{ "componentChunkName": "component---src-templates-blog-post-js", "path": "/pandas-isnull-and-notnull/", "result": {"data":{"markdownRemark":{"html":"

First, let’s read an Excel file into a DataFrame.

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import pandas as pd\n\nbudget = pd.read_excel("budget.xlsx")\n\nbudget
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Output:

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Let’s say we want to return the rows where the values in the July’19 Budget column are null.

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We can do so using the isnull() method.

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condition1 = budget["July'19 Budget"].isnull()\n\ncondition1
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Output:

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There is a complementary method called notnull() that returns the non-null values.

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condition2 = budget["July'19 Budget"].notnull()\n\nbudget[condition2]
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\n \n \n \n \n

\n","frontmatter":{"title":"Pandas - isnull() & notnull()","date":"2021-08-08"}}},"pageContext":{"slug":"/pandas-isnull-and-notnull/","prev":{"fields":{"slug":"/find-duplicate-rows-in-a-pandas-dataframe/"},"frontmatter":{"modules":null}},"next":{"fields":{"slug":"/remove-duplicate-rows-from-a-pandas-dataframe/"},"frontmatter":{"modules":null}}}}, "staticQueryHashes": ["3159585216"]}