Creating an array
import numpy as np
a = np.array([2,4,6,8,10])
print(a)
[ 2 4 6 8 10]
a = np.array([2,"a",1])
print(a)
['2' 'a' '1']
Creating an array using arange()
import numpy as np
a = np.arange(1,11)
print(a)
[ 1 2 3 4 5 6 7 8 9 10]
a=np.arange(3,20)
print(a)
[ 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]
Create an array of all zeros
import numpy as np
p = np.zeros((2,4))
print(p)
[[0. 0. 0. 0.] [0. 0. 0. 0.]]
Create an array of all ones
q = np.ones((2,3))
print(q)
[[1. 1. 1.] [1. 1. 1.]]
Create a constant array
r = np.full((2,2), 4)
print(r)
[[4 4] [4 4]]
Create an indentity matrix
s=np.eye(4)
print(s)
[[1. 0. 0. 0.] [0. 1. 0. 0.] [0. 0. 1. 0.] [0. 0. 0. 1.]]
Create a random matrix (uniform distribution on (0,1))
import numpy as np
t = np.random.random((3,3))
print(t)
[[0.29857454 0.64971355 0.54179297] [0.40194791 0.27904836 0.89676051] [0.96472405 0.06345763 0.10786494]]
type() and dtype functions
import numpy as np
a = np.arange(1,11)
print(type(a))
<class 'numpy.ndarray'>
print(a.dtype)
int32
t = np.random.random((3,3))
print(t.dtype)
float64
Shape of an array and getting specific elements
a = np.array([[5,6],[7,8]])
print(a)
[[5 6] [7 8]]
a.shape
(2, 2)
a = np.array([[5,6],[7,8]])
print(a)
print(a[0,1])
[[5 6] [7 8]] 6
a = np.arange(1,11)
print(a)
a.shape
[ 1 2 3 4 5 6 7 8 9 10]
(10,)
print(a[8])
9
print(a[0])
1
Reshaping an array
import numpy as np
arr = np.arange(12)
print(arr)
[ 0 1 2 3 4 5 6 7 8 9 10 11]
new_arr=arr.reshape(2,6)
print(new_arr)
[[ 0 1 2 3 4 5] [ 6 7 8 9 10 11]]
new_arr2=arr.reshape(3,4)
print(new_arr2)
[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]]
flatten/transpose/ resize an array
arr=np.arange(1,10).reshape(3,3)
print(arr)
[[1 2 3] [4 5 6] [7 8 9]]
print(arr.flatten())
[1 2 3 4 5 6 7 8 9]
print(arr.transpose())
[[1 4 7] [2 5 8] [3 6 9]]
arr.resize(1,9)
print(arr)
[[1 2 3 4 5 6 7 8 9]]
arr1 = np.arange(1,10).reshape(3,3)
print(arr1)
[[1 2 3] [4 5 6] [7 8 9]]
multiplying by a number
arr2 = 2*arr1
print(arr2)
[[ 2 4 6] [ 8 10 12] [14 16 18]]
Two arrays are stacked horizontally along the $x$ axis..
arr3=np.hstack((arr1, arr2))
print(arr3)
[[ 1 2 3 2 4 6] [ 4 5 6 8 10 12] [ 7 8 9 14 16 18]]
Horizontal stacking using concatenate() function
arr4=np.concatenate((arr1, arr2), axis=1)
print(arr4)
[[ 1 2 3 2 4 6] [ 4 5 6 8 10 12] [ 7 8 9 14 16 18]]
Vertical stacking
arr4=np.concatenate((arr1, arr2), axis=0)
print(arr4)
[[ 1 2 3] [ 4 5 6] [ 7 8 9] [ 2 4 6] [ 8 10 12] [14 16 18]]
Or we can proceed as following
arr5=np.vstack((arr1, arr2))
print(arr5)
[[ 1 2 3] [ 4 5 6] [ 7 8 9] [ 2 4 6] [ 8 10 12] [14 16 18]]
Stack by columns
arr7=np.dstack((arr1, arr2))
print(arr7)
[[[ 1 2] [ 2 4] [ 3 6]] [[ 4 8] [ 5 10] [ 6 12]] [[ 7 14] [ 8 16] [ 9 18]]]
arr1 = np.arange(4,7)
print(arr1)
[4 5 6]
Create column stack
Create 1-D array
arr2 = 2 * arr1
print(arr2)
[ 8 10 12]
Create column stack
arr_col_stack = np.column_stack((arr1,arr2))
print(arr_col_stack)
[[ 4 8] [ 5 10] [ 6 12]]
# Create row stack
arr_row_stack = np.row_stack((arr1,arr2))
print(arr_row_stack)
[[ 4 5 6] [ 8 10 12]]
Perform horizontal splitting
arr=np.arange(1,10).reshape(3,3)
print(arr)
[[1 2 3] [4 5 6] [7 8 9]]
arr_hor_split=np.hsplit(arr, 3)
print(arr_hor_split)
[array([[1],
[4],
[7]]), array([[2],
[5],
[8]]), array([[3],
[6],
[9]])]
Vertical split
arr_ver_split=np.vsplit(arr, 3)
print(arr_ver_split)
[array([[1, 2, 3]]), array([[4, 5, 6]]), array([[7, 8, 9]])]
Split with axis=0
arr_split=np.split(arr,3,axis=0)
print(arr_split)
[array([[1, 2, 3]]), array([[4, 5, 6]]), array([[7, 8, 9]])]
# split with axis=1
np.split(arr,3,axis=1)
[array([[1],
[4],
[7]]),
array([[2],
[5],
[8]]),
array([[3],
[6],
[9]])]
arr=np.arange(1,10).reshape(3,3)
print(arr)
[[1 2 3] [4 5 6] [7 8 9]]
arr.dtype
dtype('int32')
Change datatype of array
arr=arr.astype(float)
Check new data type of array
print(arr.dtype)
float64
Convert NumPy array to Python List
arr=np.arange(1,10)
list1=arr.tolist()
print(list1)
[1, 2, 3, 4, 5, 6, 7, 8, 9]
arr
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
arr = np.arange(10)
print(arr)
[0 1 2 3 4 5 6 7 8 9]
Coefficients from the 3rd to the 6th
print(arr[3:6])
[3 4 5]
Coefficients from the 3rd
print(arr[3:])
[3 4 5 6 7 8 9]
The last 3 coefficients
print(arr[-3:])
[7 8 9]
arr = np.arange(21,41,2)
print("Orignial Array:\n",arr)
Orignial Array: [21 23 25 27 29 31 33 35 37 39]
Boolean Indexing
print("After Boolean Condition:",arr[arr>30])
After Boolean Condition: [31 33 35 37 39]
arr = np.arange(1,21).reshape(5,4)
print("Orignial Array:\n",arr)
Orignial Array: [[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12] [13 14 15 16] [17 18 19 20]]
Selecting 2nd and 3rd row
indices = [1,2]
print("Selected 1st and 2nd Row:\n", arr[indices])
Selected 1st and 2nd Row: [[ 5 6 7 8] [ 9 10 11 12]]
Selecting 3nd and 4th row
indices = [2,3]
print("Selected 3rd and 4th Row:\n", arr[indices])
Selected 3rd and 4th Row: [[ 9 10 11 12] [13 14 15 16]]
Create row and column indices
row = np.array([1, 2])
print(row)
[1 2]
col = np.array([2, 3])
print(col)
[2 3]
print("Selected Sub-Array:", arr[row, col])
Selected Sub-Array: [ 7 12]
arr1 = np.arange(1,5).reshape(2,2)
print(arr1)
[[1 2] [3 4]]
arr2 = np.arange(5,9).reshape(2,2)
print(arr2)
[[5 6] [7 8]]
Sum two matrices
print(arr1+arr2)
[[ 6 8] [10 12]]
Multiply two matrices: $A=(a_{ij})$ and $B=(b_{ij})$, $AB=(a_{ij}b_{ij})$ ($A$ and $B$ are with same dimensions)
print(arr1*arr2)
[[ 5 12] [21 32]]
Add a scaler value
print(arr1 + 3)
[[4 5] [6 7]]
Multiply with a scalar value
print(arr1 * 3)
[[ 3 6] [ 9 12]]
Multiply matrices
a = np.array([[1, 0, 4],
[0, 1, 2],
[0, 0, 2]])
print(a)
[[1 0 4] [0 1 2] [0 0 2]]
a.shape
(3, 3)
b=np.array([[2, 4],
[1, 1],
[3, 2]])
print(b)
[[2 4] [1 1] [3 2]]
b.shape
(3, 2)
$A=(a_{ij})$ is an $n\times p$ matrix and $B=(b_{ij})$ is an $p\times q$ matrix. The $C=(c_{ij})$, where $c_{ij}=\displaystyle\sum_{k=1}^p a_{ik}b_{kj}$, is an $n\times q$ matrix.
c=np.matmul(a,b)
print(c)
[[14 12] [ 7 5] [ 6 4]]