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Pandas DataFrame.dropna()

If your dataset consists of null values, we can use the dropna() function to analyze and drop the rows/columns in the dataset.



  • axis : {0 or 'index', 1 or 'columns'}, default value 0
    It takes int or string values for rows/columns. The input can be 0 and 1 for the integers and index or columns for the string.
    • 0, or 'index': Drop the rows which contain missing values.
    • 1, or 'columns': Drop the columns which contain the missing value.
  • how :
    It determines if row or column is removed from DataFrame when we have at least one NA or all NA.
    It takes a string value of only two kinds ('any' or 'all').
    • any: It drops the row/column if any value is null.
    • all: It drops only if all values are null.
  • thresh:
    It takes integer value that defines the minimum amount of NA values to drop.
  • subset:
    It is an array that limits the dropping process to passed rows/columns through the list.
  • inplace:
    It returns a boolean value that makes the changes in data frame itself if it is True.


It returns the DataFrame from which NA entries has been dropped.

For Demonstration, first, we are taking a csv file that will drop any column from the dataset.


Name Hire Date Salary Leaves Remaining
0 John Idle 03/15/14 50000.0 10
1 Smith Gilliam 06/01/15 65000.0 8
2 Parker Chapman 05/12/14 45000.0 10
3 Jones Palin 11/01/13 70000.0 3
4 Terry Gilliam 08/12/14 48000.0 7
5 Michael Palin 05/23/13 66000.0 8



['	Name	Hire Date	Salary	Leaves Remaining'] 
 ['	Name	Hire Date	Salary	Leaves Remaining'
 'Null Column']

Column number before dropping Null column
 1 2

Column number after dropping Null column
 1 1

The above code dropped the null column from the dataset and returned a new DataFrame.

Next TopicDataFrame.fillna()

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