Numpy 🚀 - Real start of your data analyst/scientist journey
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Welcome back to Pandas Daily! Your daily 5-minute boost to becoming confident in Python. Python lists are flexible but slow at scale. Enter NumPy - a Python library for fast numerical computing. It has arrays—a special data structure to store numbers efficiently. Arrays outperform regular Python lists in speed and memory. NumPy also has powerful math functions: statistics, linear algebra, random numbers, and more. Every major data science library - scikit learn, pandas, Tensorflow is built on top of it. Today we’ll start with basics — how to create and explore arrays. 🔫 Load/Import NumPy
In:
import numpy as np
🌀 List ➡ NumPy array
In:
data = [1, 2, 3, 4, 5] arr = np.array(data) print(arr)
Out:
[1 2 3 4 5]
ꪜ Make sure to check 'type'
In:
print(type(data)) print(type(arr))
Out:
📶 Generate range of numbers (start, end, interval)
In:
arr = np.arange(0, 10, 2) print(arr)
Out:
[0 2 4 6 8]
⚖ Produce evenly spaced numbers (start, end, num of items)
In:
arr = np.linspace(0, 1, 5) print(arr)
Out:
[0. 0.25 0.5 0.75 1. ]
🌌 Arrays can be multi dimensional as well
In:
arr2d = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) print(arr2d)
Out:
[[1 2 3] [4 5 6] [7 8 9]] 🧮 Count number of elements
In:
print(arr.size) print(arr2d.size)
Out:
5 9 🔶 Know shape (rows, columns) of array For 1D it tells the number of elements
In:
print(arr.shape) print(arr2d.shape)
Out:
(5,) (3, 3) 🏷️ dtype to find datatype of elements in array
In:
print(arr.dtype)
Out:
int64
📌 For float datatype
In:
arrfloat = np.array([10.1, 20.5, 30.3, 40, 50]) print(arrfloat.dtype)
Out:
float64
Quick Excel Shortcut 💡 Ctrl + Shift + ⭐📣 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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