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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyzing Patients Data # \n",
+    "\n",
+    "\n",
+    "* Code is resuable -- Many times simple progammatic tasks have been done before\n",
+    "    - No need to reinvent the wheel!\n",
+    "    \n",
+    "Code (words) -> Functions/Methods -> Objects -> Modules -> libraries\n",
+    "\n",
+    "Libaries are organized code available to Python coders.  It allows coders to extend the \"out-of-the-box\" functionalities provided by Python by giving users the availablity of previously written code.  Access to these libraries is done by letting Python know that you plan to use these libraries by calling on them or using `import` in Python.  \n",
+    "\n",
+    "In this excerise we are going to explore one of the most import data science libraries called `numpy` and explore some of the functionalities contained within.  Importing a library is easy.  Simply use the word `import` followed by the name of the library you want to use (provided it is installed in your Python environment)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Importantion of the numpy give us access to all the code contained within.  For example, the numpy package contains a method called `loadtxt` that will load our data into our computers memory.  Using this function is simple, we just need to tell the function the name of the file and where it is (i.e, the directory) and what time of delimiter is separating our data.  Access to the function is by first telling Python to search the numpy module by writing `numpy` like so...."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  0.,  1., ...,  3.,  0.,  0.],\n",
+       "       [ 0.,  1.,  2., ...,  1.,  0.,  1.],\n",
+       "       [ 0.,  1.,  1., ...,  2.,  1.,  1.],\n",
+       "       ..., \n",
+       "       [ 0.,  1.,  1., ...,  1.,  1.,  1.],\n",
+       "       [ 0.,  0.,  0., ...,  0.,  2.,  0.],\n",
+       "       [ 0.,  0.,  1., ...,  1.,  1.,  0.]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numpy.loadtxt(fname='../data/inflammation-01.csv', delimiter=',')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) `.loadtxt(...)` -> function call  \n",
+    "2) `fname='../data/inflammation-01.csv'` -> parameter  \n",
+    "3) `delimiter=','` -> parameter\n",
+    "\n",
+    "Notice that the results of the function call to `loadtxt(...)` (i.e., the reading of our datafile, printed to the screen.  The reason this happened is because we didnt store the information for further use.  That is Python didn't know what to do with it.  But, obviously, we're going to want to use that information for later.\n",
+    "\n",
+    "A variable in Python is how we'll refer to that information in the Python program.  Variables can be any amount of letters and numbers, with the stipulation that it must begin with with a letter and is case sensitive.  \n",
+    "\n",
+    "As an example, let's store a small amount of data (a number for exmaple), into a variable called `weight_kg`.  We assing data to a variable using the `=` operator.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "weight_kg = 55"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have \"assigned\" the variable `weight_kg` to the number 55.  We can now think of that variable containing that information.  We can see that by using the `print()` function to get to print the contents of `weight_kg` to the screen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(weight_kg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While we see this variable as storing the information `weight_kg`, Python reads it as the data that it was assigned.  This can be seen by writing an equation to convery the contents of `weight_kg` pounds.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "weight in pounds:  121.00000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"weight in pounds: \", weight_kg * 2.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While we see the variable on the screen `weight_kg`, Python recognizes the data contained within and makes does the appropriate arithmetic.  But information stored in variable is not static.  It can be changed, manipulated, or updated to contain new information.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "weight in kilograms is now: 57.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "weight_kg = 57.5\n",
+    "print('weight in kilograms is now:', weight_kg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, the assignment of data to a variable does not have to be explicitly declared in a program, like `weight_kg = 55`.  We can store the results of Python expression to NEW variables, without disturbing the old variables.  Consider our kg to lbs conversion.  We can store the result of that expression into a new variable called `weight_lb` using the same syntax."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The weight in kilograms: 57.5 The weight in pounds: 126.50000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "weight_lb = weight_kg * 2.2\n",
+    "print(\"The weight in kilograms:\", weight_kg, \"The weight in pounds:\", weight_lb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What we did was multiply the data stored within `weight_kg`, but did not change it.  Instead, we assigned this result to `weight_lb`.  Now that we have successfully stored this result into a variable, changing the value of `weight_kg` won't affect `weight_lb`.  Let's change it's value to see this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The new value in kilograms is: 98 while the value in pounds is still: 126.50000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "weight_kg = 98\n",
+    "print(\"The new value in kilograms is:\", weight_kg, \"while the value in pounds is still:\", weight_lb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Who's who in memory?\n",
+    "\n",
+    "The neat thing about IPython (as oppose to just Python) is that it contains some built-in magic functions, with some cool features.  For example, the `%whos` function allows us to see what variables have been created and which modules have been loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variable    Type      Data/Info\n",
+      "-------------------------------\n",
+      "numpy       module    <module 'numpy' from 'C:\\<...>ges\\\\numpy\\\\__init__.py'>\n",
+      "weight_kg   int       98\n",
+      "weight_lb   float     126.50000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "%whos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, we've only created two variables, each containing a single number.  However, Python lets us assign more than a single number to a variable.  For example, lets consider the the `numpy.loadtxt()` function call we used earlier to store read our .csv file.  We can store the result of that operation into a variable called `data`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = numpy.loadtxt(fname='../data/inflammation-01.csv', delimiter=',')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unlike before, the resulting data wasn't printed to the screen.  Instead, we assigned the result of the `numpy.loadtxt()` function call to a variable called `data`.  We can confirm this by printing `data` to the screen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.  0.  1. ...,  3.  0.  0.]\n",
+      " [ 0.  1.  2. ...,  1.  0.  1.]\n",
+      " [ 0.  1.  1. ...,  2.  1.  1.]\n",
+      " ..., \n",
+      " [ 0.  1.  1. ...,  1.  1.  1.]\n",
+      " [ 0.  0.  0. ...,  0.  2.  0.]\n",
+      " [ 0.  0.  1. ...,  1.  1.  0.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unsurprisingly, this is the same information that the original `numpy.loadtxt()` call contained.  However, often times we may not be sure of the type of data stored within a variable.  We can figure this out by passing the variable as a parameter to the function `type()`.  The result of this function can be printed to the screen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output tells us that data currently refers to an N-dimensional array created by the NumPy library. These data correspond to arthritis patients’ inflammation. The rows are the individual patients and the columns are their daily inflammation measurements.\n",
+    "\n",
+    "## Data type\n",
+    "\n",
+    "The result of `type()` only lets us know that `data` is is a numpy array.  However, numpy arrays can contain one or more elements of the same type.  We can find the datatype of a within a numpy array like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the type contained within the numpy array are floating point numbers.  The numpy array stored in our `data` variable consists of the arthritis data contained within the file we loaded into it.  This data was essentially a matrix of patient data (rows) and successive days (columns).  We can see the shape of this matrix like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60, 40)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is telling us that we have 60 rows and 40 columns of data.  What's important to note is that loading our data into a numpy array didn't just store the data intsef, but stored information about that array.  We refered to this information as being an _attribute_ of the object.  In the above example `data.shape`, the attribute `shape` describes the dimensions of `data`.  \n",
+    "\n",
+    "So, thanks to the attribute `shape` we know that our variable `data` contains 60 rows and 40 columns.  We can access any element by putting the _index_ of that element, i.e., the row and column it appears in, within square brackets.  Let's look at two examples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first value in data 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('the first value in data', data[0,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a random middle value in data 13.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('a random middle value in data', data[30, 20])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You're probably wondering why accessing the first element isn't done by coding `data[1,1]`, as it is the first element in the first row.  This is because many programming languages (Python included) start counting from 0 (see [Mike Hoye’s blog post](http://exple.tive.org/blarg/2013/10/22/citation-needed/) for historical details).  But as a result, the _n_-th element in an array, is at the _n_-1 index.  So, the data point `13` we retrieved by the expression `data[30, 20]`, is actually in the 31<sup>st</sup> row in the 21<sup>st</sup> column.  \n",
+    "\n",
+    "The previous examples are only useful if we want to select single elements from our data; but what if we wanted to select pieces, or subsets, of data?  Numpy allows us to do that by slicing.  By declaring a starting point and an ending point, we can obtain all the elments contain therein.  Let's say we wanted to retrieve the first four rows and first ten columns of our data.  We can do so by slicing.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.]\n",
+      " [ 0.  1.  2.  1.  2.  1.  3.  2.  2.  6.]\n",
+      " [ 0.  1.  1.  3.  3.  2.  6.  2.  5.  9.]\n",
+      " [ 0.  0.  2.  0.  4.  2.  2.  1.  6.  7.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data[0:4, 0:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, `0:4` is saying, “start at index 0 and go up to, but not including, index 4.” Likewise, `0:10` is saying \"start at index 0 and go up to, but don't include index 10.  It's important to note, while the starting index of a slice is _inclusive_ the ending index is _exclusive_.  \n",
+    "\n",
+    "We don't have to start the slice at the 0 index, either.  For example, we can see the next five rows of data by putting the starting index at `5`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.  0.  1.  2.  2.  4.  2.  1.  6.  4.]\n",
+      " [ 0.  0.  2.  2.  4.  2.  2.  5.  5.  8.]\n",
+      " [ 0.  0.  1.  2.  3.  1.  2.  3.  5.  3.]\n",
+      " [ 0.  0.  0.  3.  1.  5.  6.  5.  5.  8.]\n",
+      " [ 0.  1.  1.  2.  1.  3.  5.  3.  5.  8.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data[5:10,0:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In fact, we're not even required to declare the starting AND ending points.  If we omit the staring index, Python starts at he beginning, or the 0 index.  If we omit the ending index, the slice continues to the the end of the array.  We can even store the results of a slice into a variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "small is:\n",
+      "[[ 2.  3.  0.  0.]\n",
+      " [ 1.  1.  0.  1.]\n",
+      " [ 2.  2.  1.  1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "small = data[:3, 36:]\n",
+    "print('small is:')\n",
+    "print(small)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as we performed mathematical operations on variables containing single real numbers (converting kilograms to pounds), we can performed operations on whole arrarys.  The simplest operations with data are arithmetic: add, subtract, multiply, and divide. When you do such operations on arrays with an indvidual scalar value, the operation is done on each individual element of the array. Thus:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "doubledata = data * 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we have TWO numpy arrays.  Our original array, `data`, and an array called `doubledata` that contains the same elements of `data` except doubled.  We can confirm this by looking at the same subset of data for each array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "original:\n",
+      "[[ 2.  3.  0.  0.]\n",
+      " [ 1.  1.  0.  1.]\n",
+      " [ 2.  2.  1.  1.]]\n",
+      "doubledata:\n",
+      "[[ 4.  6.  0.  0.]\n",
+      " [ 2.  2.  0.  2.]\n",
+      " [ 4.  4.  2.  2.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('original:')\n",
+    "print(data[:3, 36:])\n",
+    "print('doubledata:')\n",
+    "print(doubledata[:3, 36:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, this functionality isn't limited to scalar values.  The same operations can be used on arrays of equal dimensions.  Let's say we wanted to create a new array containg the elements of `data` tripled?  We can simply add our two arrays (`data` and `doubledata`) together.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tripledata = doubledata + data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tripledata:\n",
+      "[[ 6.  9.  0.  0.]\n",
+      " [ 3.  3.  0.  3.]\n",
+      " [ 6.  6.  3.  3.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('tripledata:')\n",
+    "print(tripledata[:3, 36:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition to the intrisic Python operators, numpy contains some advanced operations in the form of _functions_.  We can calculate the mean value of our data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.1487499999999997"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numpy.mean(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we are calling the function `mean` from the library `numpy`.  Some functions, such as `numpy.mean()` take arguments. Here our variable `data` is passed to the function `mean()`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Not all functions have input\n",
+    "\n",
+    "Many functions, such as `numpy.mean()` require an input to create an output.  This is not true for all functions.  For example, we can check the current time, from the library `time`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wed Nov  9 14:23:45 2016\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "print(time.ctime())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even though the function did not have any input, we are still required to put the `()` after the function name.  This tells python to perform the function.\n",
+    "\n",
+    "Many of numpy's functions accept an arrary as an input.  We can use three of them to explore some of our `data` variable.  Additionally, just as we assigned a value to a variable, we can assign multiple assignments in one line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "maximum inflammation: 20.0\n",
+      "minimum inflammation: 0.0\n",
+      "standard deviation: 4.61383319712\n"
+     ]
+    }
+   ],
+   "source": [
+    "maxval, minval, stdval = numpy.max(data), numpy.min(data), numpy.std(data)\n",
+    "\n",
+    "print('maximum inflammation:', maxval)\n",
+    "print('minimum inflammation:', minval)\n",
+    "print('standard deviation:', stdval)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mystery functions in IPython \n",
+    "\n",
+    "How did we know what functions NumPy has and how to use them? If you are working in the IPython/Jupyter Notebook there is an easy way to find out. If you type the name of something with a full-stop then you can use tab completion (e.g. type numpy. and then press tab) to see a list of all functions and attributes that you can use. After selecting one you can also add a question mark (e.g. numpy.cumprod?) and IPython will return an explanation of the method! This is the same as doing help(numpy.cumprod)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Often times, we dont want to look at the entire dataset.  Let's say we just wanted to look at one patient's data, such as the first patient.  One way to do this would be storing data of the first patient (i.e., the first row) in a new variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "maximum inflammation for patient 0: 18.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "patient_0 = data[0, :] # retrieve only the first row, all the columns\n",
+    "print(\"maximum inflammation for patient 0:\", patient_0.max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The symbol `#` allows notes to be taken with the code.  Everything to the right of the `#` symbol is ignored by computer.  Commenting allows for programmers to explain and annotate pieces of code for easier understanding and readability.  \n",
+    "\n",
+    "The above example, while readable, is sort of redudant.  Since we know that the result of patient `data[0, :]` will be all the data from the first row, we can pass this expression as input to the `numpy.max()` function and receive the same effect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "maximum inflammation for patient 0: 18.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"maximum inflammation for patient 0:\", numpy.max(data[0, :]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What if we wanted to find the max inflammation for EACH patient?  Or if we wanted to find the average patient inflammation across ALL the days?  As the following picture illustrates we can iteratively use functions across entire rows or rows, by specifying and axis.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"fig/python-operations-across-axes.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, let's first compute the average.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  0.           0.45         1.11666667   1.75         2.43333333   3.15\n",
+      "   3.8          3.88333333   5.23333333   5.51666667   5.95         5.9\n",
+      "   8.35         7.73333333   8.36666667   9.5          9.58333333\n",
+      "  10.63333333  11.56666667  12.35        13.25        11.96666667\n",
+      "  11.03333333  10.16666667  10.           8.66666667   9.15         7.25\n",
+      "   7.33333333   6.58333333   6.06666667   5.95         5.11666667   3.6\n",
+      "   3.3          3.56666667   2.48333333   1.5          1.13333333\n",
+      "   0.56666667]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(numpy.mean(data, axis=0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each element of this array corresponds to that index's columns.  We can check this by looking at the shape of the array, which should correspond to the number of columns in our `data` variable (40)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(40,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(numpy.mean(data, axis=0).shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result `(40,)` tells us that this is an $Nx1$ dimensional array, with $N = 40$. Similarly, we can perform operations on the other axis (columns).  Let's find the mean of the columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 5.45   5.425  6.1    5.9    5.55   6.225  5.975  6.65   6.625  6.525\n",
+      "  6.775  5.8    6.225  5.75   5.225  6.3    6.55   5.7    5.85   6.55\n",
+      "  5.775  5.825  6.175  6.1    5.8    6.425  6.05   6.025  6.175  6.55\n",
+      "  6.175  6.35   6.725  6.125  7.075  5.725  5.925  6.15   6.075  5.75\n",
+      "  5.975  5.725  6.3    5.9    6.75   5.925  7.225  6.15   5.95   6.275  5.7\n",
+      "  6.1    6.825  5.975  6.725  5.7    6.25   6.4    7.05   5.9  ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(numpy.mean(data, axis=1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the first, excercises in data exploration is data visualization.  The most widly used visualization tool in python is matplotlib.  With just a few lines of code we can see how powerful the matplotlib library is by creating a heatmap of our `data` variable.  One of the benefits of using Jupyter Notebooks is the ability to generate plots within the notebook.  However, first we need to explicitly declare that we want our plots to show up inline.  We can do that with IPython Magic and executing the following code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "% matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as we imported the numpy library we'll need to import the `pypot` module from the `numpy` library.  We can do that using the same syntax."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, just using two functions from the pyplot module, we can generate a heatmap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P2JxElLs2nme6f3TbPqQ8SXngkB35rI8iMu0zKwfMqz631Yiz5oSz5gFnxQM2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\nPDdYBfMKzw1WwbzCc4NVMK/w3OD/AJ2y6pQmEWHAAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7596908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image  = matplotlib.pyplot.imshow(data)\n",
+    "matplotlib.pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Blue regions in this heat map are low values, while red shows high values. As we can see, inflammation rises and falls over a 40-day period."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Combining the visualization tools of matplotlib with some of the functions we used with numpy, we can generate some more descriptive plots.  For example let's see the average inflammation over time.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ceSZ8/DH8139l5C1FROps2jQ4+2zYZBMYMAAaN878NRLdQv/qK0/mzz6rZC4i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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x76e5978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ave_inflammation = numpy.mean(data, axis=0)\n",
+    "ave_plot = matplotlib.pyplot.plot(ave_inflammation)\n",
+    "matplotlib.pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we have put the average per day across all patients in the variable ave_inflammation, then asked matplotlib.pyplot to create and display a line graph of those values. The result is roughly a linear rise and fall, which is suspicious: based on other studies, we expect a sharper rise and slower fall. Let’s have a look at two other statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bG5OOHEnaOQBkad2fftVVYfX0blU6udDNAfQqlJ5OQ29BNwfQrxB6Og09xX5zAIOIdX96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\nxP8DpFAe9vF8qV0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7bfaf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max_plot = matplotlib.pyplot.plot(numpy.max(data, axis=0))\n",
+    "matplotlib.pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fVjI7dkj790sLF1ZdCYAQLF4svfWWtG9f1ZUk17Gn7e5/VEYhWQwPs0sRQHzTpkkXXiht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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7e58da0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "min_plot = matplotlib.pyplot.plot(numpy.min(data, axis=0))\n",
+    "matplotlib.pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The maximum value rises and falls perfectly smoothly, while the minimum seems to be a step function. Neither result seems particularly likely, so either there’s a mistake in our calculations or something is wrong with our data. This insight would have been difficult to reach by examining the data without visualization tools.\n",
+    "\n",
+    "You can group similar plots in a single figure using subplots. This script below uses a number of new commands. The function matplotlib.pyplot.figure() creates a space into which we will place all of our plots. The parameter figsize tells Python how big to make this space. Each subplot is placed into the figure using its add_subplot method. The add_subplot method takes 3 parameters. The first denotes how many total rows of subplots there are, the second parameter refers to the total number of subplot columns, and the final parameter denotes which subplot your variable is referencing (left-to-right, top-to-bottom). Each subplot is stored in a different variable (axes1, axes2, axes3). Once a subplot is created, the axes can be titled using the set_xlabel() command (or set_ylabel()). Here are our three plots side by side:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1sIizJDk1bdrY777Skl1JRkROxPVIvkpEBonIoPDCym62kly+atXcCl7J1eQl\nS+Dll92uW1M5ERkHXA20B3oBL4tIDjXXS92oUfCnP8Euu/iOJDs1bw4XXwxDhviOJDZuAf5PVeur\n6raquo0lyBVTdUnyzjv7jiR+bCV5S8m2gLsXd3T0EcCDwBnAVFW9MNTgsrA9zfLlbgVl5UrbNV+e\na6+FefNg221d+61ffoHtt3f12rfc4ju68ATVTkpErgbGFE+exBWg25OZr4kE+yRgiaq2T9zWEHga\nd/jPN8CfSl1NKn5srOfrt99Chw6524IwU5Ytc/sv/vc/2H1339GkJ+wWcCLynqoeEuL4sZ6zZVmy\nBPbeG5Yu9R1J/CxfDi1buqu52ZifhNkC7mBVPR9YpqpDgIOAdlUN0Li+je3aZecLMChnnglNmsAR\nR8DEiW6jz+TJ8OCD8OOPvqOLPlW9o+Q7n6r+WoUPtOOBY0vd1g94U1V3A94G+gcTabQMHgyXXGIJ\nctgaNoS//33zU0ZNuT4WkadFpIeIdC/+8h1UlFmpReoaNHCt4H76yXck0ZHsOVK/J/5cIyLNgZ8B\neytJgZVaVO7AA91XSS1auDru225zvaRN+URkV9yG2j2BP9rBqWqlbx2qOkVEWpW6+RTg8MT3jwCT\ncYlz1pgzxx0HP2+e70hyw5VXuo1806ZB586+o4m0bYE1wDElblMgB4+bSo4lyekpLrnYYQffkURD\nsknyRBFpANwKfIqbpEEdUZ1TLElOXb9+7nJ43762qa8S44F8YDSuRKoXVdh/UIbGqroEQFUXi0jj\n9EOMlhtugOuucyspJnx160J+vjv2+803fUcTXaray3cMcWNJcnqKk+QDDvAdSTRUmiSLSDXgLVVd\nDjwrIi8DW5VVk2gqN3eu9UJO1U47uU1Vt91mPWwrUUdV3xJXcLgAGCwinwBBbbYtt4hx8ODBf3yf\nl5dHXgxO45g6FT76CB5/3HckuaVXL/jHP2DSJDj6aN/RJGfy5MlMnjw59OcRketU9RYRuYsy5puq\n9g49iJgqKIBDD/UdRXzZ5r3NVZokq2qRiIwFOib+vhZYG3Zg2cpWktPTv7/rfnHttbaaXIG1iQ+3\n80TkCmARUC+N8ZaISBNVXSIiTYFyK8NLJslxoOquUOTnQ506vqPJLTVrukNb+veHo45ynW2irvQH\nvyHhtemYk/jzYyr4UGq2VFAA55/vO4r4atvW2sCVlOyvpbdE5HQR226WjqIi+Pprt3HPpKZlS7ex\n7/bbfUeXl8otAAAgAElEQVQSaVfhutH0BvYDzgOq8rZR3I+12EvAXxLf9wReTD/EaJg0CRYtcqua\nJvPOOMN9UHn2Wd+RRIuqTkx8Oxs4DbgG6Jv4+ruvuOLAyi3SYyvJm0u2BdxKYGtgI/AbGWponm3t\nab75xl0G+s6OdUjLggXuZL6vvnKt4bJFgC3g9gcG4lq21UzcrMUt3Sp57JNAHrA9sARX2/wC8Ayw\nE7AA1wJueRmPjdV8LSpym8b693fJmvFj0iS4/HKYNcutLsdJBlrAzcUlxjOBouLbE2VUQYwfqzlb\nmd9/d/sKVq+G6tV9RxNPhYXuxNEFgbzCoiWV+ZrsiXvbpBaSKclKLYLRqhWcfjqMHu0u15otPEEZ\nb6zJUNVzyvlRt3SDippnnnGtGE8/3Xckua1bN7ffYPx4d9CI2cxSVX3JdxBx8c037rVkCXLqdtoJ\nFi+GdeugVi3f0fiXVJKcKLM4F9hZVW8SkZ2AZqo6NdTosowlycEZMAD239+9qbZs6TuayLE31kqs\nX+86Wvzzn9az3DcRtxH3tNPgvPNc5wvzh3wReRB4ixJ7gVTVWsCVwUot0lejhmu5umCBa9OY65Kt\nSb4Hd4BI8SrTKmBsKBFlMUuSg9O6tTuQ4LzzYONG39FETr6IPGgHEJTvoYfca6hb1q2Px1OXLnDQ\nQXD33b4jiZxeQAfgOODkxNdJXiOKMEuSg2F1yZsk2yf5AFXtJCLTAVR1mYjYQnwVzZ0LJ5/sO4rs\n0bcvvPEGjBgBN97oO5pI6QXsjqtHLi63sAMIEtasgaFD4cWs2X6YHYYNg65d4aKL3Kl8BoDOiZMu\nTRIsSQ6GJcmbJLuSvF5EqpNoRSMiO1DFWsfSRKS+iDwjInNEZJaIZH3raltJDlb16vDYYzB2LLz/\nvu9oIqWzqu6vqj1VtVfi6wLfQUXFnXfCIYe4ch0THbvvDqeeaidqlvK+iOzpO4i4sCQ5GJYkb5Js\nknwn8DzQWESGA1OAEWk+9xjgP6q6B7Avm/pCZqXVq9156FY/G6wdd4T77oNzz4XlW/RbyFn2xlqO\nZcvcYTQ33eQ7ElOW/Hx44AH4/nvfkUTGgcAMEZkrIp+LyEwR+dx3UFFlSXIw2rSB+fN9RxENSbWA\nAxCR3YGjcO3f3lLVlJNaEdkWmK6qbSu5X9a0p5k+3TU4nznTdyTZ6bLL4Jdf4Kmn4rsRK8AWcHOA\ntkAhbrNPccvGSlvApfm8kZ+v/fq518n99/uOxJSnb19Ytcptqoy6DLSAa1XW7dYCbkuqsM02ru95\n/fq+o4m3Tz6BCy+EGTN8RxKsVOZrsn2S7wT+paqBXNQWkX2B+3GN0vfFnSp0lar+Vup+WTOB//Uv\nmDDBfZng/fab63nbq5frubrVVr4jqroAk+RQ31greN5Iz9dFi6B9e/j8c3cFwkTTzz+7srQPPoj+\n7vqwk+SwRX3OVsWPP8Kee7ortiY9y5a5Vqu//hrfRaeypDJfky23+AS4QUTmi8g/EocVpKMG0AkY\nq6qdgDVAvzTHjDSrRw5XnTrw73/DSy9BkybucIgnnnCTPdeo6oKyvnzH5dvQofDXv1qCHHXbbw99\n+thmXFM1VmoRnIYN3Z6fn3/2HYl/yR4m8gjwiIhsB5wO3CwiLVU11c/53wHfqurHib9PAK4v646D\nBw/+4/u8vDzy8vJSfMrMGT7cXfZv08adg96mDbzzDlxgW6dCteee8O67sHQpvPyyS5ovvRTuugt6\n9vQd3ZYmT57M5MmTfYeRE776Cp57zn1YNdF31VVuFfnTT93pmqZqRKQF8CjQBLfJ/gFVvdNvVOGy\nJDlYxZv3GjXyHYlfSdckA4hIF+As4BRgjqqm3NBMRN4FLlLVr0QkH6irqteXuk/sLgU99JBrSfbY\nY+7Umvnz3Qtt4UIYM8YlzSZz3n8f/vxnlyRF/RQmu3QbnrPOgg4d3BHUJh7uucddGXrtNd+RlC+q\nc1ZEmgJNVXWGiNTDXQ0+RVW/LHW/yM7Zqho2zLV3HJFuSwEDwJ/+BN27w9ln+44kOKEdSy0itwCn\nAfOBfwE3qWq6vQR6A0+ISE2gANfbNdbeesu9Cf/3v1ZaERUHHwyNG7ueuN3tOI2c9MknMGWK+wBr\n4uOvf3WdSN55B444wnc08aKqi4HFie9XJTbz7gh8WeEDY6ygwP2+N8GwNnBOsjXJ84GDgXxcQtte\nRA5L54lV9TNV7ayqHVS1u6r+ms54vs2eDT16uEv8liBHS58+cPvtvqMwvvTv7+pbt97adySmKmrV\ncquD/fq5zgUmNSLSGndq30d+IwmXlVsEy5JkJ9kT94qAt4EWwAxc78YPgCNDiitWliyBE090qx6H\nH+47GlPaaafBddfBRx/BAVl/ZI0p6e233S/6Cy/0HYlJxVlnucNFnn/ergSlIlFqMQHXPWqV73gq\nU1TkymzWrKn6Y7/4wpLkILVp43KaW26p+mObNnUtb7NBsi3gZgKdgQ9VtUOiZ/IIVQ3111Yc6qVW\nrICjj4bjj4cSewxNxNxxh2sp9fTTviMpX1TrG5MVtfmq6j4U9emTXXV1uebVV93/w5kzoUayyzoZ\nEuU5KyI1gJeBV1V1TDn30fz8/D/+7ntz/Lx5cNBBqW1yr1cPbrgBqiV7fdxUaMUKGDUKNmyo+mPH\njHG9zmvWDD6uqii9OX7IkCGh9UmepqqdRWQGcICqrhWRWaq6V1WDrlJwEXvTLe2zz1yrseOPdy+K\nbOonmG1WroTWrV19auvWvqMpW5TfcJMRtfn67LOu08zHH9sbZ5ypuprk88+PXoegKM9ZEXkU+ElV\n+1Rwn0jN2ddec6uXkyb5jsSkY+ed3f/DXXbxHcnmwuyT/J2INABeACaJyItAzvZdVYVx46BbNxgy\nBO680xLkqNtmG/cGO6bM9RSTbTZsgIEDYeRIS5DjTsT9fxw8GH7/3Xc08SAihwDnAkeKyHQR+VRE\njvMdV2Wsrjg7ZFM9c7J9kk9LfDtYRN4B6gMRbswTntWr3Ylu06a5LhZ77OE7IpOs3r1h333dm60d\nW5rdHn4YmjWDY47xHYkJwkEHuX7JY8fCtdf6jib6VPU9IOJNL7dkSXJ2yKYkucprLKr6rqq+pKrr\nwggo6o45xm0umDrVEuS42WknOO44eOAB35GYMP32m7vCM3KkXeHJJsOHu018v8a6D5KpiCXJ2SGn\nk+RcVljoDgd5+GFrJxVXffq48pj1631HYsIydix07gwHHug7EhOkvfZyXYT+8Q/fkZiwWJKcHSxJ\nzlFvvOE6WViNY3ztvz/ssIPrdGGyz/LlrmXRsGG+IzFhGDzYtQhbvNh3JCZoqpYkZwtLknPU66/D\nscf6jsKk64gj4N13fUdhwnDrrXDSSbDnnr4jMWFo1cp1ubAPQdnn559di7+GDX1HYtLVtq276h6h\nxikpsyQ5SevXu4MJjj7adyQmXYcf7jZdmuzyww9w773WrzzbDRgATz3l3oRN9rBV5OzRsKHbD7Js\nme9I0mdJcpKmTnW9/5o08R2JSVfXru70PatLzi433QQ9e0LLlr4jMWHaYQe46ioYNMh3JCZIliRn\nD5HsKbmwJDlJVmqRPRo0cJeDPv7YdyQmKF9/Df/+t1tlNNnvmmvgrbdgxgzfkZigWJKcXSxJzjGW\nJGeXww+3uuRsMmgQXH01NGrkOxKTCdts4w6LGTjQdyQmKJYkZxdLknPIL7/AnDlw8MG+IzFBsSQ5\ne8yYAe+845Jkkzsuvhhmz7b9BdnCkuTsYklyDnnzTTjsMKhd23ckJihdu8L777vji028DRjgVhTr\n1fMdicmk2rVh6FDo3z87dtHnOkuSs4slyTnESi2yT6NG7gS+6dN9R2LS8e678OWXblXR5J5zzoEV\nK2DiRN+RmHSsW+e60+y0k+9ITFAsSc4Rqu4QkWOO8R2JCZqVXMSbKvTr57pa1KrlOxrjQ/Xq7vjx\nAQNg40bf0ZhULVwIO+4INWv6jsQEpWVLWLQo/l2kvCbJIlJNRD4VkZd8xlGROXPcL+J27XxHYoJm\nSXK8vfQSrFkDPXr4jsT4dOKJrmPNE0/4jsSkykotsk+tWtCsGXz7re9I0uN7JfkqYLbnGCpUXGoh\n4jsSE7TDDoMpU2wFKo42bnSrh8OH2zHxuU4ERo1yHU7WrvUdjUmFJcnZKRtKLry9vYhIC+AE4EFf\nMSTDSi2yV9Om7nCYmTN9R2Kq6rHHYLvt3CqiMYceCnvv7U5cNPFjSXJ2siQ5PaOBvkBk9yX//ju8\n9x4cdZTvSExYrOQifn7/HfLz3eqhXeExxUaMcPXJK1f6jsRUlSXJ2SkbkuQaPp5URE4ElqjqDBHJ\nA8p9qxs8ePAf3+fl5ZGXlxd2eH/43/9gn31cvZvJTocfDhMmuGNuM23y5MlMnjw5808cc/feC/vu\nC4cc4jsSEyXt28PRR8Ptt7sPUSY+LEnOTm3awHPP+Y4iPaIeGkyKyAjgPGADUAfYBnhOVc8vdT/N\ndHxr1sC0afDBB/DMM3DqqXDjjRkNwWTQokUu4frxR/+1rSKCqsZ2bTQT83XFCth1V9e7fJ99Qn0q\nE0OFhbD//m7DdePG4T+fzdn0qbqFqMJCV0JlssfUqXDppfDJJ74jcVKZr16S5M0CEDkcuFZV/6+M\nn2VsAk+fDhdd5H657rMPHHSQ+/q//4OttspICMaTXXaBF15wNY0+Rf0NV0S+AX4FioD1qtql1M9D\nn6/5+fDNN/DII6E+jYmx3r3dB9477gj/uaI+ZysThST555+hbVtYtszKp7LNTz+5RY1ly3xH4qQy\nX72UW0RNURH87W/w5z/DJZdYUpxriuuSfSfJMVAE5Kmql195P/4Id98dnVUJE00DB8Kee7pjylu3\n9h2NqUxxqYUlyNln++1dJ6Jly6BhQ9/RpMZ78yRVfbesVeRMevJJd8nnyistQc5FtnkvaYLH3xnD\nhsF551niYyrWpAlcfrnVJceF1SNnL5H4b97zniT7tno19O/vLs35rkk1fhx9tEuSK2oftXQpXHgh\nzI50V+/QKTBJRKaJyEWZfOLCQndYxMCBmXxWE1d//zu89hp88YXvSExlLEnObpYkx9zNN0PXrnDw\nwb4jMb40a+YOFbnrLlduU/pAgkmToEMH+PhjeDDSXb1Dd4iqdsL1N79cRA7N1BPn57srPZnYjGXi\nb9tt3ZHlAwb4jsRUpqDA1SSb7NS2bbyT5JyuSV6wAMaOhRkzfEdifNt1V/jwQzj/fDjySNcWbrvt\n3Jvs00/Do49C8+bQrRvceqs7qjzXqOoPiT+XisjzQBdgSsn7hNGy8fPP3aE+8+alPZTJIZde6q4Q\nvvdecO0CrW1j8AoK4E9/8h2FCUubNvHOsbx3t6hI2Dtve/SAdu1gyJDQnsLETFGRO+r4vvugUSM3\nwR94wG1AALeiPGaMq2MOWpR3yotIXaCaqq4Ska2BN4AhqvpGifuEMl9PPtl9OPHRy9rE28MPw7hx\n8N//hrMxLMpzNhlR6G6x886upaOtJmen11+Hf/zDXZH1LZYt4CoS5gR+7z04+2z48kvYeutQnsLE\n2H/+47op9Oy5+ZvrqFHuCsQ//xn8c0b5DVdEdgaex9Ul1wCeUNVRpe4T+HydMsVt1ps7F2rXDnRo\nkwM2bnSHjNx6K5xwQvDjR3nOJsN3krx+PdSrB6tWQc2a3sIwIZo3D447DubP9x2JJclJKyqCAw5w\nLYLOPTfw4U0WKyyELl3g+++D/6Vub7ibU3X7BS66yH1YMSYVL7zgatqnTw9+c7bN2fTMn++uEhUW\negvBhGzdOthmG9ckoYbnAt9U5mtObtybMMG9AZ9zju9ITNzsvLO7LPj2274jyX6vvALLl7uVZGNS\ndcopULcuPPWU70gyR0TGicgSEfncdywVsc4W2a9WLWjaFL791nckqcm5JHnDBhg0yNWdWvNyk4oe\nPeBf//IdRXbbuNG1ZhwxIjc3SZrgiLgyqRtvdKtaOWI8cKzvICpjSXJuiHMbuJxLkh9/3LWROuYY\n35GYuDrzTHjxxS1bxZngPPWUu0R38sm+IzHZ4PDDYbfd3CbcXKCqU4CIHAZcPkuSc0Ock+ScagG3\nbp3rZPHoo7aKbFLXvDnsu687rOCUU3xHk33WrXOrfo88YvPUBGfECLd5r2dPt1nMBGf1avjtt6o/\n7ssvrewxF7RpA7NmwU8/Vf2x9er5PQk5p5LkBx90qwldu/qOxMTd2We7kgtLkoN3332w++5w2GG+\nIzHZpGNHyMtzvZNvuMF3NNERRG/zXXZxH26r+qG2enVXCmOyW+fOrknC449X7XEbNsDee7suR6kI\noq95znS3WLPGHRjx0kuw336BDGly2E8/uTeGRYuCayFoO+Vh5Uo3T197zfWkNiZIX38NBx7oVjAb\nNUp/vCjPWRFpBUxU1fYV3CftObtsGbRqBb/+ald+TLB+/BH22AN+/jmY8ay7RQXGjnW/HC1BNkFo\n1AgOOgheftl3JNnljjvgqKMsQTbh2GUXd7pbjqxeSuIrVMV1xZYgm6DtsIPb+7N8ub8YciJJXrHC\nNZMfOtR3JCabFJdcmGAsXepOM7R5asJ0440wfjwsXOg7kvCIyJPA+0A7EVkoIr3Cei7bfGfCIuJe\nWz77aOdEkjx6NBx7LOy1l+9ITDY59VR45x345RffkWSHkSPhrLPseFoTrmbN4JJL3CbubKWq56hq\nc1WtraotVXV8WM9lSbIJk+/OGFm/ce/HH+Guu+Cjj3xHYrJN/fpw/PGuXdnll/uOJt4WLnTdLGbN\n8h2JyQXXXQft2sGcOa7m0aSuoMB1+zEmDL6TZC8rySLSQkTeFpFZIjJTRHqH9VxDh7pdlbY6ZcLQ\nq5e7dGvSk58Pl17qTmYyJmwNGkDfvjBwoO9I4q+gwN5fTXhyMkkGNgB9VHUv4CDgchHZPegnmTfP\n1Yxaux8TlqOOgiVLYOZM35HE16xZ7gjqvn19R2JyyRVXwLRpdpUxXVZuYcKUk0myqi5W1RmJ71cB\nc4Adg36eAQPg2mvdDkljwlC9ujucwFaTU3fDDXD99a58xZhMqVPHXcHo1w8i3Ak10jZsgO++cy3g\njAlD27Z+k2TvfZJFpDUwGdg7kTCX/FnKPRw//BDOOAO++grq1k03SmPK9/XXcMgh7s2iZs3Ux4ly\nz9VkpDJfP/zQteSaO9clLcZkUvFhBWPGuM3dVZWLc7akwkJ3QMuCBcHFZExJv//uFlDWrHGLUumI\nXZ9kEakHTACuKp0gp0PVbcwYOtQSZBO+XXZxJzm+8orvSOJF1a3iDR5sCbLxo0YNGD4c+veHoiLf\n0cSPlVqYsG21FTRu7BahfPDW3UJEauAS5MdU9cXy7pfKkZkTJ7q2XD17ph+nMcko3sB36qnJPyaI\nIzPj7PXXXT33+ef7jsTksu7d3eEizzzjWhCa5FmSbDKhuC7ZR1mPt3ILEXkU+ElV+1RwnypfCtqw\nAdq3d4eHnHhiulEak5xVq2Cnndxxt02apDZGLl26LSpyp1/eeKNLUozx6a234G9/g9mzq1YylUtz\ntiz9+0O9etYlxISrVy849FC48ML0xolNuYWIHAKcCxwpItNF5FMROS6IsceNc0vzJ5wQxGjGJKde\nPbeK/PjjviOJh6efhlq14LTTfEdijOtS07q1e/8wyZs/31aSTfh8drjw1d3iPVWtrqodVLWjqnZS\n1dfSHXfZMhg0CO64w86RN5nXqxc89JDtlK/MunVuBXnUKJunJjpGjoSbbnIbhExyrNzCZELOJclh\nGTTIXbrt0MF3JCYXde0Ka9e63qumfOPGubY+RxzhOxJjNtl/f9el5s47fUcSH5Ykm0zwmSR7bwFX\nkarUS33+OXTr5o4Z3X77kAMzphzDhrm6xieeqPoqaS7UN65eDbvu6jbX7rdfhgIzJklz57rax6++\ngoYNK79/LszZ8ixbBi1bwooVdkXIhGvJEteqcenS9MaJTU1y0FShd28YMsQSZOPXFVe4N9g+fazs\noixjxrgVd0uQTRTttpurkx81ynck0VdY6Fb4LEE2YWvc2JVBrViR+efOiiT53/+G5cvh4ot9R2Jy\nXYMG8Oab8P777oObJcqb/Pwz3H67q/s0Jqry8+HBB2HRIt+RRFtBgSubMiZsIu4DWWFh5p879kny\n6tXQty/cdVf6p7EYE4QGDeCNN+Djj+Gyy+yQgmI33+xOwWzXznckxpRvxx3hr391h1GZ8lk9sskk\nX3XJsU+SR450l2+7dvUdiTGb1K/vDsuYORMuucQS5e++cxv2Bg3yHYkxlbv+enjuOVejbMpmSbLJ\npDZtXMvBTIt1klxYCPfeC7fc4jsSY7a07bbw2mvugJEhQ3xH49eQIa4cqnlz35EYU7nttoNrr3Wt\nCk3ZLEk2mWQrySno1w+uvtpdHjMmiurVcwdn/POfMGOG72j8+PJLeOEFuO4635EYk7zeveG991zZ\nlNmSJckmkyxJrqIPPnCbo/qUe6i1MdHQvLmrx73gAli/3nc0mXfDDfD3vyfXUsuYqKhb160k9+/v\nO5Lo2bABvv0WWrXyHYnJFZYkV4GqS46HD3e/yIyJur/8xbWxybXSoGnT3AfaK6/0HYkxVXfhhfDN\nN65jjdnku++gSROoXdt3JCZXtG4NCxbAxo2Zfd5YJsnPPOOOtj3vPN+RGJMcEbj/fhg9GmbN8h1N\n5vTv71pq2YdZE0c1a7oDgvr3t3aOJc2fb6UWJrPq1IFGjTLfmjF2SfLata4W+bbboFrsoje5rGVL\n94Z7wQXucmW2mzQJFi6EXr18R2JM6s48061ePfus70iiw+qRjQ8+Si5il2bedRfssw/k5fmOxJiq\nu/hi2HpruOMO35GEq6jIrb4NG+ZW44yJq2rV4O67rTNLSZYkGx/ats18klwjs0+Xnp9+chugpkzx\nHYkxqalWzZ3m1aULnHsuNGvmO6JwFK+6nXGG3ziMCcLBB/uOIFoKCuCUU3xHYXKNrSRXYsgQOPts\n2G0335EYk7o2beCTT7I3QV6/HgYOdAf9WEmUMdnHVpKNDz6S5NisJD//PLz4Inz6qe9IjElfNrdO\nGj8edtoJunXzHYkxJgwFBe7StzGZlFMrySJynIh8KSJficj1Fd13xgx3tO/zz7vdjcaYzEtmzq5Z\nA0OHwqhRrqOHMcaPqrzHVsXy5a67lL0Xm0zLmSRZRKoBdwPHAnsBPURk97Luu2QJnHoqjB0L++0X\nXAyTJ08ObrAsG99i9zd+VCU7Z+++Gw48EDp3Dj4Ge934Gd9ij5+qvMdWVWGhS1Yq+xBsrxs/48c5\n9srGb9IEVq+GlStDDWEzvlaSuwDzVHWBqq4H/gWUuQ2ge3d3EMOZZwYbQDa/kKI8dtjjxzn2iEtq\nzt56q+toEQZ73fgZ32KPpaTfY6sq2Xpke934GT/OsVc2vgjsvLP7oJYpvpLkHYFvS/z9u8RtW2je\nHAYNykhMxpjyJTVnTz0Vdg9kvcoYk4ak32OryjbtGZ8yXXIR+Y17Dz9sO+SNiYv8fN8RGGOq4uST\nq3b/2bOhT59wYjGmMm3buoXTceMqvl/DhvDoo+k/n6iHszZF5EBgsKoel/h7P0BV9eZS97ODQE1O\nUdVIbndLZs7afDW5KIpz1t5jjSlbVeerryS5OjAXOAr4AZgK9FDVORkPxhhTKZuzxsSHzVdjguGl\n3EJVN4rIFcAbuLrocTZ5jYkum7PGxIfNV2OC4WUl2RhjjDHGmCiL5Ja4sJqglxj/GxH5TESmi8jU\nAMYbJyJLROTzErc1FJE3RGSuiLwuIvUDHDtfRL4TkU8TX8elEXsLEXlbRGaJyEwR6R1U/GWMfWWQ\n8YtIbRH5KPH/caaI5AcYe3ljB/lvXy0xxktBxe1LnOZsmPO1gvGDes3bfK36+DZnS4nTfE2MF8v3\n2DDnaznjBzZnbb4mqGqkvnCJ+9dAK6AmMAPYPeDnKAAaBjjeoUAH4PMSt90MXJf4/npgVIBj5wN9\nAoq9KdAh8X09XB3b7kHEX8HYQcZfN/FndeBDXH/QoP7tyxo7yNivAR4HXgryNZPpr7jN2TDnawXj\nB/K6sfma0vg2Zzf/b4jVfE2MF8v32DDnayXjBxV/zs/XKK4kh9YEvQQhwFV0VZ0CLCt18ynAI4nv\nHwFODXBscP8NaVPVxao6I/H9KmAO0IIA4i9n7OJenUHFvybxbW1cjb0S3L99WWNDALGLSAvgBODB\nEjcHErcHsZqzYc7XCsaHAF43Nl9TGh9szpYUq/kK8X2PDXO+VjB+YHPW5ms0yy1Ca4JeggKTRGSa\niFwU8NjFGqvqEnAvZKBxwONfISIzROTBoC7xiUhr3CfqD4EmQcZfYuyPEjcFEn/icsp0YDEwSVWn\nBRV7OWMHFftooC+bfikQVNweZMOcDXu+QsBz1uZr0uMHFX+2zNlsmK8Qs/fYMOdrqfEDm7M2X6OZ\nJGfCIaraCfcp43IROTQDzxnkDsl7gDaq2gH34ro93QFFpB4wAbgq8Ym0dLwpx1/G2IHFr6pFqtoR\n9+m8i4jsVUasKcVexth7BhG7iJwILEmsAFT0idl21W6S6Tkb9L99oHPW5mvS49uc9cPeY0sIc76W\nM34g8dt8jWaSvAhoWeLvLRK3BUZVf0j8uRR4Hnf5KWhLRKQJgIg0BX4MamBVXaqJohrgAaBzOuOJ\nSA3cBHtMVV9M3BxI/GWNHXT8iTFXAJOB4wj4377k2AHFfgjwfyJSADwFHCkijwGLw3rNhCwb5mxo\n8xWCfc3bfK3a+DZnt5AN8xVi8h4b5nwtb/yg52wuz9coJsnTgF1EpJWI1ALOBl4KanARqZv41IWI\nbA0cA3wRxNBs/onlJeAvie97Ai+WfkCqYyf+5xbrTvrxPwTMVtUxJW4LKv4txg4qfhFpVHwpRkTq\nAEfjarLSjr2csb8MInZVHaCqLVW1De71/baq/hmYmG7cnsRxzoY5X7cYP+A5a/M1+fFtzm4pjvMV\n4i670iEAAAEDSURBVPseG+Z8LXP8IOK3+bppsMh94T6tzAXmAf0CHntn3G7e6cDMIMYHngS+B9YC\nC4FeQEPgzcR/xxtAgwDHfhT4PPHf8QKuzibV2A8BNpb4N/k08e+/XbrxVzB2IPED+yTGnJEYb2Di\n9iBiL2/swP7tE+Mdzqadt2nH7esrTnM2zPlawfhBveZtvlZ9fJuzW/43xGa+JsaM5XtsmPO1kvHT\njt/mq/uyw0SMMcYYY4wpJYrlFsYYY4wxxnhlSbIxxhhjjDGlWJJsjDHGGGNMKZYkG2OMMcYYU4ol\nycYYY4wxxpRiSbIxxhhjjDGlWJJsjDHGGGNMKZYkG2OMMcYYU8r/A122qOZA2sl3AAAAAElFTkSu\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7bf1d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "import matplotlib.pyplot\n",
+    "\n",
+    "data = numpy.loadtxt(fname='../data/inflammation-01.csv', delimiter=',')\n",
+    "\n",
+    "fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
+    "\n",
+    "axes1 = fig.add_subplot(1, 3, 1)\n",
+    "axes2 = fig.add_subplot(1, 3, 2)\n",
+    "axes3 = fig.add_subplot(1, 3, 3)\n",
+    "\n",
+    "axes1.set_ylabel('average')\n",
+    "axes1.plot(numpy.mean(data, axis=0))\n",
+    "\n",
+    "axes2.set_ylabel('max')\n",
+    "axes2.plot(numpy.max(data, axis=0))\n",
+    "\n",
+    "axes3.set_ylabel('min')\n",
+    "axes3.plot(numpy.min(data, axis=0))\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "matplotlib.pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The call to loadtxt reads our data, and the rest of the program tells the plotting library how large we want the figure to be, that we’re creating three subplots, what to draw for each one, and that we want a tight layout. (Perversely, if we leave out that call to fig.tight_layout(), the graphs will actually be squeezed together more closely.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Excercises ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Key Points ###\n",
+    "\n",
+    "* Import a library into a program using import libraryname.\n",
+    "* Use the numpy library to work with arrays in Python.\n",
+    "* Use variable = value to assign a value to a variable in order to record it in memory.\n",
+    "* Variables are created on demand whenever a value is assigned to them.\n",
+    "* Use print(something) to display the value of something.\n",
+    "* The expression array.shape gives the shape of an array.\n",
+    "* Use array[x, y] to select a single element from an array.\n",
+    "* Array indices start at 0, not 1.\n",
+    "* Use low:high to specify a slice that includes the indices from low to high-1.\n",
+    "* All the indexing and slicing that works on arrays also works on strings.\n",
+    "* Use # some kind of explanation to add comments to programs.\n",
+    "* Use numpy.mean(array), numpy.max(array), and numpy.min(array) to calculate simple statistics.\n",
+    "* Use numpy.mean(array, axis=0) or numpy.mean(array, axis=1) to calculate statistics across the specified axis.\n",
+    "* Use the pyplot library from matplotlib for creating simple visualizations."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda env:ciipromol]",
+   "language": "python",
+   "name": "conda-env-ciipromol-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python_notebooks/10. Command-Line Programs.ipynb b/python_notebooks/10. Command-Line Programs.ipynb
new file mode 100644
index 0000000..c9c9f81
--- /dev/null
+++ b/python_notebooks/10. Command-Line Programs.ipynb	
@@ -0,0 +1,684 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Command-Line Programs ##\n",
+    "\n",
+    "The Jupyter Notebook and other interactive tools are great for prototyping code and exploring data, but sooner or later we will want to use our program in a pipeline or run it in a shell script to process thousands of data files. In order to do that, we need to make our programs work like other Unix command-line tools. For example, we may want a program that reads a dataset and prints the average inflammation per patient."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Switching to Shell Commands ##\n",
+    "\n",
+    "In this lesson we are switching from typing commands in a Python interpreter to typing commands in a shell terminal window (such as bash). When you see a `$` in front of a command that tells you to run that command in the shell rather than the Python interpreter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This program does exactly what we want - it prints the average inflammation per patient for a given file.\n",
+    "\n",
+    "```\n",
+    "$ python code/readings_04.py --mean data/inflammation-01.csv\n",
+    "5.45\n",
+    "5.425\n",
+    "6.1\n",
+    "...\n",
+    "6.4\n",
+    "7.05\n",
+    "5.9\n",
+    "```\n",
+    "\n",
+    "We might also want to look at the minimum of the first four lines\n",
+    "\n",
+    "```\n",
+    "$ head -4 data/inflammation-01.csv | python code/readings_04.py --min\n",
+    "```\n",
+    "\n",
+    "or the maximum inflammations in several files one after another:\n",
+    "\n",
+    "```\n",
+    "$ python code/readings_04.py --max data/inflammation-*.csv\n",
+    "```\n",
+    "\n",
+    "Our scripts should do the following:\n",
+    "\n",
+    "1. If no filename is given on the command line, read data from standard input.\n",
+    "2. If one or more filenames are given, read data from them and report statistics for each file separately.\n",
+    "3. Use the --min, --mean, or --max flag to determine what statistic to print.\n",
+    "\n",
+    "To make this work, we need to know how to handle command-line arguments in a program, and how to get at standard input. We’ll tackle these questions in turn below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Command-Line Arguments ##\n",
+    "\n",
+    "Using the text editor of your choice, save the following in a text file called sys_version.py:\n",
+    "\n",
+    "```python\n",
+    "import sys \n",
+    "print('version is`, sys.version)\n",
+    "```\n",
+    "\n",
+    "The first line imports a library called `sys`, which is short for “system”. It defines values such as `sys.version`, which describes which version of Python we are running. We can run this script from the command line like this:\n",
+    "\n",
+    "```\n",
+    "$ python sys_version.py\n",
+    "version is 3.4.3+ (default, Jul 28 2015, 13:17:50)\n",
+    "[GCC 4.9.3]\n",
+    "```\n",
+    "Create another file called `argv_list.py` and save the following text to it.\n",
+    "\n",
+    "```python\n",
+    "import sys\n",
+    "print('sys.argv is', sys.argv)\n",
+    "```\n",
+    "\n",
+    "The strange name `argv` stands for “argument values”. Whenever Python runs a program, it takes all of the values given on the command line and puts them in the list `sys.argv` so that the program can determine what they were. If we run this program with no arguments:\n",
+    "\n",
+    "```\n",
+    "$ python argv_list.py\n",
+    "sys.argv is ['argv_list.py']\n",
+    "```\n",
+    "\n",
+    "the only thing in the list is the full path to our script, which is always `sys.argv[0]`. If we run it with a few arguments, however:\n",
+    "\n",
+    "```\n",
+    "$ python argv_list.py first second third\n",
+    "sys.argv is ['argv_list.py', 'first', 'second', 'third']\n",
+    "```\n",
+    "\n",
+    "then Python adds each of those arguments to that magic list.\n",
+    "\n",
+    "With this in hand, let’s build a version of `readings.py` that always prints the per-patient mean of a single data file. The first step is to write a function that outlines our implementation, and a placeholder for the function that does the actual work. By convention this function is usually called main, though we can call it whatever we want:\n",
+    "\n",
+    "```\n",
+    "$ cat readings_01.py\n",
+    "```\n",
+    "\n",
+    "And in the file:\n",
+    "\n",
+    "```python  \n",
+    "import sys\n",
+    "import numpy\n",
+    "\n",
+    "def main():\n",
+    "    script = sys.argv[0]\n",
+    "    filename = sys.argv[1]\n",
+    "    data = numpy.loadtxt(filename, delimiter=',')\n",
+    "    for m in numpy.mean(data, axis=1):\n",
+    "        print(m)\n",
+    "```\n",
+    "\n",
+    "This function gets the name of the script from `sys.argv[0]`, because that’s where it’s always put, and the name of the file to process from `sys.argv[1]`. Here’s a simple test:\n",
+    "\n",
+    "```\n",
+    "$ python readings_01.py inflammation-01.csv\n",
+    "```\n",
+    "\n",
+    "There is no output because we have defined a function, but haven’t actually called it. Let’s add a call to `main`:\n",
+    "\n",
+    "```\n",
+    "$ cat readings_02.py\n",
+    "```\n",
+    "\n",
+    "```python \n",
+    "import sys\n",
+    "import numpy\n",
+    "\n",
+    "def main():\n",
+    "    script = sys.argv[0]\n",
+    "    filename = sys.argv[1]\n",
+    "    data = numpy.loadtxt(filename, delimiter=',')\n",
+    "    for m in numpy.mean(data, axis=1):\n",
+    "        print(m)\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "   main()\n",
+    "```\n",
+    "\n",
+    "and run that:\n",
+    "\n",
+    "```\n",
+    "$ python readings_02.py inflammation-01.csv\n",
+    "5.45\n",
+    "5.425\n",
+    "6.1\n",
+    "5.9\n",
+    "5.55\n",
+    "6.225\n",
+    "5.975\n",
+    "6.65\n",
+    "6.625\n",
+    "6.525\n",
+    "6.775\n",
+    "5.8\n",
+    "6.225\n",
+    "5.75\n",
+    "5.225\n",
+    "6.3\n",
+    "6.55\n",
+    "5.7\n",
+    "5.85\n",
+    "6.55\n",
+    "5.775\n",
+    "5.825\n",
+    "6.175\n",
+    "6.1\n",
+    "5.8\n",
+    "6.425\n",
+    "6.05\n",
+    "6.025\n",
+    "6.175\n",
+    "6.55\n",
+    "6.175\n",
+    "6.35\n",
+    "6.725\n",
+    "6.125\n",
+    "7.075\n",
+    "5.725\n",
+    "5.925\n",
+    "6.15\n",
+    "6.075\n",
+    "5.75\n",
+    "5.975\n",
+    "5.725\n",
+    "6.3\n",
+    "5.9\n",
+    "6.75\n",
+    "5.925\n",
+    "7.225\n",
+    "6.15\n",
+    "5.95\n",
+    "6.275\n",
+    "5.7\n",
+    "6.1\n",
+    "6.825\n",
+    "5.975\n",
+    "6.725\n",
+    "5.7\n",
+    "6.25\n",
+    "6.4\n",
+    "7.05\n",
+    "5.9\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running Versus Importing ##\n",
+    "\n",
+    "Running a Python script in bash is very similar to importing that file in Python. The biggest difference is that we don’t expect anything to happen when we import a file, whereas when running a script, we expect to see some output printed to the console.\n",
+    "\n",
+    "In order for a Python script to work as expected when imported or when run as a script, we typically put the part of the script that produces output in the following if statement:\n",
+    "\n",
+    "```python\n",
+    "if __name__ == '__main__':\n",
+    "    main()  # Or whatever function produces output\n",
+    "```\n",
+    "When you import a Python file, `__name__` is the name of that file (e.g., when importing `readings.py`, `__name__` is `'readings'`). However, when running a script in bash, `__name__` is always set to `'__main__'` in that script so that you can determine if the file is being imported or run as a script."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Right Way to Do It ##\n",
+    "\n",
+    "If our programs can take complex parameters or multiple filenames, we shouldn’t handle `sys.argv` directly. Instead, we should use Python’s `argparse` library, which handles common cases in a systematic way, and also makes it easy for us to provide sensible error messages for our users. We will not cover this module in this lesson but you can go to Tshepang Lekhonkhobe’s [Argparse tutorial](https://docs.python.org/dev/howto/argparse.html) that is part of Python’s Official Documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handling Multiple Files ##\n",
+    "\n",
+    "The next step is to teach our program how to handle multiple files. Since 60 lines of output per file is a lot to page through, we’ll start by using three smaller files, each of which has three days of data for two patients:\n",
+    "\n",
+    "```\n",
+    "$ ls small-*.csv\n",
+    "small-01.csv small-02.csv small-03.csv\n",
+    "```\n",
+    "\n",
+    "```\n",
+    "$ cat small-01.csv\n",
+    "0,0,1\n",
+    "0,1,2\n",
+    "```\n",
+    "\n",
+    "```\n",
+    "$ python readings_02.py small-01.csv\n",
+    "0.333333333333\n",
+    "1.0\n",
+    "```\n",
+    "\n",
+    "Using small data files as input also allows us to check our results more easily: here, for example, we can see that our program is calculating the mean correctly for each line, whereas we were really taking it on faith before. This is yet another rule of programming: _test the simple things first_.\n",
+    "\n",
+    "We want our program to process each file separately, so we need a loop that executes once for each filename. If we specify the files on the command line, the filenames will be in sys.argv, but we need to be careful: `sys.argv[0]` will always be the name of our script, rather than the name of a file. We also need to handle an unknown number of filenames, since our program could be run for any number of files.\n",
+    "\n",
+    "The solution to both problems is to loop over the contents of `sys.argv[1:]`. The `‘1’` tells Python to start the slice at location `1`, so the program’s name isn’t included; since we’ve left off the upper bound, the slice runs to the end of the list, and includes all the filenames. Here’s our changed program `readings_03.py`:\n",
+    "\n",
+    "```\n",
+    "$ cat readings_03.py\n",
+    "```\n",
+    "\n",
+    "```python \n",
+    "import sys\n",
+    "import numpy\n",
+    "\n",
+    "def main():\n",
+    "    script = sys.argv[0]\n",
+    "    for filename in sys.argv[1:]:\n",
+    "        data = numpy.loadtxt(filename, delimiter=',')\n",
+    "        for m in numpy.mean(data, axis=1):\n",
+    "            print(m)\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "   main()\n",
+    "```\n",
+    "\n",
+    "and here it is in action:\n",
+    "\n",
+    "```\n",
+    "$ python readings_03.py small-01.csv small-02.csv\n",
+    "0.333333333333\n",
+    "1.0\n",
+    "13.6666666667\n",
+    "11.0\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Right Way to Do It ##\n",
+    "\n",
+    "At this point, we have created three versions of our script called `readings_01.py`, `readings_02.py`, and `readings_03.py`. We wouldn’t do this in real life: instead, we would have one file called `readings.py` that we committed to version control every time we got an enhancement working. For teaching, though, we need all the successive versions side by side."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handling Command-Line Flags ##\n",
+    "\n",
+    "The next step is to teach our program to pay attention to the `--min`, `--mean`, and `--max` flags. These always appear before the names of the files, so we could just do this:\n",
+    "\n",
+    "```\n",
+    "$ cat readings_04.py\n",
+    "```\n",
+    "\n",
+    "```python\n",
+    "import sys\n",
+    "import numpy\n",
+    "\n",
+    "def main():\n",
+    "    script = sys.argv[0]\n",
+    "    action = sys.argv[1]\n",
+    "    filenames = sys.argv[2:]\n",
+    "\n",
+    "    for f in filenames:\n",
+    "        data = numpy.loadtxt(f, delimiter=',')\n",
+    "\n",
+    "        if action == '--min':\n",
+    "            values = numpy.min(data, axis=1)\n",
+    "        elif action == '--mean':\n",
+    "            values = numpy.mean(data, axis=1)\n",
+    "        elif action == '--max':\n",
+    "            values = numpy.max(data, axis=1)\n",
+    "\n",
+    "        for m in values:\n",
+    "            print(m)\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "   main()\n",
+    "```\n",
+    "\n",
+    "This works:\n",
+    "\n",
+    "```\n",
+    "$ python readings_04.py --max small-01.csv\n",
+    "1.0\n",
+    "2.0\n",
+    "```\n",
+    "\n",
+    "but there are several things wrong with it:\n",
+    "\n",
+    "1. `main` is too large to read comfortably.\n",
+    "\n",
+    "2. If we do not specify at least two additional arguments on the command-line, one for the flag and one for the filename, but only one, the program will not throw an exception but will run. It assumes that the file list is empty, as `sys.argv[1]` will be considered the `action`, even if it is a filename. Silent failures like this are always hard to debug.\n",
+    "\n",
+    "3. The program should check if the submitted `action` is one of the three recognized flags.\n",
+    "\n",
+    "This version pulls the processing of each file out of the loop into a function of its own. It also checks that `action` is one of the allowed flags before doing any processing, so that the program fails fast:\n",
+    "but there are several things wrong with it:\n",
+    "\n",
+    "main is too large to read comfortably.\n",
+    "\n",
+    "If we do not specify at least two additional arguments on the command-line, one for the flag and one for the filename, but only one, the program will not throw an exception but will run. It assumes that the file list is empty, as sys.argv[1] will be considered the action, even if it is a filename. Silent failures like this are always hard to debug.\n",
+    "\n",
+    "The program should check if the submitted action is one of the three recognized flags.\n",
+    "\n",
+    "This version pulls the processing of each file out of the loop into a function of its own. It also checks that action is one of the allowed flags before doing any processing, so that the program fails fast:\n",
+    "\n",
+    "```\n",
+    "$ cat readings_05.py\n",
+    "```\n",
+    "\n",
+    "```python\n",
+    "import sys\n",
+    "import numpy\n",
+    "\n",
+    "def main():\n",
+    "    script = sys.argv[0]\n",
+    "    action = sys.argv[1]\n",
+    "    filenames = sys.argv[2:]\n",
+    "    assert action in ['--min', '--mean', '--max'], \\\n",
+    "           'Action is not one of --min, --mean, or --max: ' + action\n",
+    "    for f in filenames:\n",
+    "        process(f, action)\n",
+    "\n",
+    "def process(filename, action):\n",
+    "    data = numpy.loadtxt(filename, delimiter=',')\n",
+    "\n",
+    "    if action == '--min':\n",
+    "        values = numpy.min(data, axis=1)\n",
+    "    elif action == '--mean':\n",
+    "        values = numpy.mean(data, axis=1)\n",
+    "    elif action == '--max':\n",
+    "        values = numpy.max(data, axis=1)\n",
+    "\n",
+    "    for m in values:\n",
+    "        print(m)\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "   main()\n",
+    "```\n",
+    "\n",
+    "This is four lines longer than its predecessor, but broken into more digestible chunks of 8 and 12 lines.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handling Standard Input ##\n",
+    "\n",
+    "The next thing our program has to do is read data from standard input if no filenames are given so that we can put it in a pipeline, redirect input to it, and so on. Let’s experiment in another script called `count_stdin.py`:\n",
+    "\n",
+    "```\n",
+    "$ cat count_stdin.py\n",
+    "```\n",
+    "\n",
+    "```\n",
+    "import sys\n",
+    "\n",
+    "count = 0\n",
+    "for line in sys.stdin:\n",
+    "    count += 1\n",
+    "\n",
+    "print(count, 'lines in standard input')\n",
+    "```\n",
+    "\n",
+    "This little program reads lines from a special “file” called `sys.stdin`, which is automatically connected to the program’s standard input. We don’t have to open it — Python and the operating system take care of that when the program starts up — but we can do almost anything with it that we could do to a regular file. Let’s try running it as if it were a regular command-line program:\n",
+    "\n",
+    "```\n",
+    "$ python count_stdin.py < small-01.csv\n",
+    "2 lines in standard input\n",
+    "```\n",
+    "A common mistake is to try to run something that reads from standard input like this:\n",
+    "\n",
+    "```\n",
+    "$ python count_stdin.py small-01.csv\n",
+    "```\n",
+    "\n",
+    "i.e., to forget the `<` character that redirects the file to standard input. In this case, there’s nothing in standard input, so the program waits at the start of the loop for someone to type something on the keyboard. Since there’s no way for us to do this, our program is stuck, and we have to halt it using the `Interrupt` option from the `Kernel` menu in the Notebook.\n",
+    "\n",
+    "We now need to rewrite the program so that it loads data from `sys.stdin` if no filenames are provided. Luckily, `numpy.loadtxt` can handle either a filename or an open file as its first parameter, so we don’t actually need to change `process`. Only `main` changes:\n",
+    "```\n",
+    "$ cat readings_06.py\n",
+    "```\n",
+    "\n",
+    "```python\n",
+    "import sys\n",
+    "import numpy\n",
+    "\n",
+    "def main():\n",
+    "    script = sys.argv[0]\n",
+    "    action = sys.argv[1]\n",
+    "    filenames = sys.argv[2:]\n",
+    "    assert action in ['--min', '--mean', '--max'], \\\n",
+    "           'Action is not one of --min, --mean, or --max: ' + action\n",
+    "    if len(filenames) == 0:\n",
+    "        process(sys.stdin, action)\n",
+    "    else:\n",
+    "        for f in filenames:\n",
+    "            process(f, action)\n",
+    "\n",
+    "def process(filename, action):\n",
+    "    data = numpy.loadtxt(filename, delimiter=',')\n",
+    "\n",
+    "    if action == '--min':\n",
+    "        values = numpy.min(data, axis=1)\n",
+    "    elif action == '--mean':\n",
+    "        values = numpy.mean(data, axis=1)\n",
+    "    elif action == '--max':\n",
+    "        values = numpy.max(data, axis=1)\n",
+    "\n",
+    "    for m in values:\n",
+    "        print(m)\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "   main()\n",
+    "```\n",
+    "\n",
+    "Let’s try it out:\n",
+    "\n",
+    "```\n",
+    "$ python readings_06.py --mean < small-01.csv\n",
+    "0.333333333333\n",
+    "1.0\n",
+    "```\n",
+    "That’s better. In fact, that’s done: the program now does everything we set out to do."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 1: Arithmetic on the Command Line\n",
+    "\n",
+    "Write a command-line program that does addition and subtraction:\n",
+    "\n",
+    "```\n",
+    "$ python arith.py add 1 2\n",
+    "3\n",
+    "$ python arith.py subtract 3 4\n",
+    "-1\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 2: Finding Particular Files ##\n",
+    "\n",
+    "Using the `glob` module introduced earlier, write a simple version of ls that shows files in the current directory with a particular suffix. A call to this script should look like this:\n",
+    "\n",
+    "```\n",
+    "$ python my_ls.py py\n",
+    "left.py\n",
+    "right.py\n",
+    "zero.py\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 3: Adding a Help Message ##\n",
+    "\n",
+    "Separately, modify `readings.py` so that if no parameters are given (i.e., no action is specified and no filenames are given), it prints a message explaining how it should be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 4: Adding a Default Action ##\n",
+    "\n",
+    "Separately, modify `readings.py` so that if no action is given it displays the means of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 5: A File-Checker ##\n",
+    "\n",
+    "Write a program called `check.py` that takes the names of one or more inflammation data files as arguments and checks that all the files have the same number of rows and columns. What is the best way to test your program?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 6: Counting Lines ##\n",
+    "\n",
+    "Write a program called `line_count.py` that works like the Unix `wc` command:\n",
+    "\n",
+    "* If no filenames are given, it reports the number of lines in standard input.\n",
+    "* If one or more filenames are given, it reports the number of lines in each, followed by the total number of lines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 7: Generate an Error Message ##\n",
+    "\n",
+    "Write a program called `check_arguments.py` that prints usage then exits the program if no arguments are provided. (Hint) You can use `sys.exit()` to exit the program.\n",
+    "\n",
+    "```\n",
+    "$ python check_arguments.py\n",
+    "usage: python check_argument.py filename.txt\n",
+    "$ python check_arguments.py filename.txt\n",
+    "Thanks for specifying arguments!\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python_notebooks/2. Repeating Actions With Loops.ipynb b/python_notebooks/2. Repeating Actions With Loops.ipynb
new file mode 100644
index 0000000..5d61494
--- /dev/null
+++ b/python_notebooks/2. Repeating Actions With Loops.ipynb	
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Repeating actions with loops #\n",
+    "\n",
+    "\"How can I do the same operations on many different values?\"   \n",
+    "objectives:  \n",
+    "\"Explain what a for loop does.\"  \n",
+    "\"Correctly write for loops to repeat simple calculations.\"  \n",
+    "\"Trace changes to a loop variable as the loop runs.\"  \n",
+    "\"Trace changes to other variables as they are updated by a for loop.\" keypoints:  \n",
+    "\"Use for variable in collection to process the elements of a collection one at a time.\"  \n",
+    "\"The body of a for loop must be indented.\"  \n",
+    "\"Use len(thing) to determine the length of something that contains other values.\"  \n",
+    "\n",
+    "In the last lesson, we noticed some abnormalities in our data, for example the file `inflammation-01.csv`\n",
+    "![Analysis of inflammation-01.csv](fig/03-loop_2_0.png)\n",
+    "\n",
+    "If you look in the data folder, you'll notice that we have dozens of datets that we plan on evaluation.  Individual evaluation is possible, but not practical.  If we want to perform the same action on several items, python allows us to do so by using `for` loops.  \n",
+    "\n",
+    "As an example, let's say have a variable called `word` which was assigned the string `\"lead\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "word = \"lead\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's suppose that we wanted to to print each character of that word.  The most straighforward and easiest way to performed this task would be access each character by its index and printing them to the screen.  This would look like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "l\n",
+      "e\n",
+      "a\n",
+      "d\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(word[0])\n",
+    "print(word[1])\n",
+    "print(word[2])\n",
+    "print(word[3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Well, that worked.  But we can see how this wouldn't scale up to larger words or even sentences.  Accessing these letters by each's index could take a long time.  Additionally, we can run in to situations where we may not be aware of the length of the word.  For example, let's say the string `\"tin\"` was stored in our variable `word`.  Taking the same approach, we would render an error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t\n",
+      "i\n",
+      "n\n"
+     ]
+    },
+    {
+     "ename": "IndexError",
+     "evalue": "string index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-d9583df8c6ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m: string index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "word = \"tin\"\n",
+    "\n",
+    "print(word[0])\n",
+    "print(word[1])\n",
+    "print(word[2])\n",
+    "print(word[3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We get an error, which is unsurprising.  The word `\"tin\"` doesn't have four characters, it has three.  So, accessing the index `word[3]`, renders an error.  Thankfully, we can circumvent this problem by using a `for` loop.  `for` loops allow us to access the idividual elements of a collection one-by-one.  Using a `for`, we can print each character of `word`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "o\n",
+      "x\n",
+      "y\n",
+      "g\n",
+      "e\n",
+      "n\n"
+     ]
+    }
+   ],
+   "source": [
+    "word = \"oxygen\"\n",
+    "for char in word:\n",
+    "    print(char)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exactly what we wanted, each letter in the variable `word` (in this case `\"oxygen\"`) was printed.  Here, the for loop allowed us to print each letter in `word`, but stopped when there were no longer any elements in the variable.  This is the general concept of a `for` loop, for each _element_ in a _collection_ do something to that _element_.  In our example above, the statment `for char in word` for each _element_ (letter) in our _collection_ (word) assign it to the variable `char`.  This allows us to treat `char` like we would any other variable in python, with the difference being that `char` will take on a different value through each element of our collection.  We can see how this is this is done graphically:\n",
+    "\n",
+    "![loop_image](fig/loops_image.png)\n",
+    "\n",
+    "One of the best things about python is the verbosity of the language.  We can call the `for` loop variable anything we want.  Sometimes, we only need to do something for the length of a variable?  What if we wanted to count the number of characters contained within a string variable.  We could accomplish that using a `for` loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 5 vowels\n"
+     ]
+    }
+   ],
+   "source": [
+    "length = 0\n",
+    "for vowel in 'aeiou':\n",
+    "    length = length + 1\n",
+    "print('There are', length, 'vowels')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at this code piece by piece.\n",
+    "\n",
+    "So how many times does the code within the `for` loop execute?  The answer is however many elements are in whatever we are using the `for` loop to iterate through.  In the case above that would be 5, because their are 5 elements in our string `'aeiou'`.  Why did the variable `length` display a value of 5 when we assigned it a value of 0?  Before the `for` loop executes we a assign it a value it a value of 0.  However, the expression\n",
+    "```\n",
+    "length = length + 1\n",
+    "```\n",
+    "is assigning a new value to length through each iteration of the `for` loop; where the new value is the old value + 1.  After the first iteration of the `for` loop, `length` would be equal to 1 (0 = 0 + 1).  Through the second iteration, it would be 2 (2 = 1 + 1). Through the third, 3 (3 = 2 + 1); And so on throughout the length of the string `aeiou`.  Which would leave us with the number 5.  It's important to remember that even though the variable `vowel` isn't being used within the `for` loop, it is still taking on the value of each element we are iterating over.  And it doesn't go after the `for` loop is over.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variable   Type    Data/Info\n",
+      "----------------------------\n",
+      "char       str     n\n",
+      "length     int     5\n",
+      "vowel      str     u\n",
+      "word       str     oxygen\n"
+     ]
+    }
+   ],
+   "source": [
+    "% whos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that it is still in memory and retains the value it was last assigned.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "u\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(vowel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, after programming awhile you'll notice that finding the length of a collection of items, such as a string, is a pretty common task.  So much so that python even has a built-in function called `len` that returns the amount of elements in collection.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len('aeiou'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Try it out  ##"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From 1 to N ###\n",
+    "\n",
+    "Python has a built-in function called range that creates a sequence of numbers. Range can accept 1-3 parameters. If one parameter is input, range creates an array of that length, starting at zero and incrementing by 1. If 2 parameters are input, range starts at the first and ends just before the second, incrementing by one. If range is passed 3 parameters, it starts at the first one, ends just before the second one, and increments by the third one. For example, range(3) produces the numbers 0, 1, 2, while range(2, 5) produces 2, 3, 4, and range(3, 10, 3) produces 3, 6, 9. Using range, write a loop that uses range to print the first 3 natural numbers:\n",
+    ">1  \n",
+    ">2  \n",
+    ">3  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Computing Powers With Loops ##\n",
+    "\n",
+    "Exponentiation is built into Python:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "125\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(5 ** 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a loop that calculates the same result as 5 \\** 3 using multiplication (and without exponentiation)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reverse a String ##\n",
+    "\n",
+    "Write a loop that takes a string, and produces a new string with the characters in reverse order, so 'Newton' becomes 'notweN'"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python_notebooks/3. Storing Multiple Values in Lists.ipynb b/python_notebooks/3. Storing Multiple Values in Lists.ipynb
new file mode 100644
index 0000000..682ffa9
--- /dev/null
+++ b/python_notebooks/3. Storing Multiple Values in Lists.ipynb	
@@ -0,0 +1,818 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Storing Multiple Values in Lists #\n",
+    "\n",
+    "In the last lesson, we explored how using `for` loops we can iterate over strings.  In that sense strings were just collections of characters (letters, numbers, spaces, etc).  Keeping collections of items isn't a trivial task in programming, and as such, python has a object, called a _list_ that allows us to do just that; store values.  \n",
+    "\n",
+    "The syntax for creating a list is simple.  We just put the values we want to keep inside square brackets separated by commas.  Let's create a list of the first four odd numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "odds are: [1, 3, 5, 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "odds = [1, 3, 5, 7]\n",
+    "print(\"odds are:\", odds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as we were able to access certain elements of a string by their index, we can do the same in a list.  To get the first and last numbers in our `odds` list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first and last 1 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('first and last', odds[0], odds[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could iterate over a string, so why not a list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "3\n",
+      "5\n",
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in odds:\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, lists and strings do have some differences.  One big one is that we can change the values within a list, unlike a string.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "names is originally: ['Newton', 'Darwing', 'Turing']\n",
+      "final value of names: ['Newton', 'Darwin', 'Turing']\n"
+     ]
+    }
+   ],
+   "source": [
+    "names = ['Newton', 'Darwing', 'Turing'] # typo in Darwin's name\n",
+    "print('names is originally:', names)\n",
+    "names[1] = 'Darwin' # correct the name\n",
+    "print('final value of names:', names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now if we tried to manipulate the typo in Darwin's name directly, we would get an error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'str' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-cb94b05a418a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Darwin'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mname\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'd'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "name = 'Darwin'\n",
+    "name[0] = 'd'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is becomes strings are what we refer to as _immutable_.  In python, things like strings and numbers are immutable.  This does not mean that a variable containing a string is constant, it just means that if we decide to change it, we need to change the entire value.  That is why we could not change the `D` in `Darwin` to `d`, or why you couldnt change `410` to `420`, by swapping the `1` with the `2`.  \n",
+    "\n",
+    "However objects like lists are _mutable_, and thus allows us to change elements at a given index with new ones. \n",
+    "\n",
+    "So far we've seen lists store several strings.  But that's not all they can store.  They can store any objects, or any mix of objects; even lists!  Storing lists within list is called _nesting_ and allows for great flexibility.  \n",
+    "\n",
+    "As an example, let's create a variable `x`, a list, who has three elements, all of which are also lists.  Within each of these three _nested_ lists, there are there elements, each being an item you may find in a grocery store.  This way, we can think of `x` as cabinet having three \"shelves\", with each shelf having an item on the left, middle, and right spots of the shelves.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = [['pepper', 'zucchini', 'onion'],\n",
+    "     ['cabbage', 'lettuce', 'garlic'],\n",
+    "     ['apple', 'pear', 'banana']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Using this \"cabinet\" analogy, we can access each shelf individually by using normal list indexing.  Let's say we wanted to obtain all the items on the first shelf.  All we would need to do is grab the first element of `x`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['pepper', 'zucchini', 'onion']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voila!  We successfully obtained the first element of the list `x`, which happens to be a list.  Well, then, since we know the statement `x[0]` returns a list.  Then that means `x[0]` should behave like a list.  Which is exactly the case, and allows us to access any of the elements contained within.  So, if we wanted to obtain the last element in the list `x[0]`, or the right-most item on the first shelf, using the cabinet analogy, we can just continue using indexing, like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'onion'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[0][2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as we say that we could access the first element of `x` be using indexing `x[0]`; Since we know `x[0]` is also a list, we can access it's elements in the same way (`x[0][2]` for example)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are several ways to manipulate lists.  For example, we can add things to the end by using the `append()` method.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "odds after adding a value: [1, 3, 5, 7, 11]\n"
+     ]
+    }
+   ],
+   "source": [
+    "odds.append(11)\n",
+    "print('odds after adding a value:', odds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can delete elements at a certain index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "odds after removing the first element: [3, 5, 7, 11]\n"
+     ]
+    }
+   ],
+   "source": [
+    "del odds[0]\n",
+    "print('odds after removing the first element:', odds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we can reverse the order by using the `reverse()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "odds after reversing: [11, 7, 5, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "odds.reverse()\n",
+    "print('odds after reversing:', odds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Everything isn't always that intuitive in Python, however.  For example, when trying to manipulate a list that is (at least what seems like it _should_ be) a copy of a list, we can run into problems.  Consider the following syntax:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "primes: [1, 3, 5, 7, 2]\n",
+      "odds: [1, 3, 5, 7, 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "odds = [1, 3, 5, 7]\n",
+    "primes = odds\n",
+    "primes += [2]\n",
+    "print('primes:', primes)\n",
+    "print('odds:', odds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the code above, it appears we create a list `odds` containing four elements.  On the second line, it would appear we create a copy of this list, by assiging it to a variable called `primes`.  However, when we try and add a list containing the number `2` to the list `primes` we see that __BOTH__ `primes` and `odds` are given this new element.  \n",
+    "\n",
+    "This is because Python stores a list in memory, and then can use multiple names to refer to the same list. So, the statement `primes = odds` would more accurately be described as having another variable, `primes`, pointing to the same list as `odds`.  And since they both point to the same list, manipulation to either variable would cause changes to the same list.  \n",
+    "\n",
+    "If all we want to do is copy a (simple) list, we can use the `list()` function, so we do not modify a list we did not mean to.  Like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "primes: [1, 3, 5, 7, 2]\n",
+      "odds: [1, 3, 5, 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "odds = [1, 3, 5, 7]\n",
+    "primes = list(odds)\n",
+    "primes += [2]\n",
+    "print('primes:', primes)\n",
+    "print('odds:', odds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 1: Turn a String Into a List ## \n",
+    "\n",
+    "Use a for-loop to convert the string “hello” into a list of letters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['h', 'e', 'l', 'l', 'o']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[\"h\", \"e\", \"l\", \"l\", \"o\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hint: You can create an empty list like this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "my_list = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Slicing ##\n",
+    "\n",
+    "Sometimes we don't always want an individual element in a list.  Sometimes we want several consecutive elements.  Just as we were able to access certaing ranges of positions in a Numpy array, we can so the same with lists and strings.  This is commonly referred to as _slicing_."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "group: Drosophila\n",
+      "species: melanogaster\n",
+      "autosomes: ['2', '3', '4']\n",
+      "last: 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "binomial_name = \"Drosophila melanogaster\"\n",
+    "group = binomial_name[0:10]\n",
+    "print(\"group:\", group)\n",
+    "\n",
+    "species = binomial_name[11:24]\n",
+    "print(\"species:\", species)\n",
+    "\n",
+    "chromosomes = [\"X\", \"Y\", \"2\", \"3\", \"4\"]\n",
+    "autosomes = chromosomes[2:5]\n",
+    "print(\"autosomes:\", autosomes)\n",
+    "\n",
+    "last = chromosomes[-1]\n",
+    "print(\"last:\", last)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 2: Slicing From the End ##\n",
+    "\n",
+    "Use slicing to access only the last four characters of a string or entries of a list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "string_for_slicing = \"Observation date: 02-Feb-2013\"\n",
+    "list_for_slicing = [[\"fluorine\", \"F\"], [\"chlorine\", \"Cl\"], [\"bromine\", \"Br\"], [\"iodine\", \"I\"], [\"astatine\", \"At\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Would your solution work regardless of whether you knew beforehand the length of the string or list (e.g. if you wanted to apply the solution to a set of lists of different lengths)? If not, try to change your approach to make it more robust."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 3: Non-Continuous Slices ##\n",
+    "\n",
+    "So far we’ve seen how to use slicing to take single blocks of successive entries from a sequence. But what if we want to take a subset of entries that aren’t next to each other in the sequence?\n",
+    "\n",
+    "You can achieve this by providing a third argument to the range within the brackets, called the step size. The example below shows how you can take every third entry in a list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "subset [2, 7, 17, 29]\n"
+     ]
+    }
+   ],
+   "source": [
+    "primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]\n",
+    "subset = primes[0:12:3]\n",
+    "print(\"subset\", subset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the slice taken begins with the first entry in the range, followed by entries taken at equally-spaced intervals (the steps) thereafter. If you wanted to begin the subset with the third entry, you would need to specify that as the starting point of the sliced range:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "subset [5, 13, 23, 37]\n"
+     ]
+    }
+   ],
+   "source": [
+    "primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]\n",
+    "subset = primes[2:12:3]\n",
+    "print(\"subset\", subset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the step size argument to create a new string that contains only every other character in the string “In an octopus’s garden in the shade”"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "beatles = \"In an octopus's garden in the shade\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to take a slice from the beginning of a sequence, you can omit the first index in the range:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using 0 to begin range: Monday\n",
+      "Omitting beginning index: Monday\n"
+     ]
+    }
+   ],
+   "source": [
+    "date = \"Monday 4 January 2016\"\n",
+    "day = date[0:6]\n",
+    "print(\"Using 0 to begin range:\", day)\n",
+    "day = date[:6]\n",
+    "print(\"Omitting beginning index:\", day)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And similarly, you can omit the ending index in the range to take a slice to the very end of the sequence:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With known last position: ['sep', 'oct', 'nov', 'dec']\n",
+      "Using len() to get last entry: ['sep', 'oct', 'nov', 'dec']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('Omitting ending index:', ['sep', 'oct', 'nov', 'dec'])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "months = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"]\n",
+    "sond = months[8:12]\n",
+    "print(\"With known last position:\", sond)\n",
+    "sond = months[8:len(months)]\n",
+    "print(\"Using len() to get last entry:\", sond)\n",
+    "sond = months[8:]\n",
+    "(\"Omitting ending index:\", sond)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 4: Tuples and Exchanges\n",
+    "\n",
+    "Explain what the overall effect of this code is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "left = 'L'\n",
+    "right = 'R'\n",
+    "\n",
+    "temp = left\n",
+    "left = right\n",
+    "right = temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compared to:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "left, right = right, left"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do they always do the same thing? Which do you find easier to read?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 5: Overloading\n",
+    "\n",
+    "+ usually means addition, but when used on strings or lists, it means “concatenate”. Given that, what do you think the multiplication operator * does on lists? In particular, what will be the output of the following code?\n",
+    "\n",
+    "```python\n",
+    "counts = [2, 4, 6, 8, 10]\n",
+    "repeats = counts * 2\n",
+    "print(repeats)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Key Points ###\n",
+    "\n",
+    "* `[value1, value2, value3, ...]` creates a list.\n",
+    "* Lists are indexed and sliced in the same way as strings and arrays.\n",
+    "* Lists are mutable (i.e., their values can be changed in place).\n",
+    "* Strings are immutable (i.e., the characters in them cannot be changed)."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda env:ciipromol]",
+   "language": "python",
+   "name": "conda-env-ciipromol-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python_notebooks/4. Analyzing Data from Multiple Files.ipynb b/python_notebooks/4. Analyzing Data from Multiple Files.ipynb
new file mode 100644
index 0000000..d86cfee
--- /dev/null
+++ b/python_notebooks/4. Analyzing Data from Multiple Files.ipynb	
@@ -0,0 +1,246 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyzing Data From Multiple Files #\n",
+    "\n",
+    "Since we have a bunch of inflammation data files to analyze, a basic grasp of numpy and some Python basics, we _almost_ have everything we need to process them.  However, do process them efficiently, we could be aided by the uses of a library with an odd sounding name, called glob.  Just as we imported numpy and matplotlib, we can import glob."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import glob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The glob library, as it would have it, also contains a function called `glob()`, which finds files and directories whose name match a pattern.  If we have some files that match a certain pattern (which we do in our inflammation data) we can use the `glob()` function to match those patters using strings.  In these strings we can use characters, like * which will match any zero or more characters, or ? which will match any one character.  As always, this is best learned by an example.  We know the inflammation file names are in the `data` folder and all start with `inflammation`, then followed by a dash a two digits (01-12) and of course end in `.csv`.  We can go ahead and grab all of these in one statement by using the `glob()` function and character string to match that pattern."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['../data\\\\inflammation-01.csv', '../data\\\\inflammation-02.csv', '../data\\\\inflammation-03.csv', '../data\\\\inflammation-04.csv', '../data\\\\inflammation-05.csv', '../data\\\\inflammation-06.csv', '../data\\\\inflammation-07.csv', '../data\\\\inflammation-08.csv', '../data\\\\inflammation-09.csv', '../data\\\\inflammation-10.csv', '../data\\\\inflammation-11.csv', '../data\\\\inflammation-12.csv']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(glob.glob('../data/inflammation*.csv'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, the `glob()` function returns all file and directory paths that match our query string in a list in aribtrary order.  Now that we matched all the files that we are interested in, we can do something to them.  In our example, the \"something\" we want to do is generate a set of plots for each file in our inflammation dataset.  To start, let's say we are only interested in analyzing the first three files in alphabetical order.  We can sort the list returned by the `glob()` function using the Python's `sorted()` function.\n",
+    "\n",
+    "As always, since we are working in the notebook we need to using some `IPython` magic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../data\\inflammation-01.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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1sIizJDk1bdrY777Skl1JRkROxPVIvkpEBonIoPDCym62kly+atXcCl7J1eQl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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7989ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../data\\inflammation-02.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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XgH2APbP5RhEZJCIzReTTEretLyLDRWSKiLy24opSvvn2W7uic+ONoSPJb9df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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7b2f518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../data\\inflammation-03.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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vK6XdYuNG93f89Kfhmmvy+10MwzDKTVyR/KiIfAMYICJnArcD9xRzYFV9TlVP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\nSkAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7b7e128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "import matplotlib.pyplot\n",
+    "\n",
+    "filenames = sorted(glob.glob('../data/inflammation*.csv'))\n",
+    "filenames = filenames[0:3]\n",
+    "for f in filenames:\n",
+    "    print(f)\n",
+    "\n",
+    "    data = numpy.loadtxt(fname=f, delimiter=',')\n",
+    "\n",
+    "    fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
+    "\n",
+    "    axes1 = fig.add_subplot(1, 3, 1)\n",
+    "    axes2 = fig.add_subplot(1, 3, 2)\n",
+    "    axes3 = fig.add_subplot(1, 3, 3)\n",
+    "\n",
+    "    axes1.set_ylabel('average')\n",
+    "    axes1.plot(numpy.mean(data, axis=0))\n",
+    "\n",
+    "    axes2.set_ylabel('max')\n",
+    "    axes2.plot(numpy.max(data, axis=0))\n",
+    "\n",
+    "    axes3.set_ylabel('min')\n",
+    "    axes3.plot(numpy.min(data, axis=0))\n",
+    "\n",
+    "    fig.tight_layout()\n",
+    "    matplotlib.pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sure enough, the maxima of the first two data sets show exactly the same ramp as the first, and their minima show the same staircase structure; a different situation has been revealed in the third dataset, where the maxima are a bit less regular, but the minima are consistently zero."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 1: Plotting Differences\n",
+    "\n",
+    "Plot the difference between the average of the first dataset and the average of the second dataset, i.e., the difference between the leftmost plot of the first two figures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 2: Generate Composite Statistics ##\n",
+    "\n",
+    "Use each of the files once to generate a dataset containing values averaged over all patients:\n",
+    "```python\n",
+    "filenames = glob.glob('data/inflammation*.csv')\n",
+    "composite_data = numpy.zeros((60,40))\n",
+    "for f in filenames:\n",
+    "    # sum each new file's data into as it's read\n",
+    "# and then divide the composite_data by number of samples\n",
+    "composite_data /= len(filenames)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Key Points ##\n",
+    "\n",
+    "* Use glob.glob(pattern) to create a list of files whose names match a pattern.\n",
+    "* Use * in a pattern to match zero or more characters, and ? to match any single character."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python_notebooks/5. Making Choices.ipynb b/python_notebooks/5. Making Choices.ipynb
new file mode 100644
index 0000000..a19e971
--- /dev/null
+++ b/python_notebooks/5. Making Choices.ipynb	
@@ -0,0 +1,532 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Making Choices #\n",
+    "\n",
+    "In our last lesson, we discovered something suspicious was going on in our inflammation data by drawing some plots. How can we use Python to automatically recognize the different features we saw, and take a different action for each? In this lesson, we’ll learn how to write code that runs only when certain conditions are true.\n",
+    "\n",
+    "## Conditionals ##\n",
+    "\n",
+    "Just as in real life, if we give a command to a person, but only want them to do so under certain situations.  _If_ it's raining, bring an umbrella.  Well the same is going to be true for out commands in Python.  If we only want certain pieces of code to be executed during certain conditions, we use what are appropriately called _conditionals_.  For example, consider the following code and its output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "not greater\n",
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "num = 37\n",
+    "if num > 100:\n",
+    "    print('greater')\n",
+    "else:\n",
+    "    print('not greater')\n",
+    "print('done')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the first line of the code, we set the variable `num` equal to the number `37`.  In the next line, using the `if` statement, we say that _if_ the variable `num` is greater than the number `100`, then execute code in the indented lines beneath it.  The `else` statement handles all case where the condition the above condition is not true.  In our example, anytime `num` is less than or equal to `100`, the indented lines after the `else` statement will be executed.  Because the `print('done')` statement occurs in neither of the indented blocks of our `if/else` statement, it will get executed 100% of the time.  The above algorithm can be visualized in the following graphic:\n",
+    "\n",
+    "![Executing a Conditional](fig/python-flowchart-conditional.png)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "In the previous example we used an `else` statement to take care of everything that did not meet the condition in our `if` statement.  However, sometimes `else` statements are trivial and are thus not required to be included after an `if` statement.  If there is no `else` clause, Python simply does nothing if the condition is evaluated to be false."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "before conditional...\n",
+      "...after conditional\n"
+     ]
+    }
+   ],
+   "source": [
+    "num = 53\n",
+    "print('before conditional...')\n",
+    "if num > 100:\n",
+    "    print('53 is greater than 100')\n",
+    "print('...after conditional')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What if we have more than a few conditional tests we need to evaluate?  Well we can link several tests together by using `elif`, which is a portmanteau of \"else if\". Consider the following code that uses several conditionals to print the sign of a number. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-3 is negative\n"
+     ]
+    }
+   ],
+   "source": [
+    "num = -3\n",
+    "\n",
+    "if num > 0:\n",
+    "    print(num, \"is positive\")\n",
+    "elif num == 0:\n",
+    "    print(num, \"is zero\")\n",
+    "else:\n",
+    "    print(num, \"is negative\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An important syntax note is when evaluating the equality of objects in Python, we use `==` as opposed to `=`, which as we know, is reserved for variable assignment.\n",
+    "\n",
+    "Sometimes it is beneficial to evaluate two tests in a single statement.  Using `and` and `or` we can do just that.  In a conditional combining two tests with `and` will only evaluate to true if both parts and true.  On the other hand, combining two tests with `or` will yield true if only one test turns out to be true.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "at least one part is false\n"
+     ]
+    }
+   ],
+   "source": [
+    "if (1 > 0) and (-1 > 0):\n",
+    "    print('both parts are true')\n",
+    "else:\n",
+    "    print('at least one part is false')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "at least one test is true\n"
+     ]
+    }
+   ],
+   "source": [
+    "if (1 < 0) or (-1 < 0):\n",
+    "    print('at least one test is true')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking Our Data ##\n",
+    "\n",
+    "Now that we have seen how conditionals are used, we can apply them to check for the suspecious features in our inflammation data.  In the first few plots, the inflammation per data seemed to rise like a straight line.  We can check for this inside a `for` loop we wrote with the following conditional:\n",
+    "```python\n",
+    "if numpy.max(data, axis=0)[0] == 0 and numpy.max(data, axis=0)[20] == 20:\n",
+    "    print('Suspicious looking maxima!')\n",
+    "```\n",
+    "Similarly, we also saw a problem in the third data set.  The Minima per day were all zero (perhaps a healthy person snuck into our study?).  We can also check for this with an `elif` confidtion.  \n",
+    "```python\n",
+    "elif numpy.sum(numpy.min(data, axis=0)) == 0:\n",
+    "    print('Minima add up to zero!')\n",
+    "```\n",
+    "Of course we can forget about the case where the data looks okay.  So, we can use a terminal `else` statement to give the all-clear! if neither of the above conditions were caught. \n",
+    "```python\n",
+    "else:\n",
+    "    print('Seems ok!')\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's bring it all together by using it on our real data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Suspicious looking maxima!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "\n",
+    "data = numpy.loadtxt(fname='../data/inflammation-01.csv', delimiter=',')\n",
+    "if numpy.max(data, axis=0)[0] == 0 and numpy.max(data, axis=0)[20] == 20:\n",
+    "    print('Suspicious looking maxima!')\n",
+    "elif numpy.sum(numpy.min(data, axis=0)) == 0:\n",
+    "    print('Minima add up to zero!')\n",
+    "else:\n",
+    "    print('Seems OK!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minima add up to zero!\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = numpy.loadtxt(fname='../data/inflammation-03.csv', delimiter=',')\n",
+    "if numpy.max(data, axis=0)[0] == 0 and numpy.max(data, axis=0)[20] == 20:\n",
+    "    print('Suspicious looking maxima!')\n",
+    "elif numpy.sum(numpy.min(data, axis=0)) == 0:\n",
+    "    print('Minima add up to zero!')\n",
+    "else:\n",
+    "    print('Seems OK!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this way, we have asked Python to do something different depending on the condition of our data. Here we printed messages in all cases, but we could also imagine not using the else catch-all so that messages are only printed when something is wrong, freeing us from having to manually examine every plot for features we’ve seen before."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 1: How Many Paths? ##\n",
+    "\n",
+    "Which of the following would be printed if you were to run this code? Why did you pick this answer?\n",
+    "\n",
+    "```python\n",
+    "if 4 > 5:\n",
+    "    print('A')\n",
+    "elif 4 == 5:\n",
+    "    print('B')\n",
+    "elif 4 < 5:\n",
+    "    print('C'\n",
+    "```\n",
+    "\n",
+    "1. A\n",
+    "2. B\n",
+    "3. C\n",
+    "4. B and C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 2: What Is Truth?\n",
+    "\n",
+    "`True` and `False` are special words in Python called booleans which represent true and false statements. However, they aren’t the only values in Python that are true and false. In fact, any value can be used in an if or elif. After reading and running the code below, explain what the rule is for which values are considered true and which are considered false."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "word is true\n",
+      "non-empty list is true\n",
+      "one is true\n"
+     ]
+    }
+   ],
+   "source": [
+    "if '':\n",
+    "    print('empty string is true')\n",
+    "if 'word':\n",
+    "    print('word is true')\n",
+    "if []:\n",
+    "    print('empty list is true')\n",
+    "if [1, 2, 3]:\n",
+    "    print('non-empty list is true')\n",
+    "if 0:\n",
+    "    print('zero is true')\n",
+    "if 1:\n",
+    "    print('one is true')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 3: That’s Not Not What I Meant.\n",
+    "\n",
+    "Sometimes it is useful to check whether some condition is not true. The Boolean operator not can do this explicitly. After reading and running the code below, write some if statements that use not to test the rule that you formulated in the previous challenge."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "empty string is not true\n",
+      "not not True is true\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not '':\n",
+    "    print('empty string is not true')\n",
+    "if not 'word':\n",
+    "    print('word is not true')\n",
+    "if not not True:\n",
+    "    print('not not True is true')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 4: Close Enough.\n",
+    "\n",
+    "Write some conditions that print True if the variable a is within 10% of the variable b and False otherwise. Compare your implementation with your partner’s: do you get the same answer for all possible pairs of numbers?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 5: In-Place Operators ##\n",
+    "\n",
+    "Python (and most other languages in the C family) provides in-place operators that work like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 1  # original value\n",
+    "x += 1 # add one to x, assigning result back to x\n",
+    "x *= 3 # multiply x by 3\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write some code that sums the positive and negative numbers in a list separately, using in-place operators. Do you think the result is more or less readable than writing the same without in-place operators?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 6: Sorting a List Into Buckets ##\n",
+    "\n",
+    "The folder containing our data files has large data sets whose names start with “inflammation-“, small ones whose names with “small-“, and possibly other files whose sizes we don’t know. Our goal is to sort those files into three lists called large_files, small_files, and other_files respectively. Add code to the template below to do this. Note that the string method startswith returns `True` if and only if the string it is called on starts with the string passed as an argument.\n",
+    "\n",
+    "Your solution should:\n",
+    "\n",
+    "1. loop over the names of the files\n",
+    "2. figure out which group each filename belongs\n",
+    "3. append the filename to that list\n",
+    "In the end the three lists should be:\n",
+    "```python\n",
+    "large_files = ['inflammation-01.csv', 'inflammation-02.csv']\n",
+    "small_files = ['small-01.csv', 'small-02.csv']\n",
+    "other_files = ['myscript.py']\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###\n",
+    "\n",
+    "files = ['inflammation-01.csv', 'myscript.py', 'inflammation-02.csv', 'small-01.csv', 'small-02.csv']\n",
+    "large_files = []\n",
+    "small_files = []\n",
+    "other_files = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Key Points ###\n",
+    "\n",
+    "* Use if condition to start a conditional statement, elif condition to provide additional tests, and else to provide a default.\n",
+    "* The bodies of the branches of conditional statements must be indented.\n",
+    "* Use == to test for equality.\n",
+    "* X and Y is only true if both X and Y are true.\n",
+    "* X or Y is true if either X or Y, or both, are true.\n",
+    "* Zero, the empty string, and the empty list are considered false; all other numbers, strings, and lists are considered true.\n",
+    "* Nest loops to operate on multi-dimensional data.\n",
+    "* Put code whose parameters change frequently in a function, then call it with different parameter values to customize its behavior."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python_notebooks/6. Creating Functions.ipynb b/python_notebooks/6. Creating Functions.ipynb
new file mode 100644
index 0000000..235faf3
--- /dev/null
+++ b/python_notebooks/6. Creating Functions.ipynb	
@@ -0,0 +1,1302 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Functions #\n",
+    "\n",
+    "At this point, we’ve written code to draw some interesting features in our inflammation data, loop over all our data files to quickly draw these plots for each of them, and have Python make decisions based on what it sees in our data. But, our code is getting pretty long and complicated; what if we had thousands of datasets, and didn’t want to generate a figure for every single one? Commenting out the figure-drawing code is a nuisance. Also, what if we want to use that code again, on a different dataset or at a different point in our program? Cutting and pasting it is going to make our code get very long and very repetitive, very quickly. We’d like a way to package our code so that it is easier to reuse, and Python provides for this by letting us define things called ‘functions’ - a shorthand way of re-executing longer pieces of code.\n",
+    "\n",
+    "Let’s start by defining a function fahr_to_kelvin that converts temperatures from Fahrenheit to Kelvin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def fahr_to_kelvin(temp):\n",
+    "    return ((temp - 32) * (5/9)) + 273.15"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![The Blueprint for a Python Function](fig/python-function.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The function definition opens with the keyword `def` followed by the name of the function and a parenthesized list of parameter names. The body of the function — the statements that are executed when it runs — is indented below the definition line.\n",
+    "\n",
+    "When we call the function, the values we pass to it are assigned to those variables so that we can use them inside the function. Inside the function, we use a return statement to send a result back to whoever asked for it.\n",
+    "\n",
+    "Let’s try running our function. Calling our own function is no different from calling any other function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "freezing point of water: 273.15\n",
+      "boiling point of water: 373.15\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('freezing point of water:', fahr_to_kelvin(32))\n",
+    "print('boiling point of water:', fahr_to_kelvin(212))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ve successfully called the function that we defined, and we have access to the value that we returned."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Integer Division ##\n",
+    "\n",
+    "An important note: We are using Python 3, where division always returns a floating point number.  For example, in Python 3:\n",
+    "```python\n",
+    "print(5/9)\n",
+    "```\n",
+    "evaluates to:\n",
+    "```\n",
+    "0.5555555555555556\n",
+    "```\n",
+    "Which seems logical.  However, this wasn't the case in Python 2.  Where the code \n",
+    "```python\n",
+    "5/9\n",
+    "```\n",
+    "evaluates to:\n",
+    "```\n",
+    "0\n",
+    "```\n",
+    "If you are using Python 2 and want to keep the fractional part of division you need to convert one or the other numbers to floating point:\n",
+    "```python\n",
+    "float(5)/9\n",
+    "```\n",
+    "```\n",
+    "0.555555555556\n",
+    "```\n",
+    "or \n",
+    "```python\n",
+    "5/float(9)\n",
+    "```\n",
+    "```\n",
+    "0.555555555556\n",
+    "```\n",
+    "or \n",
+    "```python\n",
+    "5.0/9\n",
+    "``` \n",
+    "```\n",
+    "0.555555555556\n",
+    "```\n",
+    "or \n",
+    "```python\n",
+    "5/9.0\n",
+    "``` \n",
+    "```\n",
+    "0.555555555556\n",
+    "```\n",
+    "And if you're working in Python 3 and want the integer division behavior, you can use a double-slash to acheive that result.  \n",
+    "```python\n",
+    "4//2\n",
+    "```\n",
+    "```\n",
+    "2\n",
+    "```\n",
+    "```python\n",
+    "3//2\n",
+    "```\n",
+    "```\n",
+    "1\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Composing Functions ##\n",
+    "Now that we’ve seen how to turn Fahrenheit into Kelvin, it’s easy to turn Kelvin into Celsius:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "absolute zero in Celsius: -273.15\n"
+     ]
+    }
+   ],
+   "source": [
+    "def kelvin_to_celsius(temp_k):\n",
+    "    return temp_k - 273.15\n",
+    "\n",
+    "print('absolute zero in Celsius:', kelvin_to_celsius(0.0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What about converting Fahrenheit to Celsius? We could write out the formula, but we don’t need to. Instead, we can compose the two functions we have already created:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "freezing point of water in Celsius: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "def fahr_to_celsius(temp_f):\n",
+    "    temp_k = fahr_to_kelvin(temp_f)\n",
+    "    result = kelvin_to_celsius(temp_k)\n",
+    "    return result\n",
+    "\n",
+    "print('freezing point of water in Celsius:', fahr_to_celsius(32.0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is our first taste of how larger programs are built: we define basic operations, then combine them in ever-large chunks to get the effect we want. Real-life functions will usually be larger than the ones shown here — typically half a dozen to a few dozen lines — but they shouldn’t ever be much longer than that, or the next person who reads it won’t be able to understand what’s going on.\n",
+    "\n",
+    "## Tidying Up ##\n",
+    "\n",
+    "Now that we know how to wrap bits of code up in functions, we can make our inflammation analysis easier to read and easier to reuse. First, let’s make an `analyze` function that generates our plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def analyze(filename):\n",
+    "    import numpy\n",
+    "    import matplotlib.pyplot\n",
+    "\n",
+    "    data = numpy.loadtxt(fname=filename, delimiter=',')\n",
+    "\n",
+    "    fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
+    "\n",
+    "    axes1 = fig.add_subplot(1, 3, 1)\n",
+    "    axes2 = fig.add_subplot(1, 3, 2)\n",
+    "    axes3 = fig.add_subplot(1, 3, 3)\n",
+    "\n",
+    "    axes1.set_ylabel('average')\n",
+    "    axes1.plot(numpy.mean(data, axis=0))\n",
+    "\n",
+    "    axes2.set_ylabel('max')\n",
+    "    axes2.plot(numpy.max(data, axis=0))\n",
+    "\n",
+    "    axes3.set_ylabel('min')\n",
+    "    axes3.plot(numpy.min(data, axis=0))\n",
+    "\n",
+    "    fig.tight_layout()\n",
+    "    matplotlib.pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and another function called detect_problems that checks for those systematics we noticed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def detect_problems(filename):\n",
+    "    import numpy\n",
+    "    data = numpy.loadtxt(fname=filename, delimiter=',')\n",
+    "\n",
+    "    if numpy.max(data, axis=0)[0] == 0 and numpy.max(data, axis=0)[20] == 20:\n",
+    "        print('Suspicious looking maxima!')\n",
+    "    elif numpy.sum(numpy.min(data, axis=0)) == 0:\n",
+    "        print('Minima add up to zero!')\n",
+    "    else:\n",
+    "        print('Seems OK!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that rather than jumbling this code together in one giant `for` loop, we can now read and reuse both ideas separately. We can reproduce the previous analysis with a much simpler `for` loop:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../data\\inflammation-01.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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1sIizJDk1bdrY777Skl1JRkROxPVIvkpEBonIoPDCym62kly+atXcCl7J1eQl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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x75c5780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Suspicious looking maxima!\n",
+      "../data\\inflammation-02.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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XgH2APbP5RhEZJCIzReTTEretLyLDRWSKiLy24opSvvn2W7uic+ONoSPJb9df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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x75dec18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Suspicious looking maxima!\n",
+      "../data\\inflammation-03.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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vK6XdYuNG93f89Kfhmmvy+10MwzDKTVyR/KiIfAMYICJnArcD9xRzYFV9TlVP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\nSkAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7614710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minima add up to zero!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import glob\n",
+    "filenames = glob.glob('../data/inflammation*.csv')\n",
+    "for f in filenames[:3]:\n",
+    "    print(f)\n",
+    "    analyze(f)\n",
+    "    detect_problems(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By giving our functions human-readable names, we can more easily read and understand what is happening in the for loop. Even better, if at some later date we want to use either of those pieces of code again, we can do so in a single line."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing and Documenting ##\n",
+    "\n",
+    "Once we start putting things in functions so that we can re-use them, we need to start testing that those functions are working correctly. To see how to do this, let’s write a function to center a dataset around a particular value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def center(data, desired):\n",
+    "    return (data - numpy.mean(data)) + desired"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could test this on our actual data, but since we don’t know what the values ought to be, it will be hard to tell if the result was correct. Instead, let’s use NumPy to create a matrix of 0’s and then center that around 3:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 3.  3.]\n",
+      " [ 3.  3.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "z = numpy.zeros((2,2))\n",
+    "print(center(z, 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That looks right, so let’s try center on our real data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-6.14875 -6.14875 -5.14875 ..., -3.14875 -6.14875 -6.14875]\n",
+      " [-6.14875 -5.14875 -4.14875 ..., -5.14875 -6.14875 -5.14875]\n",
+      " [-6.14875 -5.14875 -5.14875 ..., -4.14875 -5.14875 -5.14875]\n",
+      " ..., \n",
+      " [-6.14875 -5.14875 -5.14875 ..., -5.14875 -5.14875 -5.14875]\n",
+      " [-6.14875 -6.14875 -6.14875 ..., -6.14875 -4.14875 -6.14875]\n",
+      " [-6.14875 -6.14875 -5.14875 ..., -5.14875 -5.14875 -6.14875]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = numpy.loadtxt(fname='../data/inflammation-01.csv', delimiter=',')\n",
+    "print(center(data, 0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It’s hard to tell from the default output whether the result is correct, but there are a few simple tests that will reassure us:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "original min, mean, and max are: 0.0 6.14875 20.0\n",
+      "min, mean, and max of centered data are: -6.14875 2.84217094304e-16 13.85125\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('original min, mean, and max are:', numpy.min(data), numpy.mean(data), numpy.max(data))\n",
+    "centered = center(data, 0)\n",
+    "print('min, mean, and max of centered data are:', numpy.min(centered), numpy.mean(centered), numpy.max(centered))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That seems almost right: the original mean was about 6.1, so the lower bound from zero is now about -6.1. The mean of the centered data isn’t quite zero — we’ll explore why not in the challenges — but it’s pretty close. We can even go further and check that the standard deviation hasn’t changed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "std dev before and after: 4.61383319712 4.61383319712\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('std dev before and after:', numpy.std(data), numpy.std(centered))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Those values look the same, but we probably wouldn’t notice if they were different in the sixth decimal place. Let’s do this instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "difference in standard deviations before and after: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('difference in standard deviations before and after:', numpy.std(data) - numpy.std(centered))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, the difference is very small. It’s still possible that our function is wrong, but it seems unlikely enough that we should probably get back to doing our analysis. We have one more task first, though: we should write some documentation for our function to remind ourselves later what it’s for and how to use it.\n",
+    "\n",
+    "The usual way to put documentation in software is to add comments like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# center(data, desired): return a new array containing the original data centered around the desired value.\n",
+    "def center(data, desired):\n",
+    "    return (data - numpy.mean(data)) + desired"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There’s a better way, though. If the first thing in a function is a string that isn’t assigned to a variable, that string is attached to the function as its documentation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def center(data, desired):\n",
+    "    '''Return a new array containing the original data centered around the desired value.'''\n",
+    "    return (data - numpy.mean(data)) + desired"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is better because we can now ask Python’s built-in help system to show us the documentation for the function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function center in module __main__:\n",
+      "\n",
+      "center(data, desired)\n",
+      "    Return a new array containing the original data centered around the desired value.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(center)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining Defaults ##\n",
+    "\n",
+    "We have passed parameters to functions in two ways: directly, as in type(data), and by name, as in numpy.loadtxt(fname='something.csv', delimiter=','). In fact, we can pass the filename to loadtxt without the fname=:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  0.,  1., ...,  3.,  0.,  0.],\n",
+       "       [ 0.,  1.,  2., ...,  1.,  0.,  1.],\n",
+       "       [ 0.,  1.,  1., ...,  2.,  1.,  1.],\n",
+       "       ..., \n",
+       "       [ 0.,  1.,  1., ...,  1.,  1.,  1.],\n",
+       "       [ 0.,  0.,  0., ...,  0.,  2.,  0.],\n",
+       "       [ 0.,  0.,  1., ...,  1.,  1.,  0.]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numpy.loadtxt('../data/inflammation-01.csv', delimiter=',')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "but we still need to say delimiter=:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'inflammation-01.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-19-e3bc6cf4fd6a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'inflammation-01.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m','\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mC:\\Users\\Daniel\\Anaconda3\\envs\\ciipromol\\lib\\site-packages\\numpy\\lib\\npyio.py\u001b[0m in \u001b[0;36mloadtxt\u001b[1;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)\u001b[0m\n\u001b[0;32m    803\u001b[0m                 \u001b[0mfh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'U'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    804\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 805\u001b[1;33m                 \u001b[0mfh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    806\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    807\u001b[0m             \u001b[0mfh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'inflammation-01.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "numpy.loadtxt('inflammation-01.csv', ',')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To understand what’s going on, and make our own functions easier to use, let’s re-define our center function like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def center(data, desired=0.0):\n",
+    "    '''Return a new array containing the original data centered around the desired value (0 by default).\n",
+    "    Example: center([1, 2, 3], 0) => [-1, 0, 1]'''\n",
+    "    return (data - numpy.mean(data)) + desired"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The key change is that the second parameter is now written desired=0.0 instead of just desired. If we call the function with two arguments, it works as it did before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 3.  3.]\n",
+      " [ 3.  3.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_data = numpy.zeros((2, 2))\n",
+    "print(center(test_data, 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But we can also now call it with just one parameter, in which case desired is automatically assigned the default value of 0.0:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data before centering:\n",
+      "[[ 5.  5.]\n",
+      " [ 5.  5.]]\n",
+      "centered data:\n",
+      "[[ 0.  0.]\n",
+      " [ 0.  0.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "more_data = 5 + numpy.zeros((2, 2))\n",
+    "print('data before centering:')\n",
+    "print(more_data)\n",
+    "print('centered data:')\n",
+    "print(center(more_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is handy: if we usually want a function to work one way, but occasionally need it to do something else, we can allow people to pass a parameter when they need to but provide a default to make the normal case easier. The example below shows how Python matches values to parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no parameters:\n",
+      "a: 1 b: 2 c: 3\n",
+      "one parameter:\n",
+      "a: 55 b: 2 c: 3\n",
+      "two parameters:\n",
+      "a: 55 b: 66 c: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "def display(a=1, b=2, c=3):\n",
+    "    print('a:', a, 'b:', b, 'c:', c)\n",
+    "\n",
+    "print('no parameters:')\n",
+    "display()\n",
+    "print('one parameter:')\n",
+    "display(55)\n",
+    "print('two parameters:')\n",
+    "display(55, 66)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As this example shows, parameters are matched up from left to right, and any that haven’t been given a value explicitly get their default value. We can override this behavior by naming the value as we pass it in:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only setting the value of c\n",
+      "a: 1 b: 2 c: 77\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('only setting the value of c')\n",
+    "display(c=77)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With that in hand, let’s look at the help for numpy.loadtxt:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function loadtxt in module numpy.lib.npyio:\n",
+      "\n",
+      "loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)\n",
+      "    Load data from a text file.\n",
+      "    \n",
+      "    Each row in the text file must have the same number of values.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    fname : file or str\n",
+      "        File, filename, or generator to read.  If the filename extension is\n",
+      "        ``.gz`` or ``.bz2``, the file is first decompressed. Note that\n",
+      "        generators should return byte strings for Python 3k.\n",
+      "    dtype : data-type, optional\n",
+      "        Data-type of the resulting array; default: float.  If this is a\n",
+      "        structured data-type, the resulting array will be 1-dimensional, and\n",
+      "        each row will be interpreted as an element of the array.  In this\n",
+      "        case, the number of columns used must match the number of fields in\n",
+      "        the data-type.\n",
+      "    comments : str or sequence, optional\n",
+      "        The characters or list of characters used to indicate the start of a\n",
+      "        comment;\n",
+      "        default: '#'.\n",
+      "    delimiter : str, optional\n",
+      "        The string used to separate values.  By default, this is any\n",
+      "        whitespace.\n",
+      "    converters : dict, optional\n",
+      "        A dictionary mapping column number to a function that will convert\n",
+      "        that column to a float.  E.g., if column 0 is a date string:\n",
+      "        ``converters = {0: datestr2num}``.  Converters can also be used to\n",
+      "        provide a default value for missing data (but see also `genfromtxt`):\n",
+      "        ``converters = {3: lambda s: float(s.strip() or 0)}``.  Default: None.\n",
+      "    skiprows : int, optional\n",
+      "        Skip the first `skiprows` lines; default: 0.\n",
+      "    usecols : sequence, optional\n",
+      "        Which columns to read, with 0 being the first.  For example,\n",
+      "        ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.\n",
+      "        The default, None, results in all columns being read.\n",
+      "    unpack : bool, optional\n",
+      "        If True, the returned array is transposed, so that arguments may be\n",
+      "        unpacked using ``x, y, z = loadtxt(...)``.  When used with a structured\n",
+      "        data-type, arrays are returned for each field.  Default is False.\n",
+      "    ndmin : int, optional\n",
+      "        The returned array will have at least `ndmin` dimensions.\n",
+      "        Otherwise mono-dimensional axes will be squeezed.\n",
+      "        Legal values: 0 (default), 1 or 2.\n",
+      "    \n",
+      "        .. versionadded:: 1.6.0\n",
+      "    \n",
+      "    Returns\n",
+      "    -------\n",
+      "    out : ndarray\n",
+      "        Data read from the text file.\n",
+      "    \n",
+      "    See Also\n",
+      "    --------\n",
+      "    load, fromstring, fromregex\n",
+      "    genfromtxt : Load data with missing values handled as specified.\n",
+      "    scipy.io.loadmat : reads MATLAB data files\n",
+      "    \n",
+      "    Notes\n",
+      "    -----\n",
+      "    This function aims to be a fast reader for simply formatted files.  The\n",
+      "    `genfromtxt` function provides more sophisticated handling of, e.g.,\n",
+      "    lines with missing values.\n",
+      "    \n",
+      "    .. versionadded:: 1.10.0\n",
+      "    \n",
+      "    The strings produced by the Python float.hex method can be used as\n",
+      "    input for floats.\n",
+      "    \n",
+      "    Examples\n",
+      "    --------\n",
+      "    >>> from io import StringIO   # StringIO behaves like a file object\n",
+      "    >>> c = StringIO(\"0 1\\n2 3\")\n",
+      "    >>> np.loadtxt(c)\n",
+      "    array([[ 0.,  1.],\n",
+      "           [ 2.,  3.]])\n",
+      "    \n",
+      "    >>> d = StringIO(\"M 21 72\\nF 35 58\")\n",
+      "    >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),\n",
+      "    ...                      'formats': ('S1', 'i4', 'f4')})\n",
+      "    array([('M', 21, 72.0), ('F', 35, 58.0)],\n",
+      "          dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])\n",
+      "    \n",
+      "    >>> c = StringIO(\"1,0,2\\n3,0,4\")\n",
+      "    >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)\n",
+      "    >>> x\n",
+      "    array([ 1.,  3.])\n",
+      "    >>> y\n",
+      "    array([ 2.,  4.])\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(numpy.loadtxt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There’s a lot of information here, but the most important part is the first couple of lines:\n",
+    "```\n",
+    "loadtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None,\n",
+    "        unpack=False, ndmin=0)\n",
+    "```\n",
+    "This tells us that loadtxt has one parameter called fname that doesn’t have a default value, and eight others that do. If we call the function like this:\n",
+    "```python\n",
+    "numpy.loadtxt('../data/inflammation-01.csv', ',')\n",
+    "```\n",
+    "then the filename is assigned to fname (which is what we want), but the delimiter string ',' is assigned to dtype rather than delimiter, because dtype is the second parameter in the list. However ‘,’ isn’t a known dtype so our code produced an error message when we tried to run it. When we call loadtxt we don’t have to provide fname= for the filename because it’s the first item in the list, but if we want the ‘,’ to be assigned to the variable delimiter, we do have to provide delimiter= for the second parameter since delimiter is not the second parameter in the list."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Readable functions ##\n",
+    "\n",
+    "Consider these two functions:\n",
+    "```python\n",
+    "def s(p):\n",
+    "    a = 0\n",
+    "    for v in p:\n",
+    "        a += v\n",
+    "    m = a / len(p)\n",
+    "    d = 0\n",
+    "    for v in p:\n",
+    "        d += (v - m) * (v - m)\n",
+    "    return numpy.sqrt(d / (len(p) - 1))\n",
+    "\n",
+    "def std_dev(sample):\n",
+    "    sample_sum = 0\n",
+    "    for value in sample:\n",
+    "        sample_sum += value\n",
+    "\n",
+    "    sample_mean = sample_sum / len(sample)\n",
+    "\n",
+    "    sum_squared_devs = 0\n",
+    "    for value in sample:\n",
+    "        sum_squared_devs += (value - sample_mean) * (value - sample_mean)\n",
+    "\n",
+    "    return numpy.sqrt(sum_squared_devs / (len(sample) - 1))\n",
+    "```\n",
+    "The functions s and std_dev are computationally equivalent (they both calculate the sample standard deviation), but to a human reader, they look very different. You probably found std_dev much easier to read and understand than s.\n",
+    "\n",
+    "As this example illustrates, both documentation and a programmer’s coding style combine to determine how easy it is for others to read and understand the programmer’s code. Choosing meaningful variable names and using blank spaces to break the code into logical “chunks” are helpful techniques for producing readable code. This is useful not only for sharing code with others, but also for the original programmer. If you need to revisit code that you wrote months ago and haven’t thought about since then, you will appreciate the value of readable code!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 1: Combining Strings\n",
+    "\n",
+    "“Adding” two strings produces their concatenation: 'a' + 'b' is 'ab'. Write a function called fence that takes two parameters called original and wrapper and returns a new string that has the wrapper character at the beginning and end of the original. A call to your function should look like this:\n",
+    "```python\n",
+    "print(fence('name', '*'))\n",
+    "```\n",
+    "```\n",
+    "*name*\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 2: Selecting Characters From Strings\n",
+    "\n",
+    "If the variable s refers to a string, then s[0] is the string’s first character and s[-1] is its last. Write a function called outer that returns a string made up of just the first and last characters of its input. A call to your function should look like this:\n",
+    "```python\n",
+    "print(outer('helium'))\n",
+    "```\n",
+    "```\n",
+    "hm\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 3: Rescaling an Array ##\n",
+    "\n",
+    "Write a function rescale that takes an array as input and returns a corresponding array of values scaled to lie in the range 0.0 to 1.0. (Hint: If $L$ and $H$ are the lowest and highest values in the original array, then the replacement for a value $v$ should be $(v-L) / (H-L)$.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 4: Testing and Documenting Your Function ##\n",
+    "\n",
+    "Run the commands help(numpy.arange) and help(numpy.linspace) to see how to use these functions to generate regularly-spaced values, then use those values to test your rescale function. Once you’ve successfully tested your function, add a docstring that explains what it does."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 5: Defining Defaults ##\n",
+    "\n",
+    "Rewrite the rescale function so that it scales data to lie between 0.0 and 1.0 by default, but will allow the caller to specify lower and upper bounds if they want. Compare your implementation to your neighbor’s: do the two functions always behave the same way?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 6: Variables Inside and Outside Functions ##\n",
+    "\n",
+    "What does the following piece of code display when run - and why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = 0\n",
+    "k = 0\n",
+    "\n",
+    "def f2k(f):\n",
+    "  k = ((f-32)*(5.0/9.0)) + 273.15\n",
+    "  return k\n",
+    "\n",
+    "f2k(8)\n",
+    "f2k(41)\n",
+    "f2k(32)\n",
+    "\n",
+    "print(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 7: Mixing Default and Non-Default Parameters ##\n",
+    "\n",
+    "Given the following code:\n",
+    "```python\n",
+    "def numbers(one, two=2, three, four=4):\n",
+    "    n = str(one) + str(two) + str(three) + str(four)\n",
+    "    return n\n",
+    "\n",
+    "print(numbers(1, three=3))\n",
+    "```\n",
+    "What do you expect will be printed? What is actually printed? What rule do you think Python is following?\n",
+    "\n",
+    "1. 1234\n",
+    "2. one2three4\n",
+    "3. 1239\n",
+    "4. SyntaxError\n",
+    "\n",
+    "Given that, what does the following piece of code display when run?\n",
+    "```python \n",
+    "def func(a, b = 3, c = 6):\n",
+    "  print('a: ', a, 'b: ', b,'c:', c)\n",
+    "\n",
+    "func(-1, 2)\n",
+    "```\n",
+    "1. a: b: 3 c: 6\n",
+    "2. a: -1 b: 3 c: 6\n",
+    "3. a: -1 b: 2 c: 6\n",
+    "4. a: b: -1 c: 2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 8: The Old Switcheroo ##\n",
+    "\n",
+    "Which of the following would be printed if you were to run this code? Why did you pick this answer?\n",
+    "\n",
+    "```python\n",
+    "a = 3\n",
+    "b = 7\n",
+    "\n",
+    "def swap(a, b):\n",
+    "    temp = a\n",
+    "    a = b\n",
+    "    b = temp\n",
+    "\n",
+    "swap(a, b)\n",
+    "\n",
+    "print(a, b)\n",
+    "```\n",
+    "\n",
+    "1. 7 3\n",
+    "2. 3 7\n",
+    "3. 3 3\n",
+    "4. 7 7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 9. Readable Code ## \n",
+    "\n",
+    "Revise a function you wrote for one of the previous exercises to try to make the code more readable. Then, collaborate with one of your neighbors to critique each other’s functions and discuss how your function implementations could be further improved to make them more readable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Key Points ###\n",
+    "\n",
+    "* Define a function using def name(...params...).\n",
+    "* The body of a function must be indented.\n",
+    "* Call a function using name(...values...).\n",
+    "* Numbers are stored as integers or floating-point numbers.\n",
+    "* Integer division produces the whole part of the answer (not the fractional part).\n",
+    "* Each time a function is called, a new stack frame is created on the call stack to hold its parameters and local variables.\n",
+    "* Python looks for variables in the current stack frame before looking for them at the top level.\n",
+    "* Use help(thing) to view help for something.\n",
+    "* Put docstrings in functions to provide help for that function.\n",
+    "* Specify default values for parameters when defining a function using name=value in the parameter list.\n",
+    "* Parameters can be passed by matching based on name, by position, or by omitting them (in which case the default value is used).\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python_notebooks/7. Errors and Exceptions.ipynb b/python_notebooks/7. Errors and Exceptions.ipynb
new file mode 100644
index 0000000..10bc44c
--- /dev/null
+++ b/python_notebooks/7. Errors and Exceptions.ipynb	
@@ -0,0 +1,635 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Errors and Exceptions \n",
+    "\n",
+    "Every programmer encounters errors, both those who are just beginning, and those who have been programming for years. Encountering errors and exceptions can be very frustrating at times, and can make coding feel like a hopeless endeavour. However, understanding what the different types of errors are and when you are likely to encounter them can help a lot. Once you know why you get certain types of errors, they become much easier to fix.\n",
+    "\n",
+    "Errors in Python have a very specific form, called a traceback. Let’s examine one:\n",
+    "```python\n",
+    "import errors_01\n",
+    "errors_01.favorite_ice_cream()\n",
+    "```\n",
+    "```\n",
+    "---------------------------------------------------------------------------\n",
+    "IndexError                                Traceback (most recent call last)\n",
+    "<ipython-input-1-9d0462a5b07c> in <module>()\n",
+    "      1 import errors_01\n",
+    "----> 2 errors_01.favorite_ice_cream()\n",
+    "\n",
+    "/Users/jhamrick/project/swc/novice/python/errors_01.pyc in favorite_ice_cream()\n",
+    "      5         \"strawberry\"\n",
+    "      6     ]\n",
+    "----> 7     print(ice_creams[3])\n",
+    "\n",
+    "IndexError: list index out of range\n",
+    "```\n",
+    "This particular traceback has two levels. You can determine the number of levels by looking for the number of arrows on the left hand side. In this case:\n",
+    "\n",
+    "1. The first shows code from the cell above, with an arrow pointing to Line 2 (which is `favorite_ice_cream()`).\n",
+    "\n",
+    "2. The second shows some code in another function (`favorite_ice_cream()`, located in the file `errors_01.py`), with an arrow pointing to Line 7 (which is `print(ice_creams[3])`).\n",
+    "\n",
+    "The last level is the actual place where the error occurred. The other level(s) show what function the program executed to get to the next level down. So, in this case, the program first performed a function call to the function favorite_ice_cream. Inside this function, the program encountered an error on Line 7, when it tried to run the code `print(ice_creams[3])`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Long Tracebacks ##\n",
+    "\n",
+    "Sometimes, you might see a traceback that is very long – sometimes they might even be 20 levels deep! This can make it seem like something horrible happened, but really it just means that your program called many functions before it ran into the error. Most of the time, you can just pay attention to the bottom-most level, which is the actual place where the error occurred.\n",
+    "\n",
+    "So what error did the program actually encounter? In the last line of the traceback, Python helpfully tells us the category or type of error (in this case, it is an IndexError) and a more detailed error message (in this case, it says “list index out of range”).\n",
+    "\n",
+    "If you encounter an error and don’t know what it means, it is still important to read the traceback closely. That way, if you fix the error, but encounter a new one, you can tell that the error changed. Additionally, sometimes just knowing where the error occurred is enough to fix it, even if you don’t entirely understand the message.\n",
+    "\n",
+    "If you do encounter an error you don’t recognize, try looking at the official documentation on errors. However, note that you may not always be able to find the error there, as it is possible to create custom errors. In that case, hopefully the custom error message is informative enough to help you figure out what went wrong."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Syntax Errors ##\n",
+    "\n",
+    "When you forget a colon at the end of a line, accidentally add one space too many when indenting under an if statement, or forget a parenthesis, you will encounter a syntax error. This means that Python couldn’t figure out how to read your program. This is similar to forgetting punctuation in English: for example, this text is difficult to read there is no punctuation there is also no capitalization why is this hard because you have to figure out where each sentence ends you also have to figure out where each sentence begins to some extent it might be ambiguous if there should be a sentence break or not\n",
+    "\n",
+    "People can typically figure out what is meant by text with no punctuation, but people are much smarter than computers. If Python doesn’t know how to read the program, it will just give up and inform you with an error. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-1-dbf32ad5d3e8>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-1-dbf32ad5d3e8>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    def some_function()\u001b[0m\n\u001b[1;37m                       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "def some_function()\n",
+    "    msg = \"hello, world!\"\n",
+    "    print(msg)\n",
+    "     return msg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, Python tells us that there is a `SyntaxError` on line 1, and even puts a little arrow in the place where there is an issue. In this case the problem is that the function definition is missing a colon at the end.\n",
+    "\n",
+    "Actually, the function above has two issues with syntax. If we fix the problem with the colon, we see that there is also an IndentationError, which means that the lines in the function definition do not all have the same indentation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "IndentationError",
+     "evalue": "unexpected indent (<ipython-input-2-e169556d667b>, line 4)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-2-e169556d667b>\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m    return msg\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n"
+     ]
+    }
+   ],
+   "source": [
+    "def some_function():\n",
+    "    msg = \"hello, world!\"\n",
+    "    print(msg)\n",
+    "     return msg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both `SyntaxError` and `IndentationError` indicate a problem with the syntax of your program, but an `IndentationError` is more specific: it always means that there is a problem with how your code is indented."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tabs and Spaces ##\n",
+    "\n",
+    "A quick note on indentation errors: they can sometimes be insidious, especially if you are mixing spaces and tabs. Because they are both whitespace, it is difficult to visually tell the difference. The Jupyter notebook actually gives us a bit of a hint, but not all Python editors will do that. In the following example, the first two lines are using a tab for indentation, while the third line uses four spaces:\n",
+    "```python\n",
+    "def some_function():\n",
+    "    msg = \"hello, world!\"\n",
+    "    print(msg)\n",
+    "    return msg\n",
+    "```\n",
+    "```\n",
+    "  File \"<ipython-input-5-653b36fbcd41>\", line 4\n",
+    "    return msg\n",
+    "              ^\n",
+    "IndentationError: unindent does not match any outer indentation level\n",
+    "```\n",
+    "By default, one tab is equivalent to eight spaces, so the only way to mix tabs and spaces is to make it look like this. In general, it is better to just never use tabs and always use spaces, because it can make things very confusing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variable Name Errors ##\n",
+    "\n",
+    "Another very common type of error is called a NameError, and occurs when you try to use a variable that does not exist. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'a' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-c5a4f3535135>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'a' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Variable name errors come with some of the most informative error messages, which are usually of the form `“name ‘the_variable_name’ is not defined”`.\n",
+    "\n",
+    "Why does this error message occur? That’s harder question to answer, because it depends on what your code is supposed to do. However, there are a few very common reasons why you might have an undefined variable. The first is that you meant to use a string, but forgot to put quotes around it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'hello' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-43a14fcd4265>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhello\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'hello' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(hello)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second is that you just forgot to create the variable before using it. In the following example, count should have been defined (e.g., with `count = 0`) before the for loop:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'count' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-3cc942c225b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mcount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcount\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnumber\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"The count is:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'count' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "for number in range(10):\n",
+    "    count = count + number\n",
+    "print(\"The count is:\", count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, the third possibility is that you made a typo when you were writing your code. Let’s say we fixed the error above by adding the line Count = 0 before the for loop. Frustratingly, this actually does not fix the error. Remember that variables are case-sensitive, so the variable count is different from Count. We still get the same error, because we still have not defined count:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'count' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-6-79d2bb4fcc22>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mCount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mcount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcount\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnumber\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"The count is:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'count' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "Count = 0\n",
+    "for number in range(10):\n",
+    "    count = count + number\n",
+    "print(\"The count is:\", count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Item Errors ##\n",
+    "\n",
+    "Next up are errors having to do with containers (like lists and strings) and the items within them. If you try to access an item in a list or a string that does not exist, then you will get an error. This makes sense: if you asked someone what day they would like to get coffee, and they answered “caturday”, you might be a bit annoyed. Python gets similarly annoyed if you try to ask it for an item that doesn’t exist:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Letter #1 is a\n",
+      "Letter #2 is b\n",
+      "Letter #3 is c\n"
+     ]
+    },
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-7-656a22fa6ec5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Letter #2 is\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mletters\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Letter #3 is\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mletters\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Letter #4 is\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mletters\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "letters = ['a', 'b', 'c']\n",
+    "print(\"Letter #1 is\", letters[0])\n",
+    "print(\"Letter #2 is\", letters[1])\n",
+    "print(\"Letter #3 is\", letters[2])\n",
+    "print(\"Letter #4 is\", letters[3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, Python is telling us that there is an `IndexError` in our code, meaning we tried to access a list index that did not exist."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## File Errors ##\n",
+    "\n",
+    "The last type of error we’ll cover today are those associated with reading and writing files: `FileNotFoundError`. If you try to read a file that does not exist, you will receive a FileNotFoundError telling you so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'myfile.txt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-8-f6e1ac4aee96>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfile_handle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'myfile.txt'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'myfile.txt'"
+     ]
+    }
+   ],
+   "source": [
+    "file_handle = open('myfile.txt', 'r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One reason for receiving this error is that you specified an incorrect path to the file. For example, if I am currently in a folder called `myproject`, and I have a file in `/writing/myfile.txt`, but I try to just open `myfile.txt`, this will fail. The correct path would be `writing/myfile.txt`. It is also possible (like with `NameError`) that you just made a typo.\n",
+    "\n",
+    "A related issue can occur if you use the “read” flag instead of the “write” flag. Python will not give you an error if you try to open a file for writing when the file does not exist. However, if you meant to open a file for reading, but accidentally opened it for writing, and then try to read from it, you will get an `UnsupportedOperation` error telling you that the file was not opened for reading:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "UnsupportedOperation",
+     "evalue": "not readable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mUnsupportedOperation\u001b[0m                      Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-9-b846479bc61f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfile_handle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'myfile.txt'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mfile_handle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mUnsupportedOperation\u001b[0m: not readable"
+     ]
+    }
+   ],
+   "source": [
+    "file_handle = open('myfile.txt', 'w')\n",
+    "file_handle.read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the most common errors with files, though many others exist. If you get an error that you’ve never seen before, searching the Internet for that error type often reveals common reasons why you might get that error."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 1: Reading Error Messages ##\n",
+    "\n",
+    "Read the traceback below, and identify the following pieces of information about it:\n",
+    "\n",
+    "1. How many levels does the traceback have?\n",
+    "2. What is the file name where the error occurred?\n",
+    "3. What is the function name where the error occurred?\n",
+    "4. On which line number in this function did the error occurr?\n",
+    "5. What is the type of error?\n",
+    "6. What is the error message?\n",
+    "\n",
+    "```python\n",
+    "import errors_02\n",
+    "errors_02.print_friday_message()\n",
+    "```\n",
+    "```\n",
+    "---------------------------------------------------------------------------\n",
+    "KeyError                                  Traceback (most recent call last)\n",
+    "<ipython-input-2-e4c4cbafeeb5> in <module>()\n",
+    "      1 import errors_02\n",
+    "----> 2 errors_02.print_friday_message()\n",
+    "\n",
+    "/Users/jhamrick/project/swc/novice/python/errors_02.py in print_friday_message()\n",
+    "     13\n",
+    "     14 def print_friday_message():\n",
+    "---> 15     print_message(\"Friday\")\n",
+    "\n",
+    "/Users/jhamrick/project/swc/novice/python/errors_02.py in print_message(day)\n",
+    "      9         \"sunday\": \"Aw, the weekend is almost over.\"\n",
+    "     10     }\n",
+    "---> 11     print(messages[day])\n",
+    "     12\n",
+    "     13\n",
+    "\n",
+    "KeyError: 'Friday'\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 2: Identifying Syntax Errors ##\n",
+    "\n",
+    "1. Read the code below, and (without running it) try to identify what the errors are.\n",
+    "2. Run the code, and read the error message. Is it a SyntaxError or an IndentationError?\n",
+    "3. Fix the error.\n",
+    "4. Repeat steps 2 and 3, until you have fixed all the errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-11-c1c18188b735>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-11-c1c18188b735>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    def another_function\u001b[0m\n\u001b[1;37m                        ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "def another_function\n",
+    "  print(\"Syntax errors are annoying.\")\n",
+    "   print(\"But at least python tells us about them!\")\n",
+    "  print(\"So they are usually not too hard to fix.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 3: Identifying Variable Name Errors ## \n",
+    "\n",
+    "1. Read the code below, and (without running it) try to identify what the errors are.\n",
+    "2. Run the code, and read the error message. What type of NameError do you think this is? In other words, is it a string with no quotes, a misspelled variable, or a variable that should have been defined but was not?\n",
+    "3. Fix the error.\n",
+    "4. Repeat steps 2 and 3, until you have fixed all the errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Number' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-13-f5d871f5d05d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;31m# use a if the number is a multiple of 3, otherwise use b\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mNumber\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m         \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'Number' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "for number in range(10):\n",
+    "    # use a if the number is a multiple of 3, otherwise use b\n",
+    "    if (Number % 3) == 0:\n",
+    "        message = message + a\n",
+    "    else:\n",
+    "        message = message + \"b\"\n",
+    "print(message)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 4: Identifying Item Errors ##\n",
+    "\n",
+    "1. Read the code below, and (without running it) try to identify what the errors are.\n",
+    "2. Run the code, and read the error message. What type of error is it?\n",
+    "3. Fix the error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-15-3b9451ba5207>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mseasons\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'Spring'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Summer'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Fall'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Winter'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'My favorite season is '\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseasons\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "seasons = ['Spring', 'Summer', 'Fall', 'Winter']\n",
+    "print('My favorite season is ', seasons[4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Key Points ###\n",
+    "\n",
+    "* Tracebacks can look intimidating, but they give us a lot of useful information about what went wrong in our program, including where the error occurred and what type of error it was.\n",
+    "* An error having to do with the ‘grammar’ or syntax of the program is called a `SyntaxError`. If the issue has to do with how the code is indented, then it will be called an `IndentationError`.\n",
+    "* A `NameError` will occur if you use a variable that has not been defined, either because you meant to use quotes around a string, you forgot to define the variable, or you just made a typo.\n",
+    "* Containers like lists and strings will generate errors if you try to access items in them that do not exist. This type of error is called an `IndexError`.\n",
+    "* Trying to read a file that does not exist will give you an `IOError`. Trying to read a file that is open for writing, or writing to a file that is open for reading, will also give you an `IOError`."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python_notebooks/8. Defensive Programming.ipynb b/python_notebooks/8. Defensive Programming.ipynb
new file mode 100644
index 0000000..1cea9f0
--- /dev/null
+++ b/python_notebooks/8. Defensive Programming.ipynb	
@@ -0,0 +1,495 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Defensive Programming #\n",
+    "\n",
+    "Our previous lessons have introduced the basic tools of programming: variables and lists, file I/O, loops, conditionals, and functions. What they haven’t done is show us how to tell whether a program is getting the right answer, and how to tell if it’s still getting the right answer as we make changes to it.\n",
+    "\n",
+    "To achieve that, we need to:\n",
+    "\n",
+    "* Write programs that check their own operation.\n",
+    "* Write and run tests for widely-used functions.\n",
+    "* Make sure we know what “correct” actually means.  \n",
+    "\n",
+    "The good news is, doing these things will speed up our programming, not slow it down. As in real carpentry — the kind done with lumber — the time saved by measuring carefully before cutting a piece of wood is much greater than the time that measuring takes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assertions ##\n",
+    "\n",
+    "The first step toward getting the right answers from our programs is to assume that mistakes will happen and to guard against them. This is called defensive programming, and the most common way to do it is to add assertions to our code so that it checks itself as it runs. An assertion is simply a statement that something must be true at a certain point in a program. When Python sees one, it evaluates the assertion’s condition. If it’s true, Python does nothing, but if it’s false, Python halts the program immediately and prints the error message if one is provided. For example, this piece of code halts as soon as the loop encounters a value that isn’t positive:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "Data should only contain positive values",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-985f50018947>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mtotal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Data should only contain positive values'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mtotal\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'total is:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: Data should only contain positive values"
+     ]
+    }
+   ],
+   "source": [
+    "numbers = [1.5, 2.3, 0.7, -0.001, 4.4]\n",
+    "total = 0.0\n",
+    "for n in numbers:\n",
+    "    assert n > 0.0, 'Data should only contain positive values'\n",
+    "    total += n\n",
+    "print('total is:', total)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Programs like the Firefox browser are full of assertions: 10-20% of the code they contain are there to check that the other 80-90% are working correctly. Broadly speaking, assertions fall into three categories:\n",
+    "\n",
+    "* A precondition is something that must be true at the start of a function in order for it to work correctly.\n",
+    "\n",
+    "* A postcondition is something that the function guarantees is true when it finishes.\n",
+    "\n",
+    "* An invariant is something that is always true at a particular point inside a piece of code.\n",
+    "\n",
+    "For example, suppose we are representing rectangles using a tuple of four coordinates (x0, y0, x1, y1), representing the lower left and upper right corners of the rectangle. In order to do some calculations, we need to normalize the rectangle so that the lower left corner is at the origin and the longest side is 1.0 units long. This function does that, but checks that its input is correctly formatted and that its result makes sense:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def normalize_rectangle(rect):\n",
+    "    '''Normalizes a rectangle so that it is at the origin and 1.0 units long on its longest axis.'''\n",
+    "    assert len(rect) == 4, 'Rectangles must contain 4 coordinates'\n",
+    "    x0, y0, x1, y1 = rect\n",
+    "    assert x0 < x1, 'Invalid X coordinates'\n",
+    "    assert y0 < y1, 'Invalid Y coordinates'\n",
+    "\n",
+    "    dx = x1 - x0\n",
+    "    dy = y1 - y0\n",
+    "    if dx > dy:\n",
+    "        scaled = float(dx) / dy\n",
+    "        upper_x, upper_y = 1.0, scaled\n",
+    "    else:\n",
+    "        scaled = float(dx) / dy\n",
+    "        upper_x, upper_y = scaled, 1.0\n",
+    "\n",
+    "    assert 0 < upper_x <= 1.0, 'Calculated upper X coordinate invalid'\n",
+    "    assert 0 < upper_y <= 1.0, 'Calculated upper Y coordinate invalid'\n",
+    "\n",
+    "    return (0, 0, upper_x, upper_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The preconditions on lines 2, 4, and 5 catch invalid inputs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "Rectangles must contain 4 coordinates",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-147d2dc76773>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnormalize_rectangle\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2.0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# missing the fourth coordinate\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-2-9f8adbfdcfc9>\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[1;34m(rect)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mnormalize_rectangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;34m'''Normalizes a rectangle so that it is at the origin and 1.0 units long on its longest axis.'''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Rectangles must contain 4 coordinates'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrect\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mx0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Invalid X coordinates'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: Rectangles must contain 4 coordinates"
+     ]
+    }
+   ],
+   "source": [
+    "print(normalize_rectangle( (0.0, 1.0, 2.0) )) # missing the fourth coordinate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "Invalid X coordinates",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-2cfdb3417f64>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnormalize_rectangle\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5.0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# X axis inverted\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-2-9f8adbfdcfc9>\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[1;34m(rect)\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Rectangles must contain 4 coordinates'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrect\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mx0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Invalid X coordinates'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0my0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0my1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Invalid Y coordinates'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: Invalid X coordinates"
+     ]
+    }
+   ],
+   "source": [
+    "print(normalize_rectangle( (4.0, 2.0, 1.0, 5.0) )) # X axis inverted"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The post-conditions help us catch bugs by telling us when our calculations cannot have been correct. For example, if we normalize a rectangle that is taller than it is wide everything seems OK:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0, 0, 0.2, 1.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(normalize_rectangle( (0.0, 0.0, 1.0, 5.0) ))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "but if we normalize one that’s wider than it is tall, the assertion is triggered:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "Calculated upper Y coordinate invalid",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-6-a7353228dc6f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnormalize_rectangle\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-2-9f8adbfdcfc9>\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[1;34m(rect)\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mupper_x\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Calculated upper X coordinate invalid'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mupper_y\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Calculated upper Y coordinate invalid'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupper_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupper_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: Calculated upper Y coordinate invalid"
+     ]
+    }
+   ],
+   "source": [
+    "print(normalize_rectangle( (0.0, 0.0, 5.0, 1.0) ))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Re-reading our function, we realize that line 10 should divide dy by dx rather than dx by dy. (You can display line numbers by typing Ctrl-M, then L.) If we had left out the assertion at the end of the function, we would have created and returned something that had the right shape as a valid answer, but wasn’t. Detecting and debugging that would almost certainly have taken more time in the long run than writing the assertion.\n",
+    "\n",
+    "But assertions aren’t just about catching errors: they also help people understand programs. Each assertion gives the person reading the program a chance to check (consciously or otherwise) that their understanding matches what the code is doing.\n",
+    "\n",
+    "Most good programmers follow two rules when adding assertions to their code. The first is, fail early, fail often. The greater the distance between when and where an error occurs and when it’s noticed, the harder the error will be to debug, so good code catches mistakes as early as possible.\n",
+    "\n",
+    "The second rule is, turn bugs into assertions or tests. Whenever you fix a bug, write an assertion that catches the mistake should you make it again. If you made a mistake in a piece of code, the odds are good that you have made other mistakes nearby, or will make the same mistake (or a related one) the next time you change it. Writing assertions to check that you haven’t regressed (i.e., haven’t re-introduced an old problem) can save a lot of time in the long run, and helps to warn people who are reading the code (including your future self) that this bit is tricky."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test Driven Development ##\n",
+    "\n",
+    "An assertion checks that something is true at a particular point in the program. The next step is to check the overall behavior of a piece of code, i.e., to make sure that it produces the right output when it’s given a particular input. For example, suppose we need to find where two or more time series overlap. The range of each time series is represented as a pair of numbers, which are the time the interval started and ended. The output is the largest range that they all include:\n",
+    "\n",
+    "![Overlapping Ranges](../fig/python-overlapping-ranges.svg)\n",
+    "\n",
+    "Most novice programmers would solve this problem like this:\n",
+    "\n",
+    "1. Write a function range_overlap.\n",
+    "2. Call it interactively on two or three different inputs.\n",
+    "3. If it produces the wrong answer, fix the function and re-run that test.  \n",
+    "\n",
+    "This clearly works — after all, thousands of scientists are doing it right now — but there’s a better way:  \n",
+    "\n",
+    "\n",
+    "1. Write a short function for each test.\n",
+    "2. Write a range_overlap function that should pass those tests.\n",
+    "3. If range_overlap produces any wrong answers, fix it and re-run the test functions. \n",
+    "\n",
+    "Writing the tests before writing the function they exercise is called test-driven development (TDD). Its advocates believe it produces better code faster because:\n",
+    "\n",
+    "1. If people write tests after writing the thing to be tested, they are subject to confirmation bias, i.e., they subconsciously write tests to show that their code is correct, rather than to find errors.\n",
+    "2. Writing tests helps programmers figure out what the function is actually supposed to do.  \n",
+    "\n",
+    "Here are three test functions for range_overlap:\n",
+    "```python\n",
+    "assert range_overlap([ (0.0, 1.0) ]) == (0.0, 1.0)\n",
+    "assert range_overlap([ (2.0, 3.0), (2.0, 4.0) ]) == (2.0, 3.0)\n",
+    "assert range_overlap([ (0.0, 1.0), (0.0, 2.0), (-1.0, 1.0) ]) == (0.0, 1.0)\n",
+    "```\n",
+    "```\n",
+    "---------------------------------------------------------------------------\n",
+    "AssertionError                            Traceback (most recent call last)\n",
+    "<ipython-input-25-d8be150fbef6> in <module>()\n",
+    "----> 1 assert range_overlap([ (0.0, 1.0) ]) == (0.0, 1.0)\n",
+    "      2 assert range_overlap([ (2.0, 3.0), (2.0, 4.0) ]) == (2.0, 3.0)\n",
+    "      3 assert range_overlap([ (0.0, 1.0), (0.0, 2.0), (-1.0, 1.0) ]) == (0.0, 1.0)\n",
+    "\n",
+    "AssertionError:\n",
+    "```\n",
+    "\n",
+    "The error is actually reassuring: we haven’t written `range_overlap` yet, so if the tests passed, it would be a sign that someone else had and that we were accidentally using their function.\n",
+    "\n",
+    "And as a bonus of writing these tests, we’ve implicitly defined what our input and output look like: we expect a list of pairs as input, and produce a single pair as output.\n",
+    "\n",
+    "Something important is missing, though. We don’t have any tests for the case where the ranges don’t overlap at all:\n",
+    "```python\n",
+    "assert range_overlap([ (0.0, 1.0), (5.0, 6.0) ]) == ???\n",
+    "```\n",
+    "What should `range_overlap` do in this case: fail with an error message, produce a special value like (0.0, 0.0) to signal that there’s no overlap, or something else? Any actual implementation of the function will do one of these things; writing the tests first helps us figure out which is best before we’re emotionally invested in whatever we happened to write before we realized there was an issue.\n",
+    "\n",
+    "And what about this case?\n",
+    "```python\n",
+    "assert range_overlap([ (0.0, 1.0), (1.0, 2.0) ]) == ???\n",
+    "```\n",
+    "Do two segments that touch at their endpoints overlap or not? Mathematicians usually say “yes”, but engineers usually say “no”. The best answer is “whatever is most useful in the rest of our program”, but again, any actual implementation of `range_overlap` is going to do something, and whatever it is ought to be consistent with what it does when there’s no overlap at all.\n",
+    "\n",
+    "Since we’re planning to use the range this function returns as the X axis in a time series chart, we decide that:\n",
+    "\n",
+    "1. every overlap has to have non-zero width, and\n",
+    "2. we will return the special value None when there’s no overlap.  \n",
+    "\n",
+    "None is built into Python, and means “nothing here”. (Other languages often call the equivalent value null or nil). With that decision made, we can finish writing our last two tests:\n",
+    "```python\n",
+    "assert range_overlap([ (0.0, 1.0), (5.0, 6.0) ]) == None\n",
+    "assert range_overlap([ (0.0, 1.0), (1.0, 2.0) ]) == None\n",
+    "```\n",
+    "```\n",
+    "---------------------------------------------------------------------------\n",
+    "AssertionError                            Traceback (most recent call last)\n",
+    "<ipython-input-26-d877ef460ba2> in <module>()\n",
+    "----> 1 assert range_overlap([ (0.0, 1.0), (5.0, 6.0) ]) == None\n",
+    "      2 assert range_overlap([ (0.0, 1.0), (1.0, 2.0) ]) == None\n",
+    "\n",
+    "AssertionError:\n",
+    "```\n",
+    "Again, we get an error because we haven’t written our function, but we’re now ready to do so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def range_overlap(ranges):\n",
+    "    '''Return common overlap among a set of [low, high] ranges.'''\n",
+    "    lowest = 0.0\n",
+    "    highest = 1.0\n",
+    "    for (low, high) in ranges:\n",
+    "        lowest = max(lowest, low)\n",
+    "        highest = min(highest, high)\n",
+    "    return (lowest, highest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(Take a moment to think about why we use max to raise lowest and min to lower highest). We’d now like to re-run our tests, but they’re scattered across three different cells. To make running them easier, let’s put them all in a function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def test_range_overlap():\n",
+    "    assert range_overlap([ (0.0, 1.0), (5.0, 6.0) ]) == None\n",
+    "    assert range_overlap([ (0.0, 1.0), (1.0, 2.0) ]) == None\n",
+    "    assert range_overlap([ (0.0, 1.0) ]) == (0.0, 1.0)\n",
+    "    assert range_overlap([ (2.0, 3.0), (2.0, 4.0) ]) == (2.0, 3.0)\n",
+    "    assert range_overlap([ (0.0, 1.0), (0.0, 2.0), (-1.0, 1.0) ]) == (0.0, 1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now test range_overlap with a single function call:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-9-cf9215c96457>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtest_range_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-8-5d4cd6fd41d9>\u001b[0m in \u001b[0;36mtest_range_overlap\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtest_range_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m5.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m6.0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2.0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4.0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "test_range_overlap()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first test that was supposed to produce None fails, so we know something is wrong with our function. We don’t know whether the other tests passed or failed because Python halted the program as soon as it spotted the first error. Still, some information is better than none, and if we trace the behavior of the function with that input, we realize that we’re initializing lowest and highest to 0.0 and 1.0 respectively, regardless of the input values. This violates another important rule of programming: always initialize from data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 1: Pre- and Post-Conditions ##\n",
+    "\n",
+    "Suppose you are writing a function called average that calculates the average of the numbers in a list. What pre-conditions and post-conditions would you write for it? Compare your answer to your neighbor’s: can you think of a function that will pass your tests but not his/hers or vice versa?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 2: Testing Assertions ##\n",
+    "\n",
+    "Given a sequence of values, the function running returns a list containing the running totals at each index.\n",
+    "```python\n",
+    "running([1, 2, 3, 4])\n",
+    "```\n",
+    "```\n",
+    "[1, 3, 6, 10]\n",
+    "```\n",
+    "```python\n",
+    "running('abc')\n",
+    "```\n",
+    "```\n",
+    "['a', 'ab', 'abc']\n",
+    "```\n",
+    "Explain in words what the assertions in this function check, and for each one, give an example of input that will make that assertion fail.\n",
+    "```python\n",
+    "def running(values):\n",
+    "    assert len(values) > 0\n",
+    "    result = [values[0]]\n",
+    "    for v in values[1:]:\n",
+    "        assert result[-1] >= 0\n",
+    "        result.append(result[-1] + v)\n",
+    "        assert result[-1] >= result[0]\n",
+    "    return result\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 3: Fixing and Testing ##\n",
+    "\n",
+    "Fix `range_overlap`. Re-run `test_range_overlap` after each change you make."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python_notebooks/9. Debugging.ipynb b/python_notebooks/9. Debugging.ipynb
new file mode 100644
index 0000000..813f05c
--- /dev/null
+++ b/python_notebooks/9. Debugging.ipynb	
@@ -0,0 +1,213 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Debugging ##\n",
+    "\n",
+    "Once testing has uncovered problems, the next step is to fix them. Many novices do this by making more-or-less random changes to their code until it seems to produce the right answer, but that’s very inefficient (and the result is usually only correct for the one case they’re testing). The more experienced a programmer is, the more systematically they debug, and most follow some variation on the rules explained below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Know What It's Suppose to Do ##\n",
+    "\n",
+    "The first step in debugging something is to _know what it’s supposed to do_. “My program doesn’t work” isn’t good enough: in order to diagnose and fix problems, we need to be able to tell correct output from incorrect. If we can write a test case for the failing case — i.e., if we can assert that with these inputs, the function should produce that result — then we’re ready to start debugging. If we can’t, then we need to figure out how we’re going to know when we’ve fixed things.\n",
+    "\n",
+    "But writing test cases for scientific software is frequently harder than writing test cases for commercial applications, because if we knew what the output of the scientific code was supposed to be, we wouldn’t be running the software: we’d be writing up our results and moving on to the next program. In practice, scientists tend to do the following:\n",
+    "\n",
+    "1. _Test with simplified data_. Before doing statistics on a real data set, we should try calculating statistics for a single record, for two identical records, for two records whose values are one step apart, or for some other case where we can calculate the right answer by hand.\n",
+    "\n",
+    "2. _Test a simplified case_. If our program is supposed to simulate magnetic eddies in rapidly-rotating blobs of supercooled helium, our first test should be a blob of helium that isn’t rotating, and isn’t being subjected to any external electromagnetic fields. Similarly, if we’re looking at the effects of climate change on speciation, our first test should hold temperature, precipitation, and other factors constant.\n",
+    "\n",
+    "3. _Compare to an oracle_. A test oracle is something whose results are trusted, such as experimental data, an older program, or a human expert. We use to test oracles to determine if our new program produces the correct results. If we have a test oracle, we should store its output for particular cases so that we can compare it with our new results as often as we like without re-running that program.\n",
+    "\n",
+    "4. _Check conservation laws_. Mass, energy, and other quantities are conserved in physical systems, so they should be in programs as well. Similarly, if we are analyzing patient data, the number of records should either stay the same or decrease as we move from one analysis to the next (since we might throw away outliers or records with missing values). If “new” patients start appearing out of nowhere as we move through our pipeline, it’s probably a sign that something is wrong.\n",
+    "\n",
+    "5. _Visualize_. Data analysts frequently use simple visualizations to check both the science they’re doing and the correctness of their code (just as we did in the opening lesson of this tutorial). This should only be used for debugging as a last resort, though, since it’s very hard to compare two visualizations automatically."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make It Fail Every Time ##\n",
+    "\n",
+    "We can only debug something when it fails, so the second step is always to find a test case that _makes it fail every time_. The “every time” part is important because few things are more frustrating than debugging an intermittent problem: if we have to call a function a dozen times to get a single failure, the odds are good that we’ll scroll past the failure when it actually occurs.\n",
+    "\n",
+    "As part of this, it’s always important to check that our code is “plugged in”, i.e., that we’re actually exercising the problem that we think we are. Every programmer has spent hours chasing a bug, only to realize that they were actually calling their code on the wrong data set or with the wrong configuration parameters, or are using the wrong version of the software entirely. Mistakes like these are particularly likely to happen when we’re tired, frustrated, and up against a deadline, which is one of the reasons late-night (or overnight) coding sessions are almost never worthwhile."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make It Fail Fast ##\n",
+    "\n",
+    "If it takes 20 minutes for the bug to surface, we can only do three experiments an hour. That doesn’t just mean we’ll get less data in more time: we’re also more likely to be distracted by other things as we wait for our program to fail, which means the time we are spending on the problem is less focused. It’s therefore critical to _make it fail fast_.\n",
+    "\n",
+    "As well as making the program fail fast in time, we want to make it fail fast in space, i.e., we want to localize the failure to the smallest possible region of code:\n",
+    "\n",
+    "1. The smaller the gap between cause and effect, the easier the connection is to find. Many programmers therefore use a divide and conquer strategy to find bugs, i.e., if the output of a function is wrong, they check whether things are OK in the middle, then concentrate on either the first or second half, and so on.\n",
+    "\n",
+    "2. N things can interact in N<sup>2</sup> different ways, so every line of code that isn’t run as part of a test means more than one thing we don’t need to worry about."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Change One Thing at a Time, For a Reason ##\n",
+    "\n",
+    "Replacing random chunks of code is unlikely to do much good. (After all, if you got it wrong the first time, you’ll probably get it wrong the second and third as well.) Good programmers therefore change one thing at a time, for a reason They are either trying to gather more information (“is the bug still there if we change the order of the loops?”) or test a fix (“can we make the bug go away by sorting our data before processing it?”).\n",
+    "\n",
+    "Every time we make a change, however small, we should re-run our tests immediately, because the more things we change at once, the harder it is to know what’s responsible for what (those N<sup>2</sup> interactions again). And we should re-run all of our tests: more than half of fixes made to code introduce (or re-introduce) bugs, so re-running all of our tests tells us whether we have regressed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keep Track of What You've Done ##\n",
+    "\n",
+    "Good scientists keep track of what they’ve done so that they can reproduce their work, and so that they don’t waste time repeating the same experiments or running ones whose results won’t be interesting. Similarly, debugging works best when we _keep track of what we’ve done_ and how well it worked. If we find ourselves asking, “Did left followed by right with an odd number of lines cause the crash? Or was it right followed by left? Or was I using an even number of lines?” then it’s time to step away from the computer, take a deep breath, and start working more systematically.\n",
+    "\n",
+    "Records are particularly useful when the time comes to ask for help. People are more likely to listen to us when we can explain clearly what we did, and we’re better able to give them the information they need to be useful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Version Control Revistited  ##\n",
+    "\n",
+    "Version control is often used to reset software to a known state during debugging, and to explore recent changes to code that might be responsible for bugs. In particular, most version control systems have a `blame` command that will show who last changed particular lines of code…"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Be Humble ##\n",
+    "\n",
+    "And speaking of help: if we can’t find a bug in 10 minutes, we should _be humble_ and ask for help. Just explaining the problem aloud is often useful, since hearing what we’re thinking helps us spot inconsistencies and hidden assumptions.\n",
+    "\n",
+    "Asking for help also helps alleviate confirmation bias. If we have just spent an hour writing a complicated program, we want it to work, so we’re likely to keep telling ourselves why it should, rather than searching for the reason it doesn’t. People who aren’t emotionally invested in the code can be more objective, which is why they’re often able to spot the simple mistakes we have overlooked.\n",
+    "\n",
+    "Part of being humble is learning from our mistakes. Programmers tend to get the same things wrong over and over: either they don’t understand the language and libraries they’re working with, or their model of how things work is wrong. In either case, taking note of why the error occurred and checking for it next time quickly turns into not making the mistake at all.\n",
+    "\n",
+    "And that is what makes us most productive in the long run. As the saying goes, _A week of hard work can sometimes save you an hour of thought_. If we train ourselves to avoid making some kinds of mistakes, to break our code into modular, testable chunks, and to turn every assumption (or mistake) into an assertion, it will actually take us less time to produce working programs, not more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 1: Debug With a Neighbor ##\n",
+    "\n",
+    "Take a function that you have written today, and introduce a tricky bug. Your function should still run, but will give the wrong output. Switch seats with your neighbor and attempt to debug the bug that they introduced into their function. Which of the principles discussed above did you find helpful?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ex. 2: Not Suppose to be the Same ##\n",
+    "\n",
+    "You are assisting a researcher with Python code that computes the Body Mass Index (BMI) of patients. The researcher is concerned because all patients seemingly have identical BMIs, despite having different physiques. BMI is calculated as **weight in kilograms** divided by the the square of **height in metres**.\n",
+    "\n",
+    "Use the debugging principles in this exercise and locate problems with the code. What suggestions would you give the researcher for ensuring any later changes they make work correctly?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Patient's BMI is: 21.604938\n",
+      "Patient's BMI is: 21.604938\n",
+      "Patient's BMI is: 21.604938\n"
+     ]
+    }
+   ],
+   "source": [
+    "patients = [[70, 1.8], [80, 1.9], [150, 1.7]]\n",
+    "\n",
+    "def calculate_bmi(weight, height):\n",
+    "    return weight / (height ** 2)\n",
+    "\n",
+    "for patient in patients:\n",
+    "    height, weight = patients[0]\n",
+    "    bmi = calculate_bmi(height, weight)\n",
+    "    print(\"Patient's BMI is: %f\" % bmi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### answer here ###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Key Points ###\n",
+    "\n",
+    "* Know what code is supposed to do before trying to debug it.\n",
+    "* Make it fail every time.\n",
+    "* Make it fail fast.\n",
+    "* Change one thing at a time, and for a reason.\n",
+    "* Keep track of what you’ve done.\n",
+    "* Be humble."
+   ]
+  }
+ ],
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