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:
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