18.12.2022
This is too fast!! If it is expected that the student should have some background before taking this course, it needs to be mentioned in the course overview
Working with Text Files in Python
with
Martin Ganchev
Covering the essentials and providing hands-on experience working with *.csv, *.txt, *.json, and other types of text files in Python. With these tools under your belt, you’ll be an independent analyst, ready to gain more insights from your data.
2 hours 22 lessons
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22 High Quality Lessons
1 Practical Tasks
2 Hours of Content
Certificate of Achievement
Course Overview
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.
Topics covered
importing dataJupyterProgrammingPython
What You'll Learn
At the beginning of your analyses, data will rarely be waiting for you neatly and cleanly in a, say, *.csv format that you can load and use for statistical analysis directly. On the contrary, real-world data is messy, comes from various sources, and contains errors and missing or unknown values. It’s vague and, on top of that, comes in different formats, shapes, and sizes. This course teaches you how to tame this chaos.
Build from solid explanations of such essential terms as a file object, file, text file, and text data
Distinguish between reading and parsing data
Learn and understand several types of data organization
Use Python and pandas to load and parse data
Work with *.txt., *.csv, *.json, *.xlsx formats
Curriculum
- Introduction to Working with Text Files and Data Free4 Lessons 13 Min
What do you need to know to manage text files in Python easily? Apart from learning about text files and data connectivity, there are several distinctions to clarify, among which include File vs File Object, Reading vs Parsing Data, Structured vs Semi-structured and Unstructured Data.
Introduction to Working with Text Files in Python FreeThe Difference between File and File Object; Read vs. Parse FreeDefining Structured, Semi-Structured and Unstructured Data FreeWhat is Data Connectivity: A Text Files Perspective Free - Principles of Importing Data in Python Free4 Lessons 15 Min
In 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.
Principles of Importing Data in Python FreeMore on Working with Text Files (*.txt vs *.csv) FreeWhat about Fixed-width Files? FreePython Programming: Common Naming Conventions Free - Importing Text Files in Python13 Lessons 58 Min
Once 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() )Python: Importing Text Files ( with open() )pandas: Importing *.csv Files - Part Ipandas: Importing *.csv Files - Part IIpandas: Importing *.csv Files - Part IIIPython: Importing Data with the "index_col" ParameterPython: Importing *.json FilesIntroduction to Working with Excel Files in PythonDealing with Excel Data (the *.xlsx Format)Importing Data in Python - an Important ExercisePython: Importing Data with the pandas' .squeeze() MethodJupyter: A General Note on Importing Filespandas: Saving Your Data - Conclusion1 Lesson 1 Min
Through practice and taking certain risks while coding, one can become proficient in dealing with real-world raw text files.
Working with Text Files - Conclusion
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“A good data scientist spends more time preparing their data than using it for prediction afterward. In just a couple of hours, you’ll acquire reliable guidelines for handling data in various formats and learn to apply them in Python.”
Worked at the European Commission
Working with Text Files in Python
with Martin Ganchev