# Binning a column with pandas

## Question:

I have a data frame column with numeric values:

46.5
44.2
100.0
42.12

I want to see the column as bin counts:

bins = [0, 1, 5, 10, 25, 50, 100]

How can I get the result as bins with their value counts?

[0, 1] bin amount
[1, 5] etc
[5, 10] etc
...

You can use pandas.cut:

bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = pd.cut(df['percentage'], bins)
print (df)
percentage     binned
0       46.50   (25, 50]
1       44.20   (25, 50]
2      100.00  (50, 100]
3       42.12   (25, 50]

bins = [0, 1, 5, 10, 25, 50, 100]
labels = [1,2,3,4,5,6]
df['binned'] = pd.cut(df['percentage'], bins=bins, labels=labels)
print (df)
percentage binned
0       46.50      5
1       44.20      5
2      100.00      6
3       42.12      5
bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = np.searchsorted(bins, df['percentage'].values)
print (df)
percentage  binned
0       46.50       5
1       44.20       5
2      100.00       6
3       42.12       5

…and then value_counts or groupby and aggregate size:

s = pd.cut(df['percentage'], bins=bins).value_counts()
print (s)
(25, 50]     3
(50, 100]    1
(10, 25]     0
(5, 10]      0
(1, 5]       0
(0, 1]       0
Name: percentage, dtype: int64

s = df.groupby(pd.cut(df['percentage'], bins=bins)).size()
print (s)
percentage
(0, 1]       0
(1, 5]       0
(5, 10]      0
(10, 25]     0
(25, 50]     3
(50, 100]    1
dtype: int64

By default cut returns categorical.

Series methods like Series.value_counts() will use all categories, even if some categories are not present in the data, operations in categorical.

## Using the Numba module for speed up.

On big datasets (more than 500k), pd.cut can be quite slow for binning data.

I wrote my own function in Numba with just-in-time compilation, which is roughly six times faster:

from numba import njit

@njit
def cut(arr):
bins = np.empty(arr.shape[0])
for idx, x in enumerate(arr):
if (x >= 0) & (x < 1):
bins[idx] = 1
elif (x >= 1) & (x < 5):
bins[idx] = 2
elif (x >= 5) & (x < 10):
bins[idx] = 3
elif (x >= 10) & (x < 25):
bins[idx] = 4
elif (x >= 25) & (x < 50):
bins[idx] = 5
elif (x >= 50) & (x < 100):
bins[idx] = 6
else:
bins[idx] = 7

return bins
cut(df['percentage'].to_numpy())

# array([5., 5., 7., 5.])

Optional: you can also map it to bins as strings:

a = cut(df['percentage'].to_numpy())

conversion_dict = {1: 'bin1',
2: 'bin2',
3: 'bin3',
4: 'bin4',
5: 'bin5',
6: 'bin6',
7: 'bin7'}

bins = list(map(conversion_dict.get, a))

# ['bin5', 'bin5', 'bin7', 'bin5']

Speed comparison:

# Create a dataframe of 8 million rows for testing
dfbig = pd.concat([df]*2000000, ignore_index=True)

dfbig.shape

# (8000000, 1)
%%timeit
cut(dfbig['percentage'].to_numpy())

# 38 ms ± 616 µs per loop (mean ± standard deviation of 7 runs, 10 loops each)
%%timeit
bins = [0, 1, 5, 10, 25, 50, 100]
labels = [1,2,3,4,5,6]
pd.cut(dfbig['percentage'], bins=bins, labels=labels)

# 215 ms ± 9.76 ms per loop (mean ± standard deviation of 7 runs, 10 loops each)

We could also use np.select:

bins = [0, 1, 5, 10, 25, 50, 100]
df['groups'] = (np.select([df['percentage'].between(i, j, inclusive='right')
for i,j in zip(bins, bins[1:])],
[1, 2, 3, 4, 5, 6]))

Output:

percentage  groups
0       46.50       5
1       44.20       5
2      100.00       6
3       42.12       5

# Convenient and fast option using Numpy

np.digitize is a convenient and fast option:

import pandas as pd
import numpy as np

df = pd.DataFrame({'x': [1,2,3,4,5]})
df['y'] = np.digitize(df['x'], bins=[3,5]) # convert column to bin

print(df)

returns

x  y
0  1  0
1  2  0
2  3  1
3  4  1
4  5  2