Pandas: Find and replace the missing values in a given DataFrame which do not have any valuable information
Write a Pandas program to find and replace the missing values in a given DataFrame which do not have any valuable information.
Example:
Missing values: ?, --
Replace those values with NaN
Test Data:
ord_no purch_amt ord_date customer_id salesman_id 0 70001 150.5 ? 3002 5002 1 NaN 270.65 2012-09-10 3001 5003 2 70002 65.26 NaN 3001 ? 3 70004 110.5 2012-08-17 3003 5001 4 NaN 948.5 2012-09-10 3002 NaN 5 70005 2400.6 2012-07-27 3001 5002 6 -- 5760 2012-09-10 3001 5001 7 70010 ? 2012-10-10 3004 ? 8 70003 12.43 2012-10-10 -- 5003 9 70012 2480.4 2012-06-27 3002 5002 10 NaN 250.45 2012-08-17 3001 5003 11 70013 3045.6 2012-04-25 3001 --
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,"--",70010,70003,70012,np.nan,70013],
'purch_amt':[150.5,270.65,65.26,110.5,948.5,2400.6,5760,"?",12.43,2480.4,250.45, 3045.6],
'ord_date': ['?','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,"--",3002,3001,3001],
'salesman_id':[5002,5003,"?",5001,np.nan,5002,5001,"?",5003,5002,5003,"--"]})
print("Original Orders DataFrame:")
print(df)
print("\nReplace the missing values with NaN:")
result = df.replace({"?": np.nan, "--": np.nan})
print(result)
Sample Output:
Original Orders DataFrame:
ord_no purch_amt ord_date customer_id salesman_id
0 70001 150.5 ? 3002 5002
1 NaN 270.65 2012-09-10 3001 5003
2 70002 65.26 NaN 3001 ?
3 70004 110.5 2012-08-17 3003 5001
4 NaN 948.5 2012-09-10 3002 NaN
5 70005 2400.6 2012-07-27 3001 5002
6 -- 5760 2012-09-10 3001 5001
7 70010 ? 2012-10-10 3004 ?
8 70003 12.43 2012-10-10 -- 5003
9 70012 2480.4 2012-06-27 3002 5002
10 NaN 250.45 2012-08-17 3001 5003
11 70013 3045.6 2012-04-25 3001 --
Replace the missing values with NaN:
ord_no purch_amt ord_date customer_id salesman_id
0 70001.0 150.50 NaN 3002.0 5002.0
1 NaN 270.65 2012-09-10 3001.0 5003.0
2 70002.0 65.26 NaN 3001.0 NaN
3 70004.0 110.50 2012-08-17 3003.0 5001.0
4 NaN 948.50 2012-09-10 3002.0 NaN
5 70005.0 2400.60 2012-07-27 3001.0 5002.0
6 NaN 5760.00 2012-09-10 3001.0 5001.0
7 70010.0 NaN 2012-10-10 3004.0 NaN
8 70003.0 12.43 2012-10-10 NaN 5003.0
9 70012.0 2480.40 2012-06-27 3002.0 5002.0
10 NaN 250.45 2012-08-17 3001.0 5003.0
11 70013.0 3045.60 2012-04-25 3001.0 NaN
Python Code Editor:
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