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Intermediate Python

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Uppdaterad 2026-01

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

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PythonProgramming4 timmar18 videos87 exercises7,400 XP1.4M+Uttalande om prestation

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Kursbeskrivning

Improve Your Python Skills

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib’s functions and get acquainted with data structures such as the dictionary and pandas DataFrame. This four-hour intermediate course will help you to build on your existing Python skills and explore new Python applications and functions that expand your repertoire and help you work more efficiently.

Learn to Use Python Dictionaries and pandas

Dictionaries offer an alternative to Python lists, while the pandas dataframe is the most popular way of working with tabular data. In the second chapter of this course, you’ll find out how you can create and manipulate datasets, and how to access them using these structures. Hands-on practice throughout the course will build your confidence in each area.

Explore Python Boolean Logic and Python Loops

In the second half of this course, you’ll look at logic, control flow, filtering and loops. These functions work to control decision-making in Python programs and help you to perform more operations with your data, including repeated statements. You’ll finish the course by applying all of your new skills by using hacker statistics to calculate your chances of winning a bet.

Once you’ve completed all of the chapters, you’ll be ready to apply your new skills in your job, new career, or personal project, and be prepared to move onto more advanced Python learning.

Förkunskapskrav

Introduction to Python

1

Matplotlib

Data visualization is a key skill for aspiring data scientists. Matplotlib makes it easy to create meaningful and insightful plots. In this chapter, you’ll learn how to build various types of plots, and customize them to be more visually appealing and interpretable.

Basic plots with Matplotlib

Line plot (1)

Line Plot (2): Interpretation

Line plot (3)

Scatter Plot (1)

Scatter plot (2)

Build a histogram (1)

Build a histogram (2): bins

Build a histogram (3): compare

Choose the right plot (1)

Choose the right plot (2)

Customization

Additional Customizations

Interpretation

2

Dictionaries & Pandas

Learn about the dictionary, an alternative to the Python list, and the pandas DataFrame, the de facto standard to work with tabular data in Python. You will get hands-on practice with creating and manipulating datasets, and you’ll learn how to access the information you need from these data structures.

Dictionaries, Part 1

Motivation for dictionaries

Create dictionary

Access dictionary

Dictionaries, Part 2

Dictionary Manipulation (1)

Dictionary Manipulation (2)

Dictionariception

Pandas, Part 1

Dictionary to DataFrame (1)

Dictionary to DataFrame (2)

CSV to DataFrame (1)

CSV to DataFrame (2)

Pandas, Part 2

Square Brackets (1)

Square Brackets (2)

loc and iloc (1)

loc and iloc (2)

loc and iloc (3)

3

Logic, Control Flow and Filtering

Boolean logic is the foundation of decision-making in Python programs. Learn about different comparison operators, how to combine them with Boolean operators, and how to use the Boolean outcomes in control structures. You'll also learn to filter data in pandas DataFrames using logic.

Comparison Operators

Greater and less than

Compare arrays

Boolean Operators

and, or, not (1)

and, or, not (2)

Boolean operators with NumPy

if, elif, else

Customize further: elif

Filtering pandas DataFrames

Driving right (1)

Driving right (2)

Cars per capita (1)

Cars per capita (2)

4

Loops

There are several techniques you can use to repeatedly execute Python code. While loops are like repeated if statements, the for loop iterates over all kinds of data structures. Learn all about them in this chapter.

while: warming up

Basic while loop

Add conditionals

Loop over a list

Indexes and values (1)

Indexes and values (2)

Loop over list of lists

Loop Data Structures Part 1

Loop over dictionary

Loop over NumPy array

Loop Data Structures Part 2

Loop over DataFrame (1)

Loop over DataFrame (2)

Add column (1)

Add column (2)

5

Case Study: Hacker Statistics

This chapter will allow you to apply all the concepts you've learned in this course. You will use hacker statistics to calculate your chances of winning a bet. Use random number generators, loops, and Matplotlib to gain a competitive edge!

Random Numbers

Random float

Roll the dice

Determine your next move

Random Walk

The next step

How low can you go?

Visualize the walk

Distribution

Simulate multiple walks

Visualize all walks

Implement clumsiness

Plot the distribution

Calculate the odds

Intermediate Python

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