Dropping missing values with dropna() in Pandas

Introduction

Sometimes data has empty spots called missing values. We use dropna() to remove those spots so our data is clean and ready to use.

When you have a table with empty cells and want to remove rows or columns that have them.

When preparing data for analysis or machine learning and missing values can cause errors.

When you want to focus only on complete data without any gaps.

When cleaning survey results where some answers are missing.

When you want to quickly remove incomplete records from your dataset.

Syntax

Pandas

DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

axis=0 means drop rows; axis=1 means drop columns.

how='any' drops if any missing value is found; how='all' drops only if all values are missing.

Examples

Drop all rows that have any missing value.

Pandas

df.dropna()

Drop all columns that have any missing value.

Pandas

df.dropna(axis=1)

Drop rows only if all values in the row are missing.

Pandas

df.dropna(how='all')

Drop rows where Age or Salary columns have missing values.

Pandas

df.dropna(subset=['Age', 'Salary'])

Sample Program

This code creates a table with some missing ages and salaries. Then it removes any row that has a missing value and shows the cleaned table.

Pandas

import pandas as pd

# Create a sample DataFrame with missing values
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [25, None, 30, None],
    'Salary': [50000, 60000, None, 45000]
}
df = pd.DataFrame(data)

print('Original DataFrame:')
print(df)

# Drop rows with any missing values
clean_df = df.dropna()

print('\nDataFrame after dropping rows with missing values:')
print(clean_df)

Important Notes

Using inplace=True changes the original DataFrame without needing to assign it again.

Be careful: dropping too many rows can reduce your data size a lot.

You can choose to drop columns instead of rows by setting axis=1.

Summary

dropna() removes rows or columns with missing values.

You can control whether to drop rows or columns using axis.

Use how to decide if any or all missing values trigger dropping.