# 143、pandas.Series.map方法

# 143-1、使用函数进行映射

# 0 9

# 1 25

# 2 36

# 3 64

# 4 100

# 5 121

# 6 576

# dtype: int64

# 143-2、使用字典进行映射

# 0 爸爸

# 1 儿子

# 2 女儿

# dtype: object

# 143-3、处理缺失值

# 0 9.0

# 1 25.0

# 2 NaN

# 3 36.0

# 4 64.0

# dtype: float64

144、pandas.Series.groupby方法

144-1、语法

# 144、pandas.Series.groupby方法

pandas.Series.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, observed=_NoDefault.no_default, dropna=True)

Group Series using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters:

bymapping, function, label, pd.Grouper or list of such

Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align() method). If a list or ndarray of length equal to the selected axis is passed (see the groupby user guide), the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Split along rows (0) or columns (1). For Series this parameter is unused and defaults to 0.

Deprecated since version 2.1.0: Will be removed and behave like axis=0 in a future version. For axis=1, do frame.T.groupby(...) instead.

levelint, level name, or sequence of such, default None

If the axis is a MultiIndex (hierarchical), group by a particular level or levels. Do not specify both by and level.

as_indexbool, default True

Return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output. This argument has no effect on filtrations (see the filtrations in the user guide), such as head(), tail(), nth() and in transformations (see the transformations in the user guide).

sortbool, default True

Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. If False, the groups will appear in the same order as they did in the original DataFrame. This argument has no effect on filtrations (see the filtrations in the user guide), such as head(), tail(), nth() and in transformations (see the transformations in the user guide).

Changed in version 2.0.0: Specifying sort=False with an ordered categorical grouper will no longer sort the values.

group_keysbool, default True

When calling apply and the by argument produces a like-indexed (i.e. a transform) result, add group keys to index to identify pieces. By default group keys are not included when the result’s index (and column) labels match the inputs, and are included otherwise.

Changed in version 1.5.0: Warns that group_keys will no longer be ignored when the result from apply is a like-indexed Series or DataFrame. Specify group_keys explicitly to include the group keys or not.

Changed in version 2.0.0: group_keys now defaults to True.

observedbool, default False

This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

Deprecated since version 2.1.0: The default value will change to True in a future version of pandas.

dropnabool, default True

If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

Returns:

pandas.api.typing.SeriesGroupBy

Returns a groupby object that contains information about the groups.

144-2、参数

144-2-1、by(可选，默认值为None)：用于分组的标准，可以是函数、字符串、列表或字典。

144-2-2、axis(可选，默认值为0)：指定操作的轴。

144-2-3、level(可选，默认值为None)：用于多层索引分组的级别。

144-2-4、as_index(可选，默认值为True)：如果为True，分组键会成为结果DataFrame的索引。

144-2-5、sort(可选，默认值为True)：对分组键进行排序。

144-2-6、group_keys(可选，默认值为True)：如果为True，将分组键包含在结果中。

144-2-7、observed(可选)：对于分类数据类型，是否只显示观察到的分类。

144-2-8、dropna(可选，默认值为True)：如果为True，丢弃包含NA的分组。

144-3、功能

        用于对Series数据进行分组操作，然后可以对每个分组应用聚合、转换或其他操作，这在数据分析中非常有用，尤其是在需要基于某些条件对数据进行分组和处理时。

144-4、返回值

        返回一个pandas.core.groupby.SeriesGroupBy对象，可以对其应用各种聚合和转换操作。

144-5、说明

        pandas.Series.groupby是一个强大的方法，可以根据不同的标准对数据进行分组，并对每个分组进行各种操作。无论是基于索引、函数还是多层索引，groupby都提供了灵活的分组方式，并且能够与其他pandas方法结合使用，进行复杂的数据分析和处理。

144-6、用法

144-6-1、数据准备

无

144-6-2、代码示例

# 144、pandas.Series.groupby方法

# 144-1、数据聚合

import pandas as pd

# 创建一个示例Series

data = pd.Series([10, 20, 30, 40, 50, 60], index=['A', 'A', 'B', 'B', 'C', 'C'])

# 按索引分组并求和

grouped_sum = data.groupby(data.index).sum()

print(grouped_sum, end='\n\n')code>

# 144-2、数据转换

import pandas as pd

# 创建一个示例Series

data = pd.Series([10, 20, 30, 40, 50, 60], index=['A', 'A', 'B', 'B', 'C', 'C'])

# 归一化每个组的数据

grouped_normalized = data.groupby(data.index).apply(lambda x: (x - x.mean()) / x.std())

print(grouped_normalized, end='\n\n')code>

# 144-3、数据筛选

import pandas as pd

# 创建一个示例Series

data = pd.Series([10, 20, 30, 40, 50, 60], index=['A', 'A', 'B', 'B', 'C', 'C'])

# 找出每组的最大值

grouped_max = data.groupby(data.index).max()

print(grouped_max, end='\n\n')code>

# 144-4、数据填充

import pandas as pd

# 创建一个示例Series

data = pd.Series([10, 20, 30, 40, 50, 60], index=['A', 'A', 'B', 'B', 'C', 'C'])

# 创建一个带有缺失值的示例 Series

data_with_nan = pd.Series([10, 20, None, 40, 50, None], index=['A', 'A', 'B', 'B', 'C', 'C'])

# 用每组的均值填充缺失值

grouped_filled = data_with_nan.groupby(data_with_nan.index).transform(lambda x: x.fillna(x.mean()))

print(grouped_filled, end='\n\n')code>

# 144-5、分组统计

import pandas as pd

# 创建一个示例Series

data = pd.Series([10, 20, 30, 40, 50, 60], index=['A', 'A', 'B', 'B', 'C', 'C'])

# 计算每组的标准差

grouped_std = data.groupby(data.index).std()

print(grouped_std, end='\n\n')code>

# 144-6、时间序列分析

import pandas as pd

# 创建一个示例时间序列

date_range = pd.date_range(start='2024-01-01', periods=6, freq='ME')code>

time_series_data = pd.Series([100, 200, 300, 400, 500, 600], index=date_range)

# 按年分组并求和

grouped_by_year = time_series_data.groupby(time_series_data.index.year).sum()

print(grouped_by_year, end='\n\n')code>

# 144-7、分类数据分析

import pandas as pd

# 创建一个分类数据的示例Series

category_data = pd.Series([1, 2, 2, 3, 3, 3], index=['Myelsa', 'Alex', 'Lucy', 'Myelsa', 'Alex', 'Myelsa'])

# 按类别分组并计数

grouped_count = category_data.groupby(category_data.index).count()

print(grouped_count)

144-6-3、结果输出