Pandas高级教程之:Dataframe的重排和旋转

简介

使用Pandas的pivot方法可以将DF进行旋转变换 本文将会详细讲解pivot的秘密。

使用Pivot

pivot用来重组DF 使用指定的index columns和values来对现有的DF进行重构。

看一个Pivot的例子

通过pivot变化 新的DF使用foo中的值作为index 使用bar的值作为columns zoo作为对应的value。

再看一个时间变化的例子

In [1]: df
Out[1]: 
 date variable value
0 2000-01-03 A 0.469112
1 2000-01-04 A -0.282863
2 2000-01-05 A -1.509059
3 2000-01-03 B -1.135632
4 2000-01-04 B 1.212112
5 2000-01-05 B -0.173215
6 2000-01-03 C 0.119209
7 2000-01-04 C -1.044236
8 2000-01-05 C -0.861849
9 2000-01-03 D -2.104569
10 2000-01-04 D -0.494929
11 2000-01-05 D 1.071804

In [3]: df.pivot(index date , columns variable , values value )
Out[3]: 
variable A B C D
date 
2000-01-03 0.469112 -1.135632 0.119209 -2.104569
2000-01-04 -0.282863 1.212112 -1.044236 -0.494929
2000-01-05 -1.509059 -0.173215 -0.861849 1.071804

如果剩余的value 多于一列的话 每一列都会有相应的columns值

In [4]: df[ value2 ] df[ value ] * 2
In [5]: pivoted df.pivot(index date , columns variable )
In [6]: pivoted
Out[6]: 
 value value2 
variable A B C D A B C D
date 
2000-01-03 0.469112 -1.135632 0.119209 -2.104569 0.938225 -2.271265 0.238417 -4.209138
2000-01-04 -0.282863 1.212112 -1.044236 -0.494929 -0.565727 2.424224 -2.088472 -0.989859
2000-01-05 -1.509059 -0.173215 -0.861849 1.071804 -3.018117 -0.346429 -1.723698 2.143608

通过选择value2 可以得到相应的子集

In [7]: pivoted[ value2 ]
Out[7]: 
variable A B C D
date 
2000-01-03 0.938225 -2.271265 0.238417 -4.209138
2000-01-04 -0.565727 2.424224 -2.088472 -0.989859
2000-01-05 -3.018117 -0.346429 -1.723698 2.143608

使用Stack

Stack是对DF进行转换 将列转换为新的内部的index。

上面我们将列A B转成了index。

unstack是stack的反向操作 是将最内层的index转换为对应的列。

举个具体的例子

In [8]: tuples list(zip(*[[ bar , bar , baz , baz ,
 ...: foo , foo , qux , qux ],
 ...: [ one , two , one , two ,
 ...: one , two , one , two ]]))
 ...: 
In [9]: index pd.MultiIndex.from_tuples(tuples, names [ first , second ])
In [10]: df pd.DataFrame(np.random.randn(8, 2), index index, columns [ A , B ])
In [11]: df2 df[:4]
In [12]: df2
Out[12]: 
first second 
bar one 0.721555 -0.706771
 two -1.039575 0.271860
baz one -0.424972 0.567020
 two 0.276232 -1.087401

In [13]: stacked df2.stack()
In [14]: stacked
Out[14]: 
first second 
bar one A 0.721555
 B -0.706771
 two A -1.039575
 B 0.271860
baz one A -0.424972
 B 0.567020
 two A 0.276232
 B -1.087401
dtype: float64

默认情况下unstack是unstack最后一个index 我们还可以指定特定的index值

In [15]: stacked.unstack()
Out[15]: 
first second 
bar one 0.721555 -0.706771
 two -1.039575 0.271860
baz one -0.424972 0.567020
 two 0.276232 -1.087401
In [16]: stacked.unstack(1)
Out[16]: 
second one two
first 
bar A 0.721555 -1.039575
 B -0.706771 0.271860
baz A -0.424972 0.276232
 B 0.567020 -1.087401
In [17]: stacked.unstack(0)
Out[17]: 
first bar baz
second 
one A 0.721555 -0.424972
 B -0.706771 0.567020
two A -1.039575 0.276232
 B 0.271860 -1.087401

默认情况下stack只会stack一个level 还可以传入多个level

In [23]: columns pd.MultiIndex.from_tuples([
 ....: ( A , cat , long ), ( B , cat , long ),
 ....: ( A , dog , short ), ( B , dog , short )],
 ....: names [ exp , animal , hair_length ]
 ....: )
 ....: 
In [24]: df pd.DataFrame(np.random.randn(4, 4), columns columns)
In [25]: df
Out[25]: 
exp A B A B
animal cat cat dog dog
hair_length long long short short
0 1.075770 -0.109050 1.643563 -1.469388
1 0.357021 -0.674600 -1.776904 -0.968914
2 -1.294524 0.413738 0.276662 -0.472035
3 -0.013960 -0.362543 -0.006154 -0.923061
In [26]: df.stack(level [ animal , hair_length ])
Out[26]: 
exp A B
 animal hair_length 
0 cat long 1.075770 -0.109050
 dog short 1.643563 -1.469388
1 cat long 0.357021 -0.674600
 dog short -1.776904 -0.968914
2 cat long -1.294524 0.413738
 dog short 0.276662 -0.472035
3 cat long -0.013960 -0.362543
 dog short -0.006154 -0.923061

上面等价于

In [27]: df.stack(level [1, 2])

使用melt

melt指定特定的列作为标志变量 其他的列被转换为行的数据。并放置在新的两个列 variable和value中。

上面例子中我们指定了两列first和last 这两列是不变的 height和weight被变换成为行数据。

举个例子

In [41]: cheese pd.DataFrame({ first : [ John , Mary ],
 ....: last : [ Doe , Bo ],
 ....: height : [5.5, 6.0],
 ....: weight : [130, 150]})
 ....: 
In [42]: cheese
Out[42]: 
 first last height weight
0 John Doe 5.5 130
1 Mary Bo 6.0 150
In [43]: cheese.melt(id_vars [ first , last ])
Out[43]: 
 first last variable value
0 John Doe height 5.5
1 Mary Bo height 6.0
2 John Doe weight 130.0
3 Mary Bo weight 150.0
In [44]: cheese.melt(id_vars [ first , last ], var_name quantity )
Out[44]: 
 first last quantity value
0 John Doe height 5.5
1 Mary Bo height 6.0
2 John Doe weight 130.0
3 Mary Bo weight 150.0

使用Pivot tables

虽然Pivot可以进行DF的轴转置 Pandas还提供了 pivot_table() 在转置的同时可以进行数值的统计。

pivot_table() 接收下面的参数

data: 一个df对象

values:一列或者多列待聚合的数据。

Index: index的分组对象

Columns: 列的分组对象

Aggfunc: 聚合的方法。

先创建一个df

In [59]: import datetime
In [60]: df pd.DataFrame({ A : [ one , one , two , three ] * 6,
 ....: B : [ A , B , C ] * 8,
 ....: C : [ foo , foo , foo , bar , bar , bar ] * 4,
 ....: D : np.random.randn(24),
 ....: E : np.random.randn(24),
 ....: F : [datetime.datetime(2013, i, 1) for i in range(1, 13)]
 ....: [datetime.datetime(2013, i, 15) for i in range(1, 13)]})
 ....: 
In [61]: df
Out[61]: 
 A B C D E F
0 one A foo 0.341734 -0.317441 2013-01-01
1 one B foo 0.959726 -1.236269 2013-02-01
2 two C foo -1.110336 0.896171 2013-03-01
3 three A bar -0.619976 -0.487602 2013-04-01
4 one B bar 0.149748 -0.082240 2013-05-01
.. ... .. ... ... ... ...
19 three B foo 0.690579 -2.213588 2013-08-15
20 one C foo 0.995761 1.063327 2013-09-15
21 one A bar 2.396780 1.266143 2013-10-15
22 two B bar 0.014871 0.299368 2013-11-15
23 three C bar 3.357427 -0.863838 2013-12-15
[24 rows x 6 columns]

下面是几个聚合的例子

In [62]: pd.pivot_table(df, values D , index [ A , B ], columns [ C ])
Out[62]: 
C bar foo
one A 1.120915 -0.514058
 B -0.338421 0.002759
 C -0.538846 0.699535
three A -1.181568 NaN
 B NaN 0.433512
 C 0.588783 NaN
two A NaN 1.000985
 B 0.158248 NaN
 C NaN 0.176180
In [63]: pd.pivot_table(df, values D , index [ B ], columns [ A , C ], aggfunc np.sum)
Out[63]: 
A one three two 
C bar foo bar foo bar foo
A 2.241830 -1.028115 -2.363137 NaN NaN 2.001971
B -0.676843 0.005518 NaN 0.867024 0.316495 NaN
C -1.077692 1.399070 1.177566 NaN NaN 0.352360
In [64]: pd.pivot_table(df, values [ D , E ], index [ B ], columns [ A , C ],
 ....: aggfunc np.sum)
 ....: 
Out[64]: 
 D E 
A one three two one three two 
C bar foo bar foo bar foo bar foo bar foo bar foo
A 2.241830 -1.028115 -2.363137 NaN NaN 2.001971 2.786113 -0.043211 1.922577 NaN NaN 0.128491
B -0.676843 0.005518 NaN 0.867024 0.316495 NaN 1.368280 -1.103384 NaN -2.128743 -0.194294 NaN
C -1.077692 1.399070 1.177566 NaN NaN 0.352360 -1.976883 1.495717 -0.263660 NaN NaN 0.872482

添加margins True会添加一个All列 表示对所有的列进行聚合

In [69]: df.pivot_table(index [ A , B ], columns C , margins True, aggfunc np.std)
Out[69]: 
 D E 
C bar foo All bar foo All
one A 1.804346 1.210272 1.569879 0.179483 0.418374 0.858005
 B 0.690376 1.353355 0.898998 1.083825 0.968138 1.101401
 C 0.273641 0.418926 0.771139 1.689271 0.446140 1.422136
three A 0.794212 NaN 0.794212 2.049040 NaN 2.049040
 B NaN 0.363548 0.363548 NaN 1.625237 1.625237
 C 3.915454 NaN 3.915454 1.035215 NaN 1.035215
two A NaN 0.442998 0.442998 NaN 0.447104 0.447104
 B 0.202765 NaN 0.202765 0.560757 NaN 0.560757
 C NaN 1.819408 1.819408 NaN 0.650439 0.650439
All 1.556686 0.952552 1.246608 1.250924 0.899904 1.059389

使用crosstab

Crosstab 用来统计表格中元素的出现次数。

In [70]: foo, bar, dull, shiny, one, two foo , bar , dull , shiny , one , two 
In [71]: a np.array([foo, foo, bar, bar, foo, foo], dtype object)
In [72]: b np.array([one, one, two, one, two, one], dtype object)
In [73]: c np.array([dull, dull, shiny, dull, dull, shiny], dtype object)
In [74]: pd.crosstab(a, [b, c], rownames [ a ], colnames [ b , c ])
Out[74]: 
b one two 
c dull shiny dull shiny
bar 1 0 0 1
foo 2 1 1 0

crosstab可以接收两个Series:

In [75]: df pd.DataFrame({ A : [1, 2, 2, 2, 2], B : [3, 3, 4, 4, 4],
 ....: C : [1, 1, np.nan, 1, 1]})
 ....: 
In [76]: df
Out[76]: 
 A B C
0 1 3 1.0
1 2 3 1.0
2 2 4 NaN
3 2 4 1.0
4 2 4 1.0
In [77]: pd.crosstab(df[ A ], df[ B ])
Out[77]: 
B 3 4
1 1 0
2 1 3

还可以使用normalize来指定比例值

In [82]: pd.crosstab(df[ A ], df[ B ], normalize True)
Out[82]: 
B 3 4
1 0.2 0.0
2 0.2 0.6

还可以normalize行或者列

In [83]: pd.crosstab(df[ A ], df[ B ], normalize columns )
Out[83]: 
B 3 4
1 0.5 0.0
2 0.5 1.0

可以指定聚合方法

In [84]: pd.crosstab(df[ A ], df[ B ], values df[ C ], aggfunc np.sum)
Out[84]: 
B 3 4
1 1.0 NaN
2 1.0 2.0

get_dummies

get_dummies可以将DF中的一列转换成为k列的0和1组合

df pd.DataFrame({ key : list( bbacab ), data1 : range(6)})
Out[9]: 
 data1 key
0 0 b
1 1 b
2 2 a
3 3 c
4 4 a
5 5 b
pd.get_dummies(df[ key ])
Out[10]: 
 a b c
0 0 1 0
1 0 1 0
2 1 0 0
3 0 0 1
4 1 0 0
5 0 1 0

get_dummies 和 cut 可以进行结合用来统计范围内的元素

In [95]: values np.random.randn(10)
In [96]: values
Out[96]: 
array([ 0.4082, -1.0481, -0.0257, -0.9884, 0.0941, 1.2627, 1.29 ,
 0.0824, -0.0558, 0.5366])
In [97]: bins [0, 0.2, 0.4, 0.6, 0.8, 1]
In [98]: pd.get_dummies(pd.cut(values, bins))
Out[98]: 
 (0.0, 0.2] (0.2, 0.4] (0.4, 0.6] (0.6, 0.8] (0.8, 1.0]
0 0 0 1 0 0
1 0 0 0 0 0
2 0 0 0 0 0
3 0 0 0 0 0
4 1 0 0 0 0
5 0 0 0 0 0
6 0 0 0 0 0
7 1 0 0 0 0
8 0 0 0 0 0
9 0 0 1 0 0

get_dummies还可以接受一个DF参数

In [99]: df pd.DataFrame({ A : [ a , b , a ], B : [ c , c , b ],
 ....: C : [1, 2, 3]})
 ....: 
In [100]: pd.get_dummies(df)
Out[100]: 
 C A_a A_b B_b B_c
0 1 1 0 0 1
1 2 0 1 0 1
2 3 1 0 1 0

本文已收录于 http://www.flydean.com/05-python-pandas-reshaping-pivot/

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