简介
pandas中的DF数据类型可以像数据库表格一样进行groupby操作。通常来说groupby操作可以分为三部分:分割数据,应用变换和和合并数据。
本文将会详细讲解Pandas中的groupby操作。
分割数据
分割数据的目的是将DF分割成为一个个的group。为了进行groupby操作,在创建DF的时候需要指定相应的label:
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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
Out[ 61 ]:
A B C D
0 foo one - 0.490565 - 0.233106
1 bar one 0.430089 1.040789
2 foo two 0.653449 - 1.155530
3 bar three - 0.610380 - 0.447735
4 foo two - 0.934961 0.256358
5 bar two - 0.256263 - 0.661954
6 foo one - 1.132186 - 0.304330
7 foo three 2.129757 0.445744
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默认情况下,groupby的轴是x轴。可以一列group,也可以多列group:
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In [ 8 ]: grouped = df.groupby( "A" )
In [ 9 ]: grouped = df.groupby([ "A" , "B" ])
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多index
在0.24版本中,如果我们有多index,可以从中选择特定的index进行group:
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In [ 10 ]: df2 = df.set_index([ "A" , "B" ])
In [ 11 ]: grouped = df2.groupby(level = df2.index.names.difference([ "B" ]))
In [ 12 ]: grouped. sum ()
Out[ 12 ]:
C D
A
bar - 1.591710 - 1.739537
foo - 0.752861 - 1.402938
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get_group
get_group 可以获取分组之后的数据:
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In [ 24 ]: df3 = pd.DataFrame({ "X" : [ "A" , "B" , "A" , "B" ], "Y" : [ 1 , 4 , 3 , 2 ]})
In [ 25 ]: df3.groupby([ "X" ]).get_group( "A" )
Out[ 25 ]:
X Y
0 A 1
2 A 3
In [ 26 ]: df3.groupby([ "X" ]).get_group( "B" )
Out[ 26 ]:
X Y
1 B 4
3 B 2
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dropna
默认情况下,NaN数据会被排除在groupby之外,通过设置 dropna=False 可以允许NaN数据:
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In [ 27 ]: df_list = [[ 1 , 2 , 3 ], [ 1 , None , 4 ], [ 2 , 1 , 3 ], [ 1 , 2 , 2 ]]
In [ 28 ]: df_dropna = pd.DataFrame(df_list, columns = [ "a" , "b" , "c" ])
In [ 29 ]: df_dropna
Out[ 29 ]:
a b c
0 1 2.0 3
1 1 NaN 4
2 2 1.0 3
3 1 2.0 2
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# Default ``dropna`` is set to True, which will exclude NaNs in keys
In [ 30 ]: df_dropna.groupby(by = [ "b" ], dropna = True ). sum ()
Out[ 30 ]:
a c
b
1.0 2 3
2.0 2 5
# In order to allow NaN in keys, set ``dropna`` to False
In [ 31 ]: df_dropna.groupby(by = [ "b" ], dropna = False ). sum ()
Out[ 31 ]:
a c
b
1.0 2 3
2.0 2 5
NaN 1 4
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groups属性
groupby对象有个groups属性,它是一个key-value字典,key是用来分类的数据,value是分类对应的值。
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In [ 34 ]: grouped = df.groupby([ "A" , "B" ])
In [ 35 ]: grouped.groups
Out[ 35 ]: {( 'bar' , 'one' ): [ 1 ], ( 'bar' , 'three' ): [ 3 ], ( 'bar' , 'two' ): [ 5 ], ( 'foo' , 'one' ): [ 0 , 6 ], ( 'foo' , 'three' ): [ 7 ], ( 'foo' , 'two' ): [ 2 , 4 ]}
In [ 36 ]: len (grouped)
Out[ 36 ]: 6
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index的层级
对于多级index对象,groupby可以指定group的index层级:
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In [ 40 ]: arrays = [
....: [ "bar" , "bar" , "baz" , "baz" , "foo" , "foo" , "qux" , "qux" ],
....: [ "one" , "two" , "one" , "two" , "one" , "two" , "one" , "two" ],
....: ]
....:
In [ 41 ]: index = pd.MultiIndex.from_arrays(arrays, names = [ "first" , "second" ])
In [ 42 ]: s = pd.Series(np.random.randn( 8 ), index = index)
In [ 43 ]: s
Out[ 43 ]:
first second
bar one - 0.919854
two - 0.042379
baz one 1.247642
two - 0.009920
foo one 0.290213
two 0.495767
qux one 0.362949
two 1.548106
dtype: float64
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group第一级:
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In [ 44 ]: grouped = s.groupby(level = 0 )
In [ 45 ]: grouped. sum ()
Out[ 45 ]:
first
bar - 0.962232
baz 1.237723
foo 0.785980
qux 1.911055
dtype: float64
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group第二级:
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In [ 46 ]: s.groupby(level = "second" ). sum ()
Out[ 46 ]:
second
one 0.980950
two 1.991575
dtype: float64
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group的遍历
得到group对象之后,我们可以通过for语句来遍历group:
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In [ 62 ]: grouped = df.groupby( 'A' )
In [ 63 ]: for name, group in grouped:
....: print (name)
....: print (group)
....:
bar
A B C D
1 bar one 0.254161 1.511763
3 bar three 0.215897 - 0.990582
5 bar two - 0.077118 1.211526
foo
A B C D
0 foo one - 0.575247 1.346061
2 foo two - 1.143704 1.627081
4 foo two 1.193555 - 0.441652
6 foo one - 0.408530 0.268520
7 foo three - 0.862495 0.024580
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如果是多字段group,group的名字是一个元组:
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In [ 64 ]: for name, group in df.groupby([ 'A' , 'B' ]):
....: print (name)
....: print (group)
....:
( 'bar' , 'one' )
A B C D
1 bar one 0.254161 1.511763
( 'bar' , 'three' )
A B C D
3 bar three 0.215897 - 0.990582
( 'bar' , 'two' )
A B C D
5 bar two - 0.077118 1.211526
( 'foo' , 'one' )
A B C D
0 foo one - 0.575247 1.346061
6 foo one - 0.408530 0.268520
( 'foo' , 'three' )
A B C D
7 foo three - 0.862495 0.02458
( 'foo' , 'two' )
A B C D
2 foo two - 1.143704 1.627081
4 foo two 1.193555 - 0.441652
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聚合操作
分组之后,就可以进行聚合操作:
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In [ 67 ]: grouped = df.groupby( "A" )
In [ 68 ]: grouped.aggregate(np. sum )
Out[ 68 ]:
C D
A
bar 0.392940 1.732707
foo - 1.796421 2.824590
In [ 69 ]: grouped = df.groupby([ "A" , "B" ])
In [ 70 ]: grouped.aggregate(np. sum )
Out[ 70 ]:
C D
A B
bar one 0.254161 1.511763
three 0.215897 - 0.990582
two - 0.077118 1.211526
foo one - 0.983776 1.614581
three - 0.862495 0.024580
two 0.049851 1.185429
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对于多index数据来说,默认返回值也是多index的。如果想使用新的index,可以添加 as_index = False:
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In [ 71 ]: grouped = df.groupby([ "A" , "B" ], as_index = False )
In [ 72 ]: grouped.aggregate(np. sum )
Out[ 72 ]:
A B C D
0 bar one 0.254161 1.511763
1 bar three 0.215897 - 0.990582
2 bar two - 0.077118 1.211526
3 foo one - 0.983776 1.614581
4 foo three - 0.862495 0.024580
5 foo two 0.049851 1.185429
In [ 73 ]: df.groupby( "A" , as_index = False ). sum ()
Out[ 73 ]:
A C D
0 bar 0.392940 1.732707
1 foo - 1.796421 2.824590
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上面的效果等同于reset_index
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In [ 74 ]: df.groupby([ "A" , "B" ]). sum ().reset_index()
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grouped.size() 计算group的大小:
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In [ 75 ]: grouped.size()
Out[ 75 ]:
A B size
0 bar one 1
1 bar three 1
2 bar two 1
3 foo one 2
4 foo three 1
5 foo two 2
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grouped.describe() 描述group的信息:
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In [ 76 ]: grouped.describe()
Out[ 76 ]:
C ... D
count mean std min 25 % 50 % ... std min 25 % 50 % 75 % max
0 1.0 0.254161 NaN 0.254161 0.254161 0.254161 ... NaN 1.511763 1.511763 1.511763 1.511763 1.511763
1 1.0 0.215897 NaN 0.215897 0.215897 0.215897 ... NaN - 0.990582 - 0.990582 - 0.990582 - 0.990582 - 0.990582
2 1.0 - 0.077118 NaN - 0.077118 - 0.077118 - 0.077118 ... NaN 1.211526 1.211526 1.211526 1.211526 1.211526
3 2.0 - 0.491888 0.117887 - 0.575247 - 0.533567 - 0.491888 ... 0.761937 0.268520 0.537905 0.807291 1.076676 1.346061
4 1.0 - 0.862495 NaN - 0.862495 - 0.862495 - 0.862495 ... NaN 0.024580 0.024580 0.024580 0.024580 0.024580
5 2.0 0.024925 1.652692 - 1.143704 - 0.559389 0.024925 ... 1.462816 - 0.441652 0.075531 0.592714 1.109898 1.627081
[ 6 rows x 16 columns]
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通用聚合方法
下面是通用的聚合方法:
函数 | 描述 |
---|---|
mean()
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平均值 |
sum()
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求和 |
size()
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计算size |
count()
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group的统计 |
std()
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标准差 |
var()
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方差 |
sem()
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均值的标准误 |
describe()
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统计信息描述 |
first()
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第一个group值 |
last()
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最后一个group值 |
nth()
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第n个group值 |
min()
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最小值 |
max()
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最大值 |
可以同时指定多个聚合方法:
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In [ 81 ]: grouped = df.groupby( "A" )
In [ 82 ]: grouped[ "C" ].agg([np. sum , np.mean, np.std])
Out[ 82 ]:
sum mean std
A
bar 0.392940 0.130980 0.181231
foo - 1.796421 - 0.359284 0.912265
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可以重命名:
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In [ 84 ]: (
....: grouped[ "C" ]
....: .agg([np. sum , np.mean, np.std])
....: .rename(columns = { "sum" : "foo" , "mean" : "bar" , "std" : "baz" })
....: )
....:
Out[ 84 ]:
foo bar baz
A
bar 0.392940 0.130980 0.181231
foo - 1.796421 - 0.359284 0.912265
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NamedAgg
NamedAgg 可以对聚合进行更精准的定义,它包含 column 和aggfunc 两个定制化的字段。
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In [ 88 ]: animals = pd.DataFrame(
....: {
....: "kind" : [ "cat" , "dog" , "cat" , "dog" ],
....: "height" : [ 9.1 , 6.0 , 9.5 , 34.0 ],
....: "weight" : [ 7.9 , 7.5 , 9.9 , 198.0 ],
....: }
....: )
....:
In [ 89 ]: animals
Out[ 89 ]:
kind height weight
0 cat 9.1 7.9
1 dog 6.0 7.5
2 cat 9.5 9.9
3 dog 34.0 198.0
In [ 90 ]: animals.groupby( "kind" ).agg(
....: min_height = pd.NamedAgg(column = "height" , aggfunc = "min" ),
....: max_height = pd.NamedAgg(column = "height" , aggfunc = "max" ),
....: average_weight = pd.NamedAgg(column = "weight" , aggfunc = np.mean),
....: )
....:
Out[ 90 ]:
min_height max_height average_weight
kind
cat 9.1 9.5 8.90
dog 6.0 34.0 102.75
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或者直接使用一个元组:
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In [ 91 ]: animals.groupby( "kind" ).agg(
....: min_height = ( "height" , "min" ),
....: max_height = ( "height" , "max" ),
....: average_weight = ( "weight" , np.mean),
....: )
....:
Out[ 91 ]:
min_height max_height average_weight
kind
cat 9.1 9.5 8.90
dog 6.0 34.0 102.75
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不同的列指定不同的聚合方法
通过给agg方法传入一个字典,可以指定不同的列使用不同的聚合:
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In [ 95 ]: grouped.agg({ "C" : "sum" , "D" : "std" })
Out[ 95 ]:
C D
A
bar 0.392940 1.366330
foo - 1.796421 0.884785
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转换操作
转换是将对象转换为同样大小对象的操作。在数据分析的过程中,经常需要进行数据的转换操作。
可以接lambda操作:
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In [ 112 ]: ts.groupby( lambda x: x.year).transform( lambda x: x. max () - x. min ())
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填充na值:
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In [ 121 ]: transformed = grouped.transform( lambda x: x.fillna(x.mean()))
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过滤操作
filter方法可以通过lambda表达式来过滤我们不需要的数据:
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In [ 136 ]: sf = pd.Series([ 1 , 1 , 2 , 3 , 3 , 3 ])
In [ 137 ]: sf.groupby(sf). filter ( lambda x: x. sum () > 2 )
Out[ 137 ]:
3 3
4 3
5 3
dtype: int64
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Apply操作
有些数据可能不适合进行聚合或者转换操作,Pandas提供了一个 apply
方法,用来进行更加灵活的转换操作。
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In [ 156 ]: df
Out[ 156 ]:
A B C D
0 foo one - 0.575247 1.346061
1 bar one 0.254161 1.511763
2 foo two - 1.143704 1.627081
3 bar three 0.215897 - 0.990582
4 foo two 1.193555 - 0.441652
5 bar two - 0.077118 1.211526
6 foo one - 0.408530 0.268520
7 foo three - 0.862495 0.024580
In [ 157 ]: grouped = df.groupby( "A" )
# could also just call .describe()
In [ 158 ]: grouped[ "C" ]. apply ( lambda x: x.describe())
Out[ 158 ]:
A
bar count 3.000000
mean 0.130980
std 0.181231
min - 0.077118
25 % 0.069390
...
foo min - 1.143704
25 % - 0.862495
50 % - 0.575247
75 % - 0.408530
max 1.193555
Name: C, Length: 16 , dtype: float64
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可以外接函数:
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In [ 159 ]: grouped = df.groupby( 'A' )[ 'C' ]
In [ 160 ]: def f(group):
.....: return pd.DataFrame({ 'original' : group,
.....: 'demeaned' : group - group.mean()})
.....:
In [ 161 ]: grouped. apply (f)
Out[ 161 ]:
original demeaned
0 - 0.575247 - 0.215962
1 0.254161 0.123181
2 - 1.143704 - 0.784420
3 0.215897 0.084917
4 1.193555 1.552839
5 - 0.077118 - 0.208098
6 - 0.408530 - 0.049245
7 - 0.862495 - 0.503211
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本文已收录于 http://www.flydean.com/11-python-pandas-groupby/
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原文链接:https://blog.csdn.net/superfjj/article/details/118667826