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Pandas: Find and replace the missing values in a given DataFrame which do not have any valuable information

Pandas Handling Missing Values: Exercise-4 with Solution

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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Next: Write a Pandas program to drop the rows where at least one element is missing in a given DataFrame.

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