PyPackage01---Pandas05_分组统计groupby

  groupby是使用频率很高的，这里介绍三种用法：

• 常规的统计
• 把groupby的变量从index变为一列
• groupby的一些复杂用法，主要用apply方法

pandas版本

import pandas as pd
pd.__version__

'0.23.4'

数据构造

label = [0,0,1,1,0,1,0,1,0,1]
sex = ["male","female","male","female","male","female","male","female","male","female"]
age = [23,26,21,30,56,45,25,37,48,33]
province = ["江苏","湖北","河南","山东","江苏","湖北","河南","湖北","河南","山东"]
rawDf = pd.DataFrame({
     "sex":sex,"age":age,"province":province,"label":label})
rawDf

	sex	age	province	label
0	male	23	江苏	0
1	female	26	湖北	0
2	male	21	河南	1
3	female	30	山东	1
4	male	56	江苏	0
5	female	45	湖北	1
6	male	25	河南	0
7	female	37	湖北	1
8	male	48	河南	0
9	female	33	山东	1

帮助文档

help(rawDf.groupby)

Help on method groupby in module pandas.core.generic:

groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, **kwargs) method of pandas.core.frame.DataFrame instance
    Group series using mapper (dict or key function, apply given function
    to group, return result as series) or by a series of columns.
    
    Parameters
    ----------
    by : mapping, function, label, or list of labels
        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 an ndarray is passed, the
        values are used as-is 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 a (single) key.
    axis : int, default 0
    level : int, level name, or sequence of such, default None
        If the axis is a MultiIndex (hierarchical), group by a particular
        level or levels
    as_index : boolean, default True
        For aggregated output, return object with group labels as the
        index. Only relevant for DataFrame input. as_index=False is
        effectively "SQL-style" grouped output
    sort : boolean, 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.
    group_keys : boolean, default True
        When calling apply, add group keys to index to identify pieces
    squeeze : boolean, default False
        reduce the dimensionality of the return type if possible,
        otherwise return a consistent type
    observed : boolean, 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.
    
        .. versionadded:: 0.23.0
    
    Returns
    -------
    GroupBy object
    
    Examples
    --------
    DataFrame results
    
    >>> data.groupby(func, axis=0).mean()
    >>> data.groupby(['col1', 'col2'])['col3'].mean()
    
    DataFrame with hierarchical index
    
    >>> data.groupby(['col1', 'col2']).mean()
    
    Notes
    -----
    See the `user guide
    `_ for more.
    
    See also
    --------
    resample : Convenience method for frequency conversion and resampling
        of time series.

返回单个汇总统计值

rawDf.groupby(by="sex").count()

	age	province	label
sex
female	5	5	5
male	5	5	5

直接groupby+统计函数，有两个比较麻烦的地方：

• groupbykey变成index了
• 会输出所有非groupbykey列的统计数据

对于第一个问题，可通过设置参数as_index=False处理，此时sex为第一列

rawDf.groupby(by="sex",as_index=False).count()

	sex	age	province	label
0	female	5	5	5
1	male	5	5	5

如果指向输出指定列的某些统计结果，可以通过列名限制

rawDf.groupby(by="sex")["province","label"].count()

	province	label
sex
female	5	5
male	5	5

返回多个统计值

在SQL中，我们会统计多个汇总值，比如：

SELECT sex,COUNT(province),sum(label)
FROM tabledemo
GROUP BY sex

pandas可以借用apply方法实现。

rawDf.groupby(by="sex").apply(lambda x:pd.DataFrame({
     "x1":[x.province.count()],"x2":[x.label.sum()]}))

		x1	x2
sex
female	0	5	4
male	0	5	1

当然也可以通过辅助函数的形式传入

def aggF(subdf):
    provinceCnt = subdf.province.count()
    provinceDnt = subdf.province.nunique()
    labelSum = subdf.label.sum()
    return pd.DataFrame({
     
        "provinceCnt": [provinceCnt],
        "provinceDnt": [provinceDnt],
        "labelSum": [labelSum]
    })
rawDf.groupby(by="sex",as_index=False).apply(lambda x:aggF(subdf=x))

		provinceCnt	provinceDnt	labelSum
0	0	5	2	4
1	0	5	2	1

def aggF(subdf):
    provinceCnt = subdf.province.count()
    provinceDnt = subdf.province.nunique()
    labelSum = subdf.label.sum()
    return pd.DataFrame({
     
        "provinceCnt": provinceCnt,
        "provinceDnt": provinceDnt,
        "labelSum": labelSum
    },index=[0])
rawDf.groupby(by="sex",as_index=True).apply(lambda x:aggF(subdf=x))

		provinceCnt	provinceDnt	labelSum
sex
female	0	5	2	4
male	0	5	2	1

这里有坑，groupby+apply之后，groupbykey不能作为其中一列了，目前只知道用for循环解决，比较笨，但是凑合用吧

for循环

def defaultFunction(subDf, groupIndex):
    badCnt = subDf.query("label==1").shape[0]
    goodCnt = subDf.query("label==0").shape[0]
    totalCnt = badCnt + goodCnt
    badRate = badCnt / totalCnt
    return pd.DataFrame({
     
        "groupIndex": [groupIndex],
        "badCnt": [badCnt],
        "goodCnt": [goodCnt],
        "totalCnt": [totalCnt],
        "badRate": [badRate]
    })
def groupFunction(Df,groupKey):
    result = pd.DataFrame()
    for name,group in Df.groupby(by=[groupKey]):
        result = pd.concat([result,defaultFunction(group,name)])
    result.index = range(len(result))
    return result

groupFunction(rawDf,"sex")

	groupIndex	badCnt	goodCnt	totalCnt	badRate
0	female	4	1	5	0.8
1	male	1	4	5	0.2

groupFunction(rawDf,"province")

	groupIndex	badCnt	goodCnt	totalCnt	badRate
0	山东	2	0	2	1.000000
1	江苏	0	2	2	0.000000
2	河南	1	2	3	0.333333
3	湖北	2	1	3	0.666667

                                2020-02-29 于南京市栖霞区