CTR_curve.ipynb

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  "metadata": {
    "colab": {
      "name": "CTR-curve.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/akash-yede/Twittorials/blob/master/CTR_curve.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwGVrFZlotyo"
      },
      "source": [
        "#Plotting position CTR curves\n",
        "\n",
        "CTR curves can be useful for SEO traffic forecasting based on position changes\n",
        "\n",
        "For example, https://www.advancedwebranking.com/ctrstudy/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "621hO5AjgmQT"
      },
      "source": [
        "%%capture\n",
        "!pip install git+https://github.com/joshcarty/google-searchconsole\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9dUPabgnv_0"
      },
      "source": [
        "First, there is some setup to download a client_id.json file our Python code can use to connect securely to Google Search Console.\n",
        "\n",
        "Activate Search Console API in Compute Engine\n",
        "https://console.cloud.google.com/apis/api/webmasters.googleapis.com/overview?project=&folder=&organizationId=\n",
        "Create New Credentials / Help me choose (Search Console API, Other UI, User data)\n",
        "https://console.cloud.google.com/apis/credentials/wizard?api=iamcredentials.googleapis.com&project=\n",
        "Download client_id.json"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DaQ8q699f__z"
      },
      "source": [
        "#upload client_id.json\n",
        "from google.colab import files\n",
        "\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ba8KrDoegocX"
      },
      "source": [
        "import searchconsole\n",
        "\n",
        "account = searchconsole.authenticate(client_config=\"client_id.json\", serialize='credentials.json', flow=\"console\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FUrLjkU2iLk0"
      },
      "source": [
        "webproperty = account['https://www.domain.com/'] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RVEE9RXiYgh"
      },
      "source": [
        "#let's build a pandas dataframe with the search console data\n",
        "import pandas as pd\n",
        "\n",
        "def get_search_console_data(webproperty, days=-90):\n",
        "  if webproperty is not None:\n",
        "    query = webproperty.query.range(start='today', days=days).dimension('page')\n",
        "\n",
        "    r = query.get()\n",
        "\n",
        "    df = pd.DataFrame(r.rows)\n",
        "\n",
        "    return df\n",
        "\n",
        "  print(\"Web property doesn't exist, please select a valid one from this list\")\n",
        "  print(account.webproperties)\n",
        "\n",
        "  return None\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WmFWd2yio2S"
      },
      "source": [
        "df = get_search_console_data(webproperty)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yo0H09B6jJr5",
        "outputId": "cdb62f13-80c1-438c-a752-a614146b213d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 52979 entries, 0 to 52978\n",
            "Data columns (total 5 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   page         52979 non-null  object \n",
            " 1   clicks       52979 non-null  int64  \n",
            " 2   impressions  52979 non-null  int64  \n",
            " 3   ctr          52979 non-null  float64\n",
            " 4   position     52979 non-null  float64\n",
            "dtypes: float64(2), int64(2), object(1)\n",
            "memory usage: 2.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KB4La-c8nSPd"
      },
      "source": [
        "#Preserve privacy\n",
        "#df[\"page\"] = \"XXX\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbOPPkvIi86j",
        "outputId": "b4733499-39ed-4878-a746-5a381c2a8844",
        "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>page</th>\n",
              "      <th>clicks</th>\n",
              "      <th>impressions</th>\n",
              "      <th>ctr</th>\n",
              "      <th>position</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XXX</td>\n",
              "      <td>29909</td>\n",
              "      <td>228535</td>\n",
              "      <td>0.130873</td>\n",
              "      <td>16.978800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>XXX</td>\n",
              "      <td>14926</td>\n",
              "      <td>132973</td>\n",
              "      <td>0.112248</td>\n",
              "      <td>6.164424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XXX</td>\n",
              "      <td>4837</td>\n",
              "      <td>142811</td>\n",
              "      <td>0.033870</td>\n",
              "      <td>38.857679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XXX</td>\n",
              "      <td>4399</td>\n",
              "      <td>185644</td>\n",
              "      <td>0.023696</td>\n",
              "      <td>16.927194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XXX</td>\n",
              "      <td>3551</td>\n",
              "      <td>25107</td>\n",
              "      <td>0.141435</td>\n",
              "      <td>11.423786</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  page  clicks  impressions       ctr   position\n",
              "0  XXX   29909       228535  0.130873  16.978800\n",
              "1  XXX   14926       132973  0.112248   6.164424\n",
              "2  XXX    4837       142811  0.033870  38.857679\n",
              "3  XXX    4399       185644  0.023696  16.927194\n",
              "4  XXX    3551        25107  0.141435  11.423786"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odp85J6YjAxP"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9pt-WtQCjcc_"
      },
      "source": [
        "Position grouping\n",
        "\n",
        "https://stackoverflow.com/questions/34683963/pandas-group-by-rounded-floating-number"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMpNdprcjfOd",
        "outputId": "693e080c-5b98-4e73-aaf7-0f636bda838e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "df.groupby(df['position'].apply(lambda x: int(round(x, 0)))).mean()[\"ctr\"]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "position\n",
              "1      0.903335\n",
              "2      0.778616\n",
              "3      0.706077\n",
              "4      0.620655\n",
              "5      0.546682\n",
              "         ...   \n",
              "106    0.750000\n",
              "131    1.000000\n",
              "149    0.142857\n",
              "160    1.000000\n",
              "214    0.000000\n",
              "Name: ctr, Length: 103, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j51GV5WSoNxX"
      },
      "source": [
        "1. Filter to see only position 1 - 20\n",
        "2. Round position numbers to integers\n",
        "3. Calculate the CTR mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qf0xWPkOjreo",
        "outputId": "9cdeff4a-842d-486c-fb14-5194133fa602",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "df.query(\"position < 20\").groupby(df['position'].apply(lambda x: int(round(x, 0)))).mean()[\"ctr\"]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "position\n",
              "1     0.903335\n",
              "2     0.778616\n",
              "3     0.706077\n",
              "4     0.620655\n",
              "5     0.546682\n",
              "6     0.493563\n",
              "7     0.393256\n",
              "8     0.337710\n",
              "9     0.263124\n",
              "10    0.260993\n",
              "11    0.211491\n",
              "12    0.206297\n",
              "13    0.192463\n",
              "14    0.227653\n",
              "15    0.232472\n",
              "16    0.274906\n",
              "17    0.283696\n",
              "18    0.275552\n",
              "19    0.272716\n",
              "20    0.202685\n",
              "Name: ctr, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7aFE939odPz"
      },
      "source": [
        "Ploting with Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dZb3mRSk0dS",
        "outputId": "8cffcea6-95a2-44e5-ca00-0efce309b57e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "df.query(\"position < 20\").groupby(df['position'].apply(lambda x: int(round(x, 0)))).mean()[\"ctr\"].plot()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f69fd56f470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkXjAKcmlV0i"
      },
      "source": [
        "ctr_curve = df.query(\"position < 20\").groupby(df['position'].apply(lambda x: int(round(x, 0)))).mean()[\"ctr\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWjflFDuoi3j"
      },
      "source": [
        "Ploting with Plotly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnYnw6xYmJHE"
      },
      "source": [
        "new_df=pd.DataFrame(ctr_curve)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbmc2Whkmz70",
        "outputId": "511bc3d8-a378-48b8-e241-c3cbbff32be4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        }
      },
      "source": [
        "new_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>ctr</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>position</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.903335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.778616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.706077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.620655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.546682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.493563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.393256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.337710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.263124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.260993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.211491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.206297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.192463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.227653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.232472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.274906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.283696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.275552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.272716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.202685</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               ctr\n",
              "position          \n",
              "1         0.903335\n",
              "2         0.778616\n",
              "3         0.706077\n",
              "4         0.620655\n",
              "5         0.546682\n",
              "6         0.493563\n",
              "7         0.393256\n",
              "8         0.337710\n",
              "9         0.263124\n",
              "10        0.260993\n",
              "11        0.211491\n",
              "12        0.206297\n",
              "13        0.192463\n",
              "14        0.227653\n",
              "15        0.232472\n",
              "16        0.274906\n",
              "17        0.283696\n",
              "18        0.275552\n",
              "19        0.272716\n",
              "20        0.202685"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
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        "outputId": "108a4854-dbbe-4ef3-a2f2-ea9b32e80465",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fig = px.line(ctr_curve, x=new_df.index, y=\"ctr\", title='CTR by Position')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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}