Pandas: Replace NaNs with a single constant value
Pandas Handling Missing Values: Exercise-12 with Solution
Write a Pandas program to replace NaNs with a single constant value in specified columns in a DataFrame.
Test Data:
ord_no purch_amt ord_date customer_id 0 NaN NaN NaN NaN 1 NaN 270.65 2012-09-10 3001.0 2 70002.0 65.26 NaN 3001.0 3 NaN NaN NaN NaN 4 NaN 948.50 2012-09-10 3002.0 5 70005.0 2400.60 2012-07-27 3001.0 6 NaN 5760.00 2012-09-10 3001.0 7 70010.0 1983.43 2012-10-10 3004.0 8 70003.0 2480.40 2012-10-10 3003.0 9 70012.0 250.45 2012-06-27 3002.0 10 NaN 75.29 2012-08-17 3001.0 11 NaN NaN NaN NaN
Sample Solution:
Python Code :
import pandas as pd
import numpy as np
pd.set_option('display.max_rows', None)
#pd.set_option('display.max_columns', None)
df = pd.DataFrame({
'ord_no':[70001,np.nan,70002,70004,np.nan,70005,np.nan,70010,70003,70012,np.nan,70013],
'purch_amt':[150.5,270.65,65.26,110.5,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,3045.6],
'ord_date': ['2012-10-05','2012-09-10',np.nan,'2012-08-17','2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17','2012-04-25'],
'customer_id':[3002,3001,3001,3003,3002,3001,3001,3004,3003,3002,3001,3001],
'salesman_id':[5002,5003,5001,np.nan,5002,5001,5001,np.nan,5003,5002,5003,np.nan]})
print("Original Orders DataFrame:")
print(df)
print("\nReplace NaNs with a single constant value:")
result = df['ord_no'].fillna(0, inplace=False)
print(result)
Sample Output:
Original Orders DataFrame: ord_no purch_amt ord_date customer_id salesman_id 0 70001.0 150.50 2012-10-05 3002 5002.0 1 NaN 270.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN 3001 5001.0 3 70004.0 110.50 2012-08-17 3003 NaN 4 NaN 948.50 2012-09-10 3002 5002.0 5 70005.0 2400.60 2012-07-27 3001 5001.0 6 NaN 5760.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 2012-10-10 3004 NaN 8 70003.0 2480.40 2012-10-10 3003 5003.0 9 70012.0 250.45 2012-06-27 3002 5002.0 10 NaN 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 2012-04-25 3001 NaN Replace NaNs with a single constant value: 0 70001.0 1 0.0 2 70002.0 3 70004.0 4 0.0 5 70005.0 6 0.0 7 70010.0 8 70003.0 9 70012.0 10 0.0 11 70013.0 Name: ord_no, dtype: float64
Python Code Editor:
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