Question

How would you drop NaN values in a pandas dataframe (df)

A) df.drop_null()
B) df.dropna()
C) df.remove_nan()
D) df.clear_nan()

Answer 1

To remove NaN values from a pandas DataFrame, the method df.dropna() should be used. This removes rows with any NaN values or columns containing NaN values, depending on the specified parameters.

Step-by-step explanation:

To drop NaN (Not a Number) values in a pandas DataFrame, you would use the df.dropna() method. This is the correct way to remove missing values from a DataFrame. The other options listed, such as df.drop_null(), df.remove_nan(), and df.clear_nan() are not actual pandas methods and will not work. The dropna() method can be applied to the DataFrame to remove any rows or columns that contain NaN values, depending on the parameters you specify. For example, df.dropna() will drop all rows where any NaN values are present, and df.dropna(axis='columns') will drop all columns containing NaN values.

To drop NaN values in a pandas dataframe, you would use the df.dropna() method. This method removes any rows or columns that contain NaN values. It provides several parameters to customize the dropping process. For example, you can set the axis parameter to 0 or 1 to specify if you want to drop rows or columns respectivey. You can also set the how parameter to 'any' to drop rows or columns that contain any NaN value, or 'all' to drop rows or columns that contain all NaN values.