [
        "#Look at the last 5 lines (this also includes the header)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sum4dPoukwW4",
        "outputId": "ca544bc3-67c1-4e97-a48c-760fb71758db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Query</th>\n",
              "      <th>Clicks</th>\n",
              "      <th>Average Position</th>\n",
              "      <th>Site CTR</th>\n",
              "      <th>Impressions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>27900</th>\n",
              "      <td>url crawl</td>\n",
              "      <td>0</td>\n",
              "      <td>26.597458</td>\n",
              "      <td>0.0</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27901</th>\n",
              "      <td>how to do outreach</td>\n",
              "      <td>0</td>\n",
              "      <td>78.058824</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27902</th>\n",
              "      <td>keyword search terms</td>\n",
              "      <td>0</td>\n",
              "      <td>80.666667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27903</th>\n",
              "      <td>native adv</td>\n",
              "      <td>0</td>\n",
              "      <td>4.375000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27904</th>\n",
              "      <td>power bi query editor remove duplicates</td>\n",
              "      <td>0</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Query  Clicks  ...  Site CTR  Impressions\n",
              "27900                                url crawl       0  ...       0.0          236\n",
              "27901                       how to do outreach       0  ...       0.0           17\n",
              "27902                     keyword search terms       0  ...       0.0            6\n",
              "27903                               native adv       0  ...       0.0            8\n",
              "27904  power bi query editor remove duplicates       0  ...       0.0            5\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9M-wgv-vwKqM"
      },
      "source": [
        "#Load Filter-able Data Table"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnQX_Xs5o7uX"
      },
      "source": [
        " %load_ext google.colab.data_table\n",
        " df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAQcVHFCw8IC"
      },
      "source": [
        "A few interesting features of the data table display:\n",
        "\n",
        "- Clicking the <button>Filter</button> button in the upper right allows you to search for terms or values in any particular column.\n",
        "- Clicking on any column title lets you sort the results according to that column's value.\n",
        "- The table displays only a subset of the data at a time. You can navigate through pages of data using the controls on the lower right.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jR9LZnwr8VZr"
      },
      "source": [
        "#Generate Histogram of Impressions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWJaso6f8tFQ",
        "outputId": "dbab1223-8cfb-40ba-8ae4-58480d610113",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "sns.distplot(df['Impressions'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f49b3164080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjSv8iZd5nW3"
      },
      "source": [
        "\n",
        "#Describe the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSWb5R955MgE",
        "outputId": "6f639701-c082-4b72-d1e3-374bc66f8431",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "df.describe(include='all')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Query</th>\n",
              "      <th>Clicks</th>\n",
              "      <th>Average Position</th>\n",
              "      <th>Site CTR</th>\n",
              "      <th>Impressions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>27905</td>\n",
              "      <td>27905.000000</td>\n",
              "      <td>27905.000000</td>\n",
              "      <td>27905.000000</td>\n",
              "      <td>27905.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>27905</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>seo for mobile apps</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2.523455</td>\n",
              "      <td>38.792771</td>\n",
              "      <td>0.139390</td>\n",
              "      <td>314.065114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>28.043182</td>\n",
              "      <td>35.250051</td>\n",
              "      <td>0.291771</td>\n",
              "      <td>4075.845007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.080000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>28.968750</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>66.750000</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>104.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3444.000000</td>\n",
              "      <td>319.300000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>388735.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Query        Clicks  ...      Site CTR    Impressions\n",
              "count                 27905  27905.000000  ...  27905.000000   27905.000000\n",
              "unique                27905           NaN  ...           NaN            NaN\n",
              "top     seo for mobile apps           NaN  ...           NaN            NaN\n",
              "freq                      1           NaN  ...           NaN            NaN\n",
              "mean                    NaN      2.523455  ...      0.139390     314.065114\n",
              "std                     NaN     28.043182  ...      0.291771    4075.845007\n",
              "min                     NaN      0.000000  ...      0.000000       1.000000\n",
              "25%                     NaN      0.000000  ...      0.000000       6.000000\n",
              "50%                     NaN      0.000000  ...      0.000000      17.000000\n",
              "75%                     NaN      1.000000  ...      0.058824     104.000000\n",
              "max                     NaN   3444.000000  ...      1.000000  388735.000000\n",
              "\n",
              "[11 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "srvoYLNjwSzT"
      },
      "source": [
        "#Drop all rows with Impressions < 17"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWKKwMxMwXBC"
      },
      "source": [
        "df.drop(df.loc[df['Impressions']<17].index, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lEDVulKqw6nW"
      },
      "source": [
        "#Reevaluate Distribution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCk-xfKLw9hS"
      },
      "source": [
        "sns.distplot(df['Impressions'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zF3S7tlGxCgg"
      },
      "source": [
        "df.describe(include='all')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9GaE8SJi89u"
      },
      "source": [
        "##Find all Queries that include 'power bi'\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbuhQAbvhk_P",
        "outputId": "877bbf11-743d-4e06-87ed-fd487031f88e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df_filtered = df[(df['Query'].str.contains(\"power bi\", regex=True)==True)]\n",
        "df_filtered.head(100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Query</th>\n",
              "      <th>Clicks</th>\n",
              "      <th>Average Position</th>\n",
              "      <th>Site CTR</th>\n",
              "      <th>Impressions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>power bi sort legend</td>\n",
              "      <td>430</td>\n",
              "      <td>1.017974</td>\n",
              "      <td>0.702614</td>\n",
              "      <td>612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>sort legend power bi</td>\n",
              "      <td>201</td>\n",
              "      <td>1.013072</td>\n",
              "      <td>0.656863</td>\n",
              "      <td>306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>power bi legend order</td>\n",
              "      <td>181</td>\n",
              "      <td>1.229236</td>\n",
              "      <td>0.601329</td>\n",
              "      <td>301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>power bi bridge table</td>\n",
              "      <td>172</td>\n",
              "      <td>1.037647</td>\n",
              "      <td>0.404706</td>\n",
              "      <td>425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>power bi change order of legend</td>\n",
              "      <td>159</td>\n",
              "      <td>1.068966</td>\n",
              "      <td>0.685345</td>\n",
              "      <td>232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>power bi - custom sort</td>\n",
              "      <td>11</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1093</th>\n",
              "      <td>power bi order legend by value</td>\n",
              "      <td>11</td>\n",
              "      <td>1.076923</td>\n",
              "      <td>0.846154</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1099</th>\n",
              "      <td>how to create power bi template</td>\n",
              "      <td>11</td>\n",
              "      <td>2.833333</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1100</th>\n",
              "      <td>many to many in power bi</td>\n",
              "      <td>11</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.846154</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1115</th>\n",
              "      <td>power bi pie chart legend order</td>\n",
              "      <td>11</td>\n",
              "      <td>1.466667</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Query  Clicks  ...  Site CTR  Impressions\n",
              "10               power bi sort legend     430  ...  0.702614          612\n",
              "28               sort legend power bi     201  ...  0.656863          306\n",
              "36              power bi legend order     181  ...  0.601329          301\n",
              "40              power bi bridge table     172  ...  0.404706          425\n",
              "44    power bi change order of legend     159  ...  0.685345          232\n",
              "...                               ...     ...  ...       ...          ...\n",
              "1089           power bi - custom sort      11  ...  0.500000           22\n",
              "1093   power bi order legend by value      11  ...  0.846154           13\n",
              "1099  how to create power bi template      11  ...  0.611111           18\n",
              "1100         many to many in power bi      11  ...  0.846154           13\n",
              "1115  power bi pie chart legend order      11  ...  0.733333           15\n",
              "\n",
              "[100 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRxd3HcGRkK4"
      },
      "source": [
        "#Rename Column Headers\n",
        "\n",
        "The following column names below changed to:\n",
        "\n",
        "Query_column=\"Keyword\"\n",
        "Position_column=\"Rank\"\n",
        "CTR_column=\"CTR\"\n",
        "\n",
        "You can change these to whatever you'd like but make sure to modify any correlating code below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1636hMVkQ43k"
      },
      "source": [
        "\n",
        "df=df.rename(columns={'Query':'Keyword','Average Position':'Rank', 'Site CTR':'CTR'})\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZ6pKRNlRuyD",
        "outputId": "e6617d64-0928-4136-ca73-1421941cd4a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Query</th>\n",
              "      <th>Clicks</th>\n",
              "      <th>Average Position</th>\n",
              "      <th>Site CTR</th>\n",
              "      <th>Impressions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>seer interactive</td>\n",
              "      <td>3444</td>\n",
              "      <td>1.407144</td>\n",
              "      <td>0.597191</td>\n",
              "      <td>5767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>google data studio</td>\n",
              "      <td>1352</td>\n",
              "      <td>8.237460</td>\n",
              "      <td>0.003478</td>\n",
              "      <td>388735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>johari window exercise</td>\n",
              "      <td>992</td>\n",
              "      <td>1.128184</td>\n",
              "      <td>0.414196</td>\n",
              "      <td>2395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>keywords everywhere</td>\n",
              "      <td>985</td>\n",
              "      <td>6.454694</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>153930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>google discovery</td>\n",
              "      <td>910</td>\n",
              "      <td>3.242859</td>\n",
              "      <td>0.060449</td>\n",
              "      <td>15054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27900</th>\n",
              "      <td>url crawl</td>\n",
              "      <td>0</td>\n",
              "      <td>26.597458</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27901</th>\n",
              "      <td>how to do outreach</td>\n",
              "      <td>0</td>\n",
              "      <td>78.058824</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27902</th>\n",
              "      <td>keyword search terms</td>\n",
              "      <td>0</td>\n",
              "      <td>80.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27903</th>\n",
              "      <td>native adv</td>\n",
              "      <td>0</td>\n",
              "      <td>4.375000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27904</th>\n",
              "      <td>power bi query editor remove duplicates</td>\n",
              "      <td>0</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27905 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Query  Clicks  ...  Site CTR  Impressions\n",
              "0                             seer interactive    3444  ...  0.597191         5767\n",
              "1                           google data studio    1352  ...  0.003478       388735\n",
              "2                       johari window exercise     992  ...  0.414196         2395\n",
              "3                          keywords everywhere     985  ...  0.006399       153930\n",
              "4                             google discovery     910  ...  0.060449        15054\n",
              "...                                        ...     ...  ...       ...          ...\n",
              "27900                                url crawl       0  ...  0.000000          236\n",
              "27901                       how to do outreach       0  ...  0.000000           17\n",
              "27902                     keyword search terms       0  ...  0.000000            6\n",
              "27903                               native adv       0  ...  0.000000            8\n",
              "27904  power bi query editor remove duplicates       0  ...  0.000000            5\n",
              "\n",
              "[27905 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dIbFPqaTFXC"
      },
      "source": [
        "#Collect all branded keywords"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNBc3qgQTH1Z"
      },
      "source": [
        "df_branded = df[(df['Keyword'].str.contains(\"seer|wil|reynolds\", regex=True)==True)]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPRalE3WTQfr",
        "outputId": "e642708e-994b-4244-c566-3d19a34b2186",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        }
      },
      "source": [
        "df_branded"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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0.326923076923077,\n            'f': \"0.326923076923077\",\n        },\n{\n            'v': 208,\n            'f': \"208\",\n        }],\n [{\n            'v': 189,\n            'f': \"189\",\n        },\n\"seer philadelphia\",\n{\n            'v': 51,\n            'f': \"51\",\n        },\n{\n            'v': 2.32978723404255,\n            'f': \"2.32978723404255\",\n        },\n{\n            'v': 0.542553191489362,\n            'f': \"0.542553191489362\",\n        },\n{\n            'v': 94,\n            'f': \"94\",\n        }],\n [{\n            'v': 193,\n            'f': \"193\",\n        },\n\"seer interactive san diego\",\n{\n            'v': 50,\n            'f': \"50\",\n        },\n{\n            'v': 1.65420560747664,\n            'f': \"1.65420560747664\",\n        },\n{\n            'v': 0.467289719626168,\n            'f': \"0.467289719626168\",\n        },\n{\n            'v': 107,\n            'f': \"107\",\n        }],\n [{\n            'v': 195,\n            'f': 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           'v': 0.577464788732394,\n            'f': \"0.577464788732394\",\n        },\n{\n            'v': 71,\n            'f': \"71\",\n        }],\n [{\n            'v': 301,\n            'f': \"301\",\n        },\n\"seer interactive internship\",\n{\n            'v': 35,\n            'f': \"35\",\n        },\n{\n            'v': 1.125,\n            'f': \"1.125\",\n        },\n{\n            'v': 0.729166666666667,\n            'f': \"0.729166666666667\",\n        },\n{\n            'v': 48,\n            'f': \"48\",\n        }],\n [{\n            'v': 353,\n            'f': \"353\",\n        },\n\"seer san diego\",\n{\n            'v': 31,\n            'f': \"31\",\n        },\n{\n            'v': 2.4583333333333304,\n            'f': \"2.4583333333333304\",\n        },\n{\n            'v': 0.645833333333333,\n            'f': \"0.645833333333333\",\n        },\n{\n            'v': 48,\n            'f': \"48\",\n        }],\n [{\n            'v': 386,\n            'f': \"386\",\n        },\n\"will reynolds\",\n{\n            'v': 29,\n            'f': \"29\",\n        },\n{\n            'v': 6.55757026291931,\n            'f': \"6.55757026291931\",\n        },\n{\n            'v': 0.0262919310970082,\n            'f': \"0.0262919310970082\",\n        },\n{\n            'v': 1103,\n            'f': \"1103\",\n        }],\n [{\n            'v': 402,\n            'f': \"402\",\n        },\n\"seer team\",\n{\n            'v': 28,\n            'f': \"28\",\n        },\n{\n            'v': 1.24242424242424,\n            'f': \"1.24242424242424\",\n        },\n{\n            'v': 0.848484848484849,\n            'f': \"0.848484848484849\",\n        },\n{\n            'v': 33,\n            'f': \"33\",\n        }],\n [{\n            'v': 439,\n            'f': \"439\",\n        },\n\"seer interactive screaming frog\",\n{\n            'v': 26,\n            'f': \"26\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            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       'f': \"29.052631578947395\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 418,\n            'f': \"418\",\n        }],\n [{\n            'v': 12942,\n            'f': \"12942\",\n        },\n\"reynolds web solutions email\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 64.5,\n            'f': \"64.5\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 6,\n            'f': \"6\",\n        }],\n [{\n            'v': 13326,\n            'f': \"13326\",\n        },\n\"seer characteristics\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 37.3333333333333,\n            'f': \"37.3333333333333\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 27,\n            'f': \"27\",\n        }],\n [{\n            'v': 13363,\n            'f': \"13363\",\n        },\n\"when will google analytics be unable to identify sessions from the same user by default?\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 61.3333333333333,\n            'f': \"61.3333333333333\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 6,\n            'f': \"6\",\n        }],\n [{\n            'v': 13548,\n            'f': \"13548\",\n        },\n\"seer pdf\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 4.4,\n            'f': \"4.4\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 10,\n            'f': \"10\",\n        }],\n [{\n            'v': 14219,\n            'f': \"14219\",\n        },\n\"how can i be a seer\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 37.8571428571429,\n            'f': \"37.8571428571429\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 21,\n            'f': \"21\",\n        }],\n [{\n            'v': 15199,\n            'f': \"15199\",\n        },\n\"google ads wil\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 35.6924778761062,\n            'f': \"35.6924778761062\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 452,\n            'f': \"452\",\n        }],\n [{\n            'v': 15841,\n            'f': \"15841\",\n        },\n\"google seer\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 4.0,\n            'f': \"4.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 5,\n            'f': \"5\",\n        }],\n [{\n            'v': 16813,\n            'f': \"16813\",\n        },\n\"which beings are seers\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 10.875,\n            'f': \"10.875\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8,\n            'f': \"8\",\n        }],\n [{\n            'v': 16825,\n            'f': \"16825\",\n        },\n\"the seer television show\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 26.928571428571402,\n            'f': \"26.928571428571402\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 154,\n            'f': \"154\",\n        }],\n [{\n            'v': 17012,\n            'f': \"17012\",\n        },\n\"will stevens\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 66.3636363636364,\n            'f': \"66.3636363636364\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        }],\n [{\n            'v': 17637,\n            'f': \"17637\",\n        },\n\"when a new view is created, what data will be included?\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 15.666666666666698,\n            'f': \"15.666666666666698\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 9,\n            'f': \"9\",\n        }],\n [{\n            'v': 18393,\n            'f': \"18393\",\n        },\n\"seer data analytics\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 14.75,\n            'f': \"14.75\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 4,\n            'f': \"4\",\n        }],\n [{\n            'v': 18926,\n            'f': \"18926\",\n        },\n\"seer gif\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 9.5,\n            'f': \"9.5\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 4,\n            'f': \"4\",\n        }],\n [{\n            'v': 18977,\n            'f': \"18977\",\n        },\n\"will wil\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 21.0,\n            'f': \"21.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 6,\n            'f': \"6\",\n        }],\n [{\n            'v': 19792,\n            'f': \"19792\",\n        },\n\"seer covid\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 5.16666666666667,\n            'f': \"5.16666666666667\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 6,\n            'f': \"6\",\n        }],\n [{\n            'v': 19969,\n            'f': \"19969\",\n        },\n\"seer, inc\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 9.125,\n            'f': \"9.125\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 8,\n            'f': \"8\",\n        }],\n [{\n            'v': 19979,\n            'f': \"19979\",\n        },\n\"the mistakes will our\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 10.1228070175439,\n            'f': \"10.1228070175439\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 57,\n            'f': \"57\",\n        }],\n [{\n            'v': 20599,\n            'f': \"20599\",\n        },\n\"adam seer\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 3.6842105263157903,\n            'f': \"3.6842105263157903\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 19,\n            'f': \"19\",\n        }],\n [{\n            'v': 22236,\n            'f': \"22236\",\n        },\n\"when will google maps update in my area\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 67.5,\n            'f': \"67.5\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 6,\n            'f': \"6\",\n        }],\n [{\n            'v': 22320,\n            'f': \"22320\",\n        },\n\"what is seer\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 67.8655462184874,\n            'f': \"67.8655462184874\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 119,\n            'f': \"119\",\n        }],\n [{\n            'v': 22960,\n            'f': \"22960\",\n        },\n\"seer international\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 11.0,\n            'f': \"11.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 4,\n            'f': \"4\",\n        }],\n [{\n            'v': 23730,\n            'f': \"23730\",\n        },\n\"india reynolds\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 94.5,\n            'f': \"94.5\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 6,\n            'f': \"6\",\n        }],\n [{\n            'v': 24499,\n            'f': \"24499\",\n        },\n\"ecom wild\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 4.927152317880799,\n            'f': \"4.927152317880799\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 151,\n            'f': \"151\",\n        }],\n [{\n            'v': 26494,\n            'f': \"26494\",\n        },\n\"seer paragraph\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 6.428571428571429,\n            'f': \"6.428571428571429\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 7,\n          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},\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"string\", \"Query\"], [\"number\", \"Clicks\"], [\"number\", \"Average Position\"], [\"number\", \"Site CTR\"], [\"number\", \"Impressions\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
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              "      <th>45</th>\n",
              "      <td>seer interactive careers</td>\n",
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              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>wil reynolds</td>\n",
              "      <td>129</td>\n",
              "      <td>1.989627</td>\n",
              "      <td>0.133817</td>\n",
              "      <td>964</td>\n",
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              "    <tr>\n",
              "      <th>86</th>\n",
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              "      <th>27307</th>\n",
              "      <td>seers</td>\n",
              "      <td>0</td>\n",
              "      <td>64.031674</td>\n",
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              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27399</th>\n",
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              "      <td>0</td>\n",
              "      <td>51.909091</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>105 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       Query  Clicks  ...  Site CTR  Impressions\n",
              "0                           seer interactive    3444  ...  0.597191         5767\n",
              "45                  seer interactive careers     157  ...  0.740566          212\n",
              "59                           seerinteractive     129  ...  0.763314          169\n",
              "60                              wil reynolds     129  ...  0.133817          964\n",
              "86                                  seer seo     100  ...  0.709220          141\n",
              "...                                      ...     ...  ...       ...          ...\n",
              "24499                              ecom wild       0  ...  0.000000          151\n",
              "26494                         seer paragraph       0  ...  0.000000            7\n",
              "27052                               seer lab       0  ...  0.000000            5\n",
              "27307                                  seers       0  ...  0.000000          221\n",
              "27399  when will i reach number 1 on google?       0  ...  0.000000           11\n",
              "\n",
              "[105 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sgNymMQtTrjm"
      },
      "source": [
        "#Export Branded Keywords to a CSV\n",
        "\n",
        "--You can find the csv in the left sidebar to download."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daeit17gTUgA"
      },
      "source": [
        "df_branded.to_csv('branded-keyword-data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Us3CsJoUhKe"
      },
      "source": [
        "#Add a Branded Keyword Column to Original Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0MlLg8ZULhC",
        "outputId": "24326fa9-4d9f-46cb-9898-e15a4fade9be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df[\"Branded\"]=df.Keyword.str.contains(\"seer|wil|reynolds\")\n",
        "df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>True</td>\n",
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              "      <th>1</th>\n",
              "      <td>google data studio</td>\n",
              "      <td>1352</td>\n",
              "      <td>8.237460</td>\n",
              "      <td>0.003478</td>\n",
              "      <td>388735</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>johari window exercise</td>\n",
              "      <td>992</td>\n",
              "      <td>1.128184</td>\n",
              "      <td>0.414196</td>\n",
              "      <td>2395</td>\n",
              "      <td>False</td>\n",
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              "      <th>3</th>\n",
              "      <td>keywords everywhere</td>\n",
              "      <td>985</td>\n",
              "      <td>6.454694</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>153930</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>google discovery</td>\n",
              "      <td>910</td>\n",
              "      <td>3.242859</td>\n",
              "      <td>0.060449</td>\n",
              "      <td>15054</td>\n",
              "      <td>False</td>\n",
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              "      <th>27900</th>\n",
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              "      <td>236</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27901</th>\n",
              "      <td>how to do outreach</td>\n",
              "      <td>0</td>\n",
              "      <td>78.058824</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27902</th>\n",
              "      <td>keyword search terms</td>\n",
              "      <td>0</td>\n",
              "      <td>80.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27903</th>\n",
              "      <td>native adv</td>\n",
              "      <td>0</td>\n",
              "      <td>4.375000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27904</th>\n",
              "      <td>power bi query editor remove duplicates</td>\n",
              "      <td>0</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27905 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Query  Clicks  ...  Impressions  Branded\n",
              "0                             seer interactive    3444  ...         5767     True\n",
              "1                           google data studio    1352  ...       388735    False\n",
              "2                       johari window exercise     992  ...         2395    False\n",
              "3                          keywords everywhere     985  ...       153930    False\n",
              "4                             google discovery     910  ...        15054    False\n",
              "...                                        ...     ...  ...          ...      ...\n",
              "27900                                url crawl       0  ...          236    False\n",
              "27901                       how to do outreach       0  ...           17    False\n",
              "27902                     keyword search terms       0  ...            6    False\n",
              "27903                               native adv       0  ...            8    False\n",
              "27904  power bi query editor remove duplicates       0  ...            5    False\n",
              "\n",
              "[27905 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tk4f8HkKurwn"
      },
      "source": [
        "#Create a Non-Branded Keyword Table\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s30fUUGAuu5T"
      },
      "source": [
        "df_non_branded = df[(df['Branded']==False)]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7u3Mkzv0hjW",
        "outputId": "b11711b1-91ba-4149-97d2-d165ddd34a84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df_non_branded"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Query</th>\n",
              "      <th>Clicks</th>\n",
              "      <th>Average Position</th>\n",
              "      <th>Site CTR</th>\n",
              "      <th>Impressions</th>\n",
              "      <th>Branded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>google data studio</td>\n",
              "      <td>1352</td>\n",
              "      <td>8.237460</td>\n",
              "      <td>0.003478</td>\n",
              "      <td>388735</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>johari window exercise</td>\n",
              "      <td>992</td>\n",
              "      <td>1.128184</td>\n",
              "      <td>0.414196</td>\n",
              "      <td>2395</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>keywords everywhere</td>\n",
              "      <td>985</td>\n",
              "      <td>6.454694</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>153930</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>google discovery</td>\n",
              "      <td>910</td>\n",
              "      <td>3.242859</td>\n",
              "      <td>0.060449</td>\n",
              "      <td>15054</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>johari window test</td>\n",
              "      <td>902</td>\n",
              "      <td>1.767764</td>\n",
              "      <td>0.147001</td>\n",
              "      <td>6136</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27900</th>\n",
              "      <td>url crawl</td>\n",
              "      <td>0</td>\n",
              "      <td>26.597458</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>236</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27901</th>\n",
              "      <td>how to do outreach</td>\n",
              "      <td>0</td>\n",
              "      <td>78.058824</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27902</th>\n",
              "      <td>keyword search terms</td>\n",
              "      <td>0</td>\n",
              "      <td>80.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27903</th>\n",
              "      <td>native adv</td>\n",
              "      <td>0</td>\n",
              "      <td>4.375000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27904</th>\n",
              "      <td>power bi query editor remove duplicates</td>\n",
              "      <td>0</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27800 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Query  Clicks  ...  Impressions  Branded\n",
              "1                           google data studio    1352  ...       388735    False\n",
              "2                       johari window exercise     992  ...         2395    False\n",
              "3                          keywords everywhere     985  ...       153930    False\n",
              "4                             google discovery     910  ...        15054    False\n",
              "5                           johari window test     902  ...         6136    False\n",
              "...                                        ...     ...  ...          ...      ...\n",
              "27900                                url crawl       0  ...          236    False\n",
              "27901                       how to do outreach       0  ...           17    False\n",
              "27902                     keyword search terms       0  ...            6    False\n",
              "27903                               native adv       0  ...            8    False\n",
              "27904  power bi query editor remove duplicates       0  ...            5    False\n",
              "\n",
              "[27800 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hrbog3Gy0mtq"
      },
      "source": [
        "#Export All Non Branded Keywords to a CSV\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qu8lmTSd0ptl"
      },
      "source": [
        "df_non_branded.to_csv('non-branded-keyword-data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "al7NGFqv79Xa"
      },
      "source": [
        "#Quickly Identify % of Branded Clicks\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Po2Zj2DiF9Kb"
      },
      "source": [
        "df.Clicks.sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4b_RUPQHaWT"
      },
      "source": [
        "df_branded.Clicks.sum()/df_non_branded.Clicks.sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXEwdzaE8KhZ"
      },
      "source": [
        "#Evaluate Standard Deviation, Mean & Median"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAwXYK368Jwb",
        "outputId": "3646c03d-f64f-459c-971c-efe4675e6d19",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df['Rank'].std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35.25005146766555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_1ryYr_8pU_",
        "outputId": "b20bf7a1-b7ee-4604-c8c4-3ebd2f963602",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df['Rank'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38.79277068814318"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iupEEVlNNTXF",
        "outputId": "83fe5a0a-fc29-48c7-dce4-f44d6348973c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df['Rank'].median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.96875"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8BuvsQ9e8dTP"
      },
      "source": [
        "#Generate Histograms of Branded Keywords (True = Branded, False = NonBranded) & Their Average Positions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bi-Y_Igs8wy3",
        "outputId": "7ae69307-7d3c-4787-bfcf-4824b275dc5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        }
      },
      "source": [
        "df.hist(by='Branded', column='Rank')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f49b03a87b8>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f49b0356a58>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElApM6Z8udK8"
      },
      "source": [
        "#Generate Histograms of Branded Keywords (True = Branded, False = NonBranded) & Their CTR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLqH6JD586f9",
        "outputId": "7973e85c-b0ba-4f21-fa96-5474235d8503",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        }
      },
      "source": [
        "df.hist(by='Branded', column='CTR')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f49b02e1208>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f49b025ad68>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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evt1ZE1MZwMbUldXbpbVppDGZmVnrjShxRMQ04MvA8ZJ+U1P+jojYLW2/m+Im+OOpK+qliDgiIjJgBnBTOm0ZMDNtz6wpNzOzChrOcNxrgCOBnohYB8yhGEX1RmBlRMCOYbd/AFwSEb8DXgM+L6n3xvqZ7BiOe3N6AMwFFBGzgKeAaMo7MzOzlhgycUg6uU7x1Q2OvQ64rsG+NcBBdcqfB44eKg4zM6sGf3PczMxKceIwM7NSnDjMzKyUZnwB0MxKiog3AbdTDDIZA/yDpDkRsR+wFNgHuAc4RdKrnYvUbCC3OMw64xXgKEkHA1OBaRFxBPB14ApJ+wNbgVkdjNGsLrc4zDogfWn2V+np7umRA0cBn03li4GvUEwQalYZThxmHZK+LHsPsD/wHYqZFl6omUC0dk63/ue2bN42aP7cbVWZH8xxDDSSWJw4zDpE0nZgakTsCdwAvK/EuS2btw2aP3dbN8xTtivGAY1jGWzeNt/jMOswSS8AtwG/D+wZEb3/oaud082sMpw4zDogzeu2Z9p+M3AMxeqZtwF/nA7z3G1WSU4cZp0xDrgtIu4H7gZWSvon4DzgTyNiLcWQ3LrT+5h1ku9xmHWApPuBD9Qpfxw4rP0RmQ2fWxxmZlaKE4eZmZXixGFmZqUM6x5HRCykWCZ2k6SDUtnewLXAZOBJICRtTSv8zQM+CfwGOFXSvemcmcBF6bJfk7Q4lR/CjkWelgPn9C5Ha2Zm1TLcFsciYFq/svOBVZKmAKvSc4DjKJaMnULxzdb58HqimQMcTnHzb05E7JXOmQ+cXnNe/9cyM7OKGFbikHQ7sKVf8XSKuXRIP0+oKV8iKZe0muILTeOAT1AMOdwiaSuwkmJit3HA2yWtTq2MJTXXMjOzihnNcNyxkjak7WeBsWl7AvB0zXG98+0MVr6uTvkArZyfpxXzxlRlPhrHUc04zLpVU77HISmPiJbfk2jl/DytmDemKvPROI7hxzHY/DxmVhjNqKqNqZuJ9HNTKl8PTKo5rne+ncHKJ9YpNzOzChpN4lhGMZcO9J1TZxkwIyKytDDNi6lLawVwbETslW6KHwusSPteiogj0oisGXh+HjOzyhrucNxrgCOBnohYRzE6ai6giJgFPAVEOnw5xVDctRTDcT8HIGlLRFxKMS8PwCWSem+4n8mO4bg3p4eZmVXQsBKHpJMb7Dq6zrE5cFaD6ywEFtYpXwMcNJxYzMyss/zNcTMzK8WJw8zMSnHiMDOzUpw4zMysFCcOMzMrxYnDzMxKceIwM7NSnDjMzKwUJw4zMyvFicPMzEpx4jAzs1KcOMzMrBQnDjMzK8WJw8zMSnHiMDOzUka85nhEvBe4tqbo3cDFwJ7A6cBzqfxCScvTORcAs4DtwBclrUjl04B5wG7AVZLmjjQus24QEZOAJcBYIAcWSJoXEXtT1KvJwJNASNraqTjN6hlx4pD0S2AqQETsRrFO+A0UK/5dIemvao+PiAOAk4ADgfHAjyLiPWn3d4BjgHXA3RGxTNJDI43NrAtsA74k6d6IeBtwT0SsBE4FVkmaGxHnA+cD53UwTrMBmtVVdTTwmKSnBjlmOrBU0iuSnqBYWvaw9Fgr6XFJrwJL07FmOy1JGyTdm7ZfBh4GJlD87i9Ohy0GTuhMhGaNjbjF0c9JwDU1z8+OiBnAGor/VW2lqBSra45Zl8oAnu5Xfni9F4mI2cBsAEn09PTUDWbjCN5Ao2uNxpgxY1pyXcexc8TRKyImAx8A7gTGStqQdj1L0ZVV75yW1QVofn2oymfuOAYaSSyjThwRsQdwPHBBKpoPXErRb3sp8C3gtNG+DoCkBcCC9DTfvHlzMy4LQDOv1aunp6cl13UcrYtj/PjxbY0lIt4KXAecK+mliHh9n6Q8IvJ657WyLkDz60M3/NvvinFA41gGqwvN6Ko6DrhX0kYASRslbZf0GnAlRVcUFPdAJtWcNzGVNSo326lFxO4USeP7kq5PxRsjYlzaPw7Y1Kn4zBppRlfVydR0U0XEuJqm9onAA2l7GfCDiLic4ub4FOAuIAOmRMR+FAnjJOCzTYjLrLIiIgOuBh6WdHnNrmXATGBu+nlTB8IzG9SoEkdEvIViNNQZNcXfiIipFF1VT/buk/RgRAh4iGJEyVmStqfrnA2soBiOu1DSg6OJy6wLfAQ4BfhFRNyXyi6kSBiKiFnAU0A0ON+sY0aVOCT9GtinX9kpgxx/GXBZnfLlwPLRxGLWTSTdQdHarufodsZiVpa/OW5mZqU4cZiZWSlOHGZmVooTh5mZleLEYWZmpThxmJlZKU4cZmZWihOHmZmV4sRhZmalOHGYmVkpThxmZlaKE4eZmZXixGFmZqU4cZiZWSlOHGZmVkoz1hx/EngZ2A5sk3RoROwNXAtMpljMKSRtTauezQM+CfwGOFXSvek6M4GL0mW/JmnxaGMzM7Pma1aL42OSpko6ND0/H1glaQqwKj2HYn3yKekxG5gPkBLNHOBwijXK50TEXk2KzczMmqhVXVXTgd4Ww2LghJryJZJySauBPSNiHPAJYKWkLZK2AiuBaS2KzczMRmHUXVUUa4vfGhE58HeSFgBjJW1I+58FxqbtCcDTNeeuS2WNyvuIiNkULRUk0dPTUzegjSN4E42uNRpjxoxpyXUdx84Rh1m3akbi+Kik9RHxTmBlRDxSu1NSnpLKqKWktCA9zTdv3tyMywLQzGv16unpacl1HUfr4hg/fnybozHrPqPuqpK0Pv3cBNxAcY9iY+qCIv3clA5fD0yqOX1iKmtUbmZmFTOqxBERb4mIt/VuA8cCDwDLgJnpsJnATWl7GTAjIrKIOAJ4MXVprQCOjYi90k3xY1OZmZlVzGhbHGOBOyLi34C7gB9KugWYCxwTEY8CH0/PAZYDjwNrgSuBMwEkbQEuBe5Oj0tSmZmZVcyo7nFIehw4uE7588DRdcpz4KwG11oILBxNPGZm1nr+5riZmZXixGFmZqU4cZiZWSnN+B6HmY1ARCwEPgVsknRQKqs7z1unYrTusf3040d24g0/K32KWxxmnbOIgVPrNJrnzawynDjMOkTS7UD/YeeN5nkzqwx3VZlVS6N53vpo5bxt0Py526oyP9jGEz88ovPGjqA7ZzCt+DxG+m89klicOMwqarB53lo5bxs0f+62qsxTNlI78+exbdu2urEMNm+bu6rMqqXRPG9mleHEYVYtjeZ5M6sMd1WZdUhEXAMcCfRExDqKVTDnAoqIWcBTQHQitpEM7dztymUtiMSqyInDrEMkndxg14B53syqxF1VZmZWihOHmZmV4sRhZmaljPgeR0RMApZQfEEpBxZImhcRXwFOB55Lh14oaXk65wJgFrAd+KKkFal8GjAP2A24StJczMyskkZzc3wb8CVJ96blY++JiJVp3xWS/qr24Ig4ADgJOBAYD/woIt6Tdn8HOAZYB9wdEcskPTSK2MzMrEVGnDjStAgb0vbLEfEwMGGQU6YDSyW9AjwREWuBw9K+tWk1QSJiaTrWicPMKmGkM8/urEOUmzIcNyImAx8A7gQ+ApwdETOANRStkq0USWV1zWnr2JFonu5Xfngz4jIzs+YbdeKIiLcC1wHnSnopIuYDl1Lc97gU+BZw2mhfJ71WyyZ2a8UEbFWZ2M1xVDMOs241qsQREbtTJI3vS7oeQNLGmv1XAv+Unq4HJtWcPjGVMUh5H62c2K0VE45VZSIzxzH8OAab2M3MCqMZVZUBVwMPS7q8pnxczbTQJwIPpO1lwA8i4nKKm+NTgLuADJgSEftRJIyTgM+ONC4zM2ut0bQ4PgKcAvwiIu5LZRcCJ0fEVIquqieBMwAkPRgRorjpvQ04S9J2gIg4G1hBMRx3oaQHRxGXmZm10GhGVd1B0Vrob/kg51wGXFanfPlg55k1MqLRLk1elMdsV+NvjpuZWSmeHdfMrEUatYgHG/nZDd/9cIvDzMxKceIwM7NSnDjMzKwUJw4zMyvFicPMzErxqKpdwMYTPzyi87phdIeZtZ9bHGZmVooTh5mZleKuqsQLtZiZDY8Th5l1pZH+Z6/quuF9uavKzMxKcYtjlAb738FIViIcjLvFzKwK3OIwM7NSKtPiiIhpwDyKxZyukjS3wyFVTrv7Pkfyem4VNUc31od2tr6tsyrR4oiI3YDvAMcBB1CsInhAZ6My6wzXB6u6qrQ4DgPWSnocICKWAtMplpm1LuL/dTaF64NVWiVaHMAE4Oma5+tSmdmuyPXBKq0qLY5hiYjZwGwASYwfP77+gT9c08aorBs1/N3pEq4L1kxl60NVWhzrgUk1zyemsj4kLZB0qKRDgazRIyLuGWx/ux6Oo2vj6LQh64PrguNoUyx1VaXFcTcwJSL2o6ggJwGf7WxIZh3j+mCVVokWh6RtwNnACuDhokgPdjYqs85wfbCqq0qLA0nLgeVNutyCJl1ntBxHX45jmJpYH6ryXh1HX1WJA0YQS5bneSsCMTOznVQluqrMzKx7OHGYmVkpThxmZlZKZW6Oj1REZBRTNPR+s3Y9cJekjt28ScMoPwA8JOmRDrz+fwGm0fczWSHphXbHUhNTRz+TXYXrw4DXdl1oga6+OR4RxwLfBR5lxxekJgL7A2dKurVNcdwo6YS0PR34a+CfgQ8D/0fSonbEkV5/BjAHuJW+n8kxwFclLWlTHFX6TCr3x6MVXB8GxOG6UD+eUdeHbm9xzAM+LunJ2sKU0ZcDv9emON5Vs30ecJSkJyKiB1gFLGpTHAB/DhzS/5cgIvYC7gTaUlmoyGfS4I/Hx4C/jIi2/fFoE9eHvlwX+mlWfej2xDGGYgK4/tYDu7cxjtpm2xhJTwBI2hwRr7UxDiimCajXjHyNQaYQaIGqfCZV+ePRDq4PfbkuDNSU+tDtiWMhcHeadrp3NtFJFFM0XN3GOA6OiJcofhnfGBHjJG2IiD0oFuJpp8uAeyPiVnZ8JvtSNM8vbWMcVflMqvLHox1cH/pyXRioKfWhq+9xAKQFbo6nb3/dMkkdX7sgIvYEfk/Sv7b5dfcCPsHAPsyt7YyjnnZ/JhExE7iYomk+4I9Hu/uXW831YcBrui70fc2m1IeuTxy9ImJvAElbOh2LVUuV/3i0iuuDNdKM+tDViSMi9gW+ARwFvEjR1Ho78GPg/P43CVsYxyTgmxT/EDcD35T0u7Tv9REVbYrlNEkL0/YEij7LD1JMlneqpH9vUxxbgOuBa4Afd3I4aIpnLDUVRdJOtyCh68OAOFwXGhhtfej2LwBeC9wAjJM0RdL+wDjgRmBpG+NYSDG87gvp9X8SEfukfe9qdFKLnF2zfQXFZ7QPRUWe38Y4ngPuAy4B1kXEvIg4oo2vD0BETI2I1RT/Pl+n+MP6k4hYHREfbHc8Leb60JfrQj/Nqg/dfnO8R9K1tQWStgNLI6KdN7/eIen/pu0vRMT/Am6PiOOpfyOqXd4jKdL2DRFxcRtf+9eSvg18O/1P+CTgu6lfd6mkC9sUxyLgDEl31hamivs94OA2xdEOrg+NuS4UFtGE+tDtieOeiPgusJi+o0hmAj9vYxy7R8SbJP0ngKT/FxHPUqyn8JY2xgEwMSL+hqKb4h0RsXtvNwHtHZL5+ggNSf9B8T+bb0TE+4DPtDGOt/SvJCmm1RHR7n+bVnN96Mt1YaCm1IduTxwzgFnAV9nRX7cO+EfaO/zwKuBw4Ce9BZJ+FBGfpvglaaf/XbO9BngrsDUi/iuwrI1x3FavME2x8NU2xnFzRPyQon+79o/pDOCWNsbRDq4PfbkuDNSU+tDVN8fNhiMijgOmM3CIarMWDjPrGk2pD3me75SPT3/605/qdAxViqNKsVQljl3pUZXP3HFUM46yj24fVTWYD3U6gKQqcUB1YqlEHBExu9MxtFElPnMcR39ViaNUfej2exykG0z1ml1zdsU4qhRLVeIYxM425UhlPnPHUc04hjDs+tDVLY6IOI9ifHoG3JUeGXBNRJy/q8VRpViqEscQXu10AM1Ulc/ccVQzjmEYdn3o9hbHLODAmiF2AETE5cCDwNxdLI4qxVKVOAbzVYqx6zuLqnzmjqOacQxl2PWh2xPHa8B44Kl+5ePSvl0tjirFUok4IuL+BrsyYGy74miTSnzmjqOycW/x9j0AAACVSURBVDStPnR74jgXWBURj9J3psf96TvdwK4SR5ViqUocYykmdOs/gVsG/KyNcbRDVT5zx1HNOKBJ9aHrv8cREW9g4BrLd6epFna5OKoUSxXiiIirge9JuqPOvh9I+my7YmmHKnzmjqPScTSlPnR94jAzs/bq6lFVZmbWfk4cZmZWihOHmZmV4sRhZmalOHGYmVkp/x+xSzueRVMOZwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFUmVAaqugzD"
      },
      "source": [
        "#Generate Histograms of Branded Keywords (True = Branded, False = NonBranded) & Their Clicks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXR4xat-9J6t",
        "outputId": "2c782fb6-4a0c-42ae-c156-de4f99665652",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "source": [
        "df.hist(by='Branded', column='Clicks')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f49b507d160>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f49b02257f0>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHfLEPzYukQB"
      },
      "source": [
        "#Generate a Correlation Table"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JtWqTegB9WuW",
        "outputId": "d33f9265-49b9-4ba8-b853-5394a764be1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "corr = df.corr()\n",
        "corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[\"Clicks\",\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': -0.08638808351196885,\n            'f': \"-0.08638808351196885\",\n        },\n{\n            'v': 0.049744269340634945,\n            'f': \"0.049744269340634945\",\n        },\n{\n            'v': 0.34645162231319715,\n            'f': \"0.34645162231319715\",\n        },\n{\n            'v': 0.09887130101921024,\n            'f': \"0.09887130101921024\",\n        }],\n [\"Average Position\",\n{\n            'v': -0.08638808351196885,\n            'f': \"-0.08638808351196885\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': -0.3880643303054177,\n            'f': \"-0.3880643303054177\",\n        },\n{\n            'v': -0.007916736665097037,\n            'f': \"-0.007916736665097037\",\n        },\n{\n            'v': -0.04113428894762431,\n            'f': \"-0.04113428894762431\",\n        }],\n [\"Site CTR\",\n{\n            'v': 0.049744269340634945,\n            'f': \"0.049744269340634945\",\n        },\n{\n            'v': -0.3880643303054177,\n            'f': \"-0.3880643303054177\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': -0.034691602757473544,\n            'f': \"-0.034691602757473544\",\n        },\n{\n            'v': 0.030043670562816555,\n            'f': \"0.030043670562816555\",\n        }],\n [\"Impressions\",\n{\n            'v': 0.34645162231319715,\n            'f': \"0.34645162231319715\",\n        },\n{\n            'v': -0.007916736665097037,\n            'f': \"-0.007916736665097037\",\n        },\n{\n            'v': -0.034691602757473544,\n            'f': \"-0.034691602757473544\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': -0.0011008614179019401,\n            'f': \"-0.0011008614179019401\",\n        }],\n [\"Branded\",\n{\n            'v': 0.09887130101921024,\n            'f': \"0.09887130101921024\",\n        },\n{\n            'v': -0.04113428894762431,\n            'f': \"-0.04113428894762431\",\n        },\n{\n            'v': 0.030043670562816555,\n            'f': \"0.030043670562816555\",\n        },\n{\n            'v': -0.0011008614179019401,\n            'f': \"-0.0011008614179019401\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        }]],\n        columns: [[\"string\", \"index\"], [\"number\", \"Clicks\"], [\"number\", \"Average Position\"], [\"number\", \"Site CTR\"], [\"number\", \"Impressions\"], [\"number\", \"Branded\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "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>Clicks</th>\n",
              "      <th>Average Position</th>\n",
              "      <th>Site CTR</th>\n",
              "      <th>Impressions</th>\n",
              "      <th>Branded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Clicks</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.086388</td>\n",
              "      <td>0.049744</td>\n",
              "      <td>0.346452</td>\n",
              "      <td>0.098871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Average Position</th>\n",
              "      <td>-0.086388</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.388064</td>\n",
              "      <td>-0.007917</td>\n",
              "      <td>-0.041134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Site CTR</th>\n",
              "      <td>0.049744</td>\n",
              "      <td>-0.388064</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.034692</td>\n",
              "      <td>0.030044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Impressions</th>\n",
              "      <td>0.346452</td>\n",
              "      <td>-0.007917</td>\n",
              "      <td>-0.034692</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.001101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Branded</th>\n",
              "      <td>0.098871</td>\n",
              "      <td>-0.041134</td>\n",
              "      <td>0.030044</td>\n",
              "      <td>-0.001101</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Clicks  Average Position  Site CTR  Impressions   Branded\n",
              "Clicks            1.000000         -0.086388  0.049744     0.346452  0.098871\n",
              "Average Position -0.086388          1.000000 -0.388064    -0.007917 -0.041134\n",
              "Site CTR          0.049744         -0.388064  1.000000    -0.034692  0.030044\n",
              "Impressions       0.346452         -0.007917 -0.034692     1.000000 -0.001101\n",
              "Branded           0.098871         -0.041134  0.030044    -0.001101  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfCiP1m6uob7"
      },
      "source": [
        "#Correlation Heatmap (add color to make it easier to read):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rDJW32_U2lF",
        "outputId": "3598606d-0e7d-4fe9-a94c-2e18cc95e72a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "source": [
        "df_small = df.iloc[:,:6]\n",
        "\n",
        "correlation_mat = df_small.corr()\n",
        "\n",
        "sns.heatmap(correlation_mat, annot = True)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZf73HpeyJjb"
      },
      "source": [
        "#Load Filter-able Data Table"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ds3cgfzhyJjd"
      },
      "source": [
        " %load_ext google.colab.data_table\n",
        " df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EajU6RpeyJjg"
      },
      "source": [
        "A few interesting features of the data table display:\n",
        "\n",
        "- Clicking the <button>Filter</button> button in the upper right allows you to search for terms or values in any particular column.\n",
        "- Clicking on any column title lets you sort the results according to that column's value.\n",
        "- The table displays only a subset of the data at a time. You can navigate through pages of data using the controls on the lower right.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arjvl3027Kwg",
        "colab": {
          "background_save": true
        }
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ji3aim2NwSZ1"
      },
      "source": [
        "#Disabaling Table View"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNS6o4NrwU5S",
        "outputId": "a95a54e0-5455-4c3b-b72d-c96add8bfebe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "%unload_ext google.colab.data_table\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Query</th>\n",
              "      <th>Clicks</th>\n",
              "      <th>Average Position</th>\n",
              "      <th>Site CTR</th>\n",
              "      <th>Impressions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>seer interactive</td>\n",
              "      <td>3444</td>\n",
              "      <td>1.407144</td>\n",
              "      <td>0.597191</td>\n",
              "      <td>5767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>google data studio</td>\n",
              "      <td>1352</td>\n",
              "      <td>8.237460</td>\n",
              "      <td>0.003478</td>\n",
              "      <td>388735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>johari window exercise</td>\n",
              "      <td>992</td>\n",
              "      <td>1.128184</td>\n",
              "      <td>0.414196</td>\n",
              "      <td>2395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>keywords everywhere</td>\n",
              "      <td>985</td>\n",
              "      <td>6.454694</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>153930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>google discovery</td>\n",
              "      <td>910</td>\n",
              "      <td>3.242859</td>\n",
              "      <td>0.060449</td>\n",
              "      <td>15054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27900</th>\n",
              "      <td>url crawl</td>\n",
              "      <td>0</td>\n",
              "      <td>26.597458</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27901</th>\n",
              "      <td>how to do outreach</td>\n",
              "      <td>0</td>\n",
              "      <td>78.058824</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27902</th>\n",
              "      <td>keyword search terms</td>\n",
              "      <td>0</td>\n",
              "      <td>80.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27903</th>\n",
              "      <td>native adv</td>\n",
              "      <td>0</td>\n",
              "      <td>4.375000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27904</th>\n",
              "      <td>power bi query editor remove duplicates</td>\n",
              "      <td>0</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27905 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Query  Clicks  ...  Site CTR  Impressions\n",
              "0                             seer interactive    3444  ...  0.597191         5767\n",
              "1                           google data studio    1352  ...  0.003478       388735\n",
              "2                       johari window exercise     992  ...  0.414196         2395\n",
              "3                          keywords everywhere     985  ...  0.006399       153930\n",
              "4                             google discovery     910  ...  0.060449        15054\n",
              "...                                        ...     ...  ...       ...          ...\n",
              "27900                                url crawl       0  ...  0.000000          236\n",
              "27901                       how to do outreach       0  ...  0.000000           17\n",
              "27902                     keyword search terms       0  ...  0.000000            6\n",
              "27903                               native adv       0  ...  0.000000            8\n",
              "27904  power bi query editor remove duplicates       0  ...  0.000000            5\n",
              "\n",
              "[27905 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O98Z0boYM6hb"
      },
      "source": [
        "#Change Branded Boolean Values (True/False) --> Integers (1/0) for a Basic Linear Regression\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvfEMVGQZvjC"
      },
      "source": [
        "df['Branded'] = df['Branded']*1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XlDzOYnxu_wH"
      },
      "source": [
        "#Check to make sure the Branded Column is now in an int64 format:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmfXbKH7aCdW",
        "outputId": "85fa5252-1df9-4abb-dadd-680e35323c06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 27905 entries, 0 to 27904\n",
            "Data columns (total 6 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Query             27905 non-null  object \n",
            " 1   Clicks            27905 non-null  int64  \n",
            " 2   Average Position  27905 non-null  float64\n",
            " 3   Site CTR          27905 non-null  float64\n",
            " 4   Impressions       27905 non-null  int64  \n",
            " 5   Branded           27905 non-null  int64  \n",
            "dtypes: float64(2), int64(3), object(1)\n",
            "memory usage: 1.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FfAmgQJ1vIWn"
      },
      "source": [
        "#If you need to alter any other values for your Linear Regression to an integer format:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIKDJ4dpWgUL",
        "outputId": "b850f7c5-82c7-41fc-ca32-c799fef68502",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#df['Rank'].astype('int')\n",
        "#df['Rank'] = df['Rank'].astype('int')\n",
        "#df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Query                object\n",
              "Clicks                int64\n",
              "Average Position      int64\n",
              "Site CTR            float64\n",
              "Impressions           int64\n",
              "Branded                bool\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hRls8Bq8vRxO"
      },
      "source": [
        "#Check to make sure Branded Column values are showing 1 for Branded Keywords & 0 for non Branded."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNDVEimyYIdW",
        "outputId": "630191ad-424b-4365-bc17-400f5870372c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Query</th>\n",
              "      <th>Clicks</th>\n",
              "      <th>Average Position</th>\n",
              "      <th>Site CTR</th>\n",
              "      <th>Impressions</th>\n",
              "      <th>Branded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>seer interactive</td>\n",
              "      <td>3444</td>\n",
              "      <td>1.407144</td>\n",
              "      <td>0.597191</td>\n",
              "      <td>5767</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>google data studio</td>\n",
              "      <td>1352</td>\n",
              "      <td>8.237460</td>\n",
              "      <td>0.003478</td>\n",
              "      <td>388735</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>johari window exercise</td>\n",
              "      <td>992</td>\n",
              "      <td>1.128184</td>\n",
              "      <td>0.414196</td>\n",
              "      <td>2395</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>keywords everywhere</td>\n",
              "      <td>985</td>\n",
              "      <td>6.454694</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>153930</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>google discovery</td>\n",
              "      <td>910</td>\n",
              "      <td>3.242859</td>\n",
              "      <td>0.060449</td>\n",
              "      <td>15054</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27900</th>\n",
              "      <td>url crawl</td>\n",
              "      <td>0</td>\n",
              "      <td>26.597458</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27901</th>\n",
              "      <td>how to do outreach</td>\n",
              "      <td>0</td>\n",
              "      <td>78.058824</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27902</th>\n",
              "      <td>keyword search terms</td>\n",
              "      <td>0</td>\n",
              "      <td>80.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27903</th>\n",
              "      <td>native adv</td>\n",
              "      <td>0</td>\n",
              "      <td>4.375000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27904</th>\n",
              "      <td>power bi query editor remove duplicates</td>\n",
              "      <td>0</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27905 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Query  Clicks  ...  Impressions  Branded\n",
              "0                             seer interactive    3444  ...         5767        1\n",
              "1                           google data studio    1352  ...       388735        0\n",
              "2                       johari window exercise     992  ...         2395        0\n",
              "3                          keywords everywhere     985  ...       153930        0\n",
              "4                             google discovery     910  ...        15054        0\n",
              "...                                        ...     ...  ...          ...      ...\n",
              "27900                                url crawl       0  ...          236        0\n",
              "27901                       how to do outreach       0  ...           17        0\n",
              "27902                     keyword search terms       0  ...            6        0\n",
              "27903                               native adv       0  ...            8        0\n",
              "27904  power bi query editor remove duplicates       0  ...            5        0\n",
              "\n",
              "[27905 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CeuKXbAhvZLF"
      },
      "source": [
        "#Perform a Simple Linear Regression Model to Better Describe the Data\n",
        "\n",
        "Substitute your values/column names in below if different.\n",
        "\n",
        "Remember, this isn't supposed to be predictive. It's primarily to better describe/understand your data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5SIrlYxJ9xkR",
        "outputId": "0517d8ad-435e-4675-9832-8a09fe3b504b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Simple Linear Regression\n",
        "\n",
        "\n",
        "#Load function from sklearn\n",
        "from sklearn import linear_model\n",
        "\n",
        "# Create linear regression object\n",
        "regr = linear_model.LinearRegression()\n",
        "\n",
        "y = df['CTR']\n",
        "x = df[['Rank','Branded']]\n",
        "\n",
        "# Train the model using the training sets\n",
        "regr.fit(x,y)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TEzlZcbxpwV"
      },
      "source": [
        "The first coeficient # is multiplyed by your first x value above.\n",
        "\n",
        "If you see a second coeficient #, that is multiplied by your second x value above. \n",
        "\n",
        "These two values are then added together + added to the intercept below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjvURPFf-jxC",
        "outputId": "e6070a6a-d49c-4a3f-e004-6d8cc769ac0b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "regr.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.00320727,  0.06721488])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJRfK7rb-llC",
        "outputId": "97e23368-96f5-4e4e-8035-845e77f08ce3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "regr.intercept_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2635564810462664"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4zTXlqS-nes"
      },
      "source": [
        "#Example: Site CTR Predicted = 0.1809 + -0.0109*Average Position + 0.0672*Branded\n",
        "\n",
        "#Example of Described CTR curve:\n",
        "\n",
        "#1 = 17%\n",
        "#2 = 16%\n",
        "#3 = 15%\n",
        "#4 = 14%\n",
        "#5 = 13%\n",
        "#6 = 12%\n",
        "#7 = 10%\n",
        "#8 = 9%\n",
        "#9 = 8%\n",
        "#10 = 7%"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}