Data Engineers

Python Cheatsheet

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How To Code in Python 3 by Lisa Tagliaferri 📄 459 pages 🔗 Book link

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Effective Communication of Data Insights (Very Important Skill for Data Analysts) Know Your Audience: Tip: Tailor your presentation based on the technical expertise and interests of your audience. Consideration: Avoid jargon when presenting to non-technical stakeholders. Focus on Key Insights: Tip: Highlight the most relevant findings and their impact on business goals. Consideration: Avoid overwhelming your audience with excessive details or raw data. Use Visuals to Support Your Message: Tip: Leverage charts, graphs, and dashboards to make your insights more digestible. Consideration: Ensure visuals are simple and easy to interpret. Tell a Story: Tip: Present data in a narrative form to make it engaging and memorable. Consideration: Use the context of the data to tell a clear story with a beginning, middle, and end. Provide Actionable Recommendations: Tip: Focus on practical steps or decisions that can be made based on the data. Consideration: Offer clear, actionable insights that drive business outcomes. Be Transparent About Limitations: Tip: Acknowledge any data limitations or assumptions in your analysis. Consideration: Being transparent builds trust and shows a thorough understanding of the data. Encourage Questions: Tip: Allow for questions and discussions to clarify any doubts. Consideration: Engage with your audience to ensure full understanding of the insights. You can find more communication tips here: https://t.me/englishlearnerspro I have curated Data Analytics Resources 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Like this post for more content like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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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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