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：
