[英]Python/Pandas convert pivot table into percentages based on row total
I think this should be simple, but in Python I just can't figure out how to take a pivot table and get percentages based on row totals.我认为这应该很简单,但在 Python 中,我只是想不通如何使用 pivot 表并根据行总数获取百分比。
Pivot Table: Pivot表:
df.pivot_table(index='Name', columns='scenario',
values='y', aggfunc=np.count_nonzero, margins=True, margins_name='Total').fillna(0)
Which gets:哪个得到:
But what I want is each cell divided by row totals:但我想要的是每个单元格除以行总数:
How can I do this?我怎样才能做到这一点? Thank you in advance.
先感谢您。
.pipe
method combined with .div
to perform this column-wise division on all of the columns..pipe
方法和.div
对所有列执行按列划分。.applymap
to apply a string fomratting to each of your values to get the values to appear as percentages (note that they become strings and are no longer mathematically useful).applymap
将字符串格式应用于每个值,以使值显示为百分比(请注意,它们变成字符串并且在数学上不再有用)out = (
df.pivot_table(
index='Name', columns='scenario', values='y',
aggfunc=np.count_nonzero, margins=True,
margins_name='Total', fill_value=0
)
.pipe(lambda d: d.div(d['Total'], axis='index'))
.applymap('{:.0%}'.format)
)
example例子
df = pd.DataFrame({
'a': [1, 0, 0, 1, 5],
'b': [20, 20, 10, 50, 15],
'c': [50, 20, 50, 100, 20]
})
print(df)
a b c
0 1 20 50
1 0 20 20
2 0 10 50
3 1 50 100
4 5 15 20
out = (
df.pipe(lambda d: d.div(d['c'], axis='index'))
.applymap('{:.0%}'.format)
)
print(out)
a b c
0 2% 40% 100%
1 0% 100% 100%
2 0% 20% 100%
3 1% 50% 100%
4 25% 75% 100%
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