Data Analysis with Python & Pandas

Learn Python for data analysis and visualization by analyzing large datasets. Covering Python 3, Pandas, and Seaborn.
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  • Lectures 70
  • Length 5.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 8/2015 English

Course Description

This Python course will get you up and running with using Python for data analysis and visualization. You will learn how to handle, analyze and visualize data in Python by actually completing two big data analysis projects, one demonstrated through videos and another laid out through six exercises.  

The course assumes you have no prior knowledge of Python, so you also get to learn the basics of Python in the first two sections of the course. However, if you already know Python, the first two sections can serve as a refresher before you jump into the data analysis and visualization part.

In the course you will learn to use Python third-party data analysis libraries such as Pandas, Matplotlib, Seaborn, just to mention a few and tools to boost your productivity such as Spyder and Jupyter.

As you progress through the course, you will be guided step by step on building a program that uses real world data containing hundreds of files and millions of records. You will learn to write Python code that downloads, extracts, cleans, manipulates, aggregates and visualizes these datasets using Python. Apart from following the video screencasts, you will also be required to write your own Python scripts from scratch for completing a data analysis project on income data.

What are the requirements?

  • A working computer (Windows, Mac, or Linux)
  • No prior knowledge of Python is required

What am I going to get from this course?

  • Build 10 advanced Python scripts which together make up a data analysis and visualization program.
  • Solve six exercises related to processing, analyzing and visualizing US income data with Python.
  • Learn the fundamental blocks of the Python programming language such as variables, datatypes, loops, conditionals, functions and more.
  • Use Python to batch download files from FTP sites, extract, rename and store remote files locally.
  • Import data into Python for analysis and visualization from various sources such as CSV and delimited TXT files.
  • Keep the data organized inside Python in easily manageable pandas dataframes.
  • Merge large datasets taken from various data file formats.
  • Create pivot tables in Python out of large datasets.
  • Perform various operations among data columns and rows.
  • Query data from Python pandas dataframes.
  • Export data from Python into various formats such as TXT, CSV, Excel, HTML and more.
  • Use Python to perform various visualizations such as time series, plots, heatmaps, and more.
  • Create KML Google Earth files out of CSV files.

Who is the target audience?

  • Those who come from any technology field that deals with any kind of data.
  • Those who want to leverage the power of the Python programming language for handling data.
  • Those who need to learn Python basics and want to quickly advance their skills by learning how to perform data cleaning, analysis and visualization with Python - all in one single course.
  • Those who want to switch from programming languages such as Java, C, R, Matlab, etc. to Python.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Getting Started

In this very first video you will find out what you will learn in the course so that you can make the most of it.


In this lecture you will see an example of using Python for reading, manipulating and visualizing data from an Excel file. This will give you a feeling of how Python is used for data analysis and visualization.


You will learn how to install Python through the Anaconda package which is a complete package that will not only install Python into your computer, but also other libraries needed for data analysis and visualizations such as pandas, matplotlib, numpy, scipy, etc.


You will learn how to use the Spyder environment to write scripts of Python code and also learn how to use iPython which is an enhanced interactive shell where you type in and execute Python code. iPython is tailored for data analysis applications

Section 2: Python Basics

Get to know with the content of this section.


You will be able to declare variables in Python and assign different data types to them, such as strings, integers, and floats.


You will learn about strings and the different number data types used in Python and how to perform operations with them.

4 questions

You will be able to evaluate your knowledge on how to create variables and use strings and numbers.


You will learn how to write a small conditional program using the if-else clause. You will also learn about the crucial concept of indentation.


You will learn what built-in functions are and also how to create your own customized Python functions and how to call them for generating their output.

Storing a function for later use
1 page
4 questions

Let's now make sure you know how write conditional blocks and custom functions.


You will understand the structure of list and tuple datatypes and learn how to create them in Python.


You will understand the structure of set and dictionary datatypes and learn how to create them.


You will be able to perform various operations with lists, tuples and strings. You will learn how to use indexing, access list, tuple, and string elements and perform slicing operations.


You will learn how to use the for loop in Python and also how to integrate an if statement inside a for loop block.

4 questions

You will solve some Python quizzes on lists, tuples, dictionaries, strings and iterations.

Section 3: Working with Files
Section introduction

You will learn how to create and open files from within Python and write lines of text inside TXT files.


You will learn the with method which is a great shortcut for handling files in Python.

3 pages
Exercise solution
1 page

You will learn how create new directories, how get and change the current working directory, and how to get a list of files contained in a directory.


You will learn how to split file names from full file paths and create new directories if a directory path does not exits.


You will enforce your iterating skills by learning how to use the for loop for accessing and manipulating multiple files at once from within Python.

Section 4: Downloading Files from FTP Sites

Short lecture introducing you to this section of the course.


You will learn how to write Python code that establishes a connection to an FTP server and accesses the files of the FTP site.


You will learn how to use the Spyder editor for executing complete scripts of Python code.


You will learn how to create a custom FTP function that logs in to an FTP site and generates a list of file names contained in the site.


You will learn the Python code that downloads a single file from an FTP site.


Something to keep in mind for the next lecture.


Here we start building our data analysis program.

In this particular lecture, we will build an FTP function that will login to the FTP site, and download a given range of files from the site.

Section 5: Working with Archive Files

You will learn how to extract various types of archive files using the patool library and the for loop.


You will learn how to extract RAR archive files.


Here you will write a function that will fetch the archive files downloaded by the FTP function and it will extract them all in a local directory.

Section 6: Working with TXT and CSV Files

Short lecture introducing you to this section of the course.


You will learn how to easily read CSV and delimited TXT files using the pandas library and use their data inside Python.

Reading Excel files

You will learn how to export data from Python to CSV and TXT files.


You will learn how to open data from TXT files which columns are delimited by a certain width.


You will learn how to quickly export a pandas dataframe into an HTML file.

Data Analysis Exercise: 1 of 6
5 pages
Solution: 1 of 6
1 page
Section 7: Getting Started with Pandas

We already used the pandas library in the previous section. Here you will be given an official tour to the pandas data analysis library.


You will create a function that grabs all the TXT files of a folder, opens each of them in Python as dataframes, adds a column in each dataframe and exports the updated dataframes back to CSV files.

Data Analysis Exercise: 2 of 6
4 pages
Solution: 2 of 6
1 page
Section 8: Concatenating and Joining Tables of Data with Pandas

You will write a function that gets all the CSV files and concatenates them vertically using the pandas concatenate function by creating a single CSV containing everything.

Data Analysis Exercise: 3 of 6
3 pages
Solution: 3 of 6
1 page

You will write a function that will join columns of a pandas dataframe to another dataframe.

Data Analysis Exercise: 4 of 6
3 pages
Solution: 4 of 6
1 page
Data Analysis Exercise: 5 of 6
4 pages
Solution: 5 of 6
Section 9: Data Aggregation

You will learn how to use the pandas pivot function by creating a pivoted dataframe out of a large CSV file by aggregating the data values.

Section 10: Visualizing Data

You will learn how to use the visualization features available in Python and generate graphs using the matplotlib and the seaborn libraries.


You will expand your knowledge on performing visualizations of different kinds out of pandas dataframes and adding labels and legends to the generated graphs.


You will learn create a function that will access the pivoted dataframe and it will generate a graph representing the data, and save the graph inside a PNG image file.

Data Analysis Exercise: 6 of 6
4 pages
Solution: 6 of 6
1 page
Section 11: Mapping Spatial Data

You will learn how to create a point KML file using the simplekml library and display the file in Google Earth.


You will create a function that grabs the data from a pandas dataframe and creates a KML file using the latitude and the longitude information contained in the dataframe.

Section 12: Putting everything together

You will learn how to make your script interact with a user who runs it.

Exercise: User interaction
2 pages
Solution: User interaction
1 page

You will learn how to execute all the functions of the programs in one single click.


You will learn how to make your program more user friendly by integrating the user input functionality.


You will learn how to convert your program into a Python module so you can import it in other scripts.

Section 13: Bonus Section: Using Python in Jupyter Notebooks to Boost Productivity

Setting up Jupyter and learning how to use its keyboard shortcuts.


Learn how to handle a problem of joining raw data with no key column to base the join to.


Learn to apply various operations including in-line visualizations on a Jupyter browser-based notebook.

Bonus Lecture

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Instructor Biography

Ardit Sulce, Python and GIS Expert, Founder of

Ardit received his master's degree in Geospatial Technologies from the Institute of Geoinformatics at University of Muenster, Germany. He also holds a Bachelor's degree in Geodetic Engineering.

Ardit offers his expertise in Python development on Upwork where he has worked with companies such as the Swiss in-Terra,  Center for Conservation Geography, and Rapid Intelligence. He is the founder of PythonHow where he authors written tutorials about the Python programming language. 

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