pandas_top_200.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Pandas 常用方法示例\n",
        "本 Notebook 罗列了 Pandas 中大约 200 个常用方法或函数的最简示例。每个示例单独放在一个 cell 中，以便独立查看和运行。\n",
        "\n",
        "**Tips：**\n",
        "- 这些示例大多只演示基础用法；\n",
        "- 部分方法有重复或相似功能，仅作快速参考；\n",
        "- 在实际生产环境中，请结合官方文档与具体需求；\n",
        "- 某些示例需要外部文件或数据库才能完整运行，示例中多为简单演示。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Initial df:\n",
            "    A  B  C          D\n",
            "0  1  4  x 2021-01-01\n",
            "1  2  5  x 2021-01-02\n",
            "2  3  6  y 2021-01-03\n",
            "3  4  7  y 2021-01-04\n",
            "4  5  8  z 2021-01-05\n",
            "\n",
            "Initial s:\n",
            " 0    10\n",
            "1    20\n",
            "2    30\n",
            "3    40\n",
            "4    50\n",
            "Name: S, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 全局导入\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "\n",
        "# 在多数示例中，我们会用到以下 df 和 s:\n",
        "df = pd.DataFrame({\n",
        "    'A': [1, 2, 3, 4, 5],\n",
        "    'B': [4, 5, 6, 7, 8],\n",
        "    'C': [\"x\", \"x\", \"y\", \"y\", \"z\"],\n",
        "    'D': pd.date_range(\"2021-01-01\", periods=5, freq=\"D\")\n",
        "})\n",
        "\n",
        "s = pd.Series([10, 20, 30, 40, 50], name=\"S\")\n",
        "\n",
        "print(\"Initial df:\\n\", df)\n",
        "print(\"\\nInitial s:\\n\", s)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 1. 基础创建和导入导出"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x    10\n",
            "y    20\n",
            "z    30\n",
            "Name: ExampleSeries, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 1. pd.Series\n",
        "ser = pd.Series([10, 20, 30], index=[\"x\", \"y\", \"z\"], name=\"ExampleSeries\")\n",
        "print(ser)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   col1   col2 col3\n",
            "0     1   True    a\n",
            "1     2  False    b\n",
            "2     3   True    c\n"
          ]
        }
      ],
      "source": [
        "# 2. pd.DataFrame\n",
        "df2 = pd.DataFrame({\n",
        "    \"col1\": [1, 2, 3],\n",
        "    \"col2\": [True, False, True],\n",
        "    \"col3\": [\"a\", \"b\", \"c\"]\n",
        "})\n",
        "print(df2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pd.read_csv(\"path_to_file.csv\") # 读取 CSV 文件到 DataFrame\n"
          ]
        }
      ],
      "source": [
        "# 3. pd.read_csv (示例，仅打印函数用法)\n",
        "print('pd.read_csv(\"path_to_file.csv\") # 读取 CSV 文件到 DataFrame')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pd.read_excel(\"file.xlsx\", sheet_name=\"Sheet1\")\n"
          ]
        }
      ],
      "source": [
        "# 4. pd.read_excel (示例)\n",
        "print('pd.read_excel(\"file.xlsx\", sheet_name=\"Sheet1\")')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pd.read_table(\"data.txt\", sep=\"\t\")\n"
          ]
        }
      ],
      "source": [
        "# 5. pd.read_table\n",
        "print('pd.read_table(\"data.txt\", sep=\"\\t\")')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pd.read_json(\"data.json\")\n"
          ]
        }
      ],
      "source": [
        "# 6. pd.read_json\n",
        "print('pd.read_json(\"data.json\")')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dfs = pd.read_html(\"http://example.com\")  # 返回 DataFrame 列表\n"
          ]
        }
      ],
      "source": [
        "# 7. pd.read_html\n",
        "print('dfs = pd.read_html(\"http://example.com\")  # 返回 DataFrame 列表')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pd.read_sql(\"SELECT * FROM table\", con=some_db_connection)\n"
          ]
        }
      ],
      "source": [
        "# 8. pd.read_sql (示例)\n",
        "print('pd.read_sql(\"SELECT * FROM table\", con=some_db_connection)')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "导出 df 为 CSV: out.csv (index=False)\n"
          ]
        }
      ],
      "source": [
        "# 9. DataFrame.to_csv\n",
        "df.to_csv(\"out.csv\", index=False)\n",
        "print('导出 df 为 CSV: out.csv (index=False)')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "导出 df 为 Excel: out.xlsx\n"
          ]
        }
      ],
      "source": [
        "# Install openpyxl package\n",
        "# %pip install openpyxl\n",
        "\n",
        "# 10. DataFrame.to_excel\n",
        "df.to_excel(\"out.xlsx\", sheet_name=\"Sheet1\", index=False)\n",
        "print('导出 df 为 Excel: out.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[{\"A\":1,\"B\":4,\"C\":\"x\",\"D\":1609459200000},{\"A\":2,\"B\":5,\"C\":\"x\",\"D\":1609545600000},{\"A\":3,\"B\":6,\"C\":\"y\",\"D\":1609632000000},{\"A\":4,\"B\":7,\"C\":\"y\",\"D\":1609718400000},{\"A\":5,\"B\":8,\"C\":\"z\",\"D\":1609804800000}]\n"
          ]
        }
      ],
      "source": [
        "# 11. DataFrame.to_json\n",
        "json_str = df.to_json(orient=\"records\")\n",
        "print(json_str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "df.to_sql('table_name', con=connection, if_exists='replace')\n"
          ]
        }
      ],
      "source": [
        "# 12. DataFrame.to_sql (示例)\n",
        "print(\"df.to_sql('table_name', con=connection, if_exists='replace')\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. 查看数据与基本信息"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D\n",
            "0  1  4  x 2021-01-01\n",
            "1  2  5  x 2021-01-02\n",
            "2  3  6  y 2021-01-03\n"
          ]
        }
      ],
      "source": [
        "# 13. DataFrame.head\n",
        "print(df.head(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D\n",
            "3  4  7  y 2021-01-04\n",
            "4  5  8  z 2021-01-05\n"
          ]
        }
      ],
      "source": [
        "# 14. DataFrame.tail\n",
        "print(df.tail(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5 entries, 0 to 4\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype         \n",
            "---  ------  --------------  -----         \n",
            " 0   A       5 non-null      int64         \n",
            " 1   B       5 non-null      int64         \n",
            " 2   C       5 non-null      object        \n",
            " 3   D       5 non-null      datetime64[ns]\n",
            "dtypes: datetime64[ns](1), int64(2), object(1)\n",
            "memory usage: 292.0+ bytes\n"
          ]
        }
      ],
      "source": [
        "# 15. DataFrame.info\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              A         B                    D\n",
            "count  5.000000  5.000000                    5\n",
            "mean   3.000000  6.000000  2021-01-03 00:00:00\n",
            "min    1.000000  4.000000  2021-01-01 00:00:00\n",
            "25%    2.000000  5.000000  2021-01-02 00:00:00\n",
            "50%    3.000000  6.000000  2021-01-03 00:00:00\n",
            "75%    4.000000  7.000000  2021-01-04 00:00:00\n",
            "max    5.000000  8.000000  2021-01-05 00:00:00\n",
            "std    1.581139  1.581139                  NaN\n"
          ]
        }
      ],
      "source": [
        "# 16. DataFrame.describe\n",
        "print(df.describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(5, 4)\n"
          ]
        }
      ],
      "source": [
        "# 17. DataFrame.shape\n",
        "print(df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RangeIndex(start=0, stop=5, step=1)\n"
          ]
        }
      ],
      "source": [
        "# 18. DataFrame.index\n",
        "print(df.index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['A', 'B', 'C', 'D'], dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# 19. DataFrame.columns\n",
        "print(df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 4 'x' Timestamp('2021-01-01 00:00:00')]\n",
            " [2 5 'x' Timestamp('2021-01-02 00:00:00')]\n",
            " [3 6 'y' Timestamp('2021-01-03 00:00:00')]\n",
            " [4 7 'y' Timestamp('2021-01-04 00:00:00')]\n",
            " [5 8 'z' Timestamp('2021-01-05 00:00:00')]]\n"
          ]
        }
      ],
      "source": [
        "# 20. DataFrame.values\n",
        "print(df.values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A             int64\n",
            "B             int64\n",
            "C            object\n",
            "D    datetime64[ns]\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# 21. DataFrame.dtypes\n",
        "print(df.dtypes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B\n",
            "0  1  4\n",
            "1  2  5\n",
            "2  3  6\n",
            "3  4  7\n",
            "4  5  8\n"
          ]
        }
      ],
      "source": [
        "# 22. DataFrame.select_dtypes\n",
        "df_num = df.select_dtypes(include=[np.number])\n",
        "print(df_num)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A           float64\n",
            "B             int64\n",
            "C            object\n",
            "D    datetime64[ns]\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# 23. DataFrame.astype\n",
        "df_new = df.astype({'A': 'float'})\n",
        "print(df_new.dtypes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "df\n",
            "    A  B  C          D\n",
            "0  1  4  x 2021-01-01\n",
            "1  2  5  x 2021-01-02\n",
            "2  3  6  y 2021-01-03\n",
            "3  4  7  y 2021-01-04\n",
            "4  5  8  z 2021-01-05\n",
            "df_copy\n",
            "     A  B  C          D\n",
            "0  10  4  x 2021-01-01\n",
            "1  20  5  x 2021-01-02\n",
            "2  30  6  y 2021-01-03\n",
            "3  40  7  y 2021-01-04\n",
            "4  50  8  z 2021-01-05\n"
          ]
        }
      ],
      "source": [
        "# 24. DataFrame.copy\n",
        "df_copy = df.copy()\n",
        "df_copy['A'] = df_copy['A']*10\n",
        "print(\"df\\n\", df)\n",
        "print(\"df_copy\\n\", df_copy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 3. 缺失值处理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       A      B      C      D\n",
            "0  False  False  False  False\n",
            "1  False  False  False  False\n",
            "2  False  False  False  False\n",
            "3  False  False  False  False\n",
            "4  False  False  False  False\n"
          ]
        }
      ],
      "source": [
        "# 25. DataFrame.isna\n",
        "print(df.isna())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      A     B     C     D\n",
            "0  True  True  True  True\n",
            "1  True  True  True  True\n",
            "2  True  True  True  True\n",
            "3  True  True  True  True\n",
            "4  True  True  True  True\n"
          ]
        }
      ],
      "source": [
        "# 26. DataFrame.notna\n",
        "print(df.notna())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A    B\n",
            "0  1.0  4.0\n"
          ]
        }
      ],
      "source": [
        "# 27. DataFrame.dropna\n",
        "df_na = pd.DataFrame({\n",
        "  'A': [1, np.nan, 3],\n",
        "  'B': [4, 5, np.nan]\n",
        "})\n",
        "print(df_na.dropna())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A    B\n",
            "0  1.0  4.0\n",
            "1  0.0  5.0\n",
            "2  3.0  0.0\n"
          ]
        }
      ],
      "source": [
        "# 28. DataFrame.fillna\n",
        "print(df_na.fillna(0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   col1  col2\n",
            "0   1.0   NaN\n",
            "1   2.0   3.0\n",
            "2   NaN   4.0\n",
            "3   4.0   NaN\n"
          ]
        }
      ],
      "source": [
        "# 29. DataFrame.replace\n",
        "df_replace = pd.DataFrame({'col1':[1,2,999,4], 'col2':[999,3,4,999]})\n",
        "print(df_replace.replace(999, np.nan))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4. 重复值与去重"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    False\n",
            "1     True\n",
            "2    False\n",
            "3    False\n",
            "4    False\n",
            "dtype: bool\n"
          ]
        }
      ],
      "source": [
        "# 30. DataFrame.duplicated\n",
        "df_dup = pd.DataFrame({\n",
        "    'X': [1,1,2,2,3],\n",
        "    'Y': [10,10,20,21,30]\n",
        "})\n",
        "print(df_dup.duplicated())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   X   Y\n",
            "0  1  10\n",
            "2  2  20\n",
            "3  2  21\n",
            "4  3  30\n"
          ]
        }
      ],
      "source": [
        "# 31. DataFrame.drop_duplicates\n",
        "print(df_dup.drop_duplicates())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 5. 重命名与排序"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A_new  B  C          D\n",
            "0      1  4  x 2021-01-01\n",
            "1      2  5  x 2021-01-02\n",
            "2      3  6  y 2021-01-03\n",
            "3      4  7  y 2021-01-04\n",
            "4      5  8  z 2021-01-05\n"
          ]
        }
      ],
      "source": [
        "# 32. DataFrame.rename\n",
        "df_rename = df.rename(columns={'A': 'A_new'})\n",
        "print(df_rename.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D\n",
            "4  5  8  z 2021-01-05\n",
            "3  4  7  y 2021-01-04\n",
            "2  3  6  y 2021-01-03\n",
            "1  2  5  x 2021-01-02\n",
            "0  1  4  x 2021-01-01\n"
          ]
        }
      ],
      "source": [
        "# 33. DataFrame.sort_values\n",
        "print(df.sort_values(by='B', ascending=False))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D\n",
            "0  1  4  x 2021-01-01\n",
            "1  2  5  x 2021-01-02\n",
            "2  3  6  y 2021-01-03\n",
            "3  4  7  y 2021-01-04\n",
            "4  5  8  z 2021-01-05\n"
          ]
        }
      ],
      "source": [
        "# 34. DataFrame.sort_index\n",
        "df_sort_idx = df.sample(frac=1, random_state=1)  # 打乱\n",
        "print(df_sort_idx.sort_index())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D\n",
            "0  3  6  y 2021-01-03\n",
            "1  2  5  x 2021-01-02\n",
            "2  5  8  z 2021-01-05\n",
            "3  1  4  x 2021-01-01\n",
            "4  4  7  y 2021-01-04\n"
          ]
        }
      ],
      "source": [
        "# 35. DataFrame.reset_index\n",
        "df_reset = df_sort_idx.reset_index(drop=True)\n",
        "print(df_reset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B          D\n",
            "C                 \n",
            "x  1  4 2021-01-01\n",
            "x  2  5 2021-01-02\n",
            "y  3  6 2021-01-03\n",
            "y  4  7 2021-01-04\n",
            "z  5  8 2021-01-05\n"
          ]
        }
      ],
      "source": [
        "# 36. DataFrame.set_index\n",
        "df_set = df.set_index('C')\n",
        "print(df_set)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 6. 索引与选择数据"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  C\n",
            "0  1  x\n",
            "1  2  x\n",
            "2  3  y\n"
          ]
        }
      ],
      "source": [
        "# 37. DataFrame.loc\n",
        "print(df.loc[0:2, ['A','C']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B\n",
            "1  2  5\n",
            "2  3  6\n"
          ]
        }
      ],
      "source": [
        "# 38. DataFrame.iloc\n",
        "print(df.iloc[1:3, 0:2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1\n"
          ]
        }
      ],
      "source": [
        "# 39. DataFrame.at (基于标签的快速定位)\n",
        "print(df.at[0, 'A'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4\n"
          ]
        }
      ],
      "source": [
        "# 40. DataFrame.iat (基于整数位置)\n",
        "print(df.iat[0, 1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D\n",
            "2  3  6  y 2021-01-03\n",
            "3  4  7  y 2021-01-04\n"
          ]
        }
      ],
      "source": [
        "# 41. DataFrame.query\n",
        "df_query = df.query(\"A > 2 and B < 8\")\n",
        "print(df_query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D   E\n",
            "0  1  4  x 2021-01-01   5\n",
            "1  2  5  x 2021-01-02   7\n",
            "2  3  6  y 2021-01-03   9\n",
            "3  4  7  y 2021-01-04  11\n",
            "4  5  8  z 2021-01-05  13\n"
          ]
        }
      ],
      "source": [
        "# 42. DataFrame.eval\n",
        "df_eval = df.eval(\"E = A + B\")\n",
        "print(df_eval)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  AA\n",
            "0  1  4  x 2021-01-01  10\n",
            "1  2  5  x 2021-01-02  20\n",
            "2  3  6  y 2021-01-03  30\n",
            "3  4  7  y 2021-01-04  40\n",
            "4  5  8  z 2021-01-05  50\n"
          ]
        }
      ],
      "source": [
        "# 43. DataFrame.assign\n",
        "df_assign = df.assign(AA = df['A']*10)\n",
        "print(df_assign)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A    3.0\n",
            "B    6.0\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# 44. DataFrame.apply\n",
        "print(df[['A','B']].apply(np.mean))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       A      B\n",
            "0  val_1  val_4\n",
            "1  val_2  val_5\n",
            "2  val_3  val_6\n",
            "3  val_4  val_7\n",
            "4  val_5  val_8\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\minqliu\\AppData\\Local\\Temp\\ipykernel_39108\\1386170597.py:2: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
            "  df_str = df[['A','B']].applymap(lambda x: f\"val_{x}\")\n"
          ]
        }
      ],
      "source": [
        "# 45. DataFrame.applymap\n",
        "df_str = df[['A','B']].applymap(lambda x: f\"val_{x}\")\n",
        "print(df_str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        A    B\n",
            "min   1.0  4.0\n",
            "max   5.0  8.0\n",
            "mean  3.0  6.0\n"
          ]
        }
      ],
      "source": [
        "# 46. DataFrame.aggregate / agg\n",
        "print(df[['A','B']].agg(['min','max','mean']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "    A   B\n",
            "0  11  14\n",
            "1  12  15\n",
            "2  13  16\n",
            "3  14  17\n",
            "4  15  18\n"
          ]
        }
      ],
      "source": [
        "# 47. DataFrame.transform\n",
        "print(df[['A','B']].transform(lambda x: x+10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 7. 分组与透视"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "C\n",
            "x    3\n",
            "y    7\n",
            "z    5\n",
            "Name: A, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 48. DataFrame.groupby\n",
        "grouped = df.groupby('C')\n",
        "print(grouped['A'].sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "bar  A  B\n",
            "foo      \n",
            "one  1  2\n",
            "two  3  4\n"
          ]
        }
      ],
      "source": [
        "# 49. DataFrame.pivot\n",
        "df_pivot = pd.DataFrame({\n",
        "    'foo': ['one','one','two','two'],\n",
        "    'bar': ['A','B','A','B'],\n",
        "    'val': [1,2,3,4]\n",
        "})\n",
        "print(df_pivot.pivot(index='foo', columns='bar', values='val'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "B    one  two\n",
            "A            \n",
            "bar    3    4\n",
            "foo    1    2\n"
          ]
        }
      ],
      "source": [
        "# 50. DataFrame.pivot_table\n",
        "df_pivot_table = pd.DataFrame({\n",
        "    'A': ['foo','foo','bar','bar'],\n",
        "    'B': ['one','two','one','two'],\n",
        "    'C': [1,2,3,4]\n",
        "})\n",
        "pt = df_pivot_table.pivot_table(index='A', columns='B', values='C', aggfunc='sum')\n",
        "print(pt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   C variable  value\n",
            "0  x        A      1\n",
            "1  x        A      2\n",
            "2  y        A      3\n",
            "3  y        A      4\n",
            "4  z        A      5\n",
            "5  x        B      4\n",
            "6  x        B      5\n",
            "7  y        B      6\n",
            "8  y        B      7\n",
            "9  z        B      8\n"
          ]
        }
      ],
      "source": [
        "# 51. DataFrame.melt\n",
        "df_melt = pd.melt(df, id_vars=['C'], value_vars=['A','B'])\n",
        "print(df_melt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0  A                      1\n",
            "   B                      4\n",
            "   C                      x\n",
            "   D    2021-01-01 00:00:00\n",
            "1  A                      2\n",
            "   B                      5\n",
            "   C                      x\n",
            "   D    2021-01-02 00:00:00\n",
            "2  A                      3\n",
            "   B                      6\n",
            "   C                      y\n",
            "   D    2021-01-03 00:00:00\n",
            "3  A                      4\n",
            "   B                      7\n",
            "   C                      y\n",
            "   D    2021-01-04 00:00:00\n",
            "4  A                      5\n",
            "   B                      8\n",
            "   C                      z\n",
            "   D    2021-01-05 00:00:00\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# 52. DataFrame.stack\n",
        "df_stacked = df.stack()\n",
        "print(df_stacked)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C                    D\n",
            "0  1  4  x  2021-01-01 00:00:00\n",
            "1  2  5  x  2021-01-02 00:00:00\n",
            "2  3  6  y  2021-01-03 00:00:00\n",
            "3  4  7  y  2021-01-04 00:00:00\n",
            "4  5  8  z  2021-01-05 00:00:00\n"
          ]
        }
      ],
      "source": [
        "# 53. DataFrame.unstack\n",
        "print(df_stacked.unstack())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   key  left_val  right_val\n",
            "0    2        20        200\n",
            "1    3        30        300\n"
          ]
        }
      ],
      "source": [
        "# 54. DataFrame.merge\n",
        "df_left = pd.DataFrame({'key':[1,2,3], 'left_val':[10,20,30]})\n",
        "df_right = pd.DataFrame({'key':[2,3,4], 'right_val':[200,300,400]})\n",
        "df_merged = pd.merge(df_left, df_right, on='key', how='inner')\n",
        "print(df_merged)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     left_val  right_val\n",
            "key                     \n",
            "2          20        200\n",
            "3          30        300\n"
          ]
        }
      ],
      "source": [
        "# 55. DataFrame.join\n",
        "df_left2 = df_left.set_index('key')\n",
        "df_right2 = df_right.set_index('key')\n",
        "df_joined = df_left2.join(df_right2, how='inner')\n",
        "print(df_joined)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   col\n",
            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n"
          ]
        }
      ],
      "source": [
        "# 56. pd.concat\n",
        "df_a = pd.DataFrame({'col':[1,2]})\n",
        "df_b = pd.DataFrame({'col':[3,4]})\n",
        "df_cat = pd.concat([df_a, df_b], ignore_index=True)\n",
        "print(df_cat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A  B\n",
            "0  1.0  3\n",
            "1  6.0  4\n"
          ]
        }
      ],
      "source": [
        "# 57. DataFrame.combine_first\n",
        "df1 = pd.DataFrame({'A':[1,np.nan], 'B':[3,4]})\n",
        "df2 = pd.DataFrame({'A':[5,6]})\n",
        "df_combined = df1.combine_first(df2)\n",
        "print(df_combined)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "left:\n",
            "       X     Y\n",
            "0  10.0  30.0\n",
            "1  20.0  40.0\n",
            "2   NaN   NaN\n",
            "3   NaN   NaN\n",
            "\n",
            "right:\n",
            "        X   Y\n",
            "0    NaN NaN\n",
            "1  100.0 NaN\n",
            "2  200.0 NaN\n",
            "3  300.0 NaN\n"
          ]
        }
      ],
      "source": [
        "# 58. DataFrame.align\n",
        "df3 = pd.DataFrame({'X':[10,20], 'Y':[30,40]}, index=[0,1])\n",
        "df4 = pd.DataFrame({'X':[100,200,300]}, index=[1,2,3])\n",
        "left, right = df3.align(df4, join='outer')\n",
        "print('left:\\n', left)\n",
        "print('\\nright:\\n', right)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A    B\n",
            "C        \n",
            "x  3  4.5\n",
            "y  7  6.5\n",
            "z  5  8.0\n"
          ]
        }
      ],
      "source": [
        "# 59. DataFrame.groupby().agg\n",
        "print(df.groupby('C').agg({'A':'sum','B':'mean'}))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "C\n",
            "x     3.0\n",
            "y     7.0\n",
            "z    10.0\n",
            "dtype: float64\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\minqliu\\AppData\\Local\\Temp\\ipykernel_39108\\3754642921.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  res = df.groupby('C').apply(custom_func)\n"
          ]
        }
      ],
      "source": [
        "# 60. DataFrame.groupby().apply\n",
        "def custom_func(x):\n",
        "    return x['A'].mean() * 2\n",
        "res = df.groupby('C').apply(custom_func)\n",
        "print(res)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean\n",
            "0  1  4  x 2021-01-01     1.5\n",
            "1  2  5  x 2021-01-02     1.5\n",
            "2  3  6  y 2021-01-03     3.5\n",
            "3  4  7  y 2021-01-04     3.5\n",
            "4  5  8  z 2021-01-05     5.0\n"
          ]
        }
      ],
      "source": [
        "# 61. DataFrame.groupby().transform\n",
        "df['A_mean'] = df.groupby('C')['A'].transform('mean')\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "C\n",
            "x    2\n",
            "y    2\n",
            "z    1\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 62. DataFrame.groupby().size\n",
        "print(df.groupby('C').size())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   count  mean       std  min   25%  50%   75%  max\n",
            "C                                                  \n",
            "x    2.0   1.5  0.707107  1.0  1.25  1.5  1.75  2.0\n",
            "y    2.0   3.5  0.707107  3.0  3.25  3.5  3.75  4.0\n",
            "z    1.0   5.0       NaN  5.0  5.00  5.0  5.00  5.0\n"
          ]
        }
      ],
      "source": [
        "# 63. DataFrame.groupby().describe\n",
        "print(df.groupby('C')['A'].describe())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 8. 统计分析"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "               A         B    A_mean\n",
            "A       1.000000  1.000000  0.948683\n",
            "B       1.000000  1.000000  0.948683\n",
            "A_mean  0.948683  0.948683  1.000000\n"
          ]
        }
      ],
      "source": [
        "# 64. DataFrame.corr\n",
        "print(df.select_dtypes(include=[np.number]).corr())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "           A     B  A_mean\n",
            "A       2.50  2.50    2.25\n",
            "B       2.50  2.50    2.25\n",
            "A_mean  2.25  2.25    2.25\n"
          ]
        }
      ],
      "source": [
        "# 65. DataFrame.cov\n",
        "print(df.select_dtypes(include=[np.number]).cov())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A         1.0\n",
            "B         1.0\n",
            "A_mean    NaN\n",
            "C         NaN\n",
            "D         NaN\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# 66. DataFrame.corrwith\n",
        "df_extra = df[['A','B']]\n",
        "print(df.corrwith(df_extra))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B\n",
            "0  1  4\n",
            "1  2  5\n",
            "2  3  6\n",
            "3  4  7\n",
            "4  5  8\n"
          ]
        }
      ],
      "source": [
        "# 67. DataFrame.cummax\n",
        "print(df[['A','B']].cummax())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B\n",
            "0  1  4\n",
            "1  1  4\n",
            "2  1  4\n",
            "3  1  4\n",
            "4  1  4\n"
          ]
        }
      ],
      "source": [
        "# 68. DataFrame.cummin\n",
        "print(df[['A','B']].cummin())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A     B\n",
            "0    1     4\n",
            "1    2    20\n",
            "2    6   120\n",
            "3   24   840\n",
            "4  120  6720\n"
          ]
        }
      ],
      "source": [
        "# 69. DataFrame.cumprod\n",
        "print(df[['A','B']].cumprod())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "    A   B\n",
            "0   1   4\n",
            "1   3   9\n",
            "2   6  15\n",
            "3  10  22\n",
            "4  15  30\n"
          ]
        }
      ],
      "source": [
        "# 70. DataFrame.cumsum\n",
        "print(df[['A','B']].cumsum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A    B\n",
            "0  NaN  NaN\n",
            "1  1.0  1.0\n",
            "2  1.0  1.0\n",
            "3  1.0  1.0\n",
            "4  1.0  1.0\n"
          ]
        }
      ],
      "source": [
        "# 71. DataFrame.diff\n",
        "print(df[['A','B']].diff())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A    B\n",
            "0  1.0  1.0\n",
            "1  2.0  2.0\n",
            "2  3.0  3.0\n",
            "3  4.0  4.0\n",
            "4  5.0  5.0\n"
          ]
        }
      ],
      "source": [
        "# 72. DataFrame.rank\n",
        "print(df[['A','B']].rank())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      x     y\n",
            "0  1.12  3.57\n",
            "1  2.35  5.00\n"
          ]
        }
      ],
      "source": [
        "# 73. DataFrame.round\n",
        "df_float = pd.DataFrame({'x':[1.1234,2.3456],'y':[3.5678,4.9999]})\n",
        "print(df_float.round(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean\n",
            "1  2  5  x 2021-01-02     1.5\n",
            "4  5  8  z 2021-01-05     5.0\n"
          ]
        }
      ],
      "source": [
        "# 74. DataFrame.sample\n",
        "print(df.sample(n=2, random_state=42))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean\n",
            "4  5  8  z 2021-01-05     5.0\n",
            "3  4  7  y 2021-01-04     3.5\n"
          ]
        }
      ],
      "source": [
        "# 75. DataFrame.nlargest\n",
        "print(df.nlargest(2, 'A'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean\n",
            "0  1  4  x 2021-01-01     1.5\n",
            "1  2  5  x 2021-01-02     1.5\n"
          ]
        }
      ],
      "source": [
        "# 76. DataFrame.nsmallest\n",
        "print(df.nsmallest(2, 'B'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       A     B\n",
            "0  False  True\n",
            "1  False  True\n",
            "2  False  True\n",
            "3   True  True\n",
            "4   True  True\n",
            "A    True\n",
            "B    True\n",
            "dtype: bool\n"
          ]
        }
      ],
      "source": [
        "# 77. DataFrame.any\n",
        "print(df[['A','B']] > 3)\n",
        "print((df[['A','B']] > 3).any())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A    True\n",
            "B    True\n",
            "dtype: bool\n"
          ]
        }
      ],
      "source": [
        "# 78. DataFrame.all\n",
        "print((df[['A','B']] > 0).all())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A    4\n",
            "B    4\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 79. DataFrame.idxmax\n",
        "print(df[['A','B']].idxmax())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A    0\n",
            "B    0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 80. DataFrame.idxmin\n",
        "print(df[['A','B']].idxmin())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index     132\n",
            "A          40\n",
            "B          40\n",
            "C          40\n",
            "D          40\n",
            "A_mean     40\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 81. DataFrame.memory_usage\n",
        "print(df.memory_usage())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       x      y      z\n",
            "0   True  False  False\n",
            "1   True  False  False\n",
            "2  False   True  False\n",
            "3  False   True  False\n",
            "4  False  False   True\n"
          ]
        }
      ],
      "source": [
        "# 82. pd.get_dummies\n",
        "df_dum = pd.get_dummies(df['C'])\n",
        "print(df_dum)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 4 'x' Timestamp('2021-01-01 00:00:00') 1.5]\n",
            " [2 5 'x' Timestamp('2021-01-02 00:00:00') 1.5]\n",
            " [3 6 'y' Timestamp('2021-01-03 00:00:00') 3.5]\n",
            " [4 7 'y' Timestamp('2021-01-04 00:00:00') 3.5]\n",
            " [5 8 'z' Timestamp('2021-01-05 00:00:00') 5.0]]\n"
          ]
        }
      ],
      "source": [
        "# 83. DataFrame.to_numpy\n",
        "arr_np = df.to_numpy()\n",
        "print(arr_np)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_7da88_row4_col0, #T_7da88_row4_col1, #T_7da88_row4_col2, #T_7da88_row4_col3, #T_7da88_row4_col4 {\n",
              "  background-color: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_7da88\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_7da88_level0_col0\" class=\"col_heading level0 col0\" >A</th>\n",
              "      <th id=\"T_7da88_level0_col1\" class=\"col_heading level0 col1\" >B</th>\n",
              "      <th id=\"T_7da88_level0_col2\" class=\"col_heading level0 col2\" >C</th>\n",
              "      <th id=\"T_7da88_level0_col3\" class=\"col_heading level0 col3\" >D</th>\n",
              "      <th id=\"T_7da88_level0_col4\" class=\"col_heading level0 col4\" >A_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_7da88_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_7da88_row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "      <td id=\"T_7da88_row0_col1\" class=\"data row0 col1\" >4</td>\n",
              "      <td id=\"T_7da88_row0_col2\" class=\"data row0 col2\" >x</td>\n",
              "      <td id=\"T_7da88_row0_col3\" class=\"data row0 col3\" >2021-01-01 00:00:00</td>\n",
              "      <td id=\"T_7da88_row0_col4\" class=\"data row0 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7da88_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_7da88_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "      <td id=\"T_7da88_row1_col1\" class=\"data row1 col1\" >5</td>\n",
              "      <td id=\"T_7da88_row1_col2\" class=\"data row1 col2\" >x</td>\n",
              "      <td id=\"T_7da88_row1_col3\" class=\"data row1 col3\" >2021-01-02 00:00:00</td>\n",
              "      <td id=\"T_7da88_row1_col4\" class=\"data row1 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7da88_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_7da88_row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "      <td id=\"T_7da88_row2_col1\" class=\"data row2 col1\" >6</td>\n",
              "      <td id=\"T_7da88_row2_col2\" class=\"data row2 col2\" >y</td>\n",
              "      <td id=\"T_7da88_row2_col3\" class=\"data row2 col3\" >2021-01-03 00:00:00</td>\n",
              "      <td id=\"T_7da88_row2_col4\" class=\"data row2 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7da88_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_7da88_row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "      <td id=\"T_7da88_row3_col1\" class=\"data row3 col1\" >7</td>\n",
              "      <td id=\"T_7da88_row3_col2\" class=\"data row3 col2\" >y</td>\n",
              "      <td id=\"T_7da88_row3_col3\" class=\"data row3 col3\" >2021-01-04 00:00:00</td>\n",
              "      <td id=\"T_7da88_row3_col4\" class=\"data row3 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7da88_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_7da88_row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "      <td id=\"T_7da88_row4_col1\" class=\"data row4 col1\" >8</td>\n",
              "      <td id=\"T_7da88_row4_col2\" class=\"data row4 col2\" >z</td>\n",
              "      <td id=\"T_7da88_row4_col3\" class=\"data row4 col3\" >2021-01-05 00:00:00</td>\n",
              "      <td id=\"T_7da88_row4_col4\" class=\"data row4 col4\" >5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x2097a97f910>"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Install Jinja2 package\n",
        "# %pip install Jinja2\n",
        "\n",
        "# 84. DataFrame.style (简单示例)\n",
        "styled = df.style.highlight_max(axis=0)\n",
        "styled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "               A         B    C                    D  A_mean\n",
            "count   5.000000  5.000000    5                    5     5.0\n",
            "unique       NaN       NaN    3                  NaN     NaN\n",
            "top          NaN       NaN    x                  NaN     NaN\n",
            "freq         NaN       NaN    2                  NaN     NaN\n",
            "mean    3.000000  6.000000  NaN  2021-01-03 00:00:00     3.0\n",
            "min     1.000000  4.000000  NaN  2021-01-01 00:00:00     1.5\n",
            "25%     2.000000  5.000000  NaN  2021-01-02 00:00:00     1.5\n",
            "50%     3.000000  6.000000  NaN  2021-01-03 00:00:00     3.5\n",
            "75%     4.000000  7.000000  NaN  2021-01-04 00:00:00     3.5\n",
            "max     5.000000  8.000000  NaN  2021-01-05 00:00:00     5.0\n",
            "std     1.581139  1.581139  NaN                  NaN     1.5\n"
          ]
        }
      ],
      "source": [
        "# 85. DataFrame.describe(include='all')\n",
        "print(df.describe(include='all'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A         5\n",
            "B         5\n",
            "C         5\n",
            "D         5\n",
            "A_mean    5\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 86. DataFrame.count\n",
        "print(df.count())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "C\n",
            "x    2\n",
            "y    2\n",
            "z    1\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 87. DataFrame.value_counts (Series专用，或对一列)\n",
        "print(df['C'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B    C          D  A_mean\n",
            "0  1  4    x 2021-01-01     1.5\n",
            "1  2  5    y 2021-01-02     3.5\n",
            "2  3  6  NaN 2021-01-03     NaN\n",
            "3  4  7  NaN 2021-01-04     NaN\n",
            "4  5  8  NaN 2021-01-05     NaN\n"
          ]
        }
      ],
      "source": [
        "# 88. DataFrame.mode\n",
        "print(df.mode())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B\n",
            "0  2  4\n",
            "1  2  4\n",
            "2  3  4\n",
            "3  4  4\n",
            "4  4  4\n"
          ]
        }
      ],
      "source": [
        "# 89. DataFrame.clip\n",
        "print(df[['A','B']].clip(lower=2, upper=4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "            A  B  C  A_mean\n",
            "D                          \n",
            "2021-01-01  1  4  x     1.5\n",
            "2021-01-02  2  5  x     1.5\n",
            "2021-01-03  3  6  y     3.5\n",
            "2021-01-04  4  7  y     3.5\n",
            "2021-01-05  5  8  z     5.0\n"
          ]
        }
      ],
      "source": [
        "# 90. DataFrame.between_time (示例需要时间索引)\n",
        "df_time = df.set_index('D')\n",
        "print(df_time.between_time('00:00', '23:59'))  # 只示例"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(同 between_time 类似, 需特定时间)\n"
          ]
        }
      ],
      "source": [
        "# 91. DataFrame.at_time\n",
        "print(\"(同 between_time 类似, 需特定时间)\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "            A  B  C  A_mean\n",
            "D                          \n",
            "2021-01-01  1  4  x     1.5\n",
            "2021-01-02  2  5  x     1.5\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\minqliu\\AppData\\Local\\Temp\\ipykernel_39108\\3684921518.py:2: FutureWarning: first is deprecated and will be removed in a future version. Please create a mask and filter using `.loc` instead\n",
            "  df_first = df_time.first('2D')\n"
          ]
        }
      ],
      "source": [
        "# 92. DataFrame.first\n",
        "df_first = df_time.first('2D')\n",
        "print(df_first)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "            A  B  C  A_mean\n",
            "D                          \n",
            "2021-01-04  4  7  y     3.5\n",
            "2021-01-05  5  8  z     5.0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\minqliu\\AppData\\Local\\Temp\\ipykernel_39108\\1070510759.py:2: FutureWarning: last is deprecated and will be removed in a future version. Please create a mask and filter using `.loc` instead\n",
            "  df_last = df_time.last('2D')\n"
          ]
        }
      ],
      "source": [
        "# 93. DataFrame.last\n",
        "df_last = df_time.last('2D')\n",
        "print(df_last)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "            A   B   C  A_mean\n",
            "D                            \n",
            "2021-01-01  3   9  xx     3.0\n",
            "2021-01-03  7  13  yy     7.0\n",
            "2021-01-05  5   8   z     5.0\n"
          ]
        }
      ],
      "source": [
        "# 94. DataFrame.resample (示例)\n",
        "df_resample = df_time.resample('2D').sum()\n",
        "print(df_resample)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A     B\n",
            "0  NaN   NaN\n",
            "1  3.0   9.0\n",
            "2  5.0  11.0\n",
            "3  7.0  13.0\n",
            "4  9.0  15.0\n"
          ]
        }
      ],
      "source": [
        "# 95. DataFrame.rolling\n",
        "roll = df[['A','B']].rolling(window=2).sum()\n",
        "print(roll)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      A     B\n",
            "0   1.0   4.0\n",
            "1   3.0   9.0\n",
            "2   6.0  15.0\n",
            "3  10.0  22.0\n",
            "4  15.0  30.0\n"
          ]
        }
      ],
      "source": [
        "# 96. DataFrame.expanding\n",
        "exp = df[['A','B']].expanding().sum()\n",
        "print(exp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "          A         B\n",
            "0  1.000000  4.000000\n",
            "1  1.666667  4.666667\n",
            "2  2.428571  5.428571\n",
            "3  3.266667  6.266667\n",
            "4  4.161290  7.161290\n"
          ]
        }
      ],
      "source": [
        "# 97. DataFrame.ewm\n",
        "ewm_ = df[['A','B']].ewm(alpha=0.5).mean()\n",
        "print(ewm_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',\n",
            "               '2021-01-05'],\n",
            "              dtype='datetime64[ns]', freq='D')\n"
          ]
        }
      ],
      "source": [
        "# 98. pd.date_range\n",
        "date_rng = pd.date_range(start='2021-01-01', periods=5, freq='D')\n",
        "print(date_rng)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DatetimeIndex(['2021-01-01', '2021-01-04', '2021-01-05', '2021-01-06',\n",
            "               '2021-01-07'],\n",
            "              dtype='datetime64[ns]', freq='B')\n"
          ]
        }
      ],
      "source": [
        "# 99. pd.bdate_range\n",
        "bdate_rng = pd.bdate_range(start='2021-01-01', periods=5)\n",
        "print(bdate_rng)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2 days 03:00:00\n"
          ]
        }
      ],
      "source": [
        "# 100. pd.Timedelta\n",
        "delta = pd.Timedelta(\"2 days 3 hours\")\n",
        "print(delta)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-05-20 10:30:00 2023 5 20\n"
          ]
        }
      ],
      "source": [
        "# 101. pd.Timestamp\n",
        "ts = pd.Timestamp(\"2023-05-20 10:30:00\")\n",
        "print(ts, ts.year, ts.month, ts.day)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DatetimeIndex(['2021-01-01', '2021-02-02', '2021-03-03'], dtype='datetime64[ns]', freq=None)\n"
          ]
        }
      ],
      "source": [
        "# 102. pd.to_datetime\n",
        "dt_series = pd.to_datetime(['2021-01-01','2021-02-02','2021-03-03'])\n",
        "print(dt_series)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TimedeltaIndex(['1 days 00:00:00', '2 days 02:00:00'], dtype='timedelta64[ns]', freq=None)\n"
          ]
        }
      ],
      "source": [
        "# 103. pd.to_timedelta\n",
        "td_series = pd.to_timedelta(['1 days','2 days 02:00:00'])\n",
        "print(td_series)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A\n",
            "0  1.0\n",
            "1  2.0\n",
            "2  3.0\n",
            "3  4.0\n",
            "4  5.0\n"
          ]
        }
      ],
      "source": [
        "# 104. DataFrame.interpolate\n",
        "df_intp = pd.DataFrame({'A':[1, np.nan, 3, np.nan, 5]})\n",
        "print(df_intp.interpolate())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4 0\n"
          ]
        }
      ],
      "source": [
        "# 105. DataFrame.idxmax / idxmin (重复演示，也可对单列)\n",
        "print(df['A'].idxmax(), df['A'].idxmin())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   pref_A  pref_B pref_C     pref_D  pref_A_mean\n",
            "0       1       4      x 2021-01-01          1.5\n",
            "1       2       5      x 2021-01-02          1.5\n"
          ]
        }
      ],
      "source": [
        "# 106. DataFrame.add_prefix\n",
        "df_prefix = df.add_prefix('pref_')\n",
        "print(df_prefix.head(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A_suf  B_suf C_suf      D_suf  A_mean_suf\n",
            "0      1      4     x 2021-01-01         1.5\n",
            "1      2      5     x 2021-01-02         1.5\n"
          ]
        }
      ],
      "source": [
        "# 107. DataFrame.add_suffix\n",
        "df_suffix = df.add_suffix('_suf')\n",
        "print(df_suffix.head(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A\n",
            "0    1\n",
            "0    2\n",
            "1    3\n",
            "1    4\n",
            "2    5\n",
            "3  NaN\n"
          ]
        }
      ],
      "source": [
        "# 108. DataFrame.explode (针对列表/集合)\n",
        "df_ex = pd.DataFrame({\n",
        "    'A': [[1,2], [3,4], [5], []]\n",
        "})\n",
        "print(df_ex.explode('A'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean  F\n",
            "0  1  4  x 2021-01-01     1.5  3\n",
            "1  2  5  x 2021-01-02     1.5  3\n",
            "2  3  6  y 2021-01-03     3.5  3\n",
            "3  4  7  y 2021-01-04     3.5  3\n",
            "4  5  8  z 2021-01-05     5.0  3\n"
          ]
        }
      ],
      "source": [
        "# 109. DataFrame.eval (已示例过，这里再次演示)\n",
        "df_eval2 = df.eval(\"F = B - A\")\n",
        "print(df_eval2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A         1.0\n",
            "B         1.0\n",
            "A_mean    NaN\n",
            "C         NaN\n",
            "D         NaN\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# 110. DataFrame.corrwith (再演示)\n",
        "print(df.corrwith(df[['A','B']]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   C          D\n",
            "0  x 2021-01-01\n",
            "1  x 2021-01-02\n",
            "2  y 2021-01-03\n",
            "3  y 2021-01-04\n",
            "4  z 2021-01-05\n"
          ]
        }
      ],
      "source": [
        "# 111. DataFrame.select_dtypes (再次演示)\n",
        "print(df.select_dtypes(include=['object','category','datetime']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   B  C          D  A_mean\n",
            "0  4  x 2021-01-01     1.5\n",
            "1  5  x 2021-01-02     1.5\n",
            "2  6  y 2021-01-03     3.5\n",
            "3  7  y 2021-01-04     3.5\n",
            "4  8  z 2021-01-05     5.0\n"
          ]
        }
      ],
      "source": [
        "# 112. DataFrame.drop\n",
        "df_drop = df.drop(columns=['A'])\n",
        "print(df_drop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  A_mean\n",
            "0  1     1.5\n",
            "1  2     1.5\n",
            "2  3     3.5\n",
            "3  4     3.5\n",
            "4  5     5.0\n"
          ]
        }
      ],
      "source": [
        "# 113. DataFrame.filter\n",
        "df_filter = df.filter(like='A')\n",
        "print(df_filter)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  NewCol  C          D  A_mean\n",
            "0  1  4       8  x 2021-01-01     1.5\n",
            "1  2  5      10  x 2021-01-02     1.5\n",
            "2  3  6      12  y 2021-01-03     3.5\n",
            "3  4  7      14  y 2021-01-04     3.5\n",
            "4  5  8      16  z 2021-01-05     5.0\n"
          ]
        }
      ],
      "source": [
        "# 114. DataFrame.insert\n",
        "df_insert = df.copy()\n",
        "df_insert.insert(2, 'NewCol', df_insert['B']*2)\n",
        "print(df_insert)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "popped:\n",
            " 0    x\n",
            "1    x\n",
            "2    y\n",
            "3    y\n",
            "4    z\n",
            "Name: C, dtype: object\n",
            "\n",
            "remaining df:\n",
            "    A  B          D  A_mean\n",
            "0  1  4 2021-01-01     1.5\n",
            "1  2  5 2021-01-02     1.5\n",
            "2  3  6 2021-01-03     3.5\n",
            "3  4  7 2021-01-04     3.5\n",
            "4  5  8 2021-01-05     5.0\n"
          ]
        }
      ],
      "source": [
        "# 115. DataFrame.pop\n",
        "df_pop = df.copy()\n",
        "popped = df_pop.pop('C')\n",
        "print('popped:\\n', popped)\n",
        "print('\\nremaining df:\\n', df_pop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       A      B      C      D  A_mean\n",
            "0  False  False   True  False   False\n",
            "1  False  False   True  False   False\n",
            "2  False  False  False  False   False\n",
            "3  False  False  False  False   False\n",
            "4  False  False   True  False   False\n"
          ]
        }
      ],
      "source": [
        "# 116. DataFrame.isin\n",
        "print(df.isin({'C':['x','z']}))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B\n",
            "0  2  4\n",
            "1  2  4\n",
            "2  3  4\n",
            "3  4  4\n",
            "4  4  4\n"
          ]
        }
      ],
      "source": [
        "# 117. DataFrame.clip (再次演示)\n",
        "print(df[['A','B']].clip(2,4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        A    B\n",
            "0.25  2.0  5.0\n",
            "0.50  3.0  6.0\n",
            "0.75  4.0  7.0\n"
          ]
        }
      ],
      "source": [
        "# 118. DataFrame.quantile\n",
        "print(df[['A','B']].quantile([0.25, 0.5, 0.75]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index     132\n",
            "A          40\n",
            "B          40\n",
            "C         290\n",
            "D          40\n",
            "A_mean     40\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 119. DataFrame.memory_usage (再次)\n",
        "print(df.memory_usage(deep=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A         5\n",
            "B         5\n",
            "C         3\n",
            "D         5\n",
            "A_mean    3\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 120. DataFrame.nunique\n",
        "print(df.nunique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n",
            "False\n"
          ]
        }
      ],
      "source": [
        "# 121. DataFrame.is_monotonic (Series有此属性)\n",
        "s_mono = pd.Series([1,2,2,3])\n",
        "print(s_mono.is_monotonic_increasing)  # True  (非递减)\n",
        "print(s_mono.is_monotonic_decreasing)  # False (非递增)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "# 122. DataFrame.is_monotonic_increasing\n",
        "print(s_mono.is_monotonic_increasing)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "# 123. DataFrame.is_monotonic_decreasing\n",
        "s_dec = pd.Series([5,4,4,2])\n",
        "print(s_dec.is_monotonic_decreasing)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B   C                    D  A_mean\n",
            "0 -1 -1  -1                   -1    -1.0\n",
            "1 -1 -1  -1                   -1    -1.0\n",
            "2  3  6   y  2021-01-03 00:00:00     3.5\n",
            "3  4  7   y  2021-01-04 00:00:00     3.5\n",
            "4  5  8   z  2021-01-05 00:00:00     5.0\n"
          ]
        }
      ],
      "source": [
        "# 124. DataFrame.where\n",
        "df_where = df.where(df['A']>2, other=-1)\n",
        "print(df_where)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B   C                    D  A_mean\n",
            "0  1  4   x  2021-01-01 00:00:00     1.5\n",
            "1  2  5   x  2021-01-02 00:00:00     1.5\n",
            "2 -1 -1  -1                   -1    -1.0\n",
            "3 -1 -1  -1                   -1    -1.0\n",
            "4 -1 -1  -1                   -1    -1.0\n"
          ]
        }
      ],
      "source": [
        "# 125. DataFrame.mask (where的反向)\n",
        "df_mask = df.mask(df['A']>2, other=-1)\n",
        "print(df_mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean\n",
            "2  3  6  y 2021-01-03     3.5\n",
            "0  1  4  x 2021-01-01     1.5\n"
          ]
        }
      ],
      "source": [
        "# 126. DataFrame.sample (再演示)\n",
        "print(df.sample(frac=0.4, random_state=0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True False\n"
          ]
        }
      ],
      "source": [
        "# 127. DataFrame.equals\n",
        "df_copy2 = df.copy()\n",
        "print(df.equals(df_copy2), df.equals(df.assign(A=999)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 1 4\n",
            "1 2 5\n",
            "2 3 6\n"
          ]
        }
      ],
      "source": [
        "# 128. DataFrame.iterrows\n",
        "for index, row in df.iterrows():\n",
        "    print(index, row['A'], row['B'])\n",
        "    if index>1:\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pandas(Index=0, A=1, B=4, C='x', D=Timestamp('2021-01-01 00:00:00'), A_mean=1.5)\n"
          ]
        }
      ],
      "source": [
        "# 129. DataFrame.itertuples\n",
        "for row in df.itertuples():\n",
        "    print(row)\n",
        "    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A 0    1\n",
            "1    2\n",
            "Name: A, dtype: int64\n",
            "B 0    4\n",
            "1    5\n",
            "Name: B, dtype: int64\n",
            "C 0    x\n",
            "1    x\n",
            "Name: C, dtype: object\n",
            "D 0   2021-01-01\n",
            "1   2021-01-02\n",
            "Name: D, dtype: datetime64[ns]\n",
            "A_mean 0    1.5\n",
            "1    1.5\n",
            "Name: A_mean, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# 130. DataFrame.items\n",
        "for col_name, col_data in df.items():\n",
        "    print(col_name, col_data[:2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A (5,)\n"
          ]
        }
      ],
      "source": [
        "# 131. DataFrame.iteritems (items 的别名)\n",
        "for col_name, col_data in df.items():\n",
        "    print(col_name, col_data.shape)\n",
        "    break\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'A': [1, 2, 3, 4, 5], 'B': [4, 5, 6, 7, 8], 'C': ['x', 'x', 'y', 'y', 'z'], 'D': [Timestamp('2021-01-01 00:00:00'), Timestamp('2021-01-02 00:00:00'), Timestamp('2021-01-03 00:00:00'), Timestamp('2021-01-04 00:00:00'), Timestamp('2021-01-05 00:00:00')], 'A_mean': [1.5, 1.5, 3.5, 3.5, 5.0]}\n"
          ]
        }
      ],
      "source": [
        "# 132. DataFrame.to_dict\n",
        "dict_data = df.to_dict('list')\n",
        "print(dict_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean\n",
            "0  1  4  x 2021-01-01     1.5\n",
            "1  2  5  x 2021-01-02     1.5\n",
            "2  3  6  y 2021-01-03     3.5\n",
            "3  4  7  y 2021-01-04     3.5\n",
            "4  5  8  z 2021-01-05     5.0\n"
          ]
        }
      ],
      "source": [
        "# 133. DataFrame.from_dict\n",
        "df_from_dict = pd.DataFrame.from_dict(dict_data)\n",
        "print(df_from_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   num char\n",
            "0    1    x\n",
            "1    2    y\n"
          ]
        }
      ],
      "source": [
        "# 134. pd.DataFrame.from_records\n",
        "records = [(1, 'x'), (2, 'y')]\n",
        "df_rec = pd.DataFrame.from_records(records, columns=['num','char'])\n",
        "print(df_rec)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A         1.0\n",
            "B         4.0\n",
            "A_mean    1.5\n",
            "Name: (x, 2021-01-01 00:00:00), dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# 135. DataFrame.xs (cross-section)\n",
        "df_xs = df.set_index(['C','D'])\n",
        "print(df_xs.xs(('x','2021-01-01')))  # row"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean\n",
            "2  3  6  y 2021-01-03     3.5\n",
            "3  4  7  y 2021-01-04     3.5\n",
            "4  5  8  z 2021-01-05     5.0\n"
          ]
        }
      ],
      "source": [
        "# 136. DataFrame.query (再示例)\n",
        "print(df.query('B > 5'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pd.read_pickle(\"file.pkl\")\n"
          ]
        }
      ],
      "source": [
        "# 137. pd.read_pickle (示例)\n",
        "print('pd.read_pickle(\"file.pkl\")')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "df saved to df.pkl\n"
          ]
        }
      ],
      "source": [
        "# 138. DataFrame.to_pickle\n",
        "df.to_pickle('df.pkl')\n",
        "print('df saved to df.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "with pd.ExcelWriter('multi_sheet.xlsx') as writer:\n",
            "    df.to_excel(writer, sheet_name='Sheet1')\n",
            "    df2.to_excel(writer, sheet_name='Sheet2')\n"
          ]
        }
      ],
      "source": [
        "# 139. pd.ExcelWriter (写多表)\n",
        "print(\"with pd.ExcelWriter('multi_sheet.xlsx') as writer:\")\n",
        "print(\"    df.to_excel(writer, sheet_name='Sheet1')\")\n",
        "print(\"    df2.to_excel(writer, sheet_name='Sheet2')\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "df.to_clipboard(index=False)\n"
          ]
        }
      ],
      "source": [
        "# 140. DataFrame.to_clipboard\n",
        "print(\"df.to_clipboard(index=False)\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pd.read_clipboard()  # 从剪贴板读取 DataFrame\n"
          ]
        }
      ],
      "source": [
        "# 141. pd.read_clipboard\n",
        "print('pd.read_clipboard()  # 从剪贴板读取 DataFrame')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "          A  B  C          D  A_mean\n",
            "NewIndex                            \n",
            "0         1  4  x 2021-01-01     1.5\n",
            "1         2  5  x 2021-01-02     1.5\n",
            "2         3  6  y 2021-01-03     3.5\n",
            "3         4  7  y 2021-01-04     3.5\n",
            "4         5  8  z 2021-01-05     5.0\n"
          ]
        }
      ],
      "source": [
        "# 142. DataFrame.rename_axis\n",
        "df_ra = df.copy()\n",
        "df_ra = df_ra.rename_axis('NewIndex')\n",
        "print(df_ra)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B   Z\n",
            "0  1  4 NaN\n",
            "2  3  6 NaN\n",
            "4  5  8 NaN\n"
          ]
        }
      ],
      "source": [
        "# 143. DataFrame.reindex\n",
        "df_reindex = df.copy()\n",
        "df_reindex = df_reindex.reindex(index=[0,2,4], columns=['A','B','Z'])\n",
        "print(df_reindex)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A0  B0 C0         D0  A_mean\n",
            "0   1   4  x 2021-01-01     1.5\n",
            "1   2   5  x 2021-01-02     1.5\n",
            "2   3   6  y 2021-01-03     3.5\n",
            "3   4   7  y 2021-01-04     3.5\n",
            "4   5   8  z 2021-01-05     5.0\n"
          ]
        }
      ],
      "source": [
        "# 144. DataFrame.set_axis\n",
        "df_sa = df.copy()\n",
        "df_sa = df_sa.set_axis(['A0','B0','C0','D0', 'A_mean'], axis='columns')\n",
        "print(df_sa)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B\n",
            "0  2  5\n",
            "1  3  6\n",
            "2  4  7\n",
            "3  5  8\n",
            "4  6  9\n"
          ]
        }
      ],
      "source": [
        "# 145. DataFrame.pipe\n",
        "def plus_one(x):\n",
        "    return x+1\n",
        "\n",
        "res_pipe = df[['A','B']].pipe(plus_one)\n",
        "print(res_pipe)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "# 146. DataFrame.equals (再次)\n",
        "df_temp = df.copy()\n",
        "print(df_temp.equals(df))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 9. 字符串操作（Series.str）"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    True\n",
            "1    True\n",
            "2    True\n",
            "3    True\n",
            "dtype: bool\n"
          ]
        }
      ],
      "source": [
        "# 147. Series.str.contains\n",
        "s_str = pd.Series(['apple','banana','grape','avocado'])\n",
        "print(s_str.str.contains('a'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0      @pple\n",
            "1     b@n@n@\n",
            "2      gr@pe\n",
            "3    @voc@do\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# 148. Series.str.replace\n",
        "print(s_str.str.replace('a','@'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0      [John, Doe]\n",
            "1    [Jane, Smith]\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# 149. Series.str.split\n",
        "s_names = pd.Series(['John Doe','Jane Smith'])\n",
        "print(s_names.str.split())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    5\n",
            "1    6\n",
            "2    5\n",
            "3    7\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 150. Series.str.len\n",
        "print(s_str.str.len())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    Hello\n",
            "1    World\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# 151. Series.str.strip\n",
        "s_space = pd.Series(['  Hello  ','  World'])\n",
        "print(s_space.str.strip())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0      APPLE\n",
            "1     BANANA\n",
            "2      GRAPE\n",
            "3    AVOCADO\n",
            "dtype: object\n",
            "0      Apple\n",
            "1     Banana\n",
            "2      Grape\n",
            "3    Avocado\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# 152. Series.str.lower / upper / title\n",
        "print(s_str.str.upper())\n",
        "print(s_str.str.title())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 10. 时间序列 (Series.dt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    2021\n",
            "1    2021\n",
            "2    2021\n",
            "dtype: int32 0    1\n",
            "1    2\n",
            "2    3\n",
            "dtype: int32 0    31\n",
            "1    28\n",
            "2    31\n",
            "dtype: int32\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\minqliu\\AppData\\Local\\Temp\\ipykernel_39108\\1938323960.py:2: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
            "  dates = pd.Series(pd.date_range('2021-01-01', periods=3, freq='M'))\n"
          ]
        }
      ],
      "source": [
        "# 153. Series.dt.year / month / day\n",
        "dates = pd.Series(pd.date_range('2021-01-01', periods=3, freq='M'))\n",
        "print(dates.dt.year, dates.dt.month, dates.dt.day)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0       Sunday\n",
            "1       Sunday\n",
            "2    Wednesday\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# 154. Series.dt.day_name\n",
        "print(dates.dt.day_name())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    True\n",
            "1    True\n",
            "2    True\n",
            "dtype: bool\n"
          ]
        }
      ],
      "source": [
        "# 155. Series.dt.is_month_end\n",
        "print(dates.dt.is_month_end)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0   2021-01-01 10:00:00\n",
            "1   2021-01-02 23:00:00\n",
            "dtype: datetime64[ns]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\minqliu\\AppData\\Local\\Temp\\ipykernel_39108\\1022869270.py:3: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
            "  print(ts_floor.dt.floor('H'))\n"
          ]
        }
      ],
      "source": [
        "# 156. Series.dt.floor\n",
        "ts_floor = pd.Series(pd.to_datetime(['2021-01-01 10:15:30','2021-01-02 23:59:59']))\n",
        "print(ts_floor.dt.floor('H'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0   2021-01-01 11:00:00\n",
            "1   2021-01-03 00:00:00\n",
            "dtype: datetime64[ns]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\minqliu\\AppData\\Local\\Temp\\ipykernel_39108\\1764267752.py:2: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
            "  print(ts_floor.dt.ceil('H'))\n"
          ]
        }
      ],
      "source": [
        "# 157. Series.dt.ceil\n",
        "print(ts_floor.dt.ceil('H'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 11. 配置选项"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "set_option: display.max_rows=10\n"
          ]
        }
      ],
      "source": [
        "# 158. pd.set_option\n",
        "pd.set_option('display.max_rows', 10)\n",
        "print('set_option: display.max_rows=10')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "reset_option: display.max_rows\n"
          ]
        }
      ],
      "source": [
        "# 159. pd.reset_option\n",
        "pd.reset_option('display.max_rows')\n",
        "print('reset_option: display.max_rows')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "display.max_rows = 60\n"
          ]
        }
      ],
      "source": [
        "# 160. pd.get_option\n",
        "print('display.max_rows =', pd.get_option('display.max_rows'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max columns set to 8\n"
          ]
        }
      ],
      "source": [
        "# 161. pd.options.display (直接访问)\n",
        "pd.options.display.max_columns = 8\n",
        "print('Max columns set to 8')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "chained_assignment set to None\n"
          ]
        }
      ],
      "source": [
        "# 162. pd.options.mode.chained_assignment\n",
        "pd.options.mode.chained_assignment = None\n",
        "print('chained_assignment set to None')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 12. 更多重复值检测"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    False\n",
            "1     True\n",
            "2    False\n",
            "dtype: bool\n"
          ]
        }
      ],
      "source": [
        "# 163. DataFrame.duplicated (重演)\n",
        "df_dup2 = pd.DataFrame({'col1':[1,1,2], 'col2':[3,3,4]})\n",
        "print(df_dup2.duplicated())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A         1.0\n",
            "B         1.0\n",
            "A_mean    NaN\n",
            "C         NaN\n",
            "D         NaN\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# 164. DataFrame.corrwith (再演)\n",
        "print(df.corrwith(df[['A','B']]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 13. 可视化 (简要)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'A'}>, <Axes: title={'center': 'B'}>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 167,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 165. DataFrame.hist (需要 matplotlib)\n",
        "%matplotlib inline\n",
        "df[['A','B']].hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 168,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 166. DataFrame.boxplot\n",
        "df[['A','B']].boxplot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 169,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 167. DataFrame.plot\n",
        "df[['A','B']].plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 14. 数据转换与因子化"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    1.0\n",
            "1    2.0\n",
            "2    NaN\n",
            "3    4.0\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# 168. pd.to_numeric\n",
        "s_num = pd.Series(['1','2','NaN','4'])\n",
        "print(pd.to_numeric(s_num, errors='coerce'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0 1 0 2] ['apple' 'banana' 'pear']\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\minqliu\\AppData\\Local\\Temp\\ipykernel_39108\\1803597563.py:2: FutureWarning: factorize with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
            "  labels, uniques = pd.factorize(['apple','banana','apple','pear'])\n"
          ]
        }
      ],
      "source": [
        "# 169. pd.factorize\n",
        "labels, uniques = pd.factorize(['apple','banana','apple','pear'])\n",
        "print(labels, uniques)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pd.merge_asof(left, right, on='time') # 时间序列上的合并\n"
          ]
        }
      ],
      "source": [
        "# 170. pd.merge_asof\n",
        "print(\"pd.merge_asof(left, right, on='time') # 时间序列上的合并\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pd.merge_ordered(left, right, on='key') # 有序合并\n"
          ]
        }
      ],
      "source": [
        "# 171. pd.merge_ordered\n",
        "print(\"pd.merge_ordered(left, right, on='key') # 有序合并\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 15. DataFrame 样式 (Styler)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_6aede_row4_col0, #T_6aede_row4_col1, #T_6aede_row4_col2, #T_6aede_row4_col3, #T_6aede_row4_col4 {\n",
              "  background-color: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_6aede\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_6aede_level0_col0\" class=\"col_heading level0 col0\" >A</th>\n",
              "      <th id=\"T_6aede_level0_col1\" class=\"col_heading level0 col1\" >B</th>\n",
              "      <th id=\"T_6aede_level0_col2\" class=\"col_heading level0 col2\" >C</th>\n",
              "      <th id=\"T_6aede_level0_col3\" class=\"col_heading level0 col3\" >D</th>\n",
              "      <th id=\"T_6aede_level0_col4\" class=\"col_heading level0 col4\" >A_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_6aede_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_6aede_row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "      <td id=\"T_6aede_row0_col1\" class=\"data row0 col1\" >4</td>\n",
              "      <td id=\"T_6aede_row0_col2\" class=\"data row0 col2\" >x</td>\n",
              "      <td id=\"T_6aede_row0_col3\" class=\"data row0 col3\" >2021-01-01 00:00:00</td>\n",
              "      <td id=\"T_6aede_row0_col4\" class=\"data row0 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6aede_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_6aede_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "      <td id=\"T_6aede_row1_col1\" class=\"data row1 col1\" >5</td>\n",
              "      <td id=\"T_6aede_row1_col2\" class=\"data row1 col2\" >x</td>\n",
              "      <td id=\"T_6aede_row1_col3\" class=\"data row1 col3\" >2021-01-02 00:00:00</td>\n",
              "      <td id=\"T_6aede_row1_col4\" class=\"data row1 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6aede_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_6aede_row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "      <td id=\"T_6aede_row2_col1\" class=\"data row2 col1\" >6</td>\n",
              "      <td id=\"T_6aede_row2_col2\" class=\"data row2 col2\" >y</td>\n",
              "      <td id=\"T_6aede_row2_col3\" class=\"data row2 col3\" >2021-01-03 00:00:00</td>\n",
              "      <td id=\"T_6aede_row2_col4\" class=\"data row2 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6aede_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_6aede_row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "      <td id=\"T_6aede_row3_col1\" class=\"data row3 col1\" >7</td>\n",
              "      <td id=\"T_6aede_row3_col2\" class=\"data row3 col2\" >y</td>\n",
              "      <td id=\"T_6aede_row3_col3\" class=\"data row3 col3\" >2021-01-04 00:00:00</td>\n",
              "      <td id=\"T_6aede_row3_col4\" class=\"data row3 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6aede_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_6aede_row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "      <td id=\"T_6aede_row4_col1\" class=\"data row4 col1\" >8</td>\n",
              "      <td id=\"T_6aede_row4_col2\" class=\"data row4 col2\" >z</td>\n",
              "      <td id=\"T_6aede_row4_col3\" class=\"data row4 col3\" >2021-01-05 00:00:00</td>\n",
              "      <td id=\"T_6aede_row4_col4\" class=\"data row4 col4\" >5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x2097d7e3cd0>"
            ]
          },
          "execution_count": 174,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 172. DataFrame.style.highlight_max\n",
        "df.style.highlight_max(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 175,
      "metadata": {},
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "'<=' not supported between instances of 'int' and 'str'",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\IPython\\core\\formatters.py:406\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    404\u001b[0m     method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[0;32m    405\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 406\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    407\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\io\\formats\\style.py:405\u001b[0m, in \u001b[0;36mStyler._repr_html_\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    400\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    401\u001b[0m \u001b[38;5;124;03mHooks into Jupyter notebook rich display system, which calls _repr_html_ by\u001b[39;00m\n\u001b[0;32m    402\u001b[0m \u001b[38;5;124;03mdefault if an object is returned at the end of a cell.\u001b[39;00m\n\u001b[0;32m    403\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    404\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m get_option(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstyler.render.repr\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhtml\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 405\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_html\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\io\\formats\\style.py:1345\u001b[0m, in \u001b[0;36mStyler.to_html\u001b[1;34m(self, buf, table_uuid, table_attributes, sparse_index, sparse_columns, bold_headers, caption, max_rows, max_columns, encoding, doctype_html, exclude_styles, **kwargs)\u001b[0m\n\u001b[0;32m   1342\u001b[0m     obj\u001b[38;5;241m.\u001b[39mset_caption(caption)\n\u001b[0;32m   1344\u001b[0m \u001b[38;5;66;03m# Build HTML string..\u001b[39;00m\n\u001b[1;32m-> 1345\u001b[0m html \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_render_html\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1346\u001b[0m \u001b[43m    \u001b[49m\u001b[43msparse_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msparse_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1347\u001b[0m \u001b[43m    \u001b[49m\u001b[43msparse_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msparse_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_rows\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_cols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexclude_styles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude_styles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mget_option\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstyler.render.encoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1352\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdoctype_html\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdoctype_html\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1353\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1354\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m save_to_buffer(\n\u001b[0;32m   1357\u001b[0m     html, buf\u001b[38;5;241m=\u001b[39mbuf, encoding\u001b[38;5;241m=\u001b[39m(encoding \u001b[38;5;28;01mif\u001b[39;00m buf \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   1358\u001b[0m )\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\io\\formats\\style_render.py:204\u001b[0m, in \u001b[0;36mStylerRenderer._render_html\u001b[1;34m(self, sparse_index, sparse_columns, max_rows, max_cols, **kwargs)\u001b[0m\n\u001b[0;32m    192\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_render_html\u001b[39m(\n\u001b[0;32m    193\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    194\u001b[0m     sparse_index: \u001b[38;5;28mbool\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    198\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    199\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m    200\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    201\u001b[0m \u001b[38;5;124;03m    Renders the ``Styler`` including all applied styles to HTML.\u001b[39;00m\n\u001b[0;32m    202\u001b[0m \u001b[38;5;124;03m    Generates a dict with necessary kwargs passed to jinja2 template.\u001b[39;00m\n\u001b[0;32m    203\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 204\u001b[0m     d \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_render\u001b[49m\u001b[43m(\u001b[49m\u001b[43msparse_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msparse_columns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_cols\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m&nbsp;\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    205\u001b[0m     d\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[0;32m    206\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtemplate_html\u001b[38;5;241m.\u001b[39mrender(\n\u001b[0;32m    207\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39md,\n\u001b[0;32m    208\u001b[0m         html_table_tpl\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtemplate_html_table,\n\u001b[0;32m    209\u001b[0m         html_style_tpl\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtemplate_html_style,\n\u001b[0;32m    210\u001b[0m     )\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\io\\formats\\style_render.py:161\u001b[0m, in \u001b[0;36mStylerRenderer._render\u001b[1;34m(self, sparse_index, sparse_columns, max_rows, max_cols, blank)\u001b[0m\n\u001b[0;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_render\u001b[39m(\n\u001b[0;32m    148\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    149\u001b[0m     sparse_index: \u001b[38;5;28mbool\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    153\u001b[0m     blank: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    154\u001b[0m ):\n\u001b[0;32m    155\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    156\u001b[0m \u001b[38;5;124;03m    Computes and applies styles and then generates the general render dicts.\u001b[39;00m\n\u001b[0;32m    157\u001b[0m \n\u001b[0;32m    158\u001b[0m \u001b[38;5;124;03m    Also extends the `ctx` and `ctx_index` attributes with those of concatenated\u001b[39;00m\n\u001b[0;32m    159\u001b[0m \u001b[38;5;124;03m    stylers for use within `_translate_latex`\u001b[39;00m\n\u001b[0;32m    160\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 161\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    162\u001b[0m     dxs \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m    163\u001b[0m     ctx_len \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex)\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\io\\formats\\style_render.py:256\u001b[0m, in \u001b[0;36mStylerRenderer._compute\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    254\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    255\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_todo:\n\u001b[1;32m--> 256\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\io\\formats\\style.py:1728\u001b[0m, in \u001b[0;36mStyler._apply\u001b[1;34m(self, func, axis, subset, **kwargs)\u001b[0m\n\u001b[0;32m   1726\u001b[0m         result \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(func, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1727\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1728\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mT\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mT  \u001b[38;5;66;03m# see GH 42005\u001b[39;00m\n\u001b[0;32m   1730\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, Series):\n\u001b[0;32m   1731\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1732\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFunction \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(func)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m resulted in the apply method collapsing to a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1733\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mUsually, this is the result of the function returning a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1734\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle value, instead of list-like.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1735\u001b[0m     )\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[1;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m  10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[0;32m  10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[0;32m  10363\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m  10364\u001b[0m     func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  10372\u001b[0m     kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[0;32m  10373\u001b[0m )\n\u001b[1;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[0;32m    914\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[1;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m   1062\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 1063\u001b[0m         results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1064\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1065\u001b[0m         results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m   1079\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[0;32m   1080\u001b[0m         \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[1;32m-> 1081\u001b[0m         results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1082\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[0;32m   1083\u001b[0m             \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[0;32m   1084\u001b[0m             \u001b[38;5;66;03m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[0;32m   1085\u001b[0m             results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\io\\formats\\style.py:3923\u001b[0m, in \u001b[0;36m_highlight_value\u001b[1;34m(data, op, props)\u001b[0m\n\u001b[0;32m   3919\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_highlight_value\u001b[39m(data: DataFrame \u001b[38;5;241m|\u001b[39m Series, op: \u001b[38;5;28mstr\u001b[39m, props: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[0;32m   3920\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3921\u001b[0m \u001b[38;5;124;03m    Return an array of css strings based on the condition of values matching an op.\u001b[39;00m\n\u001b[0;32m   3922\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 3923\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m   3924\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, DataFrame):  \u001b[38;5;66;03m# min/max must be done twice to return scalar\u001b[39;00m\n\u001b[0;32m   3925\u001b[0m         value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(value, op)(skipna\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\series.py:6507\u001b[0m, in \u001b[0;36mSeries.min\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m   6499\u001b[0m \u001b[38;5;129m@doc\u001b[39m(make_doc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m   6500\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mmin\u001b[39m(\n\u001b[0;32m   6501\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   6505\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   6506\u001b[0m ):\n\u001b[1;32m-> 6507\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\generic.py:12388\u001b[0m, in \u001b[0;36mNDFrame.min\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  12381\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mmin\u001b[39m(\n\u001b[0;32m  12382\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m  12383\u001b[0m     axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  12386\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  12387\u001b[0m ):\n\u001b[1;32m> 12388\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  12389\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmin\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m  12390\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  12391\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  12392\u001b[0m \u001b[43m        \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  12393\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  12394\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  12395\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\generic.py:12377\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[1;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  12373\u001b[0m nv\u001b[38;5;241m.\u001b[39mvalidate_func(name, (), kwargs)\n\u001b[0;32m  12375\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m> 12377\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  12378\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[0;32m  12379\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\series.py:6457\u001b[0m, in \u001b[0;36mSeries._reduce\u001b[1;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[0;32m   6452\u001b[0m     \u001b[38;5;66;03m# GH#47500 - change to TypeError to match other methods\u001b[39;00m\n\u001b[0;32m   6453\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   6454\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not allow \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwd_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnumeric_only\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   6455\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith non-numeric dtypes.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   6456\u001b[0m     )\n\u001b[1;32m-> 6457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelegate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__.<locals>.f\u001b[1;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[0;32m    145\u001b[0m         result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 147\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat.<locals>.new_func\u001b[1;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[0;32m    401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    402\u001b[0m     mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[1;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[0;32m    407\u001b[0m     result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax.<locals>.reduction\u001b[1;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[0;32m   1093\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[0;32m   1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[0;32m   1096\u001b[0m     values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[0;32m   1097\u001b[0m )\n\u001b[1;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(values, meth)(axis)\n\u001b[0;32m   1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m   1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\numpy\\_core\\_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[1;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m     47\u001b[0m           initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m---> 48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m umr_minimum(a, axis, \u001b[38;5;28;01mNone\u001b[39;00m, out, keepdims, initial, where)\n",
            "\u001b[1;31mTypeError\u001b[0m: '<=' not supported between instances of 'int' and 'str'"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x2097d7b7b50>"
            ]
          },
          "execution_count": 175,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 173. DataFrame.style.highlight_min\n",
        "df.style.highlight_min(axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   id info.name  info.age\n",
            "0   1     Alice        30\n",
            "1   2       Bob        25\n"
          ]
        }
      ],
      "source": [
        "# 174. pd.json_normalize\n",
        "data_json = [\n",
        "  {\"id\":1, \"info\":{\"name\":\"Alice\",\"age\":30}},\n",
        "  {\"id\":2, \"info\":{\"name\":\"Bob\",\"age\":25}}\n",
        "]\n",
        "df_json = pd.json_normalize(data_json, max_level=1)\n",
        "print(df_json)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A    Sparse[int64, 0]\n",
            "B    Sparse[int64, 0]\n",
            "dtype: object \n",
            "    A  B\n",
            "0  0  0\n",
            "1  0  1\n",
            "2  1  0\n"
          ]
        }
      ],
      "source": [
        "# 175. DataFrame.sparse (示例)\n",
        "df_sparse = pd.DataFrame({'A':[0,0,1],'B':[0,1,0]}).astype(pd.SparseDtype(\"int\", fill_value=0))\n",
        "print(df_sparse.dtypes, '\\n', df_sparse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_1215d\">\n",
              "  <caption>My DataFrame</caption>\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_1215d_level0_col0\" class=\"col_heading level0 col0\" >A</th>\n",
              "      <th id=\"T_1215d_level0_col1\" class=\"col_heading level0 col1\" >B</th>\n",
              "      <th id=\"T_1215d_level0_col2\" class=\"col_heading level0 col2\" >C</th>\n",
              "      <th id=\"T_1215d_level0_col3\" class=\"col_heading level0 col3\" >D</th>\n",
              "      <th id=\"T_1215d_level0_col4\" class=\"col_heading level0 col4\" >A_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_1215d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_1215d_row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "      <td id=\"T_1215d_row0_col1\" class=\"data row0 col1\" >4</td>\n",
              "      <td id=\"T_1215d_row0_col2\" class=\"data row0 col2\" >x</td>\n",
              "      <td id=\"T_1215d_row0_col3\" class=\"data row0 col3\" >2021-01-01 00:00:00</td>\n",
              "      <td id=\"T_1215d_row0_col4\" class=\"data row0 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1215d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_1215d_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "      <td id=\"T_1215d_row1_col1\" class=\"data row1 col1\" >5</td>\n",
              "      <td id=\"T_1215d_row1_col2\" class=\"data row1 col2\" >x</td>\n",
              "      <td id=\"T_1215d_row1_col3\" class=\"data row1 col3\" >2021-01-02 00:00:00</td>\n",
              "      <td id=\"T_1215d_row1_col4\" class=\"data row1 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1215d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_1215d_row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "      <td id=\"T_1215d_row2_col1\" class=\"data row2 col1\" >6</td>\n",
              "      <td id=\"T_1215d_row2_col2\" class=\"data row2 col2\" >y</td>\n",
              "      <td id=\"T_1215d_row2_col3\" class=\"data row2 col3\" >2021-01-03 00:00:00</td>\n",
              "      <td id=\"T_1215d_row2_col4\" class=\"data row2 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1215d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_1215d_row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "      <td id=\"T_1215d_row3_col1\" class=\"data row3 col1\" >7</td>\n",
              "      <td id=\"T_1215d_row3_col2\" class=\"data row3 col2\" >y</td>\n",
              "      <td id=\"T_1215d_row3_col3\" class=\"data row3 col3\" >2021-01-04 00:00:00</td>\n",
              "      <td id=\"T_1215d_row3_col4\" class=\"data row3 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1215d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_1215d_row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "      <td id=\"T_1215d_row4_col1\" class=\"data row4 col1\" >8</td>\n",
              "      <td id=\"T_1215d_row4_col2\" class=\"data row4 col2\" >z</td>\n",
              "      <td id=\"T_1215d_row4_col3\" class=\"data row4 col3\" >2021-01-05 00:00:00</td>\n",
              "      <td id=\"T_1215d_row4_col4\" class=\"data row4 col4\" >5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x2098283a290>"
            ]
          },
          "execution_count": 178,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 176. DataFrame.style.set_caption\n",
        "df.style.set_caption(\"My DataFrame\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 179,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_17893\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_17893_level0_col0\" class=\"col_heading level0 col0\" >A</th>\n",
              "      <th id=\"T_17893_level0_col1\" class=\"col_heading level0 col1\" >B</th>\n",
              "      <th id=\"T_17893_level0_col2\" class=\"col_heading level0 col2\" >C</th>\n",
              "      <th id=\"T_17893_level0_col3\" class=\"col_heading level0 col3\" >D</th>\n",
              "      <th id=\"T_17893_level0_col4\" class=\"col_heading level0 col4\" >A_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_17893_row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "      <td id=\"T_17893_row0_col1\" class=\"data row0 col1\" >4</td>\n",
              "      <td id=\"T_17893_row0_col2\" class=\"data row0 col2\" >x</td>\n",
              "      <td id=\"T_17893_row0_col3\" class=\"data row0 col3\" >2021-01-01 00:00:00</td>\n",
              "      <td id=\"T_17893_row0_col4\" class=\"data row0 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_17893_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "      <td id=\"T_17893_row1_col1\" class=\"data row1 col1\" >5</td>\n",
              "      <td id=\"T_17893_row1_col2\" class=\"data row1 col2\" >x</td>\n",
              "      <td id=\"T_17893_row1_col3\" class=\"data row1 col3\" >2021-01-02 00:00:00</td>\n",
              "      <td id=\"T_17893_row1_col4\" class=\"data row1 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_17893_row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "      <td id=\"T_17893_row2_col1\" class=\"data row2 col1\" >6</td>\n",
              "      <td id=\"T_17893_row2_col2\" class=\"data row2 col2\" >y</td>\n",
              "      <td id=\"T_17893_row2_col3\" class=\"data row2 col3\" >2021-01-03 00:00:00</td>\n",
              "      <td id=\"T_17893_row2_col4\" class=\"data row2 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_17893_row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "      <td id=\"T_17893_row3_col1\" class=\"data row3 col1\" >7</td>\n",
              "      <td id=\"T_17893_row3_col2\" class=\"data row3 col2\" >y</td>\n",
              "      <td id=\"T_17893_row3_col3\" class=\"data row3 col3\" >2021-01-04 00:00:00</td>\n",
              "      <td id=\"T_17893_row3_col4\" class=\"data row3 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_17893_row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "      <td id=\"T_17893_row4_col1\" class=\"data row4 col1\" >8</td>\n",
              "      <td id=\"T_17893_row4_col2\" class=\"data row4 col2\" >z</td>\n",
              "      <td id=\"T_17893_row4_col3\" class=\"data row4 col3\" >2021-01-05 00:00:00</td>\n",
              "      <td id=\"T_17893_row4_col4\" class=\"data row4 col4\" >5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x2098268aa50>"
            ]
          },
          "execution_count": 179,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 177. DataFrame.style.hide\n",
        "df.style.hide(axis='index')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 180,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_fdc61_row0_col0, #T_fdc61_row0_col1, #T_fdc61_row0_col4, #T_fdc61_row1_col4 {\n",
              "  background-color: #ffffcc;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_fdc61_row1_col0, #T_fdc61_row1_col1 {\n",
              "  background-color: #fed976;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_fdc61_row2_col0, #T_fdc61_row2_col1 {\n",
              "  background-color: #fd8c3c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_fdc61_row2_col4, #T_fdc61_row3_col4 {\n",
              "  background-color: #fc6832;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_fdc61_row3_col0, #T_fdc61_row3_col1 {\n",
              "  background-color: #e2191c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_fdc61_row4_col0, #T_fdc61_row4_col1, #T_fdc61_row4_col4 {\n",
              "  background-color: #800026;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_fdc61\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_fdc61_level0_col0\" class=\"col_heading level0 col0\" >A</th>\n",
              "      <th id=\"T_fdc61_level0_col1\" class=\"col_heading level0 col1\" >B</th>\n",
              "      <th id=\"T_fdc61_level0_col2\" class=\"col_heading level0 col2\" >C</th>\n",
              "      <th id=\"T_fdc61_level0_col3\" class=\"col_heading level0 col3\" >D</th>\n",
              "      <th id=\"T_fdc61_level0_col4\" class=\"col_heading level0 col4\" >A_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_fdc61_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_fdc61_row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "      <td id=\"T_fdc61_row0_col1\" class=\"data row0 col1\" >4</td>\n",
              "      <td id=\"T_fdc61_row0_col2\" class=\"data row0 col2\" >x</td>\n",
              "      <td id=\"T_fdc61_row0_col3\" class=\"data row0 col3\" >2021-01-01 00:00:00</td>\n",
              "      <td id=\"T_fdc61_row0_col4\" class=\"data row0 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fdc61_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_fdc61_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "      <td id=\"T_fdc61_row1_col1\" class=\"data row1 col1\" >5</td>\n",
              "      <td id=\"T_fdc61_row1_col2\" class=\"data row1 col2\" >x</td>\n",
              "      <td id=\"T_fdc61_row1_col3\" class=\"data row1 col3\" >2021-01-02 00:00:00</td>\n",
              "      <td id=\"T_fdc61_row1_col4\" class=\"data row1 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fdc61_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_fdc61_row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "      <td id=\"T_fdc61_row2_col1\" class=\"data row2 col1\" >6</td>\n",
              "      <td id=\"T_fdc61_row2_col2\" class=\"data row2 col2\" >y</td>\n",
              "      <td id=\"T_fdc61_row2_col3\" class=\"data row2 col3\" >2021-01-03 00:00:00</td>\n",
              "      <td id=\"T_fdc61_row2_col4\" class=\"data row2 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fdc61_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_fdc61_row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "      <td id=\"T_fdc61_row3_col1\" class=\"data row3 col1\" >7</td>\n",
              "      <td id=\"T_fdc61_row3_col2\" class=\"data row3 col2\" >y</td>\n",
              "      <td id=\"T_fdc61_row3_col3\" class=\"data row3 col3\" >2021-01-04 00:00:00</td>\n",
              "      <td id=\"T_fdc61_row3_col4\" class=\"data row3 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fdc61_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_fdc61_row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "      <td id=\"T_fdc61_row4_col1\" class=\"data row4 col1\" >8</td>\n",
              "      <td id=\"T_fdc61_row4_col2\" class=\"data row4 col2\" >z</td>\n",
              "      <td id=\"T_fdc61_row4_col3\" class=\"data row4 col3\" >2021-01-05 00:00:00</td>\n",
              "      <td id=\"T_fdc61_row4_col4\" class=\"data row4 col4\" >5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x2097d9b2090>"
            ]
          },
          "execution_count": 180,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 178. DataFrame.style.background_gradient\n",
        "df.style.background_gradient(cmap='YlOrRd')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 16. 分类数据 (Series.cat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 181,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    a\n",
            "1    b\n",
            "2    a\n",
            "3    c\n",
            "dtype: category\n",
            "Categories (3, object): ['a', 'b', 'c']\n"
          ]
        }
      ],
      "source": [
        "# 179. Series.cat\n",
        "s_cat = pd.Series(['a','b','a','c'], dtype='category')\n",
        "print(s_cat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['a', 'b', 'c'], dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# 180. Series.cat.categories\n",
        "print(s_cat.cat.categories)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    a\n",
            "1    b\n",
            "2    a\n",
            "dtype: category\n",
            "Categories (2, object): ['a', 'b']\n"
          ]
        }
      ],
      "source": [
        "# 181. Series.cat.remove_unused_categories\n",
        "s_cat2 = s_cat[s_cat!='c']\n",
        "print(s_cat2.cat.remove_unused_categories())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    a\n",
            "1    b\n",
            "2    a\n",
            "3    c\n",
            "dtype: category\n",
            "Categories (3, object): ['c', 'a', 'b']\n"
          ]
        }
      ],
      "source": [
        "# 182. Series.cat.reorder_categories\n",
        "print(s_cat.cat.reorder_categories(['c','a','b']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 185,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    a\n",
            "1    b\n",
            "2    a\n",
            "3    c\n",
            "dtype: category\n",
            "Categories (4, object): ['a', 'b', 'c', 'd']\n"
          ]
        }
      ],
      "source": [
        "# 183. Series.cat.add_categories\n",
        "print(s_cat.cat.add_categories(['d']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 186,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    A\n",
            "1    B\n",
            "2    A\n",
            "3    C\n",
            "dtype: category\n",
            "Categories (3, object): ['A', 'B', 'C']\n"
          ]
        }
      ],
      "source": [
        "# 184. Series.cat.rename_categories\n",
        "print(s_cat.cat.rename_categories({'a':'A','b':'B','c':'C'}))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 17. 其它常见方法"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 187,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[False  True False]\n"
          ]
        }
      ],
      "source": [
        "# 185. DataFrame.index.duplicated\n",
        "df_idx_dup = pd.DataFrame({'col':[1,2,3]}, index=[0,0,1])\n",
        "print(df_idx_dup.index.duplicated())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 188,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "False\n"
          ]
        }
      ],
      "source": [
        "# 186. DataFrame.index.is_unique\n",
        "print(df_idx_dup.index.is_unique)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 189,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2D\n"
          ]
        }
      ],
      "source": [
        "# 187. pd.infer_freq\n",
        "ts_infer = pd.date_range('2021-01-01', periods=4, freq='2D')\n",
        "print(pd.infer_freq(ts_infer))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 190,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "D\n",
            "2021-01-01    3\n",
            "2021-01-03    7\n",
            "2021-01-05    5\n",
            "Freq: 2D, Name: A, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# 188. pd.Grouper (groupby 的高级用法)\n",
        "df_date = df.copy()\n",
        "df_date['D'] = pd.date_range('2021-01-01', periods=len(df_date))\n",
        "grouped_grouper = df_date.groupby(pd.Grouper(key='D', freq='2D'))['A'].sum()\n",
        "print(grouped_grouper)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 191,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean\n",
            "1  2  5  x 2021-01-02     1.5\n",
            "2  3  6  y 2021-01-03     3.5\n"
          ]
        }
      ],
      "source": [
        "# 189. DataFrame.query (再再示例)\n",
        "print(df.query('A in [2,3]'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 192,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean   G\n",
            "0  1  4  x 2021-01-01     1.5   4\n",
            "1  2  5  x 2021-01-02     1.5  10\n",
            "2  3  6  y 2021-01-03     3.5  18\n",
            "3  4  7  y 2021-01-04     3.5  28\n",
            "4  5  8  z 2021-01-05     5.0  40\n"
          ]
        }
      ],
      "source": [
        "# 190. DataFrame.eval (又一次示例)\n",
        "df_eval3 = df.eval(\"G = A * B\")\n",
        "print(df_eval3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 193,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "    A\n",
            "0   6\n",
            "1   7\n",
            "2   8\n",
            "3   9\n",
            "4  10\n"
          ]
        }
      ],
      "source": [
        "# 191. DataFrame.pipe (重复示例)\n",
        "def add_5(x):\n",
        "    return x+5\n",
        "\n",
        "res_pipe2 = df[['A']].pipe(add_5)\n",
        "print(res_pipe2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 194,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_da158_row0_col0, #T_da158_row0_col1, #T_da158_row0_col4, #T_da158_row1_col4 {\n",
              "  background-color: #f7fcf5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_da158_row1_col0, #T_da158_row1_col1 {\n",
              "  background-color: #c7e9c0;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_da158_row2_col0, #T_da158_row2_col1 {\n",
              "  background-color: #73c476;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_da158_row2_col4, #T_da158_row3_col4 {\n",
              "  background-color: #56b567;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_da158_row3_col0, #T_da158_row3_col1 {\n",
              "  background-color: #228a44;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_da158_row4_col0, #T_da158_row4_col1, #T_da158_row4_col4 {\n",
              "  background-color: #00441b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_da158\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_da158_level0_col0\" class=\"col_heading level0 col0\" >A</th>\n",
              "      <th id=\"T_da158_level0_col1\" class=\"col_heading level0 col1\" >B</th>\n",
              "      <th id=\"T_da158_level0_col2\" class=\"col_heading level0 col2\" >C</th>\n",
              "      <th id=\"T_da158_level0_col3\" class=\"col_heading level0 col3\" >D</th>\n",
              "      <th id=\"T_da158_level0_col4\" class=\"col_heading level0 col4\" >A_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_da158_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_da158_row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "      <td id=\"T_da158_row0_col1\" class=\"data row0 col1\" >4</td>\n",
              "      <td id=\"T_da158_row0_col2\" class=\"data row0 col2\" >x</td>\n",
              "      <td id=\"T_da158_row0_col3\" class=\"data row0 col3\" >2021-01-01 00:00:00</td>\n",
              "      <td id=\"T_da158_row0_col4\" class=\"data row0 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da158_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_da158_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "      <td id=\"T_da158_row1_col1\" class=\"data row1 col1\" >5</td>\n",
              "      <td id=\"T_da158_row1_col2\" class=\"data row1 col2\" >x</td>\n",
              "      <td id=\"T_da158_row1_col3\" class=\"data row1 col3\" >2021-01-02 00:00:00</td>\n",
              "      <td id=\"T_da158_row1_col4\" class=\"data row1 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da158_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_da158_row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "      <td id=\"T_da158_row2_col1\" class=\"data row2 col1\" >6</td>\n",
              "      <td id=\"T_da158_row2_col2\" class=\"data row2 col2\" >y</td>\n",
              "      <td id=\"T_da158_row2_col3\" class=\"data row2 col3\" >2021-01-03 00:00:00</td>\n",
              "      <td id=\"T_da158_row2_col4\" class=\"data row2 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da158_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_da158_row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "      <td id=\"T_da158_row3_col1\" class=\"data row3 col1\" >7</td>\n",
              "      <td id=\"T_da158_row3_col2\" class=\"data row3 col2\" >y</td>\n",
              "      <td id=\"T_da158_row3_col3\" class=\"data row3 col3\" >2021-01-04 00:00:00</td>\n",
              "      <td id=\"T_da158_row3_col4\" class=\"data row3 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da158_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_da158_row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "      <td id=\"T_da158_row4_col1\" class=\"data row4 col1\" >8</td>\n",
              "      <td id=\"T_da158_row4_col2\" class=\"data row4 col2\" >z</td>\n",
              "      <td id=\"T_da158_row4_col3\" class=\"data row4 col3\" >2021-01-05 00:00:00</td>\n",
              "      <td id=\"T_da158_row4_col4\" class=\"data row4 col4\" >5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x209829fc5d0>"
            ]
          },
          "execution_count": 194,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 192. DataFrame.style.background_gradient (再示例)\n",
        "df.style.background_gradient(cmap='Greens')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 18. 进一步分类操作"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 195,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    0\n",
            "1    1\n",
            "2    0\n",
            "3    2\n",
            "dtype: int8\n"
          ]
        }
      ],
      "source": [
        "# 193. Series.cat (已示例, 此处再示例)\n",
        "sc = pd.Series(['x','y','x','z'], dtype='category')\n",
        "print(sc.cat.codes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 196,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    x\n",
            "1    y\n",
            "2    x\n",
            "dtype: category\n",
            "Categories (2, object): ['x', 'y']\n"
          ]
        }
      ],
      "source": [
        "# 194. Series.cat.remove_unused_categories (再示例)\n",
        "sc2 = sc[sc != 'z']\n",
        "print(sc2.cat.remove_unused_categories())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 19. 其他"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 197,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  C          D  A_mean\n",
            "2  3  6  y 2021-01-03     3.5\n",
            "3  4  7  y 2021-01-04     3.5\n",
            "4  5  8  z 2021-01-05     5.0\n"
          ]
        }
      ],
      "source": [
        "# 195. DataFrame.drop_labels (别名, 实际多用 drop)\n",
        "df_drop2 = df.drop(labels=[0,1])\n",
        "print(df_drop2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 198,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "# 196. DataFrame.equals (又示例)\n",
        "df_test_eq = df.copy()\n",
        "print(df_test_eq.equals(df))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 199,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_108a1_row0_col0, #T_108a1_row0_col1, #T_108a1_row0_col2, #T_108a1_row0_col3, #T_108a1_row0_col4, #T_108a1_row1_col0, #T_108a1_row1_col1, #T_108a1_row1_col2, #T_108a1_row1_col3, #T_108a1_row1_col4, #T_108a1_row2_col0, #T_108a1_row2_col1, #T_108a1_row2_col2, #T_108a1_row2_col3, #T_108a1_row2_col4, #T_108a1_row3_col0, #T_108a1_row3_col1, #T_108a1_row3_col2, #T_108a1_row3_col3, #T_108a1_row3_col4, #T_108a1_row4_col0, #T_108a1_row4_col1, #T_108a1_row4_col2, #T_108a1_row4_col3, #T_108a1_row4_col4 {\n",
              "  background-color: lightblue;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_108a1\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_108a1_level0_col0\" class=\"col_heading level0 col0\" >A</th>\n",
              "      <th id=\"T_108a1_level0_col1\" class=\"col_heading level0 col1\" >B</th>\n",
              "      <th id=\"T_108a1_level0_col2\" class=\"col_heading level0 col2\" >C</th>\n",
              "      <th id=\"T_108a1_level0_col3\" class=\"col_heading level0 col3\" >D</th>\n",
              "      <th id=\"T_108a1_level0_col4\" class=\"col_heading level0 col4\" >A_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_108a1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_108a1_row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "      <td id=\"T_108a1_row0_col1\" class=\"data row0 col1\" >4</td>\n",
              "      <td id=\"T_108a1_row0_col2\" class=\"data row0 col2\" >x</td>\n",
              "      <td id=\"T_108a1_row0_col3\" class=\"data row0 col3\" >2021-01-01 00:00:00</td>\n",
              "      <td id=\"T_108a1_row0_col4\" class=\"data row0 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_108a1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_108a1_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "      <td id=\"T_108a1_row1_col1\" class=\"data row1 col1\" >5</td>\n",
              "      <td id=\"T_108a1_row1_col2\" class=\"data row1 col2\" >x</td>\n",
              "      <td id=\"T_108a1_row1_col3\" class=\"data row1 col3\" >2021-01-02 00:00:00</td>\n",
              "      <td id=\"T_108a1_row1_col4\" class=\"data row1 col4\" >1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_108a1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_108a1_row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "      <td id=\"T_108a1_row2_col1\" class=\"data row2 col1\" >6</td>\n",
              "      <td id=\"T_108a1_row2_col2\" class=\"data row2 col2\" >y</td>\n",
              "      <td id=\"T_108a1_row2_col3\" class=\"data row2 col3\" >2021-01-03 00:00:00</td>\n",
              "      <td id=\"T_108a1_row2_col4\" class=\"data row2 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_108a1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_108a1_row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "      <td id=\"T_108a1_row3_col1\" class=\"data row3 col1\" >7</td>\n",
              "      <td id=\"T_108a1_row3_col2\" class=\"data row3 col2\" >y</td>\n",
              "      <td id=\"T_108a1_row3_col3\" class=\"data row3 col3\" >2021-01-04 00:00:00</td>\n",
              "      <td id=\"T_108a1_row3_col4\" class=\"data row3 col4\" >3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_108a1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_108a1_row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "      <td id=\"T_108a1_row4_col1\" class=\"data row4 col1\" >8</td>\n",
              "      <td id=\"T_108a1_row4_col2\" class=\"data row4 col2\" >z</td>\n",
              "      <td id=\"T_108a1_row4_col3\" class=\"data row4 col3\" >2021-01-05 00:00:00</td>\n",
              "      <td id=\"T_108a1_row4_col4\" class=\"data row4 col4\" >5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x20981176bd0>"
            ]
          },
          "execution_count": 199,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 197. DataFrame.style (更多设置)\n",
        "df.style.set_properties(**{'background-color': 'lightblue'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 200,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    4\n",
            "1    5\n",
            "2    6\n",
            "3    0\n",
            "4    1\n",
            "dtype: int32 0     True\n",
            "1    False\n",
            "2    False\n",
            "3    False\n",
            "4    False\n",
            "dtype: bool\n"
          ]
        }
      ],
      "source": [
        "# 198. Series.dt (再示例)\n",
        "dts = pd.Series(pd.date_range('2021-01-01', periods=5, freq='D'))\n",
        "print(dts.dt.weekday, dts.dt.is_month_start)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 201,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['p', 'q', 'r'], dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# 199. Series.cat (再示例)\n",
        "cat_s = pd.Series(['p','q','p','r'], dtype='category')\n",
        "print(cat_s.cat.categories)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 202,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    P\n",
            "1    Q\n",
            "2    P\n",
            "3    R\n",
            "dtype: category\n",
            "Categories (3, object): ['P', 'Q', 'R']\n"
          ]
        }
      ],
      "source": [
        "# 200. Series.cat.rename_categories (再示例)\n",
        "cat_s_ren = cat_s.cat.rename_categories({'p':'P','q':'Q','r':'R'})\n",
        "print(cat_s_ren)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "----\n",
        "**以上即约 200 个 Pandas 常用方法/函数示例**。\n",
        "\n",
        "- 某些函数有多次演示、或仅做用法展示；\n",
        "- 复杂场景请参考官方文档和更多实践案例。\n",
        "\n",
        "祝学习愉快！"
      ]
    }
  ],
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      "display_name": "Python 3",
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        "name": "ipython",
        "version": 3
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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