Python数据处理小技巧：pivot_table后如何拍平columns

机器学习的过程中很多时候需要用到类似透视表的功能。Pandas提供了pivot和pivot_table实现透视表功能。相对比而言，pivot_table更加强大，在实现透视表的时候可以进行聚类等操作。

pivot_table帮助地址：

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html

官方给的几个例子：

df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",

... "bar", "bar", "bar", "bar"],

... "B": ["one", "one", "one", "two", "two",

... "one", "one", "two", "two"],

... "C": ["small", "large", "large", "small",

... "small", "large", "small", "small",

... "large"],

... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],

... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})

df

A B C D E

0 foo one small 1 2

1 foo one large 2 4

2 foo one large 2 5

3 foo two small 3 5

4 foo two small 3 6

5 bar one large 4 6

6 bar one small 5 8

7 bar two small 6 9

8 bar two large 7 9

This first example aggregates values by taking the sum.

table = pd.pivot_table(df, values='D', index=['A', 'B'],

... columns=['C'], aggfunc=np.sum)

table

C large small

A B

bar one 4.0 5.0

two 7.0 6.0

foo one 4.0 1.0

two NaN 6.0

We can also fill missing values using the fill_value parameter.

table = pd.pivot_table(df, values='D', index=['A', 'B'],

... columns=['C'], aggfunc=np.sum, fill_value=0)

table

C large small

A B

bar one 4 5

two 7 6

foo one 4 1

two 0 6

The next example aggregates by taking the mean across multiple columns.

table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],

... aggfunc={'D': np.mean,

... 'E': np.mean})

table

D E

A C

bar large 5.500000 7.500000

small 5.500000 8.500000

foo large 2.000000 4.500000

small 2.333333 4.333333

We can also calculate multiple types of aggregations for any given value column.

table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],

... aggfunc={'D': np.mean,

... 'E': [min, max, np.mean]})

table

D E

mean max mean min

A C

bar large 5.500000 9.0 7.500000 6.0

small 5.500000 9.0 8.500000 8.0

foo large 2.000000 5.0 4.500000 4.0

small 2.333333 6.0 4.333333 2.0

现在的一个问题是，处理后的dataframe的columns是多层的，例如最后一个例子的columns是这个样子的:

table.columns：

MultiIndex(levels=[['D', 'E'], ['max', 'mean', 'min']],

labels=[[0, 1, 1, 1], [1, 0, 1, 2]])

为了后续的运算，我们经常希望它能简化，便于处理。也就是说吧columns拍平。大家可以这么处理：

table.columns =[s1 +'_'+ str(s2) for (s1,s2) in table.columns.tolist()]

table.reset_index(inplace=True)

效果如下：

table.columns

Index(['A', 'C', 'D_mean', 'E_max', 'E_mean', 'E_min'], dtype='object')

效果如图：
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