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| 1 | +# NumPy is used to work with arrays. The array object in NumPy is called ndarray. We can create a NumPy ndarray object by using the array() function. |
| 2 | +# 1 Introction to numpy |
| 3 | +import numpy as np |
| 4 | +arr = np.array([1, 2, 3, 4, 5]) |
| 5 | +print(arr) |
| 6 | +print(type(arr)) |
| 7 | +""" |
| 8 | +output: |
| 9 | +[1 2 3 4 5] |
| 10 | +<class 'numpy.ndarray'> |
| 11 | +""" |
| 12 | +_________________________________________________ |
1 | 13 |
|
| 14 | +# Use a tuple to create a NumPy array: |
| 15 | +import numpy as np |
| 16 | +arr = np.array((1, 2, 3, 4, 5)) |
| 17 | +print(arr) #[1 2 3 4 5] |
| 18 | +__________________________________________________ |
| 19 | + |
| 20 | +# Dimensions in Arrays : |
| 21 | +""" |
| 22 | +A dimension in arrays is one level of array depth (nested arrays). |
| 23 | +nested array: are arrays that have arrays as their elements. |
| 24 | +> 0-D Arrays : |
| 25 | +0-D arrays, or Scalars, are the elements in an array. Each value in an array is a 0-D array. |
| 26 | +""" |
| 27 | +import numpy as np |
| 28 | +arr = np.array(42) |
| 29 | +print(arr) # 42 |
| 30 | + |
| 31 | +# 1-D Arrays :An array that has 0-D arrays as its elements is called uni-dimensional or 1-D array. |
| 32 | + |
| 33 | +import numpy as np |
| 34 | +arr = np.array([1, 2, 3, 4, 5]) |
| 35 | +print(arr) #[1 2 3 4 5] |
| 36 | + |
| 37 | +#2-D Arrays |
| 38 | +""" |
| 39 | +An array that has 1-D arrays as its elements is called a 2-D array. |
| 40 | +These are often used to represent matrix or 2nd order tensors. |
| 41 | +NumPy has a whole sub module dedicated towards matrix operations called numpy.mat |
| 42 | +""" |
| 43 | +import numpy as np |
| 44 | +arr = np.array([[1, 2, 3], [4, 5, 6]]) |
| 45 | +print(arr) |
| 46 | +# output [[1 2 3][4 5 6]] |
| 47 | + |
| 48 | +# 3-D arrays |
| 49 | +""" |
| 50 | +An array that has 2-D arrays (matrices) as its elements is called 3-D array. |
| 51 | +These are often used to represent a 3rd order tensor. |
| 52 | +""" |
| 53 | +import numpy as np |
| 54 | +arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]]) |
| 55 | +print(arr) |
| 56 | +""" |
| 57 | +[[[1 2 3] |
| 58 | + [4 5 6]] |
| 59 | + |
| 60 | + [[1 2 3] |
| 61 | + [4 5 6]]] |
| 62 | + """ |
| 63 | + |
| 64 | +# Check Number of Dimensions : NumPy Arrays provides the ndim attribute that returns an integer that tells us how many dimensions the array have. |
| 65 | +import numpy as np |
| 66 | +a = np.array(42) |
| 67 | +b = np.array([1, 2, 3, 4, 5]) |
| 68 | +c = np.array([[1, 2, 3], [4, 5, 6]]) |
| 69 | +d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]]) |
| 70 | +print(a.ndim) |
| 71 | +print(b.ndim) |
| 72 | +print(c.ndim) |
| 73 | +print(d.ndim) |
| 74 | +""" |
| 75 | +Output: |
| 76 | +0 |
| 77 | +1 |
| 78 | +2 |
| 79 | +3 |
| 80 | +""" |
| 81 | +# Higher Dimensional Arrays |
| 82 | +""" |
| 83 | +An array can have any number of dimensions. |
| 84 | +When the array is created, you can define the number of dimensions by using the ndmin argument. |
| 85 | +Example |
| 86 | +Create an array with 5 dimensions and verify that it has 5 dimensions: |
| 87 | +""" |
| 88 | +import numpy as np |
| 89 | +arr = np.array([1, 2, 3, 4], ndmin=5) |
| 90 | +print(arr) |
| 91 | +print('number of dimensions :', arr.ndim) |
| 92 | + |
| 93 | +""" |
| 94 | +[[[[[1 2 3 4]]]]] |
| 95 | +number of dimensions : 5 |
| 96 | +""" |
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