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Pandas First Steps
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Course Content

Pandas First Steps

Pandas First Steps

1. The Very First Steps
What is pandas? SeriesCreating a SeriesDataFrameSeries QuizCreating a DataFrame QuizAdding a New ColumnInserting a New ColumnDeleting a Row/Column Matching the FunctionsWorking with ColumnsExtracting Columns Quiziloc BasicsUsing iloc
2. Reading Files in Pandas
CSV FilesTXT FilesWorking with Files QuizAccessing the Columns
3. Analyzing the Data
Viewing the DataHead QuizHead and Tail QuizInformation on the DataColumn Names and Data TypesFinding Null ValuesIdentifying Null Values QuizDropping Null ValuesFilling Null ValuesNull Values QuizDescribing the DataCalculating Descriptive Statisticsmax() and min()Statistical Operations Quizsum() and count()Unique Values

bookUsing 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.


              
123456

            
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])
copy

Running the above code will return the row highlighted in the image below:

It's time to practice!

Task

We have a DataFrame called audi_cars.

  1. Display all the details from the DataFrame for the Audi A1 model from the year 2017. To do this, you'll need to use positive indexing.
  2. Display all the details from the DataFrame for the Audi A1 model from the year 2016 using negative indexing.
  3. Display all the details from the DataFrame for the Audi A3 model using positive indexing.

Make sure to use the iloc attribute. Give it a try!

Task Table

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 14
toggle bottom row

bookUsing 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.


              
123456

            
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])
copy

Running the above code will return the row highlighted in the image below:

It's time to practice!

Task

We have a DataFrame called audi_cars.

  1. Display all the details from the DataFrame for the Audi A1 model from the year 2017. To do this, you'll need to use positive indexing.
  2. Display all the details from the DataFrame for the Audi A1 model from the year 2016 using negative indexing.
  3. Display all the details from the DataFrame for the Audi A3 model using positive indexing.

Make sure to use the iloc attribute. Give it a try!

Task Table

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 14
toggle bottom row

bookUsing 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.


              
123456

            
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])
copy

Running the above code will return the row highlighted in the image below:

It's time to practice!

Task

We have a DataFrame called audi_cars.

  1. Display all the details from the DataFrame for the Audi A1 model from the year 2017. To do this, you'll need to use positive indexing.
  2. Display all the details from the DataFrame for the Audi A1 model from the year 2016 using negative indexing.
  3. Display all the details from the DataFrame for the Audi A3 model using positive indexing.

Make sure to use the iloc attribute. Give it a try!

Task Table

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your 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.


              
123456

            
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])
copy

Running the above code will return the row highlighted in the image below:

It's time to practice!

Task

We have a DataFrame called audi_cars.

  1. Display all the details from the DataFrame for the Audi A1 model from the year 2017. To do this, you'll need to use positive indexing.
  2. Display all the details from the DataFrame for the Audi A1 model from the year 2016 using negative indexing.
  3. Display all the details from the DataFrame for the Audi A3 model using positive indexing.

Make sure to use the iloc attribute. Give it a try!

Task Table

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 14
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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