diff --git a/Rohit-2020-09-24.ipynb b/Rohit-2020-09-24.ipynb
new file mode 100644
index 0000000..114534d
--- /dev/null
+++ b/Rohit-2020-09-24.ipynb
@@ -0,0 +1,991 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.3"
+ },
+ "colab": {
+ "name": "Rohit-2020-09-24.ipynb",
+ "provenance": []
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ysZ_Zc7jXQLm"
+ },
+ "source": [
+ "# GRAY INTERFACE \n",
+ "\n",
+ "## HackSlash "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SeiOUaMLXQLo"
+ },
+ "source": [
+ "----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0VcY6iWPXQLu"
+ },
+ "source": [
+ "# NumPy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sveBt_S5XQLv"
+ },
+ "source": [
+ "## Exercise 1: Creating Numpy arrays"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fnIyQKKoXQLx"
+ },
+ "source": [
+ "----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FKz52UNSXQLy"
+ },
+ "source": [
+ "#### Write & Run CODE to answer each of the given problem statement.\n",
+ "##### Note that Sample Output of the problem statement is also given for reference. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UPzeXKd-XQL0"
+ },
+ "source": [
+ "----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JR3ziOY4XQL1"
+ },
+ "source": [
+ "## Import numpy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xorAeajEXQL3"
+ },
+ "source": [
+ "import numpy as np"
+ ],
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_9FTYkoYXQL_"
+ },
+ "source": [
+ "## Create Numpy arrays from Python list\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "v9ebkdjTXQMB"
+ },
+ "source": [
+ "#### Create Numpy 1d array from list\n",
+ "\n",
+ "`array([1, 2, 3])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uxR42QFrXQMC"
+ },
+ "source": [
+ "list_1d = np.array[1,2,3]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jnwKmdKIXQMI",
+ "outputId": "c760bd50-451c-444c-b4c1-45c2344b799f"
+ },
+ "source": [
+ "list_1d"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[1, 2, 3]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 3
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Nku-ubYJXQMR"
+ },
+ "source": [
+ "#### Create Numpy 2d array from list\n",
+ "\n",
+ "`array([[1, 2, 3],\n",
+ " [4, 5, 6]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kNVCtO9aXQMT"
+ },
+ "source": [
+ "list_2d = np.array([[1,2,3],\n",
+ " [4,5,6]])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "07BN26LPXQMY",
+ "outputId": "fd475be6-6b2c-47c6-b321-7f4266b2b941"
+ },
+ "source": [
+ "list_2d"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1, 2, 3],\n",
+ " [4, 5, 6]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5dKPATOKXQMg"
+ },
+ "source": [
+ "#### Create Numpy 3d array from list"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GMOEEx7eXQMi"
+ },
+ "source": [
+ "`array([[[1, 2, 3],\n",
+ " [4, 5, 6]],\n",
+ " [[1, 2, 3],\n",
+ " [4, 5, 6]]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PrcKqmOkXQMj"
+ },
+ "source": [
+ "list_3d = np.array([[[1,2,3],\n",
+ " [4,5,6]], \n",
+ " [[1,2,3],\n",
+ " [4,5,6]]])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "OT3ILqxOXQMq",
+ "outputId": "25f5fcdf-a5e9-472f-a570-dc7a9eea2a4a"
+ },
+ "source": [
+ "list_3d"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[[1, 2, 3],\n",
+ " [4, 5, 6]],\n",
+ "\n",
+ " [[1, 2, 3],\n",
+ " [4, 5, 6]]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ic_ihn5RXQMy"
+ },
+ "source": [
+ "-----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3nK7dFFSXQMz"
+ },
+ "source": [
+ "## Create Numpy arrays using functions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cZiD_nJqXQM1"
+ },
+ "source": [
+ "#### Create a Numpy 1d array of 10 zeros \n",
+ "\n",
+ " `array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VNejzKqOXQM2",
+ "outputId": "c594ed73-c97e-4702-cf26-2861c234378c"
+ },
+ "source": [
+ "zeros = np.zeros(10)\n",
+ "zeros"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2AQwry-EXQM9"
+ },
+ "source": [
+ "#### Create a Numpy 1d array of 10 ones\n",
+ "\n",
+ "`array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "scrolled": true,
+ "id": "DI1iHZ1MXQNB",
+ "outputId": "4862d3f1-3eca-403a-e514-88034aab76a3"
+ },
+ "source": [
+ "ones = np.ones(10)\n",
+ "ones"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 10
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F0DmLwW6XQNJ"
+ },
+ "source": [
+ "#### Create a Numpy 1d array of 10 fives\n",
+ "\n",
+ "`array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2jEuCWsnXQNL",
+ "outputId": "151f8cb2-f5e5-4067-fde6-1e7fffd8125b"
+ },
+ "source": [
+ "fives = ones*5\n",
+ "fives.astype(int)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e7Im8l-SXQNR"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of all the integers from 10 to 50\n",
+ "\n",
+ "`array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
+ " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
+ " 44, 45, 46, 47, 48, 49, 50])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "F4P_lpCpXQNT",
+ "outputId": "30d7f654-a3e4-45a4-e687-ceffd49a1dc2"
+ },
+ "source": [
+ "a1 = np.arange(10, 50)\n",
+ "a1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
+ " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
+ " 44, 45, 46, 47, 48, 49])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 17
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "S8xN5LSmXQNZ"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of all the even integers only from 10 to 50\n",
+ "\n",
+ "`array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
+ " 44, 46, 48, 50])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nvFKpXqlXQNb",
+ "outputId": "f23b9d3b-f3ea-4cb1-a82a-d1cf3f68f31f"
+ },
+ "source": [
+ "a2 = np.arange(10, 50, 2)\n",
+ "a2"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
+ " 44, 46, 48])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 18
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "C1Ljd4C7XQNj"
+ },
+ "source": [
+ "#### Create a Numpy 2d array with shape (3,3) having all entries as 1\n",
+ "\n",
+ "`array([[1., 1., 1.],\n",
+ " [1., 1., 1.],\n",
+ " [1., 1., 1.]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PJ4tous6XQNk",
+ "outputId": "2dd9f0db-a315-435a-8b97-a0950a5fa5eb"
+ },
+ "source": [
+ "a3 = np.ones((3,3))\n",
+ "a3"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1., 1., 1.],\n",
+ " [1., 1., 1.],\n",
+ " [1., 1., 1.]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 19
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LzW6gWxRXQNp"
+ },
+ "source": [
+ "#### Create a Numpy 2d array with shape (10,10) having all diagonal entries as 1 and all others as 0\n",
+ "\n",
+ "`array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Jktw1-IuXQNq",
+ "outputId": "8124bf1d-e304-4a2d-ff4e-60219e7ddf5e"
+ },
+ "source": [
+ "a4 = np.zeros((10, 10))\n",
+ "np.fill_diagonal(a4, 1)\n",
+ "a4"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 27
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LPRJ-r8oXQNv"
+ },
+ "source": [
+ "#### Create a Numpy 2d array with shape (5,6) consisting of values ranging from 1 to 30\n",
+ "\n",
+ "`array([[ 1, 2, 3, 4, 5, 6],\n",
+ " [ 7, 8, 9, 10, 11, 12],\n",
+ " [13, 14, 15, 16, 17, 18],\n",
+ " [19, 20, 21, 22, 23, 24],\n",
+ " [25, 26, 27, 28, 29, 30]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0wbQZ01ZXQNw",
+ "outputId": "cd67b846-132a-45ef-f155-e579a0fd4563"
+ },
+ "source": [
+ "a5 = np.arange(1, 31).reshape(5, 6)\n",
+ "a5"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 1, 2, 3, 4, 5, 6],\n",
+ " [ 7, 8, 9, 10, 11, 12],\n",
+ " [13, 14, 15, 16, 17, 18],\n",
+ " [19, 20, 21, 22, 23, 24],\n",
+ " [25, 26, 27, 28, 29, 30]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 37
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cfqhkD61XQN1"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of a random number between 0 and 1\n",
+ "\n",
+ "`array([0.42829726])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "F69msW1lXQN2",
+ "outputId": "501ee287-a66f-4885-c307-e3d00a3cf676"
+ },
+ "source": [
+ "a6 = np.random.random(1)\n",
+ "a6"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.07729339])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 41
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TMRKw8DSXQN7"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of six *random* numbers between 0 and 1\n",
+ "\n",
+ "`array([0.48986417, 0.61869789, 0.83943457, 0.94889576, 0.79561515, 0.28859058])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gF-6VJb3XQN9",
+ "outputId": "08b401f2-4fe4-4baa-f710-96f2b47540ea"
+ },
+ "source": [
+ "a7 = np.random.random(6)\n",
+ "a7"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.31603255, 0.94442351, 0.67546733, 0.49993517, 0.12707087,\n",
+ " 0.49862089])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 40
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tXPgOjFmXQOD"
+ },
+ "source": [
+ "#### Create a Numpy 2d array of shape (2,3) consisting of five *random* numbers between 0 and 1\n",
+ "\n",
+ "`array([[0.3991418 , 0.62631826, 0.82763635],\n",
+ " [0.9232742 , 0.08517427, 0.20155819]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "hfaVKpSfXQOF",
+ "outputId": "807fa887-49af-43ec-fbdb-64c02a422a38"
+ },
+ "source": [
+ "a8 = np.random.random(size=(2, 3))\n",
+ "a8"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[0.39039111, 0.82852577, 0.76315103],\n",
+ " [0.82700325, 0.08247063, 0.13188691]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 43
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Cf6WLC80XQOK"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of 25 random numbers sampled from a *Standard Normal Distribution*\n",
+ "\n",
+ "`array([-0.01804177, -0.11468092, -0.4790744 , -0.44706856, 0.49891779,\n",
+ " 1.53806364, -1.03689237, 0.04379123, -0.6659274 , -0.54294319,\n",
+ " -0.08016372, -0.64441992, -0.12417271, 0.89901988, -0.08695241,\n",
+ " 0.23540321, 1.77803936, 1.39912268, 0.19886053, -0.56546787,\n",
+ " 0.09538678, -0.47930312, 2.15532489, 0.62643712, 1.19941788])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8O_rJstOXQOL",
+ "outputId": "9822152c-5351-4e72-b43f-31d181e4dc4a"
+ },
+ "source": [
+ "a9 = np.random.normal(0, 1, 25)\n",
+ "a9"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([-1.28311083, -0.51166075, -1.16968094, 1.40954369, 0.69034305,\n",
+ " -1.24372526, -0.27167964, 0.62141372, -1.49565467, -0.45851534,\n",
+ " 0.36675755, 0.7458972 , 0.2076125 , 1.4570458 , -0.2218839 ,\n",
+ " -1.38782674, -1.85398042, -0.45296259, 0.91931724, 0.12496521,\n",
+ " -1.27608387, -0.60804623, 0.43132898, -1.44829818, -0.73356109])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 45
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ANYwg3TgXQOQ"
+ },
+ "source": [
+ "#### Generate a *random* integer between 1 and 50\n",
+ "\n",
+ "`32`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_5zQOBImXQOT",
+ "outputId": "aaf7a36f-a343-4f92-ede7-30f73735f6b9"
+ },
+ "source": [
+ "a10 = np.random.randint(1, 50)\n",
+ "a10"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "36"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 47
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WDhy2BFOXQOb"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of five *random* integers between 1 and 50\n",
+ "\n",
+ "`array([36, 30, 32, 14, 2])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tsljNldUXQOc",
+ "outputId": "14723c71-0b7d-4ab2-faf6-7fb291529576"
+ },
+ "source": [
+ "a11 = np.random.randint(1, 50, size=(5))\n",
+ "a11"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([41, 43, 23, 28, 14])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 48
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tZcYx6kVXQOg"
+ },
+ "source": [
+ "#### Create a Numpy array consisting of 20 linearly spaced points between 0 and 1\n",
+ "\n",
+ "`array([ 0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
+ " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
+ " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
+ " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "41ed4-0rXQOh",
+ "outputId": "bf01dd86-ebc5-440e-ad0d-05bdb0c6c5e1",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 87
+ }
+ },
+ "source": [
+ "a12 = np.linspace(0, 1, num=20)\n",
+ "a12"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
+ " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
+ " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
+ " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 3
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SmGanf3EXQOp"
+ },
+ "source": [
+ "#### Create the following Numpy 2d array:\n",
+ "\n",
+ "`array([[ 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
+ " [ 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
+ " [ 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
+ " [ 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
+ " [ 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
+ " [ 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
+ " [ 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
+ " [ 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
+ " [ 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
+ " [ 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "srIwqxEgXQOq",
+ "outputId": "6ffc7798-9e50-4f61-e4a9-8c9af9459586"
+ },
+ "source": [
+ "a13 = np.arange(0.01, 1.01, 0.01).reshape(10, 10)\n",
+ "a13"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
+ " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
+ " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
+ " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
+ " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
+ " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
+ " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
+ " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
+ " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
+ " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 56
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LHQU5IEnXQOu"
+ },
+ "source": [
+ "-----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true,
+ "id": "XURw5nAGXQOv"
+ },
+ "source": [
+ "# Great Job!"
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/Rohit_assignment_1.ipynb b/Rohit_assignment_1.ipynb
new file mode 100644
index 0000000..48f50d4
--- /dev/null
+++ b/Rohit_assignment_1.ipynb
@@ -0,0 +1,1066 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.3"
+ },
+ "colab": {
+ "name": "Rohit-2020-09-24.ipynb",
+ "provenance": []
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ysZ_Zc7jXQLm",
+ "colab_type": "text"
+ },
+ "source": [
+ "# GRAY INTERFACE \n",
+ "\n",
+ "## HackSlash "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SeiOUaMLXQLo",
+ "colab_type": "text"
+ },
+ "source": [
+ "----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0VcY6iWPXQLu",
+ "colab_type": "text"
+ },
+ "source": [
+ "# NumPy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sveBt_S5XQLv",
+ "colab_type": "text"
+ },
+ "source": [
+ "## Exercise 1: Creating Numpy arrays"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fnIyQKKoXQLx",
+ "colab_type": "text"
+ },
+ "source": [
+ "----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FKz52UNSXQLy",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Write & Run CODE to answer each of the given problem statement.\n",
+ "##### Note that Sample Output of the problem statement is also given for reference. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UPzeXKd-XQL0",
+ "colab_type": "text"
+ },
+ "source": [
+ "----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JR3ziOY4XQL1",
+ "colab_type": "text"
+ },
+ "source": [
+ "## Import numpy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xorAeajEXQL3",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import numpy as np"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_9FTYkoYXQL_",
+ "colab_type": "text"
+ },
+ "source": [
+ "## Create Numpy arrays from Python list\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "v9ebkdjTXQMB",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create Numpy 1d array from list\n",
+ "\n",
+ "`array([1, 2, 3])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uxR42QFrXQMC",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "list_1d = np.array[1,2,3]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jnwKmdKIXQMI",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "c760bd50-451c-444c-b4c1-45c2344b799f"
+ },
+ "source": [
+ "list_1d"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[1, 2, 3]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 3
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Nku-ubYJXQMR",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create Numpy 2d array from list\n",
+ "\n",
+ "`array([[1, 2, 3],\n",
+ " [4, 5, 6]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kNVCtO9aXQMT",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "list_2d = np.array([[1,2,3],\n",
+ " [4,5,6]])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "07BN26LPXQMY",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "fd475be6-6b2c-47c6-b321-7f4266b2b941"
+ },
+ "source": [
+ "list_2d"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1, 2, 3],\n",
+ " [4, 5, 6]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5dKPATOKXQMg",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create Numpy 3d array from list"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GMOEEx7eXQMi",
+ "colab_type": "text"
+ },
+ "source": [
+ "`array([[[1, 2, 3],\n",
+ " [4, 5, 6]],\n",
+ " [[1, 2, 3],\n",
+ " [4, 5, 6]]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PrcKqmOkXQMj",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "list_3d = np.array([[[1,2,3],\n",
+ " [4,5,6]], \n",
+ " [[1,2,3],\n",
+ " [4,5,6]]])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "OT3ILqxOXQMq",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "25f5fcdf-a5e9-472f-a570-dc7a9eea2a4a"
+ },
+ "source": [
+ "list_3d"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[[1, 2, 3],\n",
+ " [4, 5, 6]],\n",
+ "\n",
+ " [[1, 2, 3],\n",
+ " [4, 5, 6]]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ic_ihn5RXQMy",
+ "colab_type": "text"
+ },
+ "source": [
+ "-----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3nK7dFFSXQMz",
+ "colab_type": "text"
+ },
+ "source": [
+ "## Create Numpy arrays using functions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cZiD_nJqXQM1",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 1d array of 10 zeros \n",
+ "\n",
+ " `array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VNejzKqOXQM2",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "c594ed73-c97e-4702-cf26-2861c234378c"
+ },
+ "source": [
+ "zeros = np.zeros(10)\n",
+ "zeros"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2AQwry-EXQM9",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 1d array of 10 ones\n",
+ "\n",
+ "`array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "scrolled": true,
+ "id": "DI1iHZ1MXQNB",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "4862d3f1-3eca-403a-e514-88034aab76a3"
+ },
+ "source": [
+ "ones = np.ones(10)\n",
+ "ones"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 10
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F0DmLwW6XQNJ",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 1d array of 10 fives\n",
+ "\n",
+ "`array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2jEuCWsnXQNL",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "151f8cb2-f5e5-4067-fde6-1e7fffd8125b"
+ },
+ "source": [
+ "fives = ones*5\n",
+ "fives.astype(int)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e7Im8l-SXQNR",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of all the integers from 10 to 50\n",
+ "\n",
+ "`array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
+ " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
+ " 44, 45, 46, 47, 48, 49, 50])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "F4P_lpCpXQNT",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "30d7f654-a3e4-45a4-e687-ceffd49a1dc2"
+ },
+ "source": [
+ "a1 = np.arange(10, 50)\n",
+ "a1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
+ " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
+ " 44, 45, 46, 47, 48, 49])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 17
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "S8xN5LSmXQNZ",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of all the even integers only from 10 to 50\n",
+ "\n",
+ "`array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
+ " 44, 46, 48, 50])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nvFKpXqlXQNb",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "f23b9d3b-f3ea-4cb1-a82a-d1cf3f68f31f"
+ },
+ "source": [
+ "a2 = np.arange(10, 50, 2)\n",
+ "a2"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
+ " 44, 46, 48])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 18
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "C1Ljd4C7XQNj",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 2d array with shape (3,3) having all entries as 1\n",
+ "\n",
+ "`array([[1., 1., 1.],\n",
+ " [1., 1., 1.],\n",
+ " [1., 1., 1.]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PJ4tous6XQNk",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "2dd9f0db-a315-435a-8b97-a0950a5fa5eb"
+ },
+ "source": [
+ "a3 = np.ones((3,3))\n",
+ "a3"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1., 1., 1.],\n",
+ " [1., 1., 1.],\n",
+ " [1., 1., 1.]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 19
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LzW6gWxRXQNp",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 2d array with shape (10,10) having all diagonal entries as 1 and all others as 0\n",
+ "\n",
+ "`array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Jktw1-IuXQNq",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "8124bf1d-e304-4a2d-ff4e-60219e7ddf5e"
+ },
+ "source": [
+ "a4 = np.zeros((10, 10))\n",
+ "np.fill_diagonal(a4, 1)\n",
+ "a4"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 27
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LPRJ-r8oXQNv",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 2d array with shape (5,6) consisting of values ranging from 1 to 30\n",
+ "\n",
+ "`array([[ 1, 2, 3, 4, 5, 6],\n",
+ " [ 7, 8, 9, 10, 11, 12],\n",
+ " [13, 14, 15, 16, 17, 18],\n",
+ " [19, 20, 21, 22, 23, 24],\n",
+ " [25, 26, 27, 28, 29, 30]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0wbQZ01ZXQNw",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "cd67b846-132a-45ef-f155-e579a0fd4563"
+ },
+ "source": [
+ "a5 = np.arange(1, 31).reshape(5, 6)\n",
+ "a5"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 1, 2, 3, 4, 5, 6],\n",
+ " [ 7, 8, 9, 10, 11, 12],\n",
+ " [13, 14, 15, 16, 17, 18],\n",
+ " [19, 20, 21, 22, 23, 24],\n",
+ " [25, 26, 27, 28, 29, 30]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 37
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cfqhkD61XQN1",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of a random number between 0 and 1\n",
+ "\n",
+ "`array([0.42829726])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "F69msW1lXQN2",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "501ee287-a66f-4885-c307-e3d00a3cf676"
+ },
+ "source": [
+ "a6 = np.random.random(1)\n",
+ "a6"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.07729339])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 41
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TMRKw8DSXQN7",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of six *random* numbers between 0 and 1\n",
+ "\n",
+ "`array([0.48986417, 0.61869789, 0.83943457, 0.94889576, 0.79561515, 0.28859058])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gF-6VJb3XQN9",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "08b401f2-4fe4-4baa-f710-96f2b47540ea"
+ },
+ "source": [
+ "a7 = np.random.random(6)\n",
+ "a7"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.31603255, 0.94442351, 0.67546733, 0.49993517, 0.12707087,\n",
+ " 0.49862089])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 40
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tXPgOjFmXQOD",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 2d array of shape (2,3) consisting of five *random* numbers between 0 and 1\n",
+ "\n",
+ "`array([[0.3991418 , 0.62631826, 0.82763635],\n",
+ " [0.9232742 , 0.08517427, 0.20155819]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "hfaVKpSfXQOF",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "807fa887-49af-43ec-fbdb-64c02a422a38"
+ },
+ "source": [
+ "a8 = np.random.random(size=(2, 3))\n",
+ "a8"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[0.39039111, 0.82852577, 0.76315103],\n",
+ " [0.82700325, 0.08247063, 0.13188691]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 43
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Cf6WLC80XQOK",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of 25 random numbers sampled from a *Standard Normal Distribution*\n",
+ "\n",
+ "`array([-0.01804177, -0.11468092, -0.4790744 , -0.44706856, 0.49891779,\n",
+ " 1.53806364, -1.03689237, 0.04379123, -0.6659274 , -0.54294319,\n",
+ " -0.08016372, -0.64441992, -0.12417271, 0.89901988, -0.08695241,\n",
+ " 0.23540321, 1.77803936, 1.39912268, 0.19886053, -0.56546787,\n",
+ " 0.09538678, -0.47930312, 2.15532489, 0.62643712, 1.19941788])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8O_rJstOXQOL",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "9822152c-5351-4e72-b43f-31d181e4dc4a"
+ },
+ "source": [
+ "a9 = np.random.normal(0, 1, 25)\n",
+ "a9"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([-1.28311083, -0.51166075, -1.16968094, 1.40954369, 0.69034305,\n",
+ " -1.24372526, -0.27167964, 0.62141372, -1.49565467, -0.45851534,\n",
+ " 0.36675755, 0.7458972 , 0.2076125 , 1.4570458 , -0.2218839 ,\n",
+ " -1.38782674, -1.85398042, -0.45296259, 0.91931724, 0.12496521,\n",
+ " -1.27608387, -0.60804623, 0.43132898, -1.44829818, -0.73356109])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 45
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ANYwg3TgXQOQ",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Generate a *random* integer between 1 and 50\n",
+ "\n",
+ "`32`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_5zQOBImXQOT",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "aaf7a36f-a343-4f92-ede7-30f73735f6b9"
+ },
+ "source": [
+ "a10 = np.random.randint(1, 50)\n",
+ "a10"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "36"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 47
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WDhy2BFOXQOb",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy 1d array consisting of five *random* integers between 1 and 50\n",
+ "\n",
+ "`array([36, 30, 32, 14, 2])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tsljNldUXQOc",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "14723c71-0b7d-4ab2-faf6-7fb291529576"
+ },
+ "source": [
+ "a11 = np.random.randint(1, 50, size=(5))\n",
+ "a11"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([41, 43, 23, 28, 14])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 48
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tZcYx6kVXQOg",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create a Numpy array consisting of 20 linearly spaced points between 0 and 1\n",
+ "\n",
+ "`array([ 0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
+ " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
+ " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
+ " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "41ed4-0rXQOh",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "7b3ad7ed-b2f0-4fbc-dd48-3af036bde2f8"
+ },
+ "source": [
+ "a12 = np.random.random(20)\n",
+ "a12"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.35461186, 0.71027829, 0.51195462, 0.84389804, 0.51595563,\n",
+ " 0.41546301, 0.10441333, 0.46561315, 0.63735201, 0.16660066,\n",
+ " 0.11609039, 0.66684294, 0.44709994, 0.14481748, 0.39836158,\n",
+ " 0.04509681, 0.69683096, 0.10345124, 0.75850487, 0.25542027])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 53
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SmGanf3EXQOp",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### Create the following Numpy 2d array:\n",
+ "\n",
+ "`array([[ 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
+ " [ 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
+ " [ 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
+ " [ 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
+ " [ 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
+ " [ 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
+ " [ 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
+ " [ 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
+ " [ 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
+ " [ 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "srIwqxEgXQOq",
+ "colab_type": "code",
+ "colab": {},
+ "outputId": "6ffc7798-9e50-4f61-e4a9-8c9af9459586"
+ },
+ "source": [
+ "a13 = np.arange(0.01, 1.01, 0.01).reshape(10, 10)\n",
+ "a13"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
+ " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
+ " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
+ " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
+ " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
+ " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
+ " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
+ " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
+ " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
+ " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 56
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LHQU5IEnXQOu",
+ "colab_type": "text"
+ },
+ "source": [
+ "-----"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true,
+ "id": "XURw5nAGXQOv",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Great Job!"
+ ]
+ }
+ ]
+}
\ No newline at end of file