Pandas DataFrame.dropna() Method (original) (raw)

Last Updated : 25 Jun, 2025

**DataFrame.dropna() function remove missing values (NaN or None) from a DataFrame. It can drop entire rows or columns depending on the axis and threshold you specify. This method is commonly used during data cleaning to eliminate incomplete data before analysis.

**For Example:

Python `

import pandas as pd import numpy as np df = pd.DataFrame({'A': [1, np.nan, 3], 'B': [4, 5, None]}) print(df.dropna())

**Explanation: By default, **dropna() removes rows with any missing values. Row 0 has no missing data, so it's kept. Rows 1 and 2 contain NaN or None, so they're dropped. Only row 0 remains.

Syntax

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

**Parameters:

Parameter	Description
axis	0 to drop rows (default), 1 to drop columns
how	'any' (default): drop if any value is missing and 'all': drop if all are missing
thresh	Minimum number of non-NA values required to keep the row/column
subset	Labels to consider for NA checks (subset of columns)
inplace	If True, modifies the original DataFrame; if False (default), returns a new one

**Returns: A new DataFrame with the specified rows or columns removed unless **inplace=True.

Examples

**Example 1: We drop rows only if all values are missing.

Python `

import pandas as pd import numpy as np

df = pd.DataFrame({'A': [np.nan, np.nan, 3], 'B': [None, np.nan, 4]}) print(df.dropna(how='all'))

**Explanation: Only the first two rows contain all missing values. The third row is kept because it has valid values.

**Example 2: We drop columns that contain any missing values by setting **axis=1.

Python `

import pandas as pd import numpy as np df = pd.DataFrame({'A': [1, 2, np.nan], 'B': [4, None, 6]}) print(df.dropna(axis=1))

Output

Empty DataFrame Columns: [] Index: [0, 1, 2]

**Explanation: Since both columns '**A' and '**B' have at least one missing value (NaN or None), using dropna(axis=1) drops them. This leaves an empty DataFrame with only row indices and no columns.

**Example 3: We use thresh to keep rows that have at least 2 non-null values.

Python `

import pandas as pd import numpy as np

df = pd.DataFrame({'A': [1, np.nan, 3], 'B': [None, 5, None]}) print(df.dropna(thresh=2))

Output

Empty DataFrame Columns: [A, B] Index: []

**Explanation: thresh=2 keep rows that have at least 2 non-null values. Each row in the DataFrame has only 1 non-null value, so all rows are dropped.

**Example 4: In this example, we drop rows that have missing values only in a specific column ('A') using subset.

Python `

import pandas as pd import numpy as np

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

Output

 A    B

0 1.0 4.0 2 3.0 NaN

**Explanation: Only rows where column 'A' is NaN are dropped. Other missing values are ignored.

**Example 5: In this example, we use **inplace=True to modify the DataFrame directly.

Python `

import pandas as pd import numpy as np

df = pd.DataFrame({'X': [1, np.nan, 3], 'Y': [np.nan, 5, 6]}) df.dropna(inplace=True) print(df)

**Explanation: Only the last row has no missing values. **inplace=True updates **df directly without returning a new object.