Python NumPy Cheat Sheet
Quick reference for NumPy.
What is NumPy?
NumPy is an open-source numerical Python library. NumPy contains a multi-dimensional array and matrix data structures. It can be utilised to perform a number of mathematical operations on arrays.
Why use NumPy?
Using NumPy, mathematical and logical operations on arrays can be performed. NumPy also provides high performance.Some of the key features that contribute in the popularity of NumPyare:
- It is a powerful N-dimensional array object
- It is a sophisticated broadcasting functions
- It is a tool for integrating C/C++ and Fortran code
- It is useful for linear algebra, Fourier transform, and random number capabilities.
Numpy Cheat Sheet
IMPORT
import numpy as np
CREATING ARRAY
a = np.array( [1,2,3] )
CREATE AN ARRAY OF ZEROS
np.zeros( (5,5) )
CREATE AN ARRAY OF ONES
np.ones( (3,2) )
CREATE A IDENTITY MATRIX
np.eye(5)
CREATE A CONSTANT ARRAY
np.full( (5,5),7 )
CREATE AN RANDOM ARRAY
np.empty( (3,2) )
CREATE RANDOM VALUES ARRAY
np.random.random( (2,2) )
CREATE AN ARRAY OF EVENLY SPACED VALUES (STEP VALUE)
np.arange(0,10,1)
CREATE AN ARRAY OF EVENLY SPACED VALUES
np.linspace(0,10,3)
CREATE WITH RANDOM SAMPLES FROM A UNIFORM DISTRIBUTION OVER (0, 1)
np.random.rand(5,5)
CREATE ARRAY FROM THE STANDARD NORMAL DISTRIBUTION (CONTAIN NEGATIVE ALSO)
np.random.randn( (2,2) )
RETURN RANDOM INTEGERS
np.random.randint(1,100,10)
CHANGES SHAPE OF ARRAY
arr.reshape(5,5)
RETURN MAX ELEMENT FROM ARRAY
arr.max()
RETURN POSITION OF MAX ELEMENT
arr.argmax()
Built — in methods
Shape is an attribute , return shape of array
arr.shape
RETURN DATA TYPE OF ARRAY
arr.dtype
LENGTH OF ARRAY
len(arr) or arr.size
COPYING AN ARRAY
arr2 = arr.copy()
CONDITIONAL SELECTION
arr[arr>2]
Indexing particular element : Value at 1th ROW & 0th COLUMN
arr_2d[1,0]
INDEXING 1st ROW
arr_2d[1]
INDEXING 2nd COLUMN
arr[:,2]
FIND NUMBER OF ARRAY DIMENSIONS
arr.ndim
BROADCASTING
arr[0:5]=100
2D ARRAY SLICING
arr_2d[:2,1:]
ROW ONLY INDEXING
arr_2d[2,:]
CONDITIONAL SELECTION OR BOOLEAN INDEXING
arr[arr>2]
FLATTEN ARRAY
a.ravel() or arr.flatten()
TRANSPOSE ARRAY
np.transpose(a)
ARRAY STACKING
REVERSE ARRAY
np.flip(arr)
or
a[: : -1]
DOT PRODUCT
arr1.dot(arr2)
APPENDS VALUES TO END OF ARRAY
np.append(arr,values)
INSERTS VALUES INTO ARR BEFORE INDEX 2
np.insert(arr,2,values)
DELETES ROW ON INDEX 3 OF ARR
np.delete(arr,3,axis=0)
DELETES COLUMN ON INDEX 3 OF ARR
np.delete(arr,3,axis=1)
RETURNS CORRELATION COEFFICIENT OF ARRAY
arr.corrcoef()
RETURNS MEAN ALONG SPECIFIC AXIS
np.mean(arr,axis=0)
CONCATENATE
np.concatenate((arr1,arr2),axis=0) #ROW KE NICHE ROW ADD HONGE
np.concatenate((arr1,arr2),axis=1) #COLUMN KE SIDE MEIN COLUMN ADD HONGE
CREATES VIEW OF ARRAY ELEMENT
arr.view()
SPLITS ARR INTO 3 SUB-ARRAYS
np.split(arr,3)
NumPy Operations
ARITHMETIC
arr + arr
arr * arr
arr**3
UNIVERSAL ARRAY FUNCTIONS
np.sqrt(arr)
np.sin(arr)
np.log(arr)
np.exp(arr)
By this, we come to the end of this python numpy tutorial. We have covered all the basics of python numpy, so you can start practicing now. The more you practice, the more you will learn.
