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Welcome back to Pandas Daily! Your daily 5-minute boost to becoming confident in Python. β TGIF!! As I wrap up for the weekend, I remember the times I scrolled endlessly through long Python notebooks just to find one bug. Frustrating π© Thanks to a friend who told me about Jupyter shortcuts. Life has changed completely after knowing them. I only use keyboard while coding, and can surely log-off for the weekend early π Sharing a few with you. These will save hours. Should work for most ipynb (Jupyter notebook style) files. Learn a few for a fruitful weekend ππΎ β π¬ Cell Editing & Modes
β βΆοΈ Cell Navigation and execution
β π Productivity Boosters
βπ£ That's it for today! If you liked it, please share it with anyone who will find it useful and share your feedback below πΌ
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Beginner to Expert in Python in just 5 minutes
Welcome back to Pandas Daily! Your daily 5-minute boost to becoming confident in Python. You have a spreadsheet with 500 numbers. Your boss asks: "What do you see?" Most will freeze, scroll, guess. Not you! 3 Python functions to turn raw numbers into answers. No calculator, excel sheets. Just code. Statistics isn't boring math. It's how you make decisions with data. Using just the statistics module we find "center" of any dataset. Any data analyst must know the basicsβΌοΈ π― Compute simple...
Welcome back to Pandas Daily! Your daily 5-minute boost to becoming confident in Python. Last week we introduced numpy. Created bunch of arrays, sliced them and did some math with them. To prepare data for machine learning or any analysis, we need to learn to control their shape. Today - Reshape flat lists into grids. Flatten 2D tables back to 1D arrays. Create arrays filled with zeros, ones, or random numbers. βͺ Recap: See dimensions first In: import numpy as np sales = np.array([120, 340,...
Welcome back to Pandas Daily! Your daily 5-minute boost to becoming confident in Python. TGIF! Everyone is talking about AI. Most people take courses. You're going to build it instead. Let's create a simple sentiment analyzer that reads customer reviews and classifies them as positive or negative. No machine learning librariesβjust Python. π― Define list of positive and negative words In: positive_words = ["good", "great", "excellent", "amazing", "love", "best"] negative_words = ["bad",...