CODEWITHSUNDEEP
pythonpandasdataframemulti-index
danielhadar
danielhadar

Reputation: 2161

Slicing a MultiIndex DataFrame by multiple values from a specified level

I want to slice a MultiIndex DataFrame by multiple values from a secondary level. For example, in the following DataFrame:

                val1  val2
ind1 ind2 ind3            
1    6    s1      10     8
2    7    s1      20     6
3    8    s2      30     4
4    9    s2      50     2
5    10   s3      60     0

I wish to slice only the rows in which ind3 == s1 or ind3 == s3:

           val1  val2
ind1 ind2            
1    6       10     8
2    7       20     6
5    10      60     0

Best hypothetical option would be to pass multiple arguments to .xs, since it is possible to explicitly state the desired level.

I could obviously concat all the sliced-by-single-value DataFrames:

In[2]: pd.concat([df.xs('s1',level=2), df.xs('s3',level=2)])
Out[2]:
           val1  val2
ind1 ind2            
1    6       10     8
2    7       20     6
5    10      60     0

But (a) it's tedious and not so readable when using more than 2 values, and (b) for large DataFrames it's quite heavy (or at least heavier than a multi-value slicing option, if that exists).

Here's the code to build the example DataFrame:

import pandas as pd
df = pd.DataFrame({'ind1':[1,2,3,4,5], 'ind2':[6,7,8,9,10], 'ind3':['s1','s1','s2','s2','s3'], 'val1':[10,20,30,50,60], 'val2':[8,6,4,2,0]}).set_index(['ind1','ind2','ind3'])

Upvotes: 15

Views: 7086

Answers (2)

Alex
Alex

Reputation: 19104

As with most selection from a DataFrame, you can use a mask or an indexer (loc in this case).

To get the mask, you can use get_level_values (docs) on the MultiIndex followed by isin (docs).

m = df.index.get_level_values('ind3').isin(['s1', 's3'])
df[m].reset_index(level=2, drop=True)

To use loc:

df.loc[(slice(None), slice(None), ['s1', 's3']), :].reset_index(level=2, drop=True)

both output

           val1  val2
ind1 ind2            
1    6       10     8
2    7       20     6
5    10      60     0

Note: the loc way can also be written as seen in Alberto Garcia-Raboso's answer. Many people prefer that syntax as it is more consistent with loc syntax for an Index. Both syntax styles are discussed in the docs.

Upvotes: 14

Alicia Garcia-Raboso
Alicia Garcia-Raboso

Reputation: 13913

You can use an IndexSlice:

idx = pd.IndexSlice
result = df.loc[idx[:, :, ['s1', 's3']], idx[:]]
result.index = result.index.droplevel('ind3')
print(result)

Output:

           val1  val2
ind1 ind2            
1    6       10     8
2    7       20     6
5    10      60     0

The second line above can also be written as

result = df.loc(axis=0)[idx[:, :, ['s1', 's3']]]

Upvotes: 11

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