Conteúdo do Curso
Pandas First Steps
Pandas First Steps
Using iloc
The DataFrame we are working with:
In the DataFrame we're working with, you can also use negative indexing. Negative indexing starts from the end of the DataFrame: index -1 points to the last row, -2 to the second to last, and so on.
To access the seventh row (which refers to Latvia), you can use either index 6 or -1. Let's see how this works in practice.
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 seventh row using negative indexing print(countries.iloc[-1])
Running the above code will return the row highlighted in the image below:
It's time to practice!
Swipe to show code editor
We have a DataFrame called audi_cars.
- Display all the details from the DataFrame for the
Audi A1model from the year 2017. To do this, you'll need to use positive indexing. - Display all the details from the DataFrame for the
Audi A1model from the year 2016 using negative indexing. - Display all the details from the DataFrame for the
Audi A3model using positive indexing.
Make sure to use the iloc attribute. Give it a try!
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoObrigado pelo seu feedback!
Using iloc
The DataFrame we are working with:
In the DataFrame we're working with, you can also use negative indexing. Negative indexing starts from the end of the DataFrame: index -1 points to the last row, -2 to the second to last, and so on.
To access the seventh row (which refers to Latvia), you can use either index 6 or -1. Let's see how this works in practice.
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 seventh row using negative indexing print(countries.iloc[-1])
Running the above code will return the row highlighted in the image below:
It's time to practice!
Swipe to show code editor
We have a DataFrame called audi_cars.
- Display all the details from the DataFrame for the
Audi A1model from the year 2017. To do this, you'll need to use positive indexing. - Display all the details from the DataFrame for the
Audi A1model from the year 2016 using negative indexing. - Display all the details from the DataFrame for the
Audi A3model using positive indexing.
Make sure to use the iloc attribute. Give it a try!
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoObrigado pelo seu feedback!
Using iloc
The DataFrame we are working with:
In the DataFrame we're working with, you can also use negative indexing. Negative indexing starts from the end of the DataFrame: index -1 points to the last row, -2 to the second to last, and so on.
To access the seventh row (which refers to Latvia), you can use either index 6 or -1. Let's see how this works in practice.
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 seventh row using negative indexing print(countries.iloc[-1])
Running the above code will return the row highlighted in the image below:
It's time to practice!
Swipe to show code editor
We have a DataFrame called audi_cars.
- Display all the details from the DataFrame for the
Audi A1model from the year 2017. To do this, you'll need to use positive indexing. - Display all the details from the DataFrame for the
Audi A1model from the year 2016 using negative indexing. - Display all the details from the DataFrame for the
Audi A3model using positive indexing.
Make sure to use the iloc attribute. Give it a try!
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoObrigado pelo seu feedback!
The DataFrame we are working with:
In the DataFrame we're working with, you can also use negative indexing. Negative indexing starts from the end of the DataFrame: index -1 points to the last row, -2 to the second to last, and so on.
To access the seventh row (which refers to Latvia), you can use either index 6 or -1. Let's see how this works in practice.
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 seventh row using negative indexing print(countries.iloc[-1])
Running the above code will return the row highlighted in the image below:
It's time to practice!
Swipe to show code editor
We have a DataFrame called audi_cars.
- Display all the details from the DataFrame for the
Audi A1model from the year 2017. To do this, you'll need to use positive indexing. - Display all the details from the DataFrame for the
Audi A1model from the year 2016 using negative indexing. - Display all the details from the DataFrame for the
Audi A3model using positive indexing.
Make sure to use the iloc attribute. Give it a try!
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo