Pandas: Select Rows between two values in DataFrame

# Table of Contents

# Pandas: Select Rows between two values in DataFrame

To select rows between two values in a Pandas DataFrame:

Select the specific column using bracket or dot notation.
Use the DataFrame.between() method to select the rows between the two values.
THe expression will return a new DataFrame containing only the matching rows.

main.py

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import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 5, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

print(df)

print('-' * 50)

output = df[df['salary'].between(180, 200)]

print(output)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
--------------------------------------------------
    name  experience  salary
1  Bobby           3   180.2
2   Carl           5   190.3

select rows between two values in dataframe

We used bracket notation to select the salary column and called the between() method on the resulting DataFrame.

main.py

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output = df[df['salary'].between(180, 200)]

The between() method takes a left and a right boundary as parameters and returns a series representing whether each element is between left and right (inclusive).

main.py

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import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 5, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

# 0    False
# 1     True
# 2     True
# 3    False
# Name: salary, dtype: bool
print(df['salary'].between(180, 200))

The code for this article is available on GitHub

The last step is to filter the original DataFrame using the Series.

By default, the between() method is inclusive for both the left and right boundaries.

You can pass an "inclusive" keyword argument to between() if you need to adjust the behavior.

The available values for the inclusive argument are "both", "neither", "left" and "right".

# Pandas: Select Rows that are NOT between two values in DataFrame

If you need to select the rows that are NOT between two values in a DataFrame, use the tilde ~ operator.

main.py

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import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 5, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

print(df)

print('-' * 50)

output = df[~df['salary'].between(180, 200)]

print(output)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
--------------------------------------------------
    name  experience  salary
0  Alice           1   175.1
3    Dan           7   205.4

select rows that are not between two values in dataframe

We used the tilde ~ operator to invert the call to the between() method.

main.py

Copied!
import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 5, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

# 0     True
# 1    False
# 2    False
# 3     True
# Name: salary, dtype: bool
print(~df['salary'].between(180, 200))

The code for this article is available on GitHub

The last step is to filter the original DataFrame with the boolean Series.

main.py

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output = df[~df['salary'].between(180, 200)]

# Pandas: Select Rows between two values in DataFrame using boolean AND operator

You can also use the boolean AND & operator to select the rows between two values in a DataFrame.

main.py

Copied!
import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 5, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

print(df)

print('-' * 50)

output = df[(df['salary'] >= 180) & (df['salary'] <= 200)]

print(output)

The code for this article is available on GitHub

The code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
--------------------------------------------------
    name  experience  salary
1  Bobby           3   180.2
2   Carl           5   190.3

select rows between two values using boolean and operator

Notice that we wrapped each condition in parentheses ().

main.py

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output = df[(df['salary'] >= 180) & (df['salary'] <= 200)]

This makes the code quite easy to read and makes the precedence of operations obvious.

We first check if the salary value for a specific row is greater than or equal to 180.

Then we check if the same salary value is less than or equal to 200.

If both conditions are met, the expression returns True and the corresponding row gets included in the results.

You can use the boolean AND & operator to chain as many conditions as necessary.

# Pandas: Select Rows between two values in DataFrame using `query()`

You can also use the DataFrame.query() method to select the rows between two values in a DataFrame.

main.py

Copied!
import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 5, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

print(df)

print('-' * 50)

output = df.query('180 <= salary <= 200')

print(output)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
--------------------------------------------------
    name  experience  salary
1  Bobby           3   180.2
2   Carl           5   190.3

select rows between two values using query

The DataFrame.query() method enables you to query the columns of a DataFrame with a boolean expression.

Want to learn more about selecting rows in a Pandas DataFrame? Check out these resources: Get the first Row of each Group in a Pandas DataFrame,Calculate the Average for each Row in a Pandas DataFrame.

# Additional Resources

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Pandas: Select Rows between two values in DataFrame

# Table of Contents

# Pandas: Select Rows between two values in DataFrame

# Pandas: Select Rows that are NOT between two values in DataFrame

# Pandas: Select Rows between two values in DataFrame using boolean AND operator

# Pandas: Select Rows between two values in DataFrame using query()

# Additional Resources

Borislav Hadzhiev

# Pandas: Select Rows between two values in DataFrame using `query()`