📦 Raw arrays to actionable insights
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Welcome back to Pandas Daily! Your daily 5-minute boost to becoming confident in Python. We created arrays on Day 1. Sliced them on Day 2. Now comes the real computational power of NumPy. Doing math on entire datasets. No loops. No iteration. Just one line. Add monthly revenues across regions. Calculate profit margins. Find averages. All in seconds!! Let's dive in.. 🍃 Add/Subtract like a breeze
In:
import numpy as np jan_sales = np.array([120, 340, 210]) feb_sales = np.array([150, 290, 180]) print(jan_sales + feb_sales) print(jan_sales - feb_sales)
Out:
[270 630 390] [-30 50 30] 🍰 Multiply/Divide like a piece of cake
In:
revenue = np.array([5000, 7500, 6200]) costs = np.array([2000, 3000, 2500]) print(revenue * costs) print(revenue / costs)
Out:
[10000000 22500000 15500000] [2.5 2.5 2.48] ⚔️ Arrays & Numbers Crossover Calculate discount or markup price
In:
prices = np.array([50, 75, 100, 125]) discounted = prices * 0.9 increased = prices + 15 print(discounted) print(increased)
Out:
[ 45. 67.5 90. 112.5] [ 65 90 115 140] ✳️ Aggregate however you want
In:
daily_orders = np.array([23, 45, 67, 34, 89, 56, 78]) print(daily_orders.sum()) print(daily_orders.mean()) print(daily_orders.max()) print(daily_orders.min())
Out:
392 56.0 89 23 ⏹ Square to calculate area
In:
sides = np.array([3, 5, 7, 9]) areas = sides ** 2 print(areas)
Out:
[ 9 25 49 81]
💎 Identify elements that satisfy a condition (premium products)
In:
prices = np.array([45, 78, 120, 95, 200]) print(prices > 100)
Out:
[False False True False True]
🌓 Subset data based on given conditions
In:
print(prices[prices > 100])
Out:
[120 200]
Quick Excel Shortcut 💡Ctrl + 1 Open the Format Cells dialog box. Quickly change number format, font, borders, or alignment — all from one place. ⭐📣 That's it for today! If you liked it, please share it with anyone who will find it useful and share your feedback below 🐼
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Pandas Daily
Beginner to Expert in Python in just 5 minutes
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