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pandas.cut()The function can distribute the given data into a range, which can also be called bins.

We will use the following DataFrame in this article.

import pandas as pd

df = pd.DataFrame(
    {
        "Name": ["Anish", "Birat", "Chirag", "Kabin", "Sachin"],
        "Age": [23, 34, 38, 45, 27],
        "Score": [316, 322, 332, 330, 325],
    }
)

print(df)

Output:

     Name  Age  Score
0   Anish   23    316
1   Birat   34    322
2  Chirag   38    332
3   Kabin   45    330
4  Sachin   27    325

pandas.cut()Function syntax

pandas.cut(
    x,
    bins,
    right=True,
    labels=None,
    retbins=False,
    precision=3,
    include_lowest=False,
    duplicates="raise",
    ordered=True,
)

parameter

Return Value

It returns an array representing the value xof each element in bin, and if we set it retbins=True, it will also return it bins.

Example: Use pandas.cut()the method to distribute the column values ​​of a DataFrame into bins

import pandas as pd

df = pd.DataFrame(
    {
        "Name": ["Anish", "Birat", "Chirag", "Kabin", "Sachin"],
        "Age": [23, 34, 38, 45, 27],
        "Score": [316, 322, 332, 330, 325],
    }
)

print("Initial DataFrame:")
print(df, "\n")

df["Age-Range"] = pd.cut(x=df["Age"], bins=[20, 30, 40, 50])

print("DataFrame with Age-Range:")
print(df)

Output:

Initial DataFrame:
     Name  Age  Score
0   Anish   23    316
1   Birat   34    322
2  Chirag   38    332
3   Kabin   45    330
4  Sachin   27    325

DataFrame with Age-Range:
     Name  Age  Score Age-Range
0   Anish   23    316  (20, 30]
1   Birat   34    322  (30, 40]
2  Chirag   38    332  (30, 40]
3   Kabin   45    330  (40, 50]
4  Sachin   27    325  (20, 30]

It divides the values ​​of the column dfin the DataFrame into age ranges calculated using the value of the parameter in the method and finally displays the value of for each row in the DataFrame.Agepandas.cut()binsAge-Range

Here, (20,30]it means the values ​​from 20 to 30, excluding 20 and including 30.

Example: Use pandas.cut()the method to distribute values ​​into different regions and assign a label to each region

By default, the label assigned to each bin will be the range of the bin. We can set custom bin labels using the argument pandas.cut()in the bin function .labels

import pandas as pd

df = pd.DataFrame(
    {
        "Name": ["Anish", "Birat", "Chirag", "Kabin", "Sachin"],
        "Age": [23, 34, 38, 45, 27],
        "Score": [316, 322, 332, 330, 325],
    }
)

print("Initial DataFrame:")
print(df, "\n")

bin_labels = labels = ["21 to 30", "31 to 40", "41 to 50"]
df["Age-Range"] = pd.cut(x=df["Age"], bins=[20, 30, 40, 50], labels=bin_labels)

print("DataFrame with Age-Range:")
print(df)

Output:

Initial DataFrame:
     Name  Age  Score
0   Anish   23    316
1   Birat   34    322
2  Chirag   38    332
3   Kabin   45    330
4  Sachin   27    325

DataFrame with Age-Range:
     Name  Age  Score Age-Range
0   Anish   23    316  21 to 30
1   Birat   34    322  31 to 40
2  Chirag   38    332  31 to 40
3   Kabin   45    330  41 to 50
4  Sachin   27    325  21 to 30

It Ageassigns each value of the column to a different bin and adds a label to each bin.

Example: pandas.cut()Set in method retbins=Trueto return bin value

import pandas as pd

df = pd.DataFrame(
    {
        "Name": ["Anish", "Birat", "Chirag", "Kabin", "Sachin"],
        "Age": [23, 34, 38, 45, 27],
        "Score": [316, 322, 332, 330, 325],
    }
)

print("Initial DataFrame:")
print(df, "\n")

bin_labels = labels = ["21 to 30", "31 to 40", "41 to 50"]
df["Age-Range"], bin_values = pd.cut(
    x=df["Age"], bins=[20, 30, 40, 50], labels=bin_labels, retbins=True
)

print("DataFrame with Age-Range:")
print(df, "\n")

print("The bin values are:")
print(bin_values)

Output:

Initial DataFrame:
     Name  Age  Score
0   Anish   23    316
1   Birat   34    322
2  Chirag   38    332
3   Kabin   45    330
4  Sachin   27    325

DataFrame with Age-Range:
     Name  Age  Score Age-Range
0   Anish   23    316  21 to 30
1   Birat   34    322  31 to 40
2  Chirag   38    332  31 to 40
3   Kabin   45    330  41 to 50
4  Sachin   27    325  21 to 30

The bin values are:
[20 30 40 50]

It displays Age-Rangethe DataFrame with values ​​and bin values.