Here is a quick NumPy cheat code for beginners! These 30 code snippets cover essential operations like array creation, reshaping, basic operations, and more. Copy these examples to quickly get started with NumPy!
1. Import NumPy
import numpy as np2. Create Arrays
arr = np.array([1, 2, 3, 4]) # 1D array
mat = np.array([[1, 2], [3, 4]]) # 2D array3. Array of Zeros, Ones, and Random Values
zeros = np.zeros((2, 3)) # 2x3 array of zeros
ones = np.ones((2, 3)) # 2x3 array of ones
rand = np.random.rand(2, 3) # 2x3 array of random values4. Arange and Linspace
arr = np.arange(0, 10, 2) # [0, 2, 4, 6, 8]
lin = np.linspace(0, 1, 5) # [0. 0.25 0.5 0.75 1.]5. Reshape
reshaped = np.arange(6).reshape(2, 3) # [[0, 1, 2], [3, 4, 5]]6. Basic Operations
arr = np.array([1, 2, 3])
result = arr + 2 # [3, 4, 5]
dot = np.dot(arr, arr) # 1*1 + 2*2 + 3*3 = 147. Matrix Multiplication
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
result = np.matmul(A, B) # [[19, 22], [43, 50]]8. Indexing and Slicing
arr = np.array([1, 2, 3, 4, 5])
sub = arr[1:4] # [2, 3, 4]
mat = np.array([[1, 2], [3, 4]])
elem = mat[0, 1] # 29. Boolean Masking
arr = np.array([1, 2, 3, 4])
mask = arr > 2 # [False, False, True, True]
filtered = arr[mask] # [3, 4]10. Statistical Operations
arr = np.array([1, 2, 3, 4])
mean = np.mean(arr) # 2.5
std = np.std(arr) # 1.11811. Element-Wise Operations
arr = np.array([1, 2, 3])
squared = arr ** 2 # [1, 4, 9]12. Concatenation and Stacking
a = np.array([1, 2])
b = np.array([3, 4])
concat = np.concatenate([a, b]) # [1, 2, 3, 4]
stack = np.stack([a, b]) # [[1, 2], [3, 4]]13. Transpose
mat = np.array([[1, 2], [3, 4]])
transposed = mat.T # [[1, 3], [2, 4]]14. Unique Elements
arr = np.array([1, 2, 2, 3])
unique = np.unique(arr) # [1, 2, 3]15. Save and Load
np.save('array.npy', arr) # Save array
loaded = np.load('array.npy') # Load array16. Create Meshgrid
x = np.arange(0, 3)
y = np.arange(0, 3)
xx, yy = np.meshgrid(x, y)
# xx = [[0, 1, 2], [0, 1, 2], [0, 1, 2]]
# yy = [[0, 0, 0], [1, 1, 1], [2, 2, 2]]17. Conditional Statements with np.where
arr = np.array([10, 20, 30, 40])
result = np.where(arr > 25, arr, -1) # [ -1, -1, 30, 40]18. Logical Operations
arr = np.array([1, 2, 3, 4])
logical = np.logical_and(arr > 1, arr < 4) # [False, True, True, False]19. Broadcasting
arr = np.array([[1, 2, 3]])
broadcasted = arr + np.array([[10], [20]])
# [[11, 12, 13], [21, 22, 23]]20. Tile and Repeat
arr = np.array([1, 2, 3])
tiled = np.tile(arr, 2) # [1, 2, 3, 1, 2, 3]
repeated = np.repeat(arr, 2) # [1, 1, 2, 2, 3, 3]21. Stack Arrays Vertically and Horizontally
arr1 = np.array([1, 2])
arr2 = np.array([3, 4])
vstack = np.vstack([arr1, arr2]) # [[1, 2], [3, 4]]
hstack = np.hstack([arr1, arr2]) # [1, 2, 3, 4]22. Flatten an Array
arr = np.array([[1, 2], [3, 4]])
flattened = arr.flatten() # [1, 2, 3, 4]23. Generate Random Integers
random_integers = np.random.randint(0, 10, size=(2, 3))
# Example: [[3, 7, 2], [9, 4, 5]]24. Cumulative Sum
arr = np.array([1, 2, 3])
cumsum = np.cumsum(arr) # [1, 3, 6]25. Cumulative Product
arr = np.array([1, 2, 3])
cumprod = np.cumprod(arr) # [1, 2, 6]26. Sorting an Array
arr = np.array([3, 1, 2])
sorted_arr = np.sort(arr) # [1, 2, 3]27. Find Min, Max, and Argmax
arr = np.array([3, 1, 2])
min_val = np.min(arr) # 1
max_val = np.max(arr) # 3
argmax = np.argmax(arr) # Index 0 (value 3)28. Advanced Indexing with Boolean Arrays
arr = np.array([10, 20, 30, 40])
bool_idx = arr > 25 # [False, False, True, True]
filtered = arr[bool_idx] # [30, 40]29. Matrix Determinant
mat = np.array([[1, 2], [3, 4]])
det = np.linalg.det(mat) # -2.030. Solve a Linear System
A = np.array([[2, 3], [1, 2]])
b = np.array([8, 5])
solution = np.linalg.solve(A, b) # [1, 2]