You can also access rows in a DataFrame by their index. There are multiple ways to do this:
- `.iloc` - is used to access rows by their numerical index, starting from 0;
- `.loc` - is used to access rows by their string label.

In this course, we will focus exclusively on using the `.iloc` attribute.

First, let's create a DataFrame to work with.

import pandas

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'], 'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'], 'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pandas.DataFrame(dataset)
print(countries)

The DataFrame has the following structure:

You can notice the first column, which serves as the row index. We'll use these indexes to access specific rows in the DataFrame. Now let's examine the syntax of this attribute:

```python
dataframe.iloc[index]
```

Now it's time to apply this function to our DataFrame.

import pandas

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'], 'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'], 'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}

countries = pandas.DataFrame(dataset)
# Accessing to the third and seventh rows
print(countries.iloc[2])
print(countries.iloc[6])

After running the above code, you'll get rows that correspond to the indexes indicated in the image below:

Pandas is an extremely user-friendly library for data analysis. It's also designed to handle large datasets, using data structures like DataFrame and Series. This makes it an invaluable tool for Data Science. In this guide, you'll get acquainted with a range of statistical functions, including how to find correlations, modes, medians, and maximum and minimum values within a dataset. You'll also learn how to handle missing values and manipulate specific values, as well as how to remove them.

In this section, we'll explore the fundamentals of Series and DataFrame structures. You'll also learn about the distinctions between these two types of structures.

Data can be sourced in various formats, such as CSV, JSON, SQL, HTML, and more. With Pandas, you're not limited to a single format — you can work with data across a multitude of file types. In this chapter, we'll specifically focus on the CSV and TXT formats.

Here, you'll learn how to process raw data by removing extraneous information and managing null values in a dataset.

Pandas First Steps

iloc Basics