Programming-Assignment-3

A Programming Assignment of Shinade T. Cancino from 2ECEC for her class, Advanced Computer Programming.

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Overview

This repository contains Python programs using Pandas to practice loading datasets, displaying data, subsetting, slicing, and indexing. The problems are:

1. Loading and Displaying the Dataset — loads cars.csv into a DataFrame and displays the first and last five rows.

2. Subsetting, Slicing, and Indexing — extracts specific information from the DataFrame using Pandas methods.

This README provides step-by-step explanations for every line of code, making the logic and flow clear.

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How to Run

1. Open Jupyter Notebook.
2. Ensure cars.csv is in the same directory as your notebook.
3. Run the cells from top to bottom:
   • Press Shift + Enter on each cell, or
   • Click the play icon.

Tip

Make sure your Python kernel is active.
Outputs will appear inline after each cell.

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Problem 1: Loading and Displaying the Dataset

Load the dataset cars.csv into a Pandas DataFrame named cars and display its first and last five rows.

Full Code:

import pandas as pd                   #Import the pandas library

cars = pd.read_csv("cars.csv")        #Load the dataset into a DataFrame called cars
cars                                  #Display the entire DataFrame

cars.head()                           #Display the first five rows of the DataFrame

cars.tail()                           #Display the last five rows of the DataFrame

Cell 1: Import Pandas and Load Dataset

import pandas as pd
cars = pd.read_csv("cars.csv")
cars

Explanation:

1. import pandas as pd → Imports the Pandas library to work with tabular data.
2. pd.read_csv("cars.csv") → Reads the CSV file into a Pandas DataFrame.
3. cars = ... → Stores the dataset in the variable cars.
4. cars → Displays the full DataFrame in the notebook.

Cell 2: Display First 5 Rows

cars.head()

Explanation:

1. cars.head() → Shows the first five rows of the DataFrame.
   • Useful to quickly preview the top of the dataset

Cell 3: Display Last 5 Rows

cars.tail()

Explanation:

1. cars.tail() → Shows the last five rows of the DataFrame.
2. Useful to inspect the bottom of the dataset.

Flow: Dataset loaded → first 5 rows displayed → last 5 rows displayed.

Example Outputs:

• First five rows

• Last five rows

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Problem 2: Subsetting, Slicing, and Indexing

Extract specific information from the cars DataFrame using slicing, indexing, and conditional selection.

Full Code:

import pandas as pd

cars = pd.read_csv("cars.csv")                                                                                  #Load the dataset into a DataFrame called cars
cars #Display the entire DataFrame

cars.iloc[0:5, ::2]                                                                                             # a. Display the first five rows with odd-numbered columns 
                                                                                                                # ::2 means select every other column starting from index 0 
    
(cars[cars['Model']=='Mazda RX4'])                                                                              # b. Display the row that contains the 'Model' of 'Mazda RX4'

cylinders = cars.loc[cars['Model'] == 'Camaro Z28', 'cyl'].values[0]                                            # c. Display how many cylinders does Camaro Z28 have
print("Camaro Z28 has", cylinders, "cylinders.")

(cars.loc[cars['Model'].isin(['Mazda RX4 Wag', 'Ford Pantera L', 'Honda Civic']), ['Model', 'cyl', 'gear']])    # d. Display the cylinders and gear type for Mazda RX4 Wag, Ford Pantera L, and Honda Civic

Cell 1: Import pandas

import pandas as pd

Explanation:

1. import pandas as pd imports the pandas library, a powerful tool for working with tabular data.
   • pd is the alias commonly used to call pandas functions.

Cell 2: Load dataset into a DataFrame

cars = pd.read_csv("cars.csv")
cars

Explanation:

1. pd.read_csv("cars.csv") reads the CSV file into a DataFrame, which is like a table in Python.
2. cars = ... stores the dataset in the variable cars.
3. cars on the last line of the cell displays the full DataFrame in Jupyter Notebook.

Cell 3a: Display the first five rows with odd-numbered columns

cars.iloc[0:5, ::2]

Explanation:

1. cars.iloc[...] allows index-based selection of rows and columns.
2. 0:5 selects the first five rows (row indices 0 to 4).
3. ::2 selects every other column starting from index 0, i.e., odd-numbered columns.

The result shows a subset of the DataFrame with only the specified rows and columns.

Cell 3b: Display the row for Mazda RX4

(cars[cars['Model']=='Mazda RX4'])

Explanation:

1. cars['Model'] == 'Mazda RX4' → Creates a boolean mask where the model is "Mazda RX4".
2. cars[...] → Filters the DataFrame to only that row.

The result is a DataFrame containing all columns for the 'Mazda RX4' model.

Cell 3c: Find Cylinders for "Camaro Z28"

cylinders = cars.loc[cars['Model'] == 'Camaro Z28', 'cyl'].values[0]
print("Camaro Z28 has", cylinders, "cylinders.")

Explanation:

1. cars.loc[...] allows label-based selection of rows and columns.
2. cars['Model'] == 'Camaro Z28' selects the row where Model is 'Camaro Z28'.
3. 'cyl' selects the cylinders column.
4. .values[0] → Extracts the integer value of cylinders.
5. print(...) → Displays the number of cylinders.

Cell 3d: Find Cylinders and Gear Type for Selected Models

(cars.loc[cars['Model'].isin(['Mazda RX4 Wag', 'Ford Pantera L', 'Honda Civic']), ['Model', 'cyl', 'gear']])

Explanation:

1. cars['Model'].isin([...]) creates a boolean mask for rows where the Model is one of the specified models.
2. cars.loc[mask, ['Model', 'cyl', 'gear']] selects the rows that match the mask and only the specified columns: 'Model', 'cyl', and 'gear'.
3. The resulting DataFrame shows the cylinders and gear type for 'Mazda RX4 Wag', 'Ford Pantera L', and 'Honda Civic'.

Example Outputs:

• First five rows with odd-numbered columns

  

• Row that contains the 'Model' of 'Mazda RX4'

• Cylinders of Camaro Z28

  

• Cylinders and gear type for Mazda RX4 Wag, Ford Pantera L, and Honda Civic

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Version 2: The README has been completely revised to provide a cell-by-cell explanation of each code segment, making it easier for viewers to understand the flow and logic of the notebook.