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How to Easily Search Value in Column in Pandas Dataframe

Rajiv Chandra
Name
Rajiv Chandra

Updated on

Searching for values in a Pandas DataFrame column is a fundamental operation for filtering, cleaning, and analyzing data. Pandas offers many powerful ways to search—including boolean indexing, isin(), query(), and string operations—that make this task fast and intuitive.

This updated guide covers the most useful and modern techniques for searching values in a DataFrame, along with examples you can directly apply in real workflows.

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Pandas DataFrame Basics

A Pandas DataFrame is a two-dimensional tabular structure with labeled rows and columns. Here's a simple example:

import pandas as pd
 
data = {
    'Name': ['John', 'Emma', 'Peter', 'David', 'Sophie'],
    'Age': [27, 21, 24, 30, 29],
    'Gender': ['Male', 'Female', 'Male', 'Male', 'Female'],
    'City': ['New York', 'London', 'Paris', 'Tokyo', 'Rio de Janeiro']
}
 
df = pd.DataFrame(data)
print(df)

Output:

     Name  Age  Gender             City
0    John   27    Male         New York
1    Emma   21  Female           London
2   Peter   24    Male            Paris
3   David   30    Male            Tokyo
4  Sophie   29  Female  Rio de Janeiro

Searching for Values in a DataFrame Column

1. Search for an Exact Match (Boolean Indexing)

result = df[df['Age'] == 27]
print(result)

Output:

   Name  Age Gender      City
0  John   27   Male  New York

You can use any comparison operator:

OperatorMeaning
==equal
!=not equal
> / <greater / less
>= / <=greater or equal / less or equal

Example: find rows where Age ≥ 25

df[df['Age'] >= 25]

2. Search for Multiple Values Using isin()

cities = ['Paris', 'Tokyo']
df[df['City'].isin(cities)]

Output:

    Name  Age Gender   City
2  Peter   24   Male  Paris
3  David   30   Male  Tokyo

Use ~df['col'].isin() to exclude values.

3. Search Using query() (Readable & Fast)

query() lets you filter rows using an SQL-like syntax—great for readability.

df.query("Age == 27")

Or multiple conditions:

df.query("Age > 25 and Gender == 'Female'")

This often results in cleaner code than nested Boolean indexing.

4. Search for String Patterns (str.contains())

Useful for filtering text columns.

Contains substring

df[df['City'].str.contains('on', case=False, na=False)]

Starts or ends with

df[df['Name'].str.startswith('J')]
df[df['City'].str.endswith('o')]

5. Search for Missing/Non-Missing Values

df[df['City'].isna()]      # missing
df[df['City'].notna()]     # non-missing

6. Search Across Multiple Columns

Find rows where any column matches a value:

df[df.eq('Male').any(axis=1)]

Find rows where all conditions match:

df[(df['Gender'] == 'Female') & (df['Age'] > 25)]

Performance Tips (Realistic & Accurate)

Some performance tips were previously misunderstood in older tutorials. Here is the accurate guidance:

✔ 1. Convert columns to category if they have repeated values

This speeds up comparisons:

df['City'] = df['City'].astype('category')

✔ 2. Use NumPy arrays for very large datasets

import numpy as np
 
ages = df['Age'].to_numpy()
df[ages == 27]

✔ 3. Avoid apply() for searching

Vectorized operations (boolean indexing, isin(), query()) are always faster.

❌ Removed: “.loc[] is faster than boolean indexing”

This is incorrect—they behave the same under the hood. .loc[] is for label-based selection, not a speed enhancement.

⚠ About searchsorted()

searchsorted() only works properly if the column is sorted and does not confirm that the value exists. For example:

df_sorted = df.sort_values('Age')
idx = df_sorted['Age'].searchsorted(27)

This finds the insertion position—not necessarily the row with Age = 27.

Use it only for advanced workflows.

Conclusion

Searching for values in a Pandas column is essential for data exploration and cleaning. Pandas provides many efficient ways to search:

  • Boolean indexing for exact matches
  • isin() for multiple values
  • query() for clean, SQL-like filtering
  • String searching with str.contains()
  • Missing value filtering
  • Multi-column filtering

These methods help you extract the exact data you need quickly, accurately, and cleanly.

Links

Frequently Asked Questions

  1. How do I search for a specific value in a DataFrame column? Use boolean indexing:

    df[df['Age'] == 27]

  2. How do I retrieve a specific value from a column? Use row index + column name:

    df['Age'][0]

  3. How do I get a single value quickly? Use .at or .iat:

    df.at[0, 'Age']
df.iat[0, 1]

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