Course Content
Pandas First Steps
Pandas First Steps
iloc Basics
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, we will focus exclusively on using the .iloc
attribute.
First, let's create a DataFrame to 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)
The DataFrame has the following structure:
You can notice the first column, which serves as the row index. We'll use these indexes to access specific rows in the DataFrame. Now let's examine the syntax of this attribute:
Now it's time to apply this function to 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 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:
Everything was clear?