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Tutorial Reference

Python Pandas: How to Search for a Value Within a Pandas DataFrame Row in Python

Learn how to filter Pandas DataFrames by value using boolean indexing, multiple conditions (AND/OR/NOT), isin() for lists, string pattern matching with .str, and the query() method for readable filtering.

Searching for specific values within a DataFrame is one of the most fundamental operations in data analysis. Whether you're filtering customer records, finding specific transactions, or isolating outliers, Pandas provides powerful and flexible methods to search rows based on single or multiple conditions.

In this guide, you'll learn how to search for values within DataFrame rows using boolean indexing, multiple conditions, string matching, and other techniques.

Creating a Sample DataFrame

Let's create a DataFrame to use throughout all examples:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 38],
    'City': ['New York', 'Chicago', 'Boston', 'Chicago', 'New York'],
    'Purchased': ['Yes', 'No', 'Yes', 'Yes', 'No'],
    'Amount': [250.0, 0.0, 180.0, 320.0, 0.0]
}

df = pd.DataFrame(data)
print(df)

Output:

      Name  Age      City Purchased  Amount
0    Alice   28  New York       Yes   250.0
1      Bob   35   Chicago        No     0.0
2  Charlie   42    Boston       Yes   180.0
3    Diana   31   Chicago       Yes   320.0
4      Eve   38  New York        No     0.0

Searching with a Single Condition

The most straightforward way to search is using boolean indexing - comparing a column to a value and using the resulting boolean mask to filter rows:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 38],
    'City': ['New York', 'Chicago', 'Boston', 'Chicago', 'New York'],
    'Purchased': ['Yes', 'No', 'Yes', 'Yes', 'No'],
    'Amount': [250.0, 0.0, 180.0, 320.0, 0.0]
}

df = pd.DataFrame(data)

result = df[df['Purchased'] == 'Yes']
print(result)

Output:

      Name  Age      City Purchased  Amount
0    Alice   28  New York       Yes   250.0
2  Charlie   42    Boston       Yes   180.0
3    Diana   31   Chicago       Yes   320.0

How It Works

  1. df['Purchased'] == 'Yes' creates a boolean Series: [True, False, True, True, False].
  2. df[boolean_series] returns only the rows where the value is True.

You can use any comparison operator:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 38],
    'City': ['New York', 'Chicago', 'Boston', 'Chicago', 'New York'],
    'Purchased': ['Yes', 'No', 'Yes', 'Yes', 'No'],
    'Amount': [250.0, 0.0, 180.0, 320.0, 0.0]
}
df = pd.DataFrame(data)

result = df[df['Amount'] > 200]
print(result)

Output:

    Name  Age      City Purchased  Amount
0  Alice   28  New York       Yes   250.0
3  Diana   31   Chicago       Yes   320.0

Searching with Multiple Conditions

Combine conditions using & (AND), | (OR), and ~ (NOT). Each condition must be wrapped in parentheses.

AND Condition (&)

Find rows that satisfy all conditions:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 38],
    'City': ['New York', 'Chicago', 'Boston', 'Chicago', 'New York'],
    'Purchased': ['Yes', 'No', 'Yes', 'Yes', 'No'],
    'Amount': [250.0, 0.0, 180.0, 320.0, 0.0]
}
df = pd.DataFrame(data)

result = df[(df['Age'] >= 30) & (df['Age'] <= 40)]
print(result)

Output:

    Name  Age      City Purchased  Amount
1    Bob   35   Chicago        No     0.0
3  Diana   31   Chicago       Yes   320.0
4    Eve   38  New York        No     0.0

OR Condition (|)

Find rows that satisfy any condition:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 38],
    'City': ['New York', 'Chicago', 'Boston', 'Chicago', 'New York'],
    'Purchased': ['Yes', 'No', 'Yes', 'Yes', 'No'],
    'Amount': [250.0, 0.0, 180.0, 320.0, 0.0]
}
df = pd.DataFrame(data)

result = df[(df['City'] == 'Boston') | (df['Amount'] > 300)]
print(result)

Output:

      Name  Age     City Purchased  Amount
2  Charlie   42   Boston       Yes   180.0
3    Diana   31  Chicago       Yes   320.0

NOT Condition (~)

Find rows that don't match a condition:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 38],
    'City': ['New York', 'Chicago', 'Boston', 'Chicago', 'New York'],
    'Purchased': ['Yes', 'No', 'Yes', 'Yes', 'No'],
    'Amount': [250.0, 0.0, 180.0, 320.0, 0.0]
}
df = pd.DataFrame(data)

result = df[~(df['Purchased'] == 'Yes')]
print(result)

Output:

  Name  Age      City Purchased  Amount
1  Bob   35   Chicago        No     0.0
4  Eve   38  New York        No     0.0

Searching with isin() for Multiple Values

When you want to match against a list of values, use isin():

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 38],
    'City': ['New York', 'Chicago', 'Boston', 'Chicago', 'New York'],
    'Purchased': ['Yes', 'No', 'Yes', 'Yes', 'No'],
    'Amount': [250.0, 0.0, 180.0, 320.0, 0.0]
}
df = pd.DataFrame(data)

cities = ['Chicago', 'Boston']
result = df[df['City'].isin(cities)]
print(result)

Output:

      Name  Age     City Purchased  Amount
1      Bob   35  Chicago        No     0.0
2  Charlie   42   Boston       Yes   180.0
3    Diana   31  Chicago       Yes   320.0

To find rows that are not in the list, negate with ~:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 38],
    'City': ['New York', 'Chicago', 'Boston', 'Chicago', 'New York'],
    'Purchased': ['Yes', 'No', 'Yes', 'Yes', 'No'],
    'Amount': [250.0, 0.0, 180.0, 320.0, 0.0]
}
df = pd.DataFrame(data)
cities = ['Chicago', 'Boston']

result = df[~df['City'].isin(cities)]
print(result)

Output:

    Name  Age      City Purchased  Amount
0  Alice   28  New York       Yes   250.0
4    Eve   38  New York        No     0.0

Searching with String Methods

Pandas provides .str accessor methods for pattern matching on string columns:

Exact Match

result = df[df['Name'] == 'Alice']
print(result)

Output:

    Name  Age      City Purchased  Amount
0  Alice   28  New York       Yes   250.0

Contains a Substring

result = df[df['Name'].str.contains('li', case=False)]
print(result)

Output:

      Name  Age      City Purchased  Amount
0    Alice   28  New York       Yes   250.0
2  Charlie   42    Boston       Yes   180.0

Starts With / Ends With

result = df[df['Name'].str.startswith('D')]
print(result)

result = df[df['City'].str.endswith('go')]
print(result)

Output:

    Name  Age     City Purchased  Amount
3  Diana   31  Chicago       Yes   320.0
    Name  Age     City Purchased  Amount
1    Bob   35  Chicago        No     0.0
3  Diana   31  Chicago       Yes   320.0

Regular Expression Matching

result = df[df['Name'].str.match(r'^[AE]')]
print(result)

Output:

    Name  Age      City Purchased  Amount
0  Alice   28  New York       Yes   250.0
4    Eve   38  New York        No     0.0

Using query() for Readable Filtering

The query() method lets you write filter conditions as strings, which can be more readable for complex queries:

result = df.query('Age > 30 and Purchased == "Yes"')
print(result)

Output:

      Name  Age     City Purchased  Amount
2  Charlie   42   Boston       Yes   180.0
3    Diana   31  Chicago       Yes   320.0

You can also reference variables using @:

min_age = 35
target_city = 'Chicago'

result = df.query('Age >= @min_age and City == @target_city')
print(result)

Output:

  Name  Age     City Purchased  Amount
1  Bob   35  Chicago        No     0.0

Searching Across All Columns

To find rows where any column contains a specific value, use isin() on the entire DataFrame:

import pandas as pd

df = pd.DataFrame({
    'A': ['apple', 'banana', 'cherry'],
    'B': ['dog', 'apple', 'fish'],
    'C': ['red', 'blue', 'apple']
})

result = df[df.isin(['apple']).any(axis=1)]
print(result)

Output:

        A      B      C
0   apple    dog    red
1  banana  apple   blue
2  cherry   fish  apple

Using loc[] for Searching and Selecting Specific Columns

Combine searching with column selection using loc[]:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 38],
    'City': ['New York', 'Chicago', 'Boston', 'Chicago', 'New York'],
    'Purchased': ['Yes', 'No', 'Yes', 'Yes', 'No'],
    'Amount': [250.0, 0.0, 180.0, 320.0, 0.0]
}
df = pd.DataFrame(data)

result = df.loc[df['Purchased'] == 'Yes', ['Name', 'Amount']]
print(result)

Output:

      Name  Amount
0    Alice   250.0
2  Charlie   180.0
3    Diana   320.0

Summary

Method Use Case Example
df[df['col'] == value] Single condition df[df['Age'] == 30]
df[(cond1) & (cond2)] Multiple conditions (AND) df[(df['Age'] > 30) & (df['City'] == 'NY')]
df[(cond1) | (cond2)] Multiple conditions (OR) df[(df['Age'] < 25) | (df['Age'] > 40)]
df[df['col'].isin(list)] Match against a list df[df['City'].isin(['NY', 'LA'])]
df[df['col'].str.contains()] Substring / pattern search df[df['Name'].str.contains('li')]
df.query('expression') Readable string-based filtering df.query('Age > 30')
df.loc[condition, cols] Search + select columns df.loc[df['Age'] > 30, ['Name']]

To search for values within a Pandas DataFrame, use boolean indexing with comparison operators for simple filters, combine conditions with & and | (always in parentheses), use isin() for matching against lists, and leverage .str methods for pattern matching on string columns. For complex queries, query() provides a more readable alternative.

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