Remember the dataframe we are working with.

We can also use negative indexing. Negative indexing means starting from the end, i.e. index -1 refers to the last element, index -2 refers to the penultimate element, and so on.

In order tо access the seventh row (Latvia), we can use not only index **6**, but also index **-1**. Let's look at how this works.

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[-1])

If we run the code above, we will get the row that is 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.