Different ways to create Pandas Dataframe

Ways to Create NaN Values in Pandas DataFrame

Last Updated : 08 Dec, 2021

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: pandas-create-nan-11

pandas-create-nan-11

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: pandas-create-nan-2

pandas-create-nan-2

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: pandas-create-nan-4

pandas-create-nan-4

Different ways to create Pandas Dataframe

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