CODEWITHSUNDEEP
pythondataframe
Osora
Osora

Reputation: 585

Select from pandas dataframe using boolean series/array

I have a dataframe:

             High    Low  Close
Date                           
2009-02-11  30.20  29.41  29.87
2009-02-12  30.28  29.32  30.24
2009-02-13  30.45  29.96  30.10
2009-02-17  29.35  28.74  28.90
2009-02-18  29.35  28.56  28.92

and a boolean series:

     bools
1    True
2    False
3    False
4    True
5    False

how could I select from the dataframe using the boolean array to obtain result like:

             High   
Date                           
2009-02-11  30.20  
2009-02-17  29.35

Upvotes: 42

Views: 60526

Answers (2)

Roter Rudi
Roter Rudi

Reputation: 1

just a small variation on the answer above. By applying the index of the dataframe on the series, it can be utilized as df[s]

import pandas as pd

df = pd.DataFrame([
            [30.20,  29.41,  29.87],
            [30.28,  29.32,  30.24],
            [30.45,  29.96,  30.10],
            [29.35,  28.74,  28.90],
            [29.35,  28.56,  28.92],
        ],
        columns=['High','Low','Close'], 
        index=['2009-02-11','2009-02-12','2009-02-13','2009-02-17','2009-02-18']
        )

s = pd.Series([True, False, False, True, False], index=df.index, name='bools')

# df[s.values]
df[s]

Upvotes: 0

mfitzp
mfitzp

Reputation: 15545

For the indexing to work with two DataFrames they have to have comparable indexes. In this case it won't work because one DataFrame has an integer index, while the other has dates.

However, as you say you can filter using a bool array. You can access the array for a Series via .values. This can be then applied as a filter as follows:

df # pandas.DataFrame
s  # pandas.Series 

df[s.values] # df, filtered by the bool array in s

For example, with your data:

import pandas as pd

df = pd.DataFrame([
            [30.20,  29.41,  29.87],
            [30.28,  29.32,  30.24],
            [30.45,  29.96,  30.10],
            [29.35,  28.74,  28.90],
            [29.35,  28.56,  28.92],
        ],
        columns=['High','Low','Close'], 
        index=['2009-02-11','2009-02-12','2009-02-13','2009-02-17','2009-02-18']
        )

s = pd.Series([True, False, False, True, False], name='bools')

df[s.values]

Returns the following:

            High    Low     Close
2009-02-11  30.20   29.41   29.87
2009-02-17  29.35   28.74   28.90

If you just want the High column, you can filter this as normal (before, or after the bool filter):

df['High'][s.values]
# Or: df[s.values]['High']

To get your target output (as a Series):

 2009-02-11    30.20
 2009-02-17    29.35
 Name: High, dtype: float64

Upvotes: 51

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