How to Drop Missing Values in Pandas: Simple Guide

Use the dropna() method on a pandas DataFrame or Series to remove rows or columns containing missing values. You can specify axis=0 to drop rows or axis=1 to drop columns with missing data.

Syntax

The basic syntax to drop missing values in pandas is:

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

Explanation:

• axis=0: Drop rows containing missing values (default).
• axis=1: Drop columns containing missing values.
• how='any': Drop if any missing values are present.
• how='all': Drop only if all values are missing.
• thresh=n: Require at least n non-missing values to keep the row/column.
• subset: Specify columns to check for missing values.
• inplace=False: Return a new DataFrame by default; set to True to modify in place.

python

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

Example

This example shows how to drop rows with any missing values from a DataFrame.

python

import pandas as pd

# Create a sample DataFrame with missing values
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, None, 30, 22],
        'City': ['New York', 'Los Angeles', None, 'Chicago']}

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)

Output

Original DataFrame:
      Name   Age         City
0    Alice  25.0     New York
1      Bob   NaN  Los Angeles
2  Charlie  30.0         None
3    David  22.0      Chicago

DataFrame after dropping rows with missing values:
    Name   Age      City
0  Alice  25.0  New York
3  David  22.0   Chicago

Common Pitfalls

Common mistakes when dropping missing values include:

• Forgetting to assign the result back or use inplace=True, so the original DataFrame remains unchanged.
• Using dropna() without specifying axis when you want to drop columns instead of rows.
• Not using subset when you want to check missing values only in specific columns.

python

import pandas as pd

df = pd.DataFrame({'A': [1, None, 3], 'B': [4, 5, None]})

# Wrong: This does not change df
df.dropna()
print('After dropna without assignment:')
print(df)

# Right: Assign back or use inplace
clean_df = df.dropna()
print('\nAfter dropna with assignment:')
print(clean_df)

Output

After dropna without assignment:
     A    B
0  1.0  4.0
1  NaN  5.0
2  3.0  NaN

After dropna with assignment:
     A    B
0  1.0  4.0

Quick Reference

Parameter	Description	Default
axis	0 to drop rows, 1 to drop columns	0
how	'any' drops if any missing, 'all' if all missing	'any'
thresh	Require minimum non-NA values to keep	None
subset	Columns to check for missing values	None
inplace	Modify original DataFrame if True	False

Key Takeaways

Use df.dropna() to remove rows or columns with missing values in pandas.

Set axis=0 to drop rows, axis=1 to drop columns containing missing data.

Remember to assign the result back or use inplace=True to update the DataFrame.

Use subset parameter to focus on specific columns when dropping missing values.

Use how='all' to drop only if all values are missing in a row or column.