Equal Frequency Binning (Part of Data Analytics Course)

import pandas as pd
l = [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]
s = pd.Series(l)
s.nunique()


12


bins = pd.qcut(s, 3) # Equal frequency binning

How do we make data smooth using mean?

df = pd.DataFrame(
    {
        'data': l,
        'bins': bins
    }
)

df



t = df.groupby(['bins']).mean(['data'])

t



map_of_mean_values = {}
for i in t.iterrows():
    
    # type(i[0]): Interval
    # type(i[1]): Series
    
    map_of_mean_values.update({str(i[0]): i[1][0]})

map_of_mean_values

{'(4.999, 14.333]': 9.75, '(14.333, 60.667]': 38.75, '(60.667, 215.0]': 145.75}

df['bins'] = df['bins'].astype(str)

df['smoothed_values'] = df['bins'].apply(lambda x: map_of_mean_values[x])

df