Lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introductory applied machine learning (INFR10069)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 1: Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first part of this lab we will set-up a working environment for the IAML course. In the second part we will introduce some packages we will be using throughout the course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up for DICE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within this course we will be using Python along with a few open-source libraries (packages). These packages cannot be installed directly, so we will have to create a virtual environment. We are using virtual enviroments to make the installation of packages and retention of correct versions as simple as possible. You can read [here](https://virtualenv.pypa.io/en/stable/) if you want to learn about virtual environments, but this is **not** neccessary for this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now open a terminal and follow these instructions. We are expecting you to enter these commands in **one-by-one**. Waiting for each command to complete will help catch any unexpected warnings and errors. Please read heed any warnings and errors you may encounter. We are on standby in the labs to help if required.\n",
    "\n",
    "1. Change directory to home and create a virtual enviroment\n",
    "```bash\n",
    "cd\n",
    "virtualenv --distribute virtualenvs/iaml_env  # Creates a virtual environment called iaml_env\n",
    "```\n",
    "   \n",
    "2. Navigate to and activate the virtual enviroment (you will need to activate the virtual environment every time you open a new terminal - this adds the correct python version with all installed packages to your system's `$PATH` environment variable)\n",
    "```bash\n",
    "cd virtualenvs/iaml_env\n",
    "source ./bin/activate  # Activates the environment, your shell prompt should now \n",
    "                          # change to reflect you are in the `iaml_env` enviornment\n",
    "```\n",
    "\n",
    "3. Install all the python packages we need (once the correct virtual environment is activated, pip install will install packages to the virtual environent - if you're ever unsure which python you are using, type `which python` in the terminal) **WATCH FOR WARNINGS AND ERRORS HERE**. We have split these commands up to encourage you to enter them one-by-one.\n",
    "   \n",
    "    * `pip install -U setuptools  # The -U flag upgrades the current version`\n",
    "    * `pip install -U pip`\n",
    "    * `pip install yolk`\n",
    "    * `pip install jupyter`\n",
    "    * `pip install numpy`\n",
    "    * `pip install scipy`\n",
    "    * `pip install matplotlib`\n",
    "    * `pip install pandas`\n",
    "    * `pip install statsmodels`\n",
    "    * `pip install scikit-learn`\n",
    "    * `pip install seaborn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should now have all the required modules installed. Our next step is to make a new directory where we will keep all the lab notebooks, datasets and assignments. Within your terminal:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Navigate back to your home directory\n",
    "    * `cd`\n",
    "1. Make a new directory and navigate to it\n",
    "    * `mkdir iaml_2016`\n",
    "    * `cd iaml_2016`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you have two options:\n",
    "\n",
    "1. We recommend that you directly download a .zip file from https://github.com/agamemnonc/iaml which will contain everything you need and save it in the folder you have just created. You can do this from the terminal by typing:\n",
    "    * `wget https://github.com/agamemnonc/iaml/archive/master.zip`\n",
    "2. If **and only if** you are familiar and confident with using Git/GitHub, you can initiliaze a git directory, add the above repo as remote and pull everything into your local directory. Please use this option only if you really know what you are doing. Unfortunately, we won't be able to provide you with Git/Github support if you run into issues with syncing and using version control in general. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have downloaded the material, you are now ready to start working in the Jupyter environment. First you need to activate the `iaml_env` environment and start a Jupyter Notebook session from within the folder where the material is stored. **Note that you will have to follow this procedure for all labs and assignments.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Navigate home and ensure the `iaml_env` virtualenv is activated\n",
    "    * `cd`\n",
    "    * `source virtualenvs/iaml_env/bin/activate` (Activate environment)\n",
    "1. Enter the directory you downloaded the course material\n",
    "    * `cd iaml_2016`\n",
    "1. Start a jupyter notebook\n",
    "    * `jupyter notebook`\n",
    "1. This should automatically open your browser\n",
    "    * Click on `01_Lab_1_Introduction.ipynb` to open it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up for personal machine (Windows / OS X / Ubuntu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are using a personal machine, you can choose whether to do as above or use the Anaconda distribution (Python version 2.7, choose the appropriate installer according to your operating system). Anaconda is a standard set of packages used in scientific computing which the Anaconda team curate to keep them consistent.\n",
    "\n",
    "Once the installation is complete, we need to install the Seaborn package which is the only one not included in the distribution. It's also recommended that you set up a virtual environment for this project. This way, if you update anything in your anaconda base install, this virtual environment will remain unchanged. To create a virtual environment called `iaml`, open a Terminal (or Command Prompt window if you are running Windows) and type:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```bash\n",
    "conda create -n iaml python=2.7 anaconda seaborn=0.7.0\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to activate the virtual environment every time you begin work from a new terminal:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```bash\n",
    "source activate iaml\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have finished installed everything, open a terminal (or Command Prompt in Windows), navigate to the folder where you have downloaded the course material and type:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```bash\n",
    "jupyter notebook\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then click on `01_Lab_1_Introduction.ipynb` to open it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducing main packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following section we introduce the main **packages** we will be using in this course. If you think that you are already familiar with the packages outlined below you are free to skip this section. We do advise you, however, to go quickly through this tutorial to make sure that you are familiar with all the tools you will need for the rest of the course.\n",
    "\n",
    "The packages that we will use are the following:\n",
    "* [numpy:](http://www.numpy.org/) scientific computing by using array objects\n",
    "\n",
    "\n",
    "* [pandas](http://pandas.pydata.org/): data structures and data analysis tools\n",
    "\n",
    "\n",
    "* [matplotlib](http://matplotlib.org/): plotting library (similar to MATLAB's plot interface)\n",
    "\n",
    "\n",
    "* [seaborn](https://stanford.edu/~mwaskom/software/seaborn/): data visualisation library which works on top of matplotlib\n",
    "\n",
    "Throughout the course, we will also make heavy use of [scikit-learn](http://scikit-learn.org/stable/) which is a machine learning library implementing many learning algorithms and useful tools. This is introduced in Lab 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IPython / Jupyter environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic knowledge of `python` and working with `notebooks` in the Jupyter environment is assumed for this course. If you haven't used python before, you are strongly advised to familiarise yourselves with basic python syntax and working in the Jupyter environment. There are many excellent tutorials available on the web and you can choose the ones you like the most. If you are not sure which ones to choose, these are good starting points:\n",
    "\n",
    "[Introduction to Python for scientific computing](http://bebi103.caltech.edu/2015/tutorials/t1a_intro_to_python.html)\n",
    "\n",
    "[Introduction to Jupyter notebooks](http://bebi103.caltech.edu/2015/tutorials/t0b_intro_to_jupyter_notebooks.html)\n",
    "\n",
    "[Python/Numpy tutorial](http://cs231n.github.io/python-numpy-tutorial/#python)\n",
    "(A large part of Lab 1 is based on this tutorial)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line magics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes we might want to connect to the same notebook kernel from multiple frontends. This is useful for excecuting quick calculations, or checking objects currently stored in the kernel, without having to create a new cell in the notebook.\n",
    "\n",
    "The `%connect_info` line magic displays the information necessary to connect another frontend to the Notebook kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"stdin_port\": 60857, \n",
      "  \"ip\": \"127.0.0.1\", \n",
      "  \"control_port\": 60858, \n",
      "  \"hb_port\": 60859, \n",
      "  \"signature_scheme\": \"hmac-sha256\", \n",
      "  \"key\": \"5e88597a-d3cb-4a92-a52a-41e0173ee202\", \n",
      "  \"kernel_name\": \"\", \n",
      "  \"shell_port\": 60855, \n",
      "  \"transport\": \"tcp\", \n",
      "  \"iopub_port\": 60856\n",
      "}\n",
      "\n",
      "Paste the above JSON into a file, and connect with:\n",
      "    $> ipython <app> --existing <file>\n",
      "or, if you are local, you can connect with just:\n",
      "    $> ipython <app> --existing /Users/sneeze/Library/Jupyter/runtime/kernel-bab6e7ce-7551-47c3-85f3-64de5676c70a.json \n",
      "or even just:\n",
      "    $> ipython <app> --existing \n",
      "if this is the most recent IPython session you have started.\n"
     ]
    }
   ],
   "source": [
    "%connect_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can use `%qtconsole` to launch a Jupyter QtConsole connected to the Notebook kernel. You can verify that the QtConsole and your Notebook share the same workspace (i.e. any variables declared in the Notebook will exist in the QtConsole and viceversa)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%qtconsole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tab completion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tab completion is a powerful method of finding out objects attributes and available methods.\n",
    "\n",
    "Let's see an example of this by using a Python [list](http://www.tutorialspoint.com/python/python_lists.htm). We will create a list and then you can see what methods are available by typing the list name followed by `.` and then hitting the < Tab > key. Then you can access any method's help documentation by hitting the method's name followed by `?`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4.2, 'hello']\n"
     ]
    }
   ],
   "source": [
    "l = [1, 4.2, 'hello']\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l.append?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we start, we need to import the packages that we will be using later. If you are having trouble importing any of these packages make sure that these are properly installed. If you still encounter issues, refer to Installing instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function # Makes division and printing work like python 3 (we're using 2)\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy is a powerful scientific computing library. The fundmental object is the (n-dimensional) numpy array and the library provides a collection of tools for working with such arrays. If you are already familiar with MATLAB you might find this [tutorial](https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html) useful. \n",
    "\n",
    "The following Numpy introduction is largely based on this [tutorial](http://cs231n.github.io/python-numpy-tutorial/#numpy)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A numpy array is a grid of values, all of the same type, and is indexed by a [tuple](http://www.tutorialspoint.com/python/python_tuples.htm) of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension. *N.B. this use of the word 'rank' is not the same as the meaning in linear algebra.*\n",
    "\n",
    "We can initialize numpy arrays from nested Python [lists](http://www.tutorialspoint.com/python/python_lists.htm), and access elements using square brackets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array a: [1 2 3]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([1, 2, 3])  # Create a rank 1 array (i.e. vector)\n",
    "print('Array a:', a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(a))                    # Prints the type of object a (array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n"
     ]
    }
   ],
   "source": [
    "print(a.shape)                    # Prints the number of elements for each dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3\n"
     ]
    }
   ],
   "source": [
    "print(a[0], a[1], a[2])           # Index array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a[0] = 5                          # Change an element of the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 2 3]\n"
     ]
    }
   ],
   "source": [
    "print(a)                          # Prints the whole array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = np.array([[1,2,3],[4,5,6]])   # Create a rank 2 array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n"
     ]
    }
   ],
   "source": [
    "print(b.shape)                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 4\n"
     ]
    }
   ],
   "source": [
    "print(b[0, 0], b[0, 1], b[1, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point it worth noting -in case you didn't know already- that Python follows [0-based indexing](https://en.wikipedia.org/wiki/Zero-based_numbering) (as opposed to MATLAB which follows 1-based indexing)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In an ipython notebook you can just run a cell with an object name and ipython will try to print it. For example, execute the below cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 2, 3])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have many options here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.]\n",
      " [ 0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "a = np.zeros((2,2))  # Create an array of all zeros\n",
    "print(a)                    # Prints \"[[ 0.  0.]\n",
    "                     #          [ 0.  0.]]\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "b = np.ones((1,2))   # Create an array of all ones\n",
    "print(b)                    # Prints \"[[ 1.  1.]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7.  7.]\n",
      " [ 7.  7.]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sneeze/anaconda/envs/iaml/lib/python2.7/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full((2, 2), 7) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "c = np.full((2,2), 7) # Create a constant array\n",
    "print(c)                     # Prints \"[[ 7.  7.]\n",
    "                      #          [ 7.  7.]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  0.]\n",
      " [ 0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "d = np.eye(2)        # Create a 2x2 identity matrix\n",
    "print(d)                    # Prints \"[[ 1.  0.]\n",
    "                     #          [ 0.  1.]]\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.44821849  0.07930664]\n",
      " [ 0.62681648  0.03745692]]\n"
     ]
    }
   ],
   "source": [
    "e = np.random.random((2,2)) # Create an array filled with random values\n",
    "print(e)                           # Might print \"[[ 0.91940167  0.08143941]\n",
    "                            #               [ 0.68744134  0.87236687]]\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  4.  6.  8.]\n"
     ]
    }
   ],
   "source": [
    "some_list = [1, 4, 6, 8]\n",
    "f = np.array(some_list, dtype=float) # Create an array from a list. Also sets the array type to float\n",
    "print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing is the most common way to index arrays. This works in a similar way to indexing python lists. \n",
    "\n",
    "There are also other options, such as integer and boolean array indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 3]\n",
      " [6 7]]\n"
     ]
    }
   ],
   "source": [
    "# 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "# A slice of an array is a view into the same data, so modifying it\n",
    "# will modify the original array. This is called \n",
    "print(a[0,1])  # Prints \"2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77\n"
     ]
    }
   ],
   "source": [
    "b[0, 0] = 77    # b[0, 0] is the same piece of data as a[0, 1]\n",
    "print(a[0,1])          # Prints \"77\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array datatypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every numpy array is a grid of elements of the same type. Numpy provides a large set of numeric datatypes that you can use to construct arrays. Numpy tries to guess a datatype when you create an array, but functions that construct arrays usually also include an optional argument to explicitly specify the datatype. Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 2])  # Let numpy choose the datatype\n",
    "print(x.dtype)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1.0, 2.0])  # Let numpy choose the datatype\n",
    "print(x.dtype)             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 2], dtype=np.int64)  # Force a particular datatype\n",
    "print(x.dtype)                 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic mathematical functions operate elementwise on arrays, and are available both as operator overloads and as functions in the numpy module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  [[ 1.  2.]\n",
      " [ 3.  4.]]\n",
      "y =  [[ 5.  6.]\n",
      " [ 7.  8.]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2],[3,4]], dtype=np.float64)\n",
    "y = np.array([[5,6],[7,8]], dtype=np.float64)\n",
    "print('x = ',x)\n",
    "print('y = ',y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x + y =\n",
      "[[  6.   8.]\n",
      " [ 10.  12.]]\n",
      "[[  6.   8.]\n",
      " [ 10.  12.]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise sum, equivalent expressions:\n",
    "print('x + y =')\n",
    "print(x + y)\n",
    "print(np.add(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x-y:\n",
      "[[-4. -4.]\n",
      " [-4. -4.]]\n",
      "[[-4. -4.]\n",
      " [-4. -4.]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise difference, equivalent expressions:\n",
    "print('x-y:')\n",
    "print(x - y)\n",
    "print(np.subtract(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x*y:\n",
      "[[  5.  12.]\n",
      " [ 21.  32.]]\n",
      "[[  5.  12.]\n",
      " [ 21.  32.]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise product, equivalent expressions:\n",
    "print('x*y:')\n",
    "print(x * y)\n",
    "print(np.multiply(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x/y:\n",
      "[[ 0.2         0.33333333]\n",
      " [ 0.42857143  0.5       ]]\n",
      "[[ 0.2         0.33333333]\n",
      " [ 0.42857143  0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise division, equivalent expressions:\n",
    "print('x/y:')\n",
    "print(x / y)\n",
    "print(np.divide(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Square root of x:\n",
      "[[ 1.          1.41421356]\n",
      " [ 1.73205081  2.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise square root\n",
    "print('Square root of x:')\n",
    "print(np.sqrt(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that unlike MATLAB, * is elementwise multiplication, not matrix multiplication. We instead use the `dot` function to compute inner products of vectors, to multiply a vector by a matrix, and to multiply matrices. `dot` is available both as a function in the numpy module and as an instance method of array objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "y = np.array([[5,6],[7,8]])\n",
    "\n",
    "v = np.array([9,10])\n",
    "w = np.array([11, 12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219\n"
     ]
    }
   ],
   "source": [
    "# Inner product of vectors, equivalent expressions\n",
    "print(np.dot(v, w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[29 67]\n",
      "[29 67]\n"
     ]
    }
   ],
   "source": [
    "# Matrix / vector product, equivalent expressions\n",
    "print(x.dot(v))\n",
    "print(np.dot(x, v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[19 22]\n",
      " [43 50]]\n",
      "[[19 22]\n",
      " [43 50]]\n"
     ]
    }
   ],
   "source": [
    "# Matrix / matrix product, equivalent expressions\n",
    "print(x.dot(y))\n",
    "print(np.dot(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mathematical functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy provides many useful functions for performing computations on arrays; one of the most useful is `sum`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x)  # Compute sum of all elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 6])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x, axis=0)  # Compute sum of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 7])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x, axis=1)  # Compute sum of each row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find the full list of mathematical functions provided by numpy in the [documentation](http://docs.scipy.org/doc/numpy/reference/routines.math.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apart from computing mathematical functions using arrays, we frequently need to reshape or otherwise manipulate data in arrays. The simplest example of this type of operation is transposing a matrix; to transpose a matrix, simply use the `T` attribute of an array object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2], [3,4]])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [2, 4]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [2, 4]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(x) # Equivalent expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "# Note that taking the transpose of a rank 1 array does nothing:\n",
    "v = np.array([1,2,3])\n",
    "print(v)  # Prints \"[1 2 3]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "print(v.T)  # Prints \"[1 2 3]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting is a powerful mechanism that allows numpy to work with arrays of different shapes when performing arithmetic operations. Frequently we have a smaller array and a larger array, and we want to use the smaller array multiple times to perform some operation on the larger array.\n",
    "\n",
    "For example, suppose that we want to add a constant vector to each row of a matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
    "v = np.array([1, 0, 1])\n",
    "y = x + v  # Add v to each row of x using broadcasting\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x shape: (4, 3)\n",
      "v shape: (3,)\n",
      "y shape: (4, 3)\n"
     ]
    }
   ],
   "source": [
    "print('x shape:', x.shape)\n",
    "print('v shape:', v.shape)\n",
    "print('y shape:', y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The line `y = x + v` works even though `x` has shape `(4, 3)` and `v` has shape `(3,)` due to broadcasting; this line works as if v actually had shape `(4, 3)`, where each row was a copy of `v`, and the sum was performed elementwise.\n",
    "\n",
    "Broadcasting two arrays together follows these rules:\n",
    "\n",
    "If the arrays do not have the same rank, prepend the shape of the lower rank array with 1s until both shapes have the same length.\n",
    "The two arrays are said to be compatible in a dimension if they have the same size in the dimension, or if one of the arrays has size 1 in that dimension.\n",
    "The arrays can be broadcast together if they are compatible in all dimensions.\n",
    "After broadcasting, each array behaves as if it had shape equal to the elementwise maximum of shapes of the two input arrays.\n",
    "In any dimension where one array had size 1 and the other array had size greater than 1, the first array behaves as if it were copied along that dimension."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This brief overview has touched on many of the important things that you need to know about numpy, but is far from complete. Check out the numpy reference to find out much more about numpy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following short exercises test your understanding of simple -yet important- numpy properties and functions. Make sure you can complete them and feel free to advise the official [documentation](http://docs.scipy.org/doc/) should you need it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 1 ==========\n",
    "Print your numpy version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lapack_opt_info:\n",
      "    libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread']\n",
      "    library_dirs = ['/Users/sneeze/anaconda/envs/iaml/lib']\n",
      "    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\n",
      "    include_dirs = ['/Users/sneeze/anaconda/envs/iaml/include']\n",
      "blas_opt_info:\n",
      "    libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread']\n",
      "    library_dirs = ['/Users/sneeze/anaconda/envs/iaml/lib']\n",
      "    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\n",
      "    include_dirs = ['/Users/sneeze/anaconda/envs/iaml/include']\n",
      "openblas_lapack_info:\n",
      "  NOT AVAILABLE\n",
      "lapack_mkl_info:\n",
      "    libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread']\n",
      "    library_dirs = ['/Users/sneeze/anaconda/envs/iaml/lib']\n",
      "    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\n",
      "    include_dirs = ['/Users/sneeze/anaconda/envs/iaml/include']\n",
      "blas_mkl_info:\n",
      "    libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread']\n",
      "    library_dirs = ['/Users/sneeze/anaconda/envs/iaml/lib']\n",
      "    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\n",
      "    include_dirs = ['/Users/sneeze/anaconda/envs/iaml/include']\n",
      "mkl_info:\n",
      "    libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread']\n",
      "    library_dirs = ['/Users/sneeze/anaconda/envs/iaml/lib']\n",
      "    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\n",
      "    include_dirs = ['/Users/sneeze/anaconda/envs/iaml/include']\n"
     ]
    }
   ],
   "source": [
    "np.show_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 2 ==========\n",
    "Create a null vector of size 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "z = np.zeros(5)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 3 ==========\n",
    "Create a null vector of size 5 and integer type. Set the third element is 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "z = np.zeros(5, dtype=np.int64)\n",
    "z[2] = 1\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 4 ==========\n",
    "Create a vector ranging from 0 to 9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n"
     ]
    }
   ],
   "source": [
    "z = np.linspace(0,9,10)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 5 ==========\n",
    "Create a vector ranging from 10 to 29."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 10.  11.  12.  13.  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.\n",
      "  25.  26.  27.  28.  29.]\n"
     ]
    }
   ],
   "source": [
    "z = np.linspace(10,29,20)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 6 ==========\n",
    "Create a vector ranging from 0 to 9 and reverse it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9.  8.  7.  6.  5.  4.  3.  2.  1.  0.]\n"
     ]
    }
   ],
   "source": [
    "z = np.linspace(0,9,10)\n",
    "z = z[::-1]\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 7 ==========\n",
    "Create a null matrix of size (5,3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "z = np.zeros((5,3))\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 8 ==========\n",
    "Crate a 3 X 3 matrix ranging from 0 to 8 and print its transpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  1.  2.]\n",
      " [ 3.  4.  5.]\n",
      " [ 6.  7.  8.]]\n"
     ]
    }
   ],
   "source": [
    "z = np.linspace(0,8,9).reshape(3,3)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 9 ==========\n",
    "Create a 3 X 3 identity matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  0.  0.]\n",
      " [ 0.  1.  0.]\n",
      " [ 0.  0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "z = np.eye(3)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 10 ==========\n",
    "Create a 2 X 2 X 2 array with random values (drawn from a normal distribution)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "z = np.random.random((2,2,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 11 ==========\n",
    "Create a 5 x 4 array with random values and find the minimum and maximum values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0792548188148 0.998611932958\n"
     ]
    }
   ],
   "source": [
    "z = np.random.random((5,4))\n",
    "zmin, zmax = z.min(), z.max()\n",
    "print(zmin, zmax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 12 ==========\n",
    "Find the mean value of the array in 11."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.504709758605\n"
     ]
    }
   ],
   "source": [
    "z = np.random.random((5,4))\n",
    "m = z.mean()\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 13 ==========\n",
    "Find the row means of the array in 11."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.53447209  0.91190071  0.56016731  0.47132924  0.68967374]\n"
     ]
    }
   ],
   "source": [
    "z = np.random.random((5,3))\n",
    "m = np.mean(z,axis=1)\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 14 ==========\n",
    "Find the column means of the array in 11."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.58182368  0.7026423   0.27706982]\n"
     ]
    }
   ],
   "source": [
    "z = np.random.random((5,3))\n",
    "m = np.mean(z, axis=0)\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 15 ==========\n",
    "Create a list with elements 2.2, 3.5, 0, 4, 0. and convert into numpy array. Find the indices of non-zero elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.2  3.5  0.   4.   0. ]\n"
     ]
    }
   ],
   "source": [
    "some_list = [2.2, 3.5, 0, 4, 0]\n",
    "f = np.array(some_list)\n",
    "print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 16 ==========\n",
    "Crate two random matrices of shape (5,4) and (4,2). Print their matrix product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.  3.]\n",
      " [ 3.  3.]\n",
      " [ 3.  3.]\n",
      " [ 3.  3.]\n",
      " [ 3.  3.]]\n"
     ]
    }
   ],
   "source": [
    "Z = np.dot(np.ones((5,3)), np.ones((3,2)))\n",
    "print(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 17 ==========\n",
    "Crate a random matrix of shape (5,3) and a random vector of size 3. Use broadcasting to add the two arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.37559322  0.06633006  1.00731754]\n",
      " [ 1.68978792  0.5084336   1.30539921]\n",
      " [ 1.92651533  0.89522302  1.33774296]\n",
      " [ 1.41983162  0.95832208  1.33758612]\n",
      " [ 1.09520463  0.29461069  1.05020131]]\n"
     ]
    }
   ],
   "source": [
    "z = np.random.random((5,3))\n",
    "v = np.array([1, 0, 1])\n",
    "y = z + v\n",
    "print(y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Pandas is a library for data manipulation and analysis. There are two fundamental data structures in pandas: the **Series** and **DataFrame** structures which are built on top of NumPy arrays.\n",
    "\n",
    "The following introduction is largely based on this [tutorial](http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/). Another useful referece is the [Pandas introduction to data structures](http://pandas.pydata.org/pandas-docs/stable/dsintro.html). Pandas is well documented and you will find good information about all methods and structures in the [API reference](http://pandas.pydata.org/pandas-docs/stable/api.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A **Series** a one-dimensional object (similar to a vector). Each element has a corresponding *index*. By default the indices range from 0 to N, where N is the length of the Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# First we need to import the pandas package\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        1\n",
      "1      4.2\n",
      "2    Hello\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Let's create a Series by passing in a list without specifying the indices.\n",
    "s = pd.Series([1, 4.2, 'Hello'])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A        1\n",
      "B      4.2\n",
      "C    Hello\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Now, let's specify the indices explicitly\n",
    "s = pd.Series([1, 4.2, 'Hello'], index=['A', 'B', 'C'])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.2"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indexing the Series\n",
    "s['B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B      4.2\n",
       "C    Hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also index by using boolean logic\n",
    "s[s>2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A DataFrame is a tabular data structure comprised of rows and columns. You can also think of the DataFrame as a collection of Series objects that share an index. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating DataFrame structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create an empty DataFrame by specifying the column names. Then we can insert data row by row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Gender, Age, Height, Weight]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(columns=['Gender', 'Age', 'Height', 'Weight'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Age  Height  Weight\n",
       "0    Male  23.0   180.0    73.0\n",
       "A  Female  27.0   167.0    59.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's add an observation\n",
    "df.loc[0] = ['Male', 23, 180, 73] # Note how we used .loc to specify the index\n",
    "df.loc['A'] = ['Female', 27, 167, 59]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating DataFrame from other structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also create a dataframe from:\n",
    "* Dict of 1D ndarrays, lists, dicts, or Series\n",
    "* 2-D numpy.ndarray\n",
    "* Structured or record ndarray\n",
    "* A Series\n",
    "* Another DataFrame\n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gender  Age  Height  Weight\n",
      "0    Male   23     180      73\n",
      "A  Female   27     167      59\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame from a list\n",
    "some_list = [['Male', 23, 180, 73], ['Female', 27, 167, 59]]\n",
    "df = pd.DataFrame(some_list, index=[0, 'A'], columns=['Gender', 'Age', 'Height', 'Weight'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gender  Age  Height  Weight\n",
      "0    Male   23     180      73\n",
      "A  Female   27     167      59\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame from a dictionary\n",
    "some_dict = {0:['Male', 23, 180, 73], 'A':['Female', 27, 167, 59]}\n",
    "df2 = pd.DataFrame.from_dict(some_dict, orient='index')\n",
    "df2.columns = ['Gender', 'Age', 'Height', 'Weight']\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gender Age Height Weight\n",
      "0    Male  23    180     73\n",
      "A  Female  27    167     59\n"
     ]
    }
   ],
   "source": [
    "# By default, dictionary keys are assumed to be the column headers, above we\n",
    "# explicitly made keys the row index using the `orient` argument. Below\n",
    "# We transpose using the `.T` method\n",
    "df3 = pd.DataFrame({0:['Male', 23, 180, 73], 'A':['Female', 27, 167, 59]}).T\n",
    "df3.columns = ['Gender', 'Age', 'Height', 'Weight']\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading a CSV into a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most commonly we create DataFrame structures by reading csv files. To run the following piece of code you need to download the datasets associated with the course and place them in a subdirectory called \"datasets\" under the same directory that your notebooks are located. Alternatively, you can specify the full path of the .csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adviser</td>\n",
       "      <td>125.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    vendor   MYCT    MMIN     MMAX   CACH  CHMIN  CHMAX    ERP\n",
       "0  adviser  125.0   256.0   6000.0  256.0   16.0  128.0  199.0\n",
       "1   amdahl   29.0  8000.0  32000.0   32.0    8.0   32.0  253.0\n",
       "2   amdahl   29.0  8000.0  32000.0   32.0    8.0   32.0  253.0\n",
       "3   amdahl   29.0  8000.0  32000.0   32.0    8.0   32.0  253.0\n",
       "4   amdahl   29.0  8000.0  16000.0   32.0    8.0   16.0  132.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu_loc = os.path.join(os.getcwd(), 'datasets', 'cpu.csv')\n",
    "cpu = pd.read_csv(cpu_loc)\n",
    "cpu.head() # Head shows the first 10 elements (unless specified otherwise) of the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that each observation in our dataset comprises 8 measurements (attributes)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic methods\n",
    "* `head(N)`: displays the first N elements of the DataFrame\n",
    "* `tail(N)`: displays the last N elements of the DataFrame\n",
    "* `info()`:  displays basic information about the variables\n",
    "* `describe()`: displays summary statistics of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the following cells and observe the outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>sperry</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>sperry</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>sratus</td>\n",
       "      <td>125.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>wang</td>\n",
       "      <td>480.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>wang</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vendor   MYCT    MMIN    MMAX  CACH  CHMIN  CHMAX   ERP\n",
       "204  sperry  124.0  1000.0  8000.0   0.0    1.0    8.0  37.0\n",
       "205  sperry   98.0  1000.0  8000.0  32.0    2.0    8.0  50.0\n",
       "206  sratus  125.0  2000.0  8000.0   0.0    2.0   14.0  41.0\n",
       "207    wang  480.0   512.0  8000.0  32.0    0.0    0.0  47.0\n",
       "208    wang  480.0  1000.0  4000.0   0.0    0.0    0.0  25.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 209 entries, 0 to 208\n",
      "Data columns (total 8 columns):\n",
      "vendor    209 non-null object\n",
      "MYCT      209 non-null float64\n",
      "MMIN      209 non-null float64\n",
      "MMAX      209 non-null float64\n",
      "CACH      209 non-null float64\n",
      "CHMIN     209 non-null float64\n",
      "CHMAX     209 non-null float64\n",
      "ERP       209 non-null float64\n",
      "dtypes: float64(7), object(1)\n",
      "memory usage: 13.1+ KB\n"
     ]
    }
   ],
   "source": [
    "cpu.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>203.822967</td>\n",
       "      <td>2867.980861</td>\n",
       "      <td>11796.153110</td>\n",
       "      <td>25.205742</td>\n",
       "      <td>4.698565</td>\n",
       "      <td>18.267943</td>\n",
       "      <td>99.330144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>260.262926</td>\n",
       "      <td>3878.742758</td>\n",
       "      <td>11726.564377</td>\n",
       "      <td>40.628722</td>\n",
       "      <td>6.816274</td>\n",
       "      <td>25.997318</td>\n",
       "      <td>154.757102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>225.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>16000.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>64000.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>1238.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MYCT          MMIN          MMAX        CACH       CHMIN  \\\n",
       "count   209.000000    209.000000    209.000000  209.000000  209.000000   \n",
       "mean    203.822967   2867.980861  11796.153110   25.205742    4.698565   \n",
       "std     260.262926   3878.742758  11726.564377   40.628722    6.816274   \n",
       "min      17.000000     64.000000     64.000000    0.000000    0.000000   \n",
       "25%      50.000000    768.000000   4000.000000    0.000000    1.000000   \n",
       "50%     110.000000   2000.000000   8000.000000    8.000000    2.000000   \n",
       "75%     225.000000   4000.000000  16000.000000   32.000000    6.000000   \n",
       "max    1500.000000  32000.000000  64000.000000  256.000000   52.000000   \n",
       "\n",
       "            CHMAX          ERP  \n",
       "count  209.000000   209.000000  \n",
       "mean    18.267943    99.330144  \n",
       "std     25.997318   154.757102  \n",
       "min      0.000000    15.000000  \n",
       "25%      5.000000    28.000000  \n",
       "50%      8.000000    45.000000  \n",
       "75%     24.000000   101.000000  \n",
       "max    176.000000  1238.000000  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can think of a DataFrame as a group of Series that share an index (in this case the column headers). This makes it easy to select specific **columns**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     6000.0\n",
       "1    32000.0\n",
       "2    32000.0\n",
       "3    32000.0\n",
       "4    16000.0\n",
       "Name: MMAX, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu['MMAX'].head(5) # Display the first 5 elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cpu['MMAX'])   # This is a Series "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select multiple columns we simple need to pass a list of column names. The resulting object is another DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>256.0</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16000.0</td>\n",
       "      <td>32000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MMIN     MMAX\n",
       "0    256.0   6000.0\n",
       "1   8000.0  32000.0\n",
       "2   8000.0  32000.0\n",
       "3   8000.0  32000.0\n",
       "4   8000.0  16000.0\n",
       "5   8000.0  32000.0\n",
       "6  16000.0  32000.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu[['MMIN', 'MMAX']].head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cpu[['MMIN', 'MMAX']].head(7)) # This is a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select specific **observations (i.e. rows)** we need to pass in the corresponding indices. This operation is called *slicing*. The resulting structure is again a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adviser</td>\n",
       "      <td>125.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    vendor   MYCT    MMIN     MMAX   CACH  CHMIN  CHMAX    ERP\n",
       "0  adviser  125.0   256.0   6000.0  256.0   16.0  128.0  199.0\n",
       "1   amdahl   29.0  8000.0  32000.0   32.0    8.0   32.0  253.0\n",
       "2   amdahl   29.0  8000.0  32000.0   32.0    8.0   32.0  253.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu[0:3]  # Do not forget that Python follows 0-based indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adviser</td>\n",
       "      <td>125.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    vendor   MYCT    MMIN     MMAX   CACH  CHMIN  CHMAX    ERP\n",
       "0  adviser  125.0   256.0   6000.0  256.0   16.0  128.0  199.0\n",
       "1   amdahl   29.0  8000.0  32000.0   32.0    8.0   32.0  253.0\n",
       "2   amdahl   29.0  8000.0  32000.0   32.0    8.0   32.0  253.0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is equivalent to using .iloc\n",
    "cpu.iloc[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now suppose that you want to select all the observations which have an MMAX value which is higher than 35000. It is easy to do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>sperry</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>sperry</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>978.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vendor  MYCT     MMIN     MMAX   CACH  CHMIN  CHMAX     ERP\n",
       "8    amdahl  23.0  16000.0  64000.0   64.0   16.0   32.0   749.0\n",
       "9    amdahl  23.0  32000.0  64000.0  128.0   32.0   64.0  1238.0\n",
       "198  sperry  30.0   8000.0  64000.0   96.0   12.0  176.0   919.0\n",
       "199  sperry  30.0   8000.0  64000.0  128.0   12.0  176.0   978.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu[cpu['MMAX'] > 35000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or equivalently:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>sperry</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>sperry</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>978.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vendor  MYCT     MMIN     MMAX   CACH  CHMIN  CHMAX     ERP\n",
       "8    amdahl  23.0  16000.0  64000.0   64.0   16.0   32.0   749.0\n",
       "9    amdahl  23.0  32000.0  64000.0  128.0   32.0   64.0  1238.0\n",
       "198  sperry  30.0   8000.0  64000.0   96.0   12.0  176.0   919.0\n",
       "199  sperry  30.0   8000.0  64000.0  128.0   12.0  176.0   978.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu[cpu.MMAX > 35000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also filter the data by using multiple attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1238.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor  MYCT     MMIN     MMAX   CACH  CHMIN  CHMAX     ERP\n",
       "9  amdahl  23.0  32000.0  64000.0  128.0   32.0   64.0  1238.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu[(cpu.MMAX > 35000) & (cpu.MMIN > 16000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We saw before how we can select rows by passing the index numbers. This most of the time works but very often our indices are not in linear ascending order. \n",
    "\n",
    "There are two basic methods of indexing DataFrame structures:\n",
    "* `loc`: works on labels in the index\n",
    "* `iloc`: works on the position in the index (so it only takes integers)\n",
    "\n",
    "The following example should clarify the difference between label-based indexing (`loc`) and positional indexing (`iloc`)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>sperry</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>sperry</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>978.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vendor  MYCT     MMIN     MMAX   CACH  CHMIN  CHMAX     ERP\n",
       "8    amdahl  23.0  16000.0  64000.0   64.0   16.0   32.0   749.0\n",
       "9    amdahl  23.0  32000.0  64000.0  128.0   32.0   64.0  1238.0\n",
       "198  sperry  30.0   8000.0  64000.0   96.0   12.0  176.0   919.0\n",
       "199  sperry  30.0   8000.0  64000.0  128.0   12.0  176.0   978.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First let's create a new dataframe\n",
    "cpu_new = cpu[cpu['MMAX'] > 35000]\n",
    "cpu_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1238.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor  MYCT     MMIN     MMAX   CACH  CHMIN  CHMAX     ERP\n",
       "8  amdahl  23.0  16000.0  64000.0   64.0   16.0   32.0   749.0\n",
       "9  amdahl  23.0  32000.0  64000.0  128.0   32.0   64.0  1238.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu_new.loc[8:10] # Looks for the rows which are labelled 8 and 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1238.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor  MYCT     MMIN     MMAX   CACH  CHMIN  CHMAX     ERP\n",
       "8  amdahl  23.0  16000.0  64000.0   64.0   16.0   32.0   749.0\n",
       "9  amdahl  23.0  32000.0  64000.0  128.0   32.0   64.0  1238.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu_new.iloc[0:2] # Look for the first and second rows (this yields the same result as before)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [vendor, MYCT, MMIN, MMAX, CACH, CHMIN, CHMAX, ERP]\n",
       "Index: []"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If we try the following we will get an empty DataFrame because there are no rows with labels 0 and 1.\n",
    "cpu_new.loc[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The result is another DataFrame\n",
    "type(cpu[0:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very common scenario will be the following. We want to select specific observations and columns of a DataFrame and convert to a NumPy array so that we can use it for feature extraction, classification etc. This can be achieved by using the `values` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   256.,   6000.],\n",
       "       [  8000.,  32000.],\n",
       "       [  8000.,  32000.],\n",
       "       [  8000.,  32000.],\n",
       "       [  8000.,  16000.],\n",
       "       [  8000.,  32000.],\n",
       "       [ 16000.,  32000.],\n",
       "       [ 16000.,  32000.],\n",
       "       [ 16000.,  64000.],\n",
       "       [ 32000.,  64000.]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the first 10 observations and the \"MMIN\" and \"MMAX\" columns only and convert to numpy array.\n",
    "cpu[:10][['MMIN', 'MMAX']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can confirm that by using the `values` method the resulting object is a NumPy array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 18 ==========\n",
    "Load the `credit` dataset and display the dataset basic information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1001 entries, 0 to 1000\n",
      "Data columns (total 21 columns):\n",
      "CheckingAccount     1001 non-null object\n",
      "Duration            1001 non-null float64\n",
      "CreditHistory       1001 non-null object\n",
      "Purpose             1001 non-null object\n",
      "CreditAmount        1001 non-null float64\n",
      "SavingsAccount      1001 non-null object\n",
      "YearsEmployed       1001 non-null object\n",
      "InstallmentRate     1001 non-null float64\n",
      "PersonalStatus      1001 non-null object\n",
      "OtherDebtors        1001 non-null object\n",
      "ResidentSince       1001 non-null float64\n",
      "Property            1001 non-null object\n",
      "Age                 1001 non-null float64\n",
      "OtherPlans          1001 non-null object\n",
      "Housing             1001 non-null object\n",
      "NumCreditsAtBank    1001 non-null float64\n",
      "Job                 1001 non-null object\n",
      "Dependents          1001 non-null float64\n",
      "Telephone           1001 non-null object\n",
      "Foreign             1001 non-null object\n",
      "Approve             1001 non-null object\n",
      "dtypes: float64(7), object(14)\n",
      "memory usage: 164.3+ KB\n"
     ]
    }
   ],
   "source": [
    "cpu_loc = os.path.join(os.getcwd(), 'datasets', 'credit.csv')\n",
    "cpu = pd.read_csv(cpu_loc)\n",
    "cpu.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 19 ==========\n",
    "Display the summary statistics of the attributes of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>CreditAmount</th>\n",
       "      <th>InstallmentRate</th>\n",
       "      <th>ResidentSince</th>\n",
       "      <th>Age</th>\n",
       "      <th>NumCreditsAtBank</th>\n",
       "      <th>Dependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1001.000000</td>\n",
       "      <td>1.001000e+03</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.882118</td>\n",
       "      <td>-9.957330e+05</td>\n",
       "      <td>-7.019980</td>\n",
       "      <td>2.842158</td>\n",
       "      <td>35.217782</td>\n",
       "      <td>0.406593</td>\n",
       "      <td>0.969031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.070878</td>\n",
       "      <td>3.160708e+07</td>\n",
       "      <td>316.165715</td>\n",
       "      <td>1.106825</td>\n",
       "      <td>15.398264</td>\n",
       "      <td>31.656714</td>\n",
       "      <td>5.894916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-293.000000</td>\n",
       "      <td>-1000.000000</td>\n",
       "      <td>-185.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.364000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.319000e+03</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>3.972000e+03</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>1.842400e+04</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Duration  CreditAmount  InstallmentRate  ResidentSince          Age  \\\n",
       "count  1001.000000  1.001000e+03      1001.000000    1001.000000  1001.000000   \n",
       "mean     20.882118 -9.957330e+05        -7.019980       2.842158    35.217782   \n",
       "std      12.070878  3.160708e+07       316.165715       1.106825    15.398264   \n",
       "min       0.000000 -1.000000e+09    -10000.000000       0.000000  -293.000000   \n",
       "25%      12.000000  1.364000e+03         2.000000       2.000000    27.000000   \n",
       "50%      18.000000  2.319000e+03         3.000000       3.000000    33.000000   \n",
       "75%      24.000000  3.972000e+03         4.000000       4.000000    42.000000   \n",
       "max      72.000000  1.842400e+04         4.000000       4.000000    75.000000   \n",
       "\n",
       "       NumCreditsAtBank   Dependents  \n",
       "count       1001.000000  1001.000000  \n",
       "mean           0.406593     0.969031  \n",
       "std           31.656714     5.894916  \n",
       "min        -1000.000000  -185.000000  \n",
       "25%            1.000000     1.000000  \n",
       "50%            1.000000     1.000000  \n",
       "75%            2.000000     1.000000  \n",
       "max            4.000000     2.000000  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 20 ==========\n",
    "Display the last 6 instances of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CheckingAccount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>CreditHistory</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>CreditAmount</th>\n",
       "      <th>SavingsAccount</th>\n",
       "      <th>YearsEmployed</th>\n",
       "      <th>InstallmentRate</th>\n",
       "      <th>PersonalStatus</th>\n",
       "      <th>OtherDebtors</th>\n",
       "      <th>...</th>\n",
       "      <th>Property</th>\n",
       "      <th>Age</th>\n",
       "      <th>OtherPlans</th>\n",
       "      <th>Housing</th>\n",
       "      <th>NumCreditsAtBank</th>\n",
       "      <th>Job</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign</th>\n",
       "      <th>Approve</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>none</td>\n",
       "      <td>12.0</td>\n",
       "      <td>ok_til_now</td>\n",
       "      <td>car_new</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>&gt;=7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>car</td>\n",
       "      <td>50.0</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1.0</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>none</td>\n",
       "      <td>12.0</td>\n",
       "      <td>ok_til_now</td>\n",
       "      <td>furniture</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>&lt;7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>female</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real_estate</td>\n",
       "      <td>31.0</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1.0</td>\n",
       "      <td>unskilled</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>&lt;0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>ok_til_now</td>\n",
       "      <td>car_used</td>\n",
       "      <td>3857.0</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>&lt;4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>male_divorced</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>savings</td>\n",
       "      <td>40.0</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1.0</td>\n",
       "      <td>management</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>none</td>\n",
       "      <td>12.0</td>\n",
       "      <td>ok_til_now</td>\n",
       "      <td>television</td>\n",
       "      <td>804.0</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>&gt;=7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>car</td>\n",
       "      <td>38.0</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1.0</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>&lt;0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>ok_til_now</td>\n",
       "      <td>television</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>&lt;4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>unknown</td>\n",
       "      <td>23.0</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>1.0</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>&lt;200</td>\n",
       "      <td>45.0</td>\n",
       "      <td>critical</td>\n",
       "      <td>car_used</td>\n",
       "      <td>4576.0</td>\n",
       "      <td>&lt;500</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>3.0</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>car</td>\n",
       "      <td>27.0</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1.0</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CheckingAccount  Duration CreditHistory     Purpose  CreditAmount  \\\n",
       "995             none      12.0    ok_til_now     car_new        2390.0   \n",
       "996             none      12.0    ok_til_now   furniture        1736.0   \n",
       "997               <0      30.0    ok_til_now    car_used        3857.0   \n",
       "998             none      12.0    ok_til_now  television         804.0   \n",
       "999               <0      45.0    ok_til_now  television        1845.0   \n",
       "1000            <200      45.0      critical    car_used        4576.0   \n",
       "\n",
       "     SavingsAccount YearsEmployed  InstallmentRate PersonalStatus  \\\n",
       "995         unknown           >=7              4.0    male_single   \n",
       "996            <100            <7              3.0         female   \n",
       "997            <100            <4              4.0  male_divorced   \n",
       "998            <100           >=7              4.0    male_single   \n",
       "999            <100            <4              4.0    male_single   \n",
       "1000           <500    unemployed              3.0    male_single   \n",
       "\n",
       "     OtherDebtors   ...       Property   Age  OtherPlans Housing  \\\n",
       "995          none   ...            car  50.0        none     own   \n",
       "996          none   ...    real_estate  31.0        none     own   \n",
       "997          none   ...        savings  40.0        none     own   \n",
       "998          none   ...            car  38.0        none     own   \n",
       "999          none   ...        unknown  23.0        none    free   \n",
       "1000         none   ...            car  27.0        none     own   \n",
       "\n",
       "     NumCreditsAtBank         Job Dependents  Telephone Foreign Approve  \n",
       "995               1.0     skilled        1.0        yes     yes    good  \n",
       "996               1.0   unskilled        1.0         no     yes    good  \n",
       "997               1.0  management        1.0        yes     yes    good  \n",
       "998               1.0     skilled        1.0         no     yes    good  \n",
       "999               1.0     skilled        1.0        yes     yes     bad  \n",
       "1000              1.0     skilled        1.0         no     yes    good  \n",
       "\n",
       "[6 rows x 21 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu.tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 21 ==========\n",
    "Print the 5th observation (do not forget that Python follows 0-base indexing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CheckingAccount              <0\n",
       "Duration                     42\n",
       "CreditHistory        ok_til_now\n",
       "Purpose               furniture\n",
       "CreditAmount               7882\n",
       "SavingsAccount             <100\n",
       "YearsEmployed                <7\n",
       "InstallmentRate               2\n",
       "PersonalStatus      male_single\n",
       "OtherDebtors          guarantor\n",
       "ResidentSince                 4\n",
       "Property                savings\n",
       "Age                          45\n",
       "OtherPlans                 none\n",
       "Housing                    free\n",
       "NumCreditsAtBank              1\n",
       "Job                     skilled\n",
       "Dependents                    2\n",
       "Telephone                    no\n",
       "Foreign                     yes\n",
       "Approve                    good\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu.iloc[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 22 ==========\n",
    "Print the standard deviation of the attribute `CreditAmount` by using the numpy method `std`. You can verify you get the correct result by inspecting the statistics table from Question 19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31591288.885041825"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = cpu[['CreditAmount']].values\n",
    "z.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is a 2D python plotting library with a similar interface to MATLAB's plot engine. The library is fully compatible with NumPy which means that we can -and most of the times will- pass numpy arrays as input arguments to the various plotting functions.\n",
    "\n",
    "There are many [resources](http://matplotlib.org/resources/index.html) for learning how to use Matplotlib. The following examples demonstrate only some basic plotting functions. When you are looking for a particular feature which you don't know yet how to implement a web search can prove very useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# First we need to import the package and force it to render plots inside the notebook\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vK+ip70VAUFXVdcArwDBVVTupqtrn2Ej6QWAN8CHwvdfrlY9YIinMmGHnyBGF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\nzGw8G6sRI3PS2MMugiJOjrfwcWx7vsV/zxBizX5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117416b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The first example creates a sine and a cosine and plots them.\n",
    "X = np.linspace(-np.pi, np.pi, 256,endpoint=True) # Evenly spaced numbers over the specified interval\n",
    "C, S = np.cos(X), np.sin(X) # Create a sine (S) and a cosine (C)\n",
    "\n",
    "plt.figure(figsize=(8,5)) # Instantiates figure with a specified size\n",
    "plt.plot(X,C, label='Cosine', color='blue')\n",
    "plt.plot(X,S, label='Sine', color='red')\n",
    "plt.legend() # Displays legend\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PTychTjq1/jZxVCYWi/vDgghfJ+tKuPvNO3iv+G3TUUSEk0Lmg/3tw4pTxww2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\nNNi7BrP2k2pq11Wb2rvd1tPW48SLFDKRdCKRCH/849N4PB5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117416310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create 4 subplots and plot cosines with different colours, linestyels and linewidths.\n",
    "X = np.linspace(-np.pi, np.pi, 256,endpoint=True) # Evenly spaced numbers over the specified interval\n",
    "C1, C2, C3, C4 = np.cos(X), np.cos(X+0.5), np.cos(X+1.), np.cos(X+1.5)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(7,7))\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(X, C1)\n",
    "plt.title('C1')\n",
    "plt.xticks([]) # Removes xticks\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(X, C2, color='green',  linestyle='-.')\n",
    "plt.title('C2')\n",
    "plt.yticks([]) # Removes yticks\n",
    "plt.subplot(2,2,3)\n",
    "plt.plot(X, C3, color='k', linestyle='--')\n",
    "plt.title('C3')\n",
    "plt.subplot(2,2,4)\n",
    "plt.plot(X, C4, color='m', linewidth = 5.)\n",
    "plt.title('C4')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d9aa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram and add some text\n",
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(10000) # Generate data from a normal distribution with mean mu and variance sigma\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)\n",
    "\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Histogram of IQ')\n",
    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$') # Adds some text by using TeX\n",
    "plt.axis([40, 160, 0, 0.03]) # Sets the axes limits\n",
    "plt.grid(True) # Enables grid\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 23 ==========\n",
    "Initialise an empty figure of size (8,6). Create a scatter plot of the observations in the `credit` dataset for the attributes `Duration` and `Age` (age should be shown on the xaxis). Use black coloured markers. Label the axes accordingly by using red colour for the xaxis and green for the yaxis. Finally, add a title of your choice by using a font size of 20. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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167nqqsvZuTPJsGHDeN/7PsCOHW9zzDGVrFr1Z9raWqmoOIKHHlrKaad9EIBkMkl9/SzW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\nEZEYUMIXERGJgf8PNt+/etZ6dC0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11beac750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "D = cpu[['Duration']].values\n",
    "A = cpu[['Age']].values\n",
    "\n",
    "plt.scatter(A, D, color='black')\n",
    "plt.xlabel('Age', color='red')\n",
    "plt.ylabel('Duration', color='green')\n",
    "plt.title('Duration vs. Age', fontsize='20')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ========== Question 24 ==========\n",
    "You should note above that there is an outlier (i.e. an observation that is very distant to the other observations). Now you think that there must be something wrong with that observation (why?) and as a next step you should remove it from both the age and duration variables and a make a scatter plot of the new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OqTEXERHJOTXmIiIiOafGXEREJOeOa3YFxpgrgSXA8cDngB3AemAceMhae1mzYxAREWln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\n/X+002PH1Q8plAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117443650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "D = cpu[1:][['Duration']].values\n",
    "A = cpu[1:][['Age']].values\n",
    "\n",
    "plt.scatter(A, D, color='black')\n",
    "plt.xlabel('Age', color='red')\n",
    "plt.ylabel('Duration', color='green')\n",
    "plt.title('Duration vs. Age', fontsize='20')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html) is a visualisation library built on top of matplotlib which offers some aesthetic enhancement and, more importantly, provides some high-level functions for  \"exploring and understanding data\". Seaborn is also tightly integrated with pandas and provides support for both numpy and pandas data structures.\n",
    "\n",
    "In the following example we will load seaborn and use its default settings to plot again the sine and cosine waves we saw earlier.\n",
    "\n",
    "Note that this example does not actually use any seaborn functions, but as soon as the library is loaded, the Seaborn plotting style is adopted by the matplotlib.pyplot library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HfPnLn+cLX/hTvvSlz+F2X+J73/sOnZ0dt79zAJAFT4SYZ9daB3i+vJ6EmDAev1e35FSh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\nLYQQQvgxKdRCCCGEH/sfmQ6djzose2QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bb3ba50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns # Load seaborn with default settings\n",
    "X = np.linspace(-np.pi, np.pi, 256,endpoint=True) # Evenly spaced numbers over the specified interval\n",
    "C, S = np.cos(X), np.sin(X) # Create a sine (S) and a cosine (C)\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(X,C, label='Cosine')\n",
    "plt.plot(X,S, label='Sine')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn implemented functions [Optional]\n",
    "The Seaborn website has many great examples. Below we show a few taken directly from the website. These are just to give you a flavour of the kinds of things that Seaborn can do. You can use it as reference later; by no means should you learn this code by heart! You should definitely not worry if some of the following commands do not make sense just yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LeuIECnNz0C5fhnrsmOg4TYmFTbvmVwJQAyHRMYhsyVoh3sxbujZTjx9H4vHHkRsZYWHX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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e02790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True)\n",
    "rs = np.random.RandomState(10)\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, axes = plt.subplots(2, 2, figsize=(7, 7), sharex=True)\n",
    "sns.despine(left=True)\n",
    "\n",
    "# Generate a random univariate dataset\n",
    "d = rs.normal(size=100)\n",
    "\n",
    "# Plot a simple histogram with binsize determined automatically\n",
    "sns.distplot(d, kde=False, color=\"b\", ax=axes[0, 0])\n",
    "\n",
    "# Plot a kernel density estimate and rug plot\n",
    "sns.distplot(d, hist=False, rug=True, color=\"r\", ax=axes[0, 1])\n",
    "\n",
    "# Plot a filled kernel density estimate\n",
    "sns.distplot(d, hist=False, color=\"g\", kde_kws={\"shade\": True}, ax=axes[1, 0])\n",
    "\n",
    "# Plot a historgram and kernel density estimate\n",
    "sns.distplot(d, color=\"m\", ax=axes[1, 1])\n",
    "\n",
    "plt.setp(axes, yticks=[])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OLjDURESkquTuobDaZBw6wfOpHcFQExGRqvr0CAUA7DxcIHgSfWCoiYhIVYmx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\ngw04v4nc2UADjDSRUg4cL8XazUdhNEh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tEj1Wp06cOIG77roLmzZtEj1KuzIzM7Fw4UI8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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b6aa290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "\n",
    "# Generate a random correlated bivariate dataset\n",
    "rs = np.random.RandomState(5)\n",
    "mean = [0, 0]\n",
    "cov = [(1, .5), (.5, 1)]\n",
    "x1, x2 = rs.multivariate_normal(mean, cov, 500).T\n",
    "x1 = pd.Series(x1, name=\"$X_1$\")\n",
    "x2 = pd.Series(x2, name=\"$X_2$\")\n",
    "\n",
    "# Show the joint distribution using kernel density estimation\n",
    "g = sns.jointplot(x1, x2, kind=\"kde\", size=7, space=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rUVtbK+l1c0rmArp8+TLkKt2wLSVpZC4unmQ+jtEdEYY7mavHkcy1WgUyM0PQ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\nEEIIEThK5oQQQojA/T86XVGQgqJy3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd70ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", palette=\"pastel\", color_codes=True)\n",
    "\n",
    "# Load the example tips dataset\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "\n",
    "# Draw a nested violinplot and split the violins for easier comparison\n",
    "sns.violinplot(x=\"day\", y=\"total_bill\", hue=\"sex\", data=tips, split=True,\n",
    "               inner=\"quart\", palette={\"Male\": \"b\", \"Female\": \"y\"})\n",
    "sns.despine(left=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mvHoynybIku4Y5bqrOFdf7y0n2/3k9ddBeenwlNMvgClaFl5wEZTRfDeQ0t8/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b686890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "# Load the example titanic dataset\n",
    "df = sns.load_dataset(\"titanic\")\n",
    "\n",
    "# Make a custom palette with gendered colors\n",
    "pal = dict(male=\"#6495ED\", female=\"#F08080\")\n",
    "\n",
    "# Show the survival proability as a function of age and sex\n",
    "g = sns.lmplot(x=\"age\", y=\"survived\", col=\"sex\", hue=\"sex\", data=df,\n",
    "               palette=pal, y_jitter=.02, logistic=True)\n",
    "g.set(xlim=(0, 80), ylim=(-.05, 1.05))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2Vg8bp6kTnRjH/EM7GdaiHVYmZiwa8MIR+P38CVzsSmll2B+lJHD0diDzewzF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\nhLkdNxKjdXnxGYmUsHh33U3frWbdmOVsGLeCnTO2oFQo2TB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XprYWeLTwIXjtMcxszMlO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\nRDHAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bdc6bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "\n",
    "# Load the example flights dataset and convert to long-form\n",
    "flights_long = sns.load_dataset(\"flights\")\n",
    "flights = flights_long.pivot(\"month\", \"year\", \"passengers\")\n",
    "\n",
    "# Draw a heatmap with the numeric values in each cell\n",
    "sns.heatmap(flights, annot=True, fmt=\"d\", linewidths=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XLlxQVFSUJMkwDN100006d+6cy3EEFgDAp1q0aKE333zTeVH1Tz75RG3atHE5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d168d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "g = sns.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n",
    "bins = np.linspace(0, 60, 13)\n",
    "g.map(plt.hist, \"total_bill\", color=\"steelblue\", bins=bins, lw=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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66quSTkEIIYQQVlDiRyKeeeYZVFUFICkpCXd395JOQQghhBBWYNMmYvXq1Xz3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bfdec90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "\n",
    "# Create a dataset with many short random walks\n",
    "rs = np.random.RandomState(4)\n",
    "pos = rs.randint(-1, 2, (20, 5)).cumsum(axis=1)\n",
    "pos -= pos[:, 0, np.newaxis]\n",
    "step = np.tile(range(5), 20)\n",
    "walk = np.repeat(range(20), 5)\n",
    "df = pd.DataFrame(np.c_[pos.flat, step, walk],\n",
    "                  columns=[\"position\", \"step\", \"walk\"])\n",
    "\n",
    "# Initialize a grid of plots with an Axes for each walk\n",
    "grid = sns.FacetGrid(df, col=\"walk\", hue=\"walk\", col_wrap=5, size=1.5)\n",
    "\n",
    "# Draw a horizontal line to show the starting point\n",
    "grid.map(plt.axhline, y=0, ls=\":\", c=\".5\")\n",
    "\n",
    "# Draw a line plot to show the trajectory of each random walk\n",
    "grid.map(plt.plot, \"step\", \"position\", marker=\"o\", ms=4)\n",
    "\n",
    "# Adjust the tick positions and labels\n",
    "grid.set(xticks=np.arange(5), yticks=[-3, 3],\n",
    "         xlim=(-.5, 4.5), ylim=(-3.5, 3.5))\n",
    "\n",
    "# Adjust the arrangement of the plots\n",
    "grid.fig.tight_layout(w_pad=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [iaml]",
   "language": "python",
   "name": "Python [iaml]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}