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Module 07 / Project 01 — Pandas Basics

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Focus

  • Loading CSV data with pd.read_csv()
  • Exploring a DataFrame: head(), tail(), shape, dtypes, info(), describe()
  • Selecting columns by name
  • Sorting rows with sort_values()

Why this project exists

Before you can analyze data, you need to know how to load it and look at it. This project teaches you how to get a CSV file into a pandas DataFrame and use built-in methods to understand what the data looks like — how many rows, what columns exist, what types the values are, and what the basic statistics tell you. These exploration steps are the first thing every data analyst does with a new data set.

Run

cd projects/modules/07-data-analysis/01-pandas-basics
python project.py

Expected output

=== Loading student data ===
Loaded 30 rows and 4 columns from data/students.csv

=== First 5 rows (head) ===
        name  subject  grade  age
0  Alice Chen     Math     92   17
1  Bob Martinez  Science   78   16
2  Carol Johnson  English   85   17
3    David Kim     Math     67   16
4    Eva Patel  Science     91   18

=== Shape ===
Rows: 30, Columns: 4

=== Column types (dtypes) ===
name       object
subject    object
grade       int64
age         int64
dtype: object

=== Summary statistics (describe) ===
            grade        age
count  30.000000  30.000000
mean   80.100000  17.000000
...

=== Selecting just name and grade columns ===
(first 5 rows)
            name  grade
0     Alice Chen     92
1   Bob Martinez     78
...

=== Sorted by grade (highest first) ===
(first 10 rows)
          name  subject  grade  age
18   Sam Turner     Math     96   17
...

Done.

The exact numbers will match the CSV data. The ... sections are abbreviated here — your output will show all rows and statistics.

Alter it

  1. Change head() to head(10) and see what happens. Try tail(3).
  2. Sort by age instead of grade. What happens when two students have the same age?
  3. Select three columns instead of two. What does df[["name", "subject", "grade"]] return?
  4. Try df["grade"].mean() and df["grade"].max() — what do they return?

Break it

  1. Change the filename in read_csv() to a file that does not exist. What error do you get?
  2. Try selecting a column that does not exist: df["score"]. Read the error message.
  3. Remove the import pandas as pd line. What happens?

Fix it

  1. Wrap read_csv() in a try/except that catches FileNotFoundError and prints a friendly message.
  2. Before selecting a column, check if it exists: if "score" in df.columns.
  3. Put the import back.

Explain it

  1. What is a DataFrame? How is it different from a list of dictionaries?
  2. What does describe() tell you that info() does not?
  3. Why does dtypes show object for the name and subject columns instead of string?
  4. What is the difference between df["grade"] (one column) and df[["grade"]] (double brackets)?

Mastery check

You can move on when you can:

  • Load any CSV file into a DataFrame from memory.
  • Use head(), shape, dtypes, info(), and describe() to explore a new data set.
  • Select one or more columns from a DataFrame.
  • Sort a DataFrame by any column, ascending or descending.

Next

Project 02 — Filtering & Grouping