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Below, we have read the budget.xlsx file into a DataFrame.

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

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\n \n \n \n \n

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As we can see, both the 3rd and the 12th rows have been removed.

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What if we want to drop rows only where all elements are missing? In other words, what if we want to remove only the 12th row?

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We can do so by passing a value of all to the how parameter in the dropna() method.

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In the dropna() method, the default value of the how parameter is any.

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\n
budget.dropna(how = "all", inplace = True)\nbudget
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Output:

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\n \n \n \n \n

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As we can see the 12th row where all values were missing has been removed; however, the 3rd row where only one value is missing remains in the DataFrame.

\n","frontmatter":{"title":"Delete Rows With Null Values in a Pandas DataFrame","date":"2021-08-07"}}},"pageContext":{"slug":"/delete-rows-with-null-values-in-a-pandas-dataframe/","prev":{"fields":{"slug":"/convert-floating-point-numbers-to-integers-in-pandas/"},"frontmatter":{"modules":null}},"next":{"fields":{"slug":"/fill-in-null-values-in-a-pandas-dataframe-using-the-fillna-method/"},"frontmatter":{"modules":null}}}}, "staticQueryHashes": ["3159585216"]}