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
import pandas as pdimport 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:
# import pandasimport pandas as pd # read filedf = pd.read_csv("Book1.csv") # print valuesdf |
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:
import pandas as pd num = {'data': [1,"hjghjd",3,"jxsh"]}df = pd.DataFrame(num) # this will convert non-numeric # values into NaN valuesdf = pd.to_numeric(df["data"], errors='coerce') df |
Output:
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