Python資料處理小技巧：pivot_table後如何拍平columns

機器學習的過程中很多時候需要用到類似透視表的功能。Pandas提供了pivot和pivot_table實作透視表功能。相對比而言，pivot_table更加強大，在實作透視表的時候可以進行聚類等操作。

pivot_table幫助位址：

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html 官方給的幾個例子：

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.

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

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})

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.

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

D E

mean max mean min

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

現在的一個問題是，處理後的dataframe的columns是多層的，例如最後一個例子的columns是這個樣子的:

table.columns：

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

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

為了後續的運算，我們經常希望它能簡化，便于處理。也就是說吧columns拍平。大家可以這麼處理：

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')

效果如圖：

​