🚀 NumPy skill that unlocks machine learning
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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. ⏪ Recap: See dimensions first
In:
import numpy as np sales = np.array([120, 340, 210, 450, 380, 290]) print(sales.shape)
Out:
(6,)
🔶 Now reshape above 1D array to 2D Assume 1st 3 are sales of week 1 and last 3 are of week 2
In:
weekly_sales = np.array([50, 60, 70, 80, 90, 100]) reshaped = weekly_sales.reshape(2, 3) print(reshaped)
Out:
[[ 50 60 70] [ 80 90 100]] 🙃 Do reverse - 2D to 1D Your model might need a linear feed rather than a 2D
In:
grid = np.array([[10, 20, 30], [40, 50, 60]]) flat = grid.flatten() print(flat)
Out:
[10 20 30 40 50 60]
🅾️ Create Zeroes Use as default values unless anything else is predicted
In:
empty_slots = np.zeros(5) print(empty_slots)
Out:
[0. 0. 0. 0. 0.]
☝️ Create Ones Can be used as default multipliers unless defined
In:
multipliers = np.ones(4) print(multipliers)
Out:
[1. 1. 1. 1.]
🤖 Generate sample (random) data 💡 We don't need the random module separately! iykyk
In:
random_data = np.random.rand(6) print(random_data)
Out:
[0.42891263 0.78546321 0.23456789 0.91234567 0.56789012 0.34567890]
🟰 How do you and I get the same random data? Using same seed. Past Pandas Daily readers would know 😉
In:
np.random.seed(42) data1 = np.random.rand(4) print(data1)
Out:
[0.37454012 0.95071431 0.73199394 0.59865848]
⭐📣 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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