这篇文章主要介绍 Pandas如何使用GroupBy分组，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！
groupby对象
import pandas as pd
import numpy as np
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)})
gb.groupby('A')
print(df.groupby('A'))
<pandas.core.groupby.DataFrameGroupBy object at 0x00000000042F3470>
In [26]: gb.<TAB>
gb.agg gb.boxplot gb.cummin gb.describe gb.filter gb.get_group gb.height gb.last gb.median gb.ngroups gb.plot gb.rank gb.std gb.transform
gb.aggregate gb.count gb.cumprod gb.dtype gb.first gb.groups gb.hist gb.max gb.min gb.nth gb.prod gb.resample gb.sum gb.var
gb.apply gb.cummax gb.cumsum gb.fillna gb.gender gb.head gb.indices gb.mean gb.name gb.ohlc gb.quantile gb.size gb.tail gb.weight分组迭代Iterating through groups
In [41]: grouped = df.groupby('A')
In [42]: for name, group in grouped:
....: print(name)
....: print(group)
....:
bar
A B C D1 bar one -0.042379 -0.0893293 bar three -0.009920 -0.9458675 bar two 0.495767 1.956030foo
A B C D0 foo one -0.919854 -1.1313452 foo two 1.247642 0.3378634 foo two 0.290213 -0.9321326 foo one 0.362949 0.0175877 foo three 1.548106 -0.016692获得一个分组get_group
In [44]: grouped.get_group('bar')Out[44]:
A B C D1 bar one -0.042379 -0.0893293 bar three -0.009920 -0.9458675 bar two 0.495767 1.956030使用多种函数agg()
相同的函数
In [56]: grouped = df.groupby('A')In [57]: grouped['C'].agg([np.sum, np.mean, np.std])Out[57]:
sum mean stdA
bar 0.443469 0.147823 0.301765foo 2.529056 0.505811 0.966450不同的函数
In [60]: grouped.agg({'C' : np.sum,
....: 'D' : lambda x: np.std(x, ddof=1)})
....:
Out[60]:
C D
A
bar 0.443469 1.490982foo 2.529056 0.645875转变数据框transformation
转变函数（transform）中需要返回一个和分组块（group chunk）同样大小的结果，比如我们需要标准化每一个分组的数据：
In [66]: index = pd.date_range('10/1/1999', periods=1100)
In [67]: ts = pd.Series(np.random.normal(0.5, 2, 1100), index)
In [68]: ts = ts.rolling(window=100,min_periods=100).mean().dropna()
In [71]: key = lambda x: x.year#使用年来分组In [72]: zscore = lambda x: (x - x.mean()) / x.std()#标准化In [73]: transformed = ts.groupby(key).transform(zscore)#使用索引的年份来分组，然后标准化各组数据In [80]: compare = pd.DataFrame({'Original': ts, 'Transformed': transformed})# 做出图形
过滤Filtration
filter方法返回一个子集（subset）。比如我们只想要组长度大于2的分组：
In [105]: dff = pd.DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')})
In [106]: dff.groupby('B').filter(lambda x: len(x) > 2)
Out[106]:
A B2 2 b3 3 b4 4 b5 5 b灵活运用apply
In [123]: df
Out[123]:
A B C D0 foo one -0.919854 -1.1313451 bar one -0.042379 -0.0893292 foo two 1.247642 0.3378633 bar three -0.009920 -0.9458674 foo two 0.290213 -0.9321325 bar two 0.495767 1.9560306 foo one 0.362949 0.0175877 foo three 1.548106 -0.016692In [124]: grouped = df.groupby('A')# could also just call .describe()In [125]: grouped['C'].apply(lambda x: x.describe())
Out[125]:
A
bar count 3.000000 mean 0.147823 std 0.301765 min -0.042379 25% -0.026149 50% -0.009920 75% 0.242924...
foo mean 0.505811 std 0.966450 min -0.919854 25% 0.290213 50% 0.362949 75% 1.247642 max 1.548106Name: C, dtype: float64以上是“ Pandas如何使用GroupBy分组”这篇文章的所有内容，感谢各位的阅读！希望分享的内容对大家有帮助，更多相关知识，欢迎关注天达云行业资讯频道！