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Python pandas: DataFrame dropna() # How to clean data in pandas with dropna() - [IONOS editorial team](https://www.ionos.com/experts/about-us/ "IONOS editorial team") 06/26/2025 3 mins - - [Share on Facebook](https://www.facebook.com/sharer/sharer.php?u=https://www.ionos.com/digitalguide/websites/web-development/python-pandas-dataframe-dropna/ "Share on Facebook") - [Share on Twitter](https://twitter.com/intent/tweet?source=https://www.ionos.com/digitalguide/websites/web-development/python-pandas-dataframe-dropna/&text=Python%20pandas%3A%20DataFrame%20dropna%28%29&hashtags=DigitalGuide&url=https://www.ionos.com/digitalguide/websites/web-development/python-pandas-dataframe-dropna/ "Share on Twitter") - [Share on LinkedIn](https://www.linkedin.com/shareArticle?mini=true&url=https://www.ionos.com/digitalguide/websites/web-development/python-pandas-dataframe-dropna/ "Share on LinkedIn") - [Python Pandas](https://www.ionos.com/digitalguide/tags/python-pandas/)  BEST-BACK­GROUNDSShut­ter­stock Contents - [What is the syntax for pandas dropna()?](https://www.ionos.com/digitalguide/websites/web-development/python-pandas-dataframe-dropna/#content-what-is-the-syntax-for-pandas-dropna) - [How to use pandas DataFrame.dropna()](https://www.ionos.com/digitalguide/websites/web-development/python-pandas-dataframe-dropna/#content-how-to-use-pandas-dataframedropna) The [Python pandas](https://www.ionos.com/digitalguide/websites/web-development/python-pandas/) `DataFrame.dropna()` function is used to remove all rows or columns con­tain­ing missing values (NaN) from a DataFrame. This makes it es­pe­cial­ly crucial for **preparing and cleaning data**. Web Hosting Hosting that scales with your ambitions - Stay online with 99.99% uptime and robust security - Add per­for­mance with a click as traffic grows - Includes free domain, SSL, email, and 24/7 support See plans ## What is the syntax for pandas `dropna()`? The `dropna()` function accepts **up to five pa­ra­me­ters**. Here’s its syntax: ``` DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False, ignore_index=False) ``` python ### Important pa­ra­me­ters for `dropna()` You can **use pa­ra­me­ters to influence the behavior** of the pandas `DataFrame.dropna()` function. Here’s an overview of the most important ones: | **Parameter** | **De­scrip­tion** | **Default Value** | |---|---|---| | `axis` | De­ter­mines whether rows (0 or `index`) or columns (1 or `columns`) will be removed | 0 | | `how` | Specifies whether all (`all`) or only some (`any`) values must be NaN | `any` | | `thresh` | Specifies the minimum number of non-NaN values a row or column must have to avoid being removed; cannot be combined with `how` | optional | | `subset` | Specifies which rows or columns should be con­sid­ered | optional | | `inplace` | De­ter­mines whether the operation is performed on the original DataFrame | `False` | | `ignore_index` | If `True`, the remaining axis is labeled from 0 to n-1 | `False` | ## How to use pandas `DataFrame.dropna()` Pandas `dropna()` is used to clean data before it’s analyzed. The removal of rows or columns with missing values helps to **prevent biases** in sta­tis­ti­cal eval­u­a­tions. Since missing values can also lead to problems with data vi­su­al­iza­tion, using the function is also **ad­van­ta­geous when creating charts** and reports. ### Removing rows with missing values In the following example, we’ll take a look at a DataFrame con­tain­ing NaN values: ``` import pandas as pd import numpy as np # Creating a DataFrame with sample data data = { 'A': [1, 2, np.nan, 4], 'B': [5, np.nan, np.nan, 8], 'C': [9, 10, 11, 12] } df = pd.DataFrame(data) print(df) ``` python The DataFrame looks like this: ``` A B C 0 1.0 5.0 9 1 2.0 NaN 10 2 NaN NaN 11 3 4.0 8.0 12 ``` Next, we’re going to apply the pandas `dropna()` function: ``` ## Remove all rows that contain at least one NaN value df_cleaned = df.dropna() print(df_cleaned) ``` python Running the code above produces the following result: ``` A B C 0 1.0 5.0 9 3 4.0 8.0 12 ``` Since all the other rows contain NaN values, only the zeroth and third rows remain. ### Removing columns with missing values Similarly, you can remove columns with missing values by setting the `axis` parameter to 1: ``` ## Remove all columns that contain at least one NaN value df_cleaned_columns = df.dropna(axis=1) print(df_cleaned_columns) ``` python Column C is the only column that remains, since it’s the only one that doesn’t contain NaN values: ``` C 0 9 1 10 2 11 3 12 ``` ### Using `thresh` If you want to remove rows that contain fewer than two non-NaN values, you can use the `thresh` parameter: ``` ## Only keeps rows that have 2 or more non-NaN values df_thresh = df.dropna(thresh=2) print(df_thresh) ``` python Running the code produces the following output: ``` A B C 0 1.0 5.0 9 1 2.0 NaN 10 3 4.0 8.0 12 ``` Row 1 is not removed from the output because it contains 2 non-NaN values (2.0 and 10). ### Using `subset` The `subset` parameter allows you to specify the columns where the program should look for missing values. Only rows that contain missing values in the columns that have been specified will be removed. ``` ## Removes all rows where column A contains a NaN value df_subset = df.dropna(subset=['A']) print(df_subset) ``` python Here, only the second row is removed. The NaN value in the first row is ignored due to the subset parameter, which only takes column A into con­sid­er­a­tion: ``` A B C 0 1.0 5.0 9 1 2.0 NaN 10 3 4.0 8.0 12 ``` Related tags - [Python Pandas](https://www.ionos.com/digitalguide/tags/python-pandas/) [Go to Main Menu](https://www.ionos.com/digitalguide/websites/web-development/python-pandas-dataframe-dropna/#navigation-main) ### Related Products  Deploy Now See plans   ### 10 Years Digital Guide: A Success Story Ex­pe­ri­ence the Evolution ### Turn calls into revenue: AI re­cep­tion­ist Free webinar Save my spot #### Popular Articles ##### [Domain Trading: How to sell a domain name](https://www.ionos.com/digitalguide/domains/domain-news/get-rich-by-selling-domain-names/ "Domain Trading: How to sell a domain name") Buying and selling domains can be lucrative – if you know how to go about it. 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The [Python pandas](https://www.ionos.com/digitalguide/websites/web-development/python-pandas/) `DataFrame.dropna()` function is used to remove all rows or columns con­tain­ing missing values (NaN) from a DataFrame. This makes it es­pe­cial­ly crucial for **preparing and cleaning data**. ## What is the syntax for pandas `dropna()`? The `dropna()` function accepts **up to five pa­ra­me­ters**. Here’s its syntax: ``` DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False, ignore_index=False) ``` python ### Important pa­ra­me­ters for `dropna()` You can **use pa­ra­me­ters to influence the behavior** of the pandas `DataFrame.dropna()` function. Here’s an overview of the most important ones: | **Parameter** | **De­scrip­tion** | **Default Value** | |---|---|---| | `axis` | De­ter­mines whether rows (0 or `index`) or columns (1 or `columns`) will be removed | 0 | | `how` | Specifies whether all (`all`) or only some (`any`) values must be NaN | `any` | | `thresh` | Specifies the minimum number of non-NaN values a row or column must have to avoid being removed; cannot be combined with `how` | optional | | `subset` | Specifies which rows or columns should be con­sid­ered | optional | | `inplace` | De­ter­mines whether the operation is performed on the original DataFrame | `False` | | `ignore_index` | If `True`, the remaining axis is labeled from 0 to n-1 | `False` | ## How to use pandas `DataFrame.dropna()` Pandas `dropna()` is used to clean data before it’s analyzed. The removal of rows or columns with missing values helps to **prevent biases** in sta­tis­ti­cal eval­u­a­tions. Since missing values can also lead to problems with data vi­su­al­iza­tion, using the function is also **ad­van­ta­geous when creating charts** and reports. ### Removing rows with missing values In the following example, we’ll take a look at a DataFrame con­tain­ing NaN values: ``` import pandas as pd import numpy as np # Creating a DataFrame with sample data data = { 'A': [1, 2, np.nan, 4], 'B': [5, np.nan, np.nan, 8], 'C': [9, 10, 11, 12] } df = pd.DataFrame(data) print(df) ``` python The DataFrame looks like this: ``` A B C 0 1.0 5.0 9 1 2.0 NaN 10 2 NaN NaN 11 3 4.0 8.0 12 ``` Next, we’re going to apply the pandas `dropna()` function: ``` ## Remove all rows that contain at least one NaN value df_cleaned = df.dropna() print(df_cleaned) ``` python Running the code above produces the following result: ``` A B C 0 1.0 5.0 9 3 4.0 8.0 12 ``` Since all the other rows contain NaN values, only the zeroth and third rows remain. ### Removing columns with missing values Similarly, you can remove columns with missing values by setting the `axis` parameter to 1: ``` ## Remove all columns that contain at least one NaN value df_cleaned_columns = df.dropna(axis=1) print(df_cleaned_columns) ``` python Column C is the only column that remains, since it’s the only one that doesn’t contain NaN values: ``` C 0 9 1 10 2 11 3 12 ``` ### Using `thresh` If you want to remove rows that contain fewer than two non-NaN values, you can use the `thresh` parameter: ``` ## Only keeps rows that have 2 or more non-NaN values df_thresh = df.dropna(thresh=2) print(df_thresh) ``` python Running the code produces the following output: ``` A B C 0 1.0 5.0 9 1 2.0 NaN 10 3 4.0 8.0 12 ``` Row 1 is not removed from the output because it contains 2 non-NaN values (2.0 and 10). ### Using `subset` The `subset` parameter allows you to specify the columns where the program should look for missing values. Only rows that contain missing values in the columns that have been specified will be removed. ``` ## Removes all rows where column A contains a NaN value df_subset = df.dropna(subset=['A']) print(df_subset) ``` python Here, only the second row is removed. The NaN value in the first row is ignored due to the subset parameter, which only takes column A into con­sid­er­a­tion: ``` A B C 0 1.0 5.0 9 1 2.0 NaN 10 3 4.0 8.0 12 ```