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.

Syntax:

DataFrameName.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

Parameters:

• 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.

Returns

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.

import pandas as pd
aa = pd.read_csv("aa.csv")
aa.head()

Output

	
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

Code:

# importing pandas module 
import pandas as pd  
# making data frame from csv file 
info = pd.read_csv("aa.csv")   
# making a copy of old data frame 
copy = pd.read_csv("aa.csv") 
  
# creating value with all null values in new data frame 
copy["Null Column"]= None
  
# checking if column is inserted properly  
print(info.columns.values, "\n", copy.columns.values) 
  
# comparing values before dropping null column 
print("\nColumn number before dropping Null column\n", 
       len(info.dtypes), len(copy.dtypes)) 
  
# dropping column with all null values 
copy.dropna(axis = 1, how ='all', inplace = True) 
  
# comparing values after dropping null column 
print("\nColumn number after dropping Null column\n", 
      len(info.dtypes), len(info.dtypes))  

Output

['	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.