100 Days of Python

Welcome to the first post in the “100 Days of Python” series!

Introducing the “100 Days of Python” blog post series

As the name suggests, we will be exploring the Python programming language over the next 100 days. We will be starting from the complete beginner level to make sure people without any programming background can get started and follow along. We will gradually cover more advanced concepts such as task automation, data visualization, and web scraping and perform some basic data-science experiments.

Python is a popular, general-purpose programming language known for its simplicity, readability, and flexibility. It is used in a wide range of applications, including web development, data analysis, scientific computing, and artificial intelligence.

Here are a few reasons why one might start learning Python:

• Python is easy to learn: Its simple syntax and readable code make it a great first programming language for beginners.
• Python is versatile: It can be used for a wide range of tasks, from web development to data analysis to artificial intelligence.
• Python has a large, active community: There are countless resources available online for learning Python, and the community is always working on new libraries and tools to make it even better.

Along with the concepts, the blog posts will also include some practical exercises with links to resources where one might write code and learn by doing.

The list of topics covered will be updated on a rolling basis every day:

1. Printing values in Python
2. Getting input from users in Python
3. Arithmetic expressions and numeric variables in Python
4. Boolean variables and boolean arithmetic in Python
5. Conditions in Python — if/else
6. Nested conditions in Python
7. Floating point numbers
8. Augmented assignments (+=, -=, etc.)
9. Strings
10. How to format text and what are f-strings in Python?
11. Lists
12. What is the range() function in Python?
13. For loops
14. While loops
15. break and continue
16. How Python almost had another keyword
17. 5 most useful string casing methods in Python
18. 5 most useful string modifying methods in Python
19. Splitting and Joining strings in Python
20. 10 most useful list methods in Python
21. Nested loops in Python
22. Python List Comprehension — Deep Dive
23. Tuples in Python
24. Sets in Python
25. Dictionaries in Python
26. Why Python Does Not Have Tuple Comprehension?
27. Functions in Python
28. What are Multiple Return Values Actually in Python?
29. 4 Types of Function Arguments in Python that You Might Not Know About
30. Python Variable Scope
31. Enumerate and Zip Functions in Python
32. Lambda Functions in Python: A Comprehensive Guide to Understanding and Using Anonymous Functions
33. Higher Order Functions in Python
34. Working With Files in Python
35. With Statement in Python
36. Automating Data Cleaning With Python
37. Positional-only and Keyword-only Arguments in Python
38. Does Python Have Pass-by-Value VS Pass-by-Reference Variables?
39. Recursion in Python
40. What is Stack Overflow Really?
41. Regular Expressions in Python
42. Regular Expressions — Grouping and Backreferences
43. Python Classes and Objects
44. Mastering Private and Protected Fields in Python Classes: A Complete Tutorial
45. What are Magic Methods in Python Classes?
46. Inheritance in Python
47. Method Overriding in Python
48. Multiple Inheritance in Python
49. Type Hints and Type Checking in Python
50. How to Create Custom Generic Types in Python
51. Abstract Classes in Python
52. Data Classes in Python
53. Properties in Python
54. Static Methods in Python
55. Implementing Custom Decorator Functions in Python
56. Class Decorators in Python
57. Python — Exception Handling
58. Exception Hierarchy in Python
59. Custom Exceptions in Python — Creating Custom Exceptions
60. Iterators in Python
61. Generators in Python
62. Iterables in Python
63. 10 Most Useful Itertools Methods
64. Glob — Working with Files in Python
65. Pathlib — The OOP Approach of Working with File System in Python
66. Creating Custom Context Managers in Python
67. All the Ways You Can Use Context Managers in Python
68. Multithreading in Python
69. Synchronizing Threads in Python With Locks
70. Synchronizing Threads in Python With Semaphores
71. Synchronizing Threads in Python With Barriers
72. Multiprocessing in Python
73. What Is the Python Global Interpreter Lock (GIL)?
74. Multithreading VS Multiprocessing in Python
75. Thread Pools and Process Pools in Python
76. Async Await in Python — Asyncio Deep Dive
77. Async with Expression in Python
78. Making Requests With asyncio in Python
79. Working With Databases Using asyncio in Python — SQLAlchemy Example
80. Multithreading VS Multiprocessing VS Asyncio in Python
81. How Modules Actually Work in Python and How to Create Your Own Custom Module
82. What are Packages in Python and What is the Role of __init__.py files?
83. Working With Third-Party Libraries in Python
84. Virtual Environments in Python
85. Unit Testing in Python with Pytest
86. Test Coverage in Python with Pytest
87. Mocking and Fixtures in Python
88. Web Scraping with Scrapy in Python
89. Working with Excel Sheets and CSV Files Using Pandas for Data Processing
90. Working With XML and JSON Data in Python
91. Mastering Image Processing in Python with Scikit-Image — A Comprehensive Guide to Image Processing Techniques
92. Mastering NumPy in Python for Numerical Computations: A Comprehensive Tutorial
93. Mastering Data Analysis with Pandas
94. Machine Learning in Python with Scikit-Learn
95. Creating an Interactive Website with Streamlit in Python
96. Creating Beautiful Data Visualizations with Plotly and Dash
97. Creating Custom ChatGPT Using the OpenAI API
98. Time Series Analysis with Python using Prophet
99. Developing Your First Flask Application in Python
100. The Grand Finale: Wrapping Up Our 100 Days of Python Journey

So whether you’re a complete beginner or an experienced programmer looking to learn Python, I hope you’ll join us on this journey!

What’s next?

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