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:
