Python数据分析

- PolarsBook中文版: https://www.pythondataanalysis.com/docs/polars_book_cn/
- Polars快速入门: https://www.pythondataanalysis.com/docs/polars_book_cn/quickstart/
- Polars表达式: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/
- Polars表达式: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/expressions/
- Polars上下文: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/contexts/
- Polars分组: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/groupby/
- Polars折叠: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/folds/
- Polars自定义函数: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/custom_functions/
- Polars实例: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/introduction_polars/
- Polars表达式方法: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/api/
- Polars视频介绍: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/video_intro/
- Polars与Numpy交互: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/numpy/
- Polars窗口函数: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/window_functions/
- Polars索引: https://www.pythondataanalysis.com/docs/polars_book_cn/indexing/
- Polars数据类型: https://www.pythondataanalysis.com/docs/polars_book_cn/datatypes/
- 来自Pandas: https://www.pythondataanalysis.com/docs/polars_book_cn/coming_from_pandas/
- 来自ApacheSpark: https://www.pythondataanalysis.com/docs/polars_book_cn/coming_from_spark/
- Polars性能: https://www.pythondataanalysis.com/docs/polars_book_cn/performance/
- 字符串: https://www.pythondataanalysis.com/docs/polars_book_cn/performance/strings/
- Polars优化: https://www.pythondataanalysis.com/docs/polars_book_cn/optimizations/
- Polars惰性方法: https://www.pythondataanalysis.com/docs/polars_book_cn/optimizations/lazy/
- 谓词下推: https://www.pythondataanalysis.com/docs/polars_book_cn/optimizations/lazy/predicate-pushdown/
- 投影下推: https://www.pythondataanalysis.com/docs/polars_book_cn/optimizations/lazy/projection-pushdown/
- 其它优化: https://www.pythondataanalysis.com/docs/polars_book_cn/optimizations/lazy/other-optimizations/
- Polars参考指南: https://www.pythondataanalysis.com/docs/polars_book_cn/references/
- Polars时间序列: https://www.pythondataanalysis.com/docs/polars_book_cn/timeseries/
- Polars时间序列实例: https://www.pythondataanalysis.com/docs/polars_book_cn/timeseries/time-series/
- Polars使用范围: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/
- IO: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/
- Polars操作CSV文件: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/csv/
- Polars操作Parquet文件: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/parquet/
- Polars处理多个文件: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/multiple_files/
- Polars读取数据库: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/read_db/
- Polars与AWS交互: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/aws/
- Polars与Google BigQuery交互: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/google-big-query/
- Polars与Postgres交互: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/postgres/
- 互通性: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/interop/
- Arrow: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/interop/arrow/
- Numpy: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/interop/numpy/
- 数据: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/data/
- 字符串: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/data/strings/
- 时间戳: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/data/timestamps/
- 数据帧: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/
- 选中行或列: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/row_col_selection/
- 常用操作: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/common-manipulations/
- 聚合: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/aggregate/
- 分组: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/groupby/
- 过滤: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/filter/
- 连接: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/join/
- 重塑: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/melt/
- 条件应用: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/conditionally-apply/
- 排序: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/sorting/
- 透视: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/pivot/
- 应用: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/apply/
- Polars自定义函数: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/apply/udfs/
- Polars窗口函数: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/apply/window-functions/
- Python数据分析 第二版: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/
- 第 1 章 准备工作: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-01/
- 第 2 章 Python 语法基础: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-02/
- 第 3 章 Python 的数据结构、函数和文件: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-03/
- 第 4 章 NumPy 基础：数组和向量计算: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-04/
- 第 5 章 Pandas 入门: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-05/
- 第 6 章 数据加载、存储与文件格式: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-06/
- 第 7 章 数据清洗和准备: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-07/
- 第 10 章 数据聚合与分组运算: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-10/
- 第 11 章 时间序列: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-11/
- 第 12 章 pandas 高级应用: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-12/
- 第 13 章 Python 建模库介绍: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-13/
-  第 14 章 数据分析案例: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-14/
-  附录 A NumPy 高级应用: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Appendix-A/
-  附录 B 更多关于 IPython 的内容: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Appendix-B/
- 第 8 章 数据规整：聚合、合并和重塑: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-08/
- 第 9 章 绘图和可视化: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-09/
- Polars用户指南: https://www.pythondataanalysis.com/docs/Polars_user_guide/
- Polars入门: https://www.pythondataanalysis.com/docs/Polars_user_guide/polars_getting_started/
- 安装Polars: https://www.pythondataanalysis.com/docs/Polars_user_guide/polars_installation/
- Polars核心概念: https://www.pythondataanalysis.com/docs/Polars_user_guide/concepts/
- Polars数据类型和结构: https://www.pythondataanalysis.com/docs/Polars_user_guide/concepts/data-types-and-structures/
- Polars表达式和上下文: https://www.pythondataanalysis.com/docs/Polars_user_guide/concepts/expressions-and-contexts/
- Polars延迟API: https://www.pythondataanalysis.com/docs/Polars_user_guide/concepts/lazy-api/
- Streaming: https://www.pythondataanalysis.com/docs/Polars_user_guide/concepts/_streaming/
- Polars表达式: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/
- Polars基本操作: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/basic-operations/
- Aggregation: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/aggregation/
- Casting: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/casting/
- Categorical Data and Enums: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/categorical-data-and-enums/
- Expression Expansion: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/expression-expansion/
- Folds: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/folds/
- Lists and Arrays: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/lists-and-arrays/
- Missing Data: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/missing-data/
- Numpy Functions: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/numpy-functions/
- Strings: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/strings/
- Structs: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/structs/
- User Defined Python Functions: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/user-defined-python-functions/
- Window Functions: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/window-functions/
- Reference: https://www.pythondataanalysis.com/docs/Polars_user_guide/api/reference/
- Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/development/contributing/
- Versioning: https://www.pythondataanalysis.com/docs/Polars_user_guide/development/versioning/
- Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/polars-cloud/
- Ecosystem: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/ecosystem/
- Gpu Support: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/gpu-support/
- Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/io/
- Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/lazy/
- Pandas: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/migration/pandas/
- Spark: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/migration/spark/
- Arrow: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/arrow/
- Comparison: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/comparison/
- Multiprocessing: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/multiprocessing/
- Polars Llms: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/polars_llms/
- Styling: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/styling/
- Visualization: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/visualization/
- Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/plugins/
- Create: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/sql/create/
- Cte: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/sql/cte/
- Intro: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/sql/intro/
- Select: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/sql/select/
- Show: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/sql/show/
- Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/transformations/

# Polars时间序列实例

```python
import polars as pl
from datetime import datetime
# 创建一个数据帧实例
df = pl.DataFrame(
    {
        "time": pl.date_range(
            low=datetime(2021, 12, 16),
            high=datetime(2021, 12, 16, 3),
            interval="30m",
        ),
        "n": range(7),
    }
)
df
```

```
shape: (7, 2)
┌─────────────────────┬─────┐
│ time                ┆ n   │
│ ---                 ┆ --- │
│ datetime            ┆ i64 │
╞═════════════════════╪═════╡
│ 2021-12-16 00:00:00 ┆ 0   │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ 2021-12-16 00:30:00 ┆ 1   │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ 2021-12-16 01:00:00 ┆ 2   │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ 2021-12-16 01:30:00 ┆ 3   │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ 2021-12-16 02:00:00 ┆ 4   │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ 2021-12-16 02:30:00 ┆ 5   │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ 2021-12-16 03:00:00 ┆ 6   │
└─────────────────────┴─────┘
```

从2021-12-16 00:00:00开始，按1小时的窗口分组。

```python
(
    df.groupby_dynamic("time", every="1h").agg(
        [pl.col("time").min(), pl.col("time").max()]
    )
)
```

```
shape: (3, 3)
┌─────────────────────┬─────────────────────┬─────────────────────┐
│ time                ┆ time_min            ┆ time_max            │
│ ---                 ┆ ---                 ┆ ---                 │
│ datetime            ┆ datetime            ┆ datetime            │
╞═════════════════════╪═════════════════════╪═════════════════════╡
│ 2021-12-16 00:00:00 ┆ 2021-12-16 00:30:00 ┆ 2021-12-16 01:00:00 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2021-12-16 01:00:00 ┆ 2021-12-16 01:30:00 ┆ 2021-12-16 02:00:00 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2021-12-16 02:00:00 ┆ 2021-12-16 02:30:00 ┆ 2021-12-16 03:00:00 │
└─────────────────────┴─────────────────────┴─────────────────────┘
```

窗口边界也可以添加到聚合结果中

```python
(
    df.groupby_dynamic("time", every="1h", include_boundaries=True).agg(
        [pl.col("time").count()]
    )
)
```

```
shape: (3, 4)
┌─────────────────────┬─────────────────────┬─────────────────────┬────────────┐
│ _lower_boundary     ┆ _upper_boundary     ┆ time                ┆ time_count │
│ ---                 ┆ ---                 ┆ ---                 ┆ ---        │
│ datetime            ┆ datetime            ┆ datetime            ┆ u32        │
╞═════════════════════╪═════════════════════╪═════════════════════╪════════════╡
│ 2021-12-16 00:00:00 ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 00:00:00 ┆ 2          │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 2          │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ 2          │
└─────────────────────┴─────────────────────┴─────────────────────┴────────────┘
```

当closed=“left”，不应包括区间\[下限、上限\]的右端

```python
(
    df.groupby_dynamic("time", every="1h", closed="left").agg(
        [pl.col("time").count(), pl.col("time").list()]
    )
)
```

```
shape: (3, 3)
┌─────────────────────┬────────────┬─────────────────────────────────────┐
│ time                ┆ time_count ┆ time_agg_list                       │
│ ---                 ┆ ---        ┆ ---                                 │
│ datetime            ┆ u32        ┆ list [datetime]                     │
╞═════════════════════╪════════════╪═════════════════════════════════════╡
│ 2021-12-16 00:00:00 ┆ 2          ┆ [2021-12-16 00:00:00, 2021-12-16... │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2021-12-16 01:00:00 ┆ 2          ┆ [2021-12-16 01:00:00, 2021-12-16... │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2021-12-16 02:00:00 ┆ 2          ┆ [2021-12-16 02:00:00, 2021-12-16... │
└─────────────────────┴────────────┴─────────────────────────────────────┘
```

当closed="both"，窗口边界处的时间值分为两组。

```python
(
    df.groupby_dynamic("time", every="1h", closed="both").agg(
        [pl.col("time").count()]
    )
)
```

```
shape: (3, 2)
┌─────────────────────┬────────────┐
│ time                ┆ time_count │
│ ---                 ┆ ---        │
│ datetime            ┆ u32        │
╞═════════════════════╪════════════╡
│ 2021-12-16 00:00:00 ┆ 3          │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2021-12-16 01:00:00 ┆ 3          │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2021-12-16 02:00:00 ┆ 3          │
└─────────────────────┴────────────┘
```

动态groupbys还可以与普通键上的分组相结合

```python
pl.DataFrame(
    {
        "time": pl.date_range(
            low=datetime(2021, 12, 16),
            high=datetime(2021, 12, 16, 3),
            interval="30m",
        ),
        "groups": ["a", "a", "a", "b", "b", "a", "a"],
    }
)
```

```
shape: (7, 2)
┌─────────────────────┬────────┐
│ time                ┆ groups │
│ ---                 ┆ ---    │
│ datetime            ┆ str    │
╞═════════════════════╪════════╡
│ 2021-12-16 00:00:00 ┆ a      │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2021-12-16 00:30:00 ┆ a      │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2021-12-16 01:00:00 ┆ a      │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2021-12-16 01:30:00 ┆ b      │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2021-12-16 02:00:00 ┆ b      │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2021-12-16 02:30:00 ┆ a      │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2021-12-16 03:00:00 ┆ a      │
└─────────────────────┴────────┘
```

```python
(
    df.groupby_dynamic(
        "time",
        every="1h",
        closed="both",
        by="groups",
        include_boundaries=True,
    ).agg([pl.col("time").count()])
)
```

```
shape: (4, 5)
┌────────┬─────────────────────┬─────────────────────┬─────────────────────┬────────────┐
│ groups ┆ _lower_boundary     ┆ _upper_boundary     ┆ time                ┆ time_count │
│ ---    ┆ ---                 ┆ ---                 ┆ ---                 ┆ ---        │
│ str    ┆ datetime            ┆ datetime            ┆ datetime            ┆ u32        │
╞════════╪═════════════════════╪═════════════════════╪═════════════════════╪════════════╡
│ a      ┆ 2021-12-16 00:00:00 ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 00:00:00 ┆ 3          │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ a      ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 1          │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ a      ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ 2          │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ b      ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 2          │
└────────┴─────────────────────┴─────────────────────┴─────────────────────┴────────────┘
```