The `sample()` method returns a random row from the **DataFrame** by default.

There are two optional named parameters which can be used to specify how many **random** rows to fetch:
- `n`: Specifies the exact number of random rows to fetch;
- `frac`: Specifies the **fraction** or **percentage** of the total rows to fetch. For example, `frac=0.2` will return **20%** of the total rows;

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Coffee': ['Espresso', 'Latte', 'Cappuccino', 'Americano', 'Macchiato'],
    'Price': [2.5, 3.5, 3.0, 2.8, 3.2],
    'Availability': ['Yes', 'Yes', 'Yes', 'Yes', 'No']
})

print("Get 2 random rows from the DataFrame:")
random_rows = df.sample(n=2)
print(random_rows)

In this course, we will explore the basics of Pandas – a powerful Python library for organizing, manipulating and analyzing data. Each concept will be explained through short and clear video lessons, followed by fun and practical microtasks, which are designed to help you understand the syntax and remember how to use different features and functions in Pandas with confidence.

In this section, we will explore some introductory concepts related to Pandas – which will help you get a basic understanding of the library and become more comfortable in working with it.

In this section, we will learn what are DataFrames, how to create them, and how to additional rows or columns to existing DataFrames. This section will prepare us for 
 the upcoming sections where we will explore more complex features of DataFrames.

In this section we will explore some methods of fetching data from DataFrames. Each topic will be accompanied with some tasks to help you with remembering the syntax and methods.

The `sample()` Method

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The `sample()` Method