Lol 😂

Python Cheatsheet ✅

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 — 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲?😍

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Python Roadmap

Stage 1 - Learn Python basics: syntax, loops, conditionals, and data types.
Stage 2 - Study OOP concepts and Python modules.
Stage 3 - Practice file handling, exceptions, and JSON handling.
Stage 4 - Use virtual environments and package management.
Stage 5 - Explore libraries like NumPy, Pandas, and Matplotlib.
Stage 6 - Learn threading, multiprocessing, and decorators.
Stage 7 - Improve testing and debugging.
Stage 8 - Contribute to open-source projects.

🏆 – Python Developer

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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To learn data structures and algorithms in Python, you can follow these steps:

1. Start with the basics: Learn about the most common data structures, such as arrays, linked lists, stacks, queues, trees, and graphs. Learn how to implement them in Python and understand their time and space complexities.

2. Study algorithms: Study the most common algorithms for searching, sorting, and traversing data structures. Understand their time and space complexities and the trade-offs between different algorithms.

3. Practice, practice, practice: The more you practice implementing data structures and algorithms, the better you will get at it. You can start by solving problems on websites like LeetCode and HackerRank, or by working on small projects of your own.

4. Read and learn from others: Read articles and blogs written by experts in the field, and learn from their experiences and insights. Follow the work of other Python developers on Github, and see how they use data structures and algorithms in their projects.

𝟲 𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜😍

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System Design Cheat Sheet

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𝟱 𝗙𝗥𝗘𝗘 𝗜𝗕𝗠 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍

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⌨️ Grammar Correction using Python

𝟰 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜😍

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10 Ways to Speed Up Your Python Code

1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)

2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.

3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.

4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.

5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.

6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post.

7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.

8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.

9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.

10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.

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𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝗵! 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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Complete roadmap to learn Python for data analysis

Step 1: Fundamentals of Python

1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)

2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions

3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions

4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)

Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)

2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully

3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation

Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations

2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data

3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn

Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering

2. Exploratory Data Analysis (EDA)
- Descriptive statistics
- Data visualization techniques
- Identifying patterns and outliers

3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions

Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models

2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models

3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)

Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects

2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects

👨‍💻 FREE Resources to Learn & Practice Python 

1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://t.me/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.me/pythonfreebootcamp/134
7. https://t.me/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://t.me/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://t.me/pythonspecialist/33

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ENJOY LEARNING 👍👍

𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘😍

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Python Programming Interview Questions for Entry Level Data Analyst

1. What is Python, and why is it popular in data analysis?

2. Differentiate between Python 2 and Python 3.

3. Explain the importance of libraries like NumPy and Pandas in data analysis.

4. How do you read and write data from/to files using Python?

5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.

6. What are list comprehensions, and how do you use them in Python?

7. Explain the concept of object-oriented programming (OOP) in Python.


8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.

9. How do you handle missing or NaN values in a DataFrame using Pandas?

10. Explain the difference between loc and iloc in Pandas DataFrame indexing.

11. Discuss the purpose and usage of lambda functions in Python.

12. What are Python decorators, and how do they work?

13. How do you handle categorical data in Python using the Pandas library?

14. Explain the concept of data normalization and its importance in data preprocessing.

15. Discuss the role of regular expressions (regex) in data cleaning with Python.

16. What are Python virtual environments, and why are they useful?

17. How do you handle outliers in a dataset using Python?

18. Explain the usage of the map and filter functions in Python.

19. Discuss the concept of recursion in Python programming.

20. How do you perform data analysis and visualization using Jupyter Notebooks?

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ENJOY LEARNING 👍👍

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗢𝗻 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍

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Many data scientists don't know how to push ML models to production. Here's the recipe 👇

𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀

🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data
🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time
🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model
🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github
🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API
🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API
🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker
🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs
🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring

𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲

𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴

Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling.

𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁

Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.

𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻

Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment

𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁

Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.

𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴

Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.