Sometimes, some columns do not carry any information, and it is better to delete them. To do this, pandas has the `drop()` function. Let's look at the syntax of this function.

`drop(index, columns, axis)`.
Here
- `index` - the indexes of the rows that we want to delete (this is used when `axis=0`).
- `columns` - the name of the columns, that we want to delete (this is used when `axis=1`).
- `axis` - choose whether to remove labels from the rows (0) or columns (1) (default is 0).

In this section, we will work with the next dataframe. Let's take a look at it.

import pandas as pd 

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : [None, None, 'Europe', None, 'Europe', None, 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(dataset)
print(countries)

We see that the `continent` column has a lot of empty rows, so it loses its informativeness. Because of this, let's delete it. 

import pandas

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : [None, None, 'Europe', None, 'Europe', None, 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pandas.DataFrame(dataset)
countries = countries.drop(columns = ['continent'],axis=1)
print(countries)

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.