Final_prepocessing_and_basic_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gfsYskP55RxD"
      },
      "source": [
        "## Merge all prepocessed data to start analysis\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vsq_Yvgj5Bib",
        "outputId": "02ae84c6-96b5-4f1b-f812-b108f4f5d78e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# connect to google drive\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 199,
      "metadata": {
        "id": "dCNobKnn6g-D"
      },
      "outputs": [],
      "source": [
        "# import necessary libraries\n",
        "import pandas as pd\n",
        "import glob\n",
        "import os\n",
        "\n",
        "path = '/content/drive/MyDrive/classified/reviewed'\n",
        "\n",
        "# use glob to get all the csv files in the folder\n",
        "csv_files = glob.glob(os.path.join(path, \"*.csv\"))\n",
        "    \n",
        "# loop over the list of csv files\n",
        "df_files = (pd.read_csv(f) for f in csv_files)\n",
        "# concat to one dataframe\n",
        "df = pd.concat(df_files, ignore_index=True)\n",
        "# reset the index of final DataFrame\n",
        "df.reset_index(inplace = True, drop = True)\n",
        "# drop columns not important for the analysis\n",
        "df.drop(['Unnamed: 0', 'review', 'classification_title'], axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 200,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lk3Q8Q0E_kO",
        "outputId": "a009a6fb-db46-473b-83d3-2d5aa05f3791"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 725 entries, 0 to 724\n",
            "Data columns (total 8 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   title           725 non-null    object\n",
            " 1   country         725 non-null    object\n",
            " 2   summary         725 non-null    object\n",
            " 3   classification  725 non-null    object\n",
            " 4   language        725 non-null    object\n",
            " 5   topic           725 non-null    object\n",
            " 6   link            725 non-null    object\n",
            " 7   published_date  725 non-null    object\n",
            "dtypes: object(8)\n",
            "memory usage: 45.4+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "print(df.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 201,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1rbn1Q9p70pO",
        "outputId": "7ee98137-e7ed-4060-81ec-f8e84bdc8c1f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 689 entries, 0 to 723\n",
            "Data columns (total 9 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   title               689 non-null    object\n",
            " 1   country             689 non-null    object\n",
            " 2   summary             689 non-null    object\n",
            " 3   classification      689 non-null    object\n",
            " 4   language            689 non-null    object\n",
            " 5   topic               689 non-null    object\n",
            " 6   link                689 non-null    object\n",
            " 7   published_date      689 non-null    object\n",
            " 8   classification_num  689 non-null    int64 \n",
            "dtypes: int64(1), object(8)\n",
            "memory usage: 53.8+ KB\n"
          ]
        }
      ],
      "source": [
        "# make classification also numerical\n",
        "df['classification_num'] = df['classification'].apply(lambda x: 1 if x == 'POSITIVE' else 0)\n",
        "# drop countries where there are to few results\n",
        "df = df.drop(df[((df.country == \"unknown\") | (df.country == \"GB\") | (df.country == \"SG\")| (df.country == \"IS\") |(df.country == \"Syria\") | (df.country == \"SA\") | (df.country == \"SY\") | (df.country == \"VI\"))].index)\n",
        "# fix country names\n",
        "df[\"country\"] = df[\"country\"].apply(lambda x: \"AE\" if x == 'UAE' else x)\n",
        "df[\"country\"] = df[\"country\"].apply(lambda x: \"EG\" if x == 'Egypt' else x)\n",
        "# drop duplicates if there are any\n",
        "df.drop_duplicates(inplace = True)\n",
        "# display info\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 202,
      "metadata": {
        "id": "EriVbSK1d5a2"
      },
      "outputs": [],
      "source": [
        "# trim the publishing date \n",
        "#df['published_date'] = pd.to_datetime(df['published_date']).dt.normalize() # this one keeps the time but sets it to 00:00:00\n",
        "df['published_date'] = pd.to_datetime(df['published_date']).dt.date\n",
        "df.sort_values(by='published_date', inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "P_H3NH_DDHUT"
      },
      "outputs": [],
      "source": [
        "# save dataframe to csv\n",
        "df.to_csv(f\"/content/drive/MyDrive/final_dataset.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8DMa8nIS2XM"
      },
      "source": [
        "## Do some basic analytics\n",
        "\n",
        "main analysis should be done in Tablou though, to practise that tool as well"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 203,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "HmmZRaqJS-b5",
        "outputId": "56604cd9-38a1-4db8-d0fe-358ee8895fda"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1296x1440 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "# exclude some columns first\n",
        "plot_df = df.drop(['link', 'summary', 'title', 'classification_num'], axis = 1)\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(18,20))\n",
        "for index in range(len(plot_df.columns)):\n",
        "    plt.subplot(9,5,index+1)\n",
        "    sns.countplot(x=plot_df.iloc[:,index], data=plot_df.dropna())\n",
        "    plt.xticks(rotation=90)\n",
        "fig.tight_layout(pad=1.0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 204,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "m65sqj_kGjwl",
        "outputId": "0a281928-cb18-4ca6-883c-0fa77f7d3a81"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4f5e1634-70e3-4761-a1ad-fa3c2f7f70cd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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></th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <th>classification</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">AE</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">CN</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">DE</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">EG</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ES</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">FR</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">IN</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">IT</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">LB</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">RU</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">TR</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">US</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">VN</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ZA</th>\n",
              "      <th>NEGATIVE</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POSITIVE</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f5e1634-70e3-4761-a1ad-fa3c2f7f70cd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4f5e1634-70e3-4761-a1ad-fa3c2f7f70cd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4f5e1634-70e3-4761-a1ad-fa3c2f7f70cd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                        title\n",
              "country classification       \n",
              "AE      NEGATIVE           13\n",
              "        POSITIVE            2\n",
              "CN      NEGATIVE           49\n",
              "        POSITIVE           33\n",
              "DE      NEGATIVE           38\n",
              "        POSITIVE           53\n",
              "EG      NEGATIVE           44\n",
              "        POSITIVE            7\n",
              "ES      NEGATIVE            4\n",
              "        POSITIVE            3\n",
              "FR      NEGATIVE           20\n",
              "        POSITIVE           13\n",
              "IN      NEGATIVE           20\n",
              "        POSITIVE           17\n",
              "IT      NEGATIVE           32\n",
              "        POSITIVE           11\n",
              "LB      NEGATIVE            7\n",
              "        POSITIVE            7\n",
              "RU      NEGATIVE           24\n",
              "        POSITIVE            8\n",
              "TR      NEGATIVE           50\n",
              "        POSITIVE           12\n",
              "US      NEGATIVE           58\n",
              "        POSITIVE           35\n",
              "VN      NEGATIVE           60\n",
              "        POSITIVE           16\n",
              "ZA      NEGATIVE           15\n",
              "        POSITIVE           38"
            ]
          },
          "execution_count": 204,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gf = df[[\"classification\", \"country\", \"title\"]].groupby([\"country\", \"classification\"]).count()\n",
        "gf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 205,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "H_etxDwFPNZD",
        "outputId": "dcc36a09-c51d-4fff-de34-f0e9a3656cce"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# need reset_index() so values can be ploted \n",
        "new = df[[\"classification\", \"country\",\"title\"]].groupby([\"country\", \"classification\"]).count().reset_index()\n",
        "\n",
        "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
        "ax = sns.barplot(x='country',y='title',hue='classification',data=new, palette=[\"#b7b7b7\", \"#b70000\"])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 206,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "0NQ6ZvhHlOiE",
        "outputId": "e410c869-eb0a-4911-d98d-04b08053b2ef"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#define data\n",
        "data = df.classification.value_counts()\n",
        "labels = [\"NEGATIVE\", \"POSITIVE\"]\n",
        "\n",
        "#define Seaborn color palette to use\n",
        "colors = [\"#b7b7b7\", \"#b70000\"]\n",
        "\n",
        "#create pie chart\n",
        "plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9i5nGfddo2X"
      },
      "source": [
        "##Does the sentiment correlates with the gold price changes?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 207,
      "metadata": {
        "id": "VHOlyKzPdUvq"
      },
      "outputs": [],
      "source": [
        "ddf = df[[\"classification\", \"published_date\",\"title\"]].groupby([\"published_date\", \"classification\"]).count().reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 210,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sySKymSZm_gl",
        "outputId": "4a300a86-fb35-4912-f053-cc7ecc25a672"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.4166666666666667\n",
            "0.75\n",
            "0.6666666666666666\n",
            "1.5625\n",
            "1.0\n",
            "1.25\n",
            "0.5303030303030303\n",
            "0.5068493150684932\n",
            "0.39622641509433965\n",
            "0.6571428571428571\n",
            "0.7307692307692307\n",
            "0.3\n",
            "0.25\n",
            "0.30303030303030304\n",
            "0.625\n"
          ]
        }
      ],
      "source": [
        "# while loop to calculate the ratio between POSITVE and NEGATIVE\n",
        "ratio_list = []\n",
        "def ratio():\n",
        "  i = 0\n",
        "  while i < len(ddf[\"title\"]):\n",
        "    ratio = (ddf[\"title\"][i+1] / ddf[\"title\"][i])\n",
        "    print(ratio)\n",
        "    i += 2\n",
        "    ratio_list.append(ratio)\n",
        "    ratio_list.append(ratio)\n",
        "\n",
        "ratio()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 216,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "vLDirXTXodlC",
        "outputId": "7acd5560-7878-45c7-d049-675c33cb6928"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c77f318b-13ac-4c88-8169-02550b8cbc61\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>date</th>\n",
              "      <th>classification</th>\n",
              "      <th>title</th>\n",
              "      <th>ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-05-24</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>12</td>\n",
              "      <td>1.416667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-05-24</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>17</td>\n",
              "      <td>1.416667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-05-25</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>28</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-05-25</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>21</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-05-26</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>24</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2022-05-26</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>16</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2022-05-27</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>16</td>\n",
              "      <td>1.562500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2022-05-27</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>25</td>\n",
              "      <td>1.562500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2022-05-28</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>8</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2022-05-28</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>8</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2022-05-29</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>4</td>\n",
              "      <td>1.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2022-05-29</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>5</td>\n",
              "      <td>1.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2022-05-30</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>66</td>\n",
              "      <td>0.530303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2022-05-30</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>35</td>\n",
              "      <td>0.530303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2022-05-31</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>73</td>\n",
              "      <td>0.506849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2022-05-31</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>37</td>\n",
              "      <td>0.506849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2022-06-01</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>53</td>\n",
              "      <td>0.396226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2022-06-01</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>21</td>\n",
              "      <td>0.396226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2022-06-02</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>35</td>\n",
              "      <td>0.657143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2022-06-02</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>23</td>\n",
              "      <td>0.657143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2022-06-03</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>26</td>\n",
              "      <td>0.730769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2022-06-03</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>19</td>\n",
              "      <td>0.730769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2022-06-04</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>20</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2022-06-04</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>6</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2022-06-05</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>28</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2022-06-05</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>7</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2022-06-06</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>33</td>\n",
              "      <td>0.303030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2022-06-06</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>10</td>\n",
              "      <td>0.303030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2022-06-07</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>8</td>\n",
              "      <td>0.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2022-06-07</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>5</td>\n",
              "      <td>0.625000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c77f318b-13ac-4c88-8169-02550b8cbc61')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c77f318b-13ac-4c88-8169-02550b8cbc61 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c77f318b-13ac-4c88-8169-02550b8cbc61');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          date classification  title     ratio\n",
              "0   2022-05-24       NEGATIVE     12  1.416667\n",
              "1   2022-05-24       POSITIVE     17  1.416667\n",
              "2   2022-05-25       NEGATIVE     28  0.750000\n",
              "3   2022-05-25       POSITIVE     21  0.750000\n",
              "4   2022-05-26       NEGATIVE     24  0.666667\n",
              "5   2022-05-26       POSITIVE     16  0.666667\n",
              "6   2022-05-27       NEGATIVE     16  1.562500\n",
              "7   2022-05-27       POSITIVE     25  1.562500\n",
              "8   2022-05-28       NEGATIVE      8  1.000000\n",
              "9   2022-05-28       POSITIVE      8  1.000000\n",
              "10  2022-05-29       NEGATIVE      4  1.250000\n",
              "11  2022-05-29       POSITIVE      5  1.250000\n",
              "12  2022-05-30       NEGATIVE     66  0.530303\n",
              "13  2022-05-30       POSITIVE     35  0.530303\n",
              "14  2022-05-31       NEGATIVE     73  0.506849\n",
              "15  2022-05-31       POSITIVE     37  0.506849\n",
              "16  2022-06-01       NEGATIVE     53  0.396226\n",
              "17  2022-06-01       POSITIVE     21  0.396226\n",
              "18  2022-06-02       NEGATIVE     35  0.657143\n",
              "19  2022-06-02       POSITIVE     23  0.657143\n",
              "20  2022-06-03       NEGATIVE     26  0.730769\n",
              "21  2022-06-03       POSITIVE     19  0.730769\n",
              "22  2022-06-04       NEGATIVE     20  0.300000\n",
              "23  2022-06-04       POSITIVE      6  0.300000\n",
              "24  2022-06-05       NEGATIVE     28  0.250000\n",
              "25  2022-06-05       POSITIVE      7  0.250000\n",
              "26  2022-06-06       NEGATIVE     33  0.303030\n",
              "27  2022-06-06       POSITIVE     10  0.303030\n",
              "28  2022-06-07       NEGATIVE      8  0.625000\n",
              "29  2022-06-07       POSITIVE      5  0.625000"
            ]
          },
          "execution_count": 216,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# insert the ratio to the DataFrame\n",
        "ddf[\"ratio\"] = ratio_list\n",
        "# rename published_date => date\n",
        "ddf = ddf.rename(columns={\"published_date\": \"date\"})\n",
        "ddf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 212,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2z9oKf16rVgh",
        "outputId": "0966dd38-c33b-47cb-844d-44f520644df0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[{\"date\":\"2022-06-07\",\"price\":1852.43,\"open\":1839.56,\"high\":1855.57,\"low\":1837.15},{\"date\":\"2022-06-06\",\"price\":1841.68,\"open\":1851.43,\"high\":1857.81,\"low\":1840.78},{\"date\":\"2022-06-05\",\"price\":1851.62,\"open\":1850.65,\"high\":1853.38,\"low\":1849.95},{\"date\":\"2022-06-03\",\"price\":1850.77,\"open\":1868.53,\"high\":1874.3,\"low\":1847.02},{\"date\":\"2022-06-02\",\"price\":1868.69,\"open\":1846.04,\"high\":1870.46,\"low\":1844.08},{\"date\":\"2022-06-01\",\"price\":1846.38,\"open\":1836.55,\"high\":1849.99,\"low\":1828.57},{\"date\":\"2022-05-31\",\"price\":1837.57,\"open\":1852.44,\"high\":1857.24,\"low\":1835.21},{\"date\":\"2022-05-30\",\"price\":1856.18,\"open\":1850.03,\"high\":1864.03,\"low\":1848.74},{\"date\":\"2022-05-29\",\"price\":1849.88,\"open\":1852.96,\"high\":1854.12,\"low\":1848.18},{\"date\":\"2022-05-27\",\"price\":1852.73,\"open\":1850.48,\"high\":1862.3,\"low\":1847.76},{\"date\":\"2022-05-26\",\"price\":1850.82,\"open\":1854.06,\"high\":1854.44,\"low\":1841.58},{\"date\":\"2022-05-25\",\"price\":1853.21,\"open\":1867.58,\"high\":1868.19,\"low\":1842.95},{\"date\":\"2022-05-24\",\"price\":1866.49,\"open\":1854.22,\"high\":1869.74,\"low\":1849.38}]\n"
          ]
        }
      ],
      "source": [
        "import requests\n",
        "\n",
        "# get daily gold prices\n",
        "\n",
        "url = \"https://live-and-historical-metal-prices-metalexpress.p.rapidapi.com/historical\"\n",
        "\n",
        "querystring = {\"metal\":\"gold\",\"interval\":\"daily\",\"currency\":\"usd\",\"from_date\":\"2022-05-24\",\"to_date\":\"2022-06-07\"}\n",
        "\n",
        "headers = {\n",
        "\t\"X-RapidAPI-Host\": \"live-and-historical-metal-prices-metalexpress.p.rapidapi.com\",\n",
        "\t\"X-RapidAPI-Key\": \"SECRET KEY\"\n",
        "}\n",
        "\n",
        "response = requests.request(\"GET\", url, headers=headers, params=querystring)\n",
        "\n",
        "print(response.text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 225,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "yHYzwAi5yWBc",
        "outputId": "c78b5919-e03d-47f6-87f9-8f42fdddefe8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0f1ab160-83d5-4ac2-ba22-477b953e6c9d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>date</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2022-05-24</td>\n",
              "      <td>1866.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2022-05-25</td>\n",
              "      <td>1853.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2022-05-26</td>\n",
              "      <td>1850.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2022-05-27</td>\n",
              "      <td>1852.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2022-05-29</td>\n",
              "      <td>1849.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2022-05-30</td>\n",
              "      <td>1856.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2022-05-31</td>\n",
              "      <td>1837.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2022-06-01</td>\n",
              "      <td>1846.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-06-02</td>\n",
              "      <td>1868.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-06-03</td>\n",
              "      <td>1850.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-06-05</td>\n",
              "      <td>1851.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-06-06</td>\n",
              "      <td>1841.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-06-07</td>\n",
              "      <td>1852.43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f1ab160-83d5-4ac2-ba22-477b953e6c9d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f1ab160-83d5-4ac2-ba22-477b953e6c9d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f1ab160-83d5-4ac2-ba22-477b953e6c9d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          date    price\n",
              "12  2022-05-24  1866.49\n",
              "11  2022-05-25  1853.21\n",
              "10  2022-05-26  1850.82\n",
              "9   2022-05-27  1852.73\n",
              "8   2022-05-29  1849.88\n",
              "7   2022-05-30  1856.18\n",
              "6   2022-05-31  1837.57\n",
              "5   2022-06-01  1846.38\n",
              "4   2022-06-02  1868.69\n",
              "3   2022-06-03  1850.77\n",
              "2   2022-06-05  1851.62\n",
              "1   2022-06-06  1841.68\n",
              "0   2022-06-07  1852.43"
            ]
          },
          "execution_count": 225,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from pandas import json_normalize\n",
        "\n",
        "# create a new DataFrame for prices\n",
        "gdf = (json_normalize(dict))\n",
        "gdf.sort_values(by='date', inplace=True)\n",
        "gdf.drop([\"high\", \"low\", \"open\"], axis = 1, inplace = True)\n",
        "gdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 226,
      "metadata": {
        "id": "Zu5aZpe75Wze"
      },
      "outputs": [],
      "source": [
        "# change the dtype to datetime in both DataFrames\n",
        "gdf['date'] = pd.to_datetime(gdf['date'])\n",
        "ddf['date'] = pd.to_datetime(ddf['date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 227,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "id": "YQuZNhvvpptF",
        "outputId": "5087f3a1-491e-48ab-8ea2-09bdb60f4d48"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([738300., 738302., 738304., 738306., 738307., 738309., 738311.,\n",
              "        738313.]), <a list of 8 Text major ticklabel objects>)"
            ]
          },
          "execution_count": 227,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot the ratio POSITIVE / NEGATIVE\n",
        "sns.set(rc={'figure.figsize':(15,10)})\n",
        "ax = sns.lineplot(x='date',y='ratio',data=ddf)\n",
        "plt.xticks(rotation = 45)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 228,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "id": "ALTYxGaWwApV",
        "outputId": "a2dc0aa8-0e3b-4c49-9953-0d58ba8f4c63"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([738300., 738302., 738304., 738306., 738307., 738309., 738311.,\n",
              "        738313.]), <a list of 8 Text major ticklabel objects>)"
            ]
          },
          "execution_count": 228,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot gold price for same period\n",
        "sns.set(rc={'figure.figsize':(15,10)})\n",
        "ax = sns.lineplot(x='date',y='price',data=gdf)\n",
        "plt.xticks(rotation = 45)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Ya8otqrxkwF"
      },
      "outputs": [],
      "source": [
        "# create a combined DataFrame\n",
        "merged_df = pd.merge(gdf, ddf, on='date', how='outer')\n",
        "# sort the values by date\n",
        "merged_df.sort_values(by='date', inplace=True)\n",
        "# fill missing values\n",
        "merged_df[\"price\"] = merged_df[\"price\"].fillna(method='ffill')\n",
        "merged_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 258,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "id": "hjSDdR9TGKRb",
        "outputId": "ae6d30bb-5596-42fb-e342-881e7090dd39"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot the ratio against the price\n",
        "sns.set(rc={'figure.figsize':(15,10)})\n",
        "\n",
        "ax = merged_df.plot(x=\"date\", y=\"price\", legend=False, color=\"g\", label=\"Price in USD\")\n",
        "ax2 = ax.twinx()\n",
        "merged_df.plot(x=\"date\", y=\"ratio\", ax=ax2, legend=False, color=\"r\", label=\"ratio POSITIVE/NEGATIVE\")\n",
        "ax.figure.legend()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Final_prepocessing_and_basic_analysis.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}