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

import pandas as pd

budget = pd.read_excel("budget.xlsx")
budget

Output:

We can see that we have two rows with missing values. The 3rd row (index no 2) has one missing value, while the 12th row (index no 11) has all missing values.

Pandas, by default, marks missing values as NaN.

dropna( )

The dropna() method will drop the rows where at least one element is missing.

budget.dropna(inplace = True)
budget

We pass inplace = True in order to make the changes to the DataFrame permanent.

Output:

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

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?

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

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

budget.dropna(how = "all", inplace = True)
budget

Output:

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.