A Python Program which takes a .csv dataset, displays the missing values and replaces them with the strategy of the user's choice.
Missing Data Manager - By Aaditya Awati
Please Enter Dataset(.csv) Path -> test_dataset.csv
Processing ...
Missing Values :-
Column: Location | Row: 8 - nan
Column: Location | Row: 9 - nan
Column Name - No. 1
Column: Unnamed: 1 | Row: 10 - nan
Column: Unnamed: 1 | Row: 12 - nan
Column: Experience(in years) | Row: 5 - nan
Column: Experience(in years) | Row: 6 - nan
Column: Experience(in years) | Row: 11 - nan
Column: Experience(in years) | Row: 13 - nan
Column: Time In Company(in years) | Row: 1 - nan
Column: Time In Company(in years) | Row: 7 - nan
Column: Time In Company(in years) | Row: 9 - nan
Column: Time In Company(in years) | Row: 11 - nan
Column: Salary(in dollars) | Row: 10 - nan
Column: Salary(in dollars) | Row: 12 - nan
Would you like to fill the numerical missing values? (Y/n) -> Y
Please Enter Strategy to be used for filling out Missing Values (mean/median/constant/most_frequent) -> median
Processing ...
All Numerical Values have been replaced with their column's median value(s).
Location Unnamed: 1 ... Time In Company(in years) Salary(in dollars)
0 New York 35.0 ... 2.5 94000.0
1 Manhattan 27.0 ... 5.0 53000.0
2 San Francisco 45.0 ... 5.0 120000.0
3 New York 62.0 ... 13.0 121000.0
4 Boston 24.0 ... 0.5 49000.0
5 Boston 39.0 ... 4.0 78000.0
6 Atlanta 30.0 ... 7.0 96000.0
7 Denver 51.0 ... 5.0 105000.0
8 NaN 42.0 ... 9.0 94000.0
9 NaN 33.0 ... 5.0 100000.0
10 Seattle 37.0 ... 4.5 94000.0
11 Austin 46.0 ... 5.0 83000.0
12 Chicago 37.0 ... 7.0 94000.0
13 New York 29.0 ... 5.0 60000.0
[14 rows x 5 columns]
Dataset 'test_dataset.csv' saved to Filled Datasets/test_dataset.csv
Process finished with exit code 0