How to Count Occurrences of Specific Value in Pandas Column?

Last Updated : 23 Jul, 2025

Let's learn how to count occurrences of a specific value in columns within a Pandas DataFrame using .value_counts() method and conditional filtering.

Count Occurrences of Specific Values using value_counts()

To count occurrences of values in a Pandas DataFrame, use the value_counts() method. This function helps analyze the frequency of values within a specific column or the entire Pandas DataFrame.

Let's start by setting up a sample DataFrame to demonstrate these methods. The DataFrame contains four columns: 'name', 'subjects', 'marks' and 'age'

Output:

Occurrences of 'sravan': 3
Occurrences of 'ojaswi': 1

Each value_counts() method call specifies the column and value of interest to return the count of occurrences.

Syntax: data['column_name'].value_counts()[value]
where
data: the input DataFrame.
column_name: the target column in the DataFrame.
value: the specific string or integer value to be counted within the column.

We can also count the occurrences of a specific value in a pandas column using following methods:

Table of Content

Using Conditional Filtering with sum()

This method compares values in the column with the specified value and then sums up True values, representing the count.

Output:

Occurrences of 'sravan': 3
Occurrences of 'gnanesh': 1

Using count() after Conditional Filtering

This approach filters the DataFrame for rows matching the condition, then counts the resulting rows.

Output:

Occurrences of 'sravan': 3
Occurrences of 'bobby': 1

Using len() after Conditional Filtering

Similar to the previous method, this one uses len() to get the length of the filtered DataFrame directly.

Output:

Occurrences of 'sravan': 3
Occurrences of 'rohith': 1

Using apply() with a Lambda Function

You can use the apply()function to create a custom function that checks each value. This method is useful if you want additional customization.

Output:

Occurrences of 'sravan': 3
Occurrences of 'ojaswi': 1

Using np.sum() for Conditional Counting

If you have a large DataFrame, using NumPy’s np.sum()can offer a performance boost by operating directly on the boolean mask.

Output:

Occurrences of 'sravan': 3
Occurrences of 'java': 3

Using Grouping and Aggregation

This approach is useful if you need counts for multiple values simultaneously. Group by the column and use .size() to count occurrences.

Output:

Occurrences of 'sravan': 3
Occurrences of 'php': 2

When to use each Pandas Method

The table combined overview of each method for counting occurrences in a Pandas column, along with when to use them and their code syntax:

Method	When to Use	Code Example
`value_counts()`	When you need to count all unique values in the column or just a specific value.	`df['column_name'].value_counts().get('value', 0)`
Conditional Filtering with `sum()`	When you're focused on one value or need clear and simple conditional checks.	`(df['column_name'] == 'value').sum()`
`count()` after Conditional Filtering	When you need to count rows matching a condition and ensure they are non-null.	`df[df['column_name'] == 'value'].count()['column_name']`
`len()` after Conditional Filtering	When you need a simple count of rows matching a condition without concern for null values.	`len(df[df['column_name'] == 'value'])`
`apply()` with Lambda Function	When you have complex row-wise logic or custom conditions.	`df['column_name'].apply(lambda x: x == 'value').sum()`
`np.sum()` for Conditional Counting	When performance is a priority, especially with large datasets.	`np.sum(df['column_name'] == 'value')`
Grouping and Aggregation	When you need counts for multiple categories or groups in the same column.	`df.groupby('column_name').size().get('value', 0)`