Christine A.
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Online Course
free
Working with Text Files in Python
with
Martin Ganchev
Take your data analyst skillset to the next level: master working with text files in Python
2630 students already have enrolled
Skill level:
Intermediate
Duration:
1 hour
-
Lessons (1 hour)
-
Practice exams (12 minutes)
- Lessons (1 hour)
- Practice exams (12 minutes)
CPE credits:
2
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Master essential Python skills to handle text files.
- Enhance your ability to read, write, and manipulate text file content.
- Understand and master different types of text files.
- Gain proficiency using Python and pandas to load and parse complex datasets.
- Organize and structure data effectively for real-world text processing tasks.
Topics & tools
jupyterpythonprogrammingimporting datatext files
Your instructor
Content Creator & Content Manager at 365 Data Science | Data Science Instructor
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Economic and Social Sciences
Course OVERVIEW
Description
CPE Credits: 2
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
By completing this course, you’ll obtain a comprehensive set of theoretical and practical tools to understand and handle raw data. We will extensively cover the difference between a file and file object, reading and parsing, and structured and unstructured data. Then, we’ll dive into the complex world of data connectivity by solving hands-on tasks in Python with *.txt, *.json, *.xlsx, and *.csv formats. The more you learn about data science and become an authentic practitioner, the more you realize that you’ll be independent in your research and making your predictions if (and only if) you can manage your data by yourself.
Prerequisites
- Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
- Completion of an introductory Python course is required.
Advanced preparation
Curriculum
22 lessons
2 exams
- 2. Principles of Importing Data in Python15 MinIn this section, we dig deeper into the contents of a text file and learn about its various types—giving you some ideas on how to work with a messy dataset stored in a text file, regardless of the latter’s format. More precisely, we will clarify terms like ‘character’, ‘separator’, ‘delimiter’, ‘interpreter’, and ‘encoding’ and discuss the most notable types of text files: plain text files and flat files, and fixed-width files. To complete the principles you’ll need, we conclude this section with a presentation of the most widely used naming conventions in programming.
- 3. Importing Text Files in Python58 MinOnce you’re equipped with general knowledge about working with text files and the specific skills to import them in Python, it’s time to see how things work in practice. In this section, we go into the details about importing data with Python’s open() function, *.csv files with pandas, *.json files in Python, and Excel files. We’ll also discuss several tips and tricks to improve the quality of your dataset in Python and show you how to save it.Python: Importing Text Files ( open() ) FreePython: Importing Text Files ( with open() ) Freepandas: Importing *.csv Files - Part I Freepandas: Importing *.csv Files - Part II Freepandas: Importing *.csv Files - Part III FreePython: Importing Data with the "index_col" Parameter FreePython: Importing *.json Files FreeIntroduction to Working with Excel Files in Python FreeDealing with Excel Data (the *.xlsx Format) FreeImporting Data in Python - an Important Exercise FreePython: Importing Data with the pandas' .squeeze() Method FreeJupyter: A General Note on Importing Files FreePractice exam Freepandas: Saving Your Data Free
- 4. Conclusion1 MinThrough practice and taking certain risks while coding, one can become proficient in dealing with real-world raw text files.
- 5. Course exam15 Min
Free preview
1.1 Introduction to Working with Text Files in Python
4 min
1.2 The Difference between File and File Object; Read vs. Parse
3 min
1.3 Defining Structured, Semi-Structured and Unstructured Data
3 min
1.4 What is Data Connectivity: A Text Files Perspective
3 min
2.1 Principles of Importing Data in Python
5 min
2.2 More on Working with Text Files (*.txt vs *.csv)
5 min
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