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Calculated Shard: 80 (from laksa091)

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curl -X POST \
  'http://laksa080.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.statology.org/pandas-drop-columns-with-nan/\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.statology.org/pandas-drop-columns-with-nan/\'))) FORMAT JSONEachRow'

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{"found_url":"https:\/\/www.statology.org\/pandas-drop-columns-with-nan\/","crawl_time":1775896616,"first_indexed_time":1672763791,"http_code":200,"src_unparsed":"org,statology!www,\/pandas-drop-columns-with-nan\/ s443","src_root_hash":"6121278954233998080","history_drop_reason":null,"meta_title":"Pandas: How to Drop Columns with NaN Values","meta_descriptions":["This tutorial explains how to drop columns in a pandas DataFrame with NaN values, including several examples."],"attrs_boilerpipe_text":"You can use the following methods to drop columns from a pandas DataFrame with NaN values:\nMethod 1: Drop Columns with Any NaN Values\ndf = df.\ndropna\n(axis=\n1\n)\nMethod 2: Drop Columns with All NaN Values\ndf = df.\ndropna\n(axis=\n1\n, how='\nall\n')\nMethod 3: Drop Columns with Minimum Number of NaN Values\ndf = df.\ndropna\n(axis=\n1\n, thresh=\n2\n)\nThe following examples show how to use each method in practice with the following pandas DataFrame:\nimport\npandas\nas\npd\nimport\nnumpy\nas\nnp\n#create DataFrame\ndf = pd.\nDataFrame\n({'\nteam\n': ['A', 'A', 'A', 'B', 'B', 'B'],\r\n                   '\nposition\n': [np.nan, 'G', 'F', 'F', 'C', 'G'],\r\n                   '\npoints\n': [11, 28, 10, 26, 6, 25],\r\n                   '\nrebounds\n': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]})\n#view DataFrame\nprint\n(df)\r\n\r\n  team position  points  rebounds\r\n0    A      NaN      11       NaN\r\n1    A        G      28       NaN\r\n2    A        F      10       NaN\r\n3    B        F      26       NaN\r\n4    B        C       6       NaN\r\n5    B        G      25       NaN\nExample 1: Drop Columns with Any NaN Values\nThe following code shows how to drop columns with any NaN values:\n#drop columns with any NaN values\ndf = df.\ndropna\n(axis=\n1\n)\n#view updated DataFrame\nprint\n(df)\r\n\r\n  team  points\r\n0    A      11\r\n1    A      28\r\n2    A      10\r\n3    B      26\r\n4    B       6\r\n5    B      25\nNotice that the\nposition\nand\nrebounds\ncolumns were dropped since they both had at least one NaN value.\nExample 2: Drop Columns with All NaN Values\nThe following code shows how to drop columns with all NaN values:\n#drop columns with all NaN values\ndf = df.\ndropna\n(axis=\n1\n, how='\nall\n')\n#view updated DataFrame\nprint\n(df)\r\n\r\n  team position  points\r\n0    A      NaN      11\r\n1    A        G      28\r\n2    A        F      10\r\n3    B        F      26\r\n4    B        C       6\r\n5    B        G      25\nNotice that the\nrebounds\ncolumn was dropped since it was the only column with all NaN values.\nExample 3: Drop Columns with Minimum Number of NaN Values\nThe following code shows how to drop columns with\nat least two\nNaN values:\n#drop columns with at least two NaN values\ndf = df.\ndropna\n(axis=\n1\n, thresh=\n2\n)\n#view updated DataFrame\nprint\n(df)\r\n\r\n  team position  points\r\n0    A      NaN      11\r\n1    A        G      28\r\n2    A        F      10\r\n3    B        F      26\r\n4    B        C       6\r\n5    B        G      25\nNotice that the\nrebounds\ncolumn was dropped since it was the only column with at least two NaN values.\nNote\n: You can find the complete documentation for the\ndropna()\nfunction in pandas\nhere\n.\nAdditional Resources\nThe following tutorials explain how to perform other common tasks in pandas:\nHow to Drop First Column in Pandas\nHow to Drop Duplicate Columns in Pandas\nHow to Drop All Columns Except Specific Ones in Pandas\nHey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.","attrs_markdown":"[![Statology](https:\/\/www.statology.org\/wp-content\/uploads\/2024\/07\/StatologyLogo_OnWhite.png)](https:\/\/www.statology.org\/)\n\n- [About](https:\/\/www.statology.org\/about\/)\n- [Course](https:\/\/www.statology.org\/course-register\/)\n- [Basic Stats](https:\/\/www.statology.org\/tutorials\/)\n- [Machine Learning](https:\/\/www.statology.org\/machine-learning-tutorials\/)\n- [Software Tutorials]()\n  - [Excel](https:\/\/www.statology.org\/excel-guides\/)\n  - [Google Sheets](https:\/\/www.statology.org\/google-sheets-guides\/)\n  - [MongoDB](https:\/\/www.statology.org\/mongodb-guides\/)\n  - [MySQL](https:\/\/www.statology.org\/mysql-guides\/)\n  - [Power BI](https:\/\/www.statology.org\/power-bi-guides\/)\n  - [PySpark](https:\/\/www.statology.org\/pyspark-guides\/)\n  - [Python](https:\/\/www.statology.org\/python-guides\/)\n  - [R](https:\/\/www.statology.org\/r-guides\/)\n  - [SAS](https:\/\/www.statology.org\/sas-guides\/)\n  - [SPSS](https:\/\/www.statology.org\/spss-guides\/)\n  - [Stata](https:\/\/www.statology.org\/stata-guides\/)\n  - [TI-84](https:\/\/www.statology.org\/ti-84-guides\/)\n  - [VBA](https:\/\/www.statology.org\/vba-guides\/)\n- [Tools]()\n  - [Calculators](https:\/\/www.statology.org\/calculators\/)\n  - [Critical Value Tables](https:\/\/www.statology.org\/tables\/)\n  - [Glossary](https:\/\/www.statology.org\/glossary\/)\n\n# Pandas: How to Drop Columns with NaN Values\nby [Zach Bobbitt](https:\/\/www.statology.org\/author\/admin\/)\n\nPublished on\n\n[Published on January 3, 2023](https:\/\/www.statology.org\/pandas-drop-columns-with-nan\/)\n\n***\nYou can use the following methods to drop columns from a pandas DataFrame with NaN values:\n\n**Method 1: Drop Columns with Any NaN Values**\n```\ndf = df.dropna(axis=1)\n```\n**Method 2: Drop Columns with All NaN Values**\n```\ndf = df.dropna(axis=1, how='all')\n```\n**Method 3: Drop Columns with Minimum Number of NaN Values**\n```\ndf = df.dropna(axis=1, thresh=2)\n```\nThe following examples show how to use each method in practice with the following pandas DataFrame:\n```\nimport pandas as pd import numpy as np #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'B'], 'position': [np.nan, 'G', 'F', 'F', 'C', 'G'], 'points': [11, 28, 10, 26, 6, 25], 'rebounds': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]}) #view DataFrame print(df) team position points rebounds 0 A NaN 11 NaN 1 A G 28 NaN 2 A F 10 NaN 3 B F 26 NaN 4 B C 6 NaN 5 B G 25 NaN\n```\n## **Example 1: Drop Columns with Any NaN Values**\nThe following code shows how to drop columns with any NaN values:\n```\n#drop columns with any NaN values df = df.dropna(axis=1) #view updated DataFrame print(df) team points 0 A 11 1 A 28 2 A 10 3 B 26 4 B 6 5 B 25\n```\nNotice that the **position** and **rebounds** columns were dropped since they both had at least one NaN value.\n\n## **Example 2: Drop Columns with All NaN Values**\nThe following code shows how to drop columns with all NaN values:\n```\n#drop columns with all NaN values df = df.dropna(axis=1, how='all') #view updated DataFrame print(df) team position points 0 A NaN 11 1 A G 28 2 A F 10 3 B F 26 4 B C 6 5 B G 25\n```\nNotice that the **rebounds** column was dropped since it was the only column with all NaN values.\n\n## **Example 3: Drop Columns with Minimum Number of NaN Values**\nThe following code shows how to drop columns with **at least two** NaN values:\n```\n#drop columns with at least two NaN values df = df.dropna(axis=1, thresh=2) #view updated DataFrame print(df) team position points 0 A NaN 11 1 A G 28 2 A F 10 3 B F 26 4 B C 6 5 B G 25\n```\nNotice that the **rebounds** column was dropped since it was the only column with at least two NaN values.\n\n**Note**: You can find the complete documentation for the **dropna()** function in pandas [here](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html).\n\n## **Additional Resources**\nThe following tutorials explain how to perform other common tasks in pandas:\n\n[How to Drop First Column in Pandas](https:\/\/www.statology.org\/pandas-drop-first-column\/)  \n [How to Drop Duplicate Columns in Pandas](https:\/\/www.statology.org\/pandas-drop-duplicate-columns\/)  \n [How to Drop All Columns Except Specific Ones in Pandas](https:\/\/www.statology.org\/pandas-drop-all-columns-except\/)\n\nPosted in [Programming](https:\/\/www.statology.org\/category\/programming\/)\n\n![](https:\/\/www.statology.org\/wp-content\/uploads\/2023\/08\/statology_gravatar-scaled.jpg)\n\n[Zach Bobbitt](https:\/\/www.statology.org\/author\/admin\/)\n\nHey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.\n\n## Post navigation\n[Prev Pandas: Extract Column Value Based on Another Column](https:\/\/www.statology.org\/pandas-extract-column-value-based-on-another-column\/)\n\n[Next How to Normalize Values in NumPy Array Between 0 and 1](https:\/\/www.statology.org\/numpy-normalize-between-0-and-1\/)\n### Leave a Reply [Cancel reply](https:\/\/www.statology.org\/pandas-drop-columns-with-nan\/#respond)\n\n## Search\n## ABOUT STATOLOGY\n[![](https:\/\/www.statology.org\/wp-content\/uploads\/2023\/08\/statology_circle-150x150.png)](https:\/\/www.statology.org\/about\/)Statology makes learning statistics easy by explaining topics in simple and straightforward ways. Our team of writers have over 40 years of experience in the fields of Machine Learning, AI and Statistics. **[Learn more about our team here.](https:\/\/www.statology.org\/about\/)**\n\n## Featured Posts\n- [![](https:\/\/www.statology.org\/wp-content\/uploads\/2026\/04\/sta-chugani-distance-metric-clustering-decision-tree-feature-150x150.png)](https:\/\/www.statology.org\/choosing-the-right-distance-metric-for-clustering-a-decision-tree-approach\/)\n  [Choosing the Right Distance Metric for Clustering: A Decision Tree Approach](https:\/\/www.statology.org\/choosing-the-right-distance-metric-for-clustering-a-decision-tree-approach\/)\n  April 10, 2026\n- [![](https:\/\/www.statology.org\/wp-content\/uploads\/2026\/04\/sta-chugani-textblob-python-simple-visual-feature-150x150.png)](https:\/\/www.statology.org\/how-to-analyze-word-frequencies-with-textblob-in-python\/)\n  [How to Analyze Word Frequencies with TextBlob in Python](https:\/\/www.statology.org\/how-to-analyze-word-frequencies-with-textblob-in-python\/)\n  April 9, 2026\n- [![](https:\/\/www.statology.org\/wp-content\/uploads\/2026\/04\/sta-chugani-raw-data-insight-copilot-microsoft-stack-feature-150x150.png)](https:\/\/www.statology.org\/from-raw-data-to-insight-with-copilot-across-the-microsoft-stack\/)\n  [From Raw Data to Insight with Copilot Across the Microsoft Stack](https:\/\/www.statology.org\/from-raw-data-to-insight-with-copilot-across-the-microsoft-stack\/)\n  April 9, 2026\n- [![](https:\/\/www.statology.org\/wp-content\/uploads\/2026\/04\/sta-chugani-textblob-python-simple-visual-feature-150x150.png)](https:\/\/www.statology.org\/how-to-measure-subjectivity-in-text-with-textblob-in-python\/)\n  [How to Measure Subjectivity in Text with TextBlob in Python](https:\/\/www.statology.org\/how-to-measure-subjectivity-in-text-with-textblob-in-python\/)\n  April 8, 2026\n- [![](https:\/\/www.statology.org\/wp-content\/uploads\/2026\/04\/sta-chugani-textblob-python-simple-visual-feature-150x150.png)](https:\/\/www.statology.org\/how-to-calculate-sentiment-polarity-scores-with-textblob-in-python\/)\n  [How to Calculate Sentiment Polarity Scores with TextBlob in Python](https:\/\/www.statology.org\/how-to-calculate-sentiment-polarity-scores-with-textblob-in-python\/)\n  April 7, 2026\n- [![](https:\/\/www.statology.org\/wp-content\/uploads\/2026\/04\/sta-chugani-getting-started-textblob-python-feature-150x150.png)](https:\/\/www.statology.org\/getting-started-with-textblob-in-python\/)\n  [Getting Started with TextBlob in Python](https:\/\/www.statology.org\/getting-started-with-textblob-in-python\/)\n  April 7, 2026\n\n## Statology Study\n**[Statology Study](https:\/\/www.statology.org\/study-register\/)** is the ultimate online statistics study guide that helps you study and practice all of the core concepts taught in any elementary statistics course and makes your life so much easier as a student.\n\n[![statology study](https:\/\/www.statology.org\/wp-content\/uploads\/2021\/01\/statology_study_cover1.png)](https:\/\/www.statology.org\/study-register\/)\n\n## Introduction to Statistics Course\n**Introduction to Statistics** is our premier online video course that teaches you all of the topics covered in introductory statistics. **[Get started](https:\/\/www.statology.org\/course-register\/)**  with our course today.\n\n[![introduction to statistics](https:\/\/www.statology.org\/wp-content\/uploads\/2022\/06\/Intro-to-Statistics-Cover-Photo-2.jpg)](https:\/\/www.statology.org\/course-register\/)\n\n## You Might Also Like\n- [Pandas: How to Use dropna() with thresh](https:\/\/www.statology.org\/pandas-dropna-thresh\/)\n- [How to Drop Rows with NaN Values in Pandas](https:\/\/www.statology.org\/drop-na-pandas\/)\n- [Pandas: How to Use dropna() with Specific Columns](https:\/\/www.statology.org\/pandas-dropna-specific-column\/)\n- [Pandas: How to Reset Index After Using dropna()](https:\/\/www.statology.org\/pandas-reset-index-after-dropna\/)\n- [How to Select Rows without NaN Values in Pandas](https:\/\/www.statology.org\/pandas-select-rows-without-nan\/)\n- [How to Select Rows with NaN Values in Pandas (With Examples)](https:\/\/www.statology.org\/pandas-select-rows-with-nan\/)\n\n© 2025 [Statology](https:\/\/www.statology.org\/) \\| [Privacy Policy](https:\/\/www.guidingtechmedia.com\/privacy\/) \\| [Terms of Use](https:\/\/www.guidingtechmedia.com\/terms-of-use\/)\n\nWisteria Theme by [WPFriendship](https:\/\/wpfriendship.com\/ \"WPFriendship\") ⋅ Powered by [WordPress](https:\/\/wordpress.org\/ \"WordPress\")","attrs_readable_markdown":"***\nYou can use the following methods to drop columns from a pandas DataFrame with NaN values:\n\n**Method 1: Drop Columns with Any NaN Values**\n```\ndf = df.dropna(axis=1)\n```\n**Method 2: Drop Columns with All NaN Values**\n```\ndf = df.dropna(axis=1, how='all')\n```\n**Method 3: Drop Columns with Minimum Number of NaN Values**\n```\ndf = df.dropna(axis=1, thresh=2)\n```\nThe following examples show how to use each method in practice with the following pandas DataFrame:\n```\nimport pandas as pd import numpy as np #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'B'], 'position': [np.nan, 'G', 'F', 'F', 'C', 'G'], 'points': [11, 28, 10, 26, 6, 25], 'rebounds': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]}) #view DataFrame print(df) team position points rebounds 0 A NaN 11 NaN 1 A G 28 NaN 2 A F 10 NaN 3 B F 26 NaN 4 B C 6 NaN 5 B G 25 NaN\n```\n## **Example 1: Drop Columns with Any NaN Values**\nThe following code shows how to drop columns with any NaN values:\n```\n#drop columns with any NaN values df = df.dropna(axis=1) #view updated DataFrame print(df) team points 0 A 11 1 A 28 2 A 10 3 B 26 4 B 6 5 B 25\n```\nNotice that the **position** and **rebounds** columns were dropped since they both had at least one NaN value.\n\n## **Example 2: Drop Columns with All NaN Values**\nThe following code shows how to drop columns with all NaN values:\n```\n#drop columns with all NaN values df = df.dropna(axis=1, how='all') #view updated DataFrame print(df) team position points 0 A NaN 11 1 A G 28 2 A F 10 3 B F 26 4 B C 6 5 B G 25\n```\nNotice that the **rebounds** column was dropped since it was the only column with all NaN values.\n\n## **Example 3: Drop Columns with Minimum Number of NaN Values**\nThe following code shows how to drop columns with **at least two** NaN values:\n```\n#drop columns with at least two NaN values df = df.dropna(axis=1, thresh=2) #view updated DataFrame print(df) team position points 0 A NaN 11 1 A G 28 2 A F 10 3 B F 26 4 B C 6 5 B G 25\n```\nNotice that the **rebounds** column was dropped since it was the only column with at least two NaN values.\n\n**Note**: You can find the complete documentation for the **dropna()** function in pandas [here](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html).\n\n## **Additional Resources**\nThe following tutorials explain how to perform other common tasks in pandas:\n\n[How to Drop First Column in Pandas](https:\/\/www.statology.org\/pandas-drop-first-column\/)  \n [How to Drop Duplicate Columns in Pandas](https:\/\/www.statology.org\/pandas-drop-duplicate-columns\/)  \n [How to Drop All Columns Except Specific Ones in Pandas](https:\/\/www.statology.org\/pandas-drop-all-columns-except\/)\n\n![](https:\/\/www.statology.org\/wp-content\/uploads\/2023\/08\/statology_gravatar-scaled.jpg)\n\nHey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.","meta_canonical":null}

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Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
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URL	https://www.statology.org/pandas-drop-columns-with-nan/
Last Crawled	2026-04-11 08:36:56 (3 hours ago)
First Indexed	2023-01-03 16:36:31 (3 years ago)
HTTP Status Code	200
Meta Title	Pandas: How to Drop Columns with NaN Values
Meta Description	This tutorial explains how to drop columns in a pandas DataFrame with NaN values, including several examples.
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Boilerpipe Text	You can use the following methods to drop columns from a pandas DataFrame with NaN values: Method 1: Drop Columns with Any NaN Values df = df. dropna (axis= 1 ) Method 2: Drop Columns with All NaN Values df = df. dropna (axis= 1 , how=' all ') Method 3: Drop Columns with Minimum Number of NaN Values df = df. dropna (axis= 1 , thresh= 2 ) The following examples show how to use each method in practice with the following pandas DataFrame: import pandas as pd import numpy as np #create DataFrame df = pd. DataFrame ({' team ': ['A', 'A', 'A', 'B', 'B', 'B'], ' position ': [np.nan, 'G', 'F', 'F', 'C', 'G'], ' points ': [11, 28, 10, 26, 6, 25], ' rebounds ': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]}) #view DataFrame print (df) team position points rebounds 0 A NaN 11 NaN 1 A G 28 NaN 2 A F 10 NaN 3 B F 26 NaN 4 B C 6 NaN 5 B G 25 NaN Example 1: Drop Columns with Any NaN Values The following code shows how to drop columns with any NaN values: #drop columns with any NaN values df = df. dropna (axis= 1 ) #view updated DataFrame print (df) team points 0 A 11 1 A 28 2 A 10 3 B 26 4 B 6 5 B 25 Notice that the position and rebounds columns were dropped since they both had at least one NaN value. Example 2: Drop Columns with All NaN Values The following code shows how to drop columns with all NaN values: #drop columns with all NaN values df = df. dropna (axis= 1 , how=' all ') #view updated DataFrame print (df) team position points 0 A NaN 11 1 A G 28 2 A F 10 3 B F 26 4 B C 6 5 B G 25 Notice that the rebounds column was dropped since it was the only column with all NaN values. Example 3: Drop Columns with Minimum Number of NaN Values The following code shows how to drop columns with at least two NaN values: #drop columns with at least two NaN values df = df. dropna (axis= 1 , thresh= 2 ) #view updated DataFrame print (df) team position points 0 A NaN 11 1 A G 28 2 A F 10 3 B F 26 4 B C 6 5 B G 25 Notice that the rebounds column was dropped since it was the only column with at least two NaN values. Note : You can find the complete documentation for the dropna() function in pandas here . Additional Resources The following tutorials explain how to perform other common tasks in pandas: How to Drop First Column in Pandas How to Drop Duplicate Columns in Pandas How to Drop All Columns Except Specific Ones in Pandas Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.
Markdown	[![Statology](https://www.statology.org/wp-content/uploads/2024/07/StatologyLogo_OnWhite.png)](https://www.statology.org/) - [About](https://www.statology.org/about/) - [Course](https://www.statology.org/course-register/) - [Basic Stats](https://www.statology.org/tutorials/) - [Machine Learning](https://www.statology.org/machine-learning-tutorials/) - [Software Tutorials]() - [Excel](https://www.statology.org/excel-guides/) - [Google Sheets](https://www.statology.org/google-sheets-guides/) - [MongoDB](https://www.statology.org/mongodb-guides/) - [MySQL](https://www.statology.org/mysql-guides/) - [Power BI](https://www.statology.org/power-bi-guides/) - [PySpark](https://www.statology.org/pyspark-guides/) - [Python](https://www.statology.org/python-guides/) - [R](https://www.statology.org/r-guides/) - [SAS](https://www.statology.org/sas-guides/) - [SPSS](https://www.statology.org/spss-guides/) - [Stata](https://www.statology.org/stata-guides/) - [TI-84](https://www.statology.org/ti-84-guides/) - [VBA](https://www.statology.org/vba-guides/) - [Tools]() - [Calculators](https://www.statology.org/calculators/) - [Critical Value Tables](https://www.statology.org/tables/) - [Glossary](https://www.statology.org/glossary/) # Pandas: How to Drop Columns with NaN Values by [Zach Bobbitt](https://www.statology.org/author/admin/) Published on [Published on January 3, 2023](https://www.statology.org/pandas-drop-columns-with-nan/) * You can use the following methods to drop columns from a pandas DataFrame with NaN values: Method 1: Drop Columns with Any NaN Values ``` df = df.dropna(axis=1) ``` Method 2: Drop Columns with All NaN Values ``` df = df.dropna(axis=1, how='all') ``` Method 3: Drop Columns with Minimum Number of NaN Values ``` df = df.dropna(axis=1, thresh=2) ``` The following examples show how to use each method in practice with the following pandas DataFrame: ``` import pandas as pd import numpy as np #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'B'], 'position': [np.nan, 'G', 'F', 'F', 'C', 'G'], 'points': [11, 28, 10, 26, 6, 25], 'rebounds': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]}) #view DataFrame print(df) team position points rebounds 0 A NaN 11 NaN 1 A G 28 NaN 2 A F 10 NaN 3 B F 26 NaN 4 B C 6 NaN 5 B G 25 NaN ``` ## Example 1: Drop Columns with Any NaN Values The following code shows how to drop columns with any NaN values: ``` #drop columns with any NaN values df = df.dropna(axis=1) #view updated DataFrame print(df) team points 0 A 11 1 A 28 2 A 10 3 B 26 4 B 6 5 B 25 ``` Notice that the position and rebounds columns were dropped since they both had at least one NaN value. ## Example 2: Drop Columns with All NaN Values The following code shows how to drop columns with all NaN values: ``` #drop columns with all NaN values df = df.dropna(axis=1, how='all') #view updated DataFrame print(df) team position points 0 A NaN 11 1 A G 28 2 A F 10 3 B F 26 4 B C 6 5 B G 25 ``` Notice that the rebounds column was dropped since it was the only column with all NaN values. ## Example 3: Drop Columns with Minimum Number of NaN Values The following code shows how to drop columns with at least two NaN values: ``` #drop columns with at least two NaN values df = df.dropna(axis=1, thresh=2) #view updated DataFrame print(df) team position points 0 A NaN 11 1 A G 28 2 A F 10 3 B F 26 4 B C 6 5 B G 25 ``` Notice that the rebounds column was dropped since it was the only column with at least two NaN values. Note: You can find the complete documentation for the dropna() function in pandas [here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html). ## Additional Resources The following tutorials explain how to perform other common tasks in pandas: [How to Drop First Column in Pandas](https://www.statology.org/pandas-drop-first-column/) [How to Drop Duplicate Columns in Pandas](https://www.statology.org/pandas-drop-duplicate-columns/) [How to Drop All Columns Except Specific Ones in Pandas](https://www.statology.org/pandas-drop-all-columns-except/) Posted in [Programming](https://www.statology.org/category/programming/) ![](https://www.statology.org/wp-content/uploads/2023/08/statology_gravatar-scaled.jpg) [Zach Bobbitt](https://www.statology.org/author/admin/) Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. 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Readable Markdown	* You can use the following methods to drop columns from a pandas DataFrame with NaN values: Method 1: Drop Columns with Any NaN Values ``` df = df.dropna(axis=1) ``` Method 2: Drop Columns with All NaN Values ``` df = df.dropna(axis=1, how='all') ``` Method 3: Drop Columns with Minimum Number of NaN Values ``` df = df.dropna(axis=1, thresh=2) ``` The following examples show how to use each method in practice with the following pandas DataFrame: ``` import pandas as pd import numpy as np #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'B'], 'position': [np.nan, 'G', 'F', 'F', 'C', 'G'], 'points': [11, 28, 10, 26, 6, 25], 'rebounds': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]}) #view DataFrame print(df) team position points rebounds 0 A NaN 11 NaN 1 A G 28 NaN 2 A F 10 NaN 3 B F 26 NaN 4 B C 6 NaN 5 B G 25 NaN ``` ## Example 1: Drop Columns with Any NaN Values The following code shows how to drop columns with any NaN values: ``` #drop columns with any NaN values df = df.dropna(axis=1) #view updated DataFrame print(df) team points 0 A 11 1 A 28 2 A 10 3 B 26 4 B 6 5 B 25 ``` Notice that the position and rebounds columns were dropped since they both had at least one NaN value. ## Example 2: Drop Columns with All NaN Values The following code shows how to drop columns with all NaN values: ``` #drop columns with all NaN values df = df.dropna(axis=1, how='all') #view updated DataFrame print(df) team position points 0 A NaN 11 1 A G 28 2 A F 10 3 B F 26 4 B C 6 5 B G 25 ``` Notice that the rebounds column was dropped since it was the only column with all NaN values. ## Example 3: Drop Columns with Minimum Number of NaN Values The following code shows how to drop columns with at least two NaN values: ``` #drop columns with at least two NaN values df = df.dropna(axis=1, thresh=2) #view updated DataFrame print(df) team position points 0 A NaN 11 1 A G 28 2 A F 10 3 B F 26 4 B C 6 5 B G 25 ``` Notice that the rebounds column was dropped since it was the only column with at least two NaN values. Note: You can find the complete documentation for the dropna() function in pandas [here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html). ## Additional Resources** The following tutorials explain how to perform other common tasks in pandas: [How to Drop First Column in Pandas](https://www.statology.org/pandas-drop-first-column/) [How to Drop Duplicate Columns in Pandas](https://www.statology.org/pandas-drop-duplicate-columns/) [How to Drop All Columns Except Specific Ones in Pandas](https://www.statology.org/pandas-drop-all-columns-except/) ![](https://www.statology.org/wp-content/uploads/2023/08/statology_gravatar-scaled.jpg) Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.
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