Pandas dropna() - Drop Null/NA Values from DataFrame Examples

1. Pandas DataFrame dropna() Function

Pandas DataFrame dropna() function is used to remove rows and columns with Null/NaN values. By default, this function returns a new DataFrame and the source DataFrame remains unchanged.

We can create null values using None, pandas.NaT, and numpy.nan variables.

The dropna() function syntax is:

  • axis: possible values are {0 or ‘index’, 1 or ‘columns’}, default 0. If 0, drop rows with null values. If 1, drop columns with missing values.
  • how: possible values are {‘any’, ‘all’}, default ‘any’. If ‘any’, drop the row/column if any of the values is null. If ‘all’, drop the row/column if all the values are missing.
  • thresh: an int value to specify the threshold for the drop operation.
  • subset: specifies the rows/columns to look for null values.
  • inplace: a boolean value. If True, the source DataFrame is changed and None is returned.

Let’s look at some examples of using dropna() function.

2. Pandas Drop All Rows with any Null/NaN/NaT Values

This is the default behavior of dropna() function.


3. Drop All Columns with Any Missing Value

We can pass axis=1 to drop columns with the missing values.


4. Drop Row/Column Only if All the Values are Null


5. DataFrame Drop Rows/Columns when the threshold of null values is crossed


The rows with 2 or more null values are dropped.

6. Define Labels to look for null values


We can specify the index values in the subset when dropping columns from the DataFrame.


The ‘ID’ column is not dropped because the missing value is looked only in index 1 and 2.

7. Dropping Rows with NA inplace

We can pass inplace=True to change the source DataFrame itself. It’s useful when the DataFrame size is huge and we want to save some memory.


8. References

By admin

Leave a Reply

%d bloggers like this: