Drop rows that contain specific values in multiple columns
We can drop specific values from multiple columns by using relational operators.
Syntax: dataframe[(dataframe.column_name operator value ) relational_operator (dataframe.column_name operator value )]
where
- dataframe is the input dataframe
- column_name is the column
- operator is the relational operator
Python3
# import pandas moduleimport pandas as pd# create dataframe with 4 columnsdata = pd.DataFrame({ "name": ['sravan', 'jyothika', 'harsha', 'ramya', 'sravan', 'jyothika', 'harsha', 'ramya', 'sravan', 'jyothika', 'harsha', 'ramya'], "subjects": ['java', 'java', 'java', 'python', 'python', 'python', 'html/php', 'html/php', 'html/php', 'php/js', 'php/js', 'php/js'], "marks": [98, 79, 89, 97, 82, 98, 90, 87, 78, 89, 93, 94]})# drop specific values# where marks is 98 and name is sravanprint(data[(data.marks != 98) & (data.name != 'sravan')])print("------------------")# drop specific values# where marks is 98 or name is sravanprint(data[(data.marks != 98) | (data.name != 'sravan')]) |
Output:
How to Drop Rows that Contain a Specific Value in Pandas?
In this article, we will discuss how to drop rows that contain a specific value in Pandas. Dropping rows means removing values from the dataframe we can drop the specific value by using conditional or relational operators.
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