🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 68 (from laksa090)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa068.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://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html\')), getAhrefsUnparsedNoserviceFromURL(\'https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html","crawl_time":1775212336,"first_indexed_time":1580533903,"http_code":200,"src_unparsed":"org,pydata!pandas,\/docs\/reference\/api\/pandas.DataFrame.dropna.html s443","src_root_hash":"10355827761381095868","history_drop_reason":null,"meta_title":"pandas.DataFrame.dropna — pandas 3.0.2 documentation","meta_descriptions":[],"attrs_boilerpipe_text":"DataFrame.\ndropna\n(\n*\n,\naxis\n=\n0\n,\nhow\n=\n<no_default>\n,\nthresh\n=\n<no_default>\n,\nsubset\n=\nNone\n,\ninplace\n=\nFalse\n,\nignore_index\n=\nFalse\n)\n[source]\n#\nRemove missing values.\nSee the\nUser Guide\nfor more on which values are\nconsidered missing, and how to work with missing data.\nParameters\n:\naxis\n{0 or ‘index’, 1 or ‘columns’}, default 0\nDetermine if rows or columns which contain missing values are\nremoved.\n0, or ‘index’ : Drop rows which contain missing values.\n1, or ‘columns’ : Drop columns which contain missing value.\nOnly a single axis is allowed.\nhow\n{‘any’, ‘all’}, default ‘any’\nDetermine if row or column is removed from DataFrame, when we have\nat least one NA or all NA.\n‘any’ : If any NA values are present, drop that row or column.\n‘all’ : If all values are NA, drop that row or column.\nthresh\nint, optional\nRequire that many non-NA values. Cannot be combined with how.\nsubset\ncolumn label or iterable of labels, optional\nLabels along other axis to consider, e.g. if you are dropping rows\nthese would be a list of columns to include.\ninplace\nbool, default False\nWhether to modify the DataFrame rather than creating a new one.\nignore_index\nbool, default\nFalse\nIf\nTrue\n, the resulting axis will be labeled 0, 1, …, n - 1.\nAdded in version 2.0.0.\nReturns\n:\nDataFrame or None\nDataFrame with NA entries dropped from it or None if\ninplace=True\n.\nExamples\n>>>\ndf\n=\npd\n.\nDataFrame\n(\n...\n{\n...\n\"name\"\n:\n[\n\"Alfred\"\n,\n\"Batman\"\n,\n\"Catwoman\"\n],\n...\n\"toy\"\n:\n[\nnp\n.\nnan\n,\n\"Batmobile\"\n,\n\"Bullwhip\"\n],\n...\n\"born\"\n:\n[\npd\n.\nNaT\n,\npd\n.\nTimestamp\n(\n\"1940-04-25\"\n),\npd\n.\nNaT\n],\n...\n}\n...\n)\n>>>\ndf\nname        toy       born\n0    Alfred        NaN        NaT\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\nDrop the rows where at least one element is missing.\n>>>\ndf\n.\ndropna\n()\nname        toy       born\n1  Batman  Batmobile 1940-04-25\nDrop the columns where at least one element is missing.\n>>>\ndf\n.\ndropna\n(\naxis\n=\n\"columns\"\n)\nname\n0    Alfred\n1    Batman\n2  Catwoman\nDrop the rows where all elements are missing.\n>>>\ndf\n.\ndropna\n(\nhow\n=\n\"all\"\n)\nname        toy       born\n0    Alfred        NaN        NaT\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\nKeep only the rows with at least 2 non-NA values.\n>>>\ndf\n.\ndropna\n(\nthresh\n=\n2\n)\nname        toy       born\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\nDefine in which columns to look for missing values.\n>>>\ndf\n.\ndropna\n(\nsubset\n=\n[\n\"name\"\n,\n\"toy\"\n])\nname        toy       born\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT","attrs_markdown":"[Skip to main content](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html#main-content)\n\nBack to top\n\nAnnouncement: pandas 3.0 released! Read more [in the blog post](https:\/\/pandas.pydata.org\/community\/blog\/pandas-3.0.html)\n\n[![pandas 3.0.2 documentation - Home](https:\/\/pandas.pydata.org\/docs\/_static\/pandas.svg) ![pandas 3.0.2 documentation - Home](https:\/\/pandas.pydata.org\/static\/img\/pandas_white.svg)](https:\/\/pandas.pydata.org\/docs\/index.html)\n\n- [Getting started](https:\/\/pandas.pydata.org\/docs\/getting_started\/index.html)\n- [User Guide](https:\/\/pandas.pydata.org\/docs\/user_guide\/index.html)\n- [API reference](https:\/\/pandas.pydata.org\/docs\/reference\/index.html)\n- [Development](https:\/\/pandas.pydata.org\/docs\/development\/index.html)\n- [Release notes](https:\/\/pandas.pydata.org\/docs\/whatsnew\/index.html)\n\nSearch `Ctrl`\\+`K`\n\n3\\.0 (stable)\n\n[dev](https:\/\/pandas.pydata.org\/docs\/dev\/reference\/api\/pandas.DataFrame.dropna.html)[3\\.0 (stable)](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html)[2\\.3](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.3\/reference\/api\/pandas.DataFrame.dropna.html)[2\\.2](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.2\/reference\/api\/pandas.DataFrame.dropna.html)[2\\.1](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.1\/reference\/api\/pandas.DataFrame.dropna.html)[2\\.0](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.0\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.5](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.5\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.4](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.4\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.3](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.3\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.2](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.2\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.1](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.1\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.0](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.0\/reference\/api\/pandas.DataFrame.dropna.html)\n\n- [GitHub](https:\/\/github.com\/pandas-dev\/pandas)\n- [X](https:\/\/x.com\/pandas_dev)\n- [Mastodon](https:\/\/fosstodon.org\/@pandas_dev)\n\nSearch `Ctrl`\\+`K`\n\n- [Getting started](https:\/\/pandas.pydata.org\/docs\/getting_started\/index.html)\n- [User Guide](https:\/\/pandas.pydata.org\/docs\/user_guide\/index.html)\n- [API reference](https:\/\/pandas.pydata.org\/docs\/reference\/index.html)\n- [Development](https:\/\/pandas.pydata.org\/docs\/development\/index.html)\n- [Release notes](https:\/\/pandas.pydata.org\/docs\/whatsnew\/index.html)\n\n3\\.0 (stable)\n\n[dev](https:\/\/pandas.pydata.org\/docs\/dev\/reference\/api\/pandas.DataFrame.dropna.html)[3\\.0 (stable)](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html)[2\\.3](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.3\/reference\/api\/pandas.DataFrame.dropna.html)[2\\.2](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.2\/reference\/api\/pandas.DataFrame.dropna.html)[2\\.1](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.1\/reference\/api\/pandas.DataFrame.dropna.html)[2\\.0](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.0\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.5](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.5\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.4](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.4\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.3](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.3\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.2](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.2\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.1](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.1\/reference\/api\/pandas.DataFrame.dropna.html)[1\\.0](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.0\/reference\/api\/pandas.DataFrame.dropna.html)\n\n- [GitHub](https:\/\/github.com\/pandas-dev\/pandas)\n- [X](https:\/\/x.com\/pandas_dev)\n- [Mastodon](https:\/\/fosstodon.org\/@pandas_dev)\n\n- [Input\/output](https:\/\/pandas.pydata.org\/docs\/reference\/io.html)\n- [General functions](https:\/\/pandas.pydata.org\/docs\/reference\/general_functions.html)\n- [Series](https:\/\/pandas.pydata.org\/docs\/reference\/series.html)\n- [DataFrame](https:\/\/pandas.pydata.org\/docs\/reference\/frame.html)\n  - [pandas.DataFrame](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.html)\n  - [pandas.DataFrame.index](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.index.html)\n  - [pandas.DataFrame.columns](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.columns.html)\n  - [pandas.DataFrame.dtypes](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dtypes.html)\n  - [pandas.DataFrame.info](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.info.html)\n  - [pandas.DataFrame.select\\_dtypes](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.select_dtypes.html)\n  - [pandas.DataFrame.values](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.values.html)\n  - [pandas.DataFrame.axes](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.axes.html)\n  - [pandas.DataFrame.ndim](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.ndim.html)\n  - [pandas.DataFrame.size](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.size.html)\n  - [pandas.DataFrame.shape](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.shape.html)\n  - [pandas.DataFrame.memory\\_usage](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.memory_usage.html)\n  - [pandas.DataFrame.empty](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.empty.html)\n  - [pandas.DataFrame.set\\_flags](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.set_flags.html)\n  - [pandas.DataFrame.astype](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.astype.html)\n  - [pandas.DataFrame.convert\\_dtypes](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.convert_dtypes.html)\n  - [pandas.DataFrame.infer\\_objects](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.infer_objects.html)\n  - [pandas.DataFrame.copy](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.copy.html)\n  - [pandas.DataFrame.to\\_numpy](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_numpy.html)\n  - 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[pandas.DataFrame.\\_\\_add\\_\\_](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.__add__.html)\n  - [pandas.DataFrame.add](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.add.html)\n  - [pandas.DataFrame.sub](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.sub.html)\n  - [pandas.DataFrame.mul](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.mul.html)\n  - [pandas.DataFrame.div](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.div.html)\n  - [pandas.DataFrame.truediv](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.truediv.html)\n  - [pandas.DataFrame.floordiv](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.floordiv.html)\n  - [pandas.DataFrame.mod](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.mod.html)\n  - [pandas.DataFrame.pow](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.pow.html)\n  - [pandas.DataFrame.dot](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dot.html)\n  - [pandas.DataFrame.radd](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.radd.html)\n  - [pandas.DataFrame.rsub](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rsub.html)\n  - [pandas.DataFrame.rmul](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rmul.html)\n  - [pandas.DataFrame.rdiv](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rdiv.html)\n  - [pandas.DataFrame.rtruediv](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rtruediv.html)\n  - [pandas.DataFrame.rfloordiv](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rfloordiv.html)\n  - [pandas.DataFrame.rmod](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rmod.html)\n  - [pandas.DataFrame.rpow](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rpow.html)\n  - [pandas.DataFrame.lt](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.lt.html)\n  - [pandas.DataFrame.gt](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.gt.html)\n  - 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[pandas.DataFrame.aggregate](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.aggregate.html)\n  - [pandas.DataFrame.transform](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.transform.html)\n  - [pandas.DataFrame.groupby](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.groupby.html)\n  - [pandas.DataFrame.rolling](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rolling.html)\n  - [pandas.DataFrame.expanding](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.expanding.html)\n  - [pandas.DataFrame.ewm](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.ewm.html)\n  - [pandas.DataFrame.abs](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.abs.html)\n  - [pandas.DataFrame.all](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.all.html)\n  - [pandas.DataFrame.any](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.any.html)\n  - [pandas.DataFrame.clip](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.clip.html)\n  - [pandas.DataFrame.corr](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.corr.html)\n  - [pandas.DataFrame.corrwith](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.corrwith.html)\n  - [pandas.DataFrame.count](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.count.html)\n  - [pandas.DataFrame.cov](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.cov.html)\n  - [pandas.DataFrame.cummax](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.cummax.html)\n  - [pandas.DataFrame.cummin](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.cummin.html)\n  - [pandas.DataFrame.cumprod](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.cumprod.html)\n  - [pandas.DataFrame.cumsum](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.cumsum.html)\n  - [pandas.DataFrame.describe](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.describe.html)\n  - [pandas.DataFrame.diff](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.diff.html)\n  - [pandas.DataFrame.eval](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.eval.html)\n  - [pandas.DataFrame.kurt](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.kurt.html)\n  - [pandas.DataFrame.kurtosis](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.kurtosis.html)\n  - [pandas.DataFrame.max](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.max.html)\n  - [pandas.DataFrame.mean](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.mean.html)\n  - [pandas.DataFrame.median](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.median.html)\n  - [pandas.DataFrame.min](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.min.html)\n  - [pandas.DataFrame.mode](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.mode.html)\n  - [pandas.DataFrame.pct\\_change](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.pct_change.html)\n  - [pandas.DataFrame.prod](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.prod.html)\n  - [pandas.DataFrame.product](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.product.html)\n  - [pandas.DataFrame.quantile](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.quantile.html)\n  - [pandas.DataFrame.rank](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rank.html)\n  - [pandas.DataFrame.round](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.round.html)\n  - [pandas.DataFrame.sem](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.sem.html)\n  - [pandas.DataFrame.skew](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.skew.html)\n  - [pandas.DataFrame.sum](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.sum.html)\n  - [pandas.DataFrame.std](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.std.html)\n  - [pandas.DataFrame.var](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.var.html)\n  - [pandas.DataFrame.nunique](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.nunique.html)\n  - [pandas.DataFrame.value\\_counts](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.value_counts.html)\n  - [pandas.DataFrame.add\\_prefix](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.add_prefix.html)\n  - [pandas.DataFrame.add\\_suffix](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.add_suffix.html)\n  - [pandas.DataFrame.align](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.align.html)\n  - [pandas.DataFrame.at\\_time](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.at_time.html)\n  - [pandas.DataFrame.between\\_time](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.between_time.html)\n  - [pandas.DataFrame.drop](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.drop.html)\n  - [pandas.DataFrame.drop\\_duplicates](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.drop_duplicates.html)\n  - [pandas.DataFrame.duplicated](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.duplicated.html)\n  - [pandas.DataFrame.equals](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.equals.html)\n  - [pandas.DataFrame.filter](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.filter.html)\n  - [pandas.DataFrame.idxmax](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.idxmax.html)\n  - [pandas.DataFrame.idxmin](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.idxmin.html)\n  - [pandas.DataFrame.reindex](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.reindex.html)\n  - [pandas.DataFrame.reindex\\_like](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.reindex_like.html)\n  - [pandas.DataFrame.rename](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rename.html)\n  - [pandas.DataFrame.rename\\_axis](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.rename_axis.html)\n  - [pandas.DataFrame.reset\\_index](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.reset_index.html)\n  - [pandas.DataFrame.sample](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.sample.html)\n  - [pandas.DataFrame.set\\_axis](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.set_axis.html)\n  - [pandas.DataFrame.set\\_index](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.set_index.html)\n  - [pandas.DataFrame.take](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.take.html)\n  - [pandas.DataFrame.truncate](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.truncate.html)\n  - [pandas.DataFrame.bfill](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.bfill.html)\n  - [pandas.DataFrame.dropna](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html)\n  - [pandas.DataFrame.ffill](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.ffill.html)\n  - [pandas.DataFrame.fillna](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.fillna.html)\n  - [pandas.DataFrame.interpolate](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.interpolate.html)\n  - [pandas.DataFrame.isna](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.isna.html)\n  - [pandas.DataFrame.isnull](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.isnull.html)\n  - [pandas.DataFrame.notna](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.notna.html)\n  - [pandas.DataFrame.notnull](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.notnull.html)\n  - [pandas.DataFrame.replace](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.replace.html)\n  - [pandas.DataFrame.droplevel](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.droplevel.html)\n  - [pandas.DataFrame.pivot](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.pivot.html)\n  - [pandas.DataFrame.pivot\\_table](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.pivot_table.html)\n  - [pandas.DataFrame.reorder\\_levels](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.reorder_levels.html)\n  - [pandas.DataFrame.sort\\_values](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.sort_values.html)\n  - [pandas.DataFrame.sort\\_index](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.sort_index.html)\n  - [pandas.DataFrame.nlargest](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.nlargest.html)\n  - [pandas.DataFrame.nsmallest](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.nsmallest.html)\n  - [pandas.DataFrame.swaplevel](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.swaplevel.html)\n  - [pandas.DataFrame.stack](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.stack.html)\n  - [pandas.DataFrame.unstack](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.unstack.html)\n  - [pandas.DataFrame.melt](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.melt.html)\n  - [pandas.DataFrame.explode](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.explode.html)\n  - [pandas.DataFrame.squeeze](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.squeeze.html)\n  - [pandas.DataFrame.to\\_xarray](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_xarray.html)\n  - [pandas.DataFrame.T](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.T.html)\n  - [pandas.DataFrame.transpose](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.transpose.html)\n  - [pandas.DataFrame.assign](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.assign.html)\n  - [pandas.DataFrame.compare](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.compare.html)\n  - [pandas.DataFrame.join](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.join.html)\n  - [pandas.DataFrame.merge](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.merge.html)\n  - [pandas.DataFrame.update](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.update.html)\n  - [pandas.DataFrame.asfreq](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.asfreq.html)\n  - [pandas.DataFrame.asof](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.asof.html)\n  - [pandas.DataFrame.shift](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.shift.html)\n  - [pandas.DataFrame.first\\_valid\\_index](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.first_valid_index.html)\n  - [pandas.DataFrame.last\\_valid\\_index](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.last_valid_index.html)\n  - [pandas.DataFrame.resample](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.resample.html)\n  - [pandas.DataFrame.to\\_period](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_period.html)\n  - [pandas.DataFrame.to\\_timestamp](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_timestamp.html)\n  - [pandas.DataFrame.tz\\_convert](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.tz_convert.html)\n  - [pandas.DataFrame.tz\\_localize](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.tz_localize.html)\n  - [pandas.Flags](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.Flags.html)\n  - [pandas.DataFrame.attrs](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.attrs.html)\n  - [pandas.DataFrame.plot](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.html)\n  - [pandas.DataFrame.plot.area](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.area.html)\n  - [pandas.DataFrame.plot.bar](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.bar.html)\n  - [pandas.DataFrame.plot.barh](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.barh.html)\n  - [pandas.DataFrame.plot.box](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.box.html)\n  - [pandas.DataFrame.plot.density](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.density.html)\n  - [pandas.DataFrame.plot.hexbin](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.hexbin.html)\n  - [pandas.DataFrame.plot.hist](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.hist.html)\n  - [pandas.DataFrame.plot.kde](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.kde.html)\n  - [pandas.DataFrame.plot.line](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.line.html)\n  - [pandas.DataFrame.plot.pie](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.pie.html)\n  - [pandas.DataFrame.plot.scatter](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.plot.scatter.html)\n  - [pandas.DataFrame.boxplot](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.boxplot.html)\n  - [pandas.DataFrame.hist](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.hist.html)\n  - [pandas.DataFrame.sparse.density](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.sparse.density.html)\n  - [pandas.DataFrame.sparse.from\\_spmatrix](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.sparse.from_spmatrix.html)\n  - [pandas.DataFrame.sparse.to\\_coo](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.sparse.to_coo.html)\n  - [pandas.DataFrame.sparse.to\\_dense](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.sparse.to_dense.html)\n  - [pandas.DataFrame.from\\_arrow](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.from_arrow.html)\n  - [pandas.DataFrame.from\\_dict](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.from_dict.html)\n  - [pandas.DataFrame.from\\_records](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.from_records.html)\n  - [pandas.DataFrame.to\\_orc](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_orc.html)\n  - [pandas.DataFrame.to\\_parquet](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_parquet.html)\n  - [pandas.DataFrame.to\\_pickle](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_pickle.html)\n  - [pandas.DataFrame.to\\_csv](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_csv.html)\n  - [pandas.DataFrame.to\\_hdf](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_hdf.html)\n  - [pandas.DataFrame.to\\_sql](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_sql.html)\n  - [pandas.DataFrame.to\\_dict](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_dict.html)\n  - [pandas.DataFrame.to\\_excel](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_excel.html)\n  - [pandas.DataFrame.to\\_json](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_json.html)\n  - [pandas.DataFrame.to\\_html](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_html.html)\n  - [pandas.DataFrame.to\\_feather](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_feather.html)\n  - [pandas.DataFrame.to\\_latex](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_latex.html)\n  - [pandas.DataFrame.to\\_stata](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_stata.html)\n  - [pandas.DataFrame.to\\_records](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_records.html)\n  - [pandas.DataFrame.to\\_string](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_string.html)\n  - [pandas.DataFrame.to\\_clipboard](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_clipboard.html)\n  - [pandas.DataFrame.to\\_markdown](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_markdown.html)\n  - [pandas.DataFrame.style](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.style.html)\n  - [pandas.DataFrame.\\_\\_dataframe\\_\\_](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.__dataframe__.html)\n- [pandas arrays, scalars, and data types](https:\/\/pandas.pydata.org\/docs\/reference\/arrays.html)\n- [Index objects](https:\/\/pandas.pydata.org\/docs\/reference\/indexing.html)\n- [Date offsets](https:\/\/pandas.pydata.org\/docs\/reference\/offset_frequency.html)\n- [Window](https:\/\/pandas.pydata.org\/docs\/reference\/window.html)\n- [GroupBy](https:\/\/pandas.pydata.org\/docs\/reference\/groupby.html)\n- [Resampling](https:\/\/pandas.pydata.org\/docs\/reference\/resampling.html)\n- [Style](https:\/\/pandas.pydata.org\/docs\/reference\/style.html)\n- [Plotting](https:\/\/pandas.pydata.org\/docs\/reference\/plotting.html)\n- [Options and settings](https:\/\/pandas.pydata.org\/docs\/reference\/options.html)\n- [Extensions](https:\/\/pandas.pydata.org\/docs\/reference\/extensions.html)\n- [Testing](https:\/\/pandas.pydata.org\/docs\/reference\/testing.html)\n- [Missing values](https:\/\/pandas.pydata.org\/docs\/reference\/missing_value.html)\n- [pandas typing aliases](https:\/\/pandas.pydata.org\/docs\/reference\/aliases.html)\n\n- [API reference](https:\/\/pandas.pydata.org\/docs\/reference\/index.html)\n- [DataFrame](https:\/\/pandas.pydata.org\/docs\/reference\/frame.html)\n- pandas.DataFrame.dropna\n\n# pandas.DataFrame.dropna[\\#](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html#pandas-dataframe-dropna \"Link to this heading\")\nDataFrame.dropna(*\\**, *axis\\=0*, *how\\=\\<no\\_default\\>*, *thresh\\=\\<no\\_default\\>*, *subset\\=None*, *inplace\\=False*, *ignore\\_index\\=False*)[\\[source\\]](https:\/\/github.com\/pandas-dev\/pandas\/blob\/v3.0.2\/pandas\/core\/frame.py#L7654-L7815)[\\#](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html#pandas.DataFrame.dropna \"Link to this definition\")\n\nRemove missing values.\n\nSee the [User Guide](https:\/\/pandas.pydata.org\/docs\/user_guide\/missing_data.html#missing-data) for more on which values are considered missing, and how to work with missing data.\n\nParameters:\n\n**axis**{0 or ‘index’, 1 or ‘columns’}, default 0\n\nDetermine if rows or columns which contain missing values are removed.\n\n- 0, or ‘index’ : Drop rows which contain missing values.\n- 1, or ‘columns’ : Drop columns which contain missing value.\n\nOnly a single axis is allowed.\n\n**how**{‘any’, ‘all’}, default ‘any’\n\nDetermine if row or column is removed from DataFrame, when we have at least one NA or all NA.\n\n- ‘any’ : If any NA values are present, drop that row or column.\n- ‘all’ : If all values are NA, drop that row or column.\n\n**thresh**int, optional\n\nRequire that many non-NA values. Cannot be combined with how.\n\n**subset**column label or iterable of labels, optional\n\nLabels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.\n\n**inplace**bool, default False\n\nWhether to modify the DataFrame rather than creating a new one.\n\n**ignore\\_index**bool, default `False`\n\nIf `True`, the resulting axis will be labeled 0, 1, …, n - 1.\n\nAdded in version 2.0.0.\n\nReturns:\n\nDataFrame or None\n\nDataFrame with NA entries dropped from it or None if `inplace=True`.\n\nSee also\n\n[`DataFrame.isna`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.isna.html#pandas.DataFrame.isna \"pandas.DataFrame.isna\")\n\nIndicate missing values.\n\n[`DataFrame.notna`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.notna.html#pandas.DataFrame.notna \"pandas.DataFrame.notna\")\n\nIndicate existing (non-missing) values.\n\n[`DataFrame.fillna`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.fillna.html#pandas.DataFrame.fillna \"pandas.DataFrame.fillna\")\n\nReplace missing values.\n\n[`Series.dropna`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.Series.dropna.html#pandas.Series.dropna \"pandas.Series.dropna\")\n\nDrop missing values.\n\n[`Index.dropna`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.Index.dropna.html#pandas.Index.dropna \"pandas.Index.dropna\")\n\nDrop missing indices.\n\nExamples\n```\n>>> df = pd.DataFrame(\n...     {\n...         \"name\": [\"Alfred\", \"Batman\", \"Catwoman\"],\n...         \"toy\": [np.nan, \"Batmobile\", \"Bullwhip\"],\n...         \"born\": [pd.NaT, pd.Timestamp(\"1940-04-25\"), pd.NaT],\n...     }\n... )\n>>> df\n       name        toy       born\n0    Alfred        NaN        NaT\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\n```\nDrop the rows where at least one element is missing.\n```\n>>> df.dropna()\n     name        toy       born\n1  Batman  Batmobile 1940-04-25\n```\nDrop the columns where at least one element is missing.\n```\n>>> df.dropna(axis=\"columns\")\n       name\n0    Alfred\n1    Batman\n2  Catwoman\n```\nDrop the rows where all elements are missing.\n```\n>>> df.dropna(how=\"all\")\n       name        toy       born\n0    Alfred        NaN        NaT\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\n```\nKeep only the rows with at least 2 non-NA values.\n```\n>>> df.dropna(thresh=2)\n       name        toy       born\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\n```\nDefine in which columns to look for missing values.\n```\n>>> df.dropna(subset=[\"name\", \"toy\"])\n       name        toy       born\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\n```\n\n[previous pandas.DataFrame.bfill](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.bfill.html \"previous page\")\n\n[next pandas.DataFrame.ffill](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.ffill.html \"next page\")\n\nOn this page\n\n- [`DataFrame.dropna()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html#pandas.DataFrame.dropna)\n\n© 2026, pandas via [NumFOCUS, Inc.](https:\/\/numfocus.org\/) Hosted by [OVHcloud](https:\/\/www.ovhcloud.com\/).\n\nCreated using [Sphinx](https:\/\/www.sphinx-doc.org\/) 9.0.4.\n\nBuilt with the [PyData Sphinx Theme](https:\/\/pydata-sphinx-theme.readthedocs.io\/en\/stable\/index.html) 0.16.1.","attrs_readable_markdown":"DataFrame.dropna(*\\**, *axis\\=0*, *how\\=\\<no\\_default\\>*, *thresh\\=\\<no\\_default\\>*, *subset\\=None*, *inplace\\=False*, *ignore\\_index\\=False*)[\\[source\\]](https:\/\/github.com\/pandas-dev\/pandas\/blob\/v3.0.2\/pandas\/core\/frame.py#L7654-L7815)[\\#](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.dropna.html#pandas.DataFrame.dropna \"Link to this definition\")\n\nRemove missing values.\n\nSee the [User Guide](https:\/\/pandas.pydata.org\/docs\/user_guide\/missing_data.html#missing-data) for more on which values are considered missing, and how to work with missing data.\n\nParameters:\n\n**axis**{0 or ‘index’, 1 or ‘columns’}, default 0\n\nDetermine if rows or columns which contain missing values are removed.\n\n- 0, or ‘index’ : Drop rows which contain missing values.\n- 1, or ‘columns’ : Drop columns which contain missing value.\n\nOnly a single axis is allowed.\n\n**how**{‘any’, ‘all’}, default ‘any’\n\nDetermine if row or column is removed from DataFrame, when we have at least one NA or all NA.\n\n- ‘any’ : If any NA values are present, drop that row or column.\n- ‘all’ : If all values are NA, drop that row or column.\n\n**thresh**int, optional\n\nRequire that many non-NA values. Cannot be combined with how.\n\n**subset**column label or iterable of labels, optional\n\nLabels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.\n\n**inplace**bool, default False\n\nWhether to modify the DataFrame rather than creating a new one.\n\n**ignore\\_index**bool, default `False`\n\nIf `True`, the resulting axis will be labeled 0, 1, …, n - 1.\n\nAdded in version 2.0.0.\n\nReturns:\n\nDataFrame or None\n\nDataFrame with NA entries dropped from it or None if `inplace=True`.\n\nExamples\n```\n>>> df = pd.DataFrame(\n...     {\n...         \"name\": [\"Alfred\", \"Batman\", \"Catwoman\"],\n...         \"toy\": [np.nan, \"Batmobile\", \"Bullwhip\"],\n...         \"born\": [pd.NaT, pd.Timestamp(\"1940-04-25\"), pd.NaT],\n...     }\n... )\n>>> df\n       name        toy       born\n0    Alfred        NaN        NaT\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\n```\nDrop the rows where at least one element is missing.\n```\n>>> df.dropna()\n     name        toy       born\n1  Batman  Batmobile 1940-04-25\n```\nDrop the columns where at least one element is missing.\n```\n>>> df.dropna(axis=\"columns\")\n       name\n0    Alfred\n1    Batman\n2  Catwoman\n```\nDrop the rows where all elements are missing.\n```\n>>> df.dropna(how=\"all\")\n       name        toy       born\n0    Alfred        NaN        NaT\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\n```\nKeep only the rows with at least 2 non-NA values.\n```\n>>> df.dropna(thresh=2)\n       name        toy       born\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\n```\nDefine in which columns to look for missing values.\n```\n>>> df.dropna(subset=[\"name\", \"toy\"])\n       name        toy       born\n1    Batman  Batmobile 1940-04-25\n2  Catwoman   Bullwhip        NaT\n```","meta_canonical":null}

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Boilerpipe Text	DataFrame. dropna ( * , axis = 0 , how = <no_default> , thresh = <no_default> , subset = None , inplace = False , ignore_index = False ) [source] # Remove missing values. See the User Guide for more on which values are considered missing, and how to work with missing data. Parameters : axis {0 or ‘index’, 1 or ‘columns’}, default 0 Determine if rows or columns which contain missing values are removed. 0, or ‘index’ : Drop rows which contain missing values. 1, or ‘columns’ : Drop columns which contain missing value. Only a single axis is allowed. how {‘any’, ‘all’}, default ‘any’ Determine if row or column is removed from DataFrame, when we have at least one NA or all NA. ‘any’ : If any NA values are present, drop that row or column. ‘all’ : If all values are NA, drop that row or column. thresh int, optional Require that many non-NA values. Cannot be combined with how. subset column label or iterable of labels, optional Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include. inplace bool, default False Whether to modify the DataFrame rather than creating a new one. ignore_index bool, default False If True , the resulting axis will be labeled 0, 1, …, n - 1. Added in version 2.0.0. Returns : DataFrame or None DataFrame with NA entries dropped from it or None if inplace=True . Examples >>> df = pd . DataFrame ( ... { ... "name" : [ "Alfred" , "Batman" , "Catwoman" ], ... "toy" : [ np . nan , "Batmobile" , "Bullwhip" ], ... "born" : [ pd . NaT , pd . Timestamp ( "1940-04-25" ), pd . NaT ], ... } ... ) >>> df name toy born 0 Alfred NaN NaT 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT Drop the rows where at least one element is missing. >>> df . dropna () name toy born 1 Batman Batmobile 1940-04-25 Drop the columns where at least one element is missing. >>> df . dropna ( axis = "columns" ) name 0 Alfred 1 Batman 2 Catwoman Drop the rows where all elements are missing. >>> df . dropna ( how = "all" ) name toy born 0 Alfred NaN NaT 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT Keep only the rows with at least 2 non-NA values. >>> df . dropna ( thresh = 2 ) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT Define in which columns to look for missing values. >>> df . dropna ( subset = [ "name" , "toy" ]) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT
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[pandas.DataFrame.infer\_objects](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.infer_objects.html) - [pandas.DataFrame.copy](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.copy.html) - [pandas.DataFrame.to\_numpy](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_numpy.html) - [pandas.DataFrame.head](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html) - [pandas.DataFrame.at](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.at.html) - [pandas.DataFrame.iat](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iat.html) - [pandas.DataFrame.loc](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html) - [pandas.DataFrame.iloc](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html) - [pandas.DataFrame.insert](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.insert.html) - [pandas.DataFrame.\_\_iter\_\_](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.__iter__.html) - [pandas.DataFrame.items](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.items.html) - [pandas.DataFrame.keys](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.keys.html) - [pandas.DataFrame.iterrows](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iterrows.html) - [pandas.DataFrame.itertuples](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.itertuples.html) - [pandas.DataFrame.pop](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pop.html) - [pandas.DataFrame.tail](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.tail.html) - [pandas.DataFrame.xs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.xs.html) - [pandas.DataFrame.get](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.get.html) - [pandas.DataFrame.isin](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isin.html) - [pandas.DataFrame.where](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.where.html) - [pandas.DataFrame.mask](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mask.html) - [pandas.DataFrame.query](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html) - [pandas.DataFrame.isetitem](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isetitem.html) - [pandas.DataFrame.\_\_add\_\_](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.__add__.html) - [pandas.DataFrame.add](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.add.html) - [pandas.DataFrame.sub](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sub.html) - [pandas.DataFrame.mul](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mul.html) - [pandas.DataFrame.div](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.div.html) - [pandas.DataFrame.truediv](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.truediv.html) - [pandas.DataFrame.floordiv](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.floordiv.html) - [pandas.DataFrame.mod](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mod.html) - [pandas.DataFrame.pow](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pow.html) - [pandas.DataFrame.dot](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dot.html) - [pandas.DataFrame.radd](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.radd.html) - [pandas.DataFrame.rsub](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rsub.html) - [pandas.DataFrame.rmul](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rmul.html) - [pandas.DataFrame.rdiv](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rdiv.html) - [pandas.DataFrame.rtruediv](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rtruediv.html) - [pandas.DataFrame.rfloordiv](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rfloordiv.html) - [pandas.DataFrame.rmod](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rmod.html) - [pandas.DataFrame.rpow](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rpow.html) - [pandas.DataFrame.lt](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.lt.html) - [pandas.DataFrame.gt](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.gt.html) - [pandas.DataFrame.le](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.le.html) - [pandas.DataFrame.ge](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ge.html) - [pandas.DataFrame.ne](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ne.html) - [pandas.DataFrame.eq](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.eq.html) - [pandas.DataFrame.combine](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.combine.html) - [pandas.DataFrame.combine\_first](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.combine_first.html) - [pandas.DataFrame.apply](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html) - [pandas.DataFrame.map](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.map.html) - [pandas.DataFrame.pipe](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pipe.html) - [pandas.DataFrame.agg](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.agg.html) - [pandas.DataFrame.aggregate](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.aggregate.html) - [pandas.DataFrame.transform](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.transform.html) - [pandas.DataFrame.groupby](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html) - [pandas.DataFrame.rolling](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html) - [pandas.DataFrame.expanding](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.expanding.html) - [pandas.DataFrame.ewm](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ewm.html) - [pandas.DataFrame.abs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.abs.html) - [pandas.DataFrame.all](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.all.html) - [pandas.DataFrame.any](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.any.html) - [pandas.DataFrame.clip](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.clip.html) - [pandas.DataFrame.corr](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html) - [pandas.DataFrame.corrwith](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corrwith.html) - [pandas.DataFrame.count](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.count.html) - [pandas.DataFrame.cov](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cov.html) - [pandas.DataFrame.cummax](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cummax.html) - [pandas.DataFrame.cummin](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cummin.html) - [pandas.DataFrame.cumprod](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cumprod.html) - [pandas.DataFrame.cumsum](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cumsum.html) - [pandas.DataFrame.describe](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html) - [pandas.DataFrame.diff](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.diff.html) - [pandas.DataFrame.eval](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.eval.html) - [pandas.DataFrame.kurt](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.kurt.html) - [pandas.DataFrame.kurtosis](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.kurtosis.html) - [pandas.DataFrame.max](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.max.html) - [pandas.DataFrame.mean](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mean.html) - [pandas.DataFrame.median](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.median.html) - [pandas.DataFrame.min](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.min.html) - [pandas.DataFrame.mode](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mode.html) - [pandas.DataFrame.pct\_change](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pct_change.html) - [pandas.DataFrame.prod](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.prod.html) - [pandas.DataFrame.product](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.product.html) - [pandas.DataFrame.quantile](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.quantile.html) - [pandas.DataFrame.rank](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rank.html) - [pandas.DataFrame.round](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.round.html) - [pandas.DataFrame.sem](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sem.html) - [pandas.DataFrame.skew](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.skew.html) - [pandas.DataFrame.sum](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sum.html) - [pandas.DataFrame.std](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.std.html) - [pandas.DataFrame.var](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.var.html) - [pandas.DataFrame.nunique](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.nunique.html) - [pandas.DataFrame.value\_counts](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.value_counts.html) - [pandas.DataFrame.add\_prefix](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.add_prefix.html) - [pandas.DataFrame.add\_suffix](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.add_suffix.html) - [pandas.DataFrame.align](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.align.html) - [pandas.DataFrame.at\_time](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.at_time.html) - [pandas.DataFrame.between\_time](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.between_time.html) - [pandas.DataFrame.drop](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html) - [pandas.DataFrame.drop\_duplicates](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop_duplicates.html) - [pandas.DataFrame.duplicated](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.duplicated.html) - [pandas.DataFrame.equals](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.equals.html) - [pandas.DataFrame.filter](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.filter.html) - [pandas.DataFrame.idxmax](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.idxmax.html) - [pandas.DataFrame.idxmin](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.idxmin.html) - [pandas.DataFrame.reindex](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reindex.html) - [pandas.DataFrame.reindex\_like](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reindex_like.html) - [pandas.DataFrame.rename](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rename.html) - [pandas.DataFrame.rename\_axis](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rename_axis.html) - [pandas.DataFrame.reset\_index](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reset_index.html) - [pandas.DataFrame.sample](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sample.html) - [pandas.DataFrame.set\_axis](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.set_axis.html) - [pandas.DataFrame.set\_index](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.set_index.html) - [pandas.DataFrame.take](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.take.html) - [pandas.DataFrame.truncate](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.truncate.html) - [pandas.DataFrame.bfill](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.bfill.html) - [pandas.DataFrame.dropna](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html) - [pandas.DataFrame.ffill](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ffill.html) - [pandas.DataFrame.fillna](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.fillna.html) - [pandas.DataFrame.interpolate](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.interpolate.html) - [pandas.DataFrame.isna](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isna.html) - [pandas.DataFrame.isnull](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isnull.html) - [pandas.DataFrame.notna](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.notna.html) - [pandas.DataFrame.notnull](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.notnull.html) - [pandas.DataFrame.replace](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.replace.html) - [pandas.DataFrame.droplevel](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.droplevel.html) - [pandas.DataFrame.pivot](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pivot.html) - [pandas.DataFrame.pivot\_table](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pivot_table.html) - [pandas.DataFrame.reorder\_levels](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reorder_levels.html) - [pandas.DataFrame.sort\_values](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html) - [pandas.DataFrame.sort\_index](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_index.html) - [pandas.DataFrame.nlargest](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.nlargest.html) - [pandas.DataFrame.nsmallest](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.nsmallest.html) - [pandas.DataFrame.swaplevel](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.swaplevel.html) - [pandas.DataFrame.stack](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.stack.html) - [pandas.DataFrame.unstack](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.unstack.html) - [pandas.DataFrame.melt](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.melt.html) - [pandas.DataFrame.explode](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.explode.html) - [pandas.DataFrame.squeeze](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.squeeze.html) - [pandas.DataFrame.to\_xarray](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_xarray.html) - [pandas.DataFrame.T](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.T.html) - [pandas.DataFrame.transpose](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.transpose.html) - [pandas.DataFrame.assign](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.assign.html) - [pandas.DataFrame.compare](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.compare.html) - [pandas.DataFrame.join](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.join.html) - [pandas.DataFrame.merge](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html) - [pandas.DataFrame.update](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.update.html) - [pandas.DataFrame.asfreq](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.asfreq.html) - [pandas.DataFrame.asof](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.asof.html) - [pandas.DataFrame.shift](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shift.html) - [pandas.DataFrame.first\_valid\_index](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.first_valid_index.html) - [pandas.DataFrame.last\_valid\_index](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.last_valid_index.html) - [pandas.DataFrame.resample](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.resample.html) - [pandas.DataFrame.to\_period](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_period.html) - [pandas.DataFrame.to\_timestamp](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_timestamp.html) - [pandas.DataFrame.tz\_convert](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.tz_convert.html) - [pandas.DataFrame.tz\_localize](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.tz_localize.html) - [pandas.Flags](https://pandas.pydata.org/docs/reference/api/pandas.Flags.html) - [pandas.DataFrame.attrs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.attrs.html) - [pandas.DataFrame.plot](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html) - [pandas.DataFrame.plot.area](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.area.html) - [pandas.DataFrame.plot.bar](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.bar.html) - [pandas.DataFrame.plot.barh](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.barh.html) - [pandas.DataFrame.plot.box](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.box.html) - [pandas.DataFrame.plot.density](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.density.html) - [pandas.DataFrame.plot.hexbin](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.hexbin.html) - [pandas.DataFrame.plot.hist](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.hist.html) - [pandas.DataFrame.plot.kde](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.kde.html) - [pandas.DataFrame.plot.line](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.line.html) - [pandas.DataFrame.plot.pie](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.pie.html) - [pandas.DataFrame.plot.scatter](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.scatter.html) - [pandas.DataFrame.boxplot](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.boxplot.html) - [pandas.DataFrame.hist](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html) - [pandas.DataFrame.sparse.density](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sparse.density.html) - [pandas.DataFrame.sparse.from\_spmatrix](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sparse.from_spmatrix.html) - [pandas.DataFrame.sparse.to\_coo](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sparse.to_coo.html) - [pandas.DataFrame.sparse.to\_dense](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sparse.to_dense.html) - [pandas.DataFrame.from\_arrow](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.from_arrow.html) - [pandas.DataFrame.from\_dict](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.from_dict.html) - [pandas.DataFrame.from\_records](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.from_records.html) - [pandas.DataFrame.to\_orc](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_orc.html) - [pandas.DataFrame.to\_parquet](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_parquet.html) - [pandas.DataFrame.to\_pickle](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_pickle.html) - [pandas.DataFrame.to\_csv](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html) - [pandas.DataFrame.to\_hdf](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_hdf.html) - [pandas.DataFrame.to\_sql](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_sql.html) - [pandas.DataFrame.to\_dict](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_dict.html) - [pandas.DataFrame.to\_excel](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_excel.html) - [pandas.DataFrame.to\_json](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) - 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[DataFrame](https://pandas.pydata.org/docs/reference/frame.html) - pandas.DataFrame.dropna # pandas.DataFrame.dropna[\#](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html#pandas-dataframe-dropna "Link to this heading") DataFrame.dropna(\, axis\=0, how\=\<no\_default\>, thresh\=\<no\_default\>, subset\=None, inplace\=False, ignore\_index\=False)[\[source\]](https://github.com/pandas-dev/pandas/blob/v3.0.2/pandas/core/frame.py#L7654-L7815)[\#](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html#pandas.DataFrame.dropna "Link to this definition") Remove missing values. See the [User Guide](https://pandas.pydata.org/docs/user_guide/missing_data.html#missing-data) for more on which values are considered missing, and how to work with missing data. Parameters: axis{0 or ‘index’, 1 or ‘columns’}, default 0 Determine if rows or columns which contain missing values are removed. - 0, or ‘index’ : Drop rows which contain missing values. - 1, or ‘columns’ : Drop columns which contain missing value. Only a single axis is allowed. how{‘any’, ‘all’}, default ‘any’ Determine if row or column is removed from DataFrame, when we have at least one NA or all NA. - ‘any’ : If any NA values are present, drop that row or column. - ‘all’ : If all values are NA, drop that row or column. threshint, optional Require that many non-NA values. Cannot be combined with how. subsetcolumn label or iterable of labels, optional Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include. inplacebool, default False Whether to modify the DataFrame rather than creating a new one. ignore\_index*bool, default `False` If `True`, the resulting axis will be labeled 0, 1, …, n - 1. Added in version 2.0.0. Returns: DataFrame or None DataFrame with NA entries dropped from it or None if `inplace=True`. See also [`DataFrame.isna`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isna.html#pandas.DataFrame.isna "pandas.DataFrame.isna") Indicate missing values. [`DataFrame.notna`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.notna.html#pandas.DataFrame.notna "pandas.DataFrame.notna") Indicate existing (non-missing) values. [`DataFrame.fillna`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.fillna.html#pandas.DataFrame.fillna "pandas.DataFrame.fillna") Replace missing values. [`Series.dropna`](https://pandas.pydata.org/docs/reference/api/pandas.Series.dropna.html#pandas.Series.dropna "pandas.Series.dropna") Drop missing values. [`Index.dropna`](https://pandas.pydata.org/docs/reference/api/pandas.Index.dropna.html#pandas.Index.dropna "pandas.Index.dropna") Drop missing indices. Examples ``` >>> df = pd.DataFrame( ... { ... "name": ["Alfred", "Batman", "Catwoman"], ... "toy": [np.nan, "Batmobile", "Bullwhip"], ... "born": [pd.NaT, pd.Timestamp("1940-04-25"), pd.NaT], ... } ... ) >>> df name toy born 0 Alfred NaN NaT 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT ``` Drop the rows where at least one element is missing. ``` >>> df.dropna() name toy born 1 Batman Batmobile 1940-04-25 ``` Drop the columns where at least one element is missing. ``` >>> df.dropna(axis="columns") name 0 Alfred 1 Batman 2 Catwoman ``` Drop the rows where all elements are missing. ``` >>> df.dropna(how="all") name toy born 0 Alfred NaN NaT 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT ``` Keep only the rows with at least 2 non-NA values. ``` >>> df.dropna(thresh=2) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT ``` Define in which columns to look for missing values. ``` >>> df.dropna(subset=["name", "toy"]) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT ``` [previous pandas.DataFrame.bfill](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.bfill.html "previous page") [next pandas.DataFrame.ffill](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ffill.html "next page") On this page - [`DataFrame.dropna()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html#pandas.DataFrame.dropna) © 2026, pandas via [NumFOCUS, Inc.](https://numfocus.org/) Hosted by [OVHcloud](https://www.ovhcloud.com/). 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Readable Markdown	DataFrame.dropna(\, axis\=0, how\=\<no\_default\>, thresh\=\<no\_default\>, subset\=None, inplace\=False, ignore\_index\=False)[\[source\]](https://github.com/pandas-dev/pandas/blob/v3.0.2/pandas/core/frame.py#L7654-L7815)[\#](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html#pandas.DataFrame.dropna "Link to this definition") Remove missing values. See the [User Guide](https://pandas.pydata.org/docs/user_guide/missing_data.html#missing-data) for more on which values are considered missing, and how to work with missing data. Parameters: axis{0 or ‘index’, 1 or ‘columns’}, default 0 Determine if rows or columns which contain missing values are removed. - 0, or ‘index’ : Drop rows which contain missing values. - 1, or ‘columns’ : Drop columns which contain missing value. Only a single axis is allowed. how{‘any’, ‘all’}, default ‘any’ Determine if row or column is removed from DataFrame, when we have at least one NA or all NA. - ‘any’ : If any NA values are present, drop that row or column. - ‘all’ : If all values are NA, drop that row or column. threshint, optional Require that many non-NA values. Cannot be combined with how. subsetcolumn label or iterable of labels, optional Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include. inplacebool, default False Whether to modify the DataFrame rather than creating a new one. ignore\_index*bool, default `False` If `True`, the resulting axis will be labeled 0, 1, …, n - 1. Added in version 2.0.0. Returns: DataFrame or None DataFrame with NA entries dropped from it or None if `inplace=True`. Examples ``` >>> df = pd.DataFrame( ... { ... "name": ["Alfred", "Batman", "Catwoman"], ... "toy": [np.nan, "Batmobile", "Bullwhip"], ... "born": [pd.NaT, pd.Timestamp("1940-04-25"), pd.NaT], ... } ... ) >>> df name toy born 0 Alfred NaN NaT 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT ``` Drop the rows where at least one element is missing. ``` >>> df.dropna() name toy born 1 Batman Batmobile 1940-04-25 ``` Drop the columns where at least one element is missing. ``` >>> df.dropna(axis="columns") name 0 Alfred 1 Batman 2 Catwoman ``` Drop the rows where all elements are missing. ``` >>> df.dropna(how="all") name toy born 0 Alfred NaN NaT 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT ``` Keep only the rows with at least 2 non-NA values. ``` >>> df.dropna(thresh=2) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT ``` Define in which columns to look for missing values. ``` >>> df.dropna(subset=["name", "toy"]) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT ```
Shard	68 (laksa)
Root Hash	10355827761381095868
Unparsed URL	org,pydata!pandas,/docs/reference/api/pandas.DataFrame.dropna.html s443