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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, you will focus exclusively on using the .iloc attribute.


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import pandas as pd

countries_data = {'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 = pd.DataFrame(countries_data)
print(countries)

The DataFrame has the following structure:

You can see the first column, which serves as the row index. Use these indexes to access specific rows in the DataFrame. The syntax of this attribute is:

df.iloc[index]

Use this attribute to access the third and seventh rows in the DataFrame:


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import pandas as pd

countries_data = {'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 = pd.DataFrame(countries_data)
# 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:

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Sektion 1. Kapitel 9

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Sektion 1. Kapitel 9