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:

Method 1: Using NumPy

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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
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Output:

Method 2: Importing the CSV file having blank instances



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


Code:

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

You will get Nan values for blank instances.

Method 3: Applying to_numeric function

to_numeric function coverts arguments to a numeric type.

Example:

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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
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Output:

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