機(jī)器學(xué)習(xí)的過程中很多時(shí)候需要用到類似透視表的功能。Pandas提供了pivot和pivot_table實(shí)現(xiàn)透視表功能奏纪。相對(duì)比而言缅糟，pivot_table更加強(qiáng)大堵漱，在實(shí)現(xiàn)透視表的時(shí)候可以進(jìn)行聚類等操作。
pivot_table幫助地址：
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html
官方給的幾個(gè)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
現(xiàn)在的一個(gè)問題是莱找，處理后的dataframe的columns是多層的酬姆，例如最后一個(gè)例子的columns是這個(gè)樣子的:
table.columns：
MultiIndex(levels=[['D', 'E'], ['max', 'mean', 'min']],
labels=[[0, 1, 1, 1], [1, 0, 1, 2]])
為了后續(xù)的運(yùn)算，我們經(jīng)常希望它能簡化奥溺，便于處理辞色。也就是說吧columns拍平。大家可以這么處理：
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')
?
整個(gè)案例效果：
?
