3-loop.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_tags": []
   },
   "source": [
    "## Repeat a block of code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_tags": [
     "objectives"
    ]
   },
   "source": [
    "#### Objectives\n",
    "\n",
    "*   Explain what a for loop does.\n",
    "*   Correctly write for loops to repeat simple calculations.\n",
    "*   Trace changes to a loop variable as the loop runs.\n",
    "*   Trace changes to other variables as they are updated by a for loop.\n",
    "*   Explain what a list is.\n",
    "*   Create and index lists of simple values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_tags": []
   },
   "source": [
    "### Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_tags": []
   },
   "source": [
    "We create a list by putting values inside square brackets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_tags": [],
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names = ['Alice', 'Bob', 'Carl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "type(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names.append('Dave')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"Printing a name: \" + names[0])\n",
    "print(\"Printing a name: \" + names[1])\n",
    "print(\"Printing a name: \" + names[2])\n",
    "print(\"Printing a name: \" + names[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_tags": [],
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for n in names:\n",
    "    print(\"Printing a name: \" + n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_tags": []
   },
   "source": [
    "### For Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_tags": []
   },
   "source": [
    "A [for loop](../../gloss.html#for-loop)\n",
    "to repeat an operation---in this case, printing---once for each thing in a collection.\n",
    "The general form of a loop is:\n",
    "\n",
    "<pre>\n",
    "<strong>for</strong> <em>variable</em> <strong>in</strong> <em>collection</em><strong>:</strong>\n",
    "    <em>do things with variable</em>\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_tags": [],
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names.reverse()\n",
    "print(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "len(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "len(names[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a cell in the notebook that computes the sum of the number of characters of each string in the names list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tot = 0\n",
    "for name in names:\n",
    "    tot = tot + len(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(tot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def compute_total_characters(names):\n",
    "    tot = 0\n",
    "    for name in names:\n",
    "        tot = tot + len(name)\n",
    "    return tot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_tags": []
   },
   "source": [
    "### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert compute_total_characters([]) == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert compute_total_characters([\"a\"]) == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert compute_total_characters([\"aa\", \"abc\"]) == 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the notebook http://nbviewer.ipython.org/github/zonca/challenges/blob/master/challenge-notebook-3.ipynb, the solution to this challenge is below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_tags": [],
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def fahr_to_celsius(temp_fahr):\n",
    "    \"\"\"Convert temperature from Fahrenheit to Celsius\"\"\"\n",
    "    temp_celsius = (temp_fahr - 32) * 5 / 9.0\n",
    "    return temp_celsius\n",
    "\n",
    "def analyze(data):\n",
    "    \"\"\"Perform regression analysis on mosquito data\n",
    "   \n",
    "    Performs a linear regression based on rainfall.\n",
    "    Creates a plot of the result and returns fit parameters.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data : pandas.Dataframe\n",
    "        Column named 'temperature', 'rainfall' and 'mosquitos'.\n",
    "            \n",
    "    Returns\n",
    "    -------\n",
    "    parameters_rainfall : pandas.Series\n",
    "        Fit parameters named Intercept and rainfall\n",
    "    parameters_temperature : pandas.Series\n",
    "        Fit parameters named Intercept and temperature\n",
    "    \"\"\"\n",
    "    data['temperature'] = fahr_to_celsius(data['temperature'])\n",
    "    output = []\n",
    "    for variable in ['rainfall', 'temperature']:\n",
    "        # linear fit\n",
    "        regr_results = sm.OLS.from_formula('mosquitos ~ ' + variable, data).fit()\n",
    "        parameters = regr_results.params\n",
    "        line_fit = parameters['Intercept'] + parameters[variable] * data[variable]\n",
    "        # plotting\n",
    "        plt.figure()\n",
    "        plt.plot(data[variable], data['mosquitos'], '.', label=\"data\")\n",
    "        plt.plot(data[variable], line_fit, 'red', label=\"fit\")\n",
    "        plt.xlabel(variable)\n",
    "        plt.ylabel('mosquitos')\n",
    "        plt.legend(loc='best')\n",
    "        output.append(parameters)\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_A2 = pd.read_csv('A2_mosquito_data.csv')\n",
    "parameters_A2 = analyze(data_A2)\n",
    "print(parameters_A2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_tags": []
   },
   "source": [
    "#### Key Points\n",
    "\n",
    "*   Use `for variable in collection` to process the elements of a collection one at a time.\n",
    "*   The body of a for loop must be indented.\n",
    "*   Use `len(thing)` to determine the length of something that contains other values.\n",
    "*   `[value1, value2, value3, ...]` creates a list.\n",
    "*   Lists are indexed and sliced in the same way as strings and arrays."
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}