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Calculated Shard: 181 (from laksa041)

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curl -X POST \
  'http://laksa181.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.plus2net.com/python/pandas-dataframe-dropna.php\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.plus2net.com/python/pandas-dataframe-dropna.php\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.plus2net.com\/python\/pandas-dataframe-dropna.php","crawl_time":1774773391,"first_indexed_time":1588518461,"http_code":200,"src_unparsed":"com,plus2net!www,\/python\/pandas-dataframe-dropna.php s443","src_root_hash":"727011249797570381","history_drop_reason":null,"meta_title":"Python Pandas DataFrame dropna() to remove labels on given axis when any data found to be missing or NaN","meta_descriptions":["Python Pandas dropna() to remove lables or columns along the axis if NA data is available"],"attrs_boilerpipe_text":"Python\nPandas\nPandas Data Cleaning\nimport pandas as pd\nimport numpy as np \nmy_dict={'NAME':['Ravi','Raju',\nNone\n,None,'King','Alex'],\n         'ID':[1,2,3,\nnp.NaN\n,5,6],\n         'MATH':[80,40,70,\nnp.NaN\n,82,30],\n         'ENGLISH':[81,70,40,\nnp.NaN\n,\nnp.NaN\n,30]}\ndf = pd.DataFrame(data=my_dict)\nprint(df)\nprint(df.dropna()) # remove all rows with NaN or None values\nOutput is here\nNAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0     81.0\n1  Raju  2.0  40.0     70.0\n2\nNone\n3.0  70.0     40.0\n3\nNone  NaN   NaN      NaN\n4  King  5.0  82.0\nNaN\n5  Alex  6.0  30.0     30.0\nNAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0     81.0\n1  Raju  2.0  40.0     70.0\n5  Alex  6.0  30.0     30.0\ndropna(): Remove rows or columns based on missing values #C01\ndropna(axis=0, how='any', thresh=None, subset=None, inplace=False)\nReturn the Modified DataFrame ( if\ninplace=True\n).\naxis\n0 (default ) or 1, decide row or column to remove.\nhow\nTakes values\nany\nor\nall\n. Check examples below.\nthresh\nint : Minimum NaN values required.\nsubset\nLabels along other axis to consider\ninplace\nBoolean , along with method  if value is True then original ( source ) dataframe is replaced after applying\ndropna()\nhow\nany : rows are removed if any value contains NaN\nall : rows are removed if all values are contains NaN\nprint(df.dropna(how='any'))\n2,3 and  4  numbered rows are removed as it contains NaN or None values ( at least one )\nOutput\nNAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0     81.0\n1  Raju  2.0  40.0     70.0\n5  Alex  6.0  30.0     30.0\nWe will use\nhow=all\n, remove the row or column if\nall\nvalues contains NaN.\nprint(df.dropna(how='all'))\nrow 3 (having all NaN values) is dropped with axis=0, output\nNAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0     81.0\n1  Raju  2.0  40.0     70.0\n2  None  3.0  70.0     40.0\n4  King  5.0  82.0      NaN\n5  Alex  6.0  30.0     30.0\nRemove the row if a perticular column has NaN value\nprint(df.dropna(axis=0,subset=['ENGLISH']))\nOutput\nNAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0     81.0\n1  Raju  2.0  40.0     70.0\n2  None  3.0  70.0     40.0\n5  Alex  6.0  30.0     30.0\nWith axis=1 and how='any', all columns are deleted.\nprint(df.dropna(how='any',axis=1))\nOutput\nEmpty DataFrame\nColumns: []\nIndex: [0, 1, 2, 3, 4, 5]\nhow=all\nprint(df.dropna(axis=1,how='all'))# Nothing will be removed.\nLet us change the dataframe by keeping all NaN values to one column.\nimport pandas as pd\nimport numpy as np \nmy_dict={'NAME':['Ravi','Raju',None,None,'King','Alex'],\n         'ID':[1,2,3,np.NaN,5,6],\n         'MATH':[80,40,70,np.NaN,82,30],\n         'ENGLISH':[np.NaN,None,np.NaN,np.NaN,np.NaN,None]}\ndf = pd.DataFrame(data=my_dict)\nprint(df)\nprint(df.dropna(axis=1,how='all')) #remove column if all data is NaN\nOutput : Column 'ENGLISH' is dropped as all are NaN with axis=1\nNAME   ID  MATH\n0  Ravi  1.0  80.0\n1  Raju  2.0  40.0\n2  None  3.0  70.0\n3  None  NaN   NaN\n4  King  5.0  82.0\n5  Alex  6.0  30.0\nthresh\nMinimum NaN values required\nimport pandas as pd\nimport numpy as np \nmy_dict={'NAME':['Ravi','Raju','Alex',None,'King',None],\n         'ID':[1,2,3,np.NaN,5,6],\n         'MATH':[80,40,70,np.NaN,82,30],\n         'ENGLISH':[np.NaN,np.NaN,np.NaN,np.NaN,np.NaN,np.NaN]}\ndf = pd.DataFrame(data=my_dict)\ndf=df.dropna(how='any',axis=0,thresh=3)\nprint(df)\nOutput\nNAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0      NaN\n1  Raju  2.0  40.0      NaN\n2  Alex  3.0  70.0      NaN\n4  King  5.0  82.0      NaN\nHandling NaT values\nNaT : Missing value in Date and time.\nimport pandas as pd\nimport numpy as np \nmy_dict={'NAME':['Ravi','Raju','Alex',None,'King',None],\n         'ID':[1,2,3,np.NaN,5,6],\n         'MATH':[80,40,70,np.NaN,82,30],\n         'ENGLISH':[81,70,40,np.NaN,np.NaN,30],\n'Entry':['1\/1\/2020','2\/1\/2020',pd.NaT,\n                   pd.NaT,'5\/1\/2020','1\/2\/2020']\n}\ndf = pd.DataFrame(data=my_dict)\nprint(df)\nRemove the row if\nEntry\ncolumn has NaT\ndf=df.dropna(axis=0,subset=['Entry'])\nprint(df)\nOutput\nNAME   ID  MATH  ENGLISH     Entry\n0  Ravi  1.0  80.0     81.0  1\/1\/2020\n1  Raju  2.0  40.0     70.0  2\/1\/2020\n4  King  5.0  82.0      NaN  5\/1\/2020\n5  None  6.0  30.0     30.0  1\/2\/2020\ninplace\nWe will use\ninplace=True\nso the original DataFrame is changed.\nimport pandas as pd\nimport numpy as np \nmy_dict={'NAME':['Ravi','Raju','Alex',None,'King',None],\n         'ID':[1,2,3,np.NaN,5,6],\n         'MATH':[80,40,70,np.NaN,82,30],\n         'ENGLISH':[np.NaN,np.NaN,np.NaN,np.NaN,np.NaN,np.NaN]}\ndf = pd.DataFrame(data=my_dict)\ndf.dropna(how='all',inplace=True,axis=1)\nprint(df)\nOutput ( ENGLISH column is removed )\nNAME   ID  MATH\n0  Ravi  1.0  80.0\n1  Raju  2.0  40.0\n2  Alex  3.0  70.0\n3  None  NaN   NaN\n4  King  5.0  82.0\n5  None  6.0  30.0\nChange the value of inplace to False and check  the output\ndf.dropna(how='all',inplace=False,axis=1)\nNAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0      NaN\n1  Raju  2.0  40.0      NaN\n2  Alex  3.0  70.0      NaN\n3  None  NaN   NaN      NaN\n4  King  5.0  82.0      NaN\n5  None  6.0  30.0      NaN\nCounting and identifying NaN values\nWe can count and display records with\nNaN\nby using\nisnull()\nisnull()\nRemoving rows or columns by using dropna()\nRows or columns can  be filled  by using fillna()\nfillna()\nQuestions\nData Cleaning\ncontains() to display and delete row based on Conditions\nloc\nat\nmask\nPandas\nPandas DataFrame\niloc - rows and columns by integers","attrs_markdown":"[![](https:\/\/www.plus2net.com\/images\/top2.jpg)](https:\/\/www.plus2net.com\/)\n\n- [plus2net Home](https:\/\/www.plus2net.com\/)\n- [HOME](https:\/\/www.plus2net.com\/python\/site_map.php)\n  [plus2net HOME](https:\/\/www.plus2net.com\/) [SQL](https:\/\/www.plus2net.com\/sql_tutorial\/site_map.php) [HTML](https:\/\/www.plus2net.com\/html_tutorial\/site_map.php) [PHP](https:\/\/www.plus2net.com\/php_tutorial\/site_map.php) [JavaScript](https:\/\/www.plus2net.com\/javascript_tutorial\/site_map.php) [ASP](https:\/\/www.plus2net.com\/asp-tutorial\/site_map.php) [JQuery](https:\/\/www.plus2net.com\/jquery\/site_map.php) [PhotoShop](https:\/\/www.plus2net.com\/ps-tutorial\/index.php)\n- [Python](https:\/\/www.plus2net.com\/python\/site_map.php)\n  [Python Home](https:\/\/www.plus2net.com\/python\/site_map.php) [Built in functions](https:\/\/www.plus2net.com\/python\/builtins.php) [date](https:\/\/www.plus2net.com\/python\/date.php) [List](https:\/\/www.plus2net.com\/python\/list.php) [Math](https:\/\/www.plus2net.com\/python\/math.php) [Online Classes](https:\/\/www.plus2net.com\/python\/online-class.php) [File Handling](https:\/\/www.plus2net.com\/python\/file.php) [Error Handling](https:\/\/www.plus2net.com\/python\/exception-handling.php) [Class Object](https:\/\/www.plus2net.com\/python\/class-object-method.php) [Samples](https:\/\/www.plus2net.com\/python\/sample-codes.php) [String](https:\/\/www.plus2net.com\/python\/string.php) [Variables](https:\/\/www.plus2net.com\/python\/variables.php)\n  \n  [pdf ( ReportLab)](https:\/\/www.plus2net.com\/python\/pdf.php) [Tkinter](https:\/\/www.plus2net.com\/python\/tkinter.php) [Json](https:\/\/www.plus2net.com\/python\/json.php)  [Numpy](https:\/\/www.plus2net.com\/python\/numpy.php) [Pandas](https:\/\/www.plus2net.com\/python\/pandas.php) [Image ( PIL)](https:\/\/www.plus2net.com\/python\/pillow.php)  [Python & MySQL](https:\/\/www.plus2net.com\/python\/mysql.php) [SQLite](https:\/\/www.plus2net.com\/python\/sqlite.php)\n\n- [Contact Us](https:\/\/www.plus2net.com\/contactus.php)\n\n# dropna(): Remove rows or columns based on missing values\n1. [Python](https:\/\/www.plus2net.com\/python\/site_map.php)\n2. [Pandas](https:\/\/www.plus2net.com\/python\/pandas.php)\n3. [Pandas Data Cleaning](https:\/\/www.plus2net.com\/python\/pandas-data-cleaning.php)\n```\nimport pandas as pd\nimport numpy as np \nmy_dict={'NAME':['Ravi','Raju',None,None,'King','Alex'],\n         'ID':[1,2,3,np.NaN,5,6],\n         'MATH':[80,40,70,np.NaN,82,30],\n         'ENGLISH':[81,70,40,np.NaN,np.NaN,30]}\ndf = pd.DataFrame(data=my_dict)\nprint(df)\nprint(df.dropna()) # remove all rows with NaN or None values\n```\nOutput is here\n\n**dropna(): Remove rows or columns based on missing values \\#C01**\n```\ndropna(axis=0, how='any', thresh=None, subset=None, inplace=False)\n```\nReturn the Modified DataFrame ( if inplace=True ).\n\n|   |   |\n|---|---|\n| `axis` | 0 (default ) or 1, decide row or column to remove. |\n| `how` | Takes values *any* or *all*. Check examples below. |\n| `thresh` | int : Minimum NaN values required. |\n| `subset` | Labels along other axis to consider |\n| `inplace` | Boolean , along with method if value is True then original ( source ) dataframe is replaced after applying *dropna()* |\n## how\nany : rows are removed if any value contains NaN   \n all : rows are removed if all values are contains NaN\n```\nprint(df.dropna(how='any'))\n```\n2,3 and 4 numbered rows are removed as it contains NaN or None values ( at least one )  \n Output\n```\n   NAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0     81.0\n1  Raju  2.0  40.0     70.0\n5  Alex  6.0  30.0     30.0\n```\nWe will use how=all , remove the row or column if **all** values contains NaN.\n```\nprint(df.dropna(how='all'))\n```\nrow 3 (having all NaN values) is dropped with axis=0, output\n```\n   NAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0     81.0\n1  Raju  2.0  40.0     70.0\n2  None  3.0  70.0     40.0\n4  King  5.0  82.0      NaN\n5  Alex  6.0  30.0     30.0\n```\n## Remove the row if a perticular column has NaN value\n```\nprint(df.dropna(axis=0,subset=['ENGLISH']))\n```\nOutput\n```\n   NAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0     81.0\n1  Raju  2.0  40.0     70.0\n2  None  3.0  70.0     40.0\n5  Alex  6.0  30.0     30.0\n```\n## axis\nWith axis=1 and how='any', all columns are deleted.\n```\nprint(df.dropna(how='any',axis=1))\n```\nOutput\n```\nEmpty DataFrame\nColumns: []\nIndex: [0, 1, 2, 3, 4, 5]\n```\nhow=all\n```\nprint(df.dropna(axis=1,how='all'))# Nothing will be removed.\n```\nLet us change the dataframe by keeping all NaN values to one column.\n```\nimport pandas as pd\nimport numpy as np \nmy_dict={'NAME':['Ravi','Raju',None,None,'King','Alex'],\n         'ID':[1,2,3,np.NaN,5,6],\n         'MATH':[80,40,70,np.NaN,82,30],\n         'ENGLISH':[np.NaN,None,np.NaN,np.NaN,np.NaN,None]}\ndf = pd.DataFrame(data=my_dict)\nprint(df)\nprint(df.dropna(axis=1,how='all')) #remove column if all data is NaN\n```\nOutput : Column 'ENGLISH' is dropped as all are NaN with axis=1\n```\n   NAME   ID  MATH\n0  Ravi  1.0  80.0\n1  Raju  2.0  40.0\n2  None  3.0  70.0\n3  None  NaN   NaN\n4  King  5.0  82.0\n5  Alex  6.0  30.0\n```\n## thresh\nMinimum NaN values required\n```\nimport pandas as pd\nimport numpy as np \nmy_dict={'NAME':['Ravi','Raju','Alex',None,'King',None],\n         'ID':[1,2,3,np.NaN,5,6],\n         'MATH':[80,40,70,np.NaN,82,30],\n         'ENGLISH':[np.NaN,np.NaN,np.NaN,np.NaN,np.NaN,np.NaN]}\ndf = pd.DataFrame(data=my_dict)\ndf=df.dropna(how='any',axis=0,thresh=3)\nprint(df)\n```\nOutput\n```\n   NAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0      NaN\n1  Raju  2.0  40.0      NaN\n2  Alex  3.0  70.0      NaN\n4  King  5.0  82.0      NaN\n```\nThresh is used for data cleaning where some threshold value of valid data is considered for retaining the rows or columns. [Check here to know how data cleaning is done by using thresh option](https:\/\/www.plus2net.com\/python\/pandas-dataframe-dropna-thresh.php).\n\n## Handling NaT values\nNaT : Missing value in Date and time.\n```\nimport pandas as pd\nimport numpy as np \nmy_dict={'NAME':['Ravi','Raju','Alex',None,'King',None],\n         'ID':[1,2,3,np.NaN,5,6],\n         'MATH':[80,40,70,np.NaN,82,30],\n         'ENGLISH':[81,70,40,np.NaN,np.NaN,30],\n         'Entry':['1\/1\/2020','2\/1\/2020',pd.NaT,\n                   pd.NaT,'5\/1\/2020','1\/2\/2020']}\ndf = pd.DataFrame(data=my_dict)\nprint(df)\n```\nRemove the row if *Entry* column has NaT\n```\ndf=df.dropna(axis=0,subset=['Entry'])\nprint(df)\n```\nOutput\n```\n   NAME   ID  MATH  ENGLISH     Entry\n0  Ravi  1.0  80.0     81.0  1\/1\/2020\n1  Raju  2.0  40.0     70.0  2\/1\/2020\n4  King  5.0  82.0      NaN  5\/1\/2020\n5  None  6.0  30.0     30.0  1\/2\/2020\n```\n## inplace\nWe will use inplace=True so the original DataFrame is changed.\n```\nimport pandas as pd\nimport numpy as np \nmy_dict={'NAME':['Ravi','Raju','Alex',None,'King',None],\n         'ID':[1,2,3,np.NaN,5,6],\n         'MATH':[80,40,70,np.NaN,82,30],\n         'ENGLISH':[np.NaN,np.NaN,np.NaN,np.NaN,np.NaN,np.NaN]}\ndf = pd.DataFrame(data=my_dict)\ndf.dropna(how='all',inplace=True,axis=1)\nprint(df)\n```\nOutput ( ENGLISH column is removed )\n```\n   NAME   ID  MATH\n0  Ravi  1.0  80.0\n1  Raju  2.0  40.0\n2  Alex  3.0  70.0\n3  None  NaN   NaN\n4  King  5.0  82.0\n5  None  6.0  30.0\n```\nChange the value of inplace to False and check the output\n```\ndf.dropna(how='all',inplace=False,axis=1)\n```\n```\n   NAME   ID  MATH  ENGLISH\n0  Ravi  1.0  80.0      NaN\n1  Raju  2.0  40.0      NaN\n2  Alex  3.0  70.0      NaN\n3  None  NaN   NaN      NaN\n4  King  5.0  82.0      NaN\n5  None  6.0  30.0      NaN\n```\n## Counting and identifying NaN values\nWe can count and display records with *NaN* by using *isnull()*   \n [« isnull()](https:\/\/www.plus2net.com\/python\/pandas-dataframe-isnull.php)\n\n## Removing rows or columns by using dropna()\nRows or columns can be filled by using fillna()   \n [« fillna()](https:\/\/www.plus2net.com\/python\/pandas-dataframe-fillna.php)\n\n## Questions\n1. What is the dropna() function in Pandas?\n2. What are the parameters of the dropna() function?\n3. What is the difference between the \\`how='any'\\` and \\`how='all'\\` options in the dropna() function?\n4. What is the \\`thresh\\` parameter in the dropna() function?\n5. What is the \\`subset\\` parameter in the dropna() function?\n6. What is the \\`inplace\\` parameter in the dropna() function?\n7. What are the advantages and disadvantages of using the dropna() function?\n8. How can you use the dropna() function to handle missing values in a Pandas DataFrame?\n\n1. How does the dropna() function interact with the fillna() function?\n2. How can you use the dropna() function to drop rows or columns that contain a certain percentage of missing values?\n3. 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Check examples below. `thresh` int : Minimum NaN values required. `subset` Labels along other axis to consider `inplace` Boolean , along with method if value is True then original ( source ) dataframe is replaced after applying *dropna()* how any : rows are removed if any value contains NaN   \n all : rows are removed if all values are contains NaN 2,3 and 4 numbered rows are removed as it contains NaN or None values ( at least one )  \n Output We will use how=all , remove the row or column if **all** values contains NaN. row 3 (having all NaN values) is dropped with axis=0, output Remove the row if a perticular column has NaN value Output With axis=1 and how='any', all columns are deleted. Output how=all Let us change the dataframe by keeping all NaN values to one column. Output : Column 'ENGLISH' is dropped as all are NaN with axis=1 thresh Minimum NaN values required Output Handling NaT values NaT : Missing value in Date and time. Remove the row if *Entry* column has NaT Output inplace We will use inplace=True so the original DataFrame is changed. Output ( ENGLISH column is removed ) Change the value of inplace to False and check the output Counting and identifying NaN values We can count and display records with *NaN* by using *isnull()*   \n  [isnull()](https:\/\/www.plus2net.com\/python\/pandas-dataframe-isnull.php) Removing rows or columns by using dropna() Rows or columns can be filled by using fillna()   \n  [fillna()](https:\/\/www.plus2net.com\/python\/pandas-dataframe-fillna.php) Questions [Data Cleaning](https:\/\/www.plus2net.com\/python\/pandas-data-cleaning.php)  \n  [contains() to display and delete row based on Conditions](https:\/\/www.plus2net.com\/python\/pandas-str-contains.php)    \n  [loc](https:\/\/www.plus2net.com\/python\/pandas-dataframe-loc.php)  [at](https:\/\/www.plus2net.com\/python\/pandas-dataframe-at.php)  [mask](https:\/\/www.plus2net.com\/python\/pandas-dataframe-mask.php)   \n  [Pandas](https:\/\/www.plus2net.com\/python\/pandas.php)  [Pandas DataFrame](https:\/\/www.plus2net.com\/python\/pandas-dataframe.php)   [iloc - rows and columns by integers](https:\/\/www.plus2net.com\/python\/pandas-dataframe-iloc.php)","meta_canonical":null}

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Boilerpipe Text	Python Pandas Pandas Data Cleaning import pandas as pd import numpy as np my_dict={'NAME':['Ravi','Raju', None ,None,'King','Alex'], 'ID':[1,2,3, np.NaN ,5,6], 'MATH':[80,40,70, np.NaN ,82,30], 'ENGLISH':[81,70,40, np.NaN , np.NaN ,30]} df = pd.DataFrame(data=my_dict) print(df) print(df.dropna()) # remove all rows with NaN or None values Output is here NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 81.0 1 Raju 2.0 40.0 70.0 2 None 3.0 70.0 40.0 3 None NaN NaN NaN 4 King 5.0 82.0 NaN 5 Alex 6.0 30.0 30.0 NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 81.0 1 Raju 2.0 40.0 70.0 5 Alex 6.0 30.0 30.0 dropna(): Remove rows or columns based on missing values #C01 dropna(axis=0, how='any', thresh=None, subset=None, inplace=False) Return the Modified DataFrame ( if inplace=True ). axis 0 (default ) or 1, decide row or column to remove. how Takes values any or all . Check examples below. thresh int : Minimum NaN values required. subset Labels along other axis to consider inplace Boolean , along with method if value is True then original ( source ) dataframe is replaced after applying dropna() how any : rows are removed if any value contains NaN all : rows are removed if all values are contains NaN print(df.dropna(how='any')) 2,3 and 4 numbered rows are removed as it contains NaN or None values ( at least one ) Output NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 81.0 1 Raju 2.0 40.0 70.0 5 Alex 6.0 30.0 30.0 We will use how=all , remove the row or column if all values contains NaN. print(df.dropna(how='all')) row 3 (having all NaN values) is dropped with axis=0, output NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 81.0 1 Raju 2.0 40.0 70.0 2 None 3.0 70.0 40.0 4 King 5.0 82.0 NaN 5 Alex 6.0 30.0 30.0 Remove the row if a perticular column has NaN value print(df.dropna(axis=0,subset=['ENGLISH'])) Output NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 81.0 1 Raju 2.0 40.0 70.0 2 None 3.0 70.0 40.0 5 Alex 6.0 30.0 30.0 With axis=1 and how='any', all columns are deleted. print(df.dropna(how='any',axis=1)) Output Empty DataFrame Columns: [] Index: [0, 1, 2, 3, 4, 5] how=all print(df.dropna(axis=1,how='all'))# Nothing will be removed. Let us change the dataframe by keeping all NaN values to one column. import pandas as pd import numpy as np my_dict={'NAME':['Ravi','Raju',None,None,'King','Alex'], 'ID':[1,2,3,np.NaN,5,6], 'MATH':[80,40,70,np.NaN,82,30], 'ENGLISH':[np.NaN,None,np.NaN,np.NaN,np.NaN,None]} df = pd.DataFrame(data=my_dict) print(df) print(df.dropna(axis=1,how='all')) #remove column if all data is NaN Output : Column 'ENGLISH' is dropped as all are NaN with axis=1 NAME ID MATH 0 Ravi 1.0 80.0 1 Raju 2.0 40.0 2 None 3.0 70.0 3 None NaN NaN 4 King 5.0 82.0 5 Alex 6.0 30.0 thresh Minimum NaN values required import pandas as pd import numpy as np my_dict={'NAME':['Ravi','Raju','Alex',None,'King',None], 'ID':[1,2,3,np.NaN,5,6], 'MATH':[80,40,70,np.NaN,82,30], 'ENGLISH':[np.NaN,np.NaN,np.NaN,np.NaN,np.NaN,np.NaN]} df = pd.DataFrame(data=my_dict) df=df.dropna(how='any',axis=0,thresh=3) print(df) Output NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 NaN 1 Raju 2.0 40.0 NaN 2 Alex 3.0 70.0 NaN 4 King 5.0 82.0 NaN Handling NaT values NaT : Missing value in Date and time. import pandas as pd import numpy as np my_dict={'NAME':['Ravi','Raju','Alex',None,'King',None], 'ID':[1,2,3,np.NaN,5,6], 'MATH':[80,40,70,np.NaN,82,30], 'ENGLISH':[81,70,40,np.NaN,np.NaN,30], 'Entry':['1/1/2020','2/1/2020',pd.NaT, pd.NaT,'5/1/2020','1/2/2020'] } df = pd.DataFrame(data=my_dict) print(df) Remove the row if Entry column has NaT df=df.dropna(axis=0,subset=['Entry']) print(df) Output NAME ID MATH ENGLISH Entry 0 Ravi 1.0 80.0 81.0 1/1/2020 1 Raju 2.0 40.0 70.0 2/1/2020 4 King 5.0 82.0 NaN 5/1/2020 5 None 6.0 30.0 30.0 1/2/2020 inplace We will use inplace=True so the original DataFrame is changed. import pandas as pd import numpy as np my_dict={'NAME':['Ravi','Raju','Alex',None,'King',None], 'ID':[1,2,3,np.NaN,5,6], 'MATH':[80,40,70,np.NaN,82,30], 'ENGLISH':[np.NaN,np.NaN,np.NaN,np.NaN,np.NaN,np.NaN]} df = pd.DataFrame(data=my_dict) df.dropna(how='all',inplace=True,axis=1) print(df) Output ( ENGLISH column is removed ) NAME ID MATH 0 Ravi 1.0 80.0 1 Raju 2.0 40.0 2 Alex 3.0 70.0 3 None NaN NaN 4 King 5.0 82.0 5 None 6.0 30.0 Change the value of inplace to False and check the output df.dropna(how='all',inplace=False,axis=1) NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 NaN 1 Raju 2.0 40.0 NaN 2 Alex 3.0 70.0 NaN 3 None NaN NaN NaN 4 King 5.0 82.0 NaN 5 None 6.0 30.0 NaN Counting and identifying NaN values We can count and display records with NaN by using isnull() isnull() Removing rows or columns by using dropna() Rows or columns can be filled by using fillna() fillna() Questions Data Cleaning contains() to display and delete row based on Conditions loc at mask Pandas Pandas DataFrame iloc - rows and columns by integers
Markdown	[![](https://www.plus2net.com/images/top2.jpg)](https://www.plus2net.com/) - [plus2net Home](https://www.plus2net.com/) - [HOME](https://www.plus2net.com/python/site_map.php) [plus2net HOME](https://www.plus2net.com/) [SQL](https://www.plus2net.com/sql_tutorial/site_map.php) [HTML](https://www.plus2net.com/html_tutorial/site_map.php) [PHP](https://www.plus2net.com/php_tutorial/site_map.php) [JavaScript](https://www.plus2net.com/javascript_tutorial/site_map.php) [ASP](https://www.plus2net.com/asp-tutorial/site_map.php) [JQuery](https://www.plus2net.com/jquery/site_map.php) [PhotoShop](https://www.plus2net.com/ps-tutorial/index.php) - [Python](https://www.plus2net.com/python/site_map.php) [Python Home](https://www.plus2net.com/python/site_map.php) [Built in functions](https://www.plus2net.com/python/builtins.php) [date](https://www.plus2net.com/python/date.php) [List](https://www.plus2net.com/python/list.php) [Math](https://www.plus2net.com/python/math.php) [Online Classes](https://www.plus2net.com/python/online-class.php) [File Handling](https://www.plus2net.com/python/file.php) [Error Handling](https://www.plus2net.com/python/exception-handling.php) [Class Object](https://www.plus2net.com/python/class-object-method.php) [Samples](https://www.plus2net.com/python/sample-codes.php) [String](https://www.plus2net.com/python/string.php) [Variables](https://www.plus2net.com/python/variables.php) [pdf ( ReportLab)](https://www.plus2net.com/python/pdf.php) [Tkinter](https://www.plus2net.com/python/tkinter.php) [Json](https://www.plus2net.com/python/json.php) [Numpy](https://www.plus2net.com/python/numpy.php) [Pandas](https://www.plus2net.com/python/pandas.php) [Image ( PIL)](https://www.plus2net.com/python/pillow.php) [Python & MySQL](https://www.plus2net.com/python/mysql.php) [SQLite](https://www.plus2net.com/python/sqlite.php) - [Contact Us](https://www.plus2net.com/contactus.php) # dropna(): Remove rows or columns based on missing values 1. [Python](https://www.plus2net.com/python/site_map.php) 2. [Pandas](https://www.plus2net.com/python/pandas.php) 3. [Pandas Data Cleaning](https://www.plus2net.com/python/pandas-data-cleaning.php) ``` import pandas as pd import numpy as np my_dict={'NAME':['Ravi','Raju',None,None,'King','Alex'], 'ID':[1,2,3,np.NaN,5,6], 'MATH':[80,40,70,np.NaN,82,30], 'ENGLISH':[81,70,40,np.NaN,np.NaN,30]} df = pd.DataFrame(data=my_dict) print(df) print(df.dropna()) # remove all rows with NaN or None values ``` Output is here dropna(): Remove rows or columns based on missing values \#C01 ``` dropna(axis=0, how='any', thresh=None, subset=None, inplace=False) ``` Return the Modified DataFrame ( if inplace=True ). \| \| \| \|---\|---\| \| `axis` \| 0 (default ) or 1, decide row or column to remove. \| \| `how` \| Takes values any or all. Check examples below. \| \| `thresh` \| int : Minimum NaN values required. \| \| `subset` \| Labels along other axis to consider \| \| `inplace` \| Boolean , along with method if value is True then original ( source ) dataframe is replaced after applying dropna() \| ## how any : rows are removed if any value contains NaN all : rows are removed if all values are contains NaN ``` print(df.dropna(how='any')) ``` 2,3 and 4 numbered rows are removed as it contains NaN or None values ( at least one ) Output ``` NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 81.0 1 Raju 2.0 40.0 70.0 5 Alex 6.0 30.0 30.0 ``` We will use how=all , remove the row or column if all values contains NaN. ``` print(df.dropna(how='all')) ``` row 3 (having all NaN values) is dropped with axis=0, output ``` NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 81.0 1 Raju 2.0 40.0 70.0 2 None 3.0 70.0 40.0 4 King 5.0 82.0 NaN 5 Alex 6.0 30.0 30.0 ``` ## Remove the row if a perticular column has NaN value ``` print(df.dropna(axis=0,subset=['ENGLISH'])) ``` Output ``` NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 81.0 1 Raju 2.0 40.0 70.0 2 None 3.0 70.0 40.0 5 Alex 6.0 30.0 30.0 ``` ## axis With axis=1 and how='any', all columns are deleted. ``` print(df.dropna(how='any',axis=1)) ``` Output ``` Empty DataFrame Columns: [] Index: [0, 1, 2, 3, 4, 5] ``` how=all ``` print(df.dropna(axis=1,how='all'))# Nothing will be removed. ``` Let us change the dataframe by keeping all NaN values to one column. ``` import pandas as pd import numpy as np my_dict={'NAME':['Ravi','Raju',None,None,'King','Alex'], 'ID':[1,2,3,np.NaN,5,6], 'MATH':[80,40,70,np.NaN,82,30], 'ENGLISH':[np.NaN,None,np.NaN,np.NaN,np.NaN,None]} df = pd.DataFrame(data=my_dict) print(df) print(df.dropna(axis=1,how='all')) #remove column if all data is NaN ``` Output : Column 'ENGLISH' is dropped as all are NaN with axis=1 ``` NAME ID MATH 0 Ravi 1.0 80.0 1 Raju 2.0 40.0 2 None 3.0 70.0 3 None NaN NaN 4 King 5.0 82.0 5 Alex 6.0 30.0 ``` ## thresh Minimum NaN values required ``` import pandas as pd import numpy as np my_dict={'NAME':['Ravi','Raju','Alex',None,'King',None], 'ID':[1,2,3,np.NaN,5,6], 'MATH':[80,40,70,np.NaN,82,30], 'ENGLISH':[np.NaN,np.NaN,np.NaN,np.NaN,np.NaN,np.NaN]} df = pd.DataFrame(data=my_dict) df=df.dropna(how='any',axis=0,thresh=3) print(df) ``` Output ``` NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 NaN 1 Raju 2.0 40.0 NaN 2 Alex 3.0 70.0 NaN 4 King 5.0 82.0 NaN ``` Thresh is used for data cleaning where some threshold value of valid data is considered for retaining the rows or columns. [Check here to know how data cleaning is done by using thresh option](https://www.plus2net.com/python/pandas-dataframe-dropna-thresh.php). ## Handling NaT values NaT : Missing value in Date and time. ``` import pandas as pd import numpy as np my_dict={'NAME':['Ravi','Raju','Alex',None,'King',None], 'ID':[1,2,3,np.NaN,5,6], 'MATH':[80,40,70,np.NaN,82,30], 'ENGLISH':[81,70,40,np.NaN,np.NaN,30], 'Entry':['1/1/2020','2/1/2020',pd.NaT, pd.NaT,'5/1/2020','1/2/2020']} df = pd.DataFrame(data=my_dict) print(df) ``` Remove the row if Entry column has NaT ``` df=df.dropna(axis=0,subset=['Entry']) print(df) ``` Output ``` NAME ID MATH ENGLISH Entry 0 Ravi 1.0 80.0 81.0 1/1/2020 1 Raju 2.0 40.0 70.0 2/1/2020 4 King 5.0 82.0 NaN 5/1/2020 5 None 6.0 30.0 30.0 1/2/2020 ``` ## inplace We will use inplace=True so the original DataFrame is changed. ``` import pandas as pd import numpy as np my_dict={'NAME':['Ravi','Raju','Alex',None,'King',None], 'ID':[1,2,3,np.NaN,5,6], 'MATH':[80,40,70,np.NaN,82,30], 'ENGLISH':[np.NaN,np.NaN,np.NaN,np.NaN,np.NaN,np.NaN]} df = pd.DataFrame(data=my_dict) df.dropna(how='all',inplace=True,axis=1) print(df) ``` Output ( ENGLISH column is removed ) ``` NAME ID MATH 0 Ravi 1.0 80.0 1 Raju 2.0 40.0 2 Alex 3.0 70.0 3 None NaN NaN 4 King 5.0 82.0 5 None 6.0 30.0 ``` Change the value of inplace to False and check the output ``` df.dropna(how='all',inplace=False,axis=1) ``` ``` NAME ID MATH ENGLISH 0 Ravi 1.0 80.0 NaN 1 Raju 2.0 40.0 NaN 2 Alex 3.0 70.0 NaN 3 None NaN NaN NaN 4 King 5.0 82.0 NaN 5 None 6.0 30.0 NaN ``` ## Counting and identifying NaN values We can count and display records with NaN by using isnull() [« isnull()](https://www.plus2net.com/python/pandas-dataframe-isnull.php) ## Removing rows or columns by using dropna() Rows or columns can be filled by using fillna() [« fillna()](https://www.plus2net.com/python/pandas-dataframe-fillna.php) ## Questions 1. What is the dropna() function in Pandas? 2. What are the parameters of the dropna() function? 3. What is the difference between the \`how='any'\` and \`how='all'\` options in the dropna() function? 4. What is the \`thresh\` parameter in the dropna() function? 5. What is the \`subset\` parameter in the dropna() function? 6. What is the \`inplace\` parameter in the dropna() function? 7. What are the advantages and disadvantages of using the dropna() function? 8. How can you use the dropna() function to handle missing values in a Pandas DataFrame? 1. How does the dropna() function interact with the fillna() function? 2. How can you use the dropna() function to drop rows or columns that contain a certain percentage of missing values? 3. How can you use the dropna() function to drop rows or columns that contain only missing values? 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Readable Markdown	[Python](https://www.plus2net.com/python/site_map.php) [Pandas](https://www.plus2net.com/python/pandas.php) [Pandas Data Cleaning](https://www.plus2net.com/python/pandas-data-cleaning.php) Output is here dropna(): Remove rows or columns based on missing values \#C01 Return the Modified DataFrame ( if inplace=True ). `axis` 0 (default ) or 1, decide row or column to remove. `how` Takes values any or all. Check examples below. `thresh` int : Minimum NaN values required. `subset` Labels along other axis to consider `inplace` Boolean , along with method if value is True then original ( source ) dataframe is replaced after applying dropna() how any : rows are removed if any value contains NaN all : rows are removed if all values are contains NaN 2,3 and 4 numbered rows are removed as it contains NaN or None values ( at least one ) Output We will use how=all , remove the row or column if all values contains NaN. row 3 (having all NaN values) is dropped with axis=0, output Remove the row if a perticular column has NaN value Output With axis=1 and how='any', all columns are deleted. Output how=all Let us change the dataframe by keeping all NaN values to one column. Output : Column 'ENGLISH' is dropped as all are NaN with axis=1 thresh Minimum NaN values required Output Handling NaT values NaT : Missing value in Date and time. Remove the row if Entry column has NaT Output inplace We will use inplace=True so the original DataFrame is changed. Output ( ENGLISH column is removed ) Change the value of inplace to False and check the output Counting and identifying NaN values We can count and display records with NaN by using isnull() [isnull()](https://www.plus2net.com/python/pandas-dataframe-isnull.php) Removing rows or columns by using dropna() Rows or columns can be filled by using fillna() [fillna()](https://www.plus2net.com/python/pandas-dataframe-fillna.php) Questions [Data Cleaning](https://www.plus2net.com/python/pandas-data-cleaning.php) [contains() to display and delete row based on Conditions](https://www.plus2net.com/python/pandas-str-contains.php) [loc](https://www.plus2net.com/python/pandas-dataframe-loc.php) [at](https://www.plus2net.com/python/pandas-dataframe-at.php) [mask](https://www.plus2net.com/python/pandas-dataframe-mask.php) [Pandas](https://www.plus2net.com/python/pandas.php) [Pandas DataFrame](https://www.plus2net.com/python/pandas-dataframe.php) [iloc - rows and columns by integers](https://www.plus2net.com/python/pandas-dataframe-iloc.php)
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