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The document details a task to analyze a dataset containing grade point marks for specific course codes. It outlines the need to create frequency tables for each course code based on unique GP marks. However, it notes a limitation in performing advanced data analysis and offers guidance for conducting the analysis using Python and pandas instead.

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Uddhav Mittal
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3 views2 pages

2

The document details a task to analyze a dataset containing grade point marks for specific course codes. It outlines the need to create frequency tables for each course code based on unique GP marks. However, it notes a limitation in performing advanced data analysis and offers guidance for conducting the analysis using Python and pandas instead.

Uploaded by

Uddhav Mittal
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as TXT, PDF, TXT or read online on Scribd
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You said:
Gazette Report Result B Tech 6th Sem 2023-24.xlsx
Spreadsheet
Task: Analyze the dataset to calculate the frequency of each unique grade point
(GP) mark awarded in specific subjects. The subjects of interest are identified by
the following course codes:

ECECE30
ECECE26
ECECE23
ECECE22
ECECE20
For each course code, provide a summary table that shows the unique GP marks and
the count (frequency) of each mark.

Output: A separate frequency table for each course code, listing each unique GP
mark and its frequency of occurrence in that subject.
ChatGPT said:
I'll start by examining the data structure in your file to locate the relevant
information for each course code. Then I'll calculate the GP frequencies for the
specified subjects.

The file contains a single sheet named "Table 1." I'll load and inspect this sheet
to identify the relevant columns and locate data for the course codes provided.

The dataset contains 154 columns with many unnamed or empty columns. To locate
relevant GP data for each course code, I'll identify specific column names or
values that align with the course codes provided: ECECE30, ECECE26, ECECE23,
ECECE22, and ECECE20. Then, I’ll isolate the unique GP marks and their frequencies
for these columns.

The relevant data starts from row 5, and while columns like "SGPA" and "TOT CRP"
appear toward the end, the columns of interest may be dispersed among the unnamed
columns. I'll now search within these columns to find those that correspond to the
specified course codes.

It seems like I can’t do more advanced data analysis right now. Please try again
later.

If you’d like, I can guide you through the steps to perform this analysis on your
own computer using Python and pandas, or I can help you with any other questions
related to the dataset.

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