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Calculated Shard: 147 (from laksa117)

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curl -X POST \
  'http://laksa147.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://sentry.io/answers/remove-dataframe-rows-with-missing-values-in-python/\')), getAhrefsUnparsedNoserviceFromURL(\'https://sentry.io/answers/remove-dataframe-rows-with-missing-values-in-python/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/sentry.io\/answers\/remove-dataframe-rows-with-missing-values-in-python\/","crawl_time":1775633986,"first_indexed_time":1706850082,"http_code":200,"src_unparsed":"io,sentry!\/answers\/remove-dataframe-rows-with-missing-values-in-python\/ s443","src_root_hash":"17800916657557307547","history_drop_reason":null,"meta_title":"Remove DataFrame rows with missing values in Python | Sentry","meta_descriptions":["Sentry helps developers monitor and fix crashes in real time. Get the details you need to resolve the most important issues quickly."],"attrs_boilerpipe_text":"The Problem\nIn Pandas, how do I remove DataFrame rows that contain\nNone\nor\nNaN\nacross all columns? How can I do this when these values are present in only some columns?\nThe Solution\nWe can achieve both of these results using the\nDataFrame.dropna\nmethod. For example:\nimport\npandas\nfrom\nnumpy\nimport\nnan\n\ndf\n=\npandas\n.\nDataFrame\n(\n{\n\"Test 1\"\n:\n[\n90\n,\n10\n,\nnan\n,\nnan\n]\n,\n\"Test 2\"\n:\n[\n41\n,\nnan\n,\n32\n,\nnan\n]\n,\n\"Test 3\"\n:\n[\n89\n,\n35\n,\n72\n,\nnan\n]\n,\n\"Test 4\"\n:\n[\n52\n,\nnan\n,\nnan\n,\nnan\n]\n,\n}\n)\nprint\n(\ndf\n)\n# output:\n#    Test 1  Test 2  Test 3  Test 4\n# 0    90.0    41.0    89.0    52.0\n# 1    10.0     NaN    35.0     NaN\n# 2     NaN    32.0    72.0     NaN\n# 3     NaN     NaN     NaN     NaN\ndf_no_empty_rows\n=\ndf\n.\ndropna\n(\nhow\n=\n\"all\"\n)\n# drop rows containing all NaNs\nprint\n(\ndf_no_empty_rows\n)\n# output:\n#    Test 1  Test 2  Test 3  Test 4\n# 0    90.0    41.0    89.0    52.0\n# 1    10.0     NaN    35.0     NaN\n# 2     NaN    32.0    72.0     NaN\ndf_no_empty_values\n=\ndf\n.\ndropna\n(\nhow\n=\n\"any\"\n)\n# drop rows containing any NaNs\nprint\n(\ndf_no_empty_values\n)\n# output:\n#    Test 1  Test 2  Test 3  Test 4\n# 0    90.0    41.0    89.0    52.0","attrs_markdown":"[Skip to main content](https:\/\/sentry.io\/answers\/remove-dataframe-rows-with-missing-values-in-python\/#main-content)\n\nMenu\n\nPlatform\n\nProducts\n\n- [Error Monitoring](https:\/\/sentry.io\/product\/error-monitoring\/)\n- [Logs](https:\/\/sentry.io\/product\/logs\/)\n- [Session Replay](https:\/\/sentry.io\/product\/session-replay\/)\n- [Tracing](https:\/\/sentry.io\/product\/tracing\/)\n- [Seer NEW](https:\/\/sentry.io\/product\/seer\/)\n- [Profiling](https:\/\/sentry.io\/product\/profiling\/)\n- [AI Code Review NEW](https:\/\/sentry.io\/product\/seer\/ai-code-review\/)\n- [Size Analysis NEW](https:\/\/sentry.io\/product\/size-analysis)\n- [Cron Monitoring](https:\/\/sentry.io\/product\/cron-monitoring\/)\n- [Uptime Monitoring](https:\/\/sentry.io\/product\/uptime-monitoring\/)\n- [Sentry CLI NEW](https:\/\/cli.sentry.dev\/)\n\nIntegrations\n\n- [![](https:\/\/sentry.io\/astro-assets\/nav-icons\/github.png)Github](https:\/\/sentry.io\/integrations\/github\/)\n- [![](https:\/\/sentry.io\/astro-assets\/nav-icons\/slack.png)Slack](https:\/\/sentry.io\/integrations\/slack\/)\n- [Sentry MCP NEW](https:\/\/mcp.sentry.dev\/)\n- [All Integrations](https:\/\/sentry.io\/integrations\/)\n\nSDKs\n\n- [![](https:\/\/sentry.io\/astro-assets\/nav-icons\/javascript.png)Javascript](https:\/\/sentry.io\/for\/javascript\/)\n- [![](https:\/\/sentry.io\/astro-assets\/nav-icons\/python.png)Python](https:\/\/sentry.io\/for\/python\/)\n- [![](https:\/\/sentry.io\/astro-assets\/nav-icons\/react.png)React](https:\/\/sentry.io\/for\/react\/)\n- [![](https:\/\/sentry.io\/astro-assets\/nav-icons\/laravel.png)Laravel](https:\/\/sentry.io\/for\/laravel\/)\n- [![](https:\/\/sentry.io\/astro-assets\/nav-icons\/nextjs.png)Next.js](https:\/\/sentry.io\/for\/nextjs\/)\n- [All SDKs](https:\/\/sentry.io\/platforms\/)\n\nSolutions\n\n### Solutions\n- [Web \/ Full Stack Development](https:\/\/sentry.io\/for\/full-stack\/)\n- [Mobile Crash Reporting](https:\/\/sentry.io\/solutions\/mobile-developers\/)\n- [Game Crash Reporting](https:\/\/sentry.io\/solutions\/game-developers\/)\n- [AI Observability](https:\/\/sentry.io\/solutions\/ai-observability\/)\n- [Application Performance Monitoring](https:\/\/sentry.io\/solutions\/application-performance-monitoring\/)\n- [Real User Monitoring](https:\/\/sentry.io\/solutions\/real-user-monitoring-rum\/)\n- [Ecommerce](https:\/\/sentry.io\/solutions\/ecommerce\/)\n- [Enterprise](https:\/\/sentry.io\/for\/enterprise\/)\n- [Startups](https:\/\/sentry.io\/for\/startups\/)\n\nResources\n\nLearn\n\n- [Blog](https:\/\/blog.sentry.io\/)\n- [Changelog](https:\/\/sentry.io\/changelog\/)\n- [Sandbox](https:\/\/sandbox.sentry.io\/)\n- [Resources](https:\/\/sentry.io\/resources\/)\n- [Syntax](https:\/\/syntax.fm\/)\n- [Customers](https:\/\/sentry.io\/customers\/)\n- [Cookbook](https:\/\/sentry.io\/cookbook\/)\n\nSupport\n\n- [Contact Us](https:\/\/sentry.io\/contact\/enterprise\/)\n- [Help Center](https:\/\/help.sentry.io\/)\n- [Status](https:\/\/status.sentry.io\/)\n\nHang out with us\n\n- [Sentry Build](https:\/\/sentry.io\/resources\/sentry-build\/)\n- [Events](https:\/\/sentry.io\/events\/)\n- [Merch](https:\/\/sentry.shop\/)\n\n[Docs](https:\/\/docs.sentry.io\/)\n\n[Pricing](https:\/\/sentry.io\/pricing\/)\n\n[Sign In](https:\/\/sentry.io\/auth\/login\/)   [Get Demo](https:\/\/sentry.io\/demo\/)   [Get Started](https:\/\/sentry.io\/signup\/)\n\n- Platform\n- Solutions\n  - [Web \/ Full Stack Development](https:\/\/sentry.io\/for\/full-stack\/)\n  - [Mobile Crash Reporting](https:\/\/sentry.io\/solutions\/mobile-developers\/)\n  - [Game Crash Reporting](https:\/\/sentry.io\/solutions\/game-developers\/)\n  - [AI Observability](https:\/\/sentry.io\/solutions\/ai-observability\/)\n  - [Application Performance Monitoring](https:\/\/sentry.io\/solutions\/application-performance-monitoring\/)\n  - [Real User Monitoring](https:\/\/sentry.io\/solutions\/real-user-monitoring-rum\/)\n  - [Ecommerce](https:\/\/sentry.io\/solutions\/ecommerce\/)\n  - [Enterprise](https:\/\/sentry.io\/for\/enterprise\/)\n  - [Startups](https:\/\/sentry.io\/for\/startups\/)\n- Resources\n- [Docs](https:\/\/docs.sentry.io\/)\n- [Pricing](https:\/\/sentry.io\/pricing\/)\n\n[Sign In](https:\/\/sentry.io\/auth\/login\/)   [Get Demo](https:\/\/sentry.io\/demo\/)   [Get Started](https:\/\/sentry.io\/signup\/)\n\n[Sentry Answers](https:\/\/sentry.io\/answers\/) \\> [Python](https:\/\/sentry.io\/answers\/python\/) \\> Remove DataFrame rows with missing values in Python\n\n# Remove DataFrame rows with missing values in Python\n![](https:\/\/sentry.io\/astro-assets\/legacy\/ghost.png)\n\nDavid Y. —\n\nOctober 15, 2023\n\n[jump to solution](https:\/\/sentry.io\/answers\/remove-dataframe-rows-with-missing-values-in-python\/#the-solution)\n\n## The Problem\nIn Pandas, how do I remove DataFrame rows that contain `None` or `NaN` across all columns? How can I do this when these values are present in only some columns?\n\n## The Solution\nWe can achieve both of these results using the [`DataFrame.dropna`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html) method. For example:\n```\nimport pandas\nfrom numpy import nan\n\ndf = pandas.DataFrame(\n    {\n        \"Test 1\": [90, 10, nan, nan],\n        \"Test 2\": [41, nan, 32, nan],\n        \"Test 3\": [89, 35, 72, nan],\n        \"Test 4\": [52, nan, nan, nan],\n    }\n)\nprint(df)\n\n# output:\n#    Test 1  Test 2  Test 3  Test 4\n# 0    90.0    41.0    89.0    52.0\n# 1    10.0     NaN    35.0     NaN\n# 2     NaN    32.0    72.0     NaN\n# 3     NaN     NaN     NaN     NaN\n\ndf_no_empty_rows = df.dropna(how=\"all\")  # drop rows containing all NaNs\nprint(df_no_empty_rows)\n\n# output:\n#    Test 1  Test 2  Test 3  Test 4\n# 0    90.0    41.0    89.0    52.0\n# 1    10.0     NaN    35.0     NaN\n# 2     NaN    32.0    72.0     NaN\n\ndf_no_empty_values = df.dropna(how=\"any\")  # drop rows containing any NaNs\nprint(df_no_empty_values)\n\n# output:\n#    Test 1  Test 2  Test 3  Test 4\n# 0    90.0    41.0    89.0    52.0\n```\n\n### More Python content\n- Codecov  [Python - Codecov (opens in a new tab)](https:\/\/about.codecov.io\/language\/python\/)\n- Sentry Blog  [Logging in Python: A Developer's Guide (opens in a new tab)](https:\/\/blog.sentry.io\/logging-in-python-a-developers-guide\/#python-logging-best-practices)\n- Sentry Blog  [Python Performance Testing: A Comprehensive Guide (opens in a new tab)](https:\/\/blog.sentry.io\/python-performance-testing-a-comprehensive-guide\/)\n- Engineering Blog  [Testing in Hundreds of Environments (opens in a new tab)](https:\/\/sentry.engineering\/blog\/how-we-run-our-python-tests-in-hundreds-of-environments-really-fast)\n- ![Syntax.fm logo](https:\/\/sentry.io\/astro-assets\/legacy\/syntax-logo.png)  [Listen to the Syntax Podcast (opens in a new tab)](https:\/\/syntax.fm\/?utm_source=sentry&utm_medium=display&utm_campaign=syntax-na-podcastpromo&utm_content=logo-answers-rail-banner-listen)\n  Tasty treats for web developers brought to you by Sentry. Get tips and tricks from Wes Bos and Scott Tolinski.\n  [SEE EPISODES](https:\/\/syntax.fm\/?utm_source=sentry&utm_medium=display&utm_campaign=syntax-na-podcastpromo&utm_content=logo-answers-rail-banner-listen)\n\n## Related Answers\n[Make a list of the column headers in a Python Pandas DataFrame](https:\/\/sentry.io\/answers\/make-a-list-of-the-column-headers-in-a-python-pandas-dataframe\/)\n\nDavid Y.  —\n\nNovember 15, 2023\n\n[Set individual font sizes for labels and titles in \\`pyplot\\` graphs](https:\/\/sentry.io\/answers\/set-individual-font-sizes-for-labels-and-titles-in-pyplot-graphs\/)\n\nDavid Y.  —\n\nNovember 15, 2023\n\n[Logical AND (&&) in Python](https:\/\/sentry.io\/answers\/logical-and-in-python\/)\n\nDavid Y.  —\n\nMay 15, 2023\n\n[Split a string into a list of words in Python](https:\/\/sentry.io\/answers\/split-a-string-into-a-list-of-words-in-python\/)\n\nDavid Y.  —\n\nJune 15, 2024\n\nConsidered \"not bad\" by 4 million developers and more than 150,000 organizations worldwide, Sentry provides code-level observability to many of the world's best-known companies like Disney, Peloton, Cloudflare, Eventbrite, Slack, Supercell, and Rockstar Games. Each month we process billions of exceptions from the most popular products on the internet.\n\n[learn more about sentry](https:\/\/sentry.io\/)   [join our discord](https:\/\/discord.com\/invite\/sentry)\n\n![Sentry](https:\/\/sentry.io\/_astro\/answer-cta-light.CRAdOWkA.svg)\n\n### Company\n- [About](https:\/\/sentry.io\/about\/)\n- [Blog](https:\/\/blog.sentry.io\/)\n- [Careers](https:\/\/sentry.io\/careers\/)\n- [Contact Us](https:\/\/sentry.io\/contact\/enterprise\/)\n- [Trust](https:\/\/sentry.io\/trust\/)\n\n### Platform\n- [Error Monitoring](https:\/\/sentry.io\/product\/error-monitoring\/)\n- [Tracing](https:\/\/sentry.io\/product\/tracing\/)\n- [Session Replay](https:\/\/sentry.io\/product\/session-replay\/)\n- [Seer](https:\/\/sentry.io\/product\/seer\/)\n- [Logs](https:\/\/sentry.io\/product\/logs\/)\n- [Uptime Monitoring](https:\/\/sentry.io\/product\/uptime-monitoring\/)\n- [Profiling](https:\/\/sentry.io\/product\/profiling\/)\n- [Cron Monitoring](https:\/\/sentry.io\/product\/cron-monitoring\/)\n- [Integrations](https:\/\/sentry.io\/integrations\/)\n\n### Solutions\n- [Web \/ Full Stack Development](https:\/\/sentry.io\/for\/full-stack\/)\n- [Mobile Crash Reporting](https:\/\/sentry.io\/solutions\/mobile-developers\/)\n- [Game Crash Reporting](https:\/\/sentry.io\/solutions\/game-developers\/)\n- [AI Observability](https:\/\/sentry.io\/solutions\/ai-observability\/)\n- [Application Performance Monitoring](https:\/\/sentry.io\/solutions\/application-performance-monitoring\/)\n- [Real User Monitoring](https:\/\/sentry.io\/solutions\/real-user-monitoring-rum\/)\n- [Ecommerce](https:\/\/sentry.io\/solutions\/ecommerce\/)\n- [Enterprise](https:\/\/sentry.io\/for\/enterprise\/)\n- [Startups](https:\/\/sentry.io\/for\/startups\/)\n\n### Get Help\n- [Docs](https:\/\/docs.sentry.io\/)\n- [Help Center](https:\/\/help.sentry.io\/)\n- [Status](https:\/\/status.sentry.io\/)\n- [Dev Resources](https:\/\/develop.sentry.dev\/getting-started\/)\n\n[Terms](https:\/\/sentry.io\/terms\/)  [Security & Compliance](https:\/\/sentry.io\/security\/)  [Privacy](https:\/\/sentry.io\/privacy\/)\n\n[![](https:\/\/sentry.io\/astro-assets\/nav-icons\/x-twitter.png) Twitter Menu Button](https:\/\/x.com\/sentry\/)  [![](https:\/\/sentry.io\/astro-assets\/nav-icons\/github.png) Github Social Menu Button](https:\/\/github.com\/getsentry\/)  [![](https:\/\/sentry.io\/astro-assets\/nav-icons\/linkedin.png) LinkedIn Menu Button](https:\/\/linkedin.com\/company\/getsentry\/)  [![](https:\/\/sentry.io\/astro-assets\/nav-icons\/discord.png) Discord Menu Button](https:\/\/discord.gg\/sentry)\n\n© 2026 • Sentry is a registered Trademark of Functional Software, Inc.","attrs_readable_markdown":"## The Problem\nIn Pandas, how do I remove DataFrame rows that contain `None` or `NaN` across all columns? How can I do this when these values are present in only some columns?\n\n## The Solution\nWe can achieve both of these results using the [`DataFrame.dropna`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html) method. For example:\n```\nimport pandas\nfrom numpy import nan\n\ndf = pandas.DataFrame(\n    {\n        \"Test 1\": [90, 10, nan, nan],\n        \"Test 2\": [41, nan, 32, nan],\n        \"Test 3\": [89, 35, 72, nan],\n        \"Test 4\": [52, nan, nan, nan],\n    }\n)\nprint(df)\n\n# output:\n#    Test 1  Test 2  Test 3  Test 4\n# 0    90.0    41.0    89.0    52.0\n# 1    10.0     NaN    35.0     NaN\n# 2     NaN    32.0    72.0     NaN\n# 3     NaN     NaN     NaN     NaN\n\ndf_no_empty_rows = df.dropna(how=\"all\")  # drop rows containing all NaNs\nprint(df_no_empty_rows)\n\n# output:\n#    Test 1  Test 2  Test 3  Test 4\n# 0    90.0    41.0    89.0    52.0\n# 1    10.0     NaN    35.0     NaN\n# 2     NaN    32.0    72.0     NaN\n\ndf_no_empty_values = df.dropna(how=\"any\")  # drop rows containing any NaNs\nprint(df_no_empty_values)\n\n# output:\n#    Test 1  Test 2  Test 3  Test 4\n# 0    90.0    41.0    89.0    52.0\n```","meta_canonical":null}

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5. Seen Status Check

ℹ️ Skipped - page is already crawled

📄

INDEXABLE

✅

CRAWLED

7 hours ago

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ROBOTS ALLOWED

Page Info Filters

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
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://sentry.io/answers/remove-dataframe-rows-with-missing-values-in-python/
Last Crawled	2026-04-08 07:39:46 (7 hours ago)
First Indexed	2024-02-02 05:01:22 (2 years ago)
HTTP Status Code	200
Meta Title	Remove DataFrame rows with missing values in Python \| Sentry
Meta Description	Sentry helps developers monitor and fix crashes in real time. Get the details you need to resolve the most important issues quickly.
Meta Canonical	null
Boilerpipe Text	The Problem In Pandas, how do I remove DataFrame rows that contain None or NaN across all columns? How can I do this when these values are present in only some columns? The Solution We can achieve both of these results using the DataFrame.dropna method. For example: import pandas from numpy import nan df = pandas . DataFrame ( { "Test 1" : [ 90 , 10 , nan , nan ] , "Test 2" : [ 41 , nan , 32 , nan ] , "Test 3" : [ 89 , 35 , 72 , nan ] , "Test 4" : [ 52 , nan , nan , nan ] , } ) print ( df ) # output: # Test 1 Test 2 Test 3 Test 4 # 0 90.0 41.0 89.0 52.0 # 1 10.0 NaN 35.0 NaN # 2 NaN 32.0 72.0 NaN # 3 NaN NaN NaN NaN df_no_empty_rows = df . dropna ( how = "all" ) # drop rows containing all NaNs print ( df_no_empty_rows ) # output: # Test 1 Test 2 Test 3 Test 4 # 0 90.0 41.0 89.0 52.0 # 1 10.0 NaN 35.0 NaN # 2 NaN 32.0 72.0 NaN df_no_empty_values = df . dropna ( how = "any" ) # drop rows containing any NaNs print ( df_no_empty_values ) # output: # Test 1 Test 2 Test 3 Test 4 # 0 90.0 41.0 89.0 52.0
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How can I do this when these values are present in only some columns? ## The Solution We can achieve both of these results using the [`DataFrame.dropna`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html) method. For example: ``` import pandas from numpy import nan df = pandas.DataFrame( { "Test 1": [90, 10, nan, nan], "Test 2": [41, nan, 32, nan], "Test 3": [89, 35, 72, nan], "Test 4": [52, nan, nan, nan], } ) print(df) # output: # Test 1 Test 2 Test 3 Test 4 # 0 90.0 41.0 89.0 52.0 # 1 10.0 NaN 35.0 NaN # 2 NaN 32.0 72.0 NaN # 3 NaN NaN NaN NaN df_no_empty_rows = df.dropna(how="all") # drop rows containing all NaNs print(df_no_empty_rows) # output: # Test 1 Test 2 Test 3 Test 4 # 0 90.0 41.0 89.0 52.0 # 1 10.0 NaN 35.0 NaN # 2 NaN 32.0 72.0 NaN df_no_empty_values = df.dropna(how="any") # drop rows containing any NaNs print(df_no_empty_values) # output: # Test 1 Test 2 Test 3 Test 4 # 0 90.0 41.0 89.0 52.0 ``` ### More Python content - Codecov [Python - Codecov (opens in a new tab)](https://about.codecov.io/language/python/) - Sentry Blog [Logging in Python: A Developer's Guide (opens in a new tab)](https://blog.sentry.io/logging-in-python-a-developers-guide/#python-logging-best-practices) - Sentry Blog [Python Performance Testing: A Comprehensive Guide (opens in a new tab)](https://blog.sentry.io/python-performance-testing-a-comprehensive-guide/) - Engineering Blog [Testing in Hundreds of Environments (opens in a new tab)](https://sentry.engineering/blog/how-we-run-our-python-tests-in-hundreds-of-environments-really-fast) - ![Syntax.fm logo](https://sentry.io/astro-assets/legacy/syntax-logo.png) [Listen to the Syntax Podcast (opens in a new tab)](https://syntax.fm/?utm_source=sentry&utm_medium=display&utm_campaign=syntax-na-podcastpromo&utm_content=logo-answers-rail-banner-listen) Tasty treats for web developers brought to you by Sentry. 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Readable Markdown	## The Problem In Pandas, how do I remove DataFrame rows that contain `None` or `NaN` across all columns? How can I do this when these values are present in only some columns? ## The Solution We can achieve both of these results using the [`DataFrame.dropna`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html) method. For example: ``` import pandas from numpy import nan df = pandas.DataFrame( { "Test 1": [90, 10, nan, nan], "Test 2": [41, nan, 32, nan], "Test 3": [89, 35, 72, nan], "Test 4": [52, nan, nan, nan], } ) print(df) # output: # Test 1 Test 2 Test 3 Test 4 # 0 90.0 41.0 89.0 52.0 # 1 10.0 NaN 35.0 NaN # 2 NaN 32.0 72.0 NaN # 3 NaN NaN NaN NaN df_no_empty_rows = df.dropna(how="all") # drop rows containing all NaNs print(df_no_empty_rows) # output: # Test 1 Test 2 Test 3 Test 4 # 0 90.0 41.0 89.0 52.0 # 1 10.0 NaN 35.0 NaN # 2 NaN 32.0 72.0 NaN df_no_empty_values = df.dropna(how="any") # drop rows containing any NaNs print(df_no_empty_values) # output: # Test 1 Test 2 Test 3 Test 4 # 0 90.0 41.0 89.0 52.0 ```
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