Python 101.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<h3 align=center>Comparison operations</h3> "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Find the value of <code>i</code> that produces a <code>True</code> "
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "True"
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "i=200\ni>100"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<h3 align=center>Branching </h3> "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Find the value of <code>x</code> that prints the statement: <code>\"this is a\"</code>"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {
                "collapsed": false,
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "this is a\n"
                }
            ],
            "source": "x='a'\nif(x=='a'):\n    print(\"this is a\")\nelse:\n    print(\"this is not a\")"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<h3 align=center>Logic Operators</h3> "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "find the value of <code>y</code> that produces a <code>True</code> statement "
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "True"
                    },
                    "execution_count": 1,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "y=1\nx=1\nx>0 and y<10"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<hr>\n<small>Copyright &copy; 2018 IBM Cognitive Class. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/).</small>"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python",
            "language": "python",
            "name": "conda-env-python-py"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.6.10"
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    "nbformat": 4,
    "nbformat_minor": 4
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