Pandas: First Steps

Course Content

Pandas: First Steps

Pandas: First Steps

1. Getting Started

Introduction to Pandas Series Task: Coffee Orders Series Task: Barista's Favorites Coffees Accessing Elements from a Series Task: Drink of the Hour Task - Signature Drinks Task: Mid-Morning Specials Task - Fetching the Mid-Shift Drinks Operations over Series Task: Modifying a single element Task: Modifying the Entire Series Task: Modifying Part of a Series Task: Processing the Restock of Inventory

2. Basics of Dataframes

What is a DataFrame?Task: Creating a Table of Popular Coffee Drinks Task: Tracking Coffee Bean Deliveries Task: Weekly Coffee Sales Report Adding new Columns Task: Finding Total Sales Per Drink Task: Calculating Total Sales Revenue Adding new Rows Task: Adding a New Employee to the Café Team Task: Adding New Coffee Types to the Café Menu

3. Fetching Data from DataFrames

The `head()` Method Task: Preview the Coffee Menu The `tail()` Method Task: Finding the Most Expensive Coffees The `sample()` Method Task: Randomly Select Customer Orders for Quality Check Extracting Multiple Columns Task: Extracting the Total Profit Task: Extract Sales Data For Analysis The `iloc` Property Task: Access Sales Data at Café Pandasia - Part 1 Task: Access Sales Data at Café Pandasia - Part 2 Task: Access Sales Data at Café Pandasia - Part 3

The `iloc` Property

The iloc property in Pandas allows us to access specific parts of a DataFrame using numerical positions.

You can use iloc to access:

A single element;
A single row;
A single column;
A range of rows;
A range of columns;
A range of rows and columns.

General Syntax

js

You can also slice:


python

Example


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import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Daisy'],
    'Age': [25, 30, 35, 28],
    'City': ['New York', 'Paris', 'London', 'Berlin']
})

# Single element
print('>> Single Element:\n', df.iloc[1, 2])

# Single row
print('\n>> Single Row:\n', df.iloc[2])

# Single column
print('\n>> Single Column:\n', df.iloc[:, 1])

# Range of rows
print('\n>> Range of Rows:\n', df.iloc[1:3])

# Range of columns
print('\n>> Range of Columns:\n', df.iloc[:, 0:2])

# Range of rows and columns
print('\n>> Range of Rows & Columns:\n', df.iloc[1:3, 0:2])

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 10

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Course Content

Pandas: First Steps

Pandas: First Steps

1. Getting Started

Introduction to Pandas Series Task: Coffee Orders Series Task: Barista's Favorites Coffees Accessing Elements from a Series Task: Drink of the Hour Task - Signature Drinks Task: Mid-Morning Specials Task - Fetching the Mid-Shift Drinks Operations over Series Task: Modifying a single element Task: Modifying the Entire Series Task: Modifying Part of a Series Task: Processing the Restock of Inventory

2. Basics of Dataframes

What is a DataFrame?Task: Creating a Table of Popular Coffee Drinks Task: Tracking Coffee Bean Deliveries Task: Weekly Coffee Sales Report Adding new Columns Task: Finding Total Sales Per Drink Task: Calculating Total Sales Revenue Adding new Rows Task: Adding a New Employee to the Café Team Task: Adding New Coffee Types to the Café Menu

3. Fetching Data from DataFrames

The `head()` Method Task: Preview the Coffee Menu The `tail()` Method Task: Finding the Most Expensive Coffees The `sample()` Method Task: Randomly Select Customer Orders for Quality Check Extracting Multiple Columns Task: Extracting the Total Profit Task: Extract Sales Data For Analysis The `iloc` Property Task: Access Sales Data at Café Pandasia - Part 1 Task: Access Sales Data at Café Pandasia - Part 2 Task: Access Sales Data at Café Pandasia - Part 3

The `iloc` Property

The iloc property in Pandas allows us to access specific parts of a DataFrame using numerical positions.

You can use iloc to access:

A single element;
A single row;
A single column;
A range of rows;
A range of columns;
A range of rows and columns.

General Syntax

js

You can also slice:


python

Example


              12345678910111213141516171819202122232425
            
import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Daisy'],
    'Age': [25, 30, 35, 28],
    'City': ['New York', 'Paris', 'London', 'Berlin']
})

# Single element
print('>> Single Element:\n', df.iloc[1, 2])

# Single row
print('\n>> Single Row:\n', df.iloc[2])

# Single column
print('\n>> Single Column:\n', df.iloc[:, 1])

# Range of rows
print('\n>> Range of Rows:\n', df.iloc[1:3])

# Range of columns
print('\n>> Range of Columns:\n', df.iloc[:, 0:2])

# Range of rows and columns
print('\n>> Range of Rows & Columns:\n', df.iloc[1:3, 0:2])

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 10

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