python pandas：pivot_table 用 nans 静默删除索引
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python pandas: pivot_table silently drops indices with nans
提问by mathtick
Is there an option not drop the the indices with 'nan' in them? I think silently dropping these rows from the pivot will at some point cause someone serious pain.
是否有选项不删除其中带有“nan”的索引？我认为默默地从枢轴中删除这些行会在某些时候给某人带来严重的痛苦。
import pandas
import numpy
a = [['a', 'b', 12, 12, 12], ['a', numpy.nan, 12.3, 233., 12], ['b', 'a', 123.23, 123, 1], ['a', 'b', 1, 1, 1.]]
df = pandas.DataFrame(a, columns=['a', 'b', 'c', 'd', 'e'])
df_pivot = df.pivot_table(rows=['a', 'b'], values=['c', 'd', 'e'], aggfunc=sum)
print(df)
print(df_pivot)
Output:
输出：
a b c d e
0 a b 12.00 12 12
1 a NaN 12.30 233 12
2 b a 123.23 123 1
3 a b 1.00 1 1
c d e
a b
a b 13.00 13 13
b a 123.23 123 1
采纳答案by Jeff
This is currently not supported, see this issue for the enhancement: https://github.com/pydata/pandas/issues/3729.
目前不支持此功能，请参阅此问题以了解增强功能：https: //github.com/pydata/pandas/issues/3729。
Workaround to fill the index with a dummy, pivot, and replace
使用虚拟、枢轴和替换填充索引的解决方法
In [28]: df = df.reset_index()
In [29]: df['b'] = df['b'].fillna('dummy')
In [30]: df['dummy'] = np.nan
In [31]: df
Out[31]:
a b c d e dummy
0 a b 12.00 12 12 NaN
1 a dummy 12.30 233 12 NaN
2 b a 123.23 123 1 NaN
3 a b 1.00 1 1 NaN
In [32]: df.pivot_table(rows=['a', 'b'], values=['c', 'd', 'e'], aggfunc=sum)
Out[32]:
c d e
a b
a b 13.00 13 13
dummy 12.30 233 12
b a 123.23 123 1
In [33]: df.pivot_table(rows=['a', 'b'], values=['c', 'd', 'e'], aggfunc=sum).reset_index().replace('dummy',np.nan).set_index(['a','b'])
Out[33]:
c d e
a b
a b 13.00 13 13
NaN 12.30 233 12
b a 123.23 123 1
