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数据分组

信息

  • 默认省略导入

    • import numpy as np
    • import pandas as pd

  • 缩写

    • DataFrame缩写为"DF"
    • Series缩写为"SE"

  • 默认使用数据集: learn_pandas.csv

    已经导入: df = pd.read_csv('data/learn_pandas.csv', usecols=['School', 'Grade', 'Name', 'Gender', 'Weight', 'Transfer'])

分组模式及其对象

要实现分组操作, 必须明确三个要素: 分组依据, 数据来源, 操作及其返回结果, 得出分组模式即:

df.groupby(分组依据)[数据来源].使用操作
例子 
[1]: df.groupby('Gender')['Height'].median()
Gender
Female    159.6
Male      173.4
Name: Height, dtype: float64

分组依据的本质

传入列名

可以传入相应的一个列名或多个列名, Pandas会自动从对应的列中获取分组依据.

例子 
[1]: df.groupby('Gender')['Height'].median()
Gender
Female    159.6
Male      173.4
Name: Height, dtype: float64

[1]: df.groupby(['School', 'Gender'])['Height'].mean()
School                         Gender
Fudan University               Female    158.776923
                               Male      174.212500
Peking University              Female    158.666667
                               Male      172.030000
Shanghai Jiao Tong University  Female    159.122500
                               Male      176.760000
Tsinghua University            Female    159.753333
                               male      171.638889
name: height, dtype: float64

传入数组/列

可以传入一个数组/列, 以这个数组中的元素作为分组依据.

例子 
[1]: condition = df.Weight > df.Weight.mean()
[2]: df.groupby(condition)['Height'].mean()
Weight
False    159.034646
True     172.705357
Name: Height, dtype: float64

[1]: item = np.random.choice(list('abc'), df.shape[0])
[2]: df.groupby(item)['Height'].mean()
a    163.924242
b    162.928814
c    162.708621
Name: Height, dtype: float64

[1]: condition = df.Weight > df.Weight.mean() 
[2]: item = np.random.choice(list('abc'), df.shape[0]) 
[3]: df.groupby([condition, item])['Height'].mean()
Weight   
False   a    160.193617
        b    158.921951
        c    157.756410
True    a    173.152632
        b    172.055556
        c    172.873684
Name: Height, dtype: float64

本质

从传入数组的情况可以看出, 之前传入列名只是一种简便的记号, 事实上等价于传入的是一个列或者多个列, 最后的分组依据来自于数组来源组合的unique值, 通过drop_duplicates就能直到具体的组类别.

例子 
[1]: df[['School', 'Gender']].drop_duplicates()
[2]: df.groupby([df['School'], df['Gender']])['Height'].mean() 这里直接传入了一个列, 相当于df.groupby(['School', 'Gender'])['Height'].mean()
School                         Gender
Fudan University               Female    158.776923
                               Male      174.212500
Peking University              Female    158.666667
                               Male      172.030000
Shanghai Jiao Tong University  Female    159.122500
                               Male      176.760000
Tsinghua University            Female    159.753333
                               Male      171.638889
Name: Height, dtype: float64