NumPy CheatSheet 🔢
Last updated: 23 Sept 2025
All important Numpy commands for data management and manipulation with examples. 🚀
#!python
import numpy as np
# Data types
>>> np.float32(1.0)
1.0
>>> np.int_([1, 2, 4])
array([1,2,4])
>>> np.arange(3, dtype=np.uint8)
array([0, 1, 2], dype=uint8)
# Create arrays
>>> np.zeros((2,3))
array([[ 0., 0., 0.], [ 0., 0., 0.]])
>>> np.arange(2, 10, dtype=np.float64)
array([2., 3., 4., 5., 6., 7., 8., 9.])
>>> np.arange(2, 3, 0.2)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(1., 4., 6)
array([1. , 1.6, 2.2, 2.8, 3.4, 4. ])
>>> np.random.random((2,3))
array([[0.27105365, 0.89937328, 0.52478529],
[0.57497815, 0.84540218, 0.64585082]])
>>> np.full((3,3), 5)
array([[5, 5, 5],
[5, 5, 5],
[5, 5, 5]])
>>> np.eye(3,3)
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
# reshape
>>> np.arange(9).reshape((3,3))
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
# Indexing
>>> x = np.arange(10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> x[2:5]
array([2, 3, 4])
>>> x[1:7:2]
array([1, 3, 5])
>>> x.shape = (2,5)
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> x[0]
array([0, 1, 2, 3, 4]
>>> y = np.arange(35).reshape(5,7)
array([[ 0, 1, 2, 3, 4, 5, 6],
[ 7, 8, 9, 10, 11, 12, 13],
[14, 15, 16, 17, 18, 19, 20],
[21, 22, 23, 24, 25, 26, 27],
[28, 29, 30, 31, 32, 33, 34]]))
>>> y[1:5:2, ::3]
array([[ 7, 10, 13],
[21, 24, 27]])
# slicing
>>> x = np.array([[10,20,30,40], [50,60, 70,80], [90,100, 110, 120 ] ] )
>>> x
array([[ 10, 20, 30, 40],
[ 50, 60, 70, 80],
[ 90, 100, 110, 120]])
>>> x[1,:]
array([50, 60, 70, 80])
>>> x[:2, :]
array([[10, 20, 30, 40],
[50, 60, 70, 80]])
>>> x[:, 2]
array([ 30, 70, 110])
>>> x[np.arange(3), np.array([0,3,1])]
array([ 10, 80, 100])
>>> x[::-1,] # reverse rows
array([[ 90, 100, 110, 120],
[ 50, 60, 70, 80],
[ 10, 20, 30, 40]])
>>> x[:, ::-1] # reverse cols
array([[ 40, 30, 20, 10],
[ 80, 70, 60, 50],
[120, 110, 100, 90]])
>>> x>30
array([[False, False, False, True],
[ True, True, True, True],
[ True, True, True, True]])
>>> x[x>30]
array([ 40, 50, 60, 70, 80, 90, 100, 110, 120])
# Arr operations
>>> a = np.arange(5)
>>> b = np.arange(5)
>>> a+b
array([0, 2, 4, 6, 8])
>>> a-b
array([0, 0, 0, 0, 0])
>>> a**2
array([ 0, 1, 4, 9, 16])
>>> a>3
array([False, False, False, False, True])
>>> 10*np.sin(a)
array([ 0. , 8.41470985, 9.09297427, 1.41120008, -7.56802495])
>>> a*b
array([ 0, 1, 4, 9, 16])
>>> x = np.random.random((2,3))
array([[0.95802248, 0.23079335, 0.51334747],
[0.5247865 , 0.43557556, 0.39688238]])
>>> x.sum()
np.float64(3.059407747578695)
>>> x.min()
np.float64(0.2307933513386181)
>>> x.max(axis=0)
array([0.95802248, 0.43557556, 0.51334747])
>>> x.min(axis=1)
array([0.23079335, 0.39688238])
>>> y = np.floor(10*x)
array([[9., 2., 5.],
[5., 4., 3.]])
>>> y.ravel()
array([9., 2., 5., 5., 4., 3.])
>>> y.transpose()
array([[9., 5.],
[2., 4.],
[5., 3.]])
# attributes
>>> x = np.array([1,2,3,4,5])
>>> x.size
5
>>> x.itemsize
8
# Broadcasting Operations
>>> x = np.arange(1, 13).reshape((3,4))
array([[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12]])
>>> x*3
array([[ 3, 6, 9, 12],
[15, 18, 21, 24],
[27, 30, 33, 36]])
>>> x/3
array([[0.33333333, 0.66666667, 1. , 1.33333333],
[1.66666667, 2. , 2.33333333, 2.66666667],
[3. , 3.33333333, 3.66666667, 4. ]])
>>> np.empty_like(x) # creates np.arr with same shape & dtype as x with uninitialized contents with arbitrary vals
array([[4599676419421066581, 4604180019048437077, 4607182418800017408,
4608683618675807573],
[4610184818551597739, 4611686018427387904, 4612436618365282987,
4613187218303178069],
[4613937818241073152, 4614688418178968235, 4615439018116863317,
4616189618054758400]])
# More random generation
>>> np.random.rand(5)
array([0.29667295, 0.65686954, 0.8066308 , 0.23597214, 0.86011729])
>>> np.random.rand(3,5)
array([[0.83296229, 0.22301045, 0.11728102, 0.40824894, 0.26658339],
[0.16682019, 0.60629984, 0.01388485, 0.78301687, 0.56539043],
[0.03591469, 0.65108814, 0.38361917, 0.18702309, 0.7940847 ]])
>>> np.random.randint(0, 25, 3)
array([ 7, 15, 14], dtype=int32)
>>> np.random.normal(1.0, 3.0, 5) # generate in normal dist format
array([ 1.23978788, 6.35655894, 5.44832977, 3.64996349, -2.602175 ])
>>> np.random.uniform(5, 10, 4) # generate 4 nos distributed uniformly b/w 5 & 10 inclusive
array([8.93810383, 5.61586006, 9.10111054, 5.77172238])
# NumPy Math Operations
>>> a = np.array([[10,20], [30,40]], dtype=np.int64)
array([[10, 20],
[30, 40]])
>>> np.sum(a)
np.int64(100)
>>> np.sum(a, axis=0)
array([40, 60])
>>> b = np.array([[50,60], [70,80]], dtype=np.int64)
array([[50, 60],
[70, 80]])
>>> np.add(a,b) # can also do a+b
array([[ 60, 80],
[100, 120]])
>>> np.subtract(a,b) # can also do a-b
array([[-40, -40],
[-40, -40]])
>>> np.multiply(a,b) # can also do a*b
array([[ 500, 1200],
[2100, 3200]])
>>> np.divide(a,b) # can also do a/b
array([[0.2 , 0.33333333],
[0.42857143, 0.5 ]])
>>> np.dot(a,b) # can also do a@b
array([[1900, 2200],
[4300, 5000]])
# numpy stat operations
>>> a
array([[10, 20],
[30, 40]])
>>> np.mean(a)
np.float64(25.0)
>>> np.std(a)
np.float64(11.180339887498949)
>>> np.var(a)
np.float64(125.0)
>>> np.linalg.det(a)
np.float64(-200.0000000000001)
>>> np.linalg.matrix_rank(a)
np.int64(2)
>>> a.diagonal()
array([10, 40])
>>> a.diagonal(offset=-1)
array([30])
>>> a.trace()
np.int64(50)
>>> eigen_vals, eigen_vectors = np.linalg.eig(a)
>>> eigen_vals, eigen_vectors
(array([-3.72281323, 53.72281323]), array([[-0.82456484, -0.41597356],
[ 0.56576746, -0.90937671]]))
>>> a = np.array([1,2])
>>> b = np.array([3,4])
>>> c = np.linalg.norm(a-b) # euclidean dist
np.float64(2.8284271247461903)
>>> a = np.array([1,0,1])
>>> b = np.array([1,0,-1])
>>> angle = np.arccos(a@b)
np.float64(1.5707963267948966)
>>> angle*(180/np.pi)
np.float64(90.0)
>>> x = np.array(np.arange(16)).reshape((4,4))
>>> x
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> u,s,v = np.linalg.svd(x) # Singular Value Decomposition
>>> u
array([[-0.09184212, -0.83160389, 0.53515573, 0.11665482],
[-0.31812733, -0.44586433, -0.80049034, 0.24334177],
[-0.54441254, -0.06012478, -0.00448651, -0.836648 ],
[-0.77069775, 0.32561478, 0.26982112, 0.47665141]])
>>> s
array([3.51399637e+01, 2.27661021e+00, 1.69970911e-15, 8.07447880e-17])
>>> v
array([[-0.42334086, -0.47243254, -0.52152422, -0.57061589],
[ 0.72165263, 0.27714165, -0.16736932, -0.6118803 ],
[-0.22125808, 0.66846675, -0.67315927, 0.2259506 ],
[ 0.50104377, -0.50314233, -0.49684666, 0.49894522]])
📌Points to remember:
-
doing y = x, copies by reference, if you want to make new copy do y = x.copy()
-
np.nan is empty value and np.inf is for infinity value
-
to generate with seed, do np.random.seed(\<seed_number>)