Pandas: First Steps

Contenu du cours

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

Extracting Multiple Columns

We can fetch a single column from a DataFrame using the following syntax:

js

Note

When we fetch a single column, it is fetched in the form of a Series. Hence, all the methods and properties of a series are applicable to it.

It's possible to extract multiple columns from a DataFrame using the following syntax:

js

Where column1, column2, and so on, represent the names of the columns in string format.

The following example demonstrates the usage:


              12345678910111213141516
            
import pandas as pd

# Create a sample DataFrame with sales data
df = pd.DataFrame({
    'Order ID': [101, 102, 103, 104],
    'Customer Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Amount Paid': [50.0, 30.0, 75.0, 40.0],
    'Product': ['Laptop', 'Phone', 'Tablet', 'Monitor'],
    'Purchase Date': ['2025-04-01', '2025-04-01', '2025-04-01', '2025-04-01']
})

# Extract specific columns from the DataFrame
selected_columns = df[['Order ID', 'Customer Name', 'Amount Paid']]

# Print the extracted columns
print(selected_columns)

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 7

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Contenu du cours

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

Extracting Multiple Columns

We can fetch a single column from a DataFrame using the following syntax:

js

Note

When we fetch a single column, it is fetched in the form of a Series. Hence, all the methods and properties of a series are applicable to it.

It's possible to extract multiple columns from a DataFrame using the following syntax:

js

Where column1, column2, and so on, represent the names of the columns in string format.

The following example demonstrates the usage:


              12345678910111213141516
            
import pandas as pd

# Create a sample DataFrame with sales data
df = pd.DataFrame({
    'Order ID': [101, 102, 103, 104],
    'Customer Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Amount Paid': [50.0, 30.0, 75.0, 40.0],
    'Product': ['Laptop', 'Phone', 'Tablet', 'Monitor'],
    'Purchase Date': ['2025-04-01', '2025-04-01', '2025-04-01', '2025-04-01']
})

# Extract specific columns from the DataFrame
selected_columns = df[['Order ID', 'Customer Name', 'Amount Paid']]

# Print the extracted columns
print(selected_columns)

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 7

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