Numpy array have functions for matrices ,linear algebra ,Fourier Transform. Numpy arrays provide 50x more speed than a python list.
Numpy Array
Table of Contents
Features
- It also has functions for matrices, linear algebra, and Fourier Transform.
- Python has lists which do the work of an array, but numpy will provide 50x more speed than a Python list.
- Numpy is written in c,c++ and Python.
- Where fast computations are required c,c++ is used.
- It works efficiently with the latest CPU architecture.
Numpy Installation
pip install numpy- Using this command will ensure the installation of the latest version of numpy that is compatible with your system.
Syntax
pip install numpyImporting Numpy into your code
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import numpy
import numpy as npNumpy datatypes
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import numpy as np
pi = np.float32(3.16)
print(type(pi))
print(pi)
Output
<class 'numpy.float32'>
3.16Create Ndarray with Numpy
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num_arr = np.array(42)
print(type(num_arr))
print(num_arr)
Create 1 Dimensional Array
- This array has a single dimension.
- It is a collection of a single unit.
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num_arr = np.array([1, 2, 3, 4, 5])
print(num_arr)
print(type(num_arr))
print(num_arr.shape)
print(num_arr.ndim)
Output
[1 2 3 4 5]
<class 'numpy.ndarray'>
(5,)
1Create 2 Dimensional Array
- An array that hosts multiple 1 Dimensional arrays is a 2 Dimensional array
- 2 Dimensional arrays store matrices, tensors and vectors.
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num_arr1=np.array([1, 2, 3, 4, 5])
num_arr2=np.array([6, 7, 8, 9, 10])
TwoD = np.array( [ num_arr1 , num_arr2 ] )
print(TwoD)
print(TwoD.shape)
print(TwoD.ndim)
Output
[[ 1 2 3 4 5]
[ 6 7 8 9 10]]
(2, 5)
2Create 3 Dimensional Array
- An array that hosts multiple-dimensional arrays is a 3 3-dimensional array.
- 3D arrays are used to represent 3rd-order Tensor.
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num_arr1=np.array([1, 2, 3])
num_arr2=np.array([4, 5, 6])
num_arr3=np.array([7, 8, 9])
T3D=np.array([
[num_arr1,num_arr2],
[num_arr2,num_arr3],
[num_arr1,num_arr3]
]
)
print(T3D)
print(T3D.shape)
print(T3D.ndim)
Output
[[[1 2 3]
[4 5 6]]
[[4 5 6]
[7 8 9]]
[[1 2 3]
[7 8 9]]]
(3, 2, 3)
3Higher Dimensions
- We can create multiple dimensions in a Numpy Array.
- We either define multidimensional arrays or we can provide them already formatted.
ndim
Furthermore, we will use ndim attribute to determine the number of dimensions.
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np.array([[[[1,2,3], [4,5,6], [7,8,9]], [[10,11,12], [13,14,15], [16,17,18]]], [[[19,20,21], [22,23,24], [25,26,27]], [[28,29,30], [31,32,33], [34,35,36]]]])
print(a.ndim)
a
Output
4
array([[[[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9]],
[[10, 11, 12],
[13, 14, 15],
[16, 17, 18]]],
[[[19, 20, 21],
[22, 23, 24],
[25, 26, 27]],
[[28, 29, 30],
[31, 32, 33],
[34, 35, 36]]]])Shape
Attribute shape determines the length of each dimension inside the whole array since we know the 4 dimensions are created in the array.
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import numpy as np
e = np.array([[[[4, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]]])
e.shape
Output
(2, 2, 3, 3)Concatenate
By using concatenate, we can combine two arrays along a given axis. In the following examples, we will be showcasing two examples where axis value is set to 0 by default, and we have to set any other value manually.
Axis 0
Python
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import numpy as np
arr1 = np.array([1.1, 2.1, 3.1])
arr2 = np.array([1.2, 2.2, 3.2])
arr = np.concatenate((arr1, arr2))
arr
Output
array([1.1, 2.1, 3.1, 1.2, 2.2, 3.2])
Axis 1
Python
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import numpy as np
arr1 = np.array([[1.1, 2.1, 3.1],[1.2, 2.2, 3.2],[1.3, 2.3, 3.3]])
arr2 = np.array([[1,2,3]])
arr = np.concatenate((arr1, arr2.T),axis=1)
arrOutput
array([[1.1, 2.1, 3.1, 1. ],
[1.2, 2.2, 3.2, 2. ],
[1.3, 2.3, 3.3, 3. ]])Stack
The Numpy stack allows us to join multiple arrays. And we can also use an axis with this method similar to concatenate.
Axis 0
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import numpy as np
arr1 = np.array([1, 2, 3,4])
arr2 = np.array([4, 5, 6,7])
arr3 = np.array([7, 8, 9,10])
arr4 = np.array([10, 11, 12,13])
arr4 = np.array([11, 12, 13,14])
arr = np.stack((arr1, arr2 , arr3 , arr4 ),axis=0)
print(arr)Output
[[ 1 2 3 4]
[ 4 5 6 7]
[ 7 8 9 10]
[11 12 13 14]]Axis 1
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import numpy as np
arr1 = np.array([1, 2, 3,4])
arr2 = np.array([4, 5, 6,7])
arr3 = np.array([7, 8, 9,10])
arr4 = np.array([10, 11, 12,13])
arr4 = np.array([11, 12, 13,14])
arr = np.stack((arr1, arr2 , arr3 , arr4 ),axis=1)
print(arr)Output
[[ 1 4 7 11]
[ 2 5 8 12]
[ 3 6 9 13]
[ 4 7 10 14]]Python
Python
Python
import numpy as np
arr1 = np.array([1, 2, 3,4])
arr2 = np.array([4, 5, 6,7])
arr3 = np.array([7, 8, 9,10])
arr4 = np.array([10, 11, 12,13])
arr4 = np.array([11, 12, 13,14])
arr = np.stack((arr1, arr2 , arr3 , arr4 ),axis=-1)
print(arr)Output
[[ 1 4 7 11]
[ 2 5 8 12]
[ 3 6 9 13]
[ 4 7 10 14]]Also, take note of axis 1 and axis-1 output
Depth stack
Depth stack combines multiple arrays depth-wise
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import numpy as np
arr1 = np.array([[1, 2, 3],[1, 2, 3],[1, 2, 3]])
arr2 = np.array([[4, 5, 6],[4, 5, 6],[4, 5, 6]])
arr = np.dstack(( arr1 , arr2 ))
print(arr)
Output
[[[1 4]
[2 5]
[3 6]]
[[1 4]
[2 5]
[3 6]]
[[1 4]
[2 5]
[3 6]]]Horizontal stack
Horizontal stack combines arrays in a horizontal direction along columns. This can be similar to concatenate.
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import numpy as np
arr1 = np.array([[1, 2, 3],[1, 2, 3],[1, 2, 3]])
arr2 = np.array([[4, 5, 6],[4, 5, 6],[4, 5, 6]])
arr = np.hstack(( arr1 , arr2 ))
print(arr)
Output
[[1 2 3 4 5 6]
[1 2 3 4 5 6]
[1 2 3 4 5 6]]
Vertical stack
Horizontal stack combines arrays in a horizontal direction along columns. And it is obviously similar to axis 0.
Python
Python
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import numpy as np
arr1 = np.array([[1, 2, 3],[1, 2, 3],[1, 2, 3]])
arr2 = np.array([[4, 5, 6],[4, 5, 6],[4, 5, 6]])
arr = np.vstack(( arr1 , arr2 ))
print(arr)
Output
[[1 2 3]
[1 2 3]
[1 2 3]
[4 5 6]
[4 5 6]
[4 5 6]]
Vertical stack
Horizontal stack combines arrays in a horizontal direction along columns. And it is obviously similar to axis 0.
Python
Python
Python
import numpy as np
arr1 = np.array([[1, 2, 3],[1, 2, 3],[1, 2, 3]])
arr2 = np.array([[4, 5, 6],[4, 5, 6],[4, 5, 6]])
arr = np.vstack(( arr1 , arr2 ))
print(arr)
Output
[[1 2 3]
[1 2 3]
[1 2 3]
[4 5 6]
[4 5 6]
[4 5 6]]
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