pandas分组统计：groupby，melt，pivot_table，crosstab的用法

groupby: 分组

melt: 宽表转长表

pivot_table: 长表转宽表，数据透视表

crosstab: 交叉表 / 列联表，主要用于分组频数统计

import numpy as np
import pandas as pd

df = pd.DataFrame({'key1':['a','a','b','b','a'],
                   'key2':['one','two','one','two','one'],
                   'data1':np.random.randn(5),
                   'data2':np.random.randn(5)
                   })
#  key1 key2     data1     data2
#0    a  one  0.498857  0.074495
#1    a  two  2.872086 -1.303973
#2    b  one -0.721171 -1.315390
#3    b  two  0.985844  0.419780
#4    a  one -0.134534 -0.732861

# ####################
'''groupby 用法'''
group1 = df.groupby('key1')
group2 = df.groupby(['key1','key2'])
[x for x in group1]
group1.size()
group1.sum()
group2.count()
group1['data1','data2'].agg(['mean','sum'])  #作用于所有列
group2(['key1','key2']).apply(lambda x: pd.Series([x.shape[0], x['key1'].mean(), x['key2'].sum()],
       index=['counts', 'key1_mean', 'key2_sum']))  #作用于指定列

# ####################
''' melt 用法 -- 宽表转长表 '''
pd.melt(df, id_vars=['key1', 'key2'], value_vars=['data1', 'data2'], var_name='var', value_name='value')  #col_level
#  key1 key2    var     value
#0    a  one  data1  0.498857
#1    a  two  data1  2.872086
#2    b  one  data1 -0.721171
#3    b  two  data1  0.985844
#4    a  one  data1 -0.134534
#5    a  one  data2  0.074495
#6    a  two  data2 -1.303973
#7    b  one  data2 -1.315390
#8    b  two  data2  0.419780
#9    a  one  data2 -0.732861

# ####################
''' crosstab 用法 -- 列联表（count） '''
pd.crosstab(df.key1, df.key2, margins=True)
#key2  one  two  All
#key1
#a       2    1    3
#b       1    1    2
#All     3    2    5

# ####################
''' pivot_table 用法 -- 长表转宽表 '''
# pd.pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None,
# 　　　　　　　　dropna=True, margins=False, margins_name='ALL')  #aggfunc={'d':np.sum, 'e':np.max}
pd.pivot_table(df, index='key1', columns='key2')
#         data1               data2
#key2       one       two       one       two
#key1
#a     0.182162  2.872086 -0.329183 -1.303973
#b    -0.721171  0.985844 -1.315390  0.419780

df.pivot_table(['data1'], index='key1', columns='key2', fill_value=0)  #['data1']
#         data1
#key2       one       two
#key1
#a     0.182162  2.872086
#b    -0.721171  0.985844

参考链接：

Pandas：透视表(pivotTab)和交叉表(crossTab)

pandas模块，Melt函数

Pandas分组统计函数：groupby、pivot_table及crosstab