{"cells":[{"cell_type":"markdown","metadata":{"id":"gDkBWfRPUHoW"},"source":["# Recitation 0C - Fundamentals of NumPy\n","\n","## Table of Contents\n","1. What is NumPy?\n","2. Installation\n","3. Basics\n","4. Initialization\n","5. Accessing\n","6. Modifying data\n","7. Pivoting data\n","8. Combining data\n","9. Math operations"]},{"cell_type":"markdown","source":["\n","##**1. What is NumPy?**"],"metadata":{"id":"gHCekKeJfcFu"}},{"cell_type":"markdown","metadata":{"id":"xyMzIie_UHoY"},"source":["NumPy is the fundamental package for scientific computing in Python.\n","It is a Python library that provides and an assortment of operations for fast operations on arrays - from mathematical, logical operations to basic linear algebra, random simulation and much more."]},{"cell_type":"markdown","metadata":{"id":"FwBx7NZuUHoY"},"source":["## **2. Installation**\n","Generally NumPy is pre-installed on CoLab/AWS. You should first check if NumPy is available and its version.\n","\n","To manually install Numpy, please follow the instructions below."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qKkwiJceUHoY","outputId":"0109dc58-ac9b-4f7c-cc8f-b9102694b271"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.26.4)\n"]}],"source":["# # Check the installation of NumPy\n","# !pip show numpy\n","\n","# Install NumPy\n","!pip install numpy\n","\n","# Import NumPy\n","import numpy as np"]},{"cell_type":"markdown","source":["##**3. Basics**"],"metadata":{"id":"Zr3gQ156gVsV"}},{"cell_type":"markdown","source":["![image.png](data:image/png;base64,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)"],"metadata":{"id":"x6JbsxbqgqQu"}},{"cell_type":"code","source":["# Scalar\n","x = np.array(6)\n","print (\"x: \", x)\n","print (\"x ndim: \", x.ndim) # number of dimensions\n","print (\"x shape:\", x.shape) # dimensions\n","print (\"x size: \", x.size) # size of elements\n","print (\"x dtype: \", x.dtype) # data type"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BT1dHYHQgxu2","outputId":"46007948-c517-4675-ec13-0ceae039496f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["x:  6\n","x ndim:  0\n","x shape: ()\n","x size:  1\n","x dtype:  int64\n"]}]},{"cell_type":"code","source":["# Vector\n","x = np.array([1.3 , 2.2 , 1.7])\n","print (\"x: \", x)\n","print (\"x ndim: \", x.ndim)\n","print (\"x shape:\", x.shape)\n","print (\"x size: \", x.size)\n","print (\"x dtype: \", x.dtype) # notice the float datatype"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"07eVaUoUg1KS","outputId":"8730e4d0-8622-42d4-ae5c-d57a44163365"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["x:  [1.3 2.2 1.7]\n","x ndim:  1\n","x shape: (3,)\n","x size:  3\n","x dtype:  float64\n"]}]},{"cell_type":"code","source":["# Matrix\n","x = np.array([[1,2], [3,4]])\n","print (\"x:\\n\", x)\n","print (\"x ndim: \", x.ndim)\n","print (\"x shape:\", x.shape)\n","print (\"x size: \", x.size)\n","print (\"x dtype: \", x.dtype)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x6c02G82g6Hd","outputId":"c73d69b7-202a-4853-8178-20d723526f34"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["x:\n"," [[1 2]\n"," [3 4]]\n","x ndim:  2\n","x shape: (2, 2)\n","x size:  4\n","x dtype:  int64\n"]}]},{"cell_type":"code","source":["# 3-D Tensor\n","x = np.array([[[1,2],[3,4]],[[5,6],[7,8]]])\n","print (\"x:\\n\", x)\n","print (\"x ndim: \", x.ndim)\n","print (\"x shape:\", x.shape)\n","print (\"x size: \", x.size)\n","print (\"x dtype: \", x.dtype)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eKVG6GB1g86D","outputId":"74d04a59-73ad-4c8e-9f5b-0dcfdae2f84f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["x:\n"," [[[1 2]\n","  [3 4]]\n","\n"," [[5 6]\n","  [7 8]]]\n","x ndim:  3\n","x shape: (2, 2, 2)\n","x size:  8\n","x dtype:  int64\n"]}]},{"cell_type":"markdown","source":["NumPy also comes with several functions that allow us to create tensors quickly.\n","\n"],"metadata":{"id":"ayYGLZSXhAQr"}},{"cell_type":"code","source":["# Functions\n","print (\"np.zeros((2,2)):\\n\", np.zeros((2,2)))\n","print (\"np.ones((2,2)):\\n\", np.ones((2,2)))\n","print (\"np.eye((2)):\\n\", np.eye((2))) # identity matrix\n","print (\"np.random.random((2,2)):\\n\", np.random.random((2,2)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ynMvfEnkhC1C","outputId":"ba9108b7-f0e7-4520-9090-113b6a4cec24"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["np.zeros((2,2)):\n"," [[0. 0.]\n"," [0. 0.]]\n","np.ones((2,2)):\n"," [[1. 1.]\n"," [1. 1.]]\n","np.eye((2)):\n"," [[1. 0.]\n"," [0. 1.]]\n","np.random.random((2,2)):\n"," [[0.14462093 0.20848441]\n"," [0.74598177 0.25008565]]\n"]}]},{"cell_type":"markdown","source":["**Dot Product**\n","\n","One of the most common NumPy operations we’ll use in machine learning is matrix multiplication using the dot product. Suppose we wanted to take the dot product of two matrices with shapes [2 X 3] and [3 X 2]. We take the rows of our first matrix (2) and the columns of our second matrix (2) to determine the dot product, giving us an output of [2 X 2]. The only requirement is that the inside dimensions match, in this case the first matrix has 3 columns and the second matrix has 3 rows.\n"],"metadata":{"id":"IYGySRkAhXd3"}},{"cell_type":"markdown","source":["![dot.gif](data:image/gif;base64,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)"],"metadata":{"id":"Jgl_tikth5ex"}},{"cell_type":"code","source":["# Dot product\n","a = np.array([[1,2,3], [4,5,6]], dtype=np.float64) # we can specify dtype\n","b = np.array([[7,8], [9,10], [11, 12]], dtype=np.float64)\n","c = a.dot(b)\n","print (f\"{a.shape} · {b.shape} = {c.shape}\")\n","print (c)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rUMgk4uKhacJ","outputId":"ad2830b9-834c-42b4-dff7-26e4337a1eca"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(2, 3) · (3, 2) = (2, 2)\n","[[ 58.  64.]\n"," [139. 154.]]\n"]}]},{"cell_type":"markdown","source":["**Axis Operation**"],"metadata":{"id":"NO5HfCsciYTj"}},{"cell_type":"markdown","source":["![axis.gif](data:image/gif;base64,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)"],"metadata":{"id":"lhwgKQ8nib-w"}},{"cell_type":"code","source":["# Sum across a dimension\n","x = np.array([[1,2],[3,4]])\n","print (x)\n","print (\"sum all: \", np.sum(x)) # adds all elements\n","print (\"sum axis=0: \", np.sum(x, axis=0)) # sum across rows\n","print (\"sum axis=1: \", np.sum(x, axis=1)) # sum across columns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"70J0QEUKipXc","outputId":"4756d9f9-12f1-442a-84d3-a6a13ccfdea1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[1 2]\n"," [3 4]]\n","sum all:  10\n","sum axis=0:  [4 6]\n","sum axis=1:  [3 7]\n"]}]},{"cell_type":"code","source":["# Min/max\n","x = np.array([[1,2,3], [4,5,6]])\n","print (\"min: \", x.min())\n","print (\"max: \", x.max())\n","print (\"min axis=0: \", x.min(axis=0))\n","print (\"min axis=1: \", x.min(axis=1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BMsCjoxpip9_","outputId":"91234072-18cd-450d-adc9-5270d749857f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["min:  1\n","max:  6\n","min axis=0:  [1 2 3]\n","min axis=1:  [1 4]\n"]}]},{"cell_type":"markdown","metadata":{"id":"Xdy_l8t1UHoZ"},"source":["## **4. Initialization**"]},{"cell_type":"markdown","metadata":{"id":"oUGb74ZlUHoZ"},"source":["### **a. Intrinsic NumPy array creation functions**\n"]},{"cell_type":"markdown","metadata":{"id":"CUwdQNjlUHoZ"},"source":["*italicised text*#### 1D array creation functions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"68584Y5lUHoa","outputId":"11a49de6-41d0-4961-f77c-0ad4f55a0ec0"},"outputs":[{"output_type":"stream","name":"stdout","text":["An array given range is \n"," [0 1 2 3 4 5 6 7 8 9]  with dimensions  (10,) \n","\n","An evenly spaced array given range is \n"," [2.  2.2 2.4 2.6 2.8]  with dimensions  (5,) \n","\n"]}],"source":["# return evenly spaced values within a given interval\n","range_arr = np.arange(10)\n","print(\"An array given range is \\n\", range_arr, \" with dimensions \", range_arr.shape, \"\\n\")\n","\n","# return evenly spaced numbers over a specified interval\n","linspace_arr = np.linspace(2.0, 3.0, num=5, endpoint=False)\n","print(\"An evenly spaced array given range is \\n\", linspace_arr, \" with dimensions \", linspace_arr.shape, \"\\n\")"]},{"cell_type":"markdown","metadata":{"id":"euoa-mHRUHoa"},"source":["#### General ndarray creation functions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ROAwgXBpUHoa","outputId":"58e198ad-2735-47d2-cbb6-3c8a7745cb8f"},"outputs":[{"output_type":"stream","name":"stdout","text":["An empty array is \n"," [[4.9e-324 9.9e-324]\n"," [1.5e-323 2.0e-323]]  with dimensions  (2, 2) \n","\n","A zeros array is \n"," [[0. 0. 0.]\n"," [0. 0. 0.]]  with dimensions  (2, 3) \n","\n","A ones array is \n"," [[1. 1.]\n"," [1. 1.]\n"," [1. 1.]\n"," [1. 1.]]  with dimensions  (4, 2) \n","\n"]}],"source":["# initialize an empty array with size 2 x 2\n","empty_arr = np.empty((2, 2))\n","print(\"An empty array is \\n\", empty_arr, \" with dimensions \", empty_arr.shape, \"\\n\")\n","\n","# initialize an all zero array with size 2 x 3\n","zeros_arr = np.zeros((2, 3))\n","print(\"A zeros array is \\n\", zeros_arr, \" with dimensions \", zeros_arr.shape, \"\\n\")\n","\n","# initialize an all one array with size 4 x 2\n","ones_arr = np.ones((4, 2))\n","print(\"A ones array is \\n\", ones_arr, \" with dimensions \", ones_arr.shape, \"\\n\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bxja6CAeUHoa","outputId":"fceb93e6-7a34-4c10-c902-128cb82b5eb5"},"outputs":[{"output_type":"stream","name":"stdout","text":["A zero like array is \n"," [[0. 0.]\n"," [0. 0.]\n"," [0. 0.]\n"," [0. 0.]]  with dimensions  (4, 2) \n","\n","A ones like array is \n"," [[1. 1. 1.]\n"," [1. 1. 1.]]  with dimensions  (2, 3) \n","\n"]}],"source":["# return an array of zeros with the same shape and type as a given array\n","zeros_like_arr = np.zeros_like(ones_arr)\n","print(\"A zero like array is \\n\", zeros_like_arr, \" with dimensions \", zeros_like_arr.shape, \"\\n\")\n","\n","# return an array of ones with the same shape and type as a given array\n","ones_like_arr = np.ones_like(zeros_arr)\n","print(\"A ones like array is \\n\", ones_like_arr, \" with dimensions \", ones_like_arr.shape, \"\\n\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"keILIJP5UHob","outputId":"1ff6191e-3a15-4db5-b924-6ca068dcee41"},"outputs":[{"output_type":"stream","name":"stdout","text":["A filled array is \n"," [[10 10]\n"," [10 10]]  with dimensions  (2, 2) \n","\n","A full like array is \n"," [[0.1 0.1 0.1]\n"," [0.1 0.1 0.1]]  with dimensions  (2, 3) \n","\n"]}],"source":["# return a new array of given shape and type, filled with fill_value\n","tens_arr = np.full((2,2), 10)\n","print(\"A filled array is \\n\", tens_arr, \" with dimensions \", tens_arr.shape, \"\\n\")\n","\n","# Return a full array with the same shape and type as a given array.\n","full_like_arr = np.full_like(zeros_arr, 0.1, dtype=np.double)\n","print(\"A full like array is \\n\", full_like_arr, \" with dimensions \", full_like_arr.shape, \"\\n\")"]},{"cell_type":"markdown","metadata":{"id":"m1E9zAYlUHob"},"source":["### **b. Create an array from existing data**"]},{"cell_type":"markdown","metadata":{"id":"AEAjp7Q1UHob"},"source":["#### Conversion from other Python structures (i.e. lists and tuples)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jYOkx6YAUHob","outputId":"049753d4-9a9a-4476-ca83-e082730636d9"},"outputs":[{"output_type":"stream","name":"stdout","text":["An array from given list is \n"," [1 2 3 4]  with dimensions  (4,) \n","\n"]}],"source":["# initialize an array with the given list\n","new_list = [1, 2, 3, 4]\n","arr_from_list = np.array(new_list)\n","print(\"An array from given list is \\n\", arr_from_list, \" with dimensions \", arr_from_list.shape, \"\\n\")"]},{"cell_type":"markdown","metadata":{"id":"MAnTiENLUHob"},"source":["#### Reading arrays from disk"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pWmfdfDbUHoc"},"outputs":[],"source":["# initialize an array by loading data from a txt file\n","#txt_arr = np.loadtxt(path_to_txt_file)\n","\n","# initialize an array by loading data from a npy file\n","#loaded_arr = np.load(path_to_npy_file)"]},{"cell_type":"markdown","metadata":{"id":"MVT_ryUvUHoc"},"source":["### **c. Use of special library functions (e.g., random)**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IY8q37hsUHoc","outputId":"37715965-f35e-4ff5-8eb7-3df3ae6b477a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Random integer array [[5 7 2 4]] of shape  (1, 4)\n","Random integer array with seed [[5 0 3 3]] of shape  (1, 4)\n"]}],"source":["a = np.random.randint(0, 10, size = (1,4))\n","print(\"Random integer array\", a, \"of shape \", a.shape)\n","\n","np.random.seed(0)\n","a = np.random.randint(0, 10, size = (1,4))\n","print(\"Random integer array with seed\", a, \"of shape \", a.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hBBaGmNvUHoc","outputId":"fc7a70de-9f5e-4328-ca95-1ab19a9b46f7"},"outputs":[{"output_type":"stream","name":"stdout","text":["A random array from a uniform distribution is \n"," [[0.84725174 0.6235637 ]\n"," [0.38438171 0.29753461]\n"," [0.05671298 0.27265629]]  with dimensions  (3, 2) \n","\n","A random array from a gaussian distribution is \n"," [[ 6.41366795  2.7557607  -3.06488643  1.8673605 ]\n"," [ 1.82307249  5.43253999 -0.19537279  6.5934267 ]]\n","with mu:  3\n","with sigma:  2.5\n","with dimensions  (2, 4)\n"]}],"source":["# create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1)\n","uniform_rand_arr = np.random.rand(3,2)\n","print(\"A random array from a uniform distribution is \\n\", uniform_rand_arr, \" with dimensions \", uniform_rand_arr.shape, \"\\n\")\n","\n","# For random samples from N(\\mu, \\sigma^2), use:\n","# sigma * np.random.randn(...) + mu\n","mu = 3\n","sigma =2.5\n","sample_normal_arr = mu + sigma * np.random.randn(2, 4)\n","print(\"A random array from a gaussian distribution is \\n\", sample_normal_arr)\n","print(\"with mu: \", mu)\n","print(\"with sigma: \", sigma)\n","print(\"with dimensions \", sample_normal_arr.shape)"]},{"cell_type":"markdown","metadata":{"id":"yQibb4OSUHoc"},"source":["## **5. Accessing data**"]},{"cell_type":"markdown","metadata":{"id":"Mp3uhjWwUHoc"},"source":["### **a. Indexing: Accessing values from Numpy Arrays**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z1i8hBBuUHod","outputId":"dcc10f24-0f19-44c6-916f-2acdc4120ee8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.21655035442437187"]},"metadata":{},"execution_count":19}],"source":["n = np.random.rand(4, 5, 6) # see this as 4 batches, each containing 5 rows and 6 columns\n","n\n","\n","n[0, 2, 3] # returns the element located in the fourth column of third row of the first batch."]},{"cell_type":"markdown","metadata":{"id":"lJDWqRiwUHoe"},"source":["### **b. Slicing: Accessing subsections of Numpy Arrays based on Indices**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZtyXvq_aUHoe","outputId":"af2b7517-34e7-40ab-c64f-00f6d1c4dc15"},"outputs":[{"output_type":"stream","name":"stdout","text":["[[0.95715516 0.14035078 0.87008726 0.47360805 0.80091075 0.52047748]\n"," [0.67887953 0.72063265 0.58201979 0.53737323 0.75861562 0.10590761]\n"," [0.47360042 0.18633234 0.73691818 0.21655035 0.13521817 0.32414101]\n"," [0.14967487 0.22232139 0.38648898 0.90259848 0.44994999 0.61306346]\n"," [0.90234858 0.09928035 0.96980907 0.65314004 0.17090959 0.35815217]]\n","[[0.95715516 0.14035078 0.87008726 0.47360805]\n"," [0.67887953 0.72063265 0.58201979 0.53737323]\n"," [0.47360042 0.18633234 0.73691818 0.21655035]]\n","[0.44994999 0.971945   0.14644176 0.38346223]\n"]}],"source":["# slice along a batch\n","print(n[0, :, :]) # same as: n[0]\n","\n","print(n[0, 0:3, 0:4])\n","\n","# slice along multiple batches\n","print(n[:, 3, 4]) # returns the elements in the 4th row and 5th column across all batches"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZGaxieiKUHoe","outputId":"0e0fc773-e860-4770-bdd5-d2099fb85845"},"outputs":[{"output_type":"stream","name":"stdout","text":["[[[0.95715516 0.14035078 0.87008726 0.47360805 0.80091075 0.52047748]\n","  [0.67887953 0.72063265 0.58201979 0.53737323 0.75861562 0.10590761]\n","  [0.47360042 0.18633234 0.73691818 0.21655035 0.13521817 0.32414101]\n","  [0.14967487 0.22232139 0.38648898 0.90259848 0.44994999 0.61306346]\n","  [0.90234858 0.09928035 0.96980907 0.65314004 0.17090959 0.35815217]]\n","\n"," [[0.35944446 0.48089353 0.68866118 0.88047589 0.91823547 0.21682214]\n","  [0.56518887 0.86510256 0.50896896 0.91672295 0.92115761 0.08311249]\n","  [0.27771856 0.0093567  0.84234208 0.64717414 0.84138612 0.26473016]\n","  [0.39782075 0.55282148 0.16494046 0.36980809 0.14644176 0.56961841]\n","  [0.70373728 0.28847644 0.43328806 0.75610669 0.39609828 0.89603839]]]\n","[[[0.72063265 0.53737323 0.10590761]\n","  [0.18633234 0.21655035 0.32414101]\n","  [0.22232139 0.90259848 0.61306346]]\n","\n"," [[0.86510256 0.91672295 0.08311249]\n","  [0.0093567  0.64717414 0.26473016]\n","  [0.55282148 0.36980809 0.56961841]]]\n"]}],"source":["#syntax for slicing at interval is start:stop:step_size\n","\n","print(n[0::2]) # slices from index 0 to the end of the dimension at intervals of 2\n","\n","print(n[0::2, 1:4, 1::2]) # rows (2-5) and columns at an interval of 2, starting from 1 ##howwwwwww"]},{"cell_type":"markdown","metadata":{"id":"pCqRKB4XUHoe"},"source":["## **6. Modifying data**"]},{"cell_type":"markdown","metadata":{"id":"V422LTUFUHoe"},"source":["### **Modify single values**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JBJjZ_oOUHoe","outputId":"0f6143db-6e0c-4361-bfec-65b20758e23c"},"outputs":[{"output_type":"stream","name":"stdout","text":["True\n","False\n"]}],"source":["#When you assign an array or its elements to a new variable,\n","#you have to explicitly numpy.copy the array, otherwise the variable is a view into the original array.\n","\n","n_copy = np.copy(n)\n","\n","# check if values in the two arrays are the same before copy\n","print(n_copy[2, 4, 1] == n[2, 4, 1])\n","\n","n_copy[2, 4, 1] = 0.005\n","\n","# check if values in the two arrays are the same after copy\n","print(n_copy[2, 4, 1] == n[2, 4, 1])"]},{"cell_type":"markdown","metadata":{"id":"kagdeyCgUHoe"},"source":["### **Modifying multiple values**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"47TDWES2UHof","outputId":"54dc2226-7c95-4a9c-f0d1-4283c86b77fa"},"outputs":[{"output_type":"stream","name":"stdout","text":["Are the arrays the same before modification: [ True  True  True  True  True  True]\n","Before modifying values: [0.39782075 0.55282148 0.16494046 0.36980809 0.14644176 0.56961841]\n","After modifying values: [0.5 0.5 0.5 0.5 0.5 0.5]\n","Are the arrays the same after modification: [False False False False False False]\n"]}],"source":["# check if values in the two arrays are the same before copy\n","print(f\"Are the arrays the same before modification: {n_copy[2, 3] == n[2, 3]}\")\n","print(f\"Before modifying values: {n_copy[2, 3]}\")\n","\n","n_copy[2, 3] = 0.5\n","\n","print(f\"After modifying values: {n_copy[2, 3]}\")\n","\n","# check if values in the two arrays are the same after copy\n","print(f\"Are the arrays the same after modification: {n_copy[2, 3] == n[2, 3]}\")"]},{"cell_type":"markdown","metadata":{"id":"qTO1JGLcUHof"},"source":["## **7.Pivoting data**"]},{"cell_type":"markdown","metadata":{"id":"QbAfPyVaUHof"},"source":["### **a. Reshaping Arrays**\n","Array reshaping is an operation that changes the shape of an array whilst maintaining the same data in the array.\n","\n","For instance, reshaping from (3, 4, 5) -> (2, 5, 6) or from (3, 4, 5) -> (6, 10). A reshape operation is valid, so long as the product of the new shape specified matches the product of the old shape."]},{"cell_type":"markdown","metadata":{"id":"1B5m4_ZPUHof"},"source":["#### Reshaping within the same number of dimensions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wIpNf86FUHof","outputId":"5e289d00-1215-4f90-c6ed-ae3d486146d7"},"outputs":[{"output_type":"stream","name":"stdout","text":["Original Shape: (3, 4, 5)\n","[[[0.96896177 0.32099724 0.22126269 0.1412639  0.09725993]\n","  [0.98404224 0.26034093 0.53702252 0.44792617 0.09956909]\n","  [0.35231166 0.46924917 0.84114013 0.90464774 0.03755938]\n","  [0.50831545 0.16684751 0.77905102 0.8649333  0.41139672]]\n","\n"," [[0.13997259 0.03322239 0.98257496 0.37329075 0.42007537]\n","  [0.05058812 0.36549611 0.01662797 0.23074234 0.7649117 ]\n","  [0.94412352 0.74999925 0.33940382 0.48954894 0.33898512]\n","  [0.17949026 0.1709866  0.46345098 0.87457296 0.94411975]]\n","\n"," [[0.60825287 0.59665541 0.78364425 0.5000263  0.05037006]\n","  [0.69909807 0.9923964  0.26726254 0.67909062 0.86428144]\n","  [0.75084425 0.96448977 0.55424244 0.2123905  0.22244322]\n","  [0.21874937 0.56957353 0.45210904 0.97023668 0.68054469]]]\n"]}],"source":["s = np.random.rand(3, 4, 5)\n","print(f\"Original Shape: {s.shape}\")\n","\n","s.size\n","\n","print(s)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AFX0YtP9UHof","outputId":"008e6d2a-89f2-4adf-9097-0bb95acb82a0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Reshaped from s: (2, 6, 5)\n","[[[0.96896177 0.32099724 0.22126269 0.1412639  0.09725993]\n","  [0.98404224 0.26034093 0.53702252 0.44792617 0.09956909]\n","  [0.35231166 0.46924917 0.84114013 0.90464774 0.03755938]\n","  [0.50831545 0.16684751 0.77905102 0.8649333  0.41139672]\n","  [0.13997259 0.03322239 0.98257496 0.37329075 0.42007537]\n","  [0.05058812 0.36549611 0.01662797 0.23074234 0.7649117 ]]\n","\n"," [[0.94412352 0.74999925 0.33940382 0.48954894 0.33898512]\n","  [0.17949026 0.1709866  0.46345098 0.87457296 0.94411975]\n","  [0.60825287 0.59665541 0.78364425 0.5000263  0.05037006]\n","  [0.69909807 0.9923964  0.26726254 0.67909062 0.86428144]\n","  [0.75084425 0.96448977 0.55424244 0.2123905  0.22244322]\n","  [0.21874937 0.56957353 0.45210904 0.97023668 0.68054469]]]\n"]}],"source":["s1 = s.reshape(2, 6, 5)\n","print(f\"Reshaped from s: {s1.shape}\")\n","\n","print(s1)"]},{"cell_type":"markdown","metadata":{"id":"lJAEwd3ZUHof"},"source":["#### Reshaping to a different number of dimensions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sAbf1RYoUHof","outputId":"0ad14916-99b3-4b17-8f58-1ae8942b7006"},"outputs":[{"output_type":"stream","name":"stdout","text":["Original shape: (120,)\n","[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n","  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n","  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n","  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n","  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n","  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n"," 108 109 110 111 112 113 114 115 116 117 118 119]\n"]}],"source":["r = np.arange(120)\n","\n","print(f\"Original shape: {r.shape}\")\n","\n","print(r)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GJm9xba5UHop","outputId":"10a73e81-d93c-4724-a398-f694048ec2fb"},"outputs":[{"output_type":"stream","name":"stdout","text":["Reshaped from r: (3, 4, 10)\n","[[[  0   1   2   3   4   5   6   7   8   9]\n","  [ 10  11  12  13  14  15  16  17  18  19]\n","  [ 20  21  22  23  24  25  26  27  28  29]\n","  [ 30  31  32  33  34  35  36  37  38  39]]\n","\n"," [[ 40  41  42  43  44  45  46  47  48  49]\n","  [ 50  51  52  53  54  55  56  57  58  59]\n","  [ 60  61  62  63  64  65  66  67  68  69]\n","  [ 70  71  72  73  74  75  76  77  78  79]]\n","\n"," [[ 80  81  82  83  84  85  86  87  88  89]\n","  [ 90  91  92  93  94  95  96  97  98  99]\n","  [100 101 102 103 104 105 106 107 108 109]\n","  [110 111 112 113 114 115 116 117 118 119]]]\n"]}],"source":["# (120,) -> (3, 4, 10)\n","r1 = r.reshape(3, 4, 10) # this can also be written as r.reshape((3, 4, 10))\n","print(f\"Reshaped from r: {r1.shape}\")\n","\n","print(r1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YbwQX4jCUHop","outputId":"5f07a4e6-1c9c-46a4-d4af-a67b3f0afb94"},"outputs":[{"output_type":"stream","name":"stdout","text":["Reshaped from r1: (6, 20)\n","[[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n","   18  19]\n"," [ 20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37\n","   38  39]\n"," [ 40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57\n","   58  59]\n"," [ 60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77\n","   78  79]\n"," [ 80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97\n","   98  99]\n"," [100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117\n","  118 119]]\n"]}],"source":["# (3, 4, 10) -> (6, 20)\n","r2 = r1.reshape(6, 20)\n","print(f\"Reshaped from r1: {r2.shape}\")\n","\n","print(r2)"]},{"cell_type":"markdown","metadata":{"id":"5G2h9RNDUHop"},"source":["### **b. Transposing Arrays**\n","The transpose operation reverses the order of an array. It switches the rows to columns and vice versa. In a multi-dimensional array, the transpose operation moves the data from one axis to another in the order specified in the transpose method."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jI_uIdvnUHop","outputId":"dca00383-8ed9-4941-b5e5-95433b121573"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of the original array (5, 10)\n","Original array\n"," [[ 0  1  2  3  4  5  6  7  8  9]\n"," [10 11 12 13 14 15 16 17 18 19]\n"," [20 21 22 23 24 25 26 27 28 29]\n"," [30 31 32 33 34 35 36 37 38 39]\n"," [40 41 42 43 44 45 46 47 48 49]]\n","Shape of the transposed array (10, 5)\n","Transposed array\n"," [[ 0 10 20 30 40]\n"," [ 1 11 21 31 41]\n"," [ 2 12 22 32 42]\n"," [ 3 13 23 33 43]\n"," [ 4 14 24 34 44]\n"," [ 5 15 25 35 45]\n"," [ 6 16 26 36 46]\n"," [ 7 17 27 37 47]\n"," [ 8 18 28 38 48]\n"," [ 9 19 29 39 49]]\n"]},{"output_type":"execute_result","data":{"text/plain":["array([[ 0, 10, 20, 30, 40],\n","       [ 1, 11, 21, 31, 41],\n","       [ 2, 12, 22, 32, 42],\n","       [ 3, 13, 23, 33, 43],\n","       [ 4, 14, 24, 34, 44],\n","       [ 5, 15, 25, 35, 45],\n","       [ 6, 16, 26, 36, 46],\n","       [ 7, 17, 27, 37, 47],\n","       [ 8, 18, 28, 38, 48],\n","       [ 9, 19, 29, 39, 49]])"]},"metadata":{},"execution_count":29}],"source":["x=np.arange(50).reshape((5,10))\n","print(\"Shape of the original array\", x.shape)\n","print(\"Original array\\n\", x)\n","\n","x1=np.transpose(x)\n","print(\"Shape of the transposed array\", x1.shape)\n","print(\"Transposed array\\n\", x1)\n","\n","x.T"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TOoA-hIyUHop","outputId":"b7d01cc4-402d-4fa5-b6c9-16502f9b8024"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of the Original array (3, 4, 5)\n","Original array\n"," [[[ 0  1  2  3  4]\n","  [ 5  6  7  8  9]\n","  [10 11 12 13 14]\n","  [15 16 17 18 19]]\n","\n"," [[20 21 22 23 24]\n","  [25 26 27 28 29]\n","  [30 31 32 33 34]\n","  [35 36 37 38 39]]\n","\n"," [[40 41 42 43 44]\n","  [45 46 47 48 49]\n","  [50 51 52 53 54]\n","  [55 56 57 58 59]]]\n"]}],"source":["w = np.arange(60).reshape((3, 4, 5))\n","w\n","\n","print(\"Shape of the Original array\", w.shape)\n","print(\"Original array\\n\", w)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"pW7nNVwMUHoq"},"source":["#### Transpose without specifying axes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PQ-ktVeLUHoq","outputId":"fee2537f-743b-4065-baec-d3649d25972c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of the transposed array (5, 4, 3)\n","Transposed array\n"," [[[ 0 20 40]\n","  [ 5 25 45]\n","  [10 30 50]\n","  [15 35 55]]\n","\n"," [[ 1 21 41]\n","  [ 6 26 46]\n","  [11 31 51]\n","  [16 36 56]]\n","\n"," [[ 2 22 42]\n","  [ 7 27 47]\n","  [12 32 52]\n","  [17 37 57]]\n","\n"," [[ 3 23 43]\n","  [ 8 28 48]\n","  [13 33 53]\n","  [18 38 58]]\n","\n"," [[ 4 24 44]\n","  [ 9 29 49]\n","  [14 34 54]\n","  [19 39 59]]]\n"]}],"source":["# Original Shape: 3,4,5\n","\n","w1 = np.transpose(w)  # 0->2, 1->1, 2->0   (0,1,2)->(2,1,0)\n","print(\"Shape of the transposed array\", w1.shape)\n","print(\"Transposed array\\n\", w1)"]},{"cell_type":"markdown","metadata":{"id":"eZxSowAjUHoq"},"source":["#### Transpose along specified axes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AipHmffuUHoq","outputId":"739d7fff-0a33-483d-b540-30e8942f70f0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of the transposed array (3, 5, 4)\n","Transposed array\n"," [[[ 0  5 10 15]\n","  [ 1  6 11 16]\n","  [ 2  7 12 17]\n","  [ 3  8 13 18]\n","  [ 4  9 14 19]]\n","\n"," [[20 25 30 35]\n","  [21 26 31 36]\n","  [22 27 32 37]\n","  [23 28 33 38]\n","  [24 29 34 39]]\n","\n"," [[40 45 50 55]\n","  [41 46 51 56]\n","  [42 47 52 57]\n","  [43 48 53 58]\n","  [44 49 54 59]]]\n","Shape of the transposed array (4, 5, 3)\n","Transposed array\n"," [[[ 0 20 40]\n","  [ 1 21 41]\n","  [ 2 22 42]\n","  [ 3 23 43]\n","  [ 4 24 44]]\n","\n"," [[ 5 25 45]\n","  [ 6 26 46]\n","  [ 7 27 47]\n","  [ 8 28 48]\n","  [ 9 29 49]]\n","\n"," [[10 30 50]\n","  [11 31 51]\n","  [12 32 52]\n","  [13 33 53]\n","  [14 34 54]]\n","\n"," [[15 35 55]\n","  [16 36 56]\n","  [17 37 57]\n","  [18 38 58]\n","  [19 39 59]]]\n"]}],"source":["w\n","\n","# Original Shape: 3,4,5\n","\n","w2 = np.transpose(w, axes=(0, 2, 1)) # 0, 1, 2\n","print(\"Shape of the transposed array\", w2.shape)\n","print(\"Transposed array\\n\", w2)\n","\n","# Original Shape: 3,4,5\n","\n","w3 = np.transpose(w, axes=(1, 2, 0))\n","print(\"Shape of the transposed array\", w3.shape)\n","print(\"Transposed array\\n\", w3)"]},{"cell_type":"markdown","metadata":{"id":"1CjVjxwlUHoq"},"source":["### **c. Flattening Arrays**\n","The flatten operation in Numpy collapses arrays of multiple dimensions into one dimension.\n","\n","\n","Different orders can be specified when flattening Numpy Arrays. See examples below."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BQoVOUpsUHoq","outputId":"823bb136-f4c5-4e02-84c9-c2923f661449"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[8.52955659e-02, 5.64183327e-02, 4.87837704e-01, 8.81004562e-01,\n","        9.76404387e-01, 6.17657916e-01],\n","       [5.42498775e-01, 8.54613580e-01, 7.43834545e-01, 4.78596326e-01,\n","        6.77081574e-01, 6.07045061e-01],\n","       [7.14696936e-01, 4.69497183e-01, 4.56014623e-01, 9.06418087e-01,\n","        1.37220420e-01, 2.29219323e-01],\n","       [8.81585399e-01, 9.04424976e-01, 6.45784599e-01, 3.24682972e-01,\n","        5.19711194e-01, 5.53568650e-05],\n","       [3.11860221e-01, 4.25451538e-01, 8.85337660e-01, 6.79879456e-01,\n","        4.56129772e-01, 4.83408617e-01]])"]},"metadata":{},"execution_count":33}],"source":["k = np.random.rand(5, 6)\n","\n","k"]},{"cell_type":"markdown","metadata":{"id":"4_2FOw7mUHoq"},"source":["#### Flattening Arrays along the row (default order)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gt9Uh1DlUHor","outputId":"ed1f32bc-4879-49ba-c5a7-9e1b696ba613"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(30,)"]},"metadata":{},"execution_count":34}],"source":["k1 = k.flatten() # all rows are stacked on each other into a 1D array.\n","k1\n","\n","k1.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7A5CQ808eRtc","outputId":"08c8e6a6-525e-46be-8e6d-7f87bb37c135"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([8.52955659e-02, 5.64183327e-02, 4.87837704e-01, 8.81004562e-01,\n","       9.76404387e-01, 6.17657916e-01, 5.42498775e-01, 8.54613580e-01,\n","       7.43834545e-01, 4.78596326e-01, 6.77081574e-01, 6.07045061e-01,\n","       7.14696936e-01, 4.69497183e-01, 4.56014623e-01, 9.06418087e-01,\n","       1.37220420e-01, 2.29219323e-01, 8.81585399e-01, 9.04424976e-01,\n","       6.45784599e-01, 3.24682972e-01, 5.19711194e-01, 5.53568650e-05,\n","       3.11860221e-01, 4.25451538e-01, 8.85337660e-01, 6.79879456e-01,\n","       4.56129772e-01, 4.83408617e-01])"]},"metadata":{},"execution_count":35}],"source":["k1"]},{"cell_type":"markdown","metadata":{"id":"rVQEPkUeUHor"},"source":["#### Flattening Arrays along the column"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pMEmQW3cUHor","outputId":"e26fea5b-a6b0-440f-9ebe-63319fb2c229"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(30,)"]},"metadata":{},"execution_count":36}],"source":["k2= k.flatten('F') # forms a 1D array where elements in a column are listed before moving to the next column.\n","k2\n","\n","k2.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c-iWlmU2eT6k","outputId":"d8755636-bf22-4ebc-ac29-a740b8f4827a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([8.52955659e-02, 5.42498775e-01, 7.14696936e-01, 8.81585399e-01,\n","       3.11860221e-01, 5.64183327e-02, 8.54613580e-01, 4.69497183e-01,\n","       9.04424976e-01, 4.25451538e-01, 4.87837704e-01, 7.43834545e-01,\n","       4.56014623e-01, 6.45784599e-01, 8.85337660e-01, 8.81004562e-01,\n","       4.78596326e-01, 9.06418087e-01, 3.24682972e-01, 6.79879456e-01,\n","       9.76404387e-01, 6.77081574e-01, 1.37220420e-01, 5.19711194e-01,\n","       4.56129772e-01, 6.17657916e-01, 6.07045061e-01, 2.29219323e-01,\n","       5.53568650e-05, 4.83408617e-01])"]},"metadata":{},"execution_count":37}],"source":["k2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3AVH56rzeWq1","outputId":"b11653c1-343a-4fee-d00f-d69bcd461dcf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[8.52955659e-02, 5.64183327e-02, 4.87837704e-01, 8.81004562e-01,\n","        9.76404387e-01, 6.17657916e-01],\n","       [5.42498775e-01, 8.54613580e-01, 7.43834545e-01, 4.78596326e-01,\n","        6.77081574e-01, 6.07045061e-01],\n","       [7.14696936e-01, 4.69497183e-01, 4.56014623e-01, 9.06418087e-01,\n","        1.37220420e-01, 2.29219323e-01],\n","       [8.81585399e-01, 9.04424976e-01, 6.45784599e-01, 3.24682972e-01,\n","        5.19711194e-01, 5.53568650e-05],\n","       [3.11860221e-01, 4.25451538e-01, 8.85337660e-01, 6.79879456e-01,\n","        4.56129772e-01, 4.83408617e-01]])"]},"metadata":{},"execution_count":38}],"source":["k"]},{"cell_type":"markdown","metadata":{"id":"8czJZ2OmUHor"},"source":["#### Flattening Arrays using Numpy Reshape\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fvBbG7XaUHor","outputId":"62c1659d-88e6-44f2-fc60-01cb9e7cffa7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(30,)"]},"metadata":{},"execution_count":39}],"source":["k4= k.reshape(-1)\n","k4\n","\n","k4.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T7LHY5wbedB9","outputId":"1362634b-781f-4865-ff2e-0ef07523d7ef"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([8.52955659e-02, 5.64183327e-02, 4.87837704e-01, 8.81004562e-01,\n","       9.76404387e-01, 6.17657916e-01, 5.42498775e-01, 8.54613580e-01,\n","       7.43834545e-01, 4.78596326e-01, 6.77081574e-01, 6.07045061e-01,\n","       7.14696936e-01, 4.69497183e-01, 4.56014623e-01, 9.06418087e-01,\n","       1.37220420e-01, 2.29219323e-01, 8.81585399e-01, 9.04424976e-01,\n","       6.45784599e-01, 3.24682972e-01, 5.19711194e-01, 5.53568650e-05,\n","       3.11860221e-01, 4.25451538e-01, 8.85337660e-01, 6.79879456e-01,\n","       4.56129772e-01, 4.83408617e-01])"]},"metadata":{},"execution_count":40}],"source":["k4"]},{"cell_type":"markdown","metadata":{"id":"33LonepSUHor"},"source":["### **d. Squeezing & Expanding Arrays**"]},{"cell_type":"markdown","metadata":{"id":"bdq97bdKUHos"},"source":["#### Squeezing Numpy Arrays\n","\n","The squeeze operation allows reduction of numpy arrays axes by dropping a specified axis, so long as it is of **unit length**. The product of the shape (overall size of the array) remains the same."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Gt4yJQejUHos","outputId":"abb57c03-4ac5-47c4-afd0-ac1e7d647bd1"},"outputs":[{"output_type":"stream","name":"stdout","text":["Original Array: \n","[[[0.78873943 0.22944183 0.8802976  0.31369239 0.95745086]]\n","\n"," [[0.47175157 0.71158382 0.15369431 0.73044218 0.64626444]]\n","\n"," [[0.21488074 0.18645822 0.80758027 0.74707947 0.67484735]]\n","\n"," [[0.27689375 0.17490887 0.70447426 0.4631502  0.84042853]]]\n","\n","Shape of Original Array: (4, 1, 5)\n","Squeezed Array: \n"," [[0.78873943 0.22944183 0.8802976  0.31369239 0.95745086]\n"," [0.47175157 0.71158382 0.15369431 0.73044218 0.64626444]\n"," [0.21488074 0.18645822 0.80758027 0.74707947 0.67484735]\n"," [0.27689375 0.17490887 0.70447426 0.4631502  0.84042853]]\n","\n","Shape of Squeezed Array: (4, 5)\n"]}],"source":["sq = np.random.rand(4, 1, 5)\n","print(f\"Original Array: \\n{sq}\\n\")\n","print(f\"Shape of Original Array: {sq.shape}\")\n","\n","# transforms from (4, 1, 5) -> (4, 5)\n","sq1 = np.squeeze(sq)\n","print(f\"Squeezed Array: \\n {sq1}\\n\")\n","print(f\"Shape of Squeezed Array: {sq1.shape}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dZOR7s60UHos","outputId":"6c082c55-5d16-40d1-a06d-ef02869e9b0a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Specified axis Squeezed Array: \n"," [[0.78873943 0.22944183 0.8802976  0.31369239 0.95745086]\n"," [0.47175157 0.71158382 0.15369431 0.73044218 0.64626444]\n"," [0.21488074 0.18645822 0.80758027 0.74707947 0.67484735]\n"," [0.27689375 0.17490887 0.70447426 0.4631502  0.84042853]]\n","\n","Shape of Specified axis Squeezed Array: (4, 5)\n"]}],"source":["# Squeezing specified axes\n","\n","q2 = np.squeeze(sq, axis=1)\n","print(f\"Specified axis Squeezed Array: \\n {q2}\\n\")\n","print(f\"Shape of Specified axis Squeezed Array: {q2.shape}\")"]},{"cell_type":"markdown","metadata":{"id":"l-zzkLDuUHos"},"source":["#### Unsqueezing (Expanding) Numpy Arrays\n","This is the direct opposite of squeezing. A new unit axis is inserted in specified position. Multiple unit axes can be inserted by using a tuple on the axis attribute of the `expand_dims` method."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3Jthh31sUHos","outputId":"0ae21a94-1ec6-4fb6-ae09-f9e4089e654d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Original Array: \n"," [[0.20486576 0.16495887 0.12483306 0.72208066 0.03045296]\n"," [0.74699424 0.09259617 0.21745049 0.74925442 0.73169376]\n"," [0.04561464 0.20915703 0.28691504 0.67726331 0.06303829]\n"," [0.55564924 0.00924007 0.8330381  0.98432943 0.70349478]]\n","\n","Shape of Original Array: (4, 5)\n","Expanded Array: \n"," [[[0.20486576 0.16495887 0.12483306 0.72208066 0.03045296]]\n","\n"," [[0.74699424 0.09259617 0.21745049 0.74925442 0.73169376]]\n","\n"," [[0.04561464 0.20915703 0.28691504 0.67726331 0.06303829]]\n","\n"," [[0.55564924 0.00924007 0.8330381  0.98432943 0.70349478]]]\n","\n","Shape of Expanded Array: (4, 1, 5)\n","Multi-axes Expanded Array: \n"," [[[[0.20486576 0.16495887 0.12483306 0.72208066 0.03045296]]\n","\n","  [[0.74699424 0.09259617 0.21745049 0.74925442 0.73169376]]\n","\n","  [[0.04561464 0.20915703 0.28691504 0.67726331 0.06303829]]\n","\n","  [[0.55564924 0.00924007 0.8330381  0.98432943 0.70349478]]]]\n","\n","Shape of Multi-axes Expanded Array: (1, 4, 1, 5)\n"]}],"source":["y = np.random.rand(4, 5)\n","print(f\"Original Array: \\n {y}\\n\")\n","print(f\"Shape of Original Array: {y.shape}\")\n","\n","# transforms from (4, 5) -> (4, 1, 5)\n","y1 = np.expand_dims(y, axis=1)\n","print(f\"Expanded Array: \\n {y1}\\n\")\n","print(f\"Shape of Expanded Array: {y1.shape}\")\n","\n","# transforms from (4, 5) -> (1, 4, 1, 5)\n","y2 = np.expand_dims(y, axis=(0, 2))\n","print(f\"Multi-axes Expanded Array: \\n {y2}\\n\")\n","print(f\"Shape of Multi-axes Expanded Array: {y2.shape}\")"]},{"cell_type":"markdown","metadata":{"id":"7hr7lrN4UHos"},"source":["## **8. Combining data**"]},{"cell_type":"markdown","metadata":{"id":"L37f1yjDUHos"},"source":["### **a. Concatenation**\n","\n","A concatenation operation joins a sequence of arrays along an *existing* axis. All arrays must either have the same shape (except in the concatenating dimension) or be empty."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ba8rV1KZUHot","outputId":"e44cecf4-1b37-48d0-b467-cbd5d4e53ef1"},"outputs":[{"output_type":"stream","name":"stdout","text":["Array 1 is \n"," [[[0 1]\n","  [0 2]]\n","\n"," [[1 0]\n","  [1 2]]\n","\n"," [[0 0]\n","  [0 2]]]  with dimensions  (3, 2, 2) \n","\n","Array 2 is \n"," [[[1 3]\n","  [0 0]]\n","\n"," [[0 0]\n","  [1 3]]\n","\n"," [[2 2]\n","  [1 0]]]  with dimensions  (3, 2, 2) \n","\n","Concatenated array 1 is \n"," [[[0 1]\n","  [0 2]]\n","\n"," [[1 0]\n","  [1 2]]\n","\n"," [[0 0]\n","  [0 2]]\n","\n"," [[1 3]\n","  [0 0]]\n","\n"," [[0 0]\n","  [1 3]]\n","\n"," [[2 2]\n","  [1 0]]] \n","\n"," and the dimensions of the concatenated array 1 are: \n"," (6, 2, 2)\n","Concatenated array 2 is \n"," [[[0 1]\n","  [0 2]\n","  [1 3]\n","  [0 0]]\n","\n"," [[1 0]\n","  [1 2]\n","  [0 0]\n","  [1 3]]\n","\n"," [[0 0]\n","  [0 2]\n","  [2 2]\n","  [1 0]]] \n","\n"," and the dimensions of the concatenated array 2 are: \n"," (3, 4, 2)\n","Concatenated array 3 is \n"," [[[0 1 1 3]\n","  [0 2 0 0]]\n","\n"," [[1 0 0 0]\n","  [1 2 1 3]]\n","\n"," [[0 0 2 2]\n","  [0 2 1 0]]] \n","\n"," and the dimensions of the concatenated array 3 are: \n"," (3, 2, 4)\n"]}],"source":["# Concatenating Numpy Arrays\n","\n","array1 = np.random.randint(3, size = (3, 2, 2))\n","print(\"Array 1 is \\n\", array1, \" with dimensions \", array1.shape, \"\\n\")\n","\n","array2 = np.random.randint(4, size = (3, 2, 2))\n","print(\"Array 2 is \\n\", array2, \" with dimensions \", array2.shape, \"\\n\")\n","\n","concatenated_array1 = np.concatenate((array1, array2), axis = 0)\n","print(\"Concatenated array 1 is \\n\", concatenated_array1, \"\\n\\n\", \"and the dimensions of the concatenated array 1 are: \\n\", concatenated_array1.shape)\n","\n","concatenated_array2 = np.concatenate((array1, array2), axis = 1)\n","print(\"Concatenated array 2 is \\n\", concatenated_array2, \"\\n\\n\", \"and the dimensions of the concatenated array 2 are: \\n\", concatenated_array2.shape)\n","\n","concatenated_array3 = np.concatenate((array1, array2), axis = 2)\n","print(\"Concatenated array 3 is \\n\", concatenated_array3, \"\\n\\n\", \"and the dimensions of the concatenated array 3 are: \\n\", concatenated_array3.shape)\n"]},{"cell_type":"markdown","metadata":{"id":"d9QRbjBpUHot"},"source":["### **b. Stacking**\n","\n","The stack operation joins a sequence of arrays along a *new* axis. The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis = 0 it will be the first dimension and if axis = -1 it will be the last dimension. All arrays need to be of the same size.  The stacked array has one more dimension than the input arrays."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y_AnhX6qUHot","outputId":"65ae2cc4-3207-4fdf-db63-e10216ab5762"},"outputs":[{"output_type":"stream","name":"stdout","text":["Array 1 is \n"," [1 2 3]  with dimensions  (3,) \n","\n","Array 2 is \n"," [4 5 6]  with dimensions  (3,) \n","\n","Stacked array 1 is \n"," [[1 2 3]\n"," [4 5 6]]  with dimensions  (2, 3)\n","Stacked array 2 is \n"," [[1 4]\n"," [2 5]\n"," [3 6]]  with dimensions  (3, 2)\n","Stacked array 3 is \n"," [[1 4]\n"," [2 5]\n"," [3 6]]  with dimensions  (3, 2)\n"]}],"source":["# Stacking 1-d Arrays\n","\n","array1 = np.array([1, 2, 3])\n","print(\"Array 1 is \\n\", array1, \" with dimensions \", array1.shape, \"\\n\")\n","\n","array2 = np.array([4, 5, 6])\n","print(\"Array 2 is \\n\", array2, \" with dimensions \", array2.shape, \"\\n\")\n","\n","\n","stacked_array1 = np.stack((array1, array2), axis = 0)\n","print(\"Stacked array 1 is \\n\", stacked_array1, \" with dimensions \", stacked_array1.shape)\n","\n","stacked_array2 = np.stack((array1, array2), axis = 1)\n","print(\"Stacked array 2 is \\n\", stacked_array2, \" with dimensions \", stacked_array2.shape)\n","\n","stacked_array3 = np.stack((array1, array2), axis = -1)\n","print(\"Stacked array 3 is \\n\", stacked_array3, \" with dimensions \", stacked_array3.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"j4xwzB_GUHot","outputId":"93e91773-11e2-42ac-a764-e4a84cb0a22b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Array 1 has dimensions  (3, 4, 5) \n","\n","Array 2 has dimensions  (3, 4, 5) \n","\n","Stacked array 1 has dimensions (2, 3, 4, 5) \n","\n","Stacked array 2 has dimensions (3, 2, 4, 5) \n","\n","Stacked array 3 has dimensions (3, 4, 2, 5) \n","\n","Stacked array 4 has dimensions (3, 4, 5, 2) \n","\n"]}],"source":["\n","# Stacking Numpy 3-d Arrays\n","\n","array1 = np.random.randint(3, size = (3, 4, 5))\n","print(\"Array 1 has dimensions \", array1.shape, \"\\n\")\n","\n","array2 = np.random.randint(4, size = (3, 4, 5))\n","print(\"Array 2 has dimensions \", array2.shape, \"\\n\")\n","\n","stacked_array1 = np.stack((array1, array2), axis = 0)\n","print(\"Stacked array 1 has dimensions\", stacked_array1.shape, \"\\n\")\n","\n","stacked_array2 = np.stack((array1, array2), axis = 1)\n","print(\"Stacked array 2 has dimensions\", stacked_array2.shape, \"\\n\")\n","\n","stacked_array3 = np.stack((array1, array2), axis = 2)\n","print(\"Stacked array 3 has dimensions\", stacked_array3.shape, \"\\n\")\n","\n","stacked_array4 = np.stack((array1, array2), axis = -1)\n","print(\"Stacked array 4 has dimensions\", stacked_array4.shape, \"\\n\")"]},{"cell_type":"markdown","metadata":{"id":"chG_xZQzUHou"},"source":["## **9. Math operations**"]},{"cell_type":"markdown","metadata":{"id":"DCOozLreUHou"},"source":["In this section we will cover some commonly used mathematical operations\n","1. Broadcasting\n","1. Point-wise/element-wise operations\n","1. Reduction operations\n","1. Comparison operations\n","1. Vector/Matrix operations\n","1. Tensordot"]},{"cell_type":"markdown","metadata":{"id":"y7LOmf3xUHou"},"source":["### **a. Broadcasting**\n","\n","What happens when we try to do operations with tensors with seemingly incompatible shapes? Their dimensions aren’t compatible as is but how does NumPy still gives us the right result? This is where broadcasting comes in. The scalar is broadcast across the vector so that they have compatible shapes.\n","\n","![broadcast.png](data:image/png;base64,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)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mjUBxfsfUHou","outputId":"88b9793d-d0c6-442e-91cb-e3d9df6db2a7"},"outputs":[{"output_type":"stream","name":"stdout","text":["A row array: \n"," [[0.43040244 0.51001685 0.53617749]]  with dimensions  (1, 3) \n","\n","A column array: \n"," [[0.68139251]\n"," [0.2775961 ]\n"," [0.12886057]\n"," [0.39267568]]  with dimensions  (4, 1) \n","\n","row array + column array = \n","[[1.11179495 1.19140936 1.21757001]\n"," [0.70799854 0.78761295 0.81377359]\n"," [0.559263   0.63887742 0.66503806]\n"," [0.82307812 0.90269253 0.92885317]]  with dimensions  (4, 3) \n","\n","row array * column array = \n","[[0.293273   0.34752166 0.36534733]\n"," [0.11947804 0.14157869 0.14884078]\n"," [0.0554619  0.06572106 0.06909214]\n"," [0.16900857 0.20027121 0.21054386]]  with dimensions  (4, 3) \n","\n"]}],"source":["# Broadcasting b/w arrays of different dimensions\n","# Note: When broadting two multi-dimensional tensors, match their corresponding dimensions beginning from the last dimension.\n","# All dimensions should either match or one of the arrays should have length 1 in that specific dimension\n","\n","row_arr = np.random.rand(1,3)\n","print(\"A row array: \\n\", row_arr, \" with dimensions \", row_arr.shape, \"\\n\")\n","col_arr = np.random.rand(4,1)\n","print(\"A column array: \\n\", col_arr, \" with dimensions \", col_arr.shape, \"\\n\")\n","\n","add_arr = row_arr + col_arr\n","print(\"row array + column array = \")\n","print(add_arr,\" with dimensions \", add_arr.shape, \"\\n\")\n","mul_arr = row_arr * col_arr\n","print(\"row array * column array = \")\n","print(mul_arr,\" with dimensions \", mul_arr.shape, \"\\n\")"]},{"cell_type":"markdown","metadata":{"id":"zMPkInHjUHou"},"source":["### **b. Element-wise operations**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"okX63kwLUHou","outputId":"2a72bf03-997b-44ec-8de0-6924c6d3f16d"},"outputs":[{"output_type":"stream","name":"stdout","text":["A random array1 : \n"," [[0.95640572 0.18713089 0.90398395]\n"," [0.54380595 0.45691142 0.88204141]]  with dimensions  (2, 3) \n","\n","A random array2 : \n"," [[0.45860396 0.72416764 0.39902532]\n"," [0.90404439 0.69002502 0.69962205]]  with dimensions  (2, 3) \n","\n","random array1 + 5.0 =\n","[[5.95640572 5.18713089 5.90398395]\n"," [5.54380595 5.45691142 5.88204141]] \n","\n","random array1 * 5.0 =\n","[[4.78202861 0.93565446 4.51991977]\n"," [2.71902975 2.28455711 4.41020705]] \n","\n","random array1 + random array2 =\n","[[1.41500968 0.91129853 1.30300928]\n"," [1.44785034 1.14693644 1.58166346]] \n","\n","random array1 * random array2 =\n","[[0.43861145 0.13551414 0.36071249]\n"," [0.49162472 0.31528031 0.61709562]] \n","\n","abs (-10 * random array1) =\n","[[9.56405723 1.87130892 9.03983955]\n"," [5.4380595  4.56911422 8.8204141 ]] \n","\n","sqrt(random array1) = \n"," [[0.97795998 0.43258628 0.95078071]\n"," [0.737432   0.67595223 0.9391706 ]] \n","\n"]}],"source":["rand_arr_1 = np.random.rand(2,3)\n","print(\"A random array1 : \\n\", rand_arr_1, \" with dimensions \", rand_arr_1.shape, \"\\n\")\n","rand_arr_2 = np.random.rand(2,3)\n","print(\"A random array2 : \\n\", rand_arr_2, \" with dimensions \", rand_arr_2.shape, \"\\n\")\n","scalar = 5.0\n","\n","# Addition with Scalars\n","new_arr_1 = rand_arr_1 + scalar\n","print(\"random array1 + 5.0 =\")\n","print(new_arr_1, \"\\n\")\n","\n","# Multiplication with Scalars\n","new_arr_2 = rand_arr_1 * scalar\n","print(\"random array1 * 5.0 =\")\n","print(new_arr_2, \"\\n\")\n","\n","# Elementwise Addition of Arrays\n","new_arr_3 = rand_arr_1 + rand_arr_2\n","print(\"random array1 + random array2 =\")\n","print(new_arr_3, \"\\n\")\n","\n","# Elementwise Multiplication of Arrays aka Hadmard Product\n","new_arr_4 = rand_arr_1 * rand_arr_2\n","print(\"random array1 * random array2 =\")\n","print(new_arr_4, \"\\n\") # also equivalent to np.multiply(array1, array2)\n","\n","# Absolute value\n","new_arr_5 = np.abs(-10*rand_arr_1)\n","print(\"abs (-10 * random array1) =\")\n","print(new_arr_5, \"\\n\")\n","\n","# Square root value\n","new_arr_6 = np.sqrt(rand_arr_1)\n","print('sqrt(random array1) = \\n', new_arr_6, \"\\n\")"]},{"cell_type":"markdown","metadata":{"id":"nqM9lvnXUHov"},"source":["### **c. Reduction**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Lbj4OzBFUHov","outputId":"8e3a88c7-2c41-4c58-b1c1-73278ffaaf2e"},"outputs":[{"output_type":"stream","name":"stdout","text":["random array: \n"," [[0.3277204  0.75677864 0.63606106]\n"," [0.24002027 0.16053882 0.79639147]] \n","\n","Maximum value of array \n"," 0.7963914745173317 \n","\n","Minimum value of array \n"," 0.16053882248525642 \n","\n","Sum of array \n"," 2.9175106701234474 \n","\n","Maximum value's index of array along axis 0 \n"," [0 0 1] \n","\n","Minimum value's index of array along axis 1 \n"," [0 1] \n","\n","Mean value of array \n"," 0.4862517783539079 \n","\n","Standard deviation value of array \n"," 0.2528719425862507 \n","\n","Norm value of array \n"," 1.3425014218979776 \n","\n"]}],"source":["rand_arr = np.random.rand(2,3)\n","print('random array: \\n', rand_arr, \"\\n\")\n","\n","max_val = np.max(rand_arr)\n","print('Maximum value of array \\n', max_val, \"\\n\")\n","min_val = np.min(rand_arr)\n","print('Minimum value of array \\n', min_val, \"\\n\")\n","\n","sum_val = np.sum(rand_arr)\n","print('Sum of array \\n', sum_val, \"\\n\")\n","max_idx = np.argmax(rand_arr, axis=0)\n","print('Maximum value\\'s index of array along axis 0 \\n', max_idx, \"\\n\")\n","min_idx = np.argmin(rand_arr, axis=1)\n","print('Minimum value\\'s index of array along axis 1 \\n', min_idx, \"\\n\")\n","\n","mean_val = np.mean(rand_arr)\n","print('Mean value of array \\n', mean_val, \"\\n\")\n","std_val = np.std(rand_arr)\n","print('Standard deviation value of array \\n', std_val, \"\\n\")\n","norm_val = np.linalg.norm(rand_arr)\n","print('Norm value of array \\n', norm_val, \"\\n\")"]},{"cell_type":"markdown","metadata":{"id":"XcB9hJBVUHov"},"source":["### **d. Comparision**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TWrd9Z5fUHov","outputId":"18e61aec-4fec-43f7-dd14-066fdf714c15"},"outputs":[{"output_type":"stream","name":"stdout","text":["random array1: \n"," [[0.9591666  0.45813883 0.59098417]\n"," [0.85772264 0.45722345 0.95187448]] \n","\n","random array2: \n"," [[0.57575116 0.82076712 0.90884372]\n"," [0.81552382 0.15941446 0.62889844]] \n","\n","random array1 > random array2\n","[[ True False False]\n"," [ True  True  True]] \n","\n","random array1 < random array2\n","[[False  True  True]\n"," [False False False]] \n","\n","random array1 != random array2\n","[[ True  True  True]\n"," [ True  True  True]] \n","\n","any values for random array1 > random array2:\n","True \n","\n","all values for random array1 > random array2:\n","False \n","\n","any values along first axis for random array1 > random array2:\n","[ True  True  True] \n","\n","any values along second axis for random array1 > random array2:\n","[ True  True] \n","\n","any values for random array1 != random array2:\n","True \n","\n","all values for random array1 != random array2:\n","True \n","\n"]}],"source":["rand_arr_1 = np.random.rand(2,3)\n","print('random array1: \\n', rand_arr_1, '\\n')\n","rand_arr_2 = np.random.rand(2,3)\n","print('random array2: \\n', rand_arr_2, '\\n')\n","\n","# Element-wise Comparison Operations\n","greater_compare = rand_arr_1 > rand_arr_2\n","print('random array1 > random array2')\n","print(greater_compare, '\\n')\n","\n","less_compare = rand_arr_1 < rand_arr_2\n","print('random array1 < random array2')\n","print(less_compare, '\\n')\n","\n","not_equal_compare = rand_arr_1 != rand_arr_2\n","print('random array1 != random array2')\n","print(not_equal_compare, '\\n')\n","\n","# Combining reduction operations with boolean arrays\n","print(\"any values for random array1 > random array2:\")\n","print((rand_arr_1 > rand_arr_2).any(), \"\\n\")\n","\n","print(\"all values for random array1 > random array2:\")\n","print((rand_arr_1 > rand_arr_2).all(), \"\\n\")\n","\n","print(\"any values along first axis for random array1 > random array2:\")\n","print((rand_arr_1 > rand_arr_2).any(axis=0), \"\\n\")\n","\n","print(\"any values along second axis for random array1 > random array2:\")\n","print((rand_arr_1 > rand_arr_2).any(axis=1), \"\\n\")\n","\n","print(\"any values for random array1 != random array2:\")\n","print((rand_arr_1 != rand_arr_2).any(), \"\\n\")\n","\n","print(\"all values for random array1 != random array2:\")\n","print((rand_arr_1 != rand_arr_2).all(), \"\\n\")"]},{"cell_type":"markdown","metadata":{"id":"awS5yNl3UHow"},"source":["### **e. Vector/Matrix operations**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vxmswikbUHow","outputId":"fb4b939c-8fd4-4f6c-f871-07e576f1739a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Array1 \n"," [-0.63947627 -0.14852688 -1.57458215] with dimension  (3,) \n","\n","Array2 \n"," [-0.4956882  -0.11757479  0.37312016] with dimension  (3,) \n","\n","Matmul of the two arrays can be derived by using np.matmul(array1, array2) \n"," -0.25306448851747204\n","Matmul of the two arrays can also be derived by using array1@array2 \n"," -0.25306448851747204\n","Dimensions of resulting product: \n"," ()\n","Array3 \n"," [[-0.38602877 -1.23059312 -1.95827161 -0.51796269]\n"," [ 0.5797988  -1.35373284 -0.71952667 -0.11861612]\n"," [-0.80811606 -0.71891095 -0.57354392 -0.38367719]] with dimension  (3, 4) \n","\n","Array4 \n"," [ 0.29163404  0.07285698 -0.01855425  0.10003728] with dimension  (4,) \n","\n","Matmul of a vector and a matrix can be derived by using np.matmul(array3, array4) \n"," [-0.21771776  0.07194441 -0.31579218]\n","Matmul of a vector and a matrix can also be derived by using array3@array4 \n"," [-0.21771776  0.07194441 -0.31579218]\n","Dimensions of resulting product: \n"," (3,)\n","Matrix1 \n"," [[0 1 2]\n"," [3 1 3]] with dimension  (2, 3) \n","\n","Matrix2 \n"," [[1 1]\n"," [2 0]\n"," [2 2]] with dimension  (3, 2) \n","\n","Matmul of two matrices can be derived by using np.matmul(matrix1, matrix2) \n"," [[ 6  4]\n"," [11  9]]\n","Dimensions of resulting product: \n"," (2, 2) \n","\n"]}],"source":["# Vector x Vector\n","array1 = np.random.randn(3)\n","array2 = np.random.randn(3)\n","\n","print('Array1 \\n', array1, 'with dimension ', array1.shape, '\\n')\n","print('Array2 \\n', array2, 'with dimension ', array2.shape, '\\n')\n","\n","matmul_arr = np.matmul(array1, array2)\n","another_arr = array1@array2\n","print('Matmul of the two arrays can be derived by using np.matmul(array1, array2) \\n', matmul_arr)\n","print(\"Matmul of the two arrays can also be derived by using array1@array2 \\n\", another_arr)\n","print('Dimensions of resulting product: \\n', matmul_arr.shape)\n","\n","# Matrix x Vector\n","array3 = np.random.randn(3, 4)\n","array4 = np.random.randn(4)\n","\n","print('Array3 \\n', array3, 'with dimension ', array3.shape, '\\n')\n","print('Array4 \\n', array4, 'with dimension ', array4.shape, '\\n')\n","\n","matmul_arr = np.matmul(array3, array4)\n","another_arr = array3@array4\n","print('Matmul of a vector and a matrix can be derived by using np.matmul(array3, array4) \\n', matmul_arr)\n","print('Matmul of a vector and a matrix can also be derived by using array3@array4 \\n', another_arr)\n","print('Dimensions of resulting product: \\n', matmul_arr.shape)\n","\n","# Matrix x Matrix\n","\n","matrix1 = np.random.randint(4, size = (2, 3))\n","matrix2 = np.random.randint(4, size = (3, 2))\n","\n","print('Matrix1 \\n', matrix1, 'with dimension ', matrix1.shape, '\\n')\n","print('Matrix2 \\n', matrix2, 'with dimension ', matrix2.shape, '\\n')\n","\n","matmul_mat = np.matmul(matrix1, matrix2)\n","print('Matmul of two matrices can be derived by using np.matmul(matrix1, matrix2) \\n', matmul_mat)\n","print('Dimensions of resulting product: \\n', matmul_mat.shape, \"\\n\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5fJcvlZ6UHow","outputId":"819f75a3-c719-4361-d8fd-c5f1e39e501d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Matrix1 \n"," [[0.2883985  0.24141862]\n"," [0.66250457 0.24606318]\n"," [0.66585912 0.51730852]\n"," [0.42408899 0.55468781]] with dimension  (4, 2) \n","\n","Matrix2 \n"," [[0.28705152 0.70657471 0.41485687]\n"," [0.36054556 0.82865691 0.92496691]] with dimension  (2, 3) \n","\n","Dot product of two matrices can be derived by using np.dot(mat1, mat2) \n"," [[0.16982764 0.40382829 0.34294833]\n"," [0.27888993 0.67201093 0.50244488]\n"," [0.37764916 0.89915049 0.75472949]\n"," [0.32172562 0.75929644 0.6890041 ]]\n","Dot product of two matrices can also be derived by using mat1@mat2 \n"," [[0.16982764 0.40382829 0.34294833]\n"," [0.27888993 0.67201093 0.50244488]\n"," [0.37764916 0.89915049 0.75472949]\n"," [0.32172562 0.75929644 0.6890041 ]]\n","Dimensions of resulting product: \n"," (4, 3)\n","array1 's dimension  (9, 5, 7, 4) \n","\n","array2 's dimension  (9, 5, 4, 3) \n","\n","Matmul of two multi-dimension arrays can be derived by using np.matmul(array1, array2) \n","\n","Dimensions of resulting product: \n"," (9, 5, 7, 3)\n"]}],"source":["rand_mat_1 = np.random.rand(4,2)\n","rand_mat_2 = np.random.rand(2,3)\n","print('Matrix1 \\n', rand_mat_1, 'with dimension ', rand_mat_1.shape, '\\n')\n","print('Matrix2 \\n', rand_mat_2, 'with dimension ', rand_mat_2.shape, '\\n')\n","\n","# dot product\n","dot_mat = np.dot(rand_mat_1, rand_mat_2)\n","another_mat = rand_mat_1@rand_mat_2\n","print('Dot product of two matrices can be derived by using np.dot(mat1, mat2) \\n', dot_mat)\n","print('Dot product of two matrices can also be derived by using mat1@mat2 \\n', another_mat)\n","print('Dimensions of resulting product: \\n', dot_mat.shape)\n","\n","a = np.ones([9, 5, 7, 4])\n","b = np.ones([9, 5, 4, 3])\n","print('array1 \\'s dimension ', a.shape, '\\n')\n","print('array2 \\'s dimension ', b.shape, '\\n')\n","\n","# matmul with multi-dimenstion arrays\n","c = np.matmul(a,b)\n","print('Matmul of two multi-dimension arrays can be derived by using np.matmul(array1, array2) \\n')\n","print('Dimensions of resulting product: \\n', c.shape)"]},{"cell_type":"markdown","metadata":{"id":"80x-iKA2UHow"},"source":["### **f. Tensordot**\n","\n","Understanding tensordot function will help you in writing succint code for your homeworks especially in Convolutional Neural Net assignment.\n","\n","To give a brief overview:\n","We input the arrays and the respective axes along which the sum-reductions are intended. The axes that take part in sum-reduction are removed in the output and all of the remaining axes from the input arrays are spread-out as different axes in the output keeping the order in which the input arrays are fed.\n","\n","To understand in depth please checkout: https://stackoverflow.com/questions/41870228/understanding-tensordot"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s8FjIoZHUHow","outputId":"3048150a-5fc8-4ab5-e35d-bf4b9806c934"},"outputs":[{"output_type":"stream","name":"stdout","text":["A 's dimension  (3, 4, 5) \n","\n","B 's dimension  (4, 3, 2) \n","\n","A⨂B =\n"," [[4400. 4730.]\n"," [4532. 4874.]\n"," [4664. 5018.]\n"," [4796. 5162.]\n"," [4928. 5306.]]  with dimension (5, 2) \n","\n","tensor dot is equal to sum over certain dimensions.\n","\n","[[ True  True]\n"," [ True  True]\n"," [ True  True]\n"," [ True  True]\n"," [ True  True]]\n"]}],"source":["a = np.arange(60.).reshape(3,4,5)\n","b = np.arange(24.).reshape(4,3,2)\n","print('A \\'s dimension ', a.shape, '\\n')\n","print('B \\'s dimension ', b.shape, '\\n')\n","\n","# compute tensor dot product along specified axes.\n","c = np.tensordot(a,b, axes=([1,0],[0,1]))\n","print(\"A⨂B =\\n\", c, ' with dimension', c.shape, '\\n')\n","\n","# this equals to\n","d = np.zeros((5,2))\n","for i in range(5):\n","  for j in range(2):\n","    for k in range(3):\n","      for n in range(4):\n","        d[i,j] += a[k,n,i] * b[n,k,j]\n","print(\"tensor dot is equal to sum over certain dimensions.\\n\")\n","print(c==d)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"h3dKTxEQrpMy"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.9.1"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"397704579725e15f5c7cb49fe5f0341eb7531c82d19f2c29d197e8b64ab5776b"}}},"nbformat":4,"nbformat_minor":0}