Numpy in Python.docx

Array :
Arrays are fundamental part of most programming languages. It is the collection of
elements of a single data type, eg. array of int, array of string.
However, in Python, there is no native array data structure. So, we use Python
lists instead of an array.
Note: If you want to create real arrays in Python, you need to use NumPy's
array data structure. For mathematical problems, NumPy Array is more efficient.
Unlike arrays, a single list can store elements of any data type and does everything
an array does. We can store an integer, a float and a string inside the same list. So,
it is more flexible to work with.
[10, 20, 30, 40, 50 ] is an example of what an array would look like in Python, but
it is actually a list.
import array
A=array.array('i',[4,3,2,5])
print(A)
print(type(A))

import array as arr
A=arr.array('i',[4,3,2,5])
print(A)
from array import *
A=array('i',[4,3,2,5])
print(A)
from array import *
A=array('i',[4,3,2,5])
print(A)
print(type(A))
print(A.typecode)
print(A.buffer_info())
print(A.remove(A[2]))
print(A)
print(A.append(33))
print(A)
print(A.reverse())
print(A)
for i in range(4):
print(A[i])
for i in range(len(A)):
print(A[i])

#copying Array
from array import *
A=array('i',[2,3,4,5])
print(A)
B=array(A.typecode,(i*i for i in
A))
print(B)
#character array
from array import *
A=array('u',['m','o','h','a','n']
)
print(A)
# Accepting the values from user and print Array:
from array import *
arr=array('i',[])
n=int(input("Enter Length of
Array"))
for i in range(n):
x=int(input("Enter the next
value:"))
arr.append(x)
print(arr)

s=int(input("Enter the value for
search:"))
#searchinng element in Array
print(arr.index(s))
What is NumPy?
NumPy is a general-purpose array-processing package.
It provides a high-performance multidimensional array object, and tools for working
with these arrays.
It is the fundamental package for scientific computing with Python.
 In NumPy dimensions are called axes. The number of axes is rank.
To create multi dimension Array in python we use Numpy.
To Install Numpy in command prompt:
Goto command prompt and type : pip3 install numpy
To Install Numpy in Pycharm:
Goto Pycharm Editor
Click on File
Click on settings
Expand Project
Click on Project Interpreter
Click on + Symbol from RHS(Right Hand Side)
Type numpy in search
Choose numpy
Then Click on install numpy package.

Creating Multi dimension Array In different ways like:
Array()
Linespace()
Logspace()
Arange()
Zeros()
Ones()
EX:
Array():
from numpy import *
arr=array([1,2,3,4,5])
print(arr)
print(arr.dtype)
from numpy import *
arr=array([1,2,3,4.7,5])
print(arr)
print(arr.dtype)
from numpy import *
arr=array([1,2,3,4.7,5],int)
print(arr)
print(arr.dtype)
Linespace()

from numpy import *
arr=linspace(0,10,11)#
start,stop,no of parts
print(arr)
Logspace()
from numpy import *
arr=logspace(1,20,5)#
start,stop,no of parts
print(arr)
Arange()
from numpy import *
arr=arange(1,20,2)#
start,stop,step
print(arr)
Zeros()
from numpy import *
arr=zeros(6,int)
print(arr)
Ones()

from numpy import *
arr=ones(6,int)
print(arr)
Creating Multidimension Array:
import numpy as np
#creating array
arr=np.array([[1,2,3],
[4,5,6]])
#printing type of array
print("Array type is:",type(arr))
# Printing array dimensions
(axes)
print("No. of dimensions: ",
arr.ndim)
# Printing shape of array
print("Shape of array: ",
arr.shape)
# Printing size (total number of
elements) of array
print("Size of array: ",
arr.size)
# Printing type of elements in
array

print("Array stores elements of
type: ", arr.dtype)
We can use flatten method to get a copy of array collapsed into one dimension. It
accepts order argument.
import numpy as np
#creating array
arr=np.array([[1,2,3],
[4,5,6]])
flat = arr.flatten()
print(flat)
Basic Operations in Multi dimension Array:
# unary operators in numpy
import numpy as np
arr = np.array([[1, 5, 6],
[4, 7, 2],
[3, 1, 9]])
# maximum element of array
print("Largest element is:",
arr.max())
print("Row-wise maximum
elements:",
arr.max(axis=1))

# minimum element of array
print("Column-wise minimum
elements:",
arr.min(axis=0))
# sum of array elements
print("Sum of all array
elements:",
arr.sum())

Numpy in Python.docx

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