Ways to Create NaN Values in Pandas DataFrame

Let’s discuss ways of creating NaN values in the Pandas Dataframe. There are various ways to create NaN values in Pandas dataFrame. Those are:

Using NumPy
Importing csv file having blank values
Applying to_numeric function

Method 1: Using NumPy

import pandas as pd 
import numpy as np 
  
num = {'number': [1,2,np.nan,6,7,np.nan,np.nan]} 
df = pd.DataFrame(num) 
  
df 

Output:

pandas-create-nan-11

Method 2: Importing the CSV file having blank instances

Consider the below csv file named “Book1.csv”:

Code:

# import pandas 
import pandas as pd 
  
# read file 
df = pd.read_csv("Book1.csv") 
  
# print values 
df 

Output:

pandas-create-nan-2

You will get Nan values for blank instances.

Method 3: Applying to_numeric function

to_numeric function coverts arguments to a numeric type.

Example:

import pandas as pd 
  
num = {'data': [1,"hjghjd",3,"jxsh"]} 
df = pd.DataFrame(num) 
  
# this will convert non-numeric  
# values into NaN values 
df = pd.to_numeric(df["data"], errors='coerce') 
  
df 

Output:

pandas-create-nan-4

My Personal Notes

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