Numpy 📌 arrays are useless without this
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Welcome back to Pandas Daily! Your daily 5-minute boost to becoming confident in Python. Yesterday we introduced NumPy and created few arrays. Today we learn how to access them. Think about it - You have a dataset with 10,000 prices. You need the first 100. Or the last one. Or every other value. That's indexing and slicing. Without it, arrays are useless. With it, you control your data. Let's get to the snippets. 🚩 Get value by position (index) [0] means 1st position and so on. This array can be daily sales value.
In:
import numpy as np prices = np.array([100, 150, 200, 250, 300, 400]) print(prices[0]) print(prices[2])
Out:
100 200 🔙 Negative Indexing - Start from back Get most recent or second to last sales
In:
print(prices[-1]) print(prices[-2])
Out:
400 300 🍰 Slice a range - first or last Example: First 3 months sales. Note: 3 is not included (only 0,1,2)
In:
print(prices[:3]) print(prices[2:])
Out:
[100 150 200] [200 250 300 400] 🍕 Slice again - from between includes 1,2,3 index positions
In:
print(prices[1:4])
Out:
[150 200 250]
🚫 Slice by skipping sales every alternate day. Start from 0 and go to 2,4..index positions
In:
print(prices[::2])
Out:
[100 200 300]
⏭ Skip with a different start Start from 1 and go to 3,5..index positions
In:
print(prices[1::2])
Out:
[150 250 400]
✍ Modify value using index
In:
print(prices) prices[5] = 29 print(prices)
Out:
[100 150 200 250 300 400] [100 150 200 250 300 29] Quick Excel Shortcut 💡Ctrl + D Fill all cells below with the content or formula from the one above. Handy when copying formulas or values down a column without dragging ⭐📣 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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