Programming-Assignment-3

Advanced Programming - Python Data Analysis

Author: SAEZ, Eljenzal Hoper U.
Course: Advanced Computer Programming and Algorithms / ECE2112

---

📌 Description

This programming exercise is designed to develop proficiency in data manipulation using the Python programming language and the pandas library within a Jupyter Notebook environment. The task involves loading a car dataset from a CSV file, performing exploratory data analysis, and extracting specific information through subsetting, slicing, and indexing techniques.

---

⚙️ Requirements

• Python 3.x
• Jupyter Notebook
• Install pandas if not already installed.
• Download the required CSV (bit.ly/Cars_file)

---

▶️ How to Run

1. Save the CSV in your default user folder
2. Open a terminal or command prompt
3. Navigate to the folder with your .py files
4. Run the scripts using:
   • python SAEZ_Pandas-P1.py
   • python SAEZ_Pandas-P2.py

---

💡 Examples w/ Explanation

01 🧠 Problem 1

#a. Load the corresponding .csv file into a data frame named cars using pandas
#b. Display the first five and last five rows of the resulting cars.
import pandas as pd #import pandas library

cars = pd.read_csv("cars.csv") #load the .csv file into a data frame named cars using pandas
p1 = pd.concat([cars.head(), cars.tail()]) #display the first 5 rows and last 5 rows of cars.csv
p1

Code Explanation:

• cars = pd.read_csv("cars.csv") → loads the dataset cars.csv into a Pandas DataFrame named cars.
• cars.head() → returns the first 5 rows of the dataset.
• cars.tail() → returns the last 5 rows of the dataset.
• pd.concat([...]) → combines both sets of rows into a single DataFrame.
• The result is stored in a new DataFrame called p1

02 🧮 Problem 2

import pandas as pd  # Import the pandas library
cars = pd.read_csv("cars.csv")  # Load the .csv file into a DataFrame named cars

dataset cars.csv → read and stored in a DataFrame called cars.

cars.iloc[:, ::2].head()

• .iloc[:, ::2] → selects all rows (:) but only every 2nd column starting from the first one (::2).
• .head() → shows the first 5 rows of those selected columns.

cars.loc[cars['Model'] == 'Mazda RX4']

• .loc[...] → used to filter rows.

• This returns the rows where the Model column is exactly 'Mazda RX4'.

cars.loc[cars['Model'] == 'Camaro Z28', ['Model', 'cyl']]

• Filters the DataFrame for rows where Model = 'Camaro Z28'.

• Returns only the Model and cyl (cylinders) columns.

models = (...)

• Creates a tuple containing the car models you want to filter.

cars['Model'].isin(models)

• Checks if each row’s Model is one of the values in the tuple.

• Returns True for matching rows, False otherwise.

cars.loc[..., ['Model','cyl','gear']]

• .loc[...] → selects the rows where the condition is True.

• ['Model','cyl','gear'] → specifies which columns to return.

---

- 🌱 "The future belongs to those who believe in the beauty of their dreams."

---

📝 Commitments

• v1.0 – Initial draft

  • Loaded CSV file into pandas DataFrame
  • Displayed first and last 5 rows
  • Basic checks with print statements

• v1.1 – Data Loading

  • Applied slicing and indexing for specific queries

• v1.2 – Final Touches

  • Cleaned up variable names and comments
  • Improved formatting for readability
  • Final review of code and README layout