Summary  
This chapter covers how to use the pandas DataFrame .iloc attribute to select rows by their integer index positions.  

General domain of usage  
data analysis

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, you will focus exclusively on using the `.iloc` attribute.


import pandas as pd

countries_data = {'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 = pd.DataFrame(countries_data)
print(countries)

The DataFrame has the following structure:

You can see the first column, which serves as the row index. Use these indexes to access specific rows in the `DataFrame`. The syntax of this attribute is:

```python
df.iloc[index]
```

Use this attribute to access the third and seventh rows in the `DataFrame`:


import pandas as pd

countries_data = {'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 = pd.DataFrame(countries_data)
# 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.

Dive into the fundamentals of the pandas library. Learn to create and manipulate Series and DataFrames, explore their structure, and build confidence in performing essential data operations.

Master how to load and manage data from CSV and TXT files. Gain practical experience importing datasets and preparing them for further analysis using pandas tools.

Learn to explore, clean, and summarize data effectively. Understand how to handle missing values, extract insights, and perform basic statistical operations with pandas.

iloc[] Basics

Awesome!

iloc[] Basics