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Post last modified:June 19, 2025 - Reading time:15 mins read  To drop rows with NaN (null) values in a Pandas DataFrame, you can use the `dropna()` function. Python doesn’t support Null hence any missing data is represented as None or NaN. NaN stands for Not A Number and serves as one of the typical methods for indicating [missing values](https://sparkbyexamples.com/pandas/pandas-handle-missing-data-in-dataframe/) within datasets. None/NaN values are one of the major problems in Data Analysis hence before we process either you need to drop rows that have NaN values or [replace NaN with empty for Strings](https://sparkbyexamples.com/pandas/pandas-replace-nan-with-blank-empty-string/) and [replace NaN with zero for numeric columns](https://sparkbyexamples.com/pandas/pandas-replace-nan-values-by-zeroes-in-a-column/). Advertisements ****Key Points –**** - Use the [dropna()](https://sparkbyexamples.com/pandas/pandas-dropna-usage-examples/) function in Pandas to remove rows containing `NaN/None` values from a DataFrame. - `numpy.nan` is [Not a Number (NaN)](https://numpy.org/devdocs/reference/constants.html), which is of Python build-in numeric type float (floating point). - `None` is of [NoneType](https://docs.python.org/2/library/types.html) and it is an object in Python. - Specify the axis parameter as 0 to drop rows with NaN values. - The `dropna()` function returns a new DataFrame with NaN-containing rows removed. - Use additional parameters like `subset` to specify columns to consider for NaN removal, and `how` to control the criteria for dropping rows. ## Quick Examples of Dropping Rows with NaN Values Below are quick examples of dropping rows with nan values. ``` # Quick examples of drop rows with nan values # Example 1: Drop all rows with NaN values df2=df.dropna() df2=df.dropna(axis=0) # Example 2: Reset index after drop df2=df.dropna().reset_index(drop=True) # Example 3: Drop row that has all NaN values df2=df.dropna(how='all') # Example 4: Drop rows that has NaN values on selected columns df2=df.dropna(subset=['Courses','Fee']) # Example 5: With threshold, # Keep only the rows with at least 2 non-NA values. df2=df.dropna(thresh=2) # Example 6: Drop Rows with NaN Values inplace df.dropna(inplace=True) ``` ## Usage of Pandas Drop Rows with NaN Values in DataFrame In real-world datasets, missing values are quite common. To remove all rows containing any NaN values in a Pandas DataFrame, you can simply use the `dropna()` method. This provides an easy way to clean your data by filtering out incomplete rows. To run some examples of drop rows with NaN values in Pandas DataFrame, let’s create a Pandas DataFrame using data from a dictionary. ``` import pandas as pd import numpy as np technologies = ({ 'Courses':["Spark",'Java',"Hadoop",'Python',np.nan], 'Fee' :[20000,np.nan,26000,24000,np.nan], 'Duration':['30days',np.nan,'35days','40days',np.nan], 'Discount':[1000,np.nan,2500,None,np.nan] }) df = pd.DataFrame(technologies) print("Create DataFrame:\n", df) ``` Yields below output.  ## Drop Rows with NaN Values You can use the `dropna()` method to remove rows with NaN (Not a Number) and None values from Pandas DataFrame. By default, it removes any row containing at least one NaN value and returns the copy of the DataFrame after removing rows. If you want to remove from the existing DataFrame, you should use `inplace=True`. With NaN values in a Pandas DataFrame. ``` # Drop all rows that have NaN/None values df2 = df.dropna() print("After dropping the rows with NaN Values:\n", df2) ``` Yields below output.  `Related:` you can use the `dropna(axis=1)` to [drop all columns with NaN values](https://sparkbyexamples.com/pandas/pandas-drop-columns-with-nan-none-values/) from DataFrame. Post-dropping rows with NaN, sometimes you may be required to reset the index, you can do so using the [DataFrame.reset\_index()](https://sparkbyexamples.com/pandas/pandas-reset-index-examples/) method. ``` # Reset index after drop df2 = df.dropna().reset_index(drop=True) print("Reset the index after dropping:\n", df2) ``` Yields below output. ``` # Output: # Reset the index after dropping Courses Fee Duration Discount 0 Spark 20000.0 30days 1000.0 1 Hadoop 26000.0 35days 2500.0 ``` ## Drop NaNs for all Columns in the DataFrame Similarly, you can use `how` parameter of the `dropna()` function to specify which rows to drop based on NaN values. By default, the Param `how=any` specifies all rows with NaN/None values on any element are removed. You can use `how='all'` to remove rows that have all NaN/None values in a row(data is missing for all elements in a row). ``` # Drop rows that has all NaN values df2 = df.dropna(how='all') print(" After dropping the rows which have all NaN values:\n", df2) ``` Yields below output. ``` # Output: # After dropping the rows which have all NaN values: Courses Fee Duration Discount 0 Spark 20000.0 30days 1000.0 1 Java NaN NaN NaN 2 Hadoop 26000.0 35days 2500.0 3 Python 24000.0 40days NaN ``` ## Drop NaN Values on Selected Columns from List Sometimes you may be required to drop rows only when selected columns have NaN/None values in DataFrame, you can achieve this by using `subset` param. This parameter takes a list of label names. ``` # Drop rows that has NaN values on selected columns df2=df.dropna(subset=['Courses','Fee']) print("After dropping rows based on specified columns:\n", df2) ``` Yields below output. ``` # Output: # After dropping rows based on specified columns: Courses Fee Duration Discount 0 Spark 20000.0 30days 1000.0 2 Hadoop 26000.0 35days 2500.0 3 Python 24000.0 40days NaN ``` ## Drop Rows with NaN Values inplace As you can see, by default `dropna()` method doesn’t drop rows from the original DataFrame; instead, it returns a copy of the DataFrame. If you intend to modify the existing DataFrame directly, you can set `inplace=True`. ``` # Drop Rows with NaN Values inplace df.dropna(inplace=True) print("After dropping the rows with NaN values:\n", df) # Output: # After dropping the rows with NaN values: # Courses Fee Duration Discount # 0 Spark 20000.0 30days 1000.0 # 2 Hadoop 26000.0 35days 2500.0 ``` ## Complete Example of Drop Rows with NaN Values ``` import pandas as pd import numpy as np technologies = ({ 'Courses':["Spark",'Java',"Hadoop",'Python',np.nan], 'Fee' :[20000,np.nan,26000,24000,np.nan], 'Duration':['30days',np.nan,'35days','40days',np.nan], 'Discount':[1000,np.nan,2500,None,np.nan] }) df = pd.DataFrame(technologies) print(df) # Drop all rows with NaN values df2=df.dropna() print(df2) df2=df.dropna(axis=0) # Reset index after drop df2=df.dropna().reset_index(drop=True) print(df2) # Drop row that has all NaN values df2=df.dropna(how='all') print(df2) # Drop rows that has null on selected columns df2=df.dropna(subset=['Courses','Fee']) print(df2) # Drop Rows with NaN Values inplace df.dropna(inplace=True) print(df) ``` ## FAQ on Drop Rows with NaN Values **How do I drop rows with NaN values in a Pandas DataFrame?** You can use the `dropna()` method to remove rows with NaN values in a Pandas DataFrame. By default, it removes any of the rows having at least one NaN value. For instance, `df.dropna()`. **What is the syntax for using the dropna() function to remove rows with NaN values?** `df.dropna()` which returns a new DataFrame with rows that don’t have NaN values. **How can I drop rows with NaN values in a specific column?** You can use the `subset` parameter of the `dropna()` method to specify a subset of columns to consider for NaN removal. For example, `df.dropna(subset=['specified_column'])` **How can I drop rows based on multiple columns with NaN values?** You can specify multiple columns in the `subset` parameter. For example, `df.dropna(subset=['specified_column1', 'specified_column2'])` **How can I drop rows if all values in a row are NaN?** You can use the `how` parameter with the value ‘all’ to drop rows where all values are NaN. For example, `df.dropna(how='all')` ## Conclusion In this article, I have explained dropping rows with NaN/None values in pandas DataFrame using `DataFrame.dropna()`. Also learned to remove rows only when all values are NaN/None, remove only when selected columns have NaN values, and remove using the `inplace` parameter. Happy Learning !\! ## Related Articles - [Pandas Drop Rows by Index](https://sparkbyexamples.com/pandas/pandas-drop-rows-by-index/) - [Pandas – Drop the First Three Rows](https://sparkbyexamples.com/pandas/pandas-drop-first-three-rows-of-a-dataframe) - [Drop Pandas rows based on condition](https://sparkbyexamples.com/pandas/select-pandas-columns-based-on-condition/) - [Delete Last Row From Pandas DataFrame](https://sparkbyexamples.com/pandas/pandas-delete-last-row-of-dataframe/) - [Pandas – Drop List of Rows From DataFrame](https://sparkbyexamples.com/pandas/pandas-drop-list-of-rows-from-dataframe/) - [Pandas Drop Last N Rows From DataFrame](https://sparkbyexamples.com/pandas/pandas-drop-last-n-rows-from-dataframe) - [How to drop duplicate rows from DataFrame?](https://sparkbyexamples.com/pandas/pandas-drop-duplicate-rows-from-dataframe/) - [Pandas Drop Rows Based on Column Value](https://sparkbyexamples.com/pandas/pandas-delete-rows-based-on-column-value/) ## References - <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html?> Tags: [DataFrame.dropna()](https://sparkbyexamples.com/tag/dataframe-dropna/), [DataFrame.reset\_index()](https://sparkbyexamples.com/tag/dataframe-reset-index/), [missing data](https://sparkbyexamples.com/tag/missing-data/) #### LOGIN for Tutorial Menu - [Log In](https://sparkbyexamples.com/login/) ## Top Tutorials - [Apache Spark Tutorial](https://sparkbyexamples.com/) - [PySpark Tutorial](https://sparkbyexamples.com/pyspark-tutorial/) - [Python Pandas Tutorial](https://sparkbyexamples.com/python-pandas-tutorial-for-beginners/) - [R Programming Tutorial](https://sparkbyexamples.com/r-tutorial-with-examples/) - [Python NumPy Tutorial](https://sparkbyexamples.com/python-numpy-tutorial-for-beginners/) - [Apache Hive Tutorial](https://sparkbyexamples.com/apache-hive-tutorial/) - [Apache HBase Tutorial](https://sparkbyexamples.com/apache-hbase-tutorial/) - [Apache Cassandra Tutorial](https://sparkbyexamples.com/apache-cassandra-tutorials-with-examples/) - [Apache Kafka Tutorial](https://sparkbyexamples.com/apache-kafka-tutorials-with-examples/) - [Snowflake Data Warehouse Tutorial](https://sparkbyexamples.com/snowflake-data-warehouse-database-tutorials/) - [H2O Sparkling Water Tutorial](https://sparkbyexamples.com/h2o-sparkling-water-tutorial-beginners/) ## Categories - [Apache Spark](https://sparkbyexamples.com/category/spark/) - [PySpark](https://sparkbyexamples.com/category/pyspark/) - [Pandas](https://sparkbyexamples.com/category/pandas/) - [R Programming](https://sparkbyexamples.com/category/r-programming/) - [Snowflake Database](https://sparkbyexamples.com/category/snowflake/) - [NumPy](https://sparkbyexamples.com/category/numpy/) - [Apache Hive](https://sparkbyexamples.com/category/apache-hive/) - [Apache HBase](https://sparkbyexamples.com/category/hbase/) - [Apache Kafka](https://sparkbyexamples.com/category/kafka/) - [Apache Cassandra](https://sparkbyexamples.com/category/cassandra/) - [H2O Sparkling Water](https://sparkbyexamples.com/category/h2o-sparkling-water/) ## Legal - [SparkByExamples.com – Privacy Policy](https://sparkbyexamples.com/privacy-policy/) - [Refund Policy](https://sparkbyexamples.com/refund-policy/) - [Terms of Use](https://sparkbyexamples.com/terms-of-use/)  - Opens in a new tab - Opens in a new tab - Opens in a new tab - Opens in a new tab - Opens in a new tab Copyright 2024 www.SparkByExamples.com. 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To drop rows with NaN (null) values in a Pandas DataFrame, you can use the `dropna()` function. Python doesn’t support Null hence any missing data is represented as None or NaN. NaN stands for Not A Number and serves as one of the typical methods for indicating [missing values](https://sparkbyexamples.com/pandas/pandas-handle-missing-data-in-dataframe/) within datasets. None/NaN values are one of the major problems in Data Analysis hence before we process either you need to drop rows that have NaN values or [replace NaN with empty for Strings](https://sparkbyexamples.com/pandas/pandas-replace-nan-with-blank-empty-string/) and [replace NaN with zero for numeric columns](https://sparkbyexamples.com/pandas/pandas-replace-nan-values-by-zeroes-in-a-column/). Advertisements ****Key Points –**** - Use the [dropna()](https://sparkbyexamples.com/pandas/pandas-dropna-usage-examples/) function in Pandas to remove rows containing `NaN/None` values from a DataFrame. - `numpy.nan` is [Not a Number (NaN)](https://numpy.org/devdocs/reference/constants.html), which is of Python build-in numeric type float (floating point). - `None` is of [NoneType](https://docs.python.org/2/library/types.html) and it is an object in Python. - Specify the axis parameter as 0 to drop rows with NaN values. - The `dropna()` function returns a new DataFrame with NaN-containing rows removed. - Use additional parameters like `subset` to specify columns to consider for NaN removal, and `how` to control the criteria for dropping rows. ## Quick Examples of Dropping Rows with NaN Values Below are quick examples of dropping rows with nan values. ``` # Quick examples of drop rows with nan values # Example 1: Drop all rows with NaN values df2=df.dropna() df2=df.dropna(axis=0) # Example 2: Reset index after drop df2=df.dropna().reset_index(drop=True) # Example 3: Drop row that has all NaN values df2=df.dropna(how='all') # Example 4: Drop rows that has NaN values on selected columns df2=df.dropna(subset=['Courses','Fee']) # Example 5: With threshold, # Keep only the rows with at least 2 non-NA values. df2=df.dropna(thresh=2) # Example 6: Drop Rows with NaN Values inplace df.dropna(inplace=True) ``` In real-world datasets, missing values are quite common. To remove all rows containing any NaN values in a Pandas DataFrame, you can simply use the `dropna()` method. This provides an easy way to clean your data by filtering out incomplete rows. To run some examples of drop rows with NaN values in Pandas DataFrame, let’s create a Pandas DataFrame using data from a dictionary. ``` import pandas as pd import numpy as np technologies = ({ 'Courses':["Spark",'Java',"Hadoop",'Python',np.nan], 'Fee' :[20000,np.nan,26000,24000,np.nan], 'Duration':['30days',np.nan,'35days','40days',np.nan], 'Discount':[1000,np.nan,2500,None,np.nan] }) df = pd.DataFrame(technologies) print("Create DataFrame:\n", df) ``` Yields below output.  ## Drop Rows with NaN Values You can use the `dropna()` method to remove rows with NaN (Not a Number) and None values from Pandas DataFrame. By default, it removes any row containing at least one NaN value and returns the copy of the DataFrame after removing rows. If you want to remove from the existing DataFrame, you should use `inplace=True`. With NaN values in a Pandas DataFrame. ``` # Drop all rows that have NaN/None values df2 = df.dropna() print("After dropping the rows with NaN Values:\n", df2) ``` Yields below output.  `Related:` you can use the `dropna(axis=1)` to [drop all columns with NaN values](https://sparkbyexamples.com/pandas/pandas-drop-columns-with-nan-none-values/) from DataFrame. Post-dropping rows with NaN, sometimes you may be required to reset the index, you can do so using the [DataFrame.reset\_index()](https://sparkbyexamples.com/pandas/pandas-reset-index-examples/) method. ``` # Reset index after drop df2 = df.dropna().reset_index(drop=True) print("Reset the index after dropping:\n", df2) ``` Yields below output. ``` # Output: # Reset the index after dropping Courses Fee Duration Discount 0 Spark 20000.0 30days 1000.0 1 Hadoop 26000.0 35days 2500.0 ``` ## Drop NaNs for all Columns in the DataFrame Similarly, you can use `how` parameter of the `dropna()` function to specify which rows to drop based on NaN values. By default, the Param `how=any` specifies all rows with NaN/None values on any element are removed. You can use `how='all'` to remove rows that have all NaN/None values in a row(data is missing for all elements in a row). ``` # Drop rows that has all NaN values df2 = df.dropna(how='all') print(" After dropping the rows which have all NaN values:\n", df2) ``` Yields below output. ``` # Output: # After dropping the rows which have all NaN values: Courses Fee Duration Discount 0 Spark 20000.0 30days 1000.0 1 Java NaN NaN NaN 2 Hadoop 26000.0 35days 2500.0 3 Python 24000.0 40days NaN ``` ## Drop NaN Values on Selected Columns from List Sometimes you may be required to drop rows only when selected columns have NaN/None values in DataFrame, you can achieve this by using `subset` param. This parameter takes a list of label names. ``` # Drop rows that has NaN values on selected columns df2=df.dropna(subset=['Courses','Fee']) print("After dropping rows based on specified columns:\n", df2) ``` Yields below output. ``` # Output: # After dropping rows based on specified columns: Courses Fee Duration Discount 0 Spark 20000.0 30days 1000.0 2 Hadoop 26000.0 35days 2500.0 3 Python 24000.0 40days NaN ``` ## Drop Rows with NaN Values inplace As you can see, by default `dropna()` method doesn’t drop rows from the original DataFrame; instead, it returns a copy of the DataFrame. If you intend to modify the existing DataFrame directly, you can set `inplace=True`. ``` # Drop Rows with NaN Values inplace df.dropna(inplace=True) print("After dropping the rows with NaN values:\n", df) # Output: # After dropping the rows with NaN values: # Courses Fee Duration Discount # 0 Spark 20000.0 30days 1000.0 # 2 Hadoop 26000.0 35days 2500.0 ``` ## Complete Example of Drop Rows with NaN Values ``` import pandas as pd import numpy as np technologies = ({ 'Courses':["Spark",'Java',"Hadoop",'Python',np.nan], 'Fee' :[20000,np.nan,26000,24000,np.nan], 'Duration':['30days',np.nan,'35days','40days',np.nan], 'Discount':[1000,np.nan,2500,None,np.nan] }) df = pd.DataFrame(technologies) print(df) # Drop all rows with NaN values df2=df.dropna() print(df2) df2=df.dropna(axis=0) # Reset index after drop df2=df.dropna().reset_index(drop=True) print(df2) # Drop row that has all NaN values df2=df.dropna(how='all') print(df2) # Drop rows that has null on selected columns df2=df.dropna(subset=['Courses','Fee']) print(df2) # Drop Rows with NaN Values inplace df.dropna(inplace=True) print(df) ``` ## FAQ on Drop Rows with NaN Values **How do I drop rows with NaN values in a Pandas DataFrame?** You can use the `dropna()` method to remove rows with NaN values in a Pandas DataFrame. By default, it removes any of the rows having at least one NaN value. For instance, `df.dropna()`. **What is the syntax for using the dropna() function to remove rows with NaN values?** `df.dropna()` which returns a new DataFrame with rows that don’t have NaN values. **How can I drop rows with NaN values in a specific column?** You can use the `subset` parameter of the `dropna()` method to specify a subset of columns to consider for NaN removal. For example, `df.dropna(subset=['specified_column'])` **How can I drop rows based on multiple columns with NaN values?** You can specify multiple columns in the `subset` parameter. For example, `df.dropna(subset=['specified_column1', 'specified_column2'])` **How can I drop rows if all values in a row are NaN?** You can use the `how` parameter with the value ‘all’ to drop rows where all values are NaN. For example, `df.dropna(how='all')` ## Conclusion In this article, I have explained dropping rows with NaN/None values in pandas DataFrame using `DataFrame.dropna()`. Also learned to remove rows only when all values are NaN/None, remove only when selected columns have NaN values, and remove using the `inplace` parameter. Happy Learning !\! ## Related Articles - [Pandas Drop Rows by Index](https://sparkbyexamples.com/pandas/pandas-drop-rows-by-index/) - [Pandas – Drop the First Three Rows](https://sparkbyexamples.com/pandas/pandas-drop-first-three-rows-of-a-dataframe) - [Drop Pandas rows based on condition](https://sparkbyexamples.com/pandas/select-pandas-columns-based-on-condition/) - [Delete Last Row From Pandas DataFrame](https://sparkbyexamples.com/pandas/pandas-delete-last-row-of-dataframe/) - [Pandas – Drop List of Rows From DataFrame](https://sparkbyexamples.com/pandas/pandas-drop-list-of-rows-from-dataframe/) - [Pandas Drop Last N Rows From DataFrame](https://sparkbyexamples.com/pandas/pandas-drop-last-n-rows-from-dataframe) - [How to drop duplicate rows from DataFrame?](https://sparkbyexamples.com/pandas/pandas-drop-duplicate-rows-from-dataframe/) - [Pandas Drop Rows Based on Column Value](https://sparkbyexamples.com/pandas/pandas-delete-rows-based-on-column-value/) ## References - <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html?>