diff --git a/Lists.ipynb b/Lists.ipynb
new file mode 100644
index 0000000..5d68619
--- /dev/null
+++ b/Lists.ipynb
@@ -0,0 +1,401 @@
+{
+ "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 = [1,2,2,4,3,31,67,98]"
+ ],
+ "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": "806d8595-b1bd-4c20-ec52-9d910de8c5a4"
+ },
+ "cell_type": "code",
+ "source": [
+ "print(dummy_list)"
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[1, 2, 2, 4, 3, 31, 67, 98]\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": "dbef7341-5d37-4c93-ef35-9df5a01ffcf8"
+ },
+ "cell_type": "code",
+ "source": [
+ "dummy_list.reverse()\n",
+ "print(dummy_list)"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[98, 67, 31, 3, 4, 2, 2, 1]\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": "d0a06d9f-a600-42b4-ce59-df4c05adf0e3"
+ },
+ "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": 18,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[98, 67, 31, 3, 4, 2, 2, 1, 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 = {key : dummy_list.count(key) for key in set(dummy_list)}"
+ ],
+ "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": "c09e2010-1f6e-4377-b364-81596e550dc6"
+ },
+ "cell_type": "code",
+ "source": [
+ "print(dummy_dict)"
+ ],
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "{0: 1, 1: 2, 98: 1, 3: 1, 67: 1, 4: 2, 2: 3, 200: 1, 9.45: 1, 12.01: 1, 45.67: 1, 12.02: 1, 16: 1, 90: 1, 31: 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": 50
+ },
+ "outputId": "8968c0ee-1c8f-441d-d54e-5532c261b7c0"
+ },
+ "cell_type": "code",
+ "source": [
+ "ascending_list = sorted(dummy_list, reverse=False)\n",
+ "descending_list = sorted(dummy_list, reverse=True)\n",
+ "print(ascending_list)\n",
+ "print(descending_list)"
+ ],
+ "execution_count": 21,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[0, 1, 1, 2, 2, 2, 3, 4, 4, 9.45, 12.01, 12.02, 16, 31, 45.67, 67, 90, 98, 200]\n",
+ "[200, 98, 90, 67, 45.67, 31, 16, 12.02, 12.01, 9.45, 4, 4, 3, 2, 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": 50
+ },
+ "outputId": "ef05e2ac-29ce-452a-9dd7-1334b7d2bd3d"
+ },
+ "cell_type": "code",
+ "source": [
+ "x = 200\n",
+ "print(dummy_list)\n",
+ "dummy_list.remove(x)\n",
+ "# Let's play: try the same with something which is not in the list to get the ValueError\n",
+ "try :\n",
+ " x = -23\n",
+ " dummy_list.remove(x)\n",
+ "except ValueError:\n",
+ " print(str(x)+' not present in list')"
+ ],
+ "execution_count": 22,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[98, 67, 31, 3, 4, 2, 2, 1, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n",
+ "-23 not present in list\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "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": 195
+ },
+ "outputId": "999f0f36-f5e5-4b4b-b4c8-8fe9b2f58d54"
+ },
+ "cell_type": "code",
+ "source": [
+ "from random import randint \n",
+ "x = randint(0, len(dummy_list))\n",
+ "dummy_list.remove(x)\n",
+ "# Let's play: try doing the same with x > len(dummy_list) + 1 and see what you get\n",
+ "dummy_list.remove(len(dummy_list) + 5)"
+ ],
+ "execution_count": 23,
+ "outputs": [
+ {
+ "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 3\u001b[0m \u001b[0mdummy_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\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[1;32m 4\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[0;32m----> 5\u001b[0;31m \u001b[0mdummy_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\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;36m5\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": "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": 101
+ },
+ "outputId": "e5bd9358-8ae0-4d8e-8335-72b600f88a75"
+ },
+ "cell_type": "code",
+ "source": [
+ "dummy_list.clear()\n",
+ "dummy_list_2.clear()\n",
+ "ascending_list.clear()\n",
+ "descending_list.clear()\n",
+ "dummy_dict.clear()\n",
+ "print(dummy_list)\n",
+ "print(dummy_list_2)\n",
+ "print(ascending_list)\n",
+ "print(descending_list)\n",
+ "print(dummy_dict)"
+ ],
+ "execution_count": 24,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[]\n",
+ "[]\n",
+ "[]\n",
+ "[]\n",
+ "{}\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..f955712
--- /dev/null
+++ b/Numpy_Examples_1.ipynb
@@ -0,0 +1,524 @@
+{
+ "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": 84
+ },
+ "outputId": "2b55c78d-6ca9-4baf-b3e7-0062b1b92ccc"
+ },
+ "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": 3,
+ "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": 218
+ },
+ "outputId": "d70ea2c5-27a1-47aa-b57b-7c238cf7b391"
+ },
+ "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": 4,
+ "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 [[0.26525234 0.7239169 ]\n",
+ " [0.6221951 0.66992727]]\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": 188
+ },
+ "outputId": "b9269bc7-e6ca-410b-c347-ee0f74ca175a"
+ },
+ "cell_type": "code",
+ "source": [
+ "a == np.arange(10)\n",
+ "b == np.linspace(0,10, num=6)\n",
+ "print(a)\n",
+ "print(b)"
+ ],
+ "execution_count": 5,
+ "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": 50
+ },
+ "outputId": "74680dd7-bf09-4ee4-fdd2-32d1d7a33443"
+ },
+ "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",
+ "\n",
+ "# A slice of an array is a view into the same data, so modifying it\n",
+ "# will modify the original array.\n",
+ "\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": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "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": 118
+ },
+ "outputId": "db5a4996-9afa-4d80-e06d-7686c8c0e7b3"
+ },
+ "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": 7,
+ "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": 67
+ },
+ "outputId": "02a017db-7f90-4d00-dcc7-76a37a54a853"
+ },
+ "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": 8,
+ "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": 67
+ },
+ "outputId": "30ef471c-9baa-4b2c-a9ce-964d820000ac"
+ },
+ "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": 9,
+ "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": "59650165-66f0-41cb-c7cf-111e75ffab6c"
+ },
+ "cell_type": "code",
+ "source": [
+ "modified_b = b\n",
+ "modified_b[mask] = -1\n",
+ "\n",
+ "print(modified_b)"
+ ],
+ "execution_count": 10,
+ "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": 118
+ },
+ "outputId": "2b170a3d-8fd8-446e-b837-869f953634bd"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.arange(9).reshape(3,3)\n",
+ "print(a)\n",
+ "\n",
+ "print(a[:, [1,0,2]])"
+ ],
+ "execution_count": 11,
+ "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": 118
+ },
+ "outputId": "4e91b06f-9a42-4bf0-a4dc-cab75d80bf67"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.arange(9).reshape(3,3)\n",
+ "print(a)\n",
+ "\n",
+ "print(a[[1,0,2], :])"
+ ],
+ "execution_count": 13,
+ "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..334d525
--- /dev/null
+++ b/Numpy_Exercises.ipynb
@@ -0,0 +1,337 @@
+{
+ "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": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "import numpy as np #import numpy"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "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": 50
+ },
+ "outputId": "6f0b86e0-b198-40c0-fef7-4ec041367f51"
+ },
+ "cell_type": "code",
+ "source": [
+ "cyclic_array = np.resize([1,2,3], 3*int(input()))\n",
+ "print(cyclic_array)"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "4\n",
+ "[1 2 3 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": "09d09ba4-022e-4473-8abb-959c97a348bb"
+ },
+ "cell_type": "code",
+ "source": [
+ "odds = np.arange(1,20,2)\n",
+ "print(odds)"
+ ],
+ "execution_count": 8,
+ "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": "174d4f93-9b43-47d4-9e29-27568c8aabaa"
+ },
+ "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",
+ "intersection = np.intersect1d(a,b)\n",
+ "print(intersection)"
+ ],
+ "execution_count": 9,
+ "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": 50
+ },
+ "outputId": "827c5cd3-eb58-4f00-dc22-d86aa4866295"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = np.arange(10)\n",
+ "reshaped_array = a.reshape((2,5))\n",
+ "print(reshaped_array)"
+ ],
+ "execution_count": 10,
+ "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": 50
+ },
+ "outputId": "efc118e9-7bc1-487d-b28d-08aa396ca859"
+ },
+ "cell_type": "code",
+ "source": [
+ "a = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
+ "numpy_array = np.array(a)\n",
+ "converted_list = list(a)\n",
+ "print(numpy_array)\n",
+ "print(converted_list)"
+ ],
+ "execution_count": 12,
+ "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": 185
+ },
+ "outputId": "8dccf8a7-b7e8-4367-be8d-12d0f5fd2aa2"
+ },
+ "cell_type": "code",
+ "source": [
+ "zeros = np.zeros((10,10))\n",
+ "zeros[0,:] = 1\n",
+ "zeros[9,:] = 1\n",
+ "zeros[:,0] = 1\n",
+ "zeros[:,9] = 1\n",
+ "print(zeros)"
+ ],
+ "execution_count": 22,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+ " [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": 151
+ },
+ "outputId": "7c75d531-e711-4301-bc06-b9ae819c0e53"
+ },
+ "cell_type": "code",
+ "source": [
+ "checkerboard = np.zeros((8,8))\n",
+ "checkerboard[1::2, ::2] = 1\n",
+ "checkerboard[::2, 1::2] = 1\n",
+ "print(checkerboard)"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[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",
+ " [1. 0. 1. 0. 1. 0. 1. 0.]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/loneWolf148.ipynb b/loneWolf148.ipynb
deleted file mode 100644
index 9e2543a..0000000
--- a/loneWolf148.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
-}