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Ways to Create NaN Values in Pandas DataFrame
  • Last Updated : 10 Jul, 2020

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

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