简介
应用Pandas的pivot办法能够将DF进行旋转变换，本文将会具体解说pivot的机密。
应用Pivot
pivot用来重组DF，应用指定的index，columns和values来对现有的DF进行重构。
看一个Pivot的例子：
通过pivot变动，新的DF应用foo中的值作为index，应用bar的值作为columns，zoo作为对应的value。
再看一个工夫变动的例子：
In [1]: dfOut[1]: date variable value0 2000-01-03 A 0.4691121 2000-01-04 A -0.2828632 2000-01-05 A -1.5090593 2000-01-03 B -1.1356324 2000-01-04 B 1.2121125 2000-01-05 B -0.1732156 2000-01-03 C 0.1192097 2000-01-04 C -1.0442368 2000-01-05 C -0.8618499 2000-01-03 D -2.10456910 2000-01-04 D -0.49492911 2000-01-05 D 1.071804In [3]: df.pivot(index='date', columns='variable', values='value')Out[3]: variable A B C Ddate 2000-01-03 0.469112 -1.135632 0.119209 -2.1045692000-01-04 -0.282863 1.212112 -1.044236 -0.4949292000-01-05 -1.509059 -0.173215 -0.861849 1.071804如果残余的value，多于一列的话，每一列都会有相应的columns值：
In [4]: df['value2'] = df['value'] * 2In [5]: pivoted = df.pivot(index='date', columns='variable')In [6]: pivotedOut[6]: value value2 variable A B C D A B C Ddate 2000-01-03 0.469112 -1.135632 0.119209 -2.104569 0.938225 -2.271265 0.238417 -4.2091382000-01-04 -0.282863 1.212112 -1.044236 -0.494929 -0.565727 2.424224 -2.088472 -0.9898592000-01-05 -1.509059 -0.173215 -0.861849 1.071804 -3.018117 -0.346429 -1.723698 2.143608通过抉择value2，能够失去相应的子集：
In [7]: pivoted['value2']Out[7]: variable A B C Ddate 2000-01-03 0.938225 -2.271265 0.238417 -4.2091382000-01-04 -0.565727 2.424224 -2.088472 -0.9898592000-01-05 -3.018117 -0.346429 -1.723698 2.143608应用Stack
Stack是对DF进行转换，将列转换为新的外部的index。
下面咱们将列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]: df2Out[12]: A Bfirst second bar one 0.721555 -0.706771 two -1.039575 0.271860baz one -0.424972 0.567020 two 0.276232 -1.087401In [13]: stacked = df2.stack()In [14]: stackedOut[14]: first second bar one A 0.721555 B -0.706771 two A -1.039575 B 0.271860baz one A -0.424972 B 0.567020 two A 0.276232 B -1.087401dtype: float64默认状况下unstack是unstack最初一个index，咱们还能够指定特定的index值：
In [15]: stacked.unstack()Out[15]: A Bfirst second bar one 0.721555 -0.706771 two -1.039575 0.271860baz one -0.424972 0.567020 two 0.276232 -1.087401In [16]: stacked.unstack(1)Out[16]: second one twofirst bar A 0.721555 -1.039575 B -0.706771 0.271860baz A -0.424972 0.276232 B 0.567020 -1.087401In [17]: stacked.unstack(0)Out[17]: first bar bazsecond one A 0.721555 -0.424972 B -0.706771 0.567020two A -1.039575 0.276232 B 0.271860 -1.087401默认状况下stack只会stack一个level，还能够传入多个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]: dfOut[25]: exp A B A Banimal cat cat dog doghair_length long long short short0 1.075770 -0.109050 1.643563 -1.4693881 0.357021 -0.674600 -1.776904 -0.9689142 -1.294524 0.413738 0.276662 -0.4720353 -0.013960 -0.362543 -0.006154 -0.923061In [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 -1.4693881 cat long 0.357021 -0.674600 dog short -1.776904 -0.9689142 cat long -1.294524 0.413738 dog short 0.276662 -0.4720353 cat long -0.013960 -0.362543 dog short -0.006154 -0.923061下面等价于：
In [27]: df.stack(level=[1, 2])应用melt
melt指定特定的列作为标记变量，其余的列被转换为行的数据。并搁置在新的两个列：variable和value中。
下面例子中咱们指定了两列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]: cheeseOut[42]: first last height weight0 John Doe 5.5 1301 Mary Bo 6.0 150In [43]: cheese.melt(id_vars=['first', 'last'])Out[43]: first last variable value0 John Doe height 5.51 Mary Bo height 6.02 John Doe weight 130.03 Mary Bo weight 150.0In [44]: cheese.melt(id_vars=['first', 'last'], var_name='quantity')Out[44]: first last quantity value0 John Doe height 5.51 Mary Bo height 6.02 John Doe weight 130.03 Mary Bo weight 150.0应用Pivot tables
尽管Pivot能够进行DF的轴转置，Pandas还提供了 pivot_table() 在转置的同时能够进行数值的统计。
pivot_table() 接管上面的参数：
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: 聚合的办法。
先创立一个df：
In [59]: import datetimeIn [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]: dfOut[61]: A B C D E F0 one A foo 0.341734 -0.317441 2013-01-011 one B foo 0.959726 -1.236269 2013-02-012 two C foo -1.110336 0.896171 2013-03-013 three A bar -0.619976 -0.487602 2013-04-014 one B bar 0.149748 -0.082240 2013-05-01.. ... .. ... ... ... ...19 three B foo 0.690579 -2.213588 2013-08-1520 one C foo 0.995761 1.063327 2013-09-1521 one A bar 2.396780 1.266143 2013-10-1522 two B bar 0.014871 0.299368 2013-11-1523 three C bar 3.357427 -0.863838 2013-12-15[24 rows x 6 columns]上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 fooA B one A 1.120915 -0.514058 B -0.338421 0.002759 C -0.538846 0.699535three A -1.181568 NaN B NaN 0.433512 C 0.588783 NaNtwo A NaN 1.000985 B 0.158248 NaN C NaN 0.176180In [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 fooB A 2.241830 -1.028115 -2.363137 NaN NaN 2.001971B -0.676843 0.005518 NaN 0.867024 0.316495 NaNC -1.077692 1.399070 1.177566 NaN NaN 0.352360In [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 fooB A 2.241830 -1.028115 -2.363137 NaN NaN 2.001971 2.786113 -0.043211 1.922577 NaN NaN 0.128491B -0.676843 0.005518 NaN 0.867024 0.316495 NaN 1.368280 -1.103384 NaN -2.128743 -0.194294 NaNC -1.077692 1.399070 1.177566 NaN NaN 0.352360 -1.976883 1.495717 -0.263660 NaN NaN 0.872482增加margins=True会增加一个All列，示意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 AllA 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 1.422136three 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 1.035215two 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 0.650439All 1.556686 0.952552 1.246608 1.250924 0.899904 1.059389应用crosstab
Crosstab 用来统计表格中元素的呈现次数。
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 shinya bar 1 0 0 1foo 2 1 1 0crosstab能够接管两个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]: dfOut[76]: A B C0 1 3 1.01 2 3 1.02 2 4 NaN3 2 4 1.04 2 4 1.0In [77]: pd.crosstab(df['A'], df['B'])Out[77]: B 3 4A 1 1 02 1 3还能够应用normalize来指定比例值：
In [82]: pd.crosstab(df['A'], df['B'], normalize=True)Out[82]: B 3 4A 1 0.2 0.02 0.2 0.6还能够normalize行或者列：
In [83]: pd.crosstab(df['A'], df['B'], normalize='columns')Out[83]: B 3 4A 1 0.5 0.02 0.5 1.0能够指定聚合办法：
In [84]: pd.crosstab(df['A'], df['B'], values=df['C'], aggfunc=np.sum)Out[84]: B 3 4A 1 1.0 NaN2 1.0 2.0get_dummies
get_dummies能够将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)})dfOut[9]: data1 key0 0 b1 1 b2 2 a3 3 c4 4 a5 5 bpd.get_dummies(df['key'])Out[10]: a b c0 0 1 01 0 1 02 1 0 03 0 0 14 1 0 05 0 1 0get_dummies 和 cut 能够进行联合用来统计范畴内的元素：
In [95]: values = np.random.randn(10)In [96]: valuesOut[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 01 0 0 0 0 02 0 0 0 0 03 0 0 0 0 04 1 0 0 0 05 0 0 0 0 06 0 0 0 0 07 1 0 0 0 08 0 0 0 0 09 0 0 1 0 0get_dummies还能够承受一个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_c0 1 1 0 0 11 2 0 1 0 12 3 1 0 1 0本文已收录于 http://www.flydean.com/05-python-pandas-reshaping-pivot/
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