We can also access data by index in the dataframe. Accessing rows by index is possible in several ways:
- `.iloc` - used to access by numeric value (starting from 0).
- `.loc` - used to access by string label.

In this course, we will get acquainted with the `.iloc()` function only.

Let's create a dataframe that we will 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)

Here we see the first column, which is responsible for indexing rows. Using these indexes, we will access the cells from the dataframe.
Let's take a look the syntax of this function:

 `dataframe.iloc[index]`.

It's time to use this function on 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 seventh row using negative indexing
print(countries.iloc[2])
print(countries.iloc[6])

If we run the code above, you will get the rows that are marked on the picture below:

In this section, we will introduce the basics of the series and dataframe  data structures. We will also learn the difference between these structures.

Raw data comes in many forms: CSV, JSON, SQL, HTML, and so on. With pandas, we can work with data in a variety of formats. In this chapter, we will take a closer look at the CSV and TXT formats.

In this section, we will learn how to work with raw data, how to remove unwanted data, and how to work with null values in a dataset.