diff --git a/Lists.ipynb b/Lists.ipynb
new file mode 100644
index 0000000..d59730c
--- /dev/null
+++ b/Lists.ipynb
@@ -0,0 +1,419 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Lists.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "e0R1W0Vzm4UU",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Exercise - List"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "TrO7XNQnnQZ7",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "1) Create any random list and assign it to a variable dummy_list"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "bjl-2QkznWid",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "dummy_list = [5,4,3,2,1]"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "cDjddNGfngnp",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "2) print dummy_list"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "RVL5178inz9M",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "5a63c74e-b503-4d44-dc61-b2e57885b279"
+ },
+ "cell_type": "code",
+ "source": [
+ "print(dummy_list)"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[5, 4, 3, 2, 1]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "15jKDXxkn16M",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "3) Reverse dummy_list and print"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "bYa9gFOOn-4o",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "b8d7cc17-5dbd-464a-e984-2e184dc3b68f"
+ },
+ "cell_type": "code",
+ "source": [
+ "dummy_list.reverse()\n",
+ "print(dummy_list)"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[1, 2, 3, 4, 5]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "EShv0nfXpUys",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "4) Add the list dummy_list_2 to the previous dummy_list and now print dummy_list"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Ngkc7hnYphg6",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "cd066fe0-d66e-4903-c32c-156b9b3fa0e4"
+ },
+ "cell_type": "code",
+ "source": [
+ "dummy_list_2 = [2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n",
+ "dummy_list.extend(dummy_list_2)\n",
+ "print(dummy_list)"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[1, 2, 3, 4, 5, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Le1aRTuYoDzS",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "5) Create a dictionary named dummy_dict which contains all the elements of dummy_list as keys and frequency as values. "
+ ]
+ },
+ {
+ "metadata": {
+ "id": "VHfSR_Csthnk",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "dummy_dict={}"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "wj0YOBynodoh",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "for i in dummy_list:\n",
+ " dummy_dict[i] = dummy_list.count(i)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "RgCYpFXGou6q",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "6) print dummy_dict"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "qe5E5IgxpTWU",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "2e280d67-c29a-4a9a-aa23-50ebd274d2fb"
+ },
+ "cell_type": "code",
+ "source": [
+ "print(dummy_dict)"
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "{1: 2, 2: 2, 3: 1, 4: 2, 5: 1, 200: 1, 16: 1, 0: 1, 9.45: 1, 45.67: 1, 90: 1, 12.01: 1, 12.02: 1}\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "8n_nsBDup4--",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "7) Sort dummy_list in ascending order as well as descending order and print the changed lists "
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Z_m7vr26qKnK",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ },
+ "outputId": "570c7571-5250-42f7-a4e9-d78c44063e3e"
+ },
+ "cell_type": "code",
+ "source": [
+ "print(\"dummy_list in ascending order: \", sorted(dummy_list))\n",
+ "print(\"dummy_list in descending order: \", sorted(dummy_list, reverse = True))"
+ ],
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "dummy_list in ascending order: [0, 1, 1, 2, 2, 3, 4, 4, 5, 9.45, 12.01, 12.02, 16, 45.67, 90, 200]\n",
+ "dummy_list in descending order: [200, 90, 45.67, 16, 12.02, 12.01, 9.45, 5, 4, 4, 3, 2, 2, 1, 1, 0]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Znm5Qo4LqPKA",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "8) Remove the first item from the list whose value is equal to x. It raises a ValueError if there is no such item."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "1-8mlngDqYvS",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 214
+ },
+ "outputId": "1e34a4c9-bc9f-482c-b5f2-afedf4c9bf48"
+ },
+ "cell_type": "code",
+ "source": [
+ "x = 200\n",
+ "x = 200\n",
+ "dummy_list.remove(x)\n",
+ "print(dummy_list)\n",
+ "\n",
+ "# Let's play: try the same with something which is not in the list to get the ValueError\n",
+ "dummy_list.remove(500)"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[1, 2, 3, 4, 5, 2, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "error",
+ "ename": "ValueError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Let's play: try the same with something which is not in the list to get the ValueError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdummy_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
+ ]
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "QPB6iGbeqviN",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "9) Remove the item at position x. x is any random integer"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "aMyo1gmRrVHo",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 231
+ },
+ "outputId": "5c174761-60dd-46e2-c417-eee584aa4ad6"
+ },
+ "cell_type": "code",
+ "source": [
+ "x=4\n",
+ "print(dummy_list)\n",
+ "dummy_list.pop(x)\n",
+ "print(dummy_list)\n",
+ "# Let's play: try doing the same with x > len(dummy_list) + 1 and see what you get\n",
+ "x=len(dummy_list) + 2\n",
+ "dummy_list.pop(x)\n"
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[1, 2, 3, 4, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n",
+ "[1, 2, 3, 4, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "error",
+ "ename": "IndexError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Let's play: try doing the same with x > len(dummy_list) + 1 and see what you get\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdummy_list\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdummy_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mIndexError\u001b[0m: pop index out of range"
+ ]
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "bqQnnsr8rm6G",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "10) Let's clean everything clear the list and then print"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "qBC8lKpLrtJW",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "818dc978-23e7-4524-cacf-fa94d2739d84"
+ },
+ "cell_type": "code",
+ "source": [
+ "dummy_list.clear()\n",
+ "print(dummy_list)"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/Numpy_Examples_1.ipynb b/Numpy_Examples_1.ipynb
new file mode 100644
index 0000000..fb7e0d9
--- /dev/null
+++ b/Numpy_Examples_1.ipynb
@@ -0,0 +1,526 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Numpy_Examples 1.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "metadata": {
+ "id": "3pSVAeWfuPcq",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Numpy Examples\n",
+ "\n",
+ "## What is numpy?\n",
+ "\n",
+ "#### Python has built-in:\n",
+ "\n",
+ "- containers: lists (costless insertion and append), dictionnaries (fast lookup)\n",
+ "- high-level number objects: integers, floating point\n",
+ "\n",
+ "#### Numpy is:\n",
+ "\n",
+ " - extension package to Python for multidimensional arrays\n",
+ " - closer to hardware (efficiency)\n",
+ " - designed for scientific computation (convenience)\n",
+ "\n",
+ "\n",
+ "#### Import numpy\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ozUi4_X55UHE",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "import numpy as np"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "3-1ghFDF5N2z",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Uncomment Print statement and run each cell to see the output\n",
+ "\n",
+ "#### Create numpy arrays\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "atYpk2ert0b-",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 85
+ },
+ "outputId": "ddf0ca58-fdf9-46bc-aa9e-8c86f401cb21"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.array([1, 2, 3]) # Create a rank 1 array\n",
+ "print(a)\n",
+ "print(type(a)) #print type of a\n",
+ "\n",
+ "b = np.array([[1,2,3],[4,5,6]]) # Create a rank 2 array\n",
+ "print(b.shape) # Prints \"(2, 3)\"\n",
+ "print(b[0, 0], b[0, 1], b[1, 0])"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[1 2 3]\n",
+ "\n",
+ "(2, 3)\n",
+ "1 2 4\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Kro5ZOwXue5n",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Some basic functions for creating arrays. Print all the defined arrays and see the results."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "V3rdzgr9uhHS",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 221
+ },
+ "outputId": "ff23b67a-5a28-4383-bde1-18a844366287"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.zeros(shape=(2,2))\n",
+ "b = np.ones(shape = (3,3))\n",
+ "c = np.eye(2)\n",
+ "d = np.full(shape=(3,3), fill_value=5)\n",
+ "e = np.random.random((2,2))\n",
+ "\n",
+ "print('a', a)\n",
+ "print('b',b)\n",
+ "print('c',c)\n",
+ "print('d',d)\n",
+ "print('e',e)"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "a [[0. 0.]\n",
+ " [0. 0.]]\n",
+ "b [[1. 1. 1.]\n",
+ " [1. 1. 1.]\n",
+ " [1. 1. 1.]]\n",
+ "c [[1. 0.]\n",
+ " [0. 1.]]\n",
+ "d [[5 5 5]\n",
+ " [5 5 5]\n",
+ " [5 5 5]]\n",
+ "e [[0.80215285 0.94329407]\n",
+ " [0.31846032 0.47054858]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "8RPW_SutukjF",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Execute and understand :)"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "-8JuqYt4upeo",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 170
+ },
+ "outputId": "456430d4-67c6-4b08-cc3e-fbbc3e1e3d81"
+ },
+ "cell_type": "code",
+ "source": [
+ "a == np.arange(10)\n",
+ "b == np.linspace(0,10, num=6)\n",
+ "print(a)\n",
+ "print(b)"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[0. 0.]\n",
+ " [0. 0.]]\n",
+ "[[1. 1. 1.]\n",
+ " [1. 1. 1.]\n",
+ " [1. 1. 1.]]\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.\n",
+ " \"\"\"Entry point for launching an IPython kernel.\n",
+ "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.\n",
+ " \n"
+ ],
+ "name": "stderr"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "MRHhbjx4uvYN",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Array Indexing"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "grF5_yUSuxVK",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 85
+ },
+ "outputId": "4a9524b2-932d-4528-d881-42cb2850b758"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
+ "\n",
+ "# Use slicing to pull out the subarray consisting of the first 2 rows\n",
+ "# and columns 1 and 2; b is the following array of shape (2, 2):\n",
+ "# [[2 3]\n",
+ "# [6 7]]\n",
+ "b = a[:2, 1:3]\n",
+ "print(b)\n",
+ "\n",
+ "# A slice of an array is a view into the same data, so modifying it\n",
+ "# will modify the original array.\n",
+ "print(a[0, 1]) # Prints \"2\"\n",
+ "\n",
+ "b[0, 0] = 77 # b[0, 0] is the same piece of data as a[0, 1]\n",
+ "print(a[0, 1]) # Prints \"77\""
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[2 3]\n",
+ " [6 7]]\n",
+ "2\n",
+ "77\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "s400Gijxu0kO",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Slicing"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "kubpegh2u4zF",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 119
+ },
+ "outputId": "434bb7d6-b148-46e3-b3af-1b1bc6e1a99e"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
+ "\n",
+ "row_r1 = a[1, :] # Rank 1 view of the second row of a\n",
+ "row_r2 = a[1:2, :] # Rank 2 view of the second row of a\n",
+ "\n",
+ "print(row_r1, row_r1.shape) # Prints \"[5 6 7 8] (4,)\"\n",
+ "print(row_r2, row_r2.shape) # Prints \"[[5 6 7 8]] (1, 4)\"\n",
+ "\n",
+ "col_r1 = a[:, 1]\n",
+ "col_r2 = a[:, 1:2]\n",
+ "\n",
+ "print(col_r1, col_r1.shape) # Prints \"[ 2 6 10] (3,)\"\n",
+ "print(col_r2, col_r2.shape)"
+ ],
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[5 6 7 8] (4,)\n",
+ "[[5 6 7 8]] (1, 4)\n",
+ "[ 2 6 10] (3,)\n",
+ "[[ 2]\n",
+ " [ 6]\n",
+ " [10]] (3, 1)\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "TmGnCO3AvE8t",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Aritmetic operations"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "YvBw3ImjvGqD",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 68
+ },
+ "outputId": "5a8f5e9d-e444-472e-f954-cd82c75ad6c2"
+ },
+ "cell_type": "code",
+ "source": [
+ "x = np.array([[1,2],[3,4]])\n",
+ "\n",
+ "print(np.sum(x)) # Compute sum of all elements; prints \"10\"\n",
+ "print(np.sum(x, axis=0)) # Compute sum of each column; prints \"[4 6]\"\n",
+ "print(np.sum(x, axis=1)) # Compute sum of each row; prints \"[3 7]\""
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "10\n",
+ "[4 6]\n",
+ "[3 7]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "uaVY3ZzD4pC2",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Using Boolean Mask"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "-PNfOMvh4_Gp",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 68
+ },
+ "outputId": "b8826d59-e6dc-4bec-c062-53a32035d4a1"
+ },
+ "cell_type": "code",
+ "source": [
+ "b = np.arange(10)\n",
+ "\n",
+ "print(b)\n",
+ "\n",
+ "mask = b%2!=0 #perform computations on the list \n",
+ "\n",
+ "print(mask)\n",
+ "\n",
+ "print(b[mask]) #applying the mask on the numpy array\n"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[0 1 2 3 4 5 6 7 8 9]\n",
+ "[False True False True False True False True False True]\n",
+ "[1 3 5 7 9]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "HbEPBbz-5J9K",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "931b28a0-b983-4927-f4dd-380b46d94ebd"
+ },
+ "cell_type": "code",
+ "source": [
+ "modified_b = b\n",
+ "modified_b[mask] = -1\n",
+ "\n",
+ "print(modified_b)"
+ ],
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[ 0 -1 2 -1 4 -1 6 -1 8 -1]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "zgSd71EEAHC7",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Swapping two columns in a 2d numpy array"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "-cvqeXd_AGo1",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 119
+ },
+ "outputId": "4167b7ef-43a5-4f6a-db50-c285e9c4b934"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.arange(9).reshape(3,3)\n",
+ "print(a)\n",
+ "\n",
+ "print(a[:, [1,0,2]])"
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[0 1 2]\n",
+ " [3 4 5]\n",
+ " [6 7 8]]\n",
+ "[[1 0 2]\n",
+ " [4 3 5]\n",
+ " [7 6 8]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "U7ifiLY3Ayky",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Swapping two rows in a 2d numpy array"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "0FrOURRDAZNP",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 119
+ },
+ "outputId": "29986753-004f-4fff-89cb-f5fae5a2a1b2"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.arange(9).reshape(3,3)\n",
+ "print(a)\n",
+ "\n",
+ "print(a[[1,0,2], :])"
+ ],
+ "execution_count": 18,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[0 1 2]\n",
+ " [3 4 5]\n",
+ " [6 7 8]]\n",
+ "[[3 4 5]\n",
+ " [0 1 2]\n",
+ " [6 7 8]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/Numpy_Exercises.ipynb b/Numpy_Exercises.ipynb
new file mode 100644
index 0000000..bccd320
--- /dev/null
+++ b/Numpy_Exercises.ipynb
@@ -0,0 +1,363 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Numpy_Exercises.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "metadata": {
+ "id": "a_4UupTr9fbX",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Numpy Exercises\n",
+ "\n",
+ "1) Create a uniform subdivision of the interval -1.3 to 2.5 with 64 subdivisions"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "LIP5u4zi0Nmg",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 204
+ },
+ "outputId": "f3d0b2ea-fa9b-4bbe-b28b-5760ecfee884"
+ },
+ "cell_type": "code",
+ "source": [
+ "import numpy as np #import numpy\n",
+ "x=np.linspace(-1.3,2.5,num=64)\n",
+ "print(x)"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[-1.3 -1.23968254 -1.17936508 -1.11904762 -1.05873016 -0.9984127\n",
+ " -0.93809524 -0.87777778 -0.81746032 -0.75714286 -0.6968254 -0.63650794\n",
+ " -0.57619048 -0.51587302 -0.45555556 -0.3952381 -0.33492063 -0.27460317\n",
+ " -0.21428571 -0.15396825 -0.09365079 -0.03333333 0.02698413 0.08730159\n",
+ " 0.14761905 0.20793651 0.26825397 0.32857143 0.38888889 0.44920635\n",
+ " 0.50952381 0.56984127 0.63015873 0.69047619 0.75079365 0.81111111\n",
+ " 0.87142857 0.93174603 0.99206349 1.05238095 1.11269841 1.17301587\n",
+ " 1.23333333 1.29365079 1.35396825 1.41428571 1.47460317 1.53492063\n",
+ " 1.5952381 1.65555556 1.71587302 1.77619048 1.83650794 1.8968254\n",
+ " 1.95714286 2.01746032 2.07777778 2.13809524 2.1984127 2.25873016\n",
+ " 2.31904762 2.37936508 2.43968254 2.5 ]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "dBoH_A7M9jjL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "2) Generate an array of length 3n filled with the cyclic pattern 1, 2, 3"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "4TxT66309n1o",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ },
+ "outputId": "7360c210-84bc-4ab3-ea29-bb727a9b2734"
+ },
+ "cell_type": "code",
+ "source": [
+ "n = input(\"enter n: \")\n",
+ "n = int(n)\n",
+ "x = np.array([1, 2, 3])\n",
+ "x = np.resize(x, 3* n)\n",
+ "print(x)"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "enter n: 3\n",
+ "[1 2 3 1 2 3 1 2 3]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Vh-UKizx9oTp",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "3) Create an array of the first 10 odd integers."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ebhEUZq29r32",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "46411c8e-1cdd-467d-e55d-baf7cd9c79e6"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.array(range(1,20,2))\n",
+ "print(a)"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[ 1 3 5 7 9 11 13 15 17 19]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "QfJRdMat90f4",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "4) Find intersection of a and b"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "gOlfuJCo-JwF",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "d9c52a2d-aeb9-4c4e-d185-ea2b76bbb606"
+ },
+ "cell_type": "code",
+ "source": [
+ "#expected output array([2, 4])\n",
+ "a = np.array([1,2,3,2,3,4,3,4,5,6])\n",
+ "b = np.array([7,2,10,2,7,4,9,4,9,8])\n",
+ "c = np.intersect1d(a,b)\n",
+ "print(c)"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[2 4]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "RtVCf0UoCeB8",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "5) Reshape 1d array a to 2d array of 2X5"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "2E8b55_2Cjx5",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ },
+ "outputId": "780edcc0-8aee-495f-a78b-34c1217c95dd"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.arange(10).reshape(2,5)\n",
+ "print(a)"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[0 1 2 3 4]\n",
+ " [5 6 7 8 9]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "dVrSBW1zEjp2",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "6) Create a numpy array to list and vice versa"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "tcBCyhXPEp9C",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ },
+ "outputId": "e3332b84-f458-4d2e-d2ec-515f3fd357f8"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
+ "b= np.array(a)\n",
+ "print(b)\n",
+ "c = list(b)\n",
+ "print(c)\n"
+ ],
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[1 2 3 4 5 6 7 8 9]\n",
+ "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JNqX8wnz9sQJ",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "7) Create a 10 x 10 arrays of zeros and then \"frame\" it with a border of ones."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "4bjP3JAc9vRD",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 221
+ },
+ "outputId": "491d1627-901e-4c13-915a-514de2b42151"
+ },
+ "cell_type": "code",
+ "source": [
+ "arr=np.ones(shape=(12,12))\n",
+ "b=np.zeros(shape=(10,10))\n",
+ "arr[1:11,1:11]=b\n",
+ "print(arr)"
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "xaQgf8tT9v-n",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "8) Create an 8 x 8 array with a checkerboard pattern of zeros and ones using a slicing+striding approach."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "No7fx0Xy9zEh",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 153
+ },
+ "outputId": "43b4c5c0-a4bb-47b8-c746-ce4812a96ac3"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.ones(shape = (8,8))\n",
+ "a[0::2,1::2] = 0\n",
+ "a[1::2,0::2] = 0\n",
+ "print(a)"
+ ],
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[1. 0. 1. 0. 1. 0. 1. 0.]\n",
+ " [0. 1. 0. 1. 0. 1. 0. 1.]\n",
+ " [1. 0. 1. 0. 1. 0. 1. 0.]\n",
+ " [0. 1. 0. 1. 0. 1. 0. 1.]\n",
+ " [1. 0. 1. 0. 1. 0. 1. 0.]\n",
+ " [0. 1. 0. 1. 0. 1. 0. 1.]\n",
+ " [1. 0. 1. 0. 1. 0. 1. 0.]\n",
+ " [0. 1. 0. 1. 0. 1. 0. 1.]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/ardev472.ipynb b/ardev472.ipynb
deleted file mode 100644
index 9e2543a..0000000
--- a/ardev472.ipynb
+++ /dev/null
@@ -1,32 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.5.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}