02_scipy_essentials.ipynb

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    "# Essential Tasks and Libraries for Scientific Python # \n",
    "\n",
    "Now that we have some experience with Python as a language its time to learn how to apply that to do useful scientific work. We're going to break this process down into several big chunks and learn the libraries and methods that are used for those chunks one at a time. Our ultimate goal will be to put these pieces back together and have a flow that takes us from raw data to usable results.\n",
    "\n",
    "## [Numeric Data with Numpy](https://docs.scipy.org/doc/numpy/) ##\n",
    "Numpy is the default library for numeric computations in Python. When we went over the different types we saw that Python's built in numeric types and structures are very flexible at the cost of speed and rigor. For scientific data we will almost always want that speed and rigor. Additionally Numpy lets us use a type of programming we have not seen yet, matrix operation. Matrix operations allow us to very efficiently do operations on elements of arrays or vectors of data. Not only is this more concise than doing similar operations by looping over elements because of numpy it is a great deal faster."
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      "Built-In Array Sum - uses itteration\n",
      "1000 loops, best of 3: 619 µs per loop\n",
      "\n",
      "Numpy Array Sum - uses numpy matrix operations\n",
      "The slowest run took 9.56 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "100000 loops, best of 3: 6.48 µs per loop\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Creates a numpy array of integers 0 to 10,000\n",
    "a = np.arange(0,10000)\n",
    "\n",
    "# Sum this array using pythons default method or numpys matrix operations\n",
    "\n",
    "print 'Built-In Array Sum - uses itteration'\n",
    "%timeit sum(a)\n",
    "\n",
    "print '\\nNumpy Array Sum - uses numpy matrix operations'\n",
    "%timeit np.sum(a)"
   ]
  },
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   "source": [
    "### Numpy ND-Array ###\n",
    "Numpy only has one data structure we need to consider, the N-Dimensional array or ndarray. Ndarrays are mutable sequences, they have a fixed size and may only contain variables of a single numpy data type (dtype). They are called N-dimensional arrays because they may have an arbitrary number of dimensions. \n",
    "\n",
    "#### Basic Properties ####\n",
    "\n",
    "**shape** - The size of the array along each dimension, stored as a tuple of length n\n",
    "\n",
    "**ndim** - The number of dimensions or axes in the ndarray (1 to n)\n",
    "\n",
    "**dtype** - The data type of all array elements\n",
    "\n",
    "#### Array Creation ####\n",
    "\n",
    "There are two general ways to create ndarrays. You can cast python lists or tuples to ndarrays or you can use numpy functions which return ndarrays by default. When casting lists or tuples, nested structures will be turned into multi-dimensional arrays. The data-type of the array will be automatically determined and is set to the highest priority numpy type that would be cast from any of the values in the list. (ex: a list of mixed floats and ints would be cast to an ndarray with dtype np.float). If any value in the list cannot be converted to a numpy type, for instance a string, python will throw an error."
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      "Shape: x=(4,) y=(4,) z=(3, 3)\n",
      "N-Dim: x=1 y=1 z=2\n",
      "D-Type: x=int64 y=float64 z=int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create 1-d arrays from lists or tuples\n",
    "x = np.array([0,1,2,3])\n",
    "y = np.array((4,5,6,7.0))\n",
    "\n",
    "# Create 2-d arrays from nested structures\n",
    "z = np.array( [[1,1,1],[2,2,2],[3,3,3]] )\n",
    "\n",
    "print 'Shape: x={} y={} z={}'.format(x.shape, y.shape, z.shape)\n",
    "print 'N-Dim: x={} y={} z={}'.format(x.ndim, y.ndim, z.ndim)\n",
    "print 'D-Type: x={} y={} z={}'.format(x.dtype, y.dtype, z.dtype)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Useful Array Creation Routines ####\n",
    "\n",
    "Numpy provides a variety of routines to generate ndarrays with specific size and content, here are some of the most commonly used ones."
   ]
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   "source": [
    "import numpy as np\n",
    "\n",
    "# Default Arrays\n",
    "\n",
    "# Zeros - creates an array of the given shape and type filled with zeros\n",
    "a = np.zeros((2,2), dtype=np.int)\n",
    "\n",
    "# Ones - creates an array of the given shape and type filled with ones\n",
    "b = np.ones((2,2), dtype=np.int)\n",
    "\n",
    "# Full - creates an array of the given shape and type filled with a specified value\n",
    "c = np.full((2,2), 42.0, dtype=np.float)\n",
    "\n",
    "# Numerical Range Arrays\n",
    "\n",
    "# ARange - creates a 1d array of values on a specified interval with specified step-length\n",
    "d = np.arange(10)    # default start is 0, default step is 1\n",
    "e = np.arange(0,10,2)\n",
    "\n",
    "# Linspace - creates a 1d array of evenly spaced values in a specified interval with a specified number of values\n",
    "# very useful for graphing\n",
    "f = np.linspace(0,10, num=5)\n",
    "\n",
    "# Randomly Generated Arrays\n",
    "#\n",
    "# Numpy also has functions to create arrays with randomly filled values of different sorts\n",
    "# this can be extremely useful for statistics and baseline comparisons\n",
    "\n",
    "# Rand - returns an array of shape (n,n) filled with values randomly sampled from a uniform distribution between 0-1\n",
    "g = np.random.rand(2,2)\n",
    "\n",
    "# Randn - returns an array of shape (n,n) filled with values randomly sampled from the standard normal distribution\n",
    "h = np.random.randn(2,2)\n",
    "\n",
    "# RandInt - returns an array of shape (n,n) filled with random integers from a specified range\n",
    "i = np.random.randint(4,8, size=(2,2))\n",
    "\n",
    "# Specific Distributions\n",
    "#\n",
    "# Full List - http://docs.scipy.org/doc/numpy/reference/routines.random.html#distributions\n",
    "#\n",
    "# For more advanced use numpy can sample from a large array of different distribution types, this can be very \n",
    "# useful in statistics, distribution fitting and other purposes. \n",
    "\n",
    "# ChiSquare - samples from a chisquare distribution of n degrees of freedom\n",
    "j = np.random.chisquare(2, size=4)\n",
    "\n",
    "# Zipf - samples from a zipf distribution with distribution parameter a\n",
    "k = np.random.zipf(2, size=4)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Indexing & Slicing ####\n",
    "\n",
    "Numpy arrays can be indexed and sliced in the same way we can for other sequence objects. There are only a few minor differences we should note. There is improved syntax for indexing multi-dimensional arrays, item locations may also be specified matrix style with a comma seperated list of integers/slices. Rows are indexed first followed by columns.\n",
    "\n",
    "*note: Both normal indexing and slicing of ndarrays returns a view into the original ndarray, this means it is a reference to the original not a copy and assigning to a slice or index will change the original.*"
   ]
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     "text": [
      "Original NDArray\n",
      "[['a' 'b' 'c']\n",
      " ['d' 'e' 'f']\n",
      " ['g' 'h' 'i']\n",
      " ['j' 'k' 'l']]\n",
      "\n",
      "Row 0, Col 2 (standard syntax)\n",
      "c\n",
      "\n",
      "Row 0, Col 2 (numpy syntax)\n",
      "c\n",
      "\n",
      "Column 0\n",
      "['a' 'd' 'g' 'j']\n",
      "\n",
      "Row 0\n",
      "['a' 'b' 'c']\n",
      "\n",
      "Even Rows\n",
      "[['a' 'b' 'c']\n",
      " ['g' 'h' 'i']]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Creates a 4x3 ndarray of strings\n",
    "a = np.array([['a','b','c'],['d','e','f'],['g','h','i'],['j','k','l']])\n",
    "print 'Original NDArray'\n",
    "print a\n",
    "\n",
    "# Standard syntax for indexing nested/multidimensional sequences\n",
    "# Row first followed by column\n",
    "print '\\nRow 0, Col 2 (standard syntax)'\n",
    "print a[0][2]\n",
    "\n",
    "# Numpy syntax for indexing multidimensional arrays\n",
    "print '\\nRow 0, Col 2 (numpy syntax)'\n",
    "print a[0,2]\n",
    "\n",
    "# Examples using slices\n",
    "\n",
    "# All rows in first column\n",
    "print '\\nColumn 0'\n",
    "print a[:,0]\n",
    "\n",
    "# All columns in first row\n",
    "print '\\nRow 0'\n",
    "print a[0,:]\n",
    "\n",
    "# Bonus\n",
    "# Every even row\n",
    "print '\\nEven Rows'\n",
    "print a[::2, :]"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Index & Mask Arrays ####\n",
    "\n",
    "#### Index Arrays ####\n",
    "Another useful technique I want to introduce you to is the concept of index arrays. When you need to extract multiple values from an ndarray we could just loop over the whole array and extract or change the values one by one. However this is highly inefficient. In numpy we can also pass ndarrays or other sequences of values (with the exception of tuples for reasons we won't go into) and use them to extract multiple values at once returning a new ndarray with our results.\n",
    "\n",
    "*note: unlike indexing and slicing using index arrays returns a copy of the original array not a view, you cannot assign to the original*"
   ]
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     "text": [
      "['a' 'c']\n",
      "['a' 'd']\n",
      "['a' 'e' 'i']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([['a','b','c'],['d','e','f'],['g','h','i'],['j','k','l']])\n",
    "\n",
    "# Index the first row using a list of indices\n",
    "b = a[0, [0,2] ]\n",
    "print b\n",
    "\n",
    "# Index the first column using a list of indices\n",
    "c = a[ [0,1], 0]\n",
    "print c\n",
    "\n",
    "# Index the 2d ndarray using two lists of indices\n",
    "# The first list passed acts as the row index and the second is the column index\n",
    "d = a[[0,1,2],[0,1,2]]\n",
    "print d"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mask Arrays ####\n",
    "Mask arrays are a special case that do not operate in the same manner as normal numerical indexing. Ndarrays may be indexed using an array of booleans with the same shape and dimensions as the original. It returns a 1d array of all values from the original where the value in the mask array was true. Mask arrays are quite often used as a filter to remove values from an array based on some condition. Lets do a simple example where we create a mask array to remove all outlier values from a dataset.\n",
    "\n",
    "*note: like index arrays mask arrays return a copy not a view of the original array*"
   ]
  },
  {
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   "execution_count": 6,
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   "source": [
    "import numpy as np\n",
    "\n",
    "# Create a 20x20 array of values drawn from a normal distribution\n",
    "normal = np.random.randn(20,20)\n",
    "\n",
    "# Defining outliers as any value 2 or more standard deviations from the mean\n",
    "# Creates a mask array of booleans by applying a conditional to the original ndarray\n",
    "# uses boolean or to merge both conditions then inverts with ~ (logical not)\n",
    "mask = ~( (normal < -2) | (normal > 2) )\n",
    "\n",
    "# Apply the mask array to our original\n",
    "# removes outliers\n",
    "no_out = normal[mask]"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Array Math Operations ####\n",
    "You may have noticed in the previous example that there was a single line where we created our mask array that did a LOT of work. The reason why numpy is so amazing for numerical data manipulation is because we can write simple functions, comparisons etc and very quickly apply them to an entire array. We can also perform fast operations between ndarrays. Because we are using numpy these operations are not only more concise but much faster. For most numerical data you should be using numpy arrays and its built in operations.\n",
    "\n",
    "#### Scalar Operations ####\n",
    "Scalar operations on matrices are ones that apply a single operation to all members of the ndarray. A simple example would be adding 1 to every value. A slightly more complex one you may often do is taking the mean and standard dev of a set of values then subtracting the mean and dividing by the std for every member of the set. All standard mathematical operations can be performed as scalars on ndarrays."
   ]
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     "text": [
      "[[ 0  2  4]\n",
      " [ 6  8 10]\n",
      " [12 14 16]]\n",
      "[[-1.54919334 -1.161895   -0.77459667]\n",
      " [-0.38729833  0.          0.38729833]\n",
      " [ 0.77459667  1.161895    1.54919334]]\n",
      "[[ 0 10 20]\n",
      " [ 3  4  5]\n",
      " [ 6  7  8]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create a 3x3 array of ints from 0-8\n",
    "a1 = np.arange(9).reshape((3,3))\n",
    "\n",
    "# Double all values\n",
    "print a1 * 2\n",
    "\n",
    "# Standardize all values\n",
    "print (a1 - np.mean(a1))/np.std(a1)\n",
    "\n",
    "# Bonus - It's simple to perform scalar operations on subsets of ndarrays using slices\n",
    "# remember that slices return views so any operations done to them can be assigned to the original\n",
    "\n",
    "# Multiply the first row by 10 \n",
    "a1[0,:] = a1[0,:] * 10\n",
    "print a1"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Elementwise Operations ####\n",
    "The default mode when using mathematical operations between two or more ndarrays is performing elementwise operations. This may surprise more math focused souls who would expect matrix operations. I won't cover matrix operations here but if you need to use them you can either use numpy functions that perform them or the matrix subclass of ndarrays. All standard math operations can be used but the shape of the arrays needs to match. We'll discuss what happens with non-matched arrays shortly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "[  1.   2.   3.   4.   5.   6.   7.   8.   9.  10.]\n",
      "[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      "[ 11.  12.  13.  14.  15.  16.  17.  18.  19.  20.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a1 = np.ones(10)\n",
    "a2 = np.arange(10)\n",
    "\n",
    "print a1 + a2\n",
    "print a1 * a2\n",
    "\n",
    "# Bonus: we can even mix elementwise and scalar operations\n",
    "print (a1 + a2) + 10"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the shape of the arrays are not matched the broadcasting rules become a little more complicated. In the case where the two arrays are matched in shape on all dimensions except for a single 'trailing dimension' the operations will be repeated across the trailing dimension. The simplest case is operations between a 2d array and a 1d array with the same shape on one axis. Take a (10,10) array added with a (1,10) array. The (10,1) array would be added to the first row of the (10,10) then the second and so on. The same pattern occurs for higher dimensional arrays but any shape mismatch other than a single trailing dimesion results in an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "[[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a1 = np.ones(100).reshape(10,10)\n",
    "a2 = np.arange(10).reshape(1,10)\n",
    "\n",
    "print a1*a2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function Toolbox ####\n",
    "Numpy functions behave identically to normal python functions with one important difference. They can take numpy arrays as arguments. There are two general types of numpy functions, functions like sum or mean that will take an array as input and return a single value and functions like round that apply to every element. I won't go over every possible useful function but here's the list of functions available and I'll show you an example of how they can be used.\n",
    "\n",
    "[Numpy Math Functions](http://docs.scipy.org/doc/numpy/reference/routines.math.html)\n",
    "\n",
    "[Numpy Stats Functions](http://docs.scipy.org/doc/numpy/reference/routines.statistics.html)\n",
    "\n",
    "[Numpy Linear Algebra Functions](http://docs.scipy.org/doc/numpy/reference/routines.linalg.html)\n",
    "\n",
    "[Numpy Logic Functions](http://docs.scipy.org/doc/numpy/reference/routines.logic.html)\n",
    "\n",
    "[Numpy Array Manipulation Functions](http://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html)\n",
    "\n",
    "These functions are optimized for use with numpy arrays and should be used over their built-in equivalents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small Sample: mean=103.810675772 median=103.923926599 std=11.3402902476\n",
      "Big Sample: mean=100.090128697 median=100.016784415 std=9.92271667167\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Lets use numpy to create a simulated distribution of iq scores\n",
    "# we assume that the mean is 100, the standard deviation is 10 and the distribution is normal\n",
    "\n",
    "# Create two sample arrays from the normal distribution\n",
    "# One is very large 10,000 and the other is small 10\n",
    "big = np.random.randn(10000)\n",
    "little = np.random.randn(10)\n",
    "\n",
    "# Using scalar operations scale both arrays to match an iq distribution\n",
    "big = (big*10) + 100\n",
    "little = (little*10) + 100\n",
    "\n",
    "# Calculate some simple statistics for each distribution\n",
    "print 'Small Sample: mean={} median={} std={}'.format(np.mean(little), np.median(little), np.std(little))\n",
    "print 'Big Sample: mean={} median={} std={}'.format(np.mean(big), np.median(big), np.std(big))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Everything Data with Pandas](http://pandas.pydata.org/pandas-docs/stable/) ##\n",
    "\n",
    "Pandas is a library for efficiently and expressively manipulating structured data in Python. I like to describe Pandas as an awesome wrapper for numpy ndarrays. Pandas provides a powerful dataframe object, a two dimensional, column-based data structure with labeling for rows and columns. If you have ever used R's dataframe or excel style spreadsheet programs you should be familiar with this kind of structure. Pandas makes it very easy to organize, aggregate, subset and manipulate data. For scientific data Pandas will be your primary means of inputing, outputing and structuring data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series ###\n",
    "A series is a one dimensional array-like object that contains a numpy array and an attached array of labels called the **index**. Each index label is associated with a value in the array. A pandas series is quite similar to a dictionary with the additional restriction that the values must be compatible with a single numpy nd-array (no mixed types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series with Default Index\n",
      "0    4\n",
      "1    5\n",
      "2    6\n",
      "3    7\n",
      "4    8\n",
      "5    9\n",
      "dtype: int64\n",
      "\n",
      "Series with Index \n",
      "a    4\n",
      "b    5\n",
      "c    6\n",
      "d    7\n",
      "e    8\n",
      "f    9\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Series declared from lists will create a 'default' index which is a numeric range from 0-N\n",
    "default = pd.Series([4,5,6,7,8,9])\n",
    "print 'Series with Default Index'\n",
    "print default\n",
    "\n",
    "# Series declared from lists will create a 'default' index which is a numeric range from 0-N\n",
    "indexed = pd.Series([4,5,6,7,8,9], index=['a','b','c','d','e','f'])\n",
    "print '\\nSeries with Index '\n",
    "print indexed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Series Indexing ####\n",
    "Pandas series can then be indexed using either the raw numeric index of each item or with their associated index values. We can use single values, index arrays or slices with either the numeric index or the series index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    10\n",
      "b     9\n",
      "c     8\n",
      "d     7\n",
      "dtype: int64\n",
      "\n",
      "Numeric Indexing\n",
      "9\n",
      "\n",
      "Using the Index\n",
      "10\n",
      "\n",
      "Index Arrays with Numeric or Index\n",
      "a    10\n",
      "b     9\n",
      "dtype: int64\n",
      "a    10\n",
      "b     9\n",
      "dtype: int64\n",
      "\n",
      "Slices Using Numeric or Index\n",
      "a    10\n",
      "b     9\n",
      "c     8\n",
      "dtype: int64\n",
      "a    10\n",
      "b     9\n",
      "c     8\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Series can also be created directly from dicts\n",
    "ex = pd.Series({'a':10, 'b':9, 'c':8, 'd':7})\n",
    "print ex\n",
    "\n",
    "print '\\nNumeric Indexing'\n",
    "print ex[1]\n",
    "\n",
    "print '\\nUsing the Index'\n",
    "print ex['a']\n",
    "\n",
    "print '\\nIndex Arrays with Numeric or Index'\n",
    "print ex[[0,1]]\n",
    "print ex[['a','b']]\n",
    "\n",
    "print '\\nSlices Using Numeric or Index'\n",
    "print ex[0:3]\n",
    "print ex['a':'c']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The DataFrame ###\n",
    "The dataframe is the primary data structure provided by Pandas. It is a tabular, spreadsheet like datastructure made up of one or more labeled columns which share an index. In essence it is a set of Pandas series with one shared index. It is very similar to the R data frame. Dataframes will use numpy arrays internally when possible so they are relatively fast.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DF from dict, keys become column headers\n",
      "   age  income  pet\n",
      "0   12      50    0\n",
      "1   34      78    1\n",
      "2   23     122    1\n",
      "3   52     200    0\n",
      "\n",
      "DF from dict, columns passed explicitly specify subset to use\n",
      "   age  income\n",
      "0   12      50\n",
      "1   34      78\n",
      "2   23     122\n",
      "3   52     200\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {'age':[12,34,23,52], 'income':[50,78,122,200],'pet':[0,1,1,0]}\n",
    "\n",
    "# DataFrames can be created from dictionaries of equal length lists/numpy arrays\n",
    "frame = pd.DataFrame(data)\n",
    "print 'DF from dict, keys become column headers'\n",
    "print frame\n",
    "\n",
    "# Passing a list of columns allows you to specify a subset of columns to include\n",
    "frame = pd.DataFrame(data, columns=['age','income'])\n",
    "print '\\nDF from dict, columns passed explicitly specify subset to use'\n",
    "print frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Indexing DataFrames ####\n",
    "DataFrames may be indexed in several ways. The column names act like a second index of the columns. We may specify the columns using dictionary style indexing or as an attribute of the dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indexing with ColumnNames\n",
      "0    0\n",
      "1    1\n",
      "2    1\n",
      "3    0\n",
      "Name: pet, dtype: int64\n",
      "   age  income\n",
      "0   12      50\n",
      "1   34      78\n",
      "2   23     122\n",
      "3   52     200\n",
      "\n",
      "Columns as Attributes\n",
      "0    12\n",
      "1    34\n",
      "2    23\n",
      "3    52\n",
      "Name: age, dtype: int64\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {'age':[12,34,23,52], 'income':[50,78,122,200],'pet':[0,1,1,0]}\n",
    "frame = pd.DataFrame(data)\n",
    "\n",
    "# Indexing with column names\n",
    "# note: we cannot use slices to index columns with this syntax\n",
    "print 'Indexing with ColumnNames'\n",
    "print frame['pet']\n",
    "print frame[['age','income']]\n",
    "\n",
    "# Access columns as attributes\n",
    "print '\\nColumns as Attributes'\n",
    "print frame.age\n",
    "\n",
    "# Bonus - standard multidimensional indexing is often used to select rows and columns\n",
    "print frame['income'][3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Indexing with IX ####\n",
    "There is also a special indexing function \"ix\" that allows us to do numpy style indexing of the rows and columns. Columns may either be indexed using their names or their numeric index. (first column is 0, second is 1.. etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    12\n",
      "1    34\n",
      "2    23\n",
      "3    52\n",
      "Name: age, dtype: int64\n",
      "0     50\n",
      "1     78\n",
      "2    122\n",
      "3    200\n",
      "Name: income, dtype: int64\n",
      "   age  income\n",
      "0   12      50\n",
      "1   34      78\n",
      "2   23     122\n",
      "3   52     200\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {'age':[12,34,23,52], 'income':[50,78,122,200],'pet':[0,1,1,0]}\n",
    "frame = pd.DataFrame(data)\n",
    "\n",
    "# Select rows 0-3 for column 0\n",
    "print frame.ix[0:3,0]\n",
    "\n",
    "# Select all rows for column 'income'\n",
    "print frame.ix[:,'income']\n",
    "\n",
    "# Use slices to select a subset of columns\n",
    "print frame.ix[:,'age':'income']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Mask Arrays with Pandas ####\n",
    "\n",
    "Dataframes may also be indexed using index and mask arrays allowing us to do various sorts of powerful subsettings very naturally. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {'age':[12,34,23,52], 'income':[50,78,122,200],'pet':[0,1,1,0]}\n",
    "frame = pd.DataFrame(data)\n",
    "\n",
    "# Create a mask vector for all people with a pet\n",
    "mask = frame['pet'] == 1\n",
    "\n",
    "# Apply the mask vector to the pandas index to get all subjects with pets (preserves index values)\n",
    "frame[mask]\n",
    "\n",
    "# An abreviated version\n",
    "frame[frame['pet']==1]\n",
    "\n",
    "# Mean income for all people with a pet in one line\n",
    "print np.mean(frame[frame['pet']==1]['income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Data with Pandas ####\n",
    "Pandas also has several very useful built in loading functions to parse data from common formats such as excel spreadsheets, csv's and etc. Pandas has some fairly powerful type inference when loading these formats and will automatically assign dtype to columns and use header rows as the names for column labels.\n",
    "\n",
    "*note: a common source of errors is a single non-numeric value in a column which will cause the entire column to be read as strings.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Machiavellianism Score = 65.8985253877, Male Mean = 67.6835782599, Female Mean=62.4240124972\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Import a csv with data for a machiavellianism test\n",
    "mach = pd.read_csv('./data/mach2/data.csv')\n",
    "# List the automatically read columns\n",
    "mach.columns\n",
    "\n",
    "# Get the mean machiavellianism score and then the sub-means for men and women\n",
    "mean = np.mean(mach['score'])\n",
    "mean_m = np.mean(mach[mach['gender']==1]['score'])\n",
    "mean_f = np.mean(mach[mach['gender']==2]['score'])\n",
    "\n",
    "print 'Mean Machiavellianism Score = {}, Male Mean = {}, Female Mean={}'.format(mean, mean_m, mean_f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "read_csv, read_table and read_xlsx are the most commonly used import functions. read_table allows you to read in arbitrary delimited data by specifying the delimiters, read_xlsx can import excel formatted data but requires external libraries. All three have various arguments that can be passed to them to customize how the imported data is processed. The full range of pandas import functionality is beyond the scope of this tutorial but some commonly used examples are.\n",
    "\n",
    "**nrows** - Specifies the number of rows to read in from the start of the file\n",
    "\n",
    "**names** - List of names for columns, can override header row or be used when header is none\n",
    "\n",
    "**sep**   - Seperator, specifies the delimiter to use, overrides default\n",
    "\n",
    "**converters** - Dictionary of column names mapped to functions, applies the assigned function to every value in that column, very powerful for data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Graphing with Matplotlib](http://matplotlib.org/) ##\n",
    "\n",
    "Matplotlib is the primary graphing library used with python, it is primarily 2d, but libraries for making 3d, animated and interactive plots are available. There are two ways of interfacing with matplotlib: the native matplotlib object methods and pyplot which provides a matlab like interface for interacing with graphics. I reccomend using the native matplotlib interface when possible as it makes the code more explicit and easier to read when working with multiple plots. \n",
    "\n",
    "### Make Matplotlib Pretty Again ###\n",
    "\n",
    "A lot of people prefer graphics from R or ggplot because they find the default matplotlib graphs to be fairly ugly and basic even if they like the syntax. Luckily python allows a lot of customization so this can easily be fixed and tweaked to your hearts desire. There is a quick fix for this in a library called Seaborn. Just importing Seaborn overrides the default color palette for matplotlib when imported to make it 'prettier'. It also adds some additional tools for customizing asthetics and doing common graphing tasks like adding regression lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8ecded9550>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BdLrYXBQAwJ7N+TDodXjzbC9Hmy4RQ/s6Tl0ehsvtxd01eTAb9aLLEerumjwAwLu87kQU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\nVIKhTUREpBL/P1PdJUsfhly8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ed9728e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "x = np.arange(0, 10, 0.1)\n",
    "y = np.sin(x)\n",
    "plt.plot(x,y)  # pyplot function call"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another quick cheat is using the **style** module of matplotlib to change the appearance of your plots to one of their available styles. You can also create your own by customizing the style parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'seaborn-darkgrid', u'seaborn-notebook', u'seaborn-muted', u'seaborn-ticks', u'grayscale', u'bmh', u'seaborn-talk', u'dark_background', u'ggplot', u'fivethirtyeight', u'seaborn-colorblind', u'seaborn-deep', u'seaborn-whitegrid', u'seaborn-bright', u'seaborn-poster', u'classic', u'seaborn-paper', u'seaborn-white', u'seaborn-pastel', u'seaborn-dark', u'seaborn-dark-palette']\n"
     ]
    },
    {
     "data": {
      "image/png": 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Gk4v6mxFp+bjSKdjjbli8VhUFU6xbhplEzQFKW9SVHVFl0Gr3BvBpjTBo3TpcHXNZrIlD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\nIUQ2KGgRQgiRDQpahBBCZOP/A0zFQZ4OVq5LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecddd2c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of available styles\n",
    "print plt.style.available\n",
    "\n",
    "# Set the style to nate silver clone\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.plot(x,y)  # pyplot function call\n",
    "# Set the style back to default seaborn\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "\n",
    "# Bonus - secret desu\n",
    "# plt.xkcd()\n",
    "# plt.rcdefaults()  # for after"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure and Plots ###\n",
    "\n",
    "The parent object for matplotlib graphics is the figure. The figure contains one or more subplots which graphing commands are drawn to. When pyplot graphing commands are called without specifying a plot they will automatically draw to the last plot created. If no plot or figure has been created default ones will be generated. Multiple calls to the same subplot will result in multiple things being drawn to that subplot.\n",
    "\n",
    "There are several ways to manage multiple figures. My preferred method is to assign figures and subplots to variables when they are created and use their native matplotlib methods to interact with them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8ecd764710>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PCOtqWIULF1Zad/nFaMT368ttUVtsvmfJT+eT4WTW5nm3TrVP7ll0XbNPs71Eb0LBulZ7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\noyNJ/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd528c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6w4mkuWc0PgxGWVLnc2BembnjM3tfWcfx/hgO9w2V8NoArGQwKlguu4bltWpV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\nEVHWMRgREVHWMRgREVHWMRgREVHW/X9/ujvhbk7L1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd6cdb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ozF6/Rm19ya5dedSoQSYyLF5MmXckimKx/yUmJn6dmJjYN2y8JTEx8Y6w8ZjE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\nvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd6cd410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Default figure\n",
    "x = np.arange(0, 10, 0.1)\n",
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "# New figure created in global namespace\n",
    "plt.figure()\n",
    "x = np.arange(0, 10, 0.1)\n",
    "plt.plot(x, np.cos(x))\n",
    "\n",
    "# New figure and subplot assigned to variables\n",
    "# a more manageable method\n",
    "fig1 = plt.figure()\n",
    "ax1 = fig1.add_subplot(1,1,1)\n",
    "x = np.arange(0, 10, 0.1)\n",
    "# Draw multiple things to our plot\n",
    "ax1.plot(x, np.sin(x),'b')\n",
    "ax1.plot(x, np.cos(x),'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subplots ###\n",
    "A figure may contain any number of subplots which are created using the **add_subplot()** method of a figure. This method takes 3 numeric arguents. The first two are the dimensions of the figure, for instance **add_subplot(2,3,1)** would create a total of 6 spaces for subplots in a 2x3 grid. The last number specifies which subplot we are creating using a numbering scheme of 1-N starting at the top-left and going to the bottom-right. There is also a convenience function **subplots()** which combines the creation of a figure and an array of subplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8ecdc936d0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wbmuDZgF/ae+LqqokY3NnKCtzZVVfFQJ3THFz2+LaL7ebAmGDK/+1m8dmljDs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\nFNYSrQbyXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd759f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xoE/MrxlrMboKWF3y71eBS8vZR77bpSrx97gjaoGvtHe3Yq/lFSeTgfXtN7Hf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\nXPc/XkXQhmcPI6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd8dca10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Create a figure\n",
    "fig1 = plt.figure()\n",
    "# Create subplots for that figure\n",
    "ax1 = fig1.add_subplot(2,1,1)\n",
    "ax2 = fig1.add_subplot(2,1,2)\n",
    "\n",
    "# Draw to our subplots\n",
    "x = np.arange(0, 10, 0.1)\n",
    "ax1.plot(x, np.sin(x))\n",
    "ax2.plot(x, np.cos(x))\n",
    "\n",
    "# bonus - the same code using subplots()\n",
    "fig, axes = plt.subplots(2,1)\n",
    "axes[0].plot(x, np.sin(x), color='r')\n",
    "axes[1].plot(x, np.cos(x), color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Titles, Axis Labels, Ticks, Tick Labels ###\n",
    "The main components of matplotlib graphics we'll wish to customize are the graph title and axes title. Additionally we may also wish to set the ticks (the labelled numeric intervals) and the tick labels. I will again be showing you the more explicit matplotlib method for doing this but there is also a pyplot interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.YTick at 0x7f8ecd539250>,\n",
       " <matplotlib.axis.YTick at 0x7f8ecd75bb50>,\n",
       " <matplotlib.axis.YTick at 0x7f8ecd694950>,\n",
       " <matplotlib.axis.YTick at 0x7f8ecd694c90>,\n",
       " <matplotlib.axis.YTick at 0x7f8ecdb29e50>,\n",
       " <matplotlib.axis.YTick at 0x7f8ecdb43610>,\n",
       " <matplotlib.axis.YTick at 0x7f8ecdb29410>,\n",
       " <matplotlib.axis.YTick at 0x7f8ecd6d9850>,\n",
       " <matplotlib.axis.YTick at 0x7f8ecd810e10>,\n",
       " <matplotlib.axis.YTick at 0x7f8ecd8b80d0>,\n",
       " <matplotlib.axis.YTick at 0x7f8ecd6a8910>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HZebpvV6/JGnlNdP1zNYDPkR1Yc73Ax/IzAf7VMtRwF19WrckaSU2U8/seuAqYMvMvL5f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\nM0lS8QwzSVLx/hty3nQh4oU9pwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd4de110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "fig1 = plt.figure()\n",
    "ax1 = fig1.add_subplot(1,1,1)\n",
    "\n",
    "# Set a title for the subplot\n",
    "ax1.set_title('Empty Plot', fontsize=18)\n",
    "\n",
    "# Set an x and y axis lable\n",
    "ax1.set_xlabel('X Axis', fontsize=14)\n",
    "ax1.set_ylabel('Y Axis', fontsize=14)\n",
    "\n",
    "# Set ticks for one of our axes\n",
    "# ticks must take the form of a list of numeric values\n",
    "ax1.set_xticks([0,1,2,3,10,20,30])\n",
    "\n",
    "# Set the labels for our axis ticks\n",
    "ax1.set_xticklabels([0,1,2,3,'ten','twenty','thirty'])\n",
    "\n",
    "# Bonus - arange() is an excellent way to explicitly set the range and intervals for your graphs\n",
    "ax1.set_yticks(np.arange(0,11,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Styles, Markers, Line Colors ###\n",
    "\n",
    "For graphing multiple sets of data in a visually pleasing and easily interpretable way we will often want to specify a color, markers and line type. These are considered properties of the individual line or graphic drawn, not the figure or the subplot so they are specified as arguments to the plotting function. Matplotlib can take both explicit arguments such as \" color='r' \" or a string which will be interpreted as a specification for rendering the line.\n",
    "\n",
    "#### Possible Arguments ####\n",
    "\n",
    "-[Color](http://matplotlib.org/api/colors_api.html)\n",
    "\n",
    "-[Line Styles](http://matplotlib.org/api/lines_api.html#matplotlib.lines.Line2D.set_linestyle)\n",
    "\n",
    "-[Markers](http://matplotlib.org/api/markers_api.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8ecd432210>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g7YpWmzkWFH97s3CdtMvhB48/geioMgoOPyj5Wbvh8elTgpueeYHwQQenvb7z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecdf0b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Create two subplots with line formatting using explicit arguments or abreviated form\n",
    "fig, axes = plt.subplots(1,2)\n",
    "x = np.arange(0, 100, 1)\n",
    "# Draw a different type of noise to each subplot\n",
    "\n",
    "# Explicit \n",
    "axes[0].plot(x, np.random.standard_normal(size=100), color='r', marker='o',linestyle='--')\n",
    "# Abreviated\n",
    "axes[1].plot(x, np.random.zipf(2.0,size=100), 'g-x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Legends ###\n",
    "There are a couple ways to create legends with matplotlib, I will again show you the most explicit one. Each call to the plotting functions may optionally contain a 'label' argument with a name for that line or element. Calling the **legend()** method of your plot object will automatically create a legend using those labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f12cf31dc10>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gB2mHIABz5thx+rQOt9/ugSgW/R0RKReDWkamTDEjM9OAxx93YtgwR9FfVqFzGREAmM3A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12eaf80a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "fig1 = plt.figure()\n",
    "ax1 = fig1.add_subplot(1,1,1)\n",
    "\n",
    "x = np.arange(0, 5, 0.1)\n",
    "ax1.plot(x, np.sin(x),'--b', label='sine')\n",
    "ax1.plot(x, np.cos(x),'.r', label='cosine')\n",
    "ax1.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Common Graph Types ###\n",
    "\n",
    "Matplotlib supports all the commonly used graph types such as historgrams, scatter plots and etc. For this section we'll start applying what we've learned so far and demonstrate each using a real dataset. The nerdy personality traits inventory, a personality inventory of nerdy traits that also includes demographic information and some other common personality measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline  \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Import a csv with data for the npas\n",
    "# uh-oh looks like some participants included commas in their free response section\n",
    "# that messed up our columns for the csv so lets exclude them\n",
    "bad_participants = [27,43,53,68,177,258,666,687,718,725,771,813,\\\n",
    "                    842,1006,1094,1095,1132,1159,1182,1189,1225,\\\n",
    "                    1238,1288,1333,1339,1350,1409]\n",
    "npas = pd.read_csv('./data/NPAS-data/NPAS-data.csv',skiprows=bad_participants)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bar Plots ####\n",
    "\n",
    "Bar plots are a simple representation of the relative values of different variables. To create a bar graph we use the matplotlib method bar() or barh() for horizontal bar graphs. Bar graphs require two lists (or arrays/series) of values with several optional parameters to control the appearance of the graph. Bar graphs can require a lot of manual tweaking.\n",
    "\n",
    "**Left** - x coordinates for the left side of bars\n",
    "\n",
    "**Height** - the heights of the bars\n",
    "\n",
    "**Width(optional)** - the width of the bars as a scalar or array if each bar needs different widths (default 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f8ecd24cd50>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nBq3L22F7NHJbFEGpNK7ZJYxYrRBc6yWNjogHgU5gNbCGvi2sTmBF7Qu7uzfm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\nZmZWKA4uMzMrlP8HD9pvkJ83uxIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd248590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a single plot\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "# Get counts of men, women and other genders\n",
    "men = len(npas['gender'][npas['gender']==1])\n",
    "women = len(npas['gender'][npas['gender']==2])\n",
    "other = len(npas['gender'][npas['gender']==3])\n",
    "# Store in a list\n",
    "genders = [men,women,other]\n",
    "\n",
    "# Create a bar graph of the gender numbers for the NPAS\n",
    "ax.bar(left=[1,2,3],height=genders, width=0.8)\n",
    "\n",
    "# Set the xlim, xticks, and ticklabels to make it prettier\n",
    "ax.set_xlim([0.5,4.3])\n",
    "ax.set_xticks([1.4,2.4,3.4])\n",
    "ax.set_xticklabels(['Men', 'Women', 'Other'], fontsize=12)\n",
    "\n",
    "# Add appropriate labels to our graph\n",
    "ax.set_title('Participant Genders', fontsize=16)\n",
    "ax.set_ylabel('Frequency', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histograms ####\n",
    "\n",
    "Histograms are a representation of the binned frequency of values in a single vector of data. To create a histogram we can use the **hist()** method and pass the number of bins to be used. The exact limits for each bin will be determined automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f8ecd248350>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dV1Z7+4BaxdZUWrFerVgncL3yptXqFQteaHQIDVPvBPYccF9ErAeekLQcWCNp64h4BRgC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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd1fd250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a single plot\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "# Display a histogram of the 'nerdy' values in our data column\n",
    "# these values represent the total nerdy score from this personality inventory\n",
    "# the range is 1-7 so we use 7 bins\n",
    "ax.hist(npas['nerdy'],bins=7)\n",
    "\n",
    "# Add appropriate labels to our graph\n",
    "ax.set_title('Nerdy Personality Traits', fontsize=16)\n",
    "ax.set_xlabel('NPAS Score', fontsize=14)\n",
    "ax.set_ylabel('Frequency', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter Plots ####\n",
    "\n",
    "Scatter plots are a representation of the relationship between two variables. We can create these using the Matplotlib **scatter()** method. Lets look at the relationship of two continuous variables, the time elapsed during the test and the time elapsed during the survey. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f8ecd06b5d0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Upm3/yjQNLr64kM7O1jgRS9VNefbZbutv2rTURaU3Me3v8xqNpm94Dfd/F/h0\n+NeXhBDTUIEjh6WUR1M9qRDiD8AUlAX3vJSyM/xUAzAZ5aqMbonTGH5ck4Bu4bTZt89Iye3pWFxL\nlrRH9udWrQp4Folo0Y49d3Exno6RKt6aofZ83adPG6xeHaSlxaC42Ob06dS7gvckppZlI2XP6Q66\nTJhGkxl6FTYhhIHKVZvhPBZ2Ce7u60mllB8XQswFduA2X5ItmZMupUtLx/R1GANKZ6fFyy+3U1Nj\nMH26zfz5+eTkmP06ZqJrLyhoc4X2FxSYlJYWJT3GJZco997rr9uRSba4GJdIzJxpeLrPzrFqagwm\nTAhxww0FACkdI5bY+zZunMXMmUbc+ObPL4qcW93foh7v79SpAb72tW6r9IUXgpSWjvI8jvnz85k5\nsz3pfXrxxQAdHbbr+Rkz3J/ZzJmBlO/zUPl7zwQj9dpH6nX3B8O2e1+lCiGeBTZJKZ/pz8mEEB8B\nGqSUdeHf30QFkZwnpWwXQlyM6sh9H7BYSrkw/LoHgJ9JKX8Vc0hb5XJlP/0taxVLaekYEl37rl0+\nvvGNbiHbsqWNz3++dysgenzl5SE2bgwk3B9LtVhzf/ePYu/bCy8EEaKz38dOdXyJPj9nby/RMXbt\n8tHWBm1tRmSR8dGPdjEnqrNhqmMoLR3D8ePNI3JfLtnf+3BnpF43QGnpmD7/YXvdYzsCbBFCHAEO\noQI6Ikgpv+TxOJ8ApgE3h5O+RwPPAleirLcFwK+BPeHzFaP28eajIiSHLAPldvLikks0oVZUxEdO\nmqYjvN3taKT00dpqRLoKJBPodO0rxt63mhqD2bP7f+xUx5f487OTHqO83OJ//kftMTrE5g+aphEO\nWFHHByK/JxMvvS+n0fSOV2HzoQSov2wGtgohXgQKgOuAvcB2IcS/oURzm5QyJIRYCTyHErbvhtML\nhiwDVZ3eSwRmssmxp4k+9j2rVwe57bbCjO8Lxd636dMHZxJP9fOrqLBoa6PX9ySrw5lMvPS+nEbT\nO16DR76WjpNJKYPA1Qme+psEr30SeDId580GMh3y7+DFEomdHKurTaBnt1ZsvtpZZ1msWhWgrMzC\nsuwe3WH9cUvG3rf584toaurq/Y1p5rzzQlRV+ampUVGj550XQqVeJsY0DS66qPfPPJFQOT9HP+Z8\nnrp9j0bTO15rRV7f0/NSyv9Iz3CGL6m4vjLVhdohdnIMBtX+UU9urdh8tejk5t7cYeksKdbfgJu+\n8uabvpTcETmaAAAgAElEQVTLb/WnnmYy8RqoBZJGM5Tx6opcEfO7D5gEtAAHAC1sacQRgkmTVMK1\nlCZCWK79l7q6IFOnqs7VBw/6EjYhTTb5OpNjdbVJMGiwaVN+UrdWdB1KlXBt0N7et1w55/VSmlRU\ndGUk6KGvi4KuLotXX/VFLLILLwzh83WLaF+sXC8kFiqSipfugqDR9I5XV+Q5sY8JIYqASuCNdA9q\nOONl4nUm0SVLggkSpd37L5WVQVauVEnQP/pRbP6ZERG2ROeNPVasW8spFLx7t4/iYti8OY8tWwKA\nnZI7LNYqaW1VwpCJoIe+Woevvuq2yKqq/Myb1/2+vli5XkgmVFq8NJq+49Vii0NK2SaEWAUcRNV/\n1HggduJNVHbKmUSbm+MtI+dn5//cXJtVq4IUF9tMnuyefMvK7IigNTUZrnJXTrj6c8+1cuKEQUOD\nOnb0fll1tbv6/dq1AVpbbQwDtm/3c+KEyaRJFjk5do/7bBUVFjt2+Hn99RyKi23uuy+f73wnmJGg\nh74GV9TUxEZfmsyb1/2+VKxcjUYzuPRZ2MLMQrWz0XgkduJNVHbKmUSbmhLvtUQ/1tVlsGZNAbm5\nNjt3tkaqaUyYYGHbNlVVObS2Gvj9iQISbEKheMHrjsBzC6thQFubyTXXdO+vbdvm5+hRk5ycEB/4\nQOJrNk2D8eNh/fr8jAc99DW4Yvr02OhL9/scywqsHq1cjUYz+HgNHnmF+HpDRcBM4Il0D2o4Ezvx\nTpigbmv06t+ZRC0rUVV+Ih20R4/uYvnyosj7jx3z8ZOf5LF4cQelpTZXXjk6SoDaEk74PVk4ZWVu\nl2NHh0F1tVvsqqtzWL8+n8ce8/OBDyR3yaUS9JDOKEqvwRUXXhjiqadaaW42OHHCpKiIhFZoNgdv\n6KLKGo3Cq8WWqOJIEBU48lT6hjP8ycmxY7pZq8cTrf572n+57LICfvOb1kiF+9xcGyEsNm4MsGDB\nKFasaHcJ0KFDJpWVQcaMsaMCUYweLZzmZlVPMSdHWXabNuWzZIm7jFRxsfr58GET1WEoMakEPWS6\nMWsifD6TvDyDRYt6Pm82B2/o5G2NRuE1eOR7mR7ISOHgQZ+rm/WmTW1s2dKWUmdqh+jGoGVlaq+r\nudmgsjJIbq7NunWBSIWQGTNCjBtH1HHcXa7r62HUKPjLX0za2lRU4KRJBl/7WoGrgenmzXns3Onn\nyBGTtjZVgUTVQUzNJedc39tvq326U6cMzj7bDveD67l4cKYY6snPQ338Gk268FIE2Qd8A/gyMB21\nLH8L2C6l3JnZ4Q0/Yi0kIayoVXW3qEVHI955ZyFbtgQSWg8AN91UGKnxeM89AZqbobhYidD69W1M\nmBAvaNHHqKiwaGkxeeUVdb5lywrZuDEQSTCODvUvL1fis3+/ze7dOSxa1EFJic2YMb1bBtFiXVZm\nuQJTVq8Ocu21BTz7rH/QkpCHevLzUB+/RpMuehS2sKj9GvgQqsv1Y+H3fBjYJoT4MnCllFJ/gzzi\nteRV7KSfbPUdvUpfvLiDL33J/b7WVoNLL+15H6i62ox73zvvmMyb55TZckSrW4BjLc/YOojJzuO4\nymJb47S0GBEr44orugZlHyub98+8MNTHr9Gki94stutRfdDmSClPRD8hhFiLavx5I3BPZoY3/OhL\nyauWFrjwwsRrh+hVenMzce+bN89i927DlXhsGIbLzRkb/djSAkKklptWXm716j6Nvq7Y1jjFxXbk\nOIO1j5XN+2deGOrj12jSRW/C9hXgllhRA5BSviOEuAHYgBa2PpOo4kV5ubvf2Lx5oaSr7+hVelmZ\nW2wuuCBEc7PBlVe68+aKiojLpYt+30c+EqK4uOfVfiLr4K23DPbu9XH6tMHJk6oqSnSblmgx3Lw5\nj6oqP++9173H5uTW6QlZo9H0h96E7TxUC5lk/A9wbvqGM/JIVPEiUfHcZGHb0at0Jz1ASpPWVoMb\nbyxi2bJgXOLx6NFuy6652XAlUN90UxHf+U6AiopQQgusu4VNd9AKwIkThqtSyo4dfubM6d57SySG\n3dU93PuMGg1078s6JeR0CoPGC70JW66U8v1kT4bby+i/sn6QuOJFfAuZWDffJZfEuwodkauthSVL\n1P7XlCnuXLTp0y2KiuLdgEVFsQnUdsLw8YoKiz173HuAlZVBurpCNDS4r6WxsecUgFh0HpYmFp3C\noOkL/a08ouknH/hAiHXrApw+rfLahEhchDi2JNYjj6hox1DIiBOC8nKLefM6Wbasg1OniITnT5vW\nvcdWVeWPRF0uW1bIgw+2xVlTVVU5ccWLAXbv9sVZfLW1qlBzbMRnNMmEMjpSctmyQmprfXoS0wA6\nhUHTN3oTtgIhREMvr8lP12BGInl5bvfdr37lx7KsuLD42ITr117zUVJi8u//XhDXBSAnx+bmm9v5\n6lcTFfU1sSybujrT1d354EEfn/98yGUlRu/ZlZeHmDxZjauiwqK8PBQRoJISOyKGPUXl1dcbkeT0\n4mKb+noDn8+9L7d8eTvLlhXpSUwDDEwKg/YUDD96E7a0NBgdqaRSyR+UYL39tsmpU0SsMycs3oka\ndL7gs2erCX/SJJslS9pd4uiIR/Rx33nHpKjIilhIra1GrxNGaWn3sSoqQnz5y91C+eCDbbz5pskF\nF4QYP95m1iy716i84mI7kujtiG3svtzWrW1A4kosmf4sUnldJrEsm7feMiLFqaNbFo00nMVSXV0O\nU6d2ZSS4SLs7hx89CpuUcttADWQ44uULk6ily5Ej3S5AJyx+06Z8KiuDjBpl09ZmsGpVocsCiq34\nf8YZqvLIz36Wy5VXduLzwd69PnJybGprTe67Lz9SMPnDH048YcyaZWPbXZw8aVBXp0pybdqUT12d\nSWOjwaWXhpImfieiocE9zkS93ZqbjYSVWPqL18krGya56mqTvXt9cS2Lxo+32Lcv3vU8nHEWS5dd\nVkBjYwup/D14XaRod+fwQ++xZRAvX5ho911JicXy5UWueoxOWHxDg3JL7t9vcuut3S7EMWNszjkn\nFFfx/9ZbVceAxx7zc9VV3RP1jh2qskd9vcFttzldBeKbfnbv7eHa21u9OsgddxRQUWG59sfOPTdE\na6uyDBM16gQoL4/t42YT29tt1qz4SiwD9Vmk8rpMUltrcvp0fMui3Nz2QRfdoYTXRcpIrNiSDZ6J\nTKKFLYN4+cJEu+/27TOorzci1ll0wWLTVNX+wR3RePbZFjfcUBi2vuBDHwpxyy3dFf8PH46PVLz4\n4t4re1RXm3zjG4XccIN7by8nx+aRR7qDPpyJY926gMvCiG3UCYlrWwqRerWMvnwpvU5e2TDJlZdb\nnDwZ7yquqckZdNEdSnhdpIzEii3Z4JnIJFrYMkiqX5hEr49180XvoZWU2IRCNrW1Pm67TVlxO3e6\nK/7Ht8mx+P3vVVJ0eTmR5qWxOWrV1SZr1gTjJljLMsgPhwtFTxyxFkZNjdrTSyQ+Tm3L6C9UKtUy\n+vKl9PpZZMMkV1GhRH/HDj+Njd17bLm5qXUuTxdDdXXvdZEyEiu2ZINnIpN47cf2TWCHlLI+w+MZ\nVqT6henp9U5hZClNV43Ge+5pc1Xxb2gwefRRP3/+swrlX7cun8cfV81AW1sNbrmliPp6g23b2lwN\nQx1xiBWNtWsDrF0bwDTh7LMtGhqUu7G42C2aJSXuSXfaNCthaL+U8ZX7Kyq6Upo4+/Kl9PpZZMMk\nZ5oGc+YQTm7vHsf8+fmDIrpDdXWfDYuUbCUbPBOZxKvFtghYK4T4X1Qx5CoppT9joxqBJFoVA3E5\nXosXd7j+INvbDW6/vYCHH1a5anfdVcCyZUFXKP+hQ53k5uLamztyJH4Pp6KiK054Tp40WLOmkHXr\nAnF7dX/91yGqqvy8847JmWdabNzYRl2dyYUXhujqiu/YDSSMxkx14uxLncrhQE6OOSiiO1RX99mw\nSMlWhrvoe+3Hdr4QYgbwRVRh5E1CiKeBh4Ff6+r+/SfR5A7umo6rV6uoxNWrgxQUKFGrqsqlsjLI\nkSMmkybZ+HyqN1p0/llZmU0o5N6bi61Icu65IfbsiU8D+MhHQtx7bxs+n1uojhwxefVV4roQrFlT\nGIlqjBUfKd3RmB/6kIrGfOaZ1PaOEn0ph6pVMRTI5Op+JCxIspHhLvqe99iklAeBu4C7hBBnofqz\nPQSEhBAPAhullEcyMsphSvSXur3dLRxSmnR2qp+nTrVYsqQdn0/lrG3enMfatQEWLRrlagLqREHe\nf38elZVBxo2zmDDB5qqrRjFpktqbGz3aZuJEi8mT3eIQChns3u3j+edz2batjaNHDc491+Lmm1Ul\nkHXrAnGWYmwFkpaW7okvvlO4TVmZOxrzkUe6IpVSYkW2p7D2RF/KTFgVetJVZHJ1rxckmkyQcvCI\nEOIyYCHweeA0yjVZDrwhhLhaSvlsWkc4jImNKozNZ7NtZWUtWdLuEq9t29rIz1fC4fPZrgl9714f\nn/hEiJUrC3nyyVZefVWJT12dwcqVypq65BIbNTF1i8OuXWpP7sorOyN7b6tWBait9QFw3335bN3a\nxv79PmbPDrFqVaErLSE31+bDH+7iuec6CYUMXn9dvW/79jzq6ky2bGljxoyQS+xKS9UEFjtxhkJG\n5L6Ul4fYuDFAQ0PyGpmQGatCT7qKTK7uh6qbU5PdeA0e+RDwVVQbm2LgZ8ACKeWLUa/5MrAJ1WVb\nE0Oi1X9tbbfF0y0cqprHjTeqkP3Vq4Pk57vFa/9+k8JCk7w8mzPPdE/oFRUWp0/DqlUBjh414/qe\n9RTmfued7vD+6PfW1xscPWqyfn0+d90VoK7OZPPmPLZta+P4cWXdXXhhiDff9MW5T++4o4DycotZ\ns2y6ukKRezBrlhLY2Ilz167u+7J4cYfL3fnCC0Fmz44fvyOO9fWqSkttrRF5vK9Wlp50M89wD2LQ\nDA5eLbY/A78FbkMFjrQleM2TwJ3pGthwI9HqP/pL7QjHmjWFbNrURn29waRJNqYJOTm4Ih8vvLAL\ny4LmZpNjx0wef9zP/v0mhmGwalUBa9YEWbMmn3XrAtx9d34kx23evBA5OTbPPaeiGhsaDMrL7XCy\ntc2WLQGamrrFLFq4Jk60OXTI5Ic/DDBlisWGDW1Mn27xzjsm55xj8eabKjgktrpIYaHNc8+1EgoZ\nPPNMDuXlFldc0RWXxhBNT81Ta2qMhMLmiCOQNitrMCfdZMFE/Xl/NrpRh3sQg2Zw8Cps5VLKOgAh\nRG6iF0gpO4GZvR1ICHEX8NeAD1gLvIJyZ5rAMWCRlLJTCHE1qjt3CLhfSvmAx7EOCKlOHIlW/1dc\n0eXqn3bfffnk5tpMnmyxa1crgYDB8uUqErK5Ge69t40jR0wCAYN333WXXNq2rY1bby2grs5k/36T\nJUvaWbWqgGuv7YiIWnGxzWc+MzqyLzdpks3y5UH27zeZONFi9GglAE884aeuTkVimiYUFqrqIwBr\n1wZcNSO3bWtj714fFRUqanPjRrdLdfp0ixMn3J0JehObnpqnTp/es0il08oazEk30UJo4sT+vT8b\n3ajDPYhBMzh4FbbjQojvo4oij0dV/S8GNgJLpZStXg4ihPgkcJ6Ucr4QYjzwGvAb4D4pZVX4HF8X\nQmwHbgcuALqAV4QQT0opT6VycZkkHSHqAHl5FtOm2bS0GKxYEcQw4K678lm2rIMTJwwWL+6I2187\nfjy+5FJ1tclNNwVZubKQadMsxo2zWb68nSlTLBobDTo6oLHRCFtB6v+bbw7wrW91J0uvWxcgJwcm\nTLA5dszA7/exeXMeP/hBIKkFpRK5CyNux+ZmwyVKy5YVsnBhZ9x7IH4x0L1Y8EUsO8AlLvPnF9HU\n1OW6t9GLjFghHKqurUQC3d/3D3QwzVCxGjXDD6/C9iPgI8BSYEf4MROYANwLfMPjcV6kuyP3KWAU\ncAlwbfixp4FbgLeBPY5gCiFeAj4O/NLjeTJOqhNH7Op/zpwQe/b4qK1VFpjjZnzwwTa+8IUurrmm\niAceUC5Jd5i9wYwZFm1t7rD8igqL1lb44Q8DjB9v86Uvufe5brihgO3b/TzyiEojyM21sW23SNk2\njBtnuxK3N2wIYBjKUisrsxk92nL1jysstCPvz8+3OessizlzoKKii9//3mThwk4qKty1LINBFRwS\nuxhItlhw3HBOvUQhbNcEGf2+8vJQpLZmf62s2ONGB7FkepLurxs0G4JphorVqBl+eBW2BcBfSSnf\nE0LYAFLKU0KIrwH7vJ4snO/m7M/9C0qoPht2YwI0AJOBiUBj1Fsbw49nDT1NHMlWqtEul337fHE5\nYLfdVsjRo91WUVOTESml5LwuFDK4+eZCNmwIsH27qigCsHFjHitXtnP4sEEwCBs2BFi/XrkmbRsq\nK4McPapy3UIhFVE5ZYr72GVldtwe2bhxNosWdY/z4Yf9LhfounUBoHtfrqtLRVtWV5sR92N5eYht\n29o4edLA71e1MBNZbrGLBed5MGImyJBrgox+X22tj4YGk89/vv+urejjxgaxZHqSTuwG7e/7+yfE\nqS7mdPCNZrDwKmw5qP2vWNqBMameVAjxD8DXgb8BDkY9leybl/QbWVqa8unTwiWXWLzwQpCaGoPp\n023mzy8iJ0eJzIsvBrj88u7J/4UXglx8caHr/XV1wYQ5YOeeqyaw3FybX/86h+uua+exx/zU1JiE\nQkakbczevTlMnqy6AYCyqP7pn7on3srKINdf385PfpKHECFX1ZBHHvGzcmUhP/95iyv8fvx4C5/P\ndIldU5Nb6GKLKts2rF/fRihkhJOvO7jssgLX9dXW+jh1yuDDH4ZPfar7vjiWm3N/Zs4MJLTsfvSj\ngOucb75pkptrcNFFuezZ00lHh+0Krpk50/D0d9HZafHyy+1Rn2F+5DMEXOOJdcHW1eVw2WUFffvj\n8UiiPbVU/t5T2ZPzQuzn09t97un1vd37RAzWd32wKS0d06f7NZLxKmx7gVuBNc4DQojRwAbgT6mc\nUAjxWVR05WellC1CiBYhRL6Ush2YAhwBjuK20KYALyc6nurRNDjMnk0kQi963+fAAV+4AagSjWPH\nQvzudy2RZpyWZXPGGW4BmTu3iyee6KKzEyZNsnj8cT+hEHzxi6NZsaI9rknn7NnKvZds4m1uNhg/\n3mLDhgB79/piJmV17oYGk1mzQpFAEcOAEydg2zaVdjBrlsXx426XZ3RVk9xcO2yhwcqVhWHLtZOn\nn+6itdV0ic0HPmAxY4ayIqqrTYLBbsvtwAGb2bNbEEJZY7HPl5a6zxkIGHz60wVUVfldVtSOHX7G\njwchLBob3ftwidi3L9YSdFthzngS7d1Nndo14H97paVjBvXvPfp+lJdbvd7nnl7f272PZbCvfbBw\nrjvV+zUc6M9CxquwLQeeFULcCOQLId5EJWUfA/7B68nCASd3AZdJKU+HH34B5ep8JPz/r1H7cFvC\nr7eA+agIyazGcUGWlFgsXRrf1bqrKxQpNLxsWaErDH/cOIs//jGXUAh8Pjh8WEUqTppkU1xss3lz\nnqs1TUuLwenTJo895uf99+M7bJeUKNHZuzcn7rnycovKyiB33lnIj3/sp63N4P/+LyeyZ3bNNYU8\n/rifri6YNcti504/r76qErgfeCCXxx9XLq5p0yzKyixaWgzuvVeF/7e2Gi6x2b7dz4QJuNyx4C6Q\n7Lhxkz1fWmonFMSaGjNGzE0++ckQsQZ+Mtdwb66yaPexZdkjPiw9nUW9tZsyNfT9Sg2vtSLfEELM\nBK5AJWAHUC7E/5ZSpnJ3vwycATwuhFCbMXANsFUIcS1wCNgmpQwJIVYCz6GE7btSyqxfrjmb5ZMm\n2dx0k3LFRZfDampSk2x9vRkO4VeC1NxsYFnqjzY/3+bAAV/E0nG6Vi9Z0s6oUSp8/sgR1Ww0WjQn\nT+7i4Yf9vPeeSVmZTVGRzTe/qVIFokXxIx8J8d57ShyWLm3Hitm6mTjRorPT4ORJNYFffrmyGNes\nUW63tWsDfOlL6hqXLm3nvfdMJkyw2bEjj9df93HXXW63YX29yaWXusWmt/2f2OeVpQuO4E2apFyP\nkyd7C5BIFsSQSoBFrMjpaL/+oROzU0Pfr9TwWnlkB/Ao8HMpZe8+niRIKe8H7k/w1N8keO2TqKTv\nIYOzqqqrMyJ/gLHlsJ591s/48UqcAAwDzjjDornZcFl4TjDJqFE2K1cGGDdOieLx4+p9sW7Ht9/2\ncc45FitXKsG75pog99wT4N13Te65J8D774Np+igosJgyxeTWW4N0dkJHh/u8Dz3kDweSqMLClZVB\nioriXZ5LlgTj3veVr4xm2rTYvLOem6sCWBYxtSHthCv9igq1r9nQEGLhwu76l9ENWRNZEclWu30N\nsNDRfn0jekEwY0ZoxFvAqaAT2VMjlVqRD6MKHv8c5Tb8rZRSf5ujiF5Vbd6cF2npEj2pOt2jo0Xh\n4YdVeHqiYJK2NoOpU21WrSrguus6uPXWwri6kiUlagXX0GCwc6efw4eVmzA65H/nTj9tbTYtLSb7\n9im34ubNeSxf7u6QfeKESVWVn2XLVPHj3FzVk80RkKlT1TU6uXDO+44dMyOCWFWlgl2mT1dltlRm\nSHK8CoVpGlx8cSFbt7ZFFhArVxby8MMqhcGpbBJrQSVb7fY1OVi7hfpGos85HdGrIwGdyJ4aXl2R\nV4crjnwG+EeU9WYJIZ4AHpNS/jGDYxwyJFpVtbe7azWWlVlxInb8uHLnRb9uzhxVMPi++/JZtKid\nu+8OcPPNKgLyvvvyeeIJJWClpRZjxij35qFDPk6dspk5s4t3341v6NnRYbB8uTvFIPa8s2er9jJO\n8ePOTgPThAsuUBaOZdlUVflpa3Nf1znnWDz/fCszZoBp2syb53wBe4/cihcKI/K4F6EqLrZ7FMZ0\nr3a1W6hv6AWBZqBIpW1NJ/Ar4Ffh/bBPAt8BlqDKY414nFWV0xH6mWdymDhRRSbW15uUlNhMmmSR\nn+8WhVBINQGtrAwyapTNGWfYbNyYxxe+0MV11wURwqKuzmTp0vbI3puKSISjR03OOsviuuu6k6of\nesjPOeeoydfZCysuthk71uK224IUFcGmTfm0tEBhoRVOaFZ1I3NybCzLXZty2rRucamuNlmwQLkB\nt21ro7paFVq++eZCtmwJhPfCeiZ2jypWKMrKUhOq2tr4pqnJgkAU/Vvt9kUoh/u+nJfr0wsCzUCR\nUtsaIUQh8LfAF4C/QwWR/CgD4xrSvPWWah9z+rTqQF1R0UV+vpqw6+tNVqzojoi88MJQJDAjL0+J\nleNuvOaaIiorg64ctO3b/fh8YBg206ZZdHVBc7MRyUcrLlbluVpblfuxo4NI37brrnNHac6ZE+Ki\ni1RghmVZ7NljsmDB6Mhrtm5t4+hRk+XLlWjNnWu79hGrq32RsQORLty9TXCxLqnnn291CUVstZXe\nhcpbB4N00RehHO77cl6uT+8TaQYKr8EjX0OJ2aeBJlTbmn/QLsjEnDjhDsjYurWNpiaTpUsLWLGi\nndpaH7fdVsjUqRYf/GAbixZ1MHGixa23FrJ8eTtLl7Zz5IgSq9iyVydOmJx7boi33sqho8Pg9tsL\n2LnTz/XXu5uNAvzgBwVcdVVHJMAk+jijRtlceGEIM2xiVVebcY1Djx9XP5eWqojOXbt8rnyukpL4\nNAIvE1ysS+rgQR+f/3yoz0KVbMLMJitpsN1wmb4XXq5P7xNpBgqvFtudQBUqqfqlDI5nWBC7h3b6\ntEFenrKS8vK6xWDp0nb++Z/de15dXcRV7c/NtTn//BC33BLk+HGT5maDc88N0d6uWtvU1Jgui+39\n99W5ly1TKQLOPlS0WLS1Gbz5pqrKX11tUl1tJizfdfvtBWzb1uYqj1VVpdIKJk60ePBB1dZm5kw1\nWT7zTE6vE1xvLqlEQmVZSnzr6oJMnWqQk2Nz8KCvxyjKbLKSBtsNl+l7MdjXp9FE06uwhfPNfiOl\nXDYA4xkWCBFbnQPuuaeAb30ryJQpoUiNx1hrLD/f5tSp2GhDg23b2sjLs/nKV9wls0DthcU+t3On\nn85O+OpXR7F6dZCtW1W7G6eiyMyZFqtWFfKd7wQAd6HfBx9UhZcnT1b91yorlcvUGVNtrY+6OpMF\nC5TLsanJYO7cbgvAywTXm0sq0co+tvJCZWUwUu0k2SSdzIrozXrJhHUz2G64TFuMg319Gk00vQqb\nlNIWQnxUCHGulPKdgRjUUCN2IjzvvO4cndGjLVasKGLJknba2gy+/GW1hxVbPNj5//zz3ZXwzzpL\nCUNsjcbm5m6rJCfHLZCvv+6jq0sJpGHAyZMGd9zhLlxcX29QVma7JrzaWh+NjQbTp8enCsRGdiZz\nK3mZ4PrikoqdmJ3r72mSjhXZkhKLffsM4osqe+sy0B8G2w2XaYtqsK9Po4nGqyvyp8DPhRDPo6qD\nuJK0pZT/ke6BDSWSTYRz54b4wx9Ud+zoiRhUyP6PfuTnoYf8vPGGKnt13335rFoViCRFh0Jw/LgZ\n7podipmk1URr21BUZMe1knHC8Tdtyufee9tcxY6nTLHCv5OgkafFq6+699reeMMXjoD0UVKiSlwl\nIpP7OPEipcbQ0yTtiKzTyHX58iLq6w3uvTfQo/Uy2PthmUBbVJqRhFdhc/qlfTHBczYwooWtp4nw\nox8N8fjjfmzbJhg0WbUqEEmOPnpUCcX69fmRCXvcOJtx40I0Npq0txuRqiXz5nWyc2f33lZ+vk1B\ngc1bb+XQFV5mPP98Dlde2UlXl4qw3LAhQCikxrdyZXd3gVWrgqxfnx8J89+2rY0jRwymTLFpbYWi\nIrclmZ8PTU3KknNKXCWaFDO5j+NMzHV1OUyd2kVOjs2WLW09TtKOFVFbC0uWdF9/b81Ih/p+kZe2\nScNZ1LIpaEgzOHhN0D4n0wMZyvQ0EebkmHzsYxavvGKyfHlBuEak6peWm2tTW+vjxz9uo6REVdsf\nM/03Jm4AACAASURBVEZVr7csmDJFFUGuqzPYvTuX9vZ2vvnN7hqRP/95Kx0dRsRSW7kyGKkP2dys\n6kIWFNi8/bbPNb6/+qsunn22i/p6g/x8XI1Fd+70s3lznitHbfPmvEi4vyLxJJFJS8eZmC+7rCBS\n5X3OHG+TdOzn4xRV9lqrcqhZN9kUNDMYjPTr13gP95/T0/NSyr+kZzhDD8tS1su99wYoK7MoLbXj\nLJrqapNXXvGxeHGHq27kQw/52b49j5Uru3PV1q0LxHUFWLmykM99rp38fFizJsCUKTb33JNPa6sR\nE0HpjztHZWWQD36wiwceaKOpyeDMM62I2E2danH0qOFqsdPZCf/5n20cOmTyV3+lWtr8+McB5swJ\nsW+fL+EqOLqrQTZaOrFCJYTFm28mrykw1K2b4ehKTURfuzZohj9eXZHVKJdj9Dc8egk0YiuPJFod\nxlbfUInLIV5/3b131dhocsstQVdgyOnT7qjI/Hybxx5rpaCASCPR8vIQGzYE4gJKTp40E/ZlO3DA\nRzCo3JqVlUGX8O3c6Y9rsVNV5eeGG4pc1ySl6Uo6z8mxmTPHfQ+cosSjR9tMnGiFq5jYnt1AmXIh\nJepePpxX9EPdleqVdHRt0AxPvApbrCvSB8wArgPuTeuIsgSvk6yX1WF5uUVbmx2XJ1ZaapGTA5Mn\nW5SXh1i8uIO8PHc36PZ2g0WLRrFhQ3fAw+LFHVx1laomEn28adMszjrLvT82d24XjY0m06YpMfT7\n3cJ56JDJ+PGWKw+uocGINDetqsqlqQlCIbd1uGOHnzlzbNc9cIoSr1oVYOnS0SmLxkC5kIb7in6o\nu1K9ku6uDZrhg9c9tkMJHq4RQrwG/AaYm9ZRZQFeJ1kvq8M5c0K8+KKPQ4dULtmBA8qCa2gwCAYN\nZs/uYsOGgKt01rZtfg4f9iXsIu1YZZs25bN6dZD8fFWEOCfH4v33VfPRQ4dMzjnH4uabu6v0V1YG\nXW1lystDlJdbvPeeiWHA9u151NcbPPqonzVrCiIJ4gsXjmLtWnckYWOjCYQS3oPiYiKvSyYaiRYO\ntbXxhZszITjZsqIfKAt1uE7q6e7aoOkb2Risk1KtyASEUI1Hhx2JVoOJ6iCed567Tct556k2Lc6H\nXV9vMGqUwV/+4qOoCFatKmDNmu49tfLyEHffbcW5FevrTTZvzmPJknaam2HsWBVd+e67SrDmzetk\n9+5c7rijgB/+MEB7O3zpS2OorAzyr/+qLKtVqwKuKv3NzcqNuHOnn7o6VTw5Ol/N6QFXV9fd8+34\n8e58uOhJRIhuMYheIZeVWRw6ZLJqVZCSEpsZMxILU6KFg1fB6e8XKVtW9JmwUPtyb7JxYvJCtnyO\nPTEY93agz5mNwTpeg0fuSvBwEXAp8FpaR5QlJJpkE32AAAsWdIvUxo0Bjh83mDDB5uabC+OCOVav\nDnL8eHfAxpQpFl/5SrxbccoUizVrgpGIxcceC7FoUfe5H33Uz5//HKKkxObMM62IEEfXhCwudotR\nSYkqpfX22z7OPtti7163heT0gHPIzbU591x1H+65R+3PjR2rAmTefNOkrU31W/P5zMgKed8+g+XL\nC2PukbeqIFdc0eVpourvFylbVvSZcIn25d5k48TkhWz5HHsi0/c2kYgN9OeZja59rxbbhQkeCwIv\nAOvTN5zsIdFqMFEdROdnULUZHZFzRMyycO1f2bbN5MlWJGBjxYp2l1uxoECJ6PHjBocPdwvP0aPu\nP5533zUjlfU3bmzDMFR5rcLC7pqQmzfn8dBDqonp+PE2TU0G69cXsGhRB/X1RpzwzZ4dYudOP4WF\nNlu2+CkvtykosNixw8+RIyqvbtw42+UyffxxPx/7WPeK0OsfeaKFQ6KJKvaLe8klVlZ+kfpCJlyi\nfbk3w+V+ZiOZvreJRGygP89sce1H43WP7VOZHki2kWiSdT5Ap8dZezuRjtLRf0gAkyapclhNTQaB\ngBHZv3riCT9jxoQ4ckT98TnFidU5wecjUkMyunr+2WdbMRZdd+WNQMDg1lsLI0EdO3f6eeMNHzNn\nKhGYNcvtciwpsSP7b077nIoK1V1g0aIO1q/Pj1ijn/rUGDo7VRrC7bcXsGaNe6/tlVd8jBlDZEUY\n+0d+xhkWzz1nMmmSHbbACAuVQVWVn/Z2yM8Hp7lorNsk9ov7wgvBrPwi9YVMuNL6cm/i++FZ7Nrl\nG1JuyWwl03+riURsoL8f2egS9lIE+XygyQkgEULMBFYCxcAvpJQ7MjvE7MH5AJuaiKt239xsRCrp\nd3aqyv1f/3p3yLyzf3X4sMn06ap01apVAcaOtXjyyVYOHvQxcaLKT3v9dR9r1yrra9s2P9XVOeTn\n25EKIWefbTF2rHr/7NmqoDF0d+MGmD7d4sQJg//4jwK++91AZF+trMxi1Cib9naDa69VUZglJUoY\nlRWnhLupCU6dMnjsMfUHO2WKxVNPtRAMmnGBItXVJqD+oCsqVLrAwYMmpaU2772nij1/7WtFEbGM\nFqqqKr/Lyo11m8R+cWtqDC6/PPu+SH0hE660vkwysXuky5Z1BxwNFbdktpLpST+RiA200GSjS7hH\nYRNC/B3wC+Bq4JAQogj4LXAa+BNwnxDCklI+mvGRZgHOB7hrV7eLsLbWR0OD+gN66y0zsldmGO58\nMmf/atw4mzffzEmYhJ2ba/Pgg21cffUoDAPq600mTFAltyZNUoEko0bZHDlicvSoyZo1haxdqwoa\ng7Le2tqUSJWXh1izJshNNwUZO9bmrrvyWbiwk64uFeJfXm7xk5/kEQoZLFnSzooVQQIBg/vuy2fp\n0nYWLlT7ftF93p54ws/HPqaCWF55xRepSrJ4cQeXXz4qMgm2t6tmqc77tm5tS+i6VULVs9sk9os7\nfbqdlV+kbKEv9yb6Pbt2+VwBR9otmRrxe16JWyqli0Qipr8fvVtsq4BbpJRPhH+/EhgNnCelPC2E\n+AVwGzAihM0h0Sqpttbk2DG177VqVTCu/9ns2SHuvDNIU5MRl4QdXSD56NHuSv9r1hRQXh7iscf8\n7N2rJpsNG/K59toOzjpLjWHTpvxIn7ezz7ZYt66AtWsDTJliuUplPfSQH9s2XI899VQrfr+y8s48\n06Kw0OY737FobzfiAlE6Ow0OHzYZPRrmzQsxZoyy1BYv7oikJDiTYGw/usZGt0vELVSp9WabP7+I\npiZXDW5NGhkubt7BYqADN7SIJaY3YfsQ8Jmo3z8LPCulPB3+/b+BhzMxsGwmump8WZmqsFFebnHy\npOOes/nFL3LYtk31Nisvt2huVu9dv76ApUvb46IVoTsK8YEH2iLuxdpalf+2Zk13Ed+WFoNTpwy2\nbm1j/34lePfcU8BNNwW58spOV1AKOBX6cwD3XmB7O9TUqGoifr/B3LldXHFFiOpqM3Id0eMcN852\nWWZgub7EziQY249u2jR1v5w9tmihim7x46U3W05OTFmXfjJUQ90zRTbulwwldCBOduAleCQY9fMn\ngB9E/d4JpHemGQI4E99NN3W7255/vhUhuti6tY2ODrj11nZXwMa6dQEsCxYtaqeioouHH/Zz/Liq\n1O/zwfr1bUyZYuP3Q2urinDcv99k1ChcSdW5uTbnnReipQVGjXJ3Bjj7bItjx9xBKd17Yd3i6TzW\n0eGuJvLoo3727++e3A4fNiLNSUeNUhX+o7+sySbBZO4RZ4J0rzDNfq04+ytMQzXUPVN4sQD0YiA5\n2uLNDnoTtjqgAnhDCPERYAoqxN9BAI0ZGltWE70yO//8EO+/r/auVPRi/MoNYOxYi5kzbd55RxUg\nHjfOIhQy+OpXu92Dd94ZxLZh2TJ3Pce1a5Uw5uTAmDFqIqmrMyMtcY4c8TF2LBQVhVwuSicnbsOG\nAhobVTDIkSNKUJ3ITGeMf/6zj/PPDzFnjhKfigqLPXtMxo5VX9T16ws8VXgYSPdIImFycnkci9op\nTN3XkmgaN3oxkBxt8WYHvQnbY8DDQohHgWuAl6SUBwCEEGNROWy/zuwQs5Po0P//396Zx0lVX4n+\nW1UN9AItgs2OEhRPYAgal5jRvKAxk2XiJE5QR4NIdJJRo8Fdlo86RoyBqGAUM07SLs2imKgfE1+e\ncZmXyTKJW94zQF5yRKSxB2ihAVm6uunuqnp//O6trlvd1d30XrfO95+uutvv9+t763fuOb+zLFrU\nyFtvOWeK99+PcuutJe0GXCcSES65JFiZ+sYbi9Mu96eemuD660uZN6+pzWQ7aVKS0tIk8XiUeDzC\nZZe1XifT+WTFCucBuXWr+2Ft2RIlmYxw882NVFfH2L49yk03OaeT7Gwi5eWwa1eUl19OeTkjXeza\nypXFfOtbh5k3r4mPf7xlUP1Y2xNMEPS8XLq0kZaWRLdTohlB7GUgN7bmNTjoTLB9FxgFXAb8Gbgu\nY989wInAP/dN1wY3ma7/mQHLfk7FzIDrCROSDB2a4s03g5k+tm2LUl0dY/Fit3720EPxtMt9tsnw\n0kvLqKqKM39+aZv1s0znk5YW2Lw5xtChzqU/kXDJiadNayGVcmt8zc2ufM3ixSVt6q5dfXUTySRs\n3x5l//4IM2cmiMVSLF5c4r2dt/TI7NTbZqxcjjzZ/59ck2+hvmH35D7Yy4DRH7z33oGPTJ1avrU7\n53Yo2FQ1AVyfY/d3getUtbk7Dec7/pvZyy9HA5lFfG9Fd4wLtt66Nca4cW5NKrOC9tixQQEWicCT\nT9ZTVxdh9Wq3BjdmTIpt21wbO3dG2l0/y3Q+OfbYZEDQ3nVXI7fdVsyaNfVEvHlryBBXffuhh+Ic\nPOjSe40aleLhh+PceWcJCxYEC5quX19PQ4NrN1cgdWf4E6lqjEOHIunqBT01Y7UvmNqmEss1+Rbq\nG3ZPzImF+jJg9C8bNsTWTp3KWd05t9tJkFV1e3fOE5FZwHPAClX9oYhMAtbgnFB2AvNUtVlE5uI0\nxATwY1V9rLt97Qv8ifrwYTj22CSbN7v1teHDXQmYiROTgQDt9evrufji1u9PPVXP8uXFrF5dz8aN\nRZSXp1i+vDid+WPp0kamTUtw8cWuztmNNzYyYULQxb+01NU9A5dWq6HB5YHMjp9zYQRRbrutmEcf\njTNiRIobbijl7rsbufzy1j499licOXOa2bUr6OZfWxtFJJlzIuzK23/2ROoHrPfUjNWeYMr2Wm2v\n+Gtvk28OFT0xJxbqy4DRv1RXRyu6e25Ps/sfEV6A9/3Ayxmb7wIeUtXnROS7wBUisga4HTgNaAHe\nFJHnVPXD/uxve/gT2N69EW69tYR77mlMm/KWLHHrZa4mWWNg4ti6NTiRbNgQ4+23Y5SVOY1i/36X\nrWTs2ARVVQlqa51Z0SVLPsytt5akC3n68V+Q4qKLhtPc7DL5+wHb2d6QvgbpC7qmJhdGsHdvUIDt\n2xdhzJgkI0cGNUKRVvPe5MlJrrnmcCDbSFfe/jMnUj/d2JIlrur4kRQjzbwHuYRI24kX+nryzTeH\niv4wJ+absDcGF1OmJLvtmNivgg0XOvAlXFC3z9nAld7nF4CbgXeAN1T1EICI/A44C/hFv/W0HVpa\nkrz+eoyLLirjO99pDGTf97WQurqgQPH3+Vny/e8zZiRYu9aZ+DJd7rM1u6VLG4nHneBpLeTZyP79\nEU4+uSVtBvWLmGYGbI8bl2L/fqiqiqdzSfrlZoYMSXHMMcE+HnNMkuHDYcGC1hySn/xkImDeu+aa\nw4FqBV1Nupo5kWanG3v22Xo+8YmuT3qDUYjkm0NFf5gTB+N9MvKH005rmdvdc/tVsKlqEmgSkczN\nZRnrdLuA8cBYgmEEu73tA8pbb8XSDiDHH5/gT3/KNvu5HI1LlzYyenSSp5+u5/33oxx3XJJx41zm\n/Opq9z0eh7/8JcbUqUnuv7+Bo492lasTCafR1NQ4baq0NMWJJwaFol/nrLHRz2qS5Kij3PW3bWv1\nZKypiXqaY5LFixs58cTWNagXX6ynqSmZzj85caJLfltdHQs4tFRWxolGU+mJcNOmriVdzX5bzwzE\nPnw4mFbrtddcrbquTnqDUYi0lyB7w4bIoNVS+sOcOBjvk5E/TJp0VHV3z+1vja0zcv26BsXMsHNn\nJO0A8sEH0TZlX2bOTHLggBNGdXVR9uxpzaWYTAa9J1evrqe8HEaMSLFnTzTgrJHpvj95cpJduyIs\nW9YAwJgxKcrK3Nv1/PnBAPBZs5Icf3wykFTYz+R/+umtQdLJpBMg8Xg0oHG6Yp+pNkIKWidCaJsJ\n3s/Uf+BAJJ3FP9fbul+zLTvM4EgmvcHolddeguywaSlHalocjPfJKAwGg2A7KCLDVPUwLgB8O7CD\noIY2EfhDeydXVIzo+x56jB17iAsvdJPW+vWHWLQoWPZlyZJirr66iVQKIhE4cIB0odHly4PlXjZu\nLOLJJ4ewbFkD0agTZg8/PIyamihlZSlWrIhz3HFJHntsCF//ejPXXhs0Tx44ENR6ioth9uxSnnnm\nsHdNV1Jn715X/Xr06OHpdFS/+U0DX/xi27RbNTVFXHrpUF59tZH33oswdWqKM88sDaSxmj07md5/\nzDEJvv3t1kzwr77ayKc/7TS9F19sbHPtc88tTl/j2WfjvPZaa5hBVVVTl+/l7NmlHfZxoBg7Fp54\nIve4e4P+fN6z8Z8b/znMvN/tkfms9MZ9GsixDySFOu6eMBgE26vAHOBJ7+8vgTeAShEpB5LAmQRj\n6NLs3n2wn7oJhw5F0mtapaUpFiw4zIcfRjjllASbN8e46qomVq0axsKFjQwblmLKlBQbNjhzZXY9\ntVNPbeGUU1oCAdt33dXIHXcUM3JkinnzytLtzpnTzJo19ezY4eqarVw5jDlzmgPXmzQpyZ49h6io\niHLZZS4zf+a1MzWHzZtdn8aNS7J8eQP790c8za6FffuamT4dpk93bbeXcNjfn50JfvPmFNOnu/sx\neXJQK5s8uSVwrz7xiRSlpSmqq6NUVjYgkmT37s6TG1dUjGDfvvpO+zhQdDbunlBRMaJfn/ds/OcG\n2t7vXPTWfRrosQ8UhTpu6JlA72+vyDOASqACaBGRq3CJlatE5EpgG1ClqgkRWYTznkwCd6rqgN/d\n8nIyHCeGUVUVp7ExQiwGBw+6/bGYq5Dd3AyLFpWwcqXzUty3L8KaNfXs3h2loiJJSwtt1uiKi1M8\n80w90WgqHe/2/PNFjBgBf/xjzPO8dNn9H3lkaKB8zIIFJTz+eJyiIpfRZMuW3OsbvomopYWA44qr\nl9Z1s1lHpqbOnBPC6jIe5hgvMy2Gn7B4sva388jrwMfa2fW5do59DhfvNuD4Nzu7dlhdXYSpUxOB\nZMdVVXE++MA5YzzySD0HDkRZtqyB0aOTDB0K27ZF2LChiOnTE228EpuanPPIBRc4F/4pUxKsXNkQ\nuL6fzeTxxw+zdWskkPV/1y6XoWTcOLfm1pnQac8RJNc6V3sP/MyZqZyTeFgFV2eEedxhFtqGIyye\nrIPBFDno8W92trBoaHBCKlM4bNoU5WMfS3DwYIRUKsrcuUGHkUwN6f77GwJrdAsXlnDDDa1rNFdd\n1dQmDVdTE5x4YpK3344yZUqKBx+sJx6P8sgjQ9N10GpqXLHRdeucYM0ldLIdQTKFX7Ygg0gOZ5Bw\nTuJGW8IstA1HWDxZTbB1Af9mFxWRXmObPj3BkiVOEGUKh5NOSnDzzaXU1jpPRhdg7c6pqws+NJEI\nDB/ugpMXLiyhtjbCxIlu3WvVqmEcOEAbz8tZs4Ia4hNPxLnppmKqqlxGEf/Y2toIo0bB2WfnnoQ6\negPPfnN74IGGUDzwhmHkJizmZhNsXcC/2bW1rkI2wLJlDdTWBoXdUUelKCqCefOaKC9PMX68i2ny\ntbRsja+lBV55pYgrrmjillsaaWiIpIXi6tX1lJUFg6VnzkyyY0cwW4j/fd++COec03JEpqKO3sCz\n39zGjAnHA28cGWFZczG6RljMzSbYukBmjNKUKQmuuqqJeJx0APaiRa3rXCtWxAHn7j9iRJKSklZB\ntGrVMNaurWfXrigtLRCLuVpuTU0ufdbCha3X2bs3yjnntFBZ2cCmTVEiEafVZQvHiRNT6YwisViw\naGcy6YKEO5qUck1c2W9uFRW519OM8BKWNReja4TF3GyC7QhwGT2Czhzr19cHBEBzc4R77ilOr6lN\nnhzMSFFXF2XcuCQ33lhKTY2L6bn33jjTpgUFiUgysBbmTy4PPjiUp56qp6bGCZiWFtLFNbMfwq5M\nSrmOyX5zc4U6yfsH3jgywrLmYhQWJti6QObkf8cdwbWmPXsiVFXF2bw5ykc/6tbX/H0bNxYxfnz7\n2f4zs4skEhF27nTXqauLMGNGUCPKzlZ/zDEpvvKVYvbtq8/oZfeqQ+c6JtebW2+ZpszElR+EZc3F\nKCxMsHWAP/lu2uTqoT377BBOOikR+KEPHw7z55fy1FP1bNkSo7bWTc5+IuSdO926XHa2/7KyFPfd\nF2fChBQPPjiU889vYcGC4gytquNs9e1lcMgWFieckOh0Uups4sq+ZlERvWKaMhNXfhCWNRejsDDB\n1gHZk29VVZx33ol6zhyu8Oa2bU7j2bo1yqpVw9L7fK/Ja6893G62/3g8ks64//TT9cTjkZwmxe72\n98UX6zudlDqbuLKvuW5dfa+YpszElR+EZc3FKCxMsHVA9uT7wQcRIpFIoGyLXx/thBOS1NZGWLy4\nJC0E5807zAknJFi+vIGRI50Ae/vtGNOmJVmypCR93fffj/K1r7VOHN0107UnLL785USHk1JnE1f2\nNXfvjvaKacpMXIZh9BUm2Doge/I9/vhkwP3+1FMTvPNOjLvvbmTECF/ziTBmTIqdOyOUl8P11zsn\nkcrKOOXlKb73vWKWLm0MmCxd0dBWumum6wthkX1Nkd4xTZmJyzCMvsIEWwcUFaUCMWqjRiWprGyg\nujrK6acnaW5228aMSVFdHWPKlBTnneccLzZsIJAJ3Z+8161zk/lTT7naaa6kTAJoXTPrrpmuL4RF\ne9d0Wl7PTFNm4jIMo68wwdYB774bC8SoVVamskx7TuPqiru8LxBGjYK5c0sCx8diQUeQ7mpefSEs\nTAAZhpFvmGDrgK4ImCN1l++KVmVmOsMwjO5jgq0DuiJgjlS76ooGZFqSYRhG9zHB1gFdETCmXRmG\nYQwuTLD1ENOuDMMwBhdt01cYhmEYRh5jgs0wDMMIFSbYDMMwjFBhgs0wDMMIFSbYDMMwjFBhgs0w\nDMMIFebubxiGYQTI90LAJtiMIyLfH3jDMDon3wsBm2Azjoh8f+ANw+icfC8EbGtsxhHR3gNvGEa4\n8HPgAnlZCNg0NuOIsMrXhhF+8j0Hrgk244jI9wfeMIzOyfccuINasInICuCTQBK4XlXfGuAuFTz5\n/sAbhhF+Bu0CiYh8GjhBVc8EvgE8OMBdMgzDMPKAQSvYgHOB5wFU9a/ASBEZPrBdMgzDMAY7g1mw\njQN2Z3yv87YZhmEYRk4Gs2DLxhZzDMMwjE4ZzM4jOwhqaBOAnVnHRCoqRvRfjwYZhTr2Qh032NgL\nkUIdd08YzBrby8AFACJyCrBdVesHtkuGYRjGYCeSSg3edEgicg8wG0gA16jqxgHukmEYhjHIGdSC\nzTAMwzCOlMG8xtYhhRC8LSKzgOeAFar6QxGZBKzBmZB3AvNUtVlE5gLX4TTbH6vqYwPW6V5ARL4P\nfAqIAcuANymMcZcATwBjgWHA3cCfKICxA4hIMbAJuAv43xTAuEVkNvBT3LgjwAbgXgpg7ADemG4B\nmoE7gI30wtgH8xpbTgoheFtESoH7cWuNPncBD6nqbGALcIV33O3AZ4BzgBtEZGR/97e3EJGzgb/x\n7u0XgQdw414V5nF7/APwpqqeDfwTsILCGTu4Me3xPof+Wc/gP1X1M6p6jqpeR4GMXURG4YTZmcB5\nwPn00tjzUrBRGMHbjcCXgA8ytp0NvOB9fgH4O+AM4A1VPaSqjcDvgLP6sZ+9zW+AC73PHwJluHXW\nn3vbwjpuVPUnqnqf9/VYoIYCGbuICCDAL3Cay2zC/6z7ZIcynU1hjP2zwCuqGlfVD1T1Snpp7Plq\nihwHZJoe/eDtdwemO72PqiaBJvd7T1Omqs3e513AeJzZKjOQfbe3PS/xxh33vv4zbqL7fNjHnYmI\n/BcwEafBvVIgY78PuAa43Pse+mc9gxki8jwwCqexlBbI2KcAZSLyM2Ak8B16aez5qrFlU4jB27nG\nHIr/hYh8BbgCuJbgmEI9bgBVPQv4MrCOAhi7iMwDfq2q7+c4JJTj9tgM3Kmq5wNfBx4lqHCEeewR\nnDD/R9wLzeP00vOer4KtK8HbYeSgiAzzPk8EtuP+F5lvLxO9bXmLiHweWAx8QVUPUjjjPlVEJgOo\n6gac80whjP1LwIUi8gecln47cKgAxo2q7lDVn3qf3wNqgaMLYey4ZZbfq2rSG3uv/dbzVbAVavD2\nq8Ac7/Mc4JfAG8BpIlLurTOeCfx2gPrXY0SkHPg+cJ6q7vc2h37cHv8DuBFARMYCw3Fjv8DbH8qx\nq+rFqnqGqv4tUIkzx4V+3AAi8jUR+Vfv8xhgDE5zCf3YcfP4Z0QkIiKj6cXnPW/j2MIevC0iZ+B+\n5BVAC7AX+DxQhXMF3wZcrqoJEfkqcCsu9OFBVV0/ML3uOSLyTeBfgXdwJocUMB9nogntuCHt7v4o\nMBkoBu4E/ohzfw712H28SX4r8BIFMG5von4SZ5KL4taZ/gSsJuRjh/Tv/Ru43/lSnO9Ej+973go2\nwzAMw2iPfDVFGoZhGEa7mGAzDMMwQoUJNsMwDCNUmGAzDMMwQoUJNsMwDCNUmGAzDMMwQoUJNsPI\nc0Rkvoi818dtrBSRtX107YiI/KeIXN8X1zcKD4tjM/IWEfkRMA8X3BkDhuCqIviB3Z9T1d/14PpH\nA19V1Udz7F8LXAw0eW0ewGVJ+DdV/V/dbbeLfbsc+Jmq7u3Ldry2voALHP+ol+KsL9o4FheY3fAd\nOAAABehJREFU/ClV/XNftGEUDqaxGXmLqv6LqpaoainwVSClqqX+tp4INY/P4bIi5CIF/MRvEzgV\nV2pjnYjc1p0GRaTTBK8iUoSrU1fRnTa6wVLgB30l1AC8BMhP4tJpGUaPyNeyNYbRZUTkIlxS5Wm4\nxKsrVPVhb9/f4gp6zsBV8X0JuBqXXf9xICIicWCGqlZ31I6q/jfwIxHZArwkIj9R1XdE5LfAb1V1\nidfm8bis7h/19tcAq3AJgH8NfNNLBL0UV6PsIPC0qt7kNXUIp52+7aWW2wHcrarjvevPwBXfPQWX\ngugF4NuqekhEzgV+AszFCceJwK+Ai1XVLxeU+b87A/g4Lp2bv+0KYKF37l7gMVW909t3NPAQriDk\nCG8831LVGm//acAPgJNwCW7vUtV13qX/Hfi/IjJeVQshqbnRR5jGZoQab2J+DFd+fgRwKfBdETnH\nO2Qt8IKqHoUTfGOAW1R1LbAMV+CwtDOhlomq/gfwZ1qTubZH9hrAJbi6c98UkTLgGWC116/ZwHwR\nme8d+zfe31mqujTzel5m9FeAN3EZ0U/GCZEHMtoaAVwEnAZ8DPgUcFmOfn4W2OCbPEXkOODHwDdU\ndTiuEOQVIvJ33vF+nr8ZXvt1uPI7eON6wRvb0cBVQKWInAzpigb7cELRMLqNaWxG2Lkc+Lmqvup9\n/4OIrMPVvvoVrsBhI4Cq7sNN1L2BAlM72J9tcnxJVbd6/agXkQlAvfd9i4i8jhNEVRnnt2e2PA8o\nAe7wCjb+t4jcCzxCq1k1BnxfVQ/hysO8CUzP0c+ZQGaC8XLvr983xVX7RkTGAX+P00T3e9sWArUi\n8hGv/0XAA6qaAn4lIhfgKqX7/D+vTcPoNibYjLBzPHC2iJzvffeFwX95fxcDD3jmtZeAdar6f3qh\n3SJc5Ymusi3r+9eABSIyBWdZKcKZRjvjI8B7GVWIwVWWH+6VBvGpzvgcxwnD9hiN0z4BUNWNIvI4\n8LqI/B73P6tS1e24/zU4E6l/SgTnXHMsTtDXeELNv94vstqro//WDo2QYqZII+w0AD/0zIm+Y0mJ\nqn4WQFV/BEwC7sNNzK+JyJW90O7JOO2jPWLtbGvxP3jra6tw5XuO8hxTXsw6Ppc787Ac27PPSXZw\nXEfnoarfwGl4P8etRf7Fq4vY4B07Iev/Xayqv/ba7GzOSRGO6tDGAGKCzQg77wKzMjeIyAQRiXmf\nR6vqXlV9QlXPx62r/UtPGhSRr+PWl37qbWoESjMOOYHcggngdOCvqvqMqrZ4XpCzso7JNflvAaaK\nyJCMbdOBD7sZGrAHp7UB6Zizo1X1XVW9X1U/CbyNc0bZihvXSRnHR0Vkkvf1PWCKNx5//yWeQ4lP\nBbC7G/00jDQm2Iyw8yPgLBG5XESGiMhM4PfAJV7s1HYROc+bgMtxjhmbvXMbgPEicrSIDO2sIREZ\n7hVO/AHOAcUvX78ZONe7zligM42wGpgkIpNFpAJ4GOdUMSGjXwAneg4ZmfxPnHfnnSIyVESm4hxn\nnuis/znYRHDNay7Oc3EGpJ1JJgKbvTXKp4Hvi8gkr3Dq3cB/ZPStAbhdRIaJyKdw9ydTyE8nuKZn\nGEeMCTYj1KjqX3Eeh7cA+4GfAatUda0XO3UZsBwXXL0Ztx60wDv9OZzZ8H2c63x7XCgicS8kYAdu\nbewSVV2VcYx//RrgZWBl1jWytbengV/i1rbeAP6Ac68/S0Qe9wTm8zjvwnuyxnsI+BLO07EWeBVn\nMlyYo/+d8Qowy3PjB+fhuBoXzlCPc+d/GueqD3Atrvr5Jtz/4+M4hxZU9TDwGeALuDCBSuBKVf0j\ngIjMwnlL/qqbfTUMwDKPGIbRCSLyBvBTVb23j9tZBYxT1Y7CJAyjU0xjMwyjM24HrvNMtX2CZxa+\nBLijr9owCgcTbIZhdIiqvoQzN/6wL67vpRGrAr6jqrk8SQ2jy5gp0jAMwwgVprEZhmEYocIEm2EY\nhhEqTLAZhmEYocIEm2EYhhEqTLAZhmEYocIEm2EYhhEq/j9qvbxzROY0JwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd14a3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a single plot\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "# Scatter of x vs y\n",
    "ax.scatter(npas['testelapse'], npas['surveyelapse'])\n",
    "\n",
    "# We have some extreme outliers lets set our axes range to something reasonable (10 minutes)\n",
    "ax.set_xlim([0,10*60])\n",
    "ax.set_ylim([0,10*60])\n",
    "\n",
    "# Add appropriate labels to our graph\n",
    "ax.set_title('Test vs Survey Time', fontsize=16)\n",
    "ax.set_xlabel('Test Duration (sec)', fontsize=14)\n",
    "ax.set_ylabel('Survey Duration (sec)', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Box Plots ####\n",
    "\n",
    "Box plots also called box and whisker plots display the quartiles of a distribution of values in visual form. The NPAS survey contains a ten item personality inventory, lets use box plots to visualize the distribution of these traits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x7f8eccfb3050>,\n",
       " <matplotlib.text.Text at 0x7f8ecd3fc510>,\n",
       " <matplotlib.text.Text at 0x7f8eccf61d90>,\n",
       " <matplotlib.text.Text at 0x7f8eccf6c550>,\n",
       " <matplotlib.text.Text at 0x7f8eccf6ccd0>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AhcBF6eVDgeFmNl+p2p9XOZtFOsmWZjA5BNjGzIamx5sB96Ya0ULAksAiqXmu\nJWs/qcmxDVg2/c/NCRzo7jcSF3mTU7+rVEhdApPNrNPM9jCzVYiAvSCwopltZmbXmNmlZvajepRB\nAagO0pfaYWaXmdlJZva99NLfgW+mZd4nTsiTzeyneZW11mZQk9kLmNfdlwI2BHY2sw3c/RrgfuDc\ntJwC0AykK1NSbXJSush5FLgOOCst9gTwZTP7M9HUe7y77wKslEuh66T8QiUFnyOJ/yWIKb9OMLOL\niSnALgZuNrPOzAvaBMzscOBfxMXx0cSk0TcBXwT2B/4L3AGcUY8+awWgGjCzpcr+ns3MViKmH/oP\n8BhwjJntQfxDLGBmW6TF3wA+BI43syWyLXXtpSvtUgfwCqVOYGBt4Lz099eJ2TCOT48PBrY2s9XT\n1ayCUAUz2xo4sSx1/WjgSDPrAHYjElo2Ah4lLnA+cffV3f0qM9sSGJZT0esi1aqHpL8nAdcAH5vZ\ndsT+OAt4HNjU3dcBZidqh/02IaE8Q7LsuSWBtYBt3H1Pd9/W3Se7+5nuvg+wvbvv6+6jiNrkIrUu\nl5IQ+qDUeWdmhwDzAlcBLwPbAhu5+w/ScqOIyQJPIb7wjYBtgBWJGkEX0TT1brM1lZjZcsA87v5k\nerwi8BsiC6sN2JvYJ/8jDuIFiaa3O4grrOuBk4EF3H3nzD9AEzCz7wI7ERc12xBXpQsCnwKHE/fb\nOogI9IcByxHH2tJEf8gv3P3S7EteG6m2N7ns8RLEVfsv3P3cFJh3Bw4A1nT3D81sDnf/1MzmBf4I\nXOLu1+VR/ryVJxmY2TLABHfvShluhxMtM28RLRWvA38D7iWC9kvEMTUfsJO7v1nLsikAzaJS2mv6\ne0Pi5ODu/hUzOwNYHDiQuPIfBOzr7o+nAai/IJpF5gf2dvd7cvkQfZSuwPcG/uruz6Tnzgdec/fj\nzWzV1EyEmW0C7OHuO6THfwe+QtwbakN31w1yknSVPlvFSfc3wLLAk+5+tJktTuz7Fd19GzN7mQj8\nvyOOu9WJWeePcvf7Mv8QNdBNdtYw4CV3f81iLNmuwBqpyXsJ4PfAGGAEcCXRArEecXwOz7r8jSS1\nRpwNfINoqn3H3fcxsyOA5Yn+wnuAz4E9iSzdPYA1gRvc/axuV9xHCkB9kL7UfYF1iH/2D919o1Qr\neJb4Bxjh7heaWTtwgrsfnN67rLu/mFfZ+yIF0UXdfXR6/HXiAL6aqNnsV5k5Y2a7Aue6+1xmdhjw\nNvA88Ei1w97JAAARa0lEQVRK0BCmu7BZEFjY3V8ws68C5xPNtt9LJ921iKvT3wOfERdBX3D3t1Ni\nxwdpPQVonkG/ZrY28Ky7j0uPtwFOAF4A1iD+554j+rmedPej0v/ikURN6OvAHERLw83u/p/MP0SO\nKmuM6bk9iP2xJ9H1cjdwp7sfml6fvfR/mC4O93P3l8rX1d16+0p9QFXqpv10LeBGIu11B6IpbW0z\n29zdXyDaoce7+4XpLfsA81m6FXkzBh8zW8PMFgE6gW3NbL8UWHYFtgKKRLPQnmn5OdLvfYlq/S1m\n9iRRtb/V3R9Q8AlmtjBEkkF6fDzwIHBpukp14HIiUWPd9LZniKbN1dz9n0R/4zfSekrBpzQAulmC\nz2rEBd2k9Lid+N850t23JJoWdwBWAf5AHIdfSJl/7xJNSYe4+6vufn4/DD6FsoCxaNlL3yMu9j4m\najXzEOejOcxsZWBzM9vezO4nLg7fhikJVdMM8K0l1YBmIl09FsrTqlOm17eJE8KP3P3y9PxIYBd3\nXyY9vhF4HzCiqWm/FJyaTur03ZJIHx9MXEGVrjLnAX5CBJlniMy2L5Tai83sduBnxFXrwu7+UuYf\noIGZ2VnAOu6+anq8N9GPuANxnH0X+DPRVn8OUbP+ubuPTxlvj7r7L8ysLXXKN52Kml8B2JEYTPsZ\nkSW5kU8dcPsbIkCdSySzfBkYDXQQg5tfyfwDNBAz+yIx1GEB4F/ufnDKdlsrLbIYcIq7X5MCfhfR\nH7sm8Bt3vz6rsioAVcnMvgSMJE6u97n7I6m/Y3l3/0ZaZl6i6e1kdz87tUsPABZvtnb4sgSLyg7g\nTYlO7nYiu+pAIrAcCqxGjE05ihgMeRrRBr8KsHHpqlxCWUd5GzCe6OS9wcyuAp539+Gppn0B0fz0\nA+BrRF/Pk8BHRPv9nu4+ujzDq1lqPCWlAJT6U8cT/TgPE9NW/Y84fu5Ly65PXAiVTqiHE7XvE/tb\njbpsv81G1I63BZYCPgFuJ85XuxH752fAG+5eujnoMKL/9ehu+tumXBDUkwJQFcxsK6LqfwXxxe5G\ntDV/QoxE/4W7X5mW3Ra4DPgY+Ka7P5ZHmWstfa5BREB5y90PM7PLiYys4cRBfwTRCXwK0Qy3IdEs\ncqS7T8ij3I3OzAYRQeVg4nbdaxHprm8TJ9/vADcT+/dOIqnlMqJJ7kF3vyHzQtdINye9lYEbiKC6\nI1HjPg7YlAiyy5ct+yDR2vBc5Xr6g+769dIF7/NEMsHm7v5xar7dmLhQXA/4PhGwlycuGk9y94vL\n1lHzfp6eKAB1o+zqf313v8PMTgVedvdz0uuPEM0DRxHNI9u7+5pl75+mE7WZmNnGxO3TXyLGLV1L\nNK8NJa7ANwW2J2o3HxJ9Xae6+3VmthuxP37r7jeWrvBz+BgNqfKq0mIGg1uBS4FXgF8Dx7r7qWa2\nOnAGsEE6kdxGZA3eS6QV31LWaZzpSaOvKprbliGy++4npqk62N33TIk8BxFX9YcAjxBT69xOHGPv\nERmkn+TwEXJl06ZVDwN2IWZYuTX1t+5LBKBX0zKPEcfY6USq9arEBc1R7v5I9p9gKgWgpJuTwxLA\na8SUJpcTB/7fgN8S1dnd3P0/ZjaYGDn8d3c/KvuS10b6HGcTY0cuIE4KE9PPLsBF7n5K2i+HEONQ\n9ibmtRtEjDhfNv19s6dxQdJ9P2J6/gfAWu6+V3q8HRFcliPG9BxIXLEuRVy93gK87+7PpuUzaSap\nlYoTZ6np6EBgCyKgPAxs5e5fS8vsQHSe/47o49mOqFXf5nVKC24WZjYP8f+3E9HvuiJxIXiFmT0O\nXAL8zt0/Si04vwG2S10HU/oK886QVBZckv4Z5rU05QnRvHYZMV5nFJEGeiVwjrtvCHSa2XB3f5vo\nCG3KsTxl9iFm517V3c9295+4+9FEk88dwGpmtoDHPHb/JMYwbQecSIw0vwaY6O6nKPhMlU60xXR8\nLW9mvzez76eX3yOSDABw96uJJszDiD6fF4HbiNrmfe7+kLs/W5aV1DTBB6ae5FKz0F/N7Eceo+43\nJD7nhsA8KfBApPWPAX4MtKWgs01/Cz5WMb2VmRmRAbgBMMzddycSVL5jMT7sGKKmswyAu19LBPBF\n0+NS8Mk9Q7LfBqBuvtQVicyRY9NTE4nZDdqJ+bZuBx5IJwmIPqAPIb5gd7+p/qWuj9QPsSERaErz\na5VOnl1EujlE0xtpuceJfTAf8EMi6+2yDIvdFFLgKaSr0MuJ2SEOtxgL9RDwupkdUPaWK4gR/bMT\nTSn7u/sa7n5H+Tqz+wSzrvw4Sr9Xt5j9fS2i6fGoVAMiNW//EhgLjLCYy21TYnqhu4mEi7qkAjey\nirTqr0CMdif6eozoP4So8cwF7Jr6BZ8DDjWz0n2htnL3v5WvuxH2Zb8MQBVf6oYWs8A+Q1zt72pm\ne7v7h8TBX2pnPgBY18wuNLOniFTGS3L6CLXWTnRKltqDKw/M54k+oXXMbDl3n0hMhXI/8Km7f6gk\ng2Ax51Yh/V0ws4FE6vRexGwYexEDR1cm2uJPJ07EHWkVHxLH4tru/om735bW1RS3q0ifeb6UyTfK\nzOYqtS4QteS9iYG0vyWu1I8v+2xrE82MQ4g75h5AJFr8yt3fyf7T5C/1Ra9qZvcCF5vZ+RZzA55J\n9EOvbmZzesxEciewspmtSezbhcvWU7rJXEPNhddv+oAq2zrT1cSZxNiBBYFLUzriFkRn+2NEtXYP\nIlPkVYtBmEsBn7v7v7P/FPVjZncDd7j7iG76ww4kUqknAh+4+8/yKmcjq+jjWA54xWPm6t2ITMGT\n3X1Uuir9DZFu/Esia3BZoonkAWIgZdPeRC0Fm1Ka+JXAQ+7+DzPbi0isWNDdP0v/k/cBT7n7D1Of\n2MrE7RT6x4mpQmVCicVsGKOIVojLiJaKc4kZIb5NjMO7ICUgDErLPunux0638gbUL2pAZe3wxbI+\nnn2JPo+ViU7QncxsPY9BWCcR1dsz07LvALj7f939wRYMPgUiS2YjM1syXbG22dTZH75B1HicCMw0\n2pVUI0jHV6eZXUdc7V9kZvu5+x+JE62Z2WIet+K4lpi+aP3Uhn8wceL9vsdEkQWz5rtVeSrzR0Rz\n9igikedci4HMlxJ9OqfClIvBnxCtDksSA0r/nfZjvzq+yi6QSy0z86SXOoja8AUeA3GXJxJ9vkSk\nrH8EDDOzwR5Zt8cQ4xVL623omnO/qQEBpCvRDYgv6ATgLHe/38x+TFyZ/Rn4obu/ZzE1ynVEe/VX\nUrtry7K4X8rpxBiUjcs6Krcjmo92dPf3cixiw+nmarWNOLlOIuYl+xrRrHs4MZjyUOAvnmamNrNz\niYvAI8trPJXrbTYWUzBdRKQGX28xD9lWxD17niEuZpbyqTNl3ERMkHmMN+lMDn1h0863ti1xbnqW\nyEp9jpgt/i3iVutvElmo7xNN5d8i+mBPcPd/la2zKTIkWzYAmdkGxLTjD1nMifRLIn36ZHcfk6qr\nEEHmNaK2cwOwHzDG3Z9JV2WfuvtbOXyEzFlMMno7sZ8eIGqBcwBHeJPO2F0PNv0Ayvnc/YNUu36S\nmNHgEYvbKPyWGKj8azP7NTFt0Tnu/qSZLU3MF/i/XD5IHdjU9OrhRN/icUTgGUrME3gq0Ww0h8fc\nbpjZ3O7+UV5lzkNqol2SaKZ9MT3+OjHP31+IaXF2IAZ3b040vZ3o7hel959IXDA/BXzJU2p+s2m5\nAFS6mkipio8RGVsHElehw4hbAtyflv0eMRVF6b49txHznL1KZI30q2k9YEpN6CtE8PnYy0ZJy3T9\nPKsSY6ZeA/5BzFb9W+ADoiO9NEPzg0T/ziCipv0Hd7+ibJ1NcbXaGxbT5fye+NyXuPt+ZrYC8FPi\n+FoDWMJrfH+ZRpea1n5B1FweISZevZJImd4OOMzdf52W/SNR83maCEhzEoPfj02Pd/Qmn/eubeaL\nNJcUfOYkms4+JtKDX0pXn0OAr5rZY6k9dSVgFYtBmD8hUmDvadariVpw97FEKuxdeZelkVQEnvmJ\nlPTliJkgxhEdw28SEztuA1zr7kem5Q8AxnrcofRgd3+ifN2tFnyST4imyMVTnxfu/pTFRKLrESfP\n/hZ8BhMDjZ8HVkgJKl8lugTeI/ZZe9lbTiMucK4iWiZGEv1qbxEzZDT93IpN18k5M6nmcy5xF84d\nga9Y3HvneWLswWbACmnxEcScW6Waz6X9OfjI9EqduKXgk36vQvTtLOjuozwG+v0W2JoYH3UzsKyZ\nLWlmhxLNum+l9z+R1tvSnezufi8xlulLAGY2e3r+CY/Bp6/kWLy8rEjMiHFACj5zuvvTRN/rO0RN\nel8zWyzVip8gMk+38LitxA+JFPYfpibfhk4wqEbTBiAz+7aZ7WZTx1x8Mb30JlG9f9HdbyWuHi5K\nr/0GmBtY38w6Uy1oJ2LS0F3TY5EpyjqHNzOzXc1saXe/ixgDtmLZoqcRNaICkYn0CtHvuC6wWXkH\ncVpva7V9V0gnx9uJVgf6Y3N2N75JzHCBxQ3gSvPYPUDMenEjUQs6n2ip6SSacR+FqCmnwFNIAapp\nE1VKmq4JLnXcnkT88x+WUja3BoanNtMXiAGSpRtR/Qx4xsw2dfebLAbIHUBMnTO2lTqApfYsBpKe\nTdSanwB2MbMLiH7Fd81sA3e/PZ0Y7iXmCLyKmO2g3dMA3Vbs5+lJagqHmA1dwttEC8w0ATntq2eJ\nptttiT7DLiJh5S6vuD9PunhpiQuYpglAKcV1JNHMcbG7f6/s5VuI2wJsTcxpNj+RL+8eE4aeAVxo\nZpcRGSYjUhOBCDBl8OTORALKm8Rs5p8R9zhaxN1XSMvtTdRwbiNqOH9IAyjvJRI37i4Fm/4afMrs\n3wpX6TV0C7C9mW3r7n9JtcRCSj1/ncgUfILoV1wfWKVsOERL3nKiaQIQMRP1ksSXUtlU9pnHzaru\nM7NniFTPky2mIT/R3Y9JJ5iliVHX72dacmloZrY/MQHo3cT9dwYTNemdiWPutZTYsgeRrPInj6lh\njjSzXYj79pRG/p9VGWz6afBpiLnGGsyLRD/04Wb2N5/2VhIbANd43KDwEGLc2LbAlc0+LqwnTZOG\nbXHfkLuIAWyldvm1iavQC9x9VHpuKHGSuJgYXb4Qcfvif+RScGlYZrYY0Vz7EHB8ytJqI6Zbup6Y\nPPQ+4op0IpEdONzdHzOzHd39SjPbmUg3/qq7v57W219rPDITaYD7FcQg0pvSz3Cipn0g0eT2uZkd\nROqz9ha+51HTBCCYkhf/MnEPmrOJfqDT3P2SsmX2ALZ0961Szv2y7v5ULgWWhpaaQN4j7snzlJXd\nQM9iQsd7iElnTyfGYHzf3T9JY1z2SY8/NbMniftBHW5l91oR6Y6ZtRN3VV6XuEB+0N2Hd7PcQe5+\nRtbly1KzBaB5iSkoXiUy206obBe1mO59PeAnHjNai8yQxR1gT3b3VcqeK43mv4MIQhcS06HMCbxB\nTAJ5ClHz/tzM1iA6jr/s7s9l/iGkKaUM3nlK56myQfT9pgbdVAEIptRwdnf39Sqe35kYYf0UcKs3\n4e2wJR9mNho43d3Pr3j+cGIg5UFm9gVi9PlywChPc7eVnTT2J5JjdFsKqUopscBiAtdcbwyXl2YM\nQLMRKYrbuvudaTqUnxPTnBzqmrNMeiml9j9E9C9+WGqKM7NrgNfd/aBu3jMA+Bxaf0yPSL00XQAC\nMLN1iHtj3ES0o57l7ufmWyppZql/cZy7H5IeL030/Qx399EVy7ZkSqxI1poyAAGY2Z3EJH2HtHKW\niGQj9S/+l7gh2vbE9DkXepPc2EukGTXTOKBKG7RqbrxkLzW9HUXcf+USYB13fy3nYom0tKatAYnU\nWspK+pq7P5AeDyBuv65/EpE6UAASqZACUaG/pMKK5EUBSEREctG0t2MQEZHmpgAkIiK5UAASEZFc\nKACJiEguFIBERCQXCkAiIpILBSAREcnF/wPEAq0kqbrduAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd06b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with one subplot\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "# Graph the first five personality items using boxplots\n",
    "personalityData1 = [npas['TIPI1'], npas['TIPI2'], npas['TIPI3'], npas['TIPI4'], npas['TIPI5']]\n",
    "ax.boxplot(personalityData1, vert=True)\n",
    "\n",
    "# Add appropriate labels to our graph\n",
    "ax.set_title('Ten Item Personality Inventory', fontsize=16)\n",
    "ax.set_ylabel('Score', fontsize=12)\n",
    "personalityLabels1 = ['Extraverted','Critical','Dependable','Anxious','Openness']\n",
    "ax.set_xticklabels(personalityLabels1, fontsize=12, rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Violin Plots ####\n",
    "\n",
    "Violin plots are boxplots but with a kernel density estimation of the values frequency added to give additional information about the distribution. Lets demonstrate them with the last five personality traits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x7f8eccf4ebd0>,\n",
       " <matplotlib.text.Text at 0x7f8ecced70d0>,\n",
       " <matplotlib.text.Text at 0x7f8ecce681d0>,\n",
       " <matplotlib.text.Text at 0x7f8ecce68950>,\n",
       " <matplotlib.text.Text at 0x7f8ecce70110>,\n",
       " <matplotlib.text.Text at 0x7f8ecce70890>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yApQb7cyw8S9xEEREUbWtnzZ5WEFm/VQ5YBvk6oZrNDZVYvXblQiCIJdJueZ+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\nbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ecd1e7790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with one subplot\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "# Graph the first five personality items using boxplots\n",
    "personalityData2 = [npas['TIPI6'], npas['TIPI7'], npas['TIPI8'], npas['TIPI9'], npas['TIPI10']]\n",
    "ax.violinplot(personalityData2, vert=True, showmedians=True)\n",
    "\n",
    "# Add appropriate labels to our graph\n",
    "ax.set_title('Ten Item Personality Inventory', fontsize=16)\n",
    "ax.set_ylabel('Score', fontsize=12)\n",
    "personalityLabels1 = ['','Reserved','Warm','Disorganized','Calm','Conventional']\n",
    "ax.set_xticklabels(personalityLabels1, fontsize=12, rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphing Resources ###\n",
    "\n",
    "Python has extensive support for different types of visualization. The basic matplotlib graphs we've gone through so far should be enough to get you started but there are a lot more graph types and advanced graphing libraries available. Most of these are beyond the scope of this tutorial but here is a list of some of what is available.\n",
    "\n",
    "[Seaborn](http://seaborn.pydata.org/index.html) -  Improved graph appearance and some extra functionality\n",
    "\n",
    "[Bokeh](http://bokeh.pydata.org/en/latest/) - D3.js style interactive graphs for the web\n",
    "\n",
    "[Animation](http://matplotlib.org/api/animation_api.html) - Animated graphs using matplotlib\n",
    "\n",
    "[Basemap](http://matplotlib.org/basemap/) - Lightweight geographical visulization library\n",
    "\n",
    "[MPlot3D](http://matplotlib.org/mpl_toolkits/mplot3d/index.html) - Matplotlib 3D toolkit, adds basic 3d plotting\n",
    "\n",
    "[DataShader](https://github.com/bokeh/datashader) - Graphics rendering for VERY large datasets\n",
    "\n",
    "[ggplot](https://pypi.python.org/pypi/ggplot) - Grammar of Graphics clone if you like that sort of thing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Statistics with Numpy and Scipy](https://docs.scipy.org/doc/scipy/reference/stats.html) ##\n",
    "\n",
    "Statistics and mathematical functions for scientific python are mostly spread between Numpy and a library called scipy. As we've already seen numpy has many useful mathematical and statistical functions for working with nd-arrays of data. Scipy is an add on library of scientific tools and functions which are also built to work with numpy data structures. This means they will generally use matrix operations, and require numpy nd-arrays or at least uniform data types that can be converted to numpy nd-arrays as input. Generally speaking simple functions like mean() median() will be found in numpy but statistical tests like anovas and regressions will be in the scipy.stats library. For this section lets quickly go over some of the common statistical tests and how to do them with python. We will again be using real data from the nerdy personality attributes inventory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive Statistics ###\n",
    "\n",
    "The basic descriptive statistics such as mean, standard deviation are available in numpy and some of the more advanced ones such as kurtosis, skewness, percentile and etc. are available in scipy. I'll assume you already have some statistical training so I'm just going to show you how to do some of these in python not what they are. Note that many of these functions may be given multi-dimensional matrices of values as input and so return arrays of values even when only a single vector of values is passed to them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Import our csv with data for the npas again\n",
    "bad_participants = [27,43,53,68,177,258,666,687,718,725,771,813,\\\n",
    "                    842,1006,1094,1095,1132,1159,1182,1189,1225,\\\n",
    "                    1238,1288,1333,1339,1350,1409]\n",
    "npas = pd.read_csv('./data/NPAS-data/NPAS-data.csv',skiprows=bad_participants)\n",
    "\n",
    "# Ages of all participants as a numpy nd-array\n",
    "ages = npas['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean = 54.3587347232 Median = 54.3587347232 Mode = 18.0\n",
      "Variance = 1081416.74047 STD = 1039.91189072\n",
      "n=1391 min/max=(14, 38822) mean=54.3587347232 var=1082194.73812 skew=37.2469772837 kurt=1385.55857963\n"
     ]
    }
   ],
   "source": [
    "# Measures of Centrality\n",
    "# Mean\n",
    "mean = np.mean(ages)\n",
    "# Median\n",
    "med = np.mean(ages)\n",
    "# Mode - returns an array of modes and their counts for each axis in a matrix\n",
    "mods, counts = stats.mode(ages)\n",
    "print 'Mean = {} Median = {} Mode = {}'.format(mean, med, mods[0])\n",
    "\n",
    "# Measures of Variance\n",
    "# Variance\n",
    "var = np.var(ages)\n",
    "# Standard Deviation\n",
    "std = np.std(ages)\n",
    "print 'Variance = {} STD = {}'.format(var, std)\n",
    "\n",
    "# Bonus\n",
    "# Scipy stats also has a convenience function describe() which returns a number of common descriptive stats for an array\n",
    "n, minmax, mean, var, skew, kurt = stats.describe(ages)\n",
    "print 'n={} min/max={} mean={} var={} skew={} kurt={}'.format(n, minmax, mean, var, skew, kurt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T-Tests ###\n",
    "\n",
    "### One Sample T-Test ###\n",
    "\n",
    "[ttest_1samp()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_1samp.html)\n",
    "\n",
    "**Arguments:** array, population mean, axis(optional), nan_policy(optional)\n",
    "\n",
    "**Returns:** t-statistic, p-value (two-tailed)\n",
    "\n",
    "Performs a t-test between an array of values and a designated mean value returns the t-stat and a two tailed p-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t=14.69 p=0.00000000\n"
     ]
    }
   ],
   "source": [
    "# Test the hypothesis that average familysize is 2 in our sample\n",
    "t, p = stats.ttest_1samp(npas['familysize'],2)\n",
    "print 't={:.2f} p={:.8f}'.format(t,p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Independent Samples T-Test ###\n",
    "\n",
    "[ttest_ind()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html)\n",
    "\n",
    "**Arguments:** array1, array2, axis(optional), equal_var(boolean, optional), nan_policy(optional)\n",
    "\n",
    "**Returns:** t-statistic, p-value (two-tailed)\n",
    "\n",
    "Performs an independent samples t-test between two arrays of values. By default it will assume equal population variances but you may change the equal_var argument to false which will cause scipy to use Welch's T-test. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t=-1.70 p=0.09004850\n"
     ]
    }
   ],
   "source": [
    "# Get The Nerdy scores for men and women\n",
    "m_nerd = npas['nerdy'][npas['gender']==1]\n",
    "f_nerd = npas['nerdy'][npas['gender']==2]\n",
    "\n",
    "# Test the hypothesis that women and men have an equal mean nerdy attributes score\n",
    "t, p = stats.ttest_ind(m_nerd, f_nerd)\n",
    "print 't={:.2f} p={:.8f}'.format(t,p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Related Samples T-Test ###\n",
    "\n",
    "[ttest_rel()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html)\n",
    "\n",
    "**Arguments:** array1, array2, axis(optional), nan_policy(optional)\n",
    "\n",
    "**Returns:** t-statistic, p-value (two-tailed)\n",
    "\n",
    "Performs a dependent samples t-test between two arrays of values. This should be used when you are testing between values from related populations such as paired-samples or repeated measures designs where you are not testing between two independent populations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t=-11.01 p=0.00000000\n"
     ]
    }
   ],
   "source": [
    "# Get The 'Reserved' and 'Anxious' scores for all subjects\n",
    "anx = npas['TIPI4']\n",
    "res = npas['TIPI6']\n",
    "\n",
    "# Test the hypothesis that mean reservation and anxiety have the same average values within subjects\n",
    "t, p = stats.ttest_rel(anx, res)\n",
    "print 't={:.2f} p={:.8f}'.format(t,p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chi Square ###\n",
    " \n",
    "[chisquare()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html)\n",
    "\n",
    "**Arguments:** observed frequencies, expected frequencies(optional), axis(optional), delta degrees of freedom (optional)\n",
    "\n",
    "**Returns:** chi-square statistic, p-value\n",
    "\n",
    "Performs a one way chi-square test. Takes an array of frequencies as an argument, optionaly takes a second array of expected frequencies to test against, the default is that each category is equally likely. Can also be passed a degree of freedom adjustment which will decrease the DF by an integer amount. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi=61.21 p=0.00000000\n"
     ]
    }
   ],
   "source": [
    "# Get the frequencies of male and female participants\n",
    "men_f = np.sum(npas['gender']==1) # remember booleans are integers\n",
    "fem_f = np.sum(npas['gender']==2)\n",
    "\n",
    "# Test the hypothesis that men and women are equally frequent in the sample\n",
    "chi, p = stats.chisquare([men_f,fem_f])\n",
    "print 'chi={:.2f} p={:.8f}'.format(chi,p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Way Anova ###\n",
    "\n",
    "[f_oneway()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html)\n",
    "\n",
    "**Arguments:** sample measurements for each group (sample1, sample2.. sampleN)\n",
    "\n",
    "**Returns:** F statistic, p-value\n",
    "\n",
    "Performs a one way anova test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f=11.05 p=0.00000036\n"
     ]
    }
   ],
   "source": [
    "# Get the nerdiness scores for each coded response to the sexual orientation question\n",
    "# 1 = heterosexual, 2=bisexual, 3=homosexual, 4=asexual, 5=other\n",
    "samples = [ npas['nerdy'][npas['orientation']==x] for x in range(1,5,1)]\n",
    "\n",
    "# Test the hypothesis that all orientation groups have an equal nerdy attributes score\n",
    "f, p = stats.f_oneway(*samples)\n",
    "print 'f={:.2f} p={:.8f}'.format(f,p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression ###\n",
    "\n",
    "There are many types of regressions and I would say this is a little bit of a weak point for scientific python. While tools do exist for doing multiple regressions, hierarchical regressions, logistic regressions and etc. they are not as straightforward to use as comparable tools in R, Matlab and etc. For now we'll skip those more advanced topics and just show you how to do a normal two variable least-squares regression.\n",
    "\n",
    "### OLS Regression ###\n",
    "\n",
    "[linregress()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html)\n",
    "\n",
    "**Arguments:** x, y (two sets of sample measurements, must be of same length)\n",
    "\n",
    "**Returns:** slope, intercept, r-value, pvalue, stderr\n",
    "\n",
    "Performs an ordinary least squares regression between two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nerd = 0.00001550 x age + 5.36\n",
      "r=0.01118619, p=0.67679593, stderr=0.00003718\n"
     ]
    }
   ],
   "source": [
    "# Get the age and nerdiness scores for all subjects\n",
    "ages = npas['age']\n",
    "nerd = npas['nerdy']\n",
    "\n",
    "# Test the hypothesis that there exists a linear relationship between nerdy personality\n",
    "# attributes + age and calculate the slope and intercept for that possible relationship\n",
    "slope, inter, r, p, stderr = stats.linregress(ages, nerd)\n",
    "print 'nerd = {:.8f} x age + {:.2f}'.format(slope, inter)\n",
    "print 'r={:.8f}, p={:.8f}, stderr={:.8f}'.format(r,p,stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Graphing #### \n",
    "\n",
    "Generally we'll want to graph the regression lines for any linear models we make. There are several ways to do this but the simplest and most versatile is to use the equation of our regression line and the plot command to overlay a line on the scatter plot of values. Recall that the plot command takes two numpy arrays for the x and y values of the line. We can grab the x-tick values for the x-axis and multiply them by our models values to display a regression line that will always fit the graph even if we resize it.\n",
    "\n",
    "Lets test a dumb hypothesis with our data and then graph it. Are older people on average slower at these sorts of tests?\n",
    "\n",
    "*note: unlike other methods this will also work for any sort of nonlinear regression*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f12ce2ea790>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TQl/naK3fANBavwW8leT22EPu7wPLlVI3YGb2L9NaB5VSdwFrMYplodZ6SA0t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\n089JPT5woJh/+Zcmq95WHgsXvkl5+Uc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12ced09e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Someone claimed to be 30,000 years old so lets manually remove outliers age > 100, time > 20 minutes\n",
    "npas_clean = npas[npas['age']< 100]\n",
    "npas_clean = npas_clean[npas_clean['testelapse']< 60*20]\n",
    "\n",
    "# Make our scatter plot\n",
    "fig, ax = plt.subplots(1,1)\n",
    "ax.scatter(npas_clean['age'], npas_clean['testelapse'])\n",
    "\n",
    "# Add appropriate labels to our graph\n",
    "ax.set_title('Age vs Test Time', fontsize=16)\n",
    "ax.set_xlabel('Age (years)', fontsize=14)\n",
    "ax.set_ylabel('TestTime (sec)', fontsize=14)\n",
    "# Set limits\n",
    "ax.set_xlim([0,100])\n",
    "ax.set_ylim([0,60*20])\n",
    "\n",
    "# Run our regression model\n",
    "slope, inter, r, p, stderr = stats.linregress(npas_clean['age'], npas_clean['testelapse'])\n",
    "\n",
    "# How to graph a regression line\n",
    "# \n",
    "# Grab the x-ticks values as an array\n",
    "x = ax.get_xticks()\n",
    "# Create a new array of y values using our slope and intercept\n",
    "y = x*slope + inter \n",
    "# Add to our plot as a solid red line\n",
    "ax.plot(x,y, color='r', linestyle='-')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}