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

Python3

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:

Method 2: Importing the CSV file having blank instances

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

Code:

Python3

# import pandas
import pandas as pd

# read file
df = pd.read_csv("Book1.csv")

# print values
df

Output:

You will get Nan values for blank instances.

Method 3: Applying to_numeric function

to_numeric function converts arguments to a numeric type.

Example:

Python3

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: