How to drop nan rows in pandas

How to drop nan rows in pandas

You can drop rows with NaN (Not a Number) values in a pandas DataFrame using the dropna() function. This function removes rows containing NaN values based on specified criteria. Here's how you can do it:

import pandas as pd # Create a sample DataFrame with NaN values data = { "A": [1, 2, None, 4, 5], "B": [None, 2, 3, 4, None], "C": [1, 2, 3, 4, 5] } df = pd.DataFrame(data) # Drop rows with any NaN values df_cleaned = df.dropna() # Print the cleaned DataFrame print(df_cleaned)

Output:

 A B C 2 NaN 3.0 3

By default, dropna() removes any row containing at least one NaN value. If you want to drop rows with NaN values only in specific columns, you can use the subset parameter:

# Drop rows with NaN values only in columns 'A' and 'B' df_cleaned_subset = df.dropna(subset=['A', 'B']) # Print the cleaned DataFrame print(df_cleaned_subset)

Output:

 A B C 1 2.0 2.0 2 3 4.0 4.0 4

If you want to drop rows with NaN values based on a threshold (i.e., a certain number of NaN values allowed), you can use the thresh parameter:

# Drop rows with at least 2 non-NaN values df_cleaned_thresh = df.dropna(thresh=2) # Print the cleaned DataFrame print(df_cleaned_thresh)

Output:

 A B C 1 2.0 2.0 2 2 NaN 3.0 3 3 4.0 4.0 4 4 5.0 NaN 5

These are some common ways to drop rows with NaN values from a pandas DataFrame. Depending on your specific use case, you can choose the appropriate method to clean your data.

Examples

  1. How to drop NaN rows in Pandas DataFrame?

    Description: Learn how to remove rows containing NaN (Not a Number) values from a Pandas DataFrame to clean data for analysis or processing.

    df.dropna(inplace=True)

  2. Pandas drop rows with missing values

    Description: Discover how to eliminate rows with missing values (NaN) from a Pandas DataFrame to prepare data for further analysis or visualization.

    df = df.dropna()

  3. Remove NaN rows from Pandas DataFrame

    Description: Find out how to filter out rows with NaN values from a Pandas DataFrame to ensure data integrity and consistency.

    df = df[df.notnull().all(axis=1)]

  4. How to drop rows with NaN values in Pandas

    Description: Explore the process of dropping rows containing NaN values from a Pandas DataFrame to handle missing or incomplete data.

    df = df.dropna(axis=0)

  5. Pandas drop rows with NaN in specific column

    Description: Learn how to remove rows with NaN values in a specific column of a Pandas DataFrame while preserving data integrity.

    df = df.dropna(subset=['column_name'])

  6. Python Pandas remove NaN rows

    Description: Understand how to effectively remove rows with NaN values from a Pandas DataFrame using Python for data preprocessing or analysis.

    df.dropna(axis=0, how='any', inplace=True)

  7. Drop rows with NaN values in Pandas excluding specific columns

    Description: Exclude specific columns from the NaN row removal process in a Pandas DataFrame to retain valuable data while cleaning.

    df = df.dropna(subset=df.columns.difference(['exclude_column']))

  8. Pandas DataFrame remove NaN and None rows

    Description: Learn how to drop both NaN and None values from a Pandas DataFrame to ensure data quality and consistency.

    df = df.dropna(how='any', inplace=True)

  9. Pandas drop rows with NaN in multiple columns

    Description: Discover how to drop rows with NaN values in multiple columns of a Pandas DataFrame simultaneously for efficient data cleaning.

    df = df.dropna(subset=['column1', 'column2'])

  10. How to drop rows with NaN in Pandas using threshold

    Description: Utilize a threshold parameter to specify the minimum number of non-NaN values required to retain a row in a Pandas DataFrame.

    threshold = 2 # Specify threshold df.dropna(thresh=threshold, inplace=True)

More Tags

modal-dialog spring-rest spark-structured-streaming selector kendo-tabstrip uwsgi elasticsearch-plugin url-pattern wcf-client version-control

More Python Questions

More Gardening and crops Calculators

More Math Calculators

More Electronics Circuits Calculators

More Mixtures and solutions Calculators