A scalar is just a single number
A vector is an array of numbers. One-dimensional array of numbers.
numpy.array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0, like=None)
Create an array.In [1]:
import numpy as np
vector1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
vector1
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vector1.ndim
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vector1.shape
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The value is 10.
A matrix is a 2-D array of numbers, so each element is identified by two indices instead of just one.
In [4]:
matrix1 = np.array([
[1, 2],
[3, 4]
])
matrix1
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In [5]:
matrix1.shape
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In [6]:
matrix1.ndim
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In [7]:
matrix2 = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12]
])
matrix2
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In [8]:
matrix2.shape
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In [9]:
matrix2.ndim
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Another way we can create a matrix is by using the matrix function.
class numpy.matrix(data, dtype=None, copy=True)
Returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D array that retains its 2-D nature through operations.
Parameters
dataarray_like or string
If data is a string, it is interpreted as a matrix with commas or spaces separating columns, and semicolons separating rows.
dtypedata-type
Data-type of the output matrix.
copybool
If data is already an ndarray, then this flag determines whether the data is copied (the default), or whether a view is constructed.In [10]:
matrix3 = np.matrix([
[1, 2],
[3, 4]
])
matrix3
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matrix3.shape
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In [12]:
matrix3.ndim
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An array with more than two axes is called tensor. We can create 3-dimensional array, or tensor, by numpy array function.
In [13]:
tensor1 = np.array([
[
[1, 2, 3],
[4, 5, 6]
],
[
[7, 8, 9],
[10, 11, 12]
]
])
tensor1
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In [14]:
tensor1.shape
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tensor1.ndim
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