Pandas简单实现groupby分组统计

Pandas实现groupby分组统计

类似SQL: select city.max(temperature) from city_weather groupby by city

groupby: 先对数据分组，然后在每个分组上应用聚合函数、转换函数

import pandas as pd import numpy as np %matplotlib inline df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar','foo', 'bar', 'foo', 'foo'], 'B' : ['one', 'one', 'two', 'three','two', 'two', 'one', 'three'], 'C' : np.random.randn(8), 'D' : np.random.randn(8)}) df ABCD0fooone-0.1907411.6129481barone-0.7812091.0021172footwo0.858491-0.1784293barthree-0.2805590.0838164footwo0.214302-0.1077175bartwo-0.134638-1.6043656fooone-2.108731-0.6538197foothree0.414685-1.717435

1、分组使用聚合函数做数据统计

1、单个列groupby，查询所有数据列的统计

df.groupby('A').sum() CDAbar-1.196406-0.518431foo-0.811994-1.044452

1、groupby中的A变成了数据的索引列 2、B列不是数字，所以被自动忽略

2、多个列groupby，查询所有数据列的统计

df.groupby(['A','B']).mean() CDABbarone-0.7812091.002117three-0.2805590.083816two-0.134638-1.604365fooone-1.1497360.479564three0.414685-1.717435two0.536397-0.143073

(A,B)成对变成了二级索引，不想改变原来索引。加一个as_index=False

df.groupby(['A','B'],as_index=False).mean() ABCD0barone-0.7812091.0021171barthree-0.2805590.0838162bartwo-0.134638-1.6043653fooone-1.1497360.4795644foothree0.414685-1.7174355footwo0.536397-0.143073

3、同时查看多种数据统计

df.groupby('A').agg([np.sum,np.mean,np.std]) CDsummeanstdsummeanstdAbar-1.196406-0.3988020.339116-0.518431-0.172811.322055foo-0.811994-0.1623991.151755-1.044452-0.208891.204567

列变成了多级索引

4、查看单列的结果数据统计

# 方法1：预过滤，性能更好 df.groupby('A')['C'].agg([np.sum,np.mean,np.std]) summeanstdAbar-1.196406-0.3988020.339116foo-0.811994-0.1623991.151755 #方法2 df.groupby('A').agg([np.sum,np.mean,np.std])['C'] summeanstdAbar-1.196406-0.3988020.339116foo-0.811994-0.1623991.151755

5、不同列使用不同的聚合函数

df.groupby('A').agg({'C':np.sum,'D':np.std}) CDAbar-1.1964061.322055foo-0.8119941.204567 欢迎关注个人公众号