CODEWITHSUNDEEP
pandasdataframegroup-byrolling-computation
bachts
bachts

Reputation: 85

Pandas rolling function on categorical variables

I have a pandas dataframe like this

group cat
1     0
2     0
1     0
1     1
2     0
2     1 
1     2
1     2

I'm trying to group the data by group, then applies a custom function to the past 5 rows. The custom function looks like this

def unalikeability(data):

    num_observations = data.shape[0]
    counts = data.value_counts()

    return 1 - ((counts / num_observations)**2).sum()

Desired output:

group unalikeability
1     result calculated by the function
1
1
1
2
2
2
2

I can get the past 5 rows using groupby().rolling(), but the rolling object in pandas doesn't have the shape/ value_counts attribute and method like a DataFrame. I tried creating a DataFrame from the rolling object, but this isn't allowed either.

Upvotes: 0

Views: 124

Answers (2)

Soudipta Dutta
Soudipta Dutta

Reputation: 2152

import pandas as pd
import numpy as np


def unalikeability(data):
    num_observations = data.shape[0]
    counts = data.value_counts()
    return 1 - ((counts / num_observations) ** 2).sum()


df = pd.DataFrame({'group': [1, 2, 1, 1, 2, 2, 1, 1], 'cat': [0, 0, 0, 1, 0, 1, 2, 2]})
print(df)
window_size = 5
def g(df):
    # Reverse and reset index
    shifted_data = df.cat.iloc[::-1].reset_index(drop=True)
    df['unalikeability']  = [unalikeability(shifted_data.iloc[max(0, i- window_size +1) : i +1])
                            for i in range(len(df))]
    return df 
    
    
    
out = df.groupby('group').apply(g).reset_index(drop=True)
print(out)    
"""
  group  cat  unalikeability
0      1    0        0.000000
1      1    0        0.000000
2      1    1        0.444444
3      1    2        0.625000
4      1    2        0.640000
5      2    0        0.000000
6      2    0        0.500000
7      2    1        0.444444
"""

Upvotes: 0

mozway
mozway

Reputation: 262224

You can apply your function. Depending on whether you want the output to be computed only on full chunks (5 values), or chunks of any size, use min_periods:

def unalikeability(data):

    num_observations = data.shape[0]
    counts = data.value_counts()

    return 1 - ((counts / num_observations)**2).sum()

# compute the score only if we have 5 rows
df['out1'] = (df.groupby('group')
                .rolling(5)['cat']
                .apply(unalikeability)
                .droplevel('group')
              )

# compute the score with incomplete chunks
df['out2'] = (df.groupby('group')
                .rolling(5, min_periods=1)['cat']
                .apply(unalikeability)
                .droplevel('group')
              )

Output:

   group  cat  out1      out2
0      1    0   NaN  0.000000
1      2    0   NaN  0.000000
2      1    0   NaN  0.000000
3      1    1   NaN  0.444444
4      2    0   NaN  0.000000
5      2    1   NaN  0.444444
6      1    2   NaN  0.625000
7      1    2  0.64  0.640000

Upvotes: 0

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