{"id":4885,"date":"2021-05-01T15:03:14","date_gmt":"2021-05-01T09:33:14","guid":{"rendered":"https:\/\/python-programs.com\/?p=4885"},"modified":"2021-11-22T18:42:58","modified_gmt":"2021-11-22T13:12:58","slug":"python-pandas-how-to-drop-rows-in-dataframe-by-index-labels","status":"publish","type":"post","link":"https:\/\/python-programs.com\/python-pandas-how-to-drop-rows-in-dataframe-by-index-labels\/","title":{"rendered":"Python Pandas : How to drop rows in DataFrame by index labels"},"content":{"rendered":"

How to drop rows in DataFrame by index labels in Python ?<\/h2>\n

In this article we are going to learn how to delete single or multiple rows from a Dataframe.<\/p>\n

For this we are going to use the drop( )<\/code> function.<\/p>\n

Syntax - DataFrame.drop( labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise' )<\/pre>\n
Where, the function accepts name\/series of names in the label and deletes the rows or columns it points to. The axis is used to alter between rows and columns, 0 means rows and 1 means columns(default value is 0).<\/p>\n
Also we have to pass inplace = True<\/code><\/em> if we want the modified values to be updated in our dataframe object, as the drop( )<\/code> function returns the modified values into a new Dataframe object. To explain this properly we will be using inplace = True<\/code> <\/em>in all our programs.<\/p>\n
We are going to use the following dataset as example<\/p>\n
      Name\u00a0         Age\u00a0\u00a0\u00a0\u00a0\u00a0  Location       Country\r\na\u00a0\u00a0\u00a0\u00a0 Jill\u00a0\u00a0             16\u00a0\u00a0\u00a0\u00a0     Tokyo\u00a0\u00a0           Japan\r\nb\u00a0\u00a0  Phoebe\u00a0\u00a0      38\u00a0       New York\u00a0\u00a0\u00a0\u00a0    USA\r\nc\u00a0\u00a0\u00a0  Kirti\u00a0\u00a0           39\u00a0        New York\u00a0\u00a0\u00a0\u00a0    USA\r\nd\u00a0\u00a0\u00a0  Veena\u00a0\u00a0       40\u00a0\u00a0\u00a0\u00a0     Delhi\u00a0\u00a0             India\r\ne\u00a0\u00a0\u00a0\u00a0 John\u00a0\u00a0         54\u00a0\u00a0\u00a0       Mumbai\u00a0\u00a0       India\r\nf\u00a0    Michael\u00a0\u00a0     21\u00a0\u00a0\u00a0\u00a0     Tokyo\u00a0\u00a0            Japan<\/pre>\n
Deleting a single Row in DataFrame by Row Index Label :<\/h3>\n
To delete a single row by the label we can just pass the label into the function.<\/p>\n
Here let\u2019s try to delete\u2018b\u2019 row<\/code><\/code>.<\/p>\n
#program :\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n#Examole data\r\nstudents = [('Jill',\u00a0\u00a0 16,\u00a0 'Tokyo',\u00a0\u00a0\u00a0\u00a0 'Japan'),\r\n('Phoebe', 38,\u00a0 'New York',\u00a0 'USA'),\r\n('Kirti',\u00a0 39,\u00a0 'New York',\u00a0 'USA'),\r\n('Veena',\u00a0 40,\u00a0 'Delhi',\u00a0\u00a0\u00a0\u00a0 'India'),\r\n('John',\u00a0\u00a0 54,\u00a0 'Mumbai',\u00a0\u00a0\u00a0 'India'),\r\n(\"Michael\",21,\u00a0 'Tokyo',\u00a0\u00a0\u00a0\u00a0 'Japan')]\r\n\r\n#Creating an object of dataframe class\r\ndfObj = pd.DataFrame(students, columns = ['Name' , 'Age', 'Location' , 'Country'], index=['a', 'b', 'c' , 'd' , 'e' , 'f'])\r\n#Deleting 'b' row\r\ndfObj.drop('b',inplace=True)\r\nprint(dfObj)<\/pre>\n
Output :<\/p>\n
        Name\u00a0    Age\u00a0\u00a0\u00a0\u00a0\u00a0 Location       Country\r\na\u00a0\u00a0\u00a0\u00a0    Jill\u00a0\u00a0        16\u00a0\u00a0\u00a0\u00a0   Tokyo\u00a0\u00a0          Japan\r\nc\u00a0\u00a0\u00a0     Kirti\u00a0\u00a0      39\u00a0      New York\u00a0\u00a0\u00a0\u00a0  USA\r\nd\u00a0\u00a0\u00a0   Veena\u00a0\u00a0    40\u00a0\u00a0\u00a0\u00a0   Delhi\u00a0\u00a0           India\r\ne\u00a0\u00a0\u00a0\u00a0  John\u00a0\u00a0       54\u00a0\u00a0\u00a0    Mumbai\u00a0\u00a0      India\r\nf\u00a0    Michael\u00a0\u00a0   21\u00a0\u00a0\u00a0\u00a0   Tokyo\u00a0\u00a0          Japan<\/pre>\n
Deleting Multiple Rows in DataFrame by Index Labels :<\/h3>\n
To delete multiple rows by their labels we can just pass the labels into the function inside a square bracket [ ]<\/code>.<\/p>\n
Here let\u2019s try to delete 'a'<\/code> and 'b'<\/code>\u00a0row.<\/p>\n
#program :\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n#Examole data\r\nstudents = [('Jill',\u00a0\u00a0 16,\u00a0 'Tokyo',\u00a0\u00a0\u00a0\u00a0 'Japan'),\r\n('Phoebe', 38,\u00a0 'New York',\u00a0 'USA'),\r\n('Kirti',\u00a0 39,\u00a0 'New York',\u00a0 'USA'),\r\n('Veena',\u00a0 40,\u00a0 'Delhi',\u00a0\u00a0\u00a0\u00a0 'India'),\r\n('John',\u00a0\u00a0 54,\u00a0 'Mumbai',\u00a0\u00a0\u00a0 'India'),\r\n(\"Michael\",21,\u00a0 'Tokyo',\u00a0\u00a0\u00a0\u00a0 'Japan')]\r\n\r\n#Creating an object of dataframe class\r\ndfObj = pd.DataFrame(students, columns = ['Name' , 'Age', 'Location' , 'Country'], index=['a', 'b', 'c' , 'd' , 'e' , 'f'])\r\n\r\n#Deleting 'a' and 'b' row\r\ndfObj.drop(['a','b'],inplace=True)\r\nprint(dfObj)<\/pre>\n
Output :\r\n  \u00a0 \u00a0 Name\u00a0 \u00a0 \u00a0 \u00a0Age\u00a0\u00a0\u00a0\u00a0\u00a0Location\u00a0 \u00a0 \u00a0 \u00a0Country\r\nc\u00a0 \u00a0 \u00a0Kirti\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 39\u00a0 \u00a0 \u00a0 New York\u00a0 \u00a0 \u00a0 USA\r\nd\u00a0 \u00a0 \u00a0Veena\u00a0 \u00a0 \u00a0 40\u00a0 \u00a0 \u00a0 Delhi\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0India\r\ne\u00a0\u00a0\u00a0\u00a0 John\u00a0 \u00a0 \u00a0 \u00a0 \u00a054\u00a0 \u00a0 \u00a0Mumbai\u00a0 \u00a0 \u00a0 \u00a0 \u00a0India\r\nf\u00a0 \u00a0 \u00a0Michael\u00a0 \u00a0 \u00a021\u00a0\u00a0\u00a0\u00a0 Tokyo\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Japan<\/pre>\n
Deleting Multiple Rows by Index Position in DataFrame :<\/h3>\n
To delete multiple rows we know the index position, however, the function drop( )<\/code> doesn\u2019t take indices as parameters. So we create the list of labels and pass them into the drop( )<\/code> function. Let\u2019s try deleting the same rows again but by index.<\/p>\n
#program :\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n#Examole data\r\nstudents = [('Jill',\u00a0\u00a0 16,\u00a0 'Tokyo',\u00a0\u00a0\u00a0\u00a0 'Japan'),\r\n('Phoebe', 38,\u00a0 'New York',\u00a0 'USA'),\r\n('Kirti',\u00a0 39,\u00a0 'New York',\u00a0 'USA'),\r\n('Veena',\u00a0 40,\u00a0 'Delhi',\u00a0\u00a0\u00a0\u00a0 'India'),\r\n('John',\u00a0\u00a0 54,\u00a0 'Mumbai',\u00a0\u00a0\u00a0 'India'),\r\n(\"Michael\",21,\u00a0 'Tokyo',\u00a0\u00a0\u00a0\u00a0 'Japan')]\r\n\r\n#Creating an object of dataframe class\r\ndfObj = pd.DataFrame(students, columns = ['Name' , 'Age', 'Location' , 'Country'], index=['a', 'b', 'c' , 'd' , 'e' , 'f'])\r\n\r\n#Deleting 1st and 2nd row\r\ndfObj.drop([dfObj.index[0] , dfObj.index[1]],inplace=True)\r\nprint(dfObj)<\/pre>\n
Output :\r\n  \u00a0 \u00a0 Name\u00a0 \u00a0 \u00a0 Age\u00a0\u00a0\u00a0\u00a0\u00a0Location\u00a0 \u00a0 \u00a0Country\r\nc\u00a0 \u00a0 \u00a0Kirti\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 39\u00a0 \u00a0 \u00a0New York\u00a0\u00a0\u00a0\u00a0 USA\r\nd\u00a0 \u00a0 \u00a0Veena\u00a0 \u00a0 \u00a0 40\u00a0\u00a0\u00a0\u00a0 Delhi\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 India\r\ne\u00a0\u00a0\u00a0\u00a0 John\u00a0 \u00a0 \u00a0 \u00a0 \u00a054\u00a0\u00a0\u00a0 Mumbai\u00a0 \u00a0 \u00a0 \u00a0 India\r\nf\u00a0 \u00a0 \u00a0Michael\u00a0 \u00a0 \u00a021\u00a0\u00a0\u00a0\u00a0 Tokyo\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Japan<\/pre>\n
Want to expert in the python programming language? Exploring\u00a0Python Data Analysis using Pandas<\/a>\u00a0tutorial changes your knowledge from basic to advance level in python concepts.<\/p>\n
Read more Articles on Python Data Analysis Using Padas \u2013 Remove Contents from a Dataframe<\/strong><\/p>\n