Write a python program to find and replace the missing values in a given Data Frame.

Program Logic:

• Create dictionary say ‘sale’ which store details of 7 years monthly sales.
• Add some missing data in it and represent it with ‘?’ and ‘—‘.
• Create dataframe object say’df’ using DataFrame method of pandas module and store necessary information in it.
• Display original dataframe say ‘df’ using print() function
• Fill its missing values from corresponding entries with NaN
• Display dataframe say ‘df’ with modified content using print() function
• Exit

Below is implementation code/Source code:

Here is the program to find and replace missing values in given dataframe. The output is also shown below

import pandas as pd
import numpy as np
sale = {'1996' : [98,99,103,102,104,'---',102,103,102,105,'?',105],
      '1997' : [78,101,103,102,84,101,89,103,90,104,65,105], 
      '1998' : [90,40,50,90,65,'?',101,103,102,104,65,109],
      '1999' : ['?',80,60,85,'?',101,'---',103,102,104,65,101], 
      '2000' : ['?',80,60,85,'?',101,201,103,100,104,65,101],
      '2001' : [101,101,103,102,104,101,101,103,102,104,123,101],
      '2002' : [ 101,101,103,102,'---',101,101,103,102,'?',65,101]}
df=pd.DataFrame(sale)
print("*******Original Dataframe************")
print(df)      
print("\nReplace the missing values with NaN:")
result = df.replace({"?": np.nan, "---": np.nan})
print(result)

Output:

*********Original Dataframe********
1996 1997 1998 1999 2000 2001 2002
0 98 78 90 ? ? 101 101
1 99 101 40 80 80 101 101
2 103 103 50 60 60 103 103
3 102 102 90 85 85 102 102
4 104 84 65 ? ? 104 —
5 — 101 ? 101 101 101 101
6 102 89 101 — 201 101 101
7 103 103 103 103 103 103 103
8 102 90 102 102 100 102 102
9 105 104 104 104 104 104 ?
10 ? 65 65 65 65 123 65
11 105 105 109 101 101 101 101

Replace the missing values with NaN:
1996 1997 1998 1999 2000 2001 2002
0 98.0 78 90.0 NaN NaN 101 101.0
1 99.0 101 40.0 80.0 80.0 101 101.0
2 103.0 103 50.0 60.0 60.0 103 103.0
3 102.0 102 90.0 85.0 85.0 102 102.0
4 104.0 84 65.0 NaN NaN 104 NaN
5 NaN 101 NaN 101.0 101.0 101 101.0
6 102.0 89 101.0 NaN 201.0 101 101.0
7 103.0 103 103.0 103.0 103.0 103 103.0
8 102.0 90 102.0 102.0 100.0 102 102.0
9 105.0 104 104.0 104.0 104.0 104 NaN
10 NaN 65 65.0 65.0 65.0 123 65.0
11 105.0 105 109.0 101.0 101.0 101 101.0

Below is Snapshot of executable code with output