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：
