By working with the dataframe, we can access each column separately. Let's look at the syntax.

We can see the next syntax for this:
- First, write the name of the dataframe we are working with.
- Then, in square brackets we put the name of the column we want to have access to. Please note that the name of the column must be in quotation marks.

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)

capitals = countries['capital']
print(capitals)

If we run this code, we will see that instead of a whole dataframe, we will see only a column with capitals.

Let's look at how to access multiple columns.

dataframe.[['the name of the first column', 'the name of the second column', 'the name of the third column']]

Compared to the previous example, we see only two differences. This time you need to place the list of columns that you want to get within square brackets, which means you will use double square brackets. Look at the example below.

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)
columns = countries[['country', 'capital']]
print(columns)

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.