Revising NumPy: A Cheatsheet
Because we all need a NumPy refresher now and then!
Introduction to NumPy
NumPy is a powerful Python library for numerical computing. It simplifies numerical computations by performing efficient operations on large multidimensional arrays and matrices.
Say goodbye to slow loops and hello to blazing speed!
import numpy as np
NumPy Array Creation
Create arrays using array() or functions such as zeros() and ones(). Think of it as building blocks for your data.
arr = np.array([1, 2, 3])
zeros = np.zeros((2, 2))
ones = np.ones((3, 3))
NumPy N-d Array Creation
Supports the creation of multi-dimensional arrays. Because 2D is cool, but N-D is cooler.
nd_array = np.array([[1, 2], [3, 4]])
higher_dim = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
NumPy Data Types
Specify or inspect array data types. Useful when you want to avoid unexpected data-type surprises.
arr = np.array([1.0, 2.0], dtype=np.float32)
print(arr.dtype) # float32
int_arr = np.array([1, 2, 3], dtype=np.int32)
print(int_arr.dtype) # int32
NumPy Array Attributes
Inspect the shape, size, and dimensions of arrays. It’s like peeking under the hood of your array.
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape, arr.size, arr.ndim) # (2, 3), 6, 2
NumPy Input Output
Save and load arrays easily. Think of it as bookmarking your progress.
np.save('array.npy', arr)
loaded = np.load('array.npy')
np.savetxt('array.txt', arr, delimiter=',')
loaded_txt = np.loadtxt('array.txt', delimiter=',')
NumPy Array Indexing
Access elements by indices. Remember, NumPy arrays are 0-indexed!
arr = np.array([10, 20, 30])
print(arr[0]) # 10
print(arr[-1]) # 30
NumPy Array Slicing
Extract sub-arrays using slicing. It’s like cutting a slice of your data pizza.
arr = np.array([1, 2, 3, 4, 5])
sub_arr = arr[1:4] # [2, 3, 4]
every_other = arr[::2] # [1, 3, 5]
NumPy Array Reshaping
Change the shape without altering data. Rearrange your data like a Rubik’s cube.
arr = np.array([1, 2, 3, 4, 5, 6])
reshaped = arr.reshape((2, 3)) # [[1, 2, 3], [4, 5, 6]]
flattened = reshaped.flatten() # [1, 2, 3, 4, 5, 6]
NumPy Arithmetic Array Operations
Perform element-wise operations. Because who wants to loop through elements manually?
arr = np.array([1, 2, 3])
result_add = arr + 10 # [11, 12, 13]
result_mul = arr * 2 # [2, 4, 6]
NumPy Array Functions
Built-in functions like sum and mean make life easier. They’re like your data’s best friends.
arr = np.array([1, 2, 3, 4])
print(np.sum(arr)) # 10
print(np.mean(arr)) # 2.5
NumPy Comparison/Logical Operations
Compare arrays element-wise. Great for filtering data with conditions.
arr = np.array([1, 2, 3, 4])
print(arr > 2) # [False, False, True, True]
print(np.logical_and(arr > 1, arr < 4)) # [False, True, True, False]
NumPy Math Functions
Apply math functions directly. No need for calculators anymore.
arr = np.array([1, 4, 9])
print(np.sqrt(arr)) # [1. 2. 3.]
print(np.power(arr, 2)) # [1 16 81]
NumPy Constants
Use predefined constants like pi. For when you don’t want to remember 3.14159.
print(np.pi) # 3.141592653589793
print(np.e) # 2.718281828459045
NumPy Statistical Functions
Compute stats like median, and variance. Perfect for understanding your data’s personality.
arr = np.array([1, 2, 3, 4])
print(np.median(arr)) # 2.5
print(np.var(arr)) # 1.25
NumPy String Functions
Operate on strings in arrays. Because even text data needs some love.
names = np.array(['Alice', 'Bob'])
print(np.char.upper(names)) # ['ALICE' 'BOB']
print(np.char.replace(names, 'o', '0')) # ['Alice' 'B0b']
NumPy Broadcasting
Perform operations on arrays with different shapes. It’s like magic, but with math.
arr = np.array([1, 2, 3])
broadcasted = arr + np.array([10]) # [11, 12, 13]
expanded = arr + np.array([[10], [20]]) # [[11, 12, 13], [21, 22, 23]]
NumPy Matrix Operations
Matrix multiplication, inversion, etc. Linear algebra geeks, rejoice!
matrix = np.array([[1, 2], [3, 4]])
print(np.dot(matrix, matrix)) # Matrix multiplication
print(np.linalg.inv(matrix)) # Matrix inversion
NumPy Set Operations
Find unique elements and intersections. Useful for deduplication and comparisons.
set1 = np.array([1, 2, 3])
set2 = np.array([2, 3, 4])
print(np.union1d(set1, set2)) # [1 2 3 4]
print(np.setdiff1d(set1, set2)) # [1]
NumPy Vectorization
Efficient operations on entire arrays. Skip the loops and embrace speed.
arr = np.array([1, 2, 3])
vectorized = np.vectorize(lambda x: x ** 2)(arr) # [1, 4, 9]
vectorized_add = np.vectorize(lambda x: x + 10)(arr) # [11, 12, 13]
NumPy Boolean Indexing
Filter elements based on conditions. Let your data speak for itself.
arr = np.array([1, 2, 3, 4])
filtered = arr[arr > 2] # [3, 4]
even = arr[arr % 2 == 0] # [2, 4]
NumPy Fancy Indexing
Access specific elements with lists/arrays. Fancy indeed!
arr = np.array([10, 20, 30, 40])
print(arr[[0, 2]]) # [10, 30]
print(arr[[1, 3]]) # [20, 40]
NumPy Random
Generate random numbers. Perfect for simulations and shuffling data.
rand_arr = np.random.rand(3, 3) # Uniform distribution
rand_ints = np.random.randint(0, 10, (2, 2)) # Random integers
NumPy Linear Algebra
Handle linear algebra operations. Your math professor would approve.
matrix = np.array([[1, 2], [3, 4]])
print(np.linalg.det(matrix)) # Determinant
print(np.linalg.eig(matrix)) # Eigenvalues and eigenvectors
NumPy Histogram
Create histograms. Visualize data distribution like a pro.
arr = np.array([1, 1, 2, 3, 3, 3, 4])
hist, bins = np.histogram(arr, bins=3)
print(hist) # [2 1 4]
print(bins) # [1. 2. 3. 4.]
NumPy Interpolation
Interpolate data. Filling gaps has never been easier.
x = [0, 1, 2]
y = [0, 1, 4]
print(np.interp(1.5, x, y)) # 2.5
print(np.interp([0.5, 1.5], x, y)) # [0.5, 2.5]
NumPy Files
Read/write text/binary files. Share or save your work effortlessly.
arr = np.array([[1, 2], [3, 4]])
np.savetxt('data.txt', arr)
loaded = np.loadtxt('data.txt')
np.save('data.npy', arr)
loaded_bin = np.load('data.npy')
NumPy Error Handling
Handle errors gracefully. Because no one likes crashing code.
try:
result = np.sqrt(-1)
except FloatingPointError as e:
print(e) # Domain error
try:
bad_index = arr[100]
except IndexError as e:
print(e) # Index out of bounds
NumPy Date and Time
Work with date/time data. Time travel, but for data.
dates = np.arange('2023-01-01', '2023-01-10', dtype='datetime64[D]')
duration = np.timedelta64(1, 'D')
print(dates + duration) # Increment dates by one day
NumPy Data Visualization
Combine with libraries like Matplotlib. Because pictures speak louder than numbers.
import matplotlib.pyplot as plt
arr = np.array([1, 2, 3, 4])
plt.plot(arr) # Line plot
plt.hist(arr) # Histogram
plt.show()
NumPy Universal Function
Operate element-wise using ufuncs. Fast and functional, just like NumPy.
arr = np.array([1, 2, 3])
print(np.add(arr, 2)) # [3, 4, 5]
print(np.multiply(arr, 2)) # [2, 4, 6]
