Popular_Data_Science_Questions.ipynb

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    "colab": {
      "name": "Popular Data Science Questions",
      "provenance": [],
      "toc_visible": true,
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      "name": "python3",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZ_VUZFMRozf",
        "colab_type": "text"
      },
      "source": [
        "# Popular Data Science Questions\n",
        "\n",
        "In this project, let's assume that I'm working for a company that creates data science content, be it books, online articles, videos or interactive text-based platforms.\n",
        "\n",
        "I'm tasked with figuring out what is best content to write about. So, I decide to scower the internet in search for the answer to the question \"What is it that people want to learn about in data science?\".\n",
        "\n",
        "Thinking back to my experience when I first started learning programming, it occurs to me that if I wanted to figure out what programming content to write, I could consult [Stack Overflow](https://stackoverflow.com/) (a question and answer website about programming) and see what kind of content is more popular.\n",
        "\n",
        "You decide to investigate Stack Overflow a little more and find out that it is part of a question and answer website network called [Stack Exchange](https://en.wikipedia.org/wiki/Stack_Exchange)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "teh_nd_AUoOC",
        "colab_type": "text"
      },
      "source": [
        "## Stack Exchange \n",
        "\n",
        "Stack Exchange hosts sites on a multitude of fields and subjects, including mathematics, physics, philosophy, and [data science](https://datascience.stackexchange.com/)!\n",
        "\n",
        "Stack Exchange employs a reputation award system for its questions and answers. Each post — each question/answer — is a post that is subject to upvotes and downvotes. This ensures that good posts are easily identifiable.\n",
        "\n",
        "If you open [this link](https://stackexchange.com/sites?view=list#percentanswered), you'll find a complete list of Stack Exchange websites sorted by percentage of questions that received answers. At the time of this writing, Data Science Stack Exchange (DSSE) is on the bottom 10 sites with respect to this metric.\n",
        "\n",
        "The fact that DSSE is a data science dedicated site (contrarily to the others), coupled with it having a lot of an unanswered questions, makes it an ideal candidate for this investigation. DSSE will be the focus of this guided project.\n",
        "\n",
        "The tagging system used by Stack Exchange looks just like what I need to solve this problem as it allow me to quantify how many questions are asked about each subject.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXkShBj_W0Jx",
        "colab_type": "text"
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      "source": [
        "### What information is available in each post?\n",
        "\n",
        "Looking, just as an example, at [this](https://datascience.stackexchange.com/questions/19141/linear-model-to-generate-probability-of-each-possible-output?rq=1) question, some of the information we see is:\n",
        "\n",
        "* For both questions and answers:\n",
        "    * The posts's score;\n",
        "    * The posts's title;\n",
        "    * The posts's author;\n",
        "    * The posts's body;\n",
        "* For questions only:\n",
        "    * The last time the question as active;\n",
        "    * How many times the question was viewed;\n",
        "    * Related questions;\n",
        "    * The question's tags;"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFRCpgtDRdVP",
        "colab_type": "text"
      },
      "source": [
        "## Stack Exchange Data Explorer\n",
        "\n",
        "Perusing the table names, a few stand out as relevant for the goal:\n",
        "\n",
        "* Posts\n",
        "* PostTags\n",
        "* Tags\n",
        "* TagSynonyms\n",
        "\n",
        "Running a few exploratory queries, leads me to focus my effort on `Posts` table. For examples, the `Tags` table looked very promising as it tells me how many times each tag was used, but there's no way to tell just from this if the interest in these tags is recent or a thing from the past.\n",
        "\n",
        "\n",
        "<table>\n",
        "  <tr>\n",
        "    <th>Id</th>\n",
        "    <th>TagName</th>\n",
        "    <th>Count</th>\n",
        "    <th>ExcerptPostId</th>\n",
        "    <th>WikiPostId</th>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>2</td>\n",
        "    <td>machine-learning</td>\n",
        "    <td>6919</td>\n",
        "    <td>4909</td>\n",
        "    <td>4908</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>46</td>\n",
        "    <td>python</td>\n",
        "    <td>3907</td>\n",
        "    <td>5523</td>\n",
        "    <td>5522</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>81</td>\n",
        "    <td>neural-network</td>\n",
        "    <td>2923</td>\n",
        "    <td>8885</td>\n",
        "    <td>8884</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>194</td>\n",
        "    <td>deep-learning</td>\n",
        "    <td>2786</td>\n",
        "    <td>8956</td>\n",
        "    <td>8955</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>77</td>\n",
        "    <td>classification</td>\n",
        "    <td>1899</td>\n",
        "    <td>4911</td>\n",
        "    <td>4910</td>\n",
        "  </tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZT2SZ2WZPvp",
        "colab_type": "text"
      },
      "source": [
        "The posts table has a lot of columns. I'll be focusing the attention on those that seem relevant towards the goal:\n",
        "\n",
        "- `Id`: An identification number for the post.\n",
        "- `PostTypeId`: An identification number for the type of post.\n",
        "- `CreationDate`: The date and time of creation of the post.\n",
        "- `Score`: The post's score.\n",
        "- `ViewCount`: How many times the post was viewed.\n",
        "- `Tags`: What tags were used.\n",
        "- `AnswerCount`: How many answers the question got (only applicable to question posts).\n",
        "-`FavoriteCount`: How many times the question was favored (only applicable to question posts).\n",
        "\n",
        "The last few columns contain information about how popular the post is — the kind of information I'm after.\n",
        "\n",
        "Anything that isn't questions or answers is mostly inconsequential. Even if it happens to be the case that such kind of posts is immensely popular, they would just be outliers and not relevant to me. I'll then just focus on the questions.\n",
        "\n",
        "Since I'm only interested in recent posts, I'll limit my analysis to the posts of 2019."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Z_tvc-pRdVQ",
        "colab_type": "text"
      },
      "source": [
        "## Getting the Data\n",
        "\n",
        "To get the relevant data I run the following query.\n",
        "\n",
        "```\n",
        "SELECT Id, CreationDate,\n",
        "       Score, ViewCount, Tags,\n",
        "       AnswerCount, FavoriteCount\n",
        "  FROM posts\n",
        " WHERE PostTypeId = 1 AND YEAR(CreationDate) = 2019;\n",
        "```\n",
        "The result of the query was stored in a file called `2019_questions.csv`.\n",
        "Here's what the first few rows look like:\n",
        "\n",
        "<table>\n",
        "  <tr>\n",
        "    <th>Id</th>\n",
        "    <th>PostTypeId</th>\n",
        "    <th>CreationDate</th>\n",
        "    <th>Score</th>\n",
        "    <th>ViewCount</th>\n",
        "    <th>Tags</th>\n",
        "    <th>AnswerCount</th>\n",
        "    <th>FavoriteCount</th>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>44419</td>\n",
        "    <td>1</td>\n",
        "    <td>2019-01-23 09:21:13</td>\n",
        "    <td>1</td>\n",
        "    <td>21</td>\n",
        "    <td>&lt;machine-learning&gt;&lt;data-mining&gt;</td>\n",
        "    <td>0</td>\n",
        "    <td></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>44420</td>\n",
        "    <td>1</td>\n",
        "    <td>2019-01-23 09:34:01</td>\n",
        "    <td>0</td>\n",
        "    <td>25</td>\n",
        "    <td>&lt;machine-learning&gt;&lt;regression&gt;&lt;linear-regression&gt;&lt;regularization&gt;</td>\n",
        "    <td>0</td>\n",
        "    <td></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>44423</td>\n",
        "    <td>1</td>\n",
        "    <td>2019-01-23 09:58:41</td>\n",
        "    <td>2</td>\n",
        "    <td>1651</td>\n",
        "    <td>&lt;python&gt;&lt;time-series&gt;&lt;forecast&gt;&lt;forecasting&gt;</td>\n",
        "    <td>0</td>\n",
        "    <td></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>44427</td>\n",
        "    <td>1</td>\n",
        "    <td>2019-01-23 10:57:09</td>\n",
        "    <td>0</td>\n",
        "    <td>55</td>\n",
        "    <td>&lt;machine-learning&gt;&lt;scikit-learn&gt;&lt;pca&gt;</td>\n",
        "    <td>1</td>\n",
        "    <td></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>44428</td>\n",
        "    <td>1</td>\n",
        "    <td>2019-01-23 11:02:15</td>\n",
        "    <td>0</td>\n",
        "    <td>19</td>\n",
        "    <td>&lt;dataset&gt;&lt;bigdata&gt;&lt;data&gt;&lt;speech-to-text&gt;</td>\n",
        "    <td>0</td>\n",
        "    <td></td>\n",
        "  </tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HphOsieGRdVR",
        "colab_type": "text"
      },
      "source": [
        "# Exploring the Data\n",
        "\n",
        "I can read in the data while immediately making sure `CreationDate` will be stored as a datetime object:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e13ZOb9PRdVS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We import everything that we'll use\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MmkyK6hRdVV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "questions = pd.read_csv(\"/content/2019_questions.csv\", parse_dates=[\"CreationDate\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QqfWCfJNRdVY",
        "colab_type": "text"
      },
      "source": [
        "Running [`questions.info()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html) should gives a lot of useful information."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNg-P8g7RdVZ",
        "colab_type": "code",
        "outputId": "38f7d160-d5ac-4be4-e6dd-0d1d7b287910",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "questions.info()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8839 entries, 0 to 8838\n",
            "Data columns (total 7 columns):\n",
            "Id               8839 non-null int64\n",
            "CreationDate     8839 non-null datetime64[ns]\n",
            "Score            8839 non-null int64\n",
            "ViewCount        8839 non-null int64\n",
            "Tags             8839 non-null object\n",
            "AnswerCount      8839 non-null int64\n",
            "FavoriteCount    1407 non-null float64\n",
            "dtypes: datetime64[ns](1), float64(1), int64(4), object(1)\n",
            "memory usage: 483.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "71ghESDbRdVc",
        "colab_type": "text"
      },
      "source": [
        "It's possible to see that only `FavoriteCount` has missing values. A missing value on this column probably means that the question wasn't present in any users' favorite list, so we can replace the missing values with zero.\n",
        "\n",
        "The types seem adequate for every column, however, after I fill in the missing values on `FavoriteCount`, there is no reason to store the values as floats."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ET961sxFRdVi",
        "colab_type": "text"
      },
      "source": [
        "# Data Cleaning\n",
        "\n",
        "I'll begin by fixing `FavoriteCount`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vtzyQXQ2RdVj",
        "colab_type": "code",
        "outputId": "abe9eeba-24a9-4029-d660-08b44e2c9d66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "questions.fillna(value={\"FavoriteCount\": 0}, inplace=True)\n",
        "questions[\"FavoriteCount\"] = questions[\"FavoriteCount\"].astype(int)\n",
        "questions.dtypes"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                        int64\n",
              "CreationDate     datetime64[ns]\n",
              "Score                     int64\n",
              "ViewCount                 int64\n",
              "Tags                     object\n",
              "AnswerCount               int64\n",
              "FavoriteCount             int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmyHIBBFRdVl",
        "colab_type": "text"
      },
      "source": [
        "Let's now modify `Tags` to make it easier to work with."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHMIPcdRRdVm",
        "colab_type": "code",
        "outputId": "051aaa5b-23c5-4032-82d4-4758d3691e32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "questions[\"Tags\"] = questions[\"Tags\"].str.strip(r\"[<>]\").str.split(\"><\")\n",
        "questions.sample(3)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>CreationDate</th>\n",
              "      <th>Score</th>\n",
              "      <th>ViewCount</th>\n",
              "      <th>Tags</th>\n",
              "      <th>AnswerCount</th>\n",
              "      <th>FavoriteCount</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6568</th>\n",
              "      <td>64304</td>\n",
              "      <td>2019-12-06 00:22:55</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>[efficiency]</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2292</th>\n",
              "      <td>58413</td>\n",
              "      <td>2019-08-30 08:03:56</td>\n",
              "      <td>0</td>\n",
              "      <td>48</td>\n",
              "      <td>[python, numpy]</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5672</th>\n",
              "      <td>51992</td>\n",
              "      <td>2019-05-15 08:12:08</td>\n",
              "      <td>0</td>\n",
              "      <td>168</td>\n",
              "      <td>[data, data-cleaning, beginner]</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "         Id        CreationDate  ...  AnswerCount  FavoriteCount\n",
              "6568  64304 2019-12-06 00:22:55  ...            0              0\n",
              "2292  58413 2019-08-30 08:03:56  ...            1              0\n",
              "5672  51992 2019-05-15 08:12:08  ...            2              0\n",
              "\n",
              "[3 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LluZEGGX05B5",
        "colab_type": "text"
      },
      "source": [
        "# Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWgSvvDmRdVp",
        "colab_type": "text"
      },
      "source": [
        "## Most Used and Most Viewed `Tags`\n",
        "\n",
        "I'll begin by creating a dataframe of all the tags to store how many times each tag was used and its post viewed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdjg33Ly43cO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "36e6f975-958a-4c34-d96a-c77817118053"
      },
      "source": [
        "# Select all the Tags\n",
        "all_tags = set(questions[\"Tags\"].sum())\n",
        "\n",
        "# Create the dataframe\n",
        "df_tag = pd.DataFrame(0, index=all_tags, columns=[\"Count\", \"ViewCount\"])\n",
        "\n",
        "df_tag.head()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Count</th>\n",
              "      <th>ViewCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>gradient-descent</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>allennlp</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>distributed</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dialog-flow</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>parquet</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Count  ViewCount\n",
              "gradient-descent      0          0\n",
              "allennlp              0          0\n",
              "distributed           0          0\n",
              "dialog-flow           0          0\n",
              "parquet               0          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VdzzpaYX7Syo",
        "colab_type": "text"
      },
      "source": [
        "Now I need to create a function that will be useful to extract how many times each tag was used and its post viewed from the `questions` dataframe.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FdIi6zAC7N3U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def extract_count(row):\n",
        "  # Take the index of the row\n",
        "  idx = row.name\n",
        "\n",
        "  for i in row[\"Tags\"]:\n",
        "    df_tag.loc[i, \"Count\"] += 1\n",
        "    df_tag.loc[i, \"ViewCount\"] += questions.loc[idx, \"ViewCount\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PuTloj1a8h7b",
        "colab_type": "text"
      },
      "source": [
        "Let's apply `extract_count` function to fill the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ycow2k1v8r1P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "1cfc3f7c-10eb-481f-e9c4-c651fbaaa4f8"
      },
      "source": [
        "questions.apply(extract_count, axis=1)\n",
        "\n",
        "df_tag.head(10)"
      ],
      "execution_count": 105,
      "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>Count</th>\n",
              "      <th>ViewCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>gradient-descent</th>\n",
              "      <td>98</td>\n",
              "      <td>14837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>allennlp</th>\n",
              "      <td>2</td>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>distributed</th>\n",
              "      <td>7</td>\n",
              "      <td>227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dialog-flow</th>\n",
              "      <td>2</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>parquet</th>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reinforcement-learning</th>\n",
              "      <td>203</td>\n",
              "      <td>20101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>competitions</th>\n",
              "      <td>2</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gaussian-process</th>\n",
              "      <td>12</td>\n",
              "      <td>471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rbf</th>\n",
              "      <td>5</td>\n",
              "      <td>359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pooling</th>\n",
              "      <td>4</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Count  ViewCount\n",
              "gradient-descent           98      14837\n",
              "allennlp                    2        183\n",
              "distributed                 7        227\n",
              "dialog-flow                 2         51\n",
              "parquet                     1         17\n",
              "reinforcement-learning    203      20101\n",
              "competitions                2        125\n",
              "gaussian-process           12        471\n",
              "rbf                         5        359\n",
              "pooling                     4        160"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzoHD_WHRdV2",
        "colab_type": "text"
      },
      "source": [
        " Let's visualize `Count` and `ViewCount` data side by side with a threshold of `20` because popularity of the tags rapidly declines, so looking at these tags should be enough to help us with the goal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wREqz9GyRdWB",
        "colab_type": "code",
        "outputId": "c268eb4f-d1a8-4030-e280-d0afc2a53523",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        }
      },
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
        "fig.set_size_inches((24, 10))\n",
        "df_tag.sort_values(\"Count\").tail(20).plot.barh(y=\"Count\", ax=axes[0], subplots=True)\n",
        "df_tag.sort_values(\"ViewCount\").tail(20).plot.barh(y=\"ViewCount\", ax=axes[1], subplots=True)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb884ec79b0>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XA7sDb6N7qvFGYI+qehrwLuD97brDgFnAnJZ7Lujp87aqejrwydbPSGa2cV4MzAdI8kK6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\n",
            "text/plain": [
              "<Figure size 1728x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVXITMauRdV5",
        "colab_type": "text"
      },
      "source": [
        "Some tags are very, very broad and are unlikely to be useful; e.g.: `python`, `dataset`, `r`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7F1SFp52RdWH",
        "colab_type": "text"
      },
      "source": [
        "## Relations Between `Tags`\n",
        "\n",
        "One way of trying to gauge how pairs of tags are related to each other, is to count how many times each pair appears together.\n",
        "\n",
        "I'll now create a dataframe where each row will represent a tag, and each column as well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqWk1gDPRdWM",
        "colab_type": "code",
        "outputId": "43d13545-068d-45c9-d572-bddf68b79b59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "associations = pd.DataFrame(0, index=all_tags, columns=all_tags, dtype=float)\n",
        "\n",
        "associations.iloc[0:4,0:4]"
      ],
      "execution_count": 107,
      "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>gradient-descent</th>\n",
              "      <th>allennlp</th>\n",
              "      <th>distributed</th>\n",
              "      <th>dialog-flow</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>gradient-descent</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>allennlp</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>distributed</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dialog-flow</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  gradient-descent  allennlp  distributed  dialog-flow\n",
              "gradient-descent               0.0       0.0          0.0          0.0\n",
              "allennlp                       0.0       0.0          0.0          0.0\n",
              "distributed                    0.0       0.0          0.0          0.0\n",
              "dialog-flow                    0.0       0.0          0.0          0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fm_kXmLCRdWO",
        "colab_type": "text"
      },
      "source": [
        "Now for each lists of tags in `questions[\"Tags\"]`, I will increment the intervening tags by one. The end result will be a dataframe that for each pair of tags, it tells us how many times they were used together."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWJBxPVsRdWP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for tags in questions[\"Tags\"]:\n",
        "    associations.loc[tags, tags] += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vuj0Q0r2RdWR",
        "colab_type": "text"
      },
      "source": [
        "This dataframe is quite large. Let's focus our attention on the most used tags."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8xdykpnRdWR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "most_used_tags = df_tag.sort_values(\"Count\").tail(20).index\n",
        "\n",
        "relations_most_used = associations.loc[most_used_tags, most_used_tags]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wAoxi22vRdWU",
        "colab_type": "text"
      },
      "source": [
        "It's hard for a human to understand what is going on in this dataframe. Let's create a heatmap. But before I do it, let's get rid of the diagonal values, otherwise the colors will be too skewed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlFVxr_mRdWV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get rid of the diagonal values\n",
        "np.fill_diagonal(relations_most_used.values, np.NaN)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MF3NdN99RdWX",
        "colab_type": "code",
        "outputId": "aa4b994e-378c-4840-dd7d-72c8f1a6c5b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        }
      },
      "source": [
        "# Plot the heatmap\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.heatmap(relations_most_used, cmap=\"Greens\", annot=False)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb885fcf908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3SPLRFu3cEUnemOSE7a2T5P8leVKb1kmSJGlYDXUPfpLJqh27S0tVnQ2cvY0q+wEvB07p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\na25XAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58zO33ASRdWa",
        "colab_type": "text"
      },
      "source": [
        "The most used tags also seem to have the strongest relationships, as given by the dark concentration in the bottom right corner. However, this could simply be because each of these tags is used a lot, and so end up being used together a lot without possibly even having any strong relation between them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsTetoUNRdWb",
        "colab_type": "text"
      },
      "source": [
        "# Enter Domain Knowledge\n",
        "\n",
        "[Keras](https://keras.io/), [scikit-learn](https://scikit-learn.org/), [TensorFlow](https://www.tensorflow.org/) are all Python libraries that allow their users to employ deep learning (a type of neural network).\n",
        "\n",
        "Most of the top tags are all intimately related with one central machine learning theme: deep learning. If I want to be very specific, I can suggest the creation of Python content that uses deep learning for classification problems (and other variations of this suggestion).\n",
        "\n",
        "At the glance of an eye, someone with sufficient domain knowledge can tell that the most popular topic at the moment, as shown by the analysis, is deep learning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQ0kN4vMRdWc",
        "colab_type": "text"
      },
      "source": [
        "## Is Deep Learning just a Fad?\n",
        "\n",
        "Before I officially make our recommendation, it would be nice to solidy the findings with additional proof. More specifically, one thing that comes to mind is \"Is deep learning just a fad?\" Ideally, the content I decide to create will be the most useful for as long as possible. Could interest in deep learning be slowing down?\n",
        "\n",
        "The file `all_questions.csv` holds the result of the query below — this query fetches all of the questions ever asked on DSSE, their dates and tags:\n",
        "```\n",
        "SELECT Id, CreationDate, Tags\n",
        "  FROM posts\n",
        " WHERE PostTypeId = 1;\n",
        "```\n",
        "\n",
        "Let's read in the file into a dataframe called `all_q`. I'll parse the dates at read-time."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjXnAnWSRdWc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_q = pd.read_csv(\"/content/all_questions.csv\", parse_dates=[\"CreationDate\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nZw3OwRRdWf",
        "colab_type": "text"
      },
      "source": [
        "It's possible to use the same technique as before to clean the tags column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlxiq7EN9Frt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_q[\"Tags\"] = all_q[\"Tags\"].str.strip(r\"[<>]\").str.split(\"><\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XW-NKX0IRdWj",
        "colab_type": "text"
      },
      "source": [
        "Before deciding which questions should be classified as being deep learning questions, I should decide what tags are deep learning tags.\n",
        "\n",
        "The definition of what constitutes a deep learning tag I'll use is: a tag that belongs to the list `[\"lstm\", \"cnn\", \"scikit-learn\", \"tensorflow\", \"keras\", \"neural-network\", \"deep-learning\"]`.\n",
        "\n",
        "This list was obtained by looking at all the tags in `most_used:tags` and seeing which ones had any relation to deep learning.\n",
        "\n",
        "I'll now create a function that assigns `1` to deep learning questions and `0` otherwise."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YS-1qbZ3RdWk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def class_deep_learning(tags):\n",
        "    for tag in tags:\n",
        "        if tag in [\"lstm\", \"cnn\", \"scikit-learn\", \"tensorflow\",\n",
        "                   \"keras\", \"neural-network\", \"deep-learning\"]:\n",
        "            return 1\n",
        "    return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFlqK-HPRdWm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_q[\"DeepLearning\"] = all_q[\"Tags\"].apply(class_deep_learning)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEJtGW03RdWp",
        "colab_type": "code",
        "outputId": "c4c6f411-9582-4b4e-fe56-a12a08b92506",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "all_q.sample(5)"
      ],
      "execution_count": 116,
      "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>Id</th>\n",
              "      <th>CreationDate</th>\n",
              "      <th>Tags</th>\n",
              "      <th>DeepLearning</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12256</th>\n",
              "      <td>9664</td>\n",
              "      <td>2016-01-07 06:01:43</td>\n",
              "      <td>[machine-learning, data-mining, clustering, k-...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21306</th>\n",
              "      <td>36313</td>\n",
              "      <td>2018-08-01 13:49:38</td>\n",
              "      <td>[nlp, text-mining, preprocessing]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2884</th>\n",
              "      <td>56598</td>\n",
              "      <td>2019-07-30 05:02:26</td>\n",
              "      <td>[machine-learning, classification, nlp, featur...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9421</th>\n",
              "      <td>19186</td>\n",
              "      <td>2017-05-24 16:13:03</td>\n",
              "      <td>[machine-learning, classification, regression,...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9879</th>\n",
              "      <td>19437</td>\n",
              "      <td>2017-06-02 20:28:51</td>\n",
              "      <td>[machine-learning, python, neural-network, cla...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Id  ... DeepLearning\n",
              "12256   9664  ...            0\n",
              "21306  36313  ...            0\n",
              "2884   56598  ...            0\n",
              "9421   19186  ...            1\n",
              "9879   19437  ...            1\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SoRFBXzVRdWs",
        "colab_type": "text"
      },
      "source": [
        "Looks good!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJ07yeniRdWs",
        "colab_type": "text"
      },
      "source": [
        "The data-science-techonology landscape isn't something as dynamic to merit daily, weekly, or even monthly tracking. Let's track it quarterly.\n",
        "\n",
        "Since I don't have all the data for the first quarter of 2020, I'll get rid of those dates:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8ExdAfmRdWt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_q = all_q[all_q[\"CreationDate\"].dt.year < 2020]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6sjH1NePRdWv",
        "colab_type": "text"
      },
      "source": [
        "Let's create a column that identifies the quarter in which a question was asked."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLGzlag8RdWw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fetch_quarter(datetime):\n",
        "    year = str(datetime.year)[-2:]\n",
        "    quarter = str(((datetime.month-1) // 3) + 1)\n",
        "    return \"{y}Q{q}\".format(y=year, q=quarter)\n",
        "\n",
        "all_q[\"Quarter\"] = all_q[\"CreationDate\"].apply(fetch_quarter)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1Fpi3F7RdWz",
        "colab_type": "code",
        "outputId": "6176ea33-aba2-4547-98b0-b9fba867325e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "all_q.head()"
      ],
      "execution_count": 119,
      "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>Id</th>\n",
              "      <th>CreationDate</th>\n",
              "      <th>Tags</th>\n",
              "      <th>DeepLearning</th>\n",
              "      <th>Quarter</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>45416</td>\n",
              "      <td>2019-02-12 00:36:29</td>\n",
              "      <td>[python, keras, tensorflow, cnn, probability]</td>\n",
              "      <td>1</td>\n",
              "      <td>19Q1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45418</td>\n",
              "      <td>2019-02-12 00:50:39</td>\n",
              "      <td>[neural-network]</td>\n",
              "      <td>1</td>\n",
              "      <td>19Q1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45422</td>\n",
              "      <td>2019-02-12 04:40:51</td>\n",
              "      <td>[python, ibm-watson, chatbot]</td>\n",
              "      <td>0</td>\n",
              "      <td>19Q1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45426</td>\n",
              "      <td>2019-02-12 04:51:49</td>\n",
              "      <td>[keras]</td>\n",
              "      <td>1</td>\n",
              "      <td>19Q1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>45427</td>\n",
              "      <td>2019-02-12 05:08:24</td>\n",
              "      <td>[r, predictive-modeling, machine-learning-mode...</td>\n",
              "      <td>0</td>\n",
              "      <td>19Q1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Id        CreationDate  ... DeepLearning  Quarter\n",
              "0  45416 2019-02-12 00:36:29  ...            1     19Q1\n",
              "1  45418 2019-02-12 00:50:39  ...            1     19Q1\n",
              "2  45422 2019-02-12 04:40:51  ...            0     19Q1\n",
              "3  45426 2019-02-12 04:51:49  ...            1     19Q1\n",
              "4  45427 2019-02-12 05:08:24  ...            0     19Q1\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aahqmnLWRdW1",
        "colab_type": "text"
      },
      "source": [
        "For the final stretch of this screen, I'll group by quarter and:\n",
        "\n",
        "* Count the number of deep learning questions.\n",
        "* Count the total number of questions.\n",
        "* Compute the ratio between the two numbers above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3c63f7ljRdW2",
        "colab_type": "code",
        "outputId": "1a4ada45-68fa-47e4-d0f8-cf75cb9a5b81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "quarterly = all_q.groupby('Quarter').agg({\"DeepLearning\": ['sum', 'size']})\n",
        "quarterly.columns = ['DeepLearningQuestions', 'TotalQuestions']\n",
        "quarterly[\"DeepLearningRate\"] = quarterly[\"DeepLearningQuestions\"]\\\n",
        "                                /quarterly[\"TotalQuestions\"]\n",
        "# The following is done to help with visualizations later.\n",
        "quarterly.reset_index(inplace=True)\n",
        "quarterly.sample(5)"
      ],
      "execution_count": 120,
      "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>Quarter</th>\n",
              "      <th>DeepLearningQuestions</th>\n",
              "      <th>TotalQuestions</th>\n",
              "      <th>DeepLearningRate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>17Q3</td>\n",
              "      <td>247</td>\n",
              "      <td>714</td>\n",
              "      <td>0.345938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>19Q2</td>\n",
              "      <td>935</td>\n",
              "      <td>2393</td>\n",
              "      <td>0.390723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>16Q2</td>\n",
              "      <td>109</td>\n",
              "      <td>518</td>\n",
              "      <td>0.210425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>18Q2</td>\n",
              "      <td>566</td>\n",
              "      <td>1448</td>\n",
              "      <td>0.390884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14Q2</td>\n",
              "      <td>9</td>\n",
              "      <td>157</td>\n",
              "      <td>0.057325</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Quarter  DeepLearningQuestions  TotalQuestions  DeepLearningRate\n",
              "13    17Q3                    247             714          0.345938\n",
              "20    19Q2                    935            2393          0.390723\n",
              "8     16Q2                    109             518          0.210425\n",
              "16    18Q2                    566            1448          0.390884\n",
              "0     14Q2                      9             157          0.057325"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuPNkM6fRdW4",
        "colab_type": "code",
        "outputId": "dc8e45b3-d8e3-4478-ba22-44a2abee7ccf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        }
      },
      "source": [
        "ax1 = quarterly.plot(x=\"Quarter\", y=\"DeepLearningRate\",\n",
        "                    kind=\"line\", linestyle=\"-\", marker=\"o\", color=\"orange\",\n",
        "                    figsize=(24,12)\n",
        "                    )\n",
        "\n",
        "ax2 = quarterly.plot(x=\"Quarter\", y=\"TotalQuestions\",\n",
        "                     kind=\"bar\", ax=ax1, secondary_y=True, alpha=0.7, rot=45)\n",
        "\n",
        "for idx, t in enumerate(quarterly[\"TotalQuestions\"]):\n",
        "    ax2.text(idx, t, str(t), ha=\"center\", va=\"bottom\")\n",
        "xlims = ax1.get_xlim()\n",
        "\n",
        "ax1.get_legend().remove()\n",
        "\n",
        "handles1, labels1 = ax1.get_legend_handles_labels()\n",
        "handles2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(handles=handles1 + handles2,\n",
        "           labels=labels1 + labels2,\n",
        "           loc=\"upper left\", prop={\"size\": 12})\n",
        "\n",
        "\n",
        "for ax in (ax1, ax2):\n",
        "    for where in (\"top\", \"right\"):\n",
        "        ax.spines[where].set_visible(False)\n",
        "        ax.tick_params(right=False, labelright=False)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2ZJLLd6ZoP/MzPVO0g36eYpsLFZaNGzcGihaWHh4eVKtWrVBhefToUVxdXfH19SUzM5Mf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GlnXrH348pX+6pUrdlx72VrpTpQ/qiDH7MO2o4dO2jbti1Llizhuuuuo0OHDnFHkiRJkiRJMbCY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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1728x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cz6Mcpw3RdW7",
        "colab_type": "text"
      },
      "source": [
        "It seems that deep learning questions was a high-growth trend since the start of DSSE and it looks like it is plateauing. There is no evidence to suggest that interest in deep learning is decreasing and so I maintain my previous idea of proposing to create deep learning content."
      ]
    }
  ]
}