Course Content
Pandas First Steps
Pandas First Steps
2. Reading Files in Pandas
3. Analyzing the Data
Viewing the DataQuiz: Using HeadQuiz: Head, Tail, and SampleExploring the DatasetColumn Names and Data TypesFinding Null ValuesQuiz: Identifying Null ValuesChallenge: Dropping Null ValuesChallenge: Filling Null ValuesQuiz: Null ValuesDescribing the Datamax() and min()Quiz: Statistical Operationssum() and count()Unique Values
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.
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 notice the first column, which serves as the row index. We'll use these indexes to access specific rows in the DataFrame. The syntax of this attribute is as follows:
python
We can apply this attribute to access the third and seventh rows our DataFrame:
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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SectionΒ 1. ChapterΒ 13
