D598 Analytics Programming Task 3: Analytics
Programming Presentation and Code Explanation 2026-
2027 Western Governors University
Caitlin Atkins
D598 – Analytics Programming
Task 3: Presentation
A. Explain how the code works for the program you submitted in Task 2.
1. Import pandas as pd
Import numpy as np
Import matplotlib.pyplot as plt
Before performing data analysis, importing Python language libraries capable of
specific performances is essential. Pandas are used in nearly every data analysis
document because of their ability to manipulate data and read, clean, filter, and group
files. While NumPy was not used during this task, it is helpful for numerical computing
and data processing. Finally, matplotlib is essential for data visualizations.
2. Data = pd.read_excel(‘D598 Data Set.xlsx)
To load the Excel file into the data frame, pd.read_excel() is used. The title of the
data file is included in parentheses. The file is then named to be called on throughout the
analysis.
3. duplicates = data.duplicated()
4. duplicates
5. data[duplicates]
These code lines are used to identify any duplicates in the data frame. First, a
, Boolean series identifies duplicate columns and marks them as a variable named ‘duplicates.
After, duplicates are used to display those duplicate items. Finally, data[duplicates] retrieves
the duplicate rows in the data frame.
6. gdata = data.groupby('Business
State').mean(numeric_only=True)
7. gdata
Using these lines, the data frame will be grouped by state; the mean for each column
will be calculated, and only numeric values will be displayed. Gdata will print the new data
frame based on the previous line of code.
8. grouped_data = gdata.agg(['mean', 'median', 'min', 'max'])
9. print(grouped_data)
10. new_dataframe = grouped_data
Programming Presentation and Code Explanation 2026-
2027 Western Governors University
Caitlin Atkins
D598 – Analytics Programming
Task 3: Presentation
A. Explain how the code works for the program you submitted in Task 2.
1. Import pandas as pd
Import numpy as np
Import matplotlib.pyplot as plt
Before performing data analysis, importing Python language libraries capable of
specific performances is essential. Pandas are used in nearly every data analysis
document because of their ability to manipulate data and read, clean, filter, and group
files. While NumPy was not used during this task, it is helpful for numerical computing
and data processing. Finally, matplotlib is essential for data visualizations.
2. Data = pd.read_excel(‘D598 Data Set.xlsx)
To load the Excel file into the data frame, pd.read_excel() is used. The title of the
data file is included in parentheses. The file is then named to be called on throughout the
analysis.
3. duplicates = data.duplicated()
4. duplicates
5. data[duplicates]
These code lines are used to identify any duplicates in the data frame. First, a
, Boolean series identifies duplicate columns and marks them as a variable named ‘duplicates.
After, duplicates are used to display those duplicate items. Finally, data[duplicates] retrieves
the duplicate rows in the data frame.
6. gdata = data.groupby('Business
State').mean(numeric_only=True)
7. gdata
Using these lines, the data frame will be grouped by state; the mean for each column
will be calculated, and only numeric values will be displayed. Gdata will print the new data
frame based on the previous line of code.
8. grouped_data = gdata.agg(['mean', 'median', 'min', 'max'])
9. print(grouped_data)
10. new_dataframe = grouped_data
