Getting Started¶

Use the following import convention

import numpy as np

Calculation¶

Element wise sum is not possible in Python list. But numpy can do that it is an advantage of numpy array

# add 2 lists 
L1 = [1, 2, 3]
L2 = [4, 5, 6]
print(L1+L2)

# element wise sum using numpy array 
import numpy as np 
A1 = np.array([1, 2, 3])
A2 = np.array([4, 5, 6])
print(A1+A2)

Less Memory¶

import numpy as np
import time
import sys
S = range(1000)
print("Python List: ", sys.getsizeof(5)*len(S))
 
D = np.arange(1000)
print("Numpy Array: ", D.size*D.itemsize)

Faster¶

import time
import sys
 
SIZE = 1000000
 
L1 = range(SIZE)
L2 = range(SIZE)
A1 = np.arange(SIZE)
A2 = np.arange(SIZE)
 
start= time.time()
result=[(x,y) for x,y in zip(L1,L2)]
# time in ms 
print((time.time()-start)*1000)
 
start = time.time()
result = A1+A2
# time in ms 
print((time.time()-start)*1000)

%timeit sum(range(1000))

%timeit np.sum(np.arange(1000))

Creating Arrays¶

Array: Ordered collection of elements of basic data types of given length.
Syntax

np.array(object)

# import numpy 
import numpy as np 

1D Array¶

# Creating 1D array
A = np.array([1, 2, 3])
A 

2D Array¶

# Creating 2D array
B = np.array([[1, 2, 3], [3, 4, 5]])
B 

3D Array¶

# Creating 3D array
C = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
C 

Printing Arrays¶

When you print an array, NumPy displays it in a similar way to nested lists, One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices.

# Printing arrays 
X = np.array([1, 2, 3, 4, 5])
print(X)

If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners

# Prinitng large array 
print(np.arange(10000))

# Set threshold
np.set_printoptions(threshold = 2**32)

# Printing large arrays 
print(np.arange(10000)) 

# type 
print(type(A))

Array with Categorical Entities¶

Numpy can handle different categorical entities.
All elements are coerced into same data type

# create an array with categorical entities. 
X = np.array([12, 13, "n"])
print(X)

# type 
print(type(X))

# Creating 2D array
A2 = np.array([[3, 4, 5], [7, 8, 9]])
print(A2) 

# Creating 3D array
A3 = np.array([[(1, 2, 3), (4, 5, 6)], [(7, 8, 9), (10, 11, 12)]])
print(A3) 

Inspecting array properties¶

Size¶

Returns number of elements in array
Syntax: array.size

A1 = np.array([1, 2, 3,4, 5])
# size 
A1.size

Shape¶

Returns dimensions of array (rows,columns)
Syntax: array.shape

A2 = np.array([[4, 5, 6], [7, 8, 9]])
# shape 
A2.shape 

# get row 
A2.shape[0]

# get column
A2.shape[1]

Data Type¶

Returns type of elements in array
Syntax: array.dtype

A3 = np.linspace(0, 100, 6)
# dtypes 
A3.dtype

Type Conversion¶

Convert array elements to type dtype
Syntax: array.astype(dtype)
- dtype - data type

A4 = np.ones((2,3))
# convert 
A4.astype(np.float16)

Numpy array to Python List¶

Returns the Python list
Syntax: array.tolist()

A5 = np.linspace(0, 100, 20)
# array to list 
A5.tolist() 

Get Help: View documentation¶

Returns a documentation
Syntax: np.info(np.function)
- function - linspace, logspace, eye, ones, zeros etc.

np.info(np.linspace)

Introduction to NumPy