Arrays :
Numpy Array is a grid of values with same type, and is
indexed by a tuple of nonnegative
integers. The number of dimensions of it
,is the rank of the array; the shape of an array depends upon a tuple of integers giving the size of the array
along each dimension.
Note:- Before numpy based programming ,it must be
installed. It can be installed using >pip
install pandas command at command prompt
Any arrays can be single or multidimensional. The number of subscript/index determines dimensions of the array.
An array of one dimension is known as a
one-dimensional array or 1-D array
In above diagram num is an array ,it’s first element is
at 0 index position ,next element is at 1
and so on till last element at n-1 index
position. At 0 index position value is 2 and at 1 index position value is 5
Example
a = np.array([500, 200, 300]) # Create a 1D Array
print(type(a)) # Prints "<class 'numpy.ndarray'>"print(a.shape) # Prints "(3,)" means dimension of array
print(a[0], a[1], a[2]) # Prints "500 200 300"a[0] = 150 # Change an element of the array
print(a)
1 D ARRAY
Creation of 1D array Using functions
import numpy as np
p = np.empty(5) # Create an array of 5 elements with random values print(p)
a1 = np.zeros(5) # Create an array of all zeros float values
print(a1) # Prints "[0. 0. 0. 0. 0.]"
a2 = np.zeros(5,
dtype = np.int) #
Create an array of all zeros int values
print(a2) # Prints "[0. 0. 0. 0. 0.]"
b = np.ones(5) # Create an array of all ones
print(b) # Prints "[1. 1. 1. 1. 1.]"
c = np.full(5, 7) # Create a constant array
print(c) # Prints "[7 7 7 7 7]"
e = np.random.random(5) # Create an array filled with random values
print(e)
1 D ARRAY
Difference between Numpy array and list
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1 D ARRAY
Create 1D from string
import numpy as np
data =np.fromstring('1 2', dtype=int, sep=' ')
print(data)
Note:- in fromstring dtype and sep
argument can be changed.
Create 1D from
buffer
numpy array from range numpy.arange(start, stop, step, dtype)
#Program 1
import numpy as np
x = np.arange(5) #for float value specify dtype = float as argumentprint(x) #print [0 1 2 3 4]
#Program 2
import numpy as np
x = np.arange(10,20,2)print (x) #print [10 12 14 16 18]
1 D ARRAY
Create 1D from array
Copy function is used to create the copy of the existing array.
#Program
import numpy as np
x = np.array([1, 2, 3])
y = x
z = np.copy(x)
x[0] = 10
print(x)
print(y)
print(z)
Note that:, when we modify x, y changes, but
not z:
1 D ARRAY SLICES
Slicing of numpy array elements is just similar to slicing of list elements.
# Program
import numpy as np
data = np.array([5,2,7,3,9])
print (data[:])#print [5 2 7 3 9]
print(data[1:3]) #print [2 7]
print(data[:2]) #print [5 2]print(data[-2:]) #print [3 9]
1 D ARRAY JOINING
Joining of two or more one dimensional array is possible with the help of concatenate()
function of numpy object.
#Program
import numpy as np
a = np.array([1, 2, 3])
b = np.array([5, 6])
c=np.concatenate([a,b,a])
print(c) #print [1 2 3 5 6 1 2 3]
Print all subsets of a 1D Array
If A {1, 3, 5}, then all the possible/proper subsets of A are { }, {1}, {3}, {5}, {1,3}, {3, 5}
#Program
import pandas as pd
import numpy as np
def sub_lists(list1):
# store all the sublists
sublist = [[]]
# first loop
for i in range(len(list1) + 1):
# second loop
for j in range(i + 1, len(list1) + 1):
# slice the subarray
sub = list1[i:j]
sublist.append(sub)
return sublist
x = np.array([1, 2, 3,4]) # driver code
print(sub_lists(x))
OUTPUT
[[], array([1]), array([1, 2]), array([1, 2, 3]), array([1, 2, 3, 4]), array([2]), array([2, 3]), array([2, 3, 4]), array([3]), array([3, 4]), array([4])]
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Basic arithmetic operation on 1D Array |
Aggregate operation on 1D Array |
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#Program import numpy as np x = np.array([1, 2, 3,4]) y = np.array([1, 2, 3,4]) z=x+y print(z) #print [2 4 6 8] z=x-y print(z) #print [0 0 0 0] z=x*y print(z) #print [ 1 4 9 16] z=x/y print(z) #print [1. 1. 1. 1.] z=x+1 print(z) #print [2 3 4 5] |
#Program import numpy as
np x = np.array([1,
2, 3,4]) print(x.sum())
#print 10 print(x.min())
#print 1 print(x.max())
#print 4 print(x.mean())#print
2.5 print(np.median(x))#print
2.5 |
2 D ARRAY
An array of two dimensions/indexes
/subscripts is known as a two- dimensional array or 2-D array.
In above diagram num is an array of two dimensions with 3 rows
and 4 columns. Subscript of rows is 0 to 2 and columns is 0 to 3.
2 D ARRAY
Creation of 2D array
Two-dimension array can be created using array method with list
object with two-dimensional elements.
#Program
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import numpy as np a = np.array([[3, 2, 1],[1, 2, 3]]) print(type(a))
print(a.shape)
print(a[0][1])
a[0][1] = 150 print(a) |
# Create a 2D Array # Prints "<class 'numpy.ndarray'>" # Prints (2, 3) # Prints 2 # Change an element of the array # prints [[ 3 150 1] [ 1 2 3]] |
2 D ARRAY
Creation of 2D array Using functions
import numpy as np
p = np.empty([2,2]) # Create an array of 4 elements with random values
print(p)
a1 = np.zeros([2,2]) # Create 2d array of all zeros float values
print(a1) # Prints [[0. 0.][0. 0.]]
a2 = np.zeros([2,2], dtype = np.int) #Create an array of all zeros int values
print(a2) # Prints [[0 0] [0 0]]
b = np.ones([2,2]) # Create an array of all ones
print(b) # Prints [[1. 1.] [1. 1.]]
c = np.full([2,2], 7) # Create a constant array
print(c) # Prints [[7 7] [7 7]]
e = np.random.random([2,2]) # Create 2d array filled with random values
print(e)
2D ARRAY
Creation of 2D array from 1D array
We can create 2D array from 1d array using reshape() function.
#Program
import numpy as np
A = np.array([1,2,3,4,5,6])
B = np.reshape(A, (2,
3)) print(B)
OUTPUT
[[1 2 3]
[4 5 6]]
2 D ARRAY SLICES
Slicing of numpy 2d array elements
is just similar to slicing of list elements with 2 dimension.
#Program
import numpy as np
A = np.array([[7, 5,
9, 4],
[ 7, 6, 8, 8],
[ 1, 6, 7, 7]])
print(A[:2, :3]) #print elements of 0,1 rows and 0,1,2 columns
print(A[:3, ::2]) #print elements of 0,1,2
rows and alternate column position
print(A[::-1, ::-1]) #print elements in reverse order
print(A[:, 0]) #print all elements of 0 column
print(A[0, :]) #print all elements of 0 rows
print(A[0]) #print all elements of 0 row
Output:
print(A[:2, :3]) #print elements of 0,1 rows and 0,1,2 columns
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7 |
5 |
9 |
4 |
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7 |
6 |
8 |
8 |
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1 |
6 |
7 |
7 |
print(A[:3, ::2]) #print elements of 0,1,2
rows and alternate column position
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7 |
5 |
9 |
4 |
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7 |
6 |
8 |
8 |
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1 |
6 |
7 |
7 |
print(A[::-1, ::-1]) #print
elements in reverse order
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7 |
5 |
9 |
4 |
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7 |
6 |
8 |
8 |
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1 |
6 |
7 |
7 |
print(A[0, :]) #print all elements of
0 rows
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7 |
5 |
9 |
4 |
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7 |
6 |
8 |
8 |
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1 |
6 |
7 |
7 |
print(A[0])
#print all elements of 0 row
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7 |
5 |
9 |
4 |
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7 |
6 |
8 |
8 |
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1 |
6 |
7 |
7 |
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