[英]Conditional Slicing from Columns in Pandas MultiIndex
I am trying to conditionally slice data from a multiindex based on column names as opposed to index.我正在尝试根据列名而不是索引有条件地从多索引中切片数据。 For example, I have the following MultiIndex Data frame:
例如,我有以下 MultiIndex 数据框:
203 204 205
TIME VALUE TIME VALUE TIME VALUE
0 1 bar 1.0 LH2 10.0 dog
1 2 baz 2.0 LOX 11.0 cat
2 3 foo 3.0 CH4 12.0 pig
3 4 qux NaN NaN 13.0 rat
4 5 qaz NaN NaN NaN NaN
5 6 qoo NaN NaN NaN NaN
(I essentially have measurement data (203, 204, etc) with a time and value, recorded using different sample rates. Thus, the number of rows will always be different. I am putting all data into a single MultiIndex since it can contain a varying number of rows.) (我基本上有带有时间和值的测量数据(203、204 等),使用不同的采样率记录。因此,行数总是不同的。我将所有数据放入一个 MultiIndex,因为它可以包含一个不同的行数。)
I want to select all data if TIME is > 3. The expected output would be the following:如果 TIME > 3,我想选择所有数据。预期的输出如下:
203 204 205
TIME VALUE TIME VALUE TIME VALUE
0 4 qux NaN NaN 10.0 dog
1 5 qaz NaN NaN 11.0 cat
2 6 qoo NaN NaN 12.0 pig
3 NaN NaN NaN NaN 13.0 rat
4 NaN NaN NaN NaN NaN NaN
5 NaN NaN NaN NaN NaN NaN
I tried using the query method but that only works on an index, not a column name.我尝试使用查询方法,但这只适用于索引,而不适用于列名。 I do not want to transpose the dataframe to use query.
我不想转置数据框以使用查询。 I also tried using loc but never seemed to find a way to get what I am looking for.
我也尝试使用 loc 但似乎从未找到一种方法来获得我正在寻找的东西。 I even looked into using xs but I don't think I can add conditional slicing with it.
我什至考虑使用 xs 但我认为我不能用它添加条件切片。
I found this on SO but it doesn't include conditional slicing: Selecting columns from pandas MultiIndex我在 SO 上找到了这个,但它不包括条件切片: 从熊猫 MultiIndex 中选择列
Here is the code that I have been using to test this:这是我用来测试的代码:
import pandas as pd
import numpy as np
d1 = {'TIME': [1,2,3,4,5,6], 'VALUE': ['bar', 'baz', 'foo', 'qux', 'qaz', 'qoo']}
df1 = pd.DataFrame(data=d1)
d2 = {'TIME': [1,2,3], 'VALUE': ['LH2', 'LOX', 'CH4']}
df2 = pd.DataFrame(data=d2)
d3 = {'TIME': [10,11,12,13], 'VALUE': ['dog', 'cat', 'pig', 'rat']}
df3 = pd.DataFrame(data=d3)
df_list = [df1, df2, df3]
pids = [203, 204, 205]
df_multi = pd.concat(df_list, axis=1, keys=list(zip(pids)))
print(df_multi)
# Slice all time columns
ALL = slice(None)
df_multi_2 = df_multi.loc[ALL, (ALL, 'TIME')]
print(df_multi_2)
# Condition based slicing - does not work
ALL = slice(None)
df_multi_3 = df_multi.loc[ALL, df_multi.loc[ALL,(ALL,'TIME')] > 3]
print(df_multi_3)
Let's try IndexSlice
to slice the data:让我们尝试使用
IndexSlice
对数据进行切片:
from pandas import IndexSlice
mask = (df_multi.loc[:, IndexSlice[:,"TIME"]].gt(3)
.reindex(df_multi.columns, axis=1)
.groupby(level=0, axis=1)
.transform('any')
)
df_multi.where(mask)
Output:输出:
203 204 205
TIME VALUE TIME VALUE TIME VALUE
0 NaN NaN NaN NaN 10.0 dog
1 NaN NaN NaN NaN 11.0 cat
2 NaN NaN NaN NaN 12.0 pig
3 4.0 qux NaN NaN 13.0 rat
4 5.0 qaz NaN NaN NaN NaN
5 6.0 qoo NaN NaN NaN NaN
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