CODEWITHSUNDEEP
pandasdataframe
maninekkalapudi
maninekkalapudi

Reputation: 1118

Assign bucket ranges in power of 2 in a separate column in pandas

I have a column of values like below:

col
12
76
34

for which I need to generate a new column with the bucket labels for col1 as mentioned below:

col1     bucket-labels
12            8-16
76            64-128 
34            32-64

Here the values in the column might vary and the number of results also.

Edit: The intervals of the bucket label should be in the range of 2^n

Upvotes: 4

Views: 566

Answers (2)

Space Impact
Space Impact

Reputation: 13255

Using pd.cut with 2 power bins:

bins = [2**i for i in range(0,int(np.log2(df.col.max()))+2)]
#alternative [2**i for i in range(0,np.ceil(np.log2(df.col.max()))+1)]
bin_labels = [f'{x}-{y}' for x, y in zip(bins[:-1], bins[1:])]
df['bucket-labels'] = pd.cut(df.col, bins=bins, labels=bin_labels)

print(df)
   col bucket-labels
0   12          8-16
1   76        64-128
2   34         32-64

Upvotes: 2

jezrael
jezrael

Reputation: 863166

First get maximal value of power 2 by one of solution from here, create bins by list comprehension, labels by zip and pass it to cut function:

import math
a = df['col'].max()
bins = [1<<exponent for exponent in range(math.ceil(math.log(a, 2))+1)]
#another solution
#bins = [1<<exponent for exponent in range((int(a)-1).bit_length() + 1)]
print (bins)
[1, 2, 4, 8, 16, 32, 64, 128]

labels = ['{}-{}'.format(i, j) for i, j in zip(bins[:-1], bins[1:])] 

df['bucket-labels'] = pd.cut(df['col'], bins=bins, labels=labels)
print (df)
   col bucket-labels
0   12          8-16
1   34         32-64
2   76        64-128

Upvotes: 7

Related Questions