HSK_Vocabulary.ipynb

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        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mauroemartinez/HSK_Vocabulary/blob/main/HSK_Vocabulary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "id": "gcHpDT2_wBn5",
      "metadata": {
        "id": "gcHpDT2_wBn5"
      },
      "source": [
        "# 汉语 书平 考试 1 - 6 级 词汇表 数据分析\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "# HSK (Chinese Proficiency Exam) 1 - 6 Vocabulary List"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3-MySAPOyoem",
      "metadata": {
        "id": "3-MySAPOyoem"
      },
      "source": [
        "By this project we would like to help students know the importance of each initial and character driven by the frequency of apparitions on the HSK vocabulary, which does not inform directly, but subtly.\n",
        "Our database will be each .xlsx and .csv files downloaded from the website https://www.purpleculture.net/textbook-vocab-lists/\n",
        "We will need to concatenate all the files. In order to do that, we need to see if columns are the same between the six files. If not, we will have to transform the data until every file is compatible with each other.\n",
        "After concatenation, we will need to **split every word/phrase into single characters. Thus, allowing us to count the frequency of each character.**\n",
        "A bar chart will be the cherry-on-top for the project, allowing the visualization of the data ordered from top most frequent to least frequent character.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3jgcJyP71g_1",
      "metadata": {
        "id": "3jgcJyP71g_1"
      },
      "source": [
        "# Google Drive mounting and databases importation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "f5aoVGqPkVXf",
      "metadata": {
        "colab": {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data reading and merging"
      ],
      "metadata": {
        "id": "7u2x88XSE_gp"
      },
      "id": "7u2x88XSE_gp"
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# CSV file paths\n",
        "file_paths = [\n",
        "    '/content/drive/MyDrive/HSK Vocabulary List/Vocabulary List for New HSK Level 1 (150 words).csv',\n",
        "    '/content/drive/MyDrive/HSK Vocabulary List/Vocabulary List for New HSK Level 2 (151 words).csv',\n",
        "    '/content/drive/MyDrive/HSK Vocabulary List/Vocabulary List for New HSK Level 3 (300 words).csv',\n",
        "    '/content/drive/MyDrive/HSK Vocabulary List/Vocabulary List for New HSK Level 4 (600 words).csv',\n",
        "    '/content/drive/MyDrive/HSK Vocabulary List/Vocabulary List for New HSK Level 5 (1300 words).csv',\n",
        "    '/content/drive/MyDrive/HSK Vocabulary List/Vocabulary List for New HSK Level 6 (2500 words).csv'\n",
        "]\n",
        "\n",
        "# HSK levels list\n",
        "hsk_levels = [1, 2, 3, 4, 5, 6]\n",
        "\n",
        "# Empty list to save the HSK vocabulary dataframes\n",
        "hsk_list = []\n",
        "\n",
        "# Iter through file paths and levels\n",
        "for file_path, hsk_level in zip(file_paths, hsk_levels):\n",
        "    hsk_df = pd.read_csv(file_path) # Read each CSV file\n",
        "    hsk_df['HSK'] = hsk_level # Add HSK level column per file\n",
        "    hsk_list.append(hsk_df) # Save each DataFrame on the empty list\n",
        "\n",
        "# Concat all Dataframes in one\n",
        "df = pd.concat(hsk_list, ignore_index=True)\n",
        "\n",
        "# Ahora tienes un único DataFrame que contiene los datos de todos los niveles HSK\n",
        "df"
      ],
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      "id": "Zu6p-MKxemX-",
      "execution_count": 124,
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            "text/plain": [
              "        ID Chinese Trad.        Pinyin  \\\n",
              "0        1       爱     愛            ài   \n",
              "1        2       八     八            bā   \n",
              "2        3      爸爸    爸爸         bà ba   \n",
              "3        4      杯子    杯子        bēi zi   \n",
              "4        5      北京    北京      běi jīng   \n",
              "...    ...     ...   ...           ...   \n",
              "4996  2496      作废    作廢       zuò fèi   \n",
              "4997  2497      作风    作風      zuò fēng   \n",
              "4998  2498      作息    作息        zuò xī   \n",
              "4999  2499     座右铭   座右銘  zuò yòu míng   \n",
              "5000  2500      做主    做主       zuò zhǔ   \n",
              "\n",
              "                                     English Definition  HSK  Unnamed: 0  \\\n",
              "0                                                  love    1         NaN   \n",
              "1                                                 eight    1         NaN   \n",
              "2                                                   Dad    1         NaN   \n",
              "3                                            cup; glass    1         NaN   \n",
              "4                                               Beijing    1         NaN   \n",
              "...                                                 ...  ...         ...   \n",
              "4996  cancellation; delete; to nullify; to expire an...    6         NaN   \n",
              "4997                                    work style; way    6         NaN   \n",
              "4998                                      work and rest    6         NaN   \n",
              "4999                                              motto    6         NaN   \n",
              "5000     decide; take the responsibility for a decision    6         NaN   \n",
              "\n",
              "      Unnamed: 6  \n",
              "0            NaN  \n",
              "1            NaN  \n",
              "2            NaN  \n",
              "3            NaN  \n",
              "4            NaN  \n",
              "...          ...  \n",
              "4996         NaN  \n",
              "4997         NaN  \n",
              "4998         NaN  \n",
              "4999         NaN  \n",
              "5000         NaN  \n",
              "\n",
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              "      <th>ID</th>\n",
              "      <th>Chinese</th>\n",
              "      <th>Trad.</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>爱</td>\n",
              "      <td>愛</td>\n",
              "      <td>ài</td>\n",
              "      <td>love</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>bā</td>\n",
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              "      <td>杯子</td>\n",
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              "      <td>bēi zi</td>\n",
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              "      <td>1</td>\n",
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              "      <td>北京</td>\n",
              "      <td>北京</td>\n",
              "      <td>běi jīng</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>4996</th>\n",
              "      <td>2496</td>\n",
              "      <td>作废</td>\n",
              "      <td>作廢</td>\n",
              "      <td>zuò fèi</td>\n",
              "      <td>cancellation; delete; to nullify; to expire an...</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>4997</th>\n",
              "      <td>2497</td>\n",
              "      <td>作风</td>\n",
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              "      <td>zuò fēng</td>\n",
              "      <td>work style; way</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>2498</td>\n",
              "      <td>作息</td>\n",
              "      <td>作息</td>\n",
              "      <td>zuò xī</td>\n",
              "      <td>work and rest</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>2499</td>\n",
              "      <td>座右铭</td>\n",
              "      <td>座右銘</td>\n",
              "      <td>zuò yòu míng</td>\n",
              "      <td>motto</td>\n",
              "      <td>6</td>\n",
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              "      <td>做主</td>\n",
              "      <td>做主</td>\n",
              "      <td>zuò zhǔ</td>\n",
              "      <td>decide; take the responsibility for a decision</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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        "id": "YROSQZ3y2Bvj"
      },
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop first and NaN columns\n",
        "df = df.dropna(axis=1, how=\"all\").iloc[:,1:]\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
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        "id": "6SwOE4UEVe9O",
        "outputId": "58cc8f3d-b64a-4b89-c2da-a03c34f31ed5"
      },
      "id": "6SwOE4UEVe9O",
      "execution_count": 125,
      "outputs": [
        {
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          "data": {
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              "     Chinese Trad.        Pinyin  \\\n",
              "0          爱     愛            ài   \n",
              "1          八     八            bā   \n",
              "2         爸爸    爸爸         bà ba   \n",
              "3         杯子    杯子        bēi zi   \n",
              "4         北京    北京      běi jīng   \n",
              "...      ...   ...           ...   \n",
              "4996      作废    作廢       zuò fèi   \n",
              "4997      作风    作風      zuò fēng   \n",
              "4998      作息    作息        zuò xī   \n",
              "4999     座右铭   座右銘  zuò yòu míng   \n",
              "5000      做主    做主       zuò zhǔ   \n",
              "\n",
              "                                     English Definition  HSK  \n",
              "0                                                  love    1  \n",
              "1                                                 eight    1  \n",
              "2                                                   Dad    1  \n",
              "3                                            cup; glass    1  \n",
              "4                                               Beijing    1  \n",
              "...                                                 ...  ...  \n",
              "4996  cancellation; delete; to nullify; to expire an...    6  \n",
              "4997                                    work style; way    6  \n",
              "4998                                      work and rest    6  \n",
              "4999                                              motto    6  \n",
              "5000     decide; take the responsibility for a decision    6  \n",
              "\n",
              "[5001 rows x 5 columns]"
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              "        document.querySelector('#df-f69b118b-8415-459d-8555-8d45d8851776 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-f69b118b-8415-459d-8555-8d45d8851776');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "id": "b3763fb7",
      "metadata": {
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          "height": 0
        },
        "outputId": "2acd6272-9f83-4f76-b8d0-03ce5f91db68"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Simplified Traditional        Pinyin  \\\n",
              "0             爱           愛            ài   \n",
              "1             八           八            bā   \n",
              "2            爸爸          爸爸         bà ba   \n",
              "3            杯子          杯子        bēi zi   \n",
              "4            北京          北京      běi jīng   \n",
              "...         ...         ...           ...   \n",
              "4996         作废          作廢       zuò fèi   \n",
              "4997         作风          作風      zuò fēng   \n",
              "4998         作息          作息        zuò xī   \n",
              "4999        座右铭         座右銘  zuò yòu míng   \n",
              "5000         做主          做主       zuò zhǔ   \n",
              "\n",
              "                                    English Translation  HSK  \n",
              "0                                                  love    1  \n",
              "1                                                 eight    1  \n",
              "2                                                   Dad    1  \n",
              "3                                            cup; glass    1  \n",
              "4                                               Beijing    1  \n",
              "...                                                 ...  ...  \n",
              "4996  cancellation; delete; to nullify; to expire an...    6  \n",
              "4997                                    work style; way    6  \n",
              "4998                                      work and rest    6  \n",
              "4999                                              motto    6  \n",
              "5000     decide; take the responsibility for a decision    6  \n",
              "\n",
              "[5001 rows x 5 columns]"
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              "      <td>cancellation; delete; to nullify; to expire an...</td>\n",
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              "    <tr>\n",
              "      <th>4997</th>\n",
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              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>座右铭</td>\n",
              "      <td>座右銘</td>\n",
              "      <td>zuò yòu míng</td>\n",
              "      <td>motto</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
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              "      <th>5000</th>\n",
              "      <td>做主</td>\n",
              "      <td>做主</td>\n",
              "      <td>zuò zhǔ</td>\n",
              "      <td>decide; take the responsibility for a decision</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5001 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3dda991f-0b5c-418f-991f-1f339b475800')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-3dda991f-0b5c-418f-991f-1f339b475800 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-3dda991f-0b5c-418f-991f-1f339b475800');\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",
              "\n",
              "\n",
              "<div id=\"df-311bc423-97a0-46ed-a59f-94f534888fa4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-311bc423-97a0-46ed-a59f-94f534888fa4')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-311bc423-97a0-46ed-a59f-94f534888fa4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ],
      "source": [
        "# Rename columns with more suitable labels\n",
        "columns_mapping = {\n",
        "    'Chinese': 'Simplified',\n",
        "    'Trad.': 'Traditional',\n",
        "    'English Definition': 'English Translation'\n",
        "}\n",
        "df = df.rename(columns=columns_mapping)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "35kpqdET2Uxy",
      "metadata": {
        "id": "35kpqdET2Uxy"
      },
      "source": [
        "# New dataframe for frequency calculation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "id": "7ee262f2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "7ee262f2",
        "outputId": "3609428b-7887-4b17-daed-c3709f94a3c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Simplified Traditional        Pinyin  \\\n",
              "0             爱           愛            ài   \n",
              "1             八           八            bā   \n",
              "2             爸          爸爸         bà ba   \n",
              "2             爸          爸爸         bà ba   \n",
              "3             杯          杯子        bēi zi   \n",
              "...         ...         ...           ...   \n",
              "4999          座         座右銘  zuò yòu míng   \n",
              "4999          右         座右銘  zuò yòu míng   \n",
              "4999          铭         座右銘  zuò yòu míng   \n",
              "5000          做          做主       zuò zhǔ   \n",
              "5000          主          做主       zuò zhǔ   \n",
              "\n",
              "                                 English Translation  HSK  \n",
              "0                                               love    1  \n",
              "1                                              eight    1  \n",
              "2                                                Dad    1  \n",
              "2                                                Dad    1  \n",
              "3                                         cup; glass    1  \n",
              "...                                              ...  ...  \n",
              "4999                                           motto    6  \n",
              "4999                                           motto    6  \n",
              "4999                                           motto    6  \n",
              "5000  decide; take the responsibility for a decision    6  \n",
              "5000  decide; take the responsibility for a decision    6  \n",
              "\n",
              "[9662 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d2e32d05-fff5-4714-af5d-251f0c63f3ee\" 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",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Simplified</th>\n",
              "      <th>Traditional</th>\n",
              "      <th>Pinyin</th>\n",
              "      <th>English Translation</th>\n",
              "      <th>HSK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>爱</td>\n",
              "      <td>愛</td>\n",
              "      <td>ài</td>\n",
              "      <td>love</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>八</td>\n",
              "      <td>八</td>\n",
              "      <td>bā</td>\n",
              "      <td>eight</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>爸</td>\n",
              "      <td>爸爸</td>\n",
              "      <td>bà ba</td>\n",
              "      <td>Dad</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>爸</td>\n",
              "      <td>爸爸</td>\n",
              "      <td>bà ba</td>\n",
              "      <td>Dad</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>杯</td>\n",
              "      <td>杯子</td>\n",
              "      <td>bēi zi</td>\n",
              "      <td>cup; glass</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>座</td>\n",
              "      <td>座右銘</td>\n",
              "      <td>zuò yòu míng</td>\n",
              "      <td>motto</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>右</td>\n",
              "      <td>座右銘</td>\n",
              "      <td>zuò yòu míng</td>\n",
              "      <td>motto</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>铭</td>\n",
              "      <td>座右銘</td>\n",
              "      <td>zuò yòu míng</td>\n",
              "      <td>motto</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5000</th>\n",
              "      <td>做</td>\n",
              "      <td>做主</td>\n",
              "      <td>zuò zhǔ</td>\n",
              "      <td>decide; take the responsibility for a decision</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5000</th>\n",
              "      <td>主</td>\n",
              "      <td>做主</td>\n",
              "      <td>zuò zhǔ</td>\n",
              "      <td>decide; take the responsibility for a decision</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9662 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d2e32d05-fff5-4714-af5d-251f0c63f3ee')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-d2e32d05-fff5-4714-af5d-251f0c63f3ee 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-d2e32d05-fff5-4714-af5d-251f0c63f3ee');\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",
              "\n",
              "\n",
              "<div id=\"df-9b6d28ff-1bf7-4da0-8b26-90f9cb581cdc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9b6d28ff-1bf7-4da0-8b26-90f9cb581cdc')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9b6d28ff-1bf7-4da0-8b26-90f9cb581cdc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ],
      "source": [
        "# We need to split the Simplified column into rows by hanzi, so that we can count each character's frequency\n",
        "# Let's do it in a new df which will be a copy of the original one\n",
        "df_hsk_freq = df.copy()\n",
        "\n",
        "# String split into a list per row with the split characters\n",
        "import re\n",
        "df_hsk_freq['Simplified'] = df_hsk_freq['Simplified'].apply(\n",
        "    lambda x : re.findall('.{1,1}', x))\n",
        "\n",
        "# Explode the dataset, split each element in a different row\n",
        "df_hsk_freq = df_hsk_freq.explode('Simplified')\n",
        "df_hsk_freq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "id": "h39S5wwd4nt3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "h39S5wwd4nt3",
        "outputId": "b75221a1-440a-464f-ecc6-78d910e99351"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Simplified  HSK\n",
              "0             爱    1\n",
              "1             八    1\n",
              "2             爸    1\n",
              "2             爸    1\n",
              "3             杯    1\n",
              "...         ...  ...\n",
              "4999          座    6\n",
              "4999          右    6\n",
              "4999          铭    6\n",
              "5000          做    6\n",
              "5000          主    6\n",
              "\n",
              "[9662 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-970d81a2-70fa-436e-abff-728ad120aecd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Simplified</th>\n",
              "      <th>HSK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>爱</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>八</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>爸</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>爸</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>杯</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>座</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>右</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>铭</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5000</th>\n",
              "      <td>做</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5000</th>\n",
              "      <td>主</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9662 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-970d81a2-70fa-436e-abff-728ad120aecd 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-970d81a2-70fa-436e-abff-728ad120aecd');\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",
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              "\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b1e58eb0-824e-4767-9395-9563e5be6529 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ],
      "source": [
        "# Only Simplified and HSK columns are neccessary for our analysis\n",
        "df_hsk_freq = df_hsk_freq[[\"Simplified\", \"HSK\"]]\n",
        "df_hsk_freq"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "M9zYGKUv3Hgm",
      "metadata": {
        "id": "M9zYGKUv3Hgm"
      },
      "source": [
        "# Hanzi frequency analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "id": "6qfDC0QqCKCJ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qfDC0QqCKCJ",
        "outputId": "643f1531-7b2d-4d3a-bb87-5b260724fa12"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-09-20 03:18:16--  https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_\n",
            "Resolving drive.google.com (drive.google.com)... 173.194.218.102, 173.194.218.113, 173.194.218.100, ...\n",
            "Connecting to drive.google.com (drive.google.com)|173.194.218.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-0k-9o-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/rpi61o227udt20r941gisv4r7h2pebk6/1695179850000/02847987870453524430/*/1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_?uuid=b027723f-4d73-49d6-a9d9-580e3864927e [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-09-20 03:18:17--  https://doc-0k-9o-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/rpi61o227udt20r941gisv4r7h2pebk6/1695179850000/02847987870453524430/*/1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_?uuid=b027723f-4d73-49d6-a9d9-580e3864927e\n",
            "Resolving doc-0k-9o-docs.googleusercontent.com (doc-0k-9o-docs.googleusercontent.com)... 173.194.212.132, 2607:f8b0:400c:c11::84\n",
            "Connecting to doc-0k-9o-docs.googleusercontent.com (doc-0k-9o-docs.googleusercontent.com)|173.194.212.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 20659344 (20M) [application/x-font-ttf]\n",
            "Saving to: ‘TaipeiSansTCBeta-Regular.ttf’\n",
            "\n",
            "TaipeiSansTCBeta-Re 100%[===================>]  19.70M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-09-20 03:18:17 (181 MB/s) - ‘TaipeiSansTCBeta-Regular.ttf’ saved [20659344/20659344]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Font installation or it won't print Chinese characters in charts\n",
        "!wget -O TaipeiSansTCBeta-Regular.ttf https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_&export=download\n",
        "\n",
        "# Import plotting libraries and font manager\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.font_manager import fontManager\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# From the characters list, we count their frequency in a new DataFrame\n",
        "df_hsk_freq_count = df_hsk_freq.Simplified.value_counts()\n",
        "df_hsk_freq_count = pd.DataFrame(df_hsk_freq_count)\n",
        "df_hsk_freq_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "KbV8kIvoXyoi",
        "outputId": "cae9cdf8-6a5a-4799-f7da-43cfaff7106c"
      },
      "id": "KbV8kIvoXyoi",
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Simplified\n",
              "不           64\n",
              "子           58\n",
              "心           41\n",
              "气           39\n",
              "发           38\n",
              "..         ...\n",
              "喘            1\n",
              "舶            1\n",
              "川            1\n",
              "熊            1\n",
              "铭            1\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    60% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# As it saved the count as Simplified, while it should be the index, we rename index and column\n",
        "df_hsk_freq_count.index.name = 'Simplified'\n",
        "df_hsk_freq_count.columns = ['Frequency']\n",
        "df_hsk_freq_count"
      ],
      "metadata": {
        "id": "H5FnmA4uP52r",
        "outputId": "b9e8134e-b7af-4136-f047-477a43c922ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "id": "H5FnmA4uP52r",
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Frequency\n",
              "Simplified           \n",
              "不                  64\n",
              "子                  58\n",
              "心                  41\n",
              "气                  39\n",
              "发                  38\n",
              "...               ...\n",
              "喘                   1\n",
              "舶                   1\n",
              "川                   1\n",
              "熊                   1\n",
              "铭                   1\n",
              "\n",
              "[2663 rows x 1 columns]"
            ],
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              "\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",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-345e414f-7963-414a-ae17-6732c0a74717 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reduce the DataFrame to top X\n",
        "top_x = 25\n",
        "df_hsk_freq_count_top_x = df_hsk_freq_count.copy()\n",
        "df_hsk_freq_count_top_x = df_hsk_freq_count_top_x.head(top_x)\n",
        "df_hsk_freq_count_top_x"
      ],
      "metadata": {
        "id": "oF_219DphHM1",
        "outputId": "023a236a-8075-48d2-9f39-3f6a0bc3269f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        }
      },
      "id": "oF_219DphHM1",
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Frequency\n",
              "Simplified           \n",
              "不                  64\n",
              "子                  58\n",
              "心                  41\n",
              "气                  39\n",
              "发                  38\n",
              "人                  34\n",
              "理                  34\n",
              "动                  33\n",
              "实                  33\n",
              "生                  33\n",
              "力                  32\n",
              "一                  31\n",
              "成                  30\n",
              "意                  27\n",
              "分                  27\n",
              "事                  26\n",
              "大                  26\n",
              "得                  26\n",
              "体                  25\n",
              "公                  25\n",
              "行                  25\n",
              "然                  25\n",
              "时                  25\n",
              "无                  25\n",
              "手                  24"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1032c050-7183-408b-88d4-dbb4e774bcac\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Frequency</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Simplified</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>不</th>\n",
              "      <td>64</td>\n",
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              "      <td>58</td>\n",
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              "      <th>心</th>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>气</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>发</th>\n",
              "      <td>38</td>\n",
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              "    <tr>\n",
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              "      <td>34</td>\n",
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              "      <td>33</td>\n",
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              "      <th>力</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>一</th>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>成</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>意</th>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>分</th>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>事</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>大</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>得</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>体</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>公</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>行</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>然</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>时</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>无</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>手</th>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1032c050-7183-408b-88d4-dbb4e774bcac')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-1032c050-7183-408b-88d4-dbb4e774bcac 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-1032c050-7183-408b-88d4-dbb4e774bcac');\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",
              "\n",
              "\n",
              "<div id=\"df-f7beed45-8d82-49ff-9bdb-a2e66a3430cf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7beed45-8d82-49ff-9bdb-a2e66a3430cf')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f7beed45-8d82-49ff-9bdb-a2e66a3430cf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "id": "1ei-8R0GSl4P",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "1ei-8R0GSl4P",
        "outputId": "dc7984e5-f0aa-46e2-f705-cd1e055e5747"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Apply font\n",
        "fontManager.addfont('TaipeiSansTCBeta-Regular.ttf')\n",
        "mpl.rcParams['font.family'] = 'Taipei Sans TC Beta'\n",
        "\n",
        "# Total of different character (no need of unique, it was already split by value_counts)\n",
        "total_hanzi = len(df_hsk_freq_count)\n",
        "\n",
        "# Barplot filtered by top 15\n",
        "plt.figure(figsize=(12, 6))\n",
        "bar_chart = sns.barplot(\n",
        "    x = df_hsk_freq_count_top_x.index,\n",
        "    y = df_hsk_freq_count_top_x.Frequency,\n",
        "    palette=sns.color_palette(\"dark:red_r\", top_x)\n",
        ")\n",
        "\n",
        "# Customize backgrounds\n",
        "bar_chart.set_facecolor('lightgrey')\n",
        "bar_chart.grid(which='major', axis=\"y\", linewidth=2, linestyle=\"-.\", color=\"darkgrey\")\n",
        "plt.gcf().patch.set_facecolor('lightblue')\n",
        "\n",
        "title = f'Top {top_x} Hanzi by Frequency'\n",
        "\n",
        "# Customize title\n",
        "bar_chart.set_title(\n",
        "    title,\n",
        "    fontsize=25,\n",
        "    fontweight='bold',\n",
        "    color = \"darkred\"\n",
        ")\n",
        "\n",
        "# Drop the Y label and ticks\n",
        "bar_chart.set(ylabel=None)\n",
        "\n",
        "# Drop the X label\n",
        "bar_chart.set(xlabel=None)\n",
        "\n",
        "# Set Y limit to be a bit higher than maximum frequency\n",
        "max_freq = max(df_hsk_freq_count_top_x.Frequency)\n",
        "bar_chart.set_ylim(0, max_freq * 1.1)\n",
        "\n",
        "# Customize the X ticks\n",
        "bar_chart.set_xticklabels(\n",
        "    df_hsk_freq_count_top_x.index.tolist(),\n",
        "    size = 20,\n",
        "    color = \"darkred\"\n",
        ")\n",
        "\n",
        "# Annotate data labels a bit above each column\n",
        "for p, label in zip(bar_chart.patches, df_hsk_freq_count_top_x.Frequency):\n",
        "    bar_chart.annotate(\n",
        "        label,\n",
        "        (p.get_x()+0.3, p.get_height()+1),\n",
        "        fontsize=12,\n",
        "        color=\"darkred\"\n",
        "    )\n",
        "plt.savefig(f\"{title}.jpg\")\n",
        "\n",
        "sns.despine()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Jl5Qb1jrGw8l",
      "metadata": {
        "id": "Jl5Qb1jrGw8l"
      },
      "source": [
        "The top 2 characters mean \"no\" and \"son\" (though not used to address a son, it is an etymological meaning, it is often used in nouns, for example, table 桌子 and chair 椅子), they are both written with three strokes and are the only characters that appear more than fifty times.\n",
        "The remaining characters, are also simple characters like heart, air, life, strength, person and one."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 195,
      "id": "o47nI6xdI4Lh",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o47nI6xdI4Lh",
        "outputId": "994fd55a-e75e-41b1-ae6d-0f7d83e077ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conclusions:\n",
            "\n",
            "1- Actually, we study 2,663 distinct hanzi, in a total of 2,663 words.\n",
            "2- The average of characters per row is: 3.63.\n",
            "3- The average of characters per row does not change much from the whole HSK 1-6 vocabulary list to only HSK 6 list since it is 0.83.\n",
            "4- The most frequent characters are beginner friendly.\n",
            "5- The distinct hanzi qunatity per HSK against total words and average are:\n",
            "\n",
            "HSK 1 = 174 distinct characters || 150 words || 1.16 average characters per word\n",
            "HSK 2 = 208 distinct characters || 151 words || 1.16 average characters per word\n",
            "HSK 3 = 399 distinct characters || 300 words || 1.33 average characters per word\n",
            "HSK 4 = 716 distinct characters || 600 words || 1.19 average characters per word\n",
            "HSK 5 = 1,207 distinct characters || 1,300 words || 0.93 average characters per word\n",
            "HSK 6 = 2,077 distinct characters || 2,500 words || 0.83 average characters per word\n",
            "HSK 1-6 = 2,663 distinct characters || 5,001 words || 0.53 average characters per word\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# These DataFrames can lead us to some conclusions\n",
        "average = len(df_hsk_freq)/total_hanzi\n",
        "\n",
        "# Unique quantities per exam\n",
        "unique_hsk1 = df_hsk_freq[df_hsk_freq['HSK']==1][\"Simplified\"].nunique()\n",
        "unique_hsk2 = df_hsk_freq[df_hsk_freq['HSK']==2][\"Simplified\"].nunique()\n",
        "unique_hsk3 = df_hsk_freq[df_hsk_freq['HSK']==3][\"Simplified\"].nunique()\n",
        "unique_hsk4 = df_hsk_freq[df_hsk_freq['HSK']==4][\"Simplified\"].nunique()\n",
        "unique_hsk5 = df_hsk_freq[df_hsk_freq['HSK']==5][\"Simplified\"].nunique()\n",
        "unique_hsk6 = df_hsk_freq[df_hsk_freq['HSK']==6][\"Simplified\"].nunique()\n",
        "unique_hsk1_6 = df_hsk_freq[\"Simplified\"].nunique()\n",
        "\n",
        "# Rows per HSK exam, which means the quantity of words\n",
        "rows_hsk1 = df[df['HSK']==1].shape[0]\n",
        "rows_hsk2 = df[df['HSK']==2].shape[0]\n",
        "rows_hsk3 = df[df['HSK']==3].shape[0]\n",
        "rows_hsk4 = df[df['HSK']==4].shape[0]\n",
        "rows_hsk5 = df[df['HSK']==5].shape[0]\n",
        "rows_hsk6 = df[df['HSK']==6].shape[0]\n",
        "rows_hsk1_6 = df.shape[0]\n",
        "\n",
        "# Average per word by HSK exam\n",
        "average_hsk1 = unique_hsk1/rows_hsk1\n",
        "average_hsk2 = unique_hsk1/rows_hsk1\n",
        "average_hsk3 = unique_hsk3/rows_hsk3\n",
        "average_hsk4 = unique_hsk4/rows_hsk4\n",
        "average_hsk5 = unique_hsk5/rows_hsk5\n",
        "average_hsk6 = unique_hsk6/rows_hsk6\n",
        "average_hsk1_6 = unique_hsk1_6/rows_hsk1_6\n",
        "\n",
        "\n",
        "print(f\"\"\"Conclusions:\n",
        "\n",
        "1- Actually, we study {unique_hsk1_6:,} distinct hanzi, in a total of {total_hanzi:,} words.\n",
        "2- The average of characters per row is: {average:.2f}.\n",
        "3- The average of characters per row does not change much from the whole HSK 1-6 vocabulary list to only HSK 6 list since it is {average_hsk6:.2f}.\n",
        "4- The most frequent characters are beginner friendly.\n",
        "5- The distinct hanzi qunatity per HSK against total words and average are:\n",
        "\n",
        "HSK 1 = {unique_hsk1} distinct characters || {rows_hsk1} words || {average_hsk1:.2f} average characters per word\n",
        "HSK 2 = {unique_hsk2} distinct characters || {rows_hsk2} words || {average_hsk2:.2f} average characters per word\n",
        "HSK 3 = {unique_hsk3} distinct characters || {rows_hsk3} words || {average_hsk3:.2f} average characters per word\n",
        "HSK 4 = {unique_hsk4} distinct characters || {rows_hsk4} words || {average_hsk4:.2f} average characters per word\n",
        "HSK 5 = {unique_hsk5:,} distinct characters || {rows_hsk5:,} words || {average_hsk5:.2f} average characters per word\n",
        "HSK 6 = {unique_hsk6:,} distinct characters || {rows_hsk6:,} words || {average_hsk6:.2f} average characters per word\n",
        "HSK 1-6 = {unique_hsk1_6:,} distinct characters || {rows_hsk1_6:,} words || {average_hsk1_6:.2f} average characters per word\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's plot the last inisghts\n",
        "hsk_levels = ['HSK 1', 'HSK 2', 'HSK 3', 'HSK 4', 'HSK 5', 'HSK 6']\n",
        "unique_counts = [unique_hsk1, unique_hsk2, unique_hsk3, unique_hsk4, unique_hsk5, unique_hsk6]\n",
        "words_counts = [rows_hsk1, rows_hsk2, rows_hsk3, rows_hsk4, rows_hsk5, rows_hsk6]\n",
        "\n",
        "# Create a line plot for unique characters\n",
        "fig,ax = plt.subplots(figsize=(12, 6))\n",
        "plt.plot(\n",
        "    hsk_levels,\n",
        "    unique_counts,\n",
        "    marker='o',\n",
        "    markersize=8,\n",
        "    color='darkred',\n",
        "    linestyle='-.',\n",
        "    linewidth=2,\n",
        "    label='Unique Characters'\n",
        "    )\n",
        "# Create a line plot for words quantity\n",
        "plt.plot(\n",
        "    hsk_levels,\n",
        "    words_counts,\n",
        "    marker='o',\n",
        "    markersize=8,\n",
        "    color='darkblue',\n",
        "    linestyle='-.',\n",
        "    linewidth=2,\n",
        "    label='Words Quantity'\n",
        ")\n",
        "\n",
        "# Customize backgrounds\n",
        "fig.patch.set_facecolor('lightblue')  # Background color of the figure\n",
        "ax.set_facecolor('lightgrey')  # Background color of the plot area\n",
        "ax.grid(which='both', linewidth=1, linestyle=\":\", color=\"grey\")\n",
        "\n",
        "plt.xlabel('')\n",
        "plt.ylabel(\n",
        "    'Counts',\n",
        "    fontsize=12,\n",
        "    color=\"darkred\"\n",
        "    )\n",
        "plt.ylim(0, max(words_counts) * 1.1)\n",
        "\n",
        "title = \"Unique Characters and Word per HSK Exam Level\"\n",
        "plt.title(\n",
        "    title,\n",
        "    fontsize=25,\n",
        "    fontweight=\"bold\",\n",
        "    color=\"darkred\"\n",
        "    )\n",
        "ax.set_xticklabels(\n",
        "    hsk_levels,\n",
        "    size=12,\n",
        "    color=\"darkred\"\n",
        ")\n",
        "\n",
        "# Annotate data labels a bit above each marker\n",
        "for x, y in zip(hsk_levels, unique_counts):\n",
        "    plt.annotate(\n",
        "        f'{y}',  # Label text\n",
        "        (x, y),  # Data point coordinates\n",
        "        textcoords=\"offset points\",  # Offset the label position\n",
        "        xytext=(-10, 5),  # How much to offset (in points)\n",
        "        ha='center',  # Horizontal alignment\n",
        "        fontsize=12,\n",
        "        color=\"darkred\"\n",
        "    )\n",
        "for x, y in zip(hsk_levels, words_counts):\n",
        "    plt.annotate(\n",
        "        f'{y}',  # Label text\n",
        "        (x, y),  # Data point coordinates\n",
        "        textcoords=\"offset points\",  # Offset the label position\n",
        "        xytext=(-10, 5),  # How much to offset (in points)\n",
        "        ha='center',  # Horizontal alignment\n",
        "        fontsize=12,\n",
        "        color=\"darkblue\"\n",
        "    )\n",
        "\n",
        "# Display the plot\n",
        "sns.despine()\n",
        "plt.legend(frameon = False)\n",
        "plt.tight_layout()\n",
        "plt.savefig(f\"{title}.jpg\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "BQU7uXPDYtPb",
        "outputId": "8951a003-d6af-4f9d-a61c-f94d96239bf9"
      },
      "id": "BQU7uXPDYtPb",
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-204-20a8e4cb4544>:50: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot the cumulative quantities\n",
        "import numpy as np\n",
        "cumulative_words_counts = np.cumsum(words_counts)\n",
        "\n",
        "# Create a line plot for cumulative word counts (in darkblue)\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "plt.plot(\n",
        "    hsk_levels,\n",
        "    cumulative_words_counts,\n",
        "    marker='o',\n",
        "    markersize=5,\n",
        "    color='darkred',\n",
        "    linestyle='-.',\n",
        "    linewidth=2\n",
        "    )\n",
        "\n",
        "# Customize backgrounds\n",
        "fig.patch.set_facecolor('lightblue')  # Background color of the figure\n",
        "ax.set_facecolor('lightgrey')  # Background color of the plot area\n",
        "ax.grid(which='both', linewidth=1, linestyle=\":\", color=\"grey\")\n",
        "\n",
        "plt.xlabel('')\n",
        "plt.ylabel(\n",
        "    'Cumulative Word Counts',\n",
        "    fontsize=12,\n",
        "    color=\"darkred\"\n",
        ")\n",
        "plt.ylim(0, max(cumulative_words_counts) * 1.1)\n",
        "\n",
        "ax.set_xticklabels(\n",
        "    hsk_levels,\n",
        "    size = 12,\n",
        "    color = \"darkred\"\n",
        ")\n",
        "\n",
        "title = 'Cumulative Word Counts per HSK Exam Level'\n",
        "plt.title(\n",
        "    title,\n",
        "    fontsize=25,\n",
        "    fontweight=\"bold\",\n",
        "    color=\"darkred\"\n",
        ")\n",
        "\n",
        "# Annotate data labels a bit below each marker for cumulative word counts\n",
        "for x, y in zip(hsk_levels, cumulative_words_counts):\n",
        "    plt.annotate(\n",
        "        f'{y}',  # Label text\n",
        "        (x, y),  # Data point coordinates\n",
        "        textcoords=\"offset points\",  # Offset the label position\n",
        "        xytext=(-10, 5),  # How much to offset (in points)\n",
        "        ha='center',  # Horizontal alignment\n",
        "        fontsize=12,\n",
        "        color=\"darkred\"\n",
        "    )\n",
        "\n",
        "plt.fill_between(\n",
        "    hsk_levels,\n",
        "    0,\n",
        "    cumulative_words_counts,\n",
        "    alpha=0.3,  # Adjust transparency of the fill\n",
        "    color='darkred'\n",
        ")\n",
        "\n",
        "# Display the plot without legend background\n",
        "sns.despine()\n",
        "plt.tight_layout()\n",
        "plt.savefig(f\"{title}.jpg\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "-Hc_WNXUmke0",
        "outputId": "ca1e7efe-75ba-4a1c-c3d1-970a9b0ae189"
      },
      "id": "-Hc_WNXUmke0",
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-202-adb0e494fd8b>:30: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "gcHpDT2_wBn5",
        "3jgcJyP71g_1",
        "YROSQZ3y2Bvj",
        "35kpqdET2Uxy"
      ],
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}