Contenuti del Corso

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

Adding new Columns

We can easily add new columns to an existing DataFrame using the indexing syntax:

js

It is important that if values is a list, it needs to have the same number of elements as there are rows in the DataFrame, otherwise you'll get an error. Alternatively, we can assign a single value to the entire new column as well.

Example 1: New Column from a Single Value


              1234567891011
            
import pandas as pd

df = pd.DataFrame({
    'Product': ['Laptop', 'Tablet', 'Phone'],
    'Old Price': [1000, 600, 400]
})

# Add a new column with a fixed price for all rows
df['New Price'] = 700

print(df)

Example 2: New Column using a List of Values


              1234567891011
            
import pandas as pd

df = pd.DataFrame({
    'Member': ['Sophie', 'Liam', 'Emma'],
    'Role': ['Designer', 'Developer', 'Tester']
})

# Add a new column for Project Name
df['Project'] = ['Marketing Website Redesign', 'Client Portal Backend Development', 'Payment Flow QA Testing']

print(df)

Example 3: New Column derived from Another Column


              1234567891011
            
import pandas as pd

df = pd.DataFrame({
    'Product': ['Laptop', 'Tablet', 'Phone'],
    'Price': [1000, 600, 400]
})

# Derive a new column 'Discounted Price' (10% off)
df['Discounted Price'] = df['Price'] * 0.9

print(df)

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Grazie per i tuoi commenti!

Sezione 2. Capitolo 5

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Contenuti del Corso

Pandas: First Steps

1. Getting Started

2. Basics of Dataframes

3. Fetching Data from DataFrames

Adding new Columns

We can easily add new columns to an existing DataFrame using the indexing syntax:

js

Example 1: New Column from a Single Value


              1234567891011
            
import pandas as pd

df = pd.DataFrame({
    'Product': ['Laptop', 'Tablet', 'Phone'],
    'Old Price': [1000, 600, 400]
})

# Add a new column with a fixed price for all rows
df['New Price'] = 700

print(df)

Example 2: New Column using a List of Values


              1234567891011
            
import pandas as pd

df = pd.DataFrame({
    'Member': ['Sophie', 'Liam', 'Emma'],
    'Role': ['Designer', 'Developer', 'Tester']
})

# Add a new column for Project Name
df['Project'] = ['Marketing Website Redesign', 'Client Portal Backend Development', 'Payment Flow QA Testing']

print(df)

Example 3: New Column derived from Another Column


              1234567891011
            
import pandas as pd

df = pd.DataFrame({
    'Product': ['Laptop', 'Tablet', 'Phone'],
    'Price': [1000, 600, 400]
})

# Derive a new column 'Discounted Price' (10% off)
df['Discounted Price'] = df['Price'] * 0.9

print(df)

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 2. Capitolo 5

Pandas: First Steps

Adding new Columns

Example 1: New Column from a Single Value

Example 2: New Column using a List of Values

Example 3: New Column derived from Another Column

1. What happens if you try to add a new column to a DataFrame with a list that has fewer elements than the number of rows?

2. Which of the following lines correctly adds a column `Discount` which is 20% of the `Price`?

3. What would be the output of the following code?

Pandas: First Steps

Adding new Columns

Example 1: New Column from a Single Value

Example 2: New Column using a List of Values

Example 3: New Column derived from Another Column

1. What happens if you try to add a new column to a DataFrame with a list that has fewer elements than the number of rows?

2. Which of the following lines correctly adds a column `Discount` which is 20% of the `Price`?

3. What would be the output of the following code?

Pandas: First Steps

Adding new Columns

Example 1: New Column from a Single Value

Example 2: New Column using a List of Values

Example 3: New Column derived from Another Column

1. What happens if you try to add a new column to a DataFrame with a list that has fewer elements than the number of rows?

2. Which of the following lines correctly adds a column Discount which is 20% of the Price?

3. What would be the output of the following code?

Pandas: First Steps

Adding new Columns

Example 1: New Column from a Single Value

Example 2: New Column using a List of Values

Example 3: New Column derived from Another Column

1. What happens if you try to add a new column to a DataFrame with a list that has fewer elements than the number of rows?

2. Which of the following lines correctly adds a column Discount which is 20% of the Price?

3. What would be the output of the following code?

2. Which of the following lines correctly adds a column `Discount` which is 20% of the `Price`?

2. Which of the following lines correctly adds a column `Discount` which is 20% of the `Price`?