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]:
A B
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]:
A B
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
A B
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
B
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
B
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
A B
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 a 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
A
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 A 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 A 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 A 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)})
df
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
到此这篇关于Pandas实现Dataframe的重排和旋转的文章就介绍到这了,更多相关Pandas Dataframe重排和旋转内容请搜索服务器之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持服务器之家！
原文链接：https://www.cnblogs.com/flydean/p/14884127.html
