NOTES_Intro_to_Data_Analysis-2-NumPy_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy and Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is efficient in handling big files. It's also pretty fast in executing statistical operations on large datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named matplotlib.pyplot",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-2c4e12a39853>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: No module named matplotlib.pyplot"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# daily_engagement = pd.read_csv('daily_engagement_full.csv')  # Very fast in reading 500 MB file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# len(daily_engagement['acct'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NumPy** or *__Num__eric __Py__thon* is a Python library that provides support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. These operations which includes mathematical, logical, shape manipulation, sorting, selecting, I/O etc. are designed for fast execution.\n",
    "\n",
    "**Pandas** is a Python library providing high-performance, easy-to-use data structures and data analysis tools.  \n",
    "Pandas is built using NumPy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "np array $\\equiv$ pd series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np array vs python lists\n",
    "\n",
    "**Similarities**  \n",
    "- Support indexing: a[0]\n",
    "- Support slicing: a[1:5]\n",
    "- Can be used with `in` operator: \n",
    "```py\n",
    "for x in a:\n",
    "    print x\n",
    "```\n",
    "\n",
    "** Differences**\n",
    "- In np array, each element should have same data type. This makes operations on the array elements faster.\n",
    "- np provides convenient functions on the arrays like `mean()`, `std()` etc. These can also be used on python lists, but these are operate faster for np arrays.\n",
    "- np arrays can be multi-dimensional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# First 20 countries with employment data\n",
    "countries = np.array([\n",
    "    'Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',\n",
    "    'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas',\n",
    "    'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belagium',\n",
    "    'Belize', 'Benin', 'Bhutan', 'Bolivia',\n",
    "    'Bosnia and Herzegovina'\n",
    "])\n",
    "\n",
    "# Employment data in 2007 for those 20 countries\n",
    "employment = np.array([\n",
    "    55.70000076,  51.40000153,  50.5       ,  75.69999695,\n",
    "    58.40000153,  40.09999847,  61.5       ,  57.09999847,\n",
    "    60.90000153,  66.59999847,  60.40000153,  68.09999847,\n",
    "    66.90000153,  53.40000153,  48.59999847,  56.79999924,\n",
    "    71.59999847,  58.40000153,  70.40000153,  41.20000076\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|S22\n",
      "float64\n",
      "int64\n",
      "float64\n",
      "bool\n",
      "|S2\n"
     ]
    }
   ],
   "source": [
    "# Element types\n",
    "if True:\n",
    "    print countries.dtype\n",
    "    print employment.dtype\n",
    "    print np.array([0, 1, 2, 3]).dtype\n",
    "    print np.array([1.0, 1.5, 2.0, 2.5]).dtype\n",
    "    print np.array([True, False, True]).dtype\n",
    "    print np.array(['AL', 'AK', 'AZ', 'AR', 'CA']).dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`dtype` field returns the datatype of the elements in the array.  \n",
    "`|S22` = datatype is string where 22 is the length of the largest string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58.6850000385\n",
      "9.33826911369\n",
      "75.69999695\n",
      "1173.70000077\n"
     ]
    }
   ],
   "source": [
    "# Numpy functions\n",
    "if True:\n",
    "    print employment.mean()\n",
    "    print employment.std()\n",
    "    print employment.max()\n",
    "    print employment.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Angola', 75.699996949999999)\n"
     ]
    }
   ],
   "source": [
    "def max_employment(countries, employment):\n",
    "    i = employment.argmax()     # argmax() returns the index of maximum element\n",
    "    \n",
    "    return (countries[i], employment[i])\n",
    "\n",
    "print max_employment(countries, employment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorized operations with np\n",
    "\n",
    "An n-dimensional vector is an n-dimensional array of numbers or booleans.  \n",
    "\n",
    "np arrays (or vectors) can have following operations - \n",
    "- maths operations(`+, -, *, /, **`)\n",
    "- logical operations(`&,|, ~`) : works with booleans, does bitwise operations with numbers\n",
    "- comparison operations(`>, >=, <, <=, ==, !=`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4 4 6]\n",
      "[0 0 2 2]\n",
      "[1 4 3 8]\n",
      "[1 1 3 2]\n",
      "[ 1  4  3 16]\n"
     ]
    }
   ],
   "source": [
    "# Arithmetic operations between 2 NumPy arrays\n",
    "if True:\n",
    "    a = np.array([1, 2, 3, 4])\n",
    "    b = np.array([1, 2, 1, 2])\n",
    "    \n",
    "    print a + b\n",
    "    print a - b\n",
    "    print a * b\n",
    "    print a / b\n",
    "    print a ** b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "print [1,2,3,4]    # ipython prints np array different from python lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.  4.  5.  6.]\n",
      "[-1.  0.  1.  2.]\n",
      "[ 2.  4.  6.  8.]\n",
      "[ 0.5  1.   1.5  2. ]\n",
      "[  1.   4.   9.  16.]\n"
     ]
    }
   ],
   "source": [
    "# Arithmetic operations between a NumPy array and a single number\n",
    "if True:\n",
    "    a = np.array([1, 2, 3, 4])\n",
    "    b = 2.0\n",
    "    \n",
    "    print a + b\n",
    "    print a - b\n",
    "    print a * b\n",
    "    print a / b\n",
    "    print a ** b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False False False]\n",
      "[ True  True  True False]\n",
      "[False False  True  True]\n",
      "[ True  True False False]\n",
      "[False False False False]\n",
      "[ True  True  True  True]\n",
      "[ True  True False False]\n"
     ]
    }
   ],
   "source": [
    "# Logical operations with NumPy arrays\n",
    "if True:\n",
    "    a = np.array([True, True, False, False])\n",
    "    b = np.array([True, False, True, False])\n",
    "    \n",
    "    print a & b\n",
    "    print a | b\n",
    "    print ~a\n",
    "    \n",
    "    print a & True\n",
    "    print a & False\n",
    "    \n",
    "    print a | True\n",
    "    print a | False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Both arrays must have same dimensionality\n",
    "if False:\n",
    "    a = np.array([True, False])\n",
    "    b = np.array([True, True, False])\n",
    "    print a & b     # ValueError: operands could not be broadcast together with shapes (2,) (3,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False  True  True]\n",
      "[False False  True  True  True]\n",
      "[ True  True False False False]\n",
      "[ True  True  True False False]\n",
      "[False False  True False False]\n",
      "[ True  True False  True  True]\n"
     ]
    }
   ],
   "source": [
    "# Comparison operations between 2 NumPy Arrays\n",
    "if True:\n",
    "    a = np.array([1, 2, 3, 4, 5])\n",
    "    b = np.array([5, 4, 3, 2, 1])\n",
    "    \n",
    "    print a > b\n",
    "    print a >= b\n",
    "    print a < b\n",
    "    print a <= b\n",
    "    print a == b\n",
    "    print a != b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False  True  True]\n",
      "[False  True  True  True]\n",
      "[ True False False False]\n",
      "[ True  True False False]\n",
      "[False  True False False]\n",
      "[ True False  True  True]\n"
     ]
    }
   ],
   "source": [
    "# Comparison operations between a NumPy array and a single number\n",
    "if True:\n",
    "    a = np.array([1, 2, 3, 4])\n",
    "    b = 2\n",
    "    \n",
    "    print a > b\n",
    "    print a >= b\n",
    "    print a < b\n",
    "    print a <= b\n",
    "    print a == b\n",
    "    print a != b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# First 20 countries with school completion data\n",
    "countries = np.array([\n",
    "       'Algeria', 'Argentina', 'Armenia', 'Aruba', 'Austria','Azerbaijan',\n",
    "       'Bahamas', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Bolivia',\n",
    "       'Botswana', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi',\n",
    "       'Cambodia', 'Cameroon', 'Cape Verde'\n",
    "])\n",
    "\n",
    "# Female school completion rate in 2007 for those 20 countries\n",
    "female_completion = np.array([\n",
    "    97.35583,  104.62379,  103.02998,   95.14321,  103.69019,\n",
    "    98.49185,  100.88828,   95.43974,   92.11484,   91.54804,\n",
    "    95.98029,   98.22902,   96.12179,  119.28105,   97.84627,\n",
    "    29.07386,   38.41644,   90.70509,   51.7478 ,   95.45072\n",
    "])\n",
    "\n",
    "# Male school completion rate in 2007 for those 20 countries\n",
    "male_completion = np.array([\n",
    "     95.47622,  100.66476,   99.7926 ,   91.48936,  103.22096,\n",
    "     97.80458,  103.81398,   88.11736,   93.55611,   87.76347,\n",
    "    102.45714,   98.73953,   92.22388,  115.3892 ,   98.70502,\n",
    "     37.00692,   45.39401,   91.22084,   62.42028,   90.66958\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  96.416025,  102.644275,  101.41129 ,   93.316285,  103.455575,\n",
       "         98.148215,  102.35113 ,   91.77855 ,   92.835475,   89.655755,\n",
       "         99.218715,   98.484275,   94.172835,  117.335125,   98.275645,\n",
       "         33.04039 ,   41.905225,   90.962965,   57.08404 ,   93.06015 ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def overall_completion_rate(female_completion, male_completion):\n",
    "    '''\n",
    "    Fill in this function to return a NumPy array containing the overall\n",
    "    school completion rate for each country. The arguments are NumPy\n",
    "    arrays giving the female and male completion of each country in\n",
    "    the same order.\n",
    "    '''\n",
    "    return (female_completion + male_completion) / 2\n",
    "\n",
    "overall_completion_rate(female_completion, male_completion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardizing each value in the distribution\n",
    "\n",
    "Each data point is represented in terms of number of standard deviations from the mean.  \n",
    "$$ \\frac{(x_i - \\mu)}{\\sigma} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.31965231, -0.780123  , -0.87650077,  1.82207181, -0.03051941,\n",
       "       -1.99019768,  0.30144772, -0.16973184,  0.23719615,  0.84758731,\n",
       "        0.18365304,  1.00821665,  0.87971351, -0.56595055, -1.07996476,\n",
       "       -0.20185762,  1.38301845, -0.03051941,  1.2545153 , -1.87240259])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First 20 countries with employment data\n",
    "countries = np.array([\n",
    "    'Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',\n",
    "    'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas',\n",
    "    'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',\n",
    "    'Belize', 'Benin', 'Bhutan', 'Bolivia',\n",
    "    'Bosnia and Herzegovina'\n",
    "])\n",
    "\n",
    "# Employment data in 2007 for those 20 countries\n",
    "employment = np.array([\n",
    "    55.70000076,  51.40000153,  50.5       ,  75.69999695,\n",
    "    58.40000153,  40.09999847,  61.5       ,  57.09999847,\n",
    "    60.90000153,  66.59999847,  60.40000153,  68.09999847,\n",
    "    66.90000153,  53.40000153,  48.59999847,  56.79999924,\n",
    "    71.59999847,  58.40000153,  70.40000153,  41.20000076\n",
    "])\n",
    "\n",
    "def standardize_data(values):\n",
    "    '''\n",
    "    Fill in this function to return a standardized version of the given values,\n",
    "    which will be in a NumPy array. Each value should be translated into the\n",
    "    number of standard deviations that value is away from the mean of the data.\n",
    "    (A positive number indicates a value higher than the mean, and a negative\n",
    "    number indicates a value lower than the mean.)\n",
    "    '''\n",
    "    sd = values.std()\n",
    "    mean = values.mean()\n",
    "    \n",
    "    return (values - mean)/sd\n",
    "\n",
    "standardize_data(employment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NumPy Index Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2]\n",
      "[1 3]\n"
     ]
    }
   ],
   "source": [
    "# Using index arrays\n",
    "if True:\n",
    "    a = np.array([1, 2, 3, 4])\n",
    "    b = np.array([True, True, False, False])\n",
    "    \n",
    "    print a[b]\n",
    "    print a[np.array([True, False, True, False])]\n",
    "    \n",
    "# array b which contains boolean elements is called 'Index Array'. It tells which element of a should be\n",
    "# considered and which should be rejected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 2]\n",
      "[2 3 2]\n"
     ]
    }
   ],
   "source": [
    "# Creating the index array using vectorized operations\n",
    "if True:\n",
    "    a = np.array([1, 2, 3, 2, 1])\n",
    "    b = (a >= 2)\n",
    "    \n",
    "    print a[b]\n",
    "    print a[a >= 2]\n",
    "    \n",
    "# a[a >= 2] has better readability. \n",
    "# It means ouput an array of elements of a which are >= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41.054003485454537"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Time spent in the classroom in the first week for 20 students\n",
    "time_spent = np.array([\n",
    "       12.89697233,    0.        ,   64.55043217,    0.        ,\n",
    "       24.2315615 ,   39.991625  ,    0.        ,    0.        ,\n",
    "      147.20683783,    0.        ,    0.        ,    0.        ,\n",
    "       45.18261617,  157.60454283,  133.2434615 ,   52.85000767,\n",
    "        0.        ,   54.9204785 ,   26.78142417,    0.\n",
    "])\n",
    "\n",
    "# Days to cancel for 20 students\n",
    "days_to_cancel = np.array([\n",
    "      4,   5,  37,   3,  12,   4,  35,  38,   5,  37,   3,   3,  68,\n",
    "     38,  98,   2, 249,   2, 127,  35\n",
    "])\n",
    "\n",
    "def mean_time_for_paid_students(time_spent, days_to_cancel):\n",
    "    '''\n",
    "    Fill in this function to calculate the mean time sent in the classroom\n",
    "    for students who stayed enrolled at least (greater than or equal to) 7 days.\n",
    "    Unlike in Lesson 1, you can assume that days_to_cancel will contain only\n",
    "    integers (there are no students who have not canceled yet).\n",
    "    \n",
    "    The arguments are NumPy arrays. time_spent contains the amount of time spent\n",
    "    in the classroom for each student, and days_to_cancel contains the number\n",
    "    of days until each student cancel. The data is given in the same order\n",
    "    in both arrays.\n",
    "    '''\n",
    "    paid_time = time_spent[days_to_cancel >= 7]\n",
    "    return paid_time.mean()\n",
    "\n",
    "mean_time_for_paid_students(time_spent, days_to_cancel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In-place vs Not In-place operators\n",
    "\n",
    "In-place operations modify the original variable.  \n",
    "\n",
    "`+=` operates in-place, while `+` does not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 4 5]\n",
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "def fn1():\n",
    "    a = np.array([1,2,3,4])\n",
    "    b = a\n",
    "    a += 1\n",
    "    print b\n",
    "    \n",
    "def fn2():\n",
    "    a = np.array([1,2,3,4])\n",
    "    b = a\n",
    "    a = a + 1\n",
    "    print b\n",
    "    \n",
    "fn1()\n",
    "fn2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`a += 1` changes the original `a` array.  \n",
    "While `a + 1` returns a new array. `a` now points to the new array and `b` still points to the original array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100   2   3   4]\n"
     ]
    }
   ],
   "source": [
    "def fn_slice():\n",
    "    a = np.array([1,2,3,4])\n",
    "    slice = a[:3]\n",
    "    slice[0] = 100\n",
    "    print a\n",
    "    \n",
    "fn_slice()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***slicing* is in-place i.e. slicing doesn't return a new array.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panda series\n",
    "\n",
    "Panda series are like NumPy array, but give extra functionalities such as `describe()` function.  \n",
    "\n",
    "Since Panda series are **implemented in C**, these are faster than native python lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries = ['Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda',\n",
    "             'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan',\n",
    "             'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus',\n",
    "             'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia']\n",
    "\n",
    "life_expectancy_values = [74.7,  75. ,  83.4,  57.6,  74.6,  75.4,  72.3,  81.5,  80.2,\n",
    "                          70.3,  72.1,  76.4,  68.1,  75.2,  69.8,  79.4,  70.8,  62.7,\n",
    "                          67.3,  70.6]\n",
    "\n",
    "gdp_values = [ 1681.61390973,   2155.48523109,  21495.80508273,    562.98768478,\n",
    "              13495.1274663 ,   9388.68852258,   1424.19056199,  24765.54890176,\n",
    "              27036.48733192,   1945.63754911,  21721.61840978,  13373.21993972,\n",
    "                483.97086804,   9783.98417323,   2253.46411147,  25034.66692293,\n",
    "               3680.91642923,    366.04496652,   1175.92638695,   1132.21387981]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74.7\n",
      "3      562.987685\n",
      "4    13495.127466\n",
      "5     9388.688523\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Life expectancy and gdp data in 2007 for 20 countries\n",
    "life_expectancy = pd.Series(life_expectancy_values)\n",
    "gdp = pd.Series(gdp_values)\n",
    "\n",
    "\n",
    "# Accessing elements and slicing\n",
    "if True:\n",
    "    print life_expectancy[0]\n",
    "    print gdp[3:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examining life expectancy 74.7\n",
      "Examining life expectancy 75.0\n",
      "Examining life expectancy 83.4\n",
      "Examining life expectancy 57.6\n",
      "Examining life expectancy 74.6\n",
      "Examining life expectancy 75.4\n",
      "Examining life expectancy 72.3\n",
      "Examining life expectancy 81.5\n",
      "Examining life expectancy 80.2\n",
      "Examining life expectancy 70.3\n",
      "Examining life expectancy 72.1\n",
      "Examining life expectancy 76.4\n",
      "Examining life expectancy 68.1\n",
      "Examining life expectancy 75.2\n",
      "Examining life expectancy 69.8\n",
      "Examining life expectancy 79.4\n",
      "Examining life expectancy 70.8\n",
      "Examining life expectancy 62.7\n",
      "Examining life expectancy 67.3\n",
      "Examining life expectancy 70.6\n"
     ]
    }
   ],
   "source": [
    "# Looping\n",
    "if True:\n",
    "    for country_life_expectancy in life_expectancy:\n",
    "        print 'Examining life expectancy {}'.format(country_life_expectancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72.87\n",
      "6.21399947487\n",
      "27036.4873319\n",
      "182957.59833\n"
     ]
    }
   ],
   "source": [
    "# Pandas functions\n",
    "if True:\n",
    "    print life_expectancy.mean()\n",
    "    print life_expectancy.std()\n",
    "    print gdp.max()\n",
    "    print gdp.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2\n",
      "1    4\n",
      "2    4\n",
      "3    6\n",
      "dtype: int64\n",
      "0    2\n",
      "1    4\n",
      "2    6\n",
      "3    8\n",
      "dtype: int64\n",
      "0    False\n",
      "1    False\n",
      "2     True\n",
      "3     True\n",
      "dtype: bool\n",
      "2    3\n",
      "3    4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Vectorized operations and index arrays\n",
    "if True:\n",
    "    a = pd.Series([1, 2, 3, 4])\n",
    "    b = pd.Series([1, 2, 1, 2])\n",
    "  \n",
    "    print a + b\n",
    "    print a * 2\n",
    "    print a >= 3\n",
    "    print a[a >= 3]\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def variable_correlation(variable1, variable2):\n",
    "    '''\n",
    "    Fill in this function to calculate the number of data points for which\n",
    "    the directions of variable1 and variable2 relative to the mean are the\n",
    "    same, and the number of data points for which they are different.\n",
    "    Direction here means whether each value is above or below its mean.\n",
    "    \n",
    "    You can classify cases where the value is equal to the mean for one or\n",
    "    both variables however you like.\n",
    "    \n",
    "    Each argument will be a Pandas series.\n",
    "    \n",
    "    For example, if the inputs were pd.Series([1, 2, 3, 4]) and\n",
    "    pd.Series([4, 5, 6, 7]), then the output would be (4, 0).\n",
    "    This is because 1 and 4 are both below their means, 2 and 5 are both\n",
    "    below, 3 and 6 are both above, and 4 and 7 are both above.\n",
    "    \n",
    "    On the other hand, if the inputs were pd.Series([1, 2, 3, 4]) and\n",
    "    pd.Series([7, 6, 5, 4]), then the output would be (0, 4).\n",
    "    This is because 1 is below its mean but 7 is above its mean, and\n",
    "    so on.\n",
    "    '''\n",
    "    both_above = (variable1 > variable1.mean()) & (variable2 > variable2.mean())\n",
    "    both_below = (variable1 < variable1.mean()) & (variable2 < variable2.mean())\n",
    "    is_same_direction = both_above | both_below\n",
    "    \n",
    "    num_same_direction = is_same_direction.sum()\n",
    "    num_different_direction = len(variable1) - num_same_direction\n",
    "    \n",
    "    return (num_same_direction, num_different_direction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 0)\n",
      "(0, 4)\n"
     ]
    }
   ],
   "source": [
    "a = pd.Series([1, 2, 3, 4])\n",
    "b = pd.Series([4, 5, 6, 7])\n",
    "\n",
    "print variable_correlation(a, b)\n",
    "\n",
    "a = pd.Series([1, 2, 3, 4])\n",
    "b = pd.Series([7, 6, 5, 4])\n",
    "\n",
    "print variable_correlation(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print True + True\n",
    "print True + False\n",
    "print False + False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 3)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable_correlation(life_expectancy, gdp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Correlation**  \n",
    "\n",
    "The above example tells that `life_expectancy` and `gdp` are +vely correlated as 17/20 data-points show +ve correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  1.,  0.,  1.,  3.,  4.,  5.,  1.,  2.,  2.]),\n",
       " array([ 57.6 ,  60.18,  62.76,  65.34,  67.92,  70.5 ,  73.08,  75.66,\n",
       "         78.24,  80.82,  83.4 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADNVJREFUeJzt3V2MXGUdx/Hfr2wptBSCUXcVTCsXDdFEW4RCqMooBCpG\nMF5IAN8wEi8k4EuIyE3XG6Mm6oUvFwpBIOALCAGjCaByUDARbIsIlCpiCzXdtWgplQah9O/FnK3L\ndnfnrWdm/qffT7Lp7PTsOc/TZ+fbM2d2WkeEAAA5LBj0AAAA7SPaAJAI0QaARIg2ACRCtAEgEaIN\nAImMtLOR7S2SdknaJ+nliFhd5aAAALNrK9pqxroRETurHAwAYH7tXh5xB9sCACrSbohD0j22H7J9\naZUDAgDMrd3LI2siYrvt16kZ700RcX+VAwMAHKitaEfE9vLXHbZvl7Ra0quibZt/xAQAOhQR7mT7\nlpdHbC+2fVR5e4mksyU9OsfBa/mxbt26gY+B+eWfX/koqeBj3Ty/l/9xOSzrV933RGfaOdMelXR7\neSY9IummiLi7q6MBAHrSMtoR8XdJK/swFgBAC/wYXxsajcagh1Ap5pddY9ADqFT9168z7va6ygE7\nsuNg7QuoI9uaus7cx6N2fe0U1bOtONgvRAIAhgfRBoBEiDYAJEK0ASARog0AiRBtAEiEaANAIkQb\nABIh2gCQCNEGgESINgAkQrQBIBGiDQCJEG0ASIRoA0AiRBsAEiHaAJAI0QaARIg2ACRCtAEgEaIN\nAIkQbQBIhGgDQCJEGwASIdoAkAjRBoBEiDYAJEK0ASARog0AiRBtAEiEaANAIkQbABIh2gCQCNEG\ngETajrbtBbY32L6zygEBAObWyZn2FZIer2ogAIDW2oq27eMlnSvpmmqHAwCYT7tn2t+SdKWkqHAs\nAIAWRlptYPv9kiYj4mHbDUmea9vx8fH9txuNhhqNRu8jBCoyNrZck5NbBz0MHEKKolBRFD3twxHz\nnzzb/oqkj0jaK+lISUsl3RYRH5uxXbTaFzBMbKu/Tx77fbzmMXlcDi/biog5T4Rn/ZpOFtT2GZK+\nEBHnzfJ7RBupEG0MWjfR5ue0ASCRjs60590RZ9pIhjNtDBpn2gBQc0QbABIh2gCQCNEGgESINgAk\nQrQBIBGiDQCJEG0ASIRoA0AiRBsAEiHaAJAI0QaARIg2ACRCtAEgEaINAIkQbQBIhGgDQCJEGwAS\nIdoAkAjRBoBEiDYAJEK0ASARog0AiRBtAEiEaANAIkQbABIh2gCQCNEGgESINgAkQrQBIBGiDQCJ\nEG0ASIRoA0AiRBsAEiHaAJDISKsNbC+S9FtJh5cfd0TE1VUPDABwoJbRjoj/2n5PROyxfZikB2yv\niYgH+jA+AMA0bV0eiYg95c1F5dfsrGxEAIA5tRVt2wtsb5Q0IamIiMerHRYAYDbtnmnvi4hVko6X\n9G7bZ1Q7LADAbFpe054uIp63/QtJJ0u6b+bvj4+P77/daDTUaDR6HB4OFWNjyzU5uXXQwwAqVRSF\niqLoaR+OiPk3sF8r6eWI2GX7SEl3SfpyRPx6xnbRal/AXGxL6vf3T7+POZg58rgcXrYVEe7ka9o5\n036DpOvdfFQtkHTjzGADAPqj5Zl22zviTBs94Ey7umPyuBxe3Zxp845IAEiEaANAIkQbABIh2gCQ\nCNEGgESINgAkQrQBIBGiDQCJEG0ASIRoA0AiRBsAEiHaAJAI0QaARIg2ACRCtAEgEaINAIkQbQBI\nhGgDQCJEGwASIdoAkAjRBoBEiDYAJEK0ASARog0AiRBtAEiEaANAIkQbABIh2gCQCNEGgESINgAk\nQrQBIBGiDQCJEG0ASIRoA0AiRBsAEiHaAJBIy2jbPt72b2w/ZvvPti/vx8AAAAdyRMy/gT0maSwi\nHrZ9lKT1ks6PiCdmbBet9gXMxbakfn//9PuYg5kjj8vhZVsR4U6+puWZdkRMRMTD5e3/SNok6bju\nhggA6EVH17RtL5e0UtIfqhgMAGB+bUe7vDRyq6QryjNuAECfjbSzke0RNYN9Y0TcMdd24+Pj+283\nGg01Go0ehwcgm7Gx5Zqc3NrXYy5YsFj79u3p6zFHR5dpYmJLR19TFIWKoujpuC1fiJQk2zdIejYi\nPj/PNrwQia7xQmR1x+z34/LQWMvmMXv9s63khUjbayRdLOm9tjfa3mB7bbeDBAB0r60z7bZ2xJk2\nenBonJ3lPBvs+IiHxFo2jzmUZ9oAgOFBtAEgEaINAIkQbQBIhGgDQCJEGwASIdoAkAjRBoBEiDYA\nJEK0ASARog0AiRBtAEiEaANAIkQbABIh2gCQCNEGgESINgAkQrQBIBGiDQCJEG0ASIRoA0AiRBsA\nEiHaAJAI0QaARIg2ACRCtAEgEaINAIkQbQBIhGgDQCJEGwASIdoAkAjRBoBEiDYAJEK0ASARog0A\niRBtAEikZbRtX2t70vYj/RgQAGBu7ZxpXyfpnKoHAgBorWW0I+J+STv7MBYAQAsjgx5At9avX68n\nn3yyr8dcsWKFVq1a1ddjAsB0BzXa4+Pj+283Gg01Go2DuftXOeusD2jv3lNkH1HZMabbt+8F7dlz\nryL29OV4U0ZHl2liYktfjzk2tlyTk1v7ekxUZZFsD3oQKBVFoaIoetqHI6L1RvYyST+PiLfNs020\ns6+D5eijR7V79yOSRvt0xKclLZPUvzk2Wf38c5VUPsj7P8/6H/NQmOOhdcxeH5u2FREd/a3a7o/8\nufwAAAxQOz/yd7Ok30taYftp25dUPywAwGxaXtOOiIv6MRAAQGu8IxIAEiHaAJAI0QaARIg2ACRC\ntAEgEaINAIkQbQBIhGgDQCJEGwASIdoAkAjRBoBEiDYAJEK0ASARog0AiRBtAEiEaANAIkQbABIh\n2gCQCNEGgESINgAkQrQBIBGiDQCJEG0ASIRoA0AiRBsAEiHaAJAI0QaARIg2ACRCtAEgEaINAIkQ\nbQBIhGgDQCJEGwASIdoAkAjRBoBE2oq27bW2n7D9F9tfrHpQAIDZtYy27QWSviPpHElvlXSh7ROr\nHthwKQY9gEoVRTHoIVSsGPQAKlYMegAVKwY9gKHSzpn2akl/jYitEfGypB9LOr/aYQ2bYtADqBTR\nzq4Y9AAqVgx6AEOlnWgfJ+mZaZ9vK+8DAPTZyKAH0K2FCxdq6dKPyl5U+bFefHGzFi36nXbvrvxQ\nADAvR8T8G9inSRqPiLXl51dJioj42ozt5t8RAOAAEeFOtm8n2odJ2izpTEnbJT0o6cKI2NTtIAEA\n3Wl5eSQiXrF9maS71bwGfi3BBoDBaHmmDQAYHl29I9L2Ftt/sr3R9oPlfetsb7O9ofxYe3CH2j+2\nj7F9i+1Nth+zfartY23fbXuz7btsHzPocXZrjvmlXz/bK8rvyQ3lr7tsX16XtZtnfunXbortL5Xf\nk4/Yvsn24XVZP2nW+S3qdP26OtO2/ZSkd0TEzmn3rZO0OyK+2fEOh4ztH0q6LyKusz0iaYmkqyX9\nKyK+Xr4r9NiIuGqQ4+zWHPP7rGqyftL+N4Vtk3SqpMtUk7WbMmN+n1QN1s72Mkn3SjoxIl6y/RNJ\nv5T0FtVg/eaZ33J1sH7d/tsjnuNrO3oVdBjZPlrSuyLiOkmKiL0RsUvNNxRdX252vaQPDmiIPZln\nflIN1m+asyT9LSKeUU3Wbobp85PqsXbPS3pJ0pLyZOJISf9QfdZv5vwWqzk/qYP16zbaIeke2w/Z\nvnTa/ZfZftj2NYmfwrxZ0rO2ryufqnzf9mJJoxExKUkRMSHp9QMdZffmmp9Uj/WbcoGkm8vbdVm7\n6S6Q9KNpn6dfu/KZ+zckPa1mzHZFxK9Uk/WbZX7PlfOTOli/bqO9JiJOknSupM/Yfqek70k6ISJW\nSpqQlPWp2oikkyR9t5zjC5KuUvMvqumyvoI7c3571JxfXdZPthdKOk/SLeVddVk7SbPOrxZrZ/sE\nSZ+TtEzSG9U8I71YNVm/WeZ3lO2L1OH6dRXtiNhe/rpD0u2SVkfEjvj/BfIfSDqlm30PgW2SnomI\nP5af/0zNyE3aHpUk22OS/jmg8fVq5vxulbSqRusnSe+TtD4ini0/r8vaTZma3w6p+TisydqdLOmB\niPh3RLyiZltOV33Wb+b8bpN0eqfr13G0bS+2fVR5e4mksyU9Wv5hTvmQpEc73fcwKJ+GPWN7RXnX\nmZIek3SnpE+U931c0h39H13v5pjf43VZv9KFevWlg1qs3TSvml+N1m6zpNNsH2HbKr83VZ/1m21+\nmzpdv45/esT2m9X8GzDUfKp9U0R81fYNklZK2idpi6RPT12Hysb22yVdI2mhpKckXSLpMEk/lfQm\nSVslfTginhvYIHswx/y+rRqsX3l9fquaTzd3l/e9RvVZu9nmV6fH3pVqBvoVSRslfUrSUtVn/abP\nb4OkSyVdqw7WjzfXAEAi/HdjAJAI0QaARIg2ACRCtAEgEaINAIkQbQBIhGgDQCJEGwAS+R+WbIKX\nUzdQPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62c0998ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.hist(life_expectancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 10.,   1.,   0.,   2.,   2.,   0.,   0.,   1.,   1.,   3.]),\n",
       " array([   366.04496652,   3033.08920306,   5700.1334396 ,   8367.17767614,\n",
       "         11034.22191268,  13701.26614922,  16368.31038576,  19035.3546223 ,\n",
       "         21702.39885884,  24369.44309538,  27036.48733192]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nACRC6ANAIoQ+ACRC6ANAIoQ+ACTyVw03/oMe7SF9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62c09406d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(gdp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panda series - Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "countries = [\n",
    "    'Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',\n",
    "    'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas',\n",
    "    'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',\n",
    "    'Belize', 'Benin', 'Bhutan', 'Bolivia',\n",
    "    'Bosnia and Herzegovina'\n",
    "]\n",
    "\n",
    "\n",
    "employment_values = [\n",
    "    55.70000076,  51.40000153,  50.5       ,  75.69999695,\n",
    "    58.40000153,  40.09999847,  61.5       ,  57.09999847,\n",
    "    60.90000153,  66.59999847,  60.40000153,  68.09999847,\n",
    "    66.90000153,  53.40000153,  48.59999847,  56.79999924,\n",
    "    71.59999847,  58.40000153,  70.40000153,  41.20000076\n",
    "]\n",
    "\n",
    "# Employment data in 2007 for 20 countries\n",
    "employment = pd.Series(employment_values, index=countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan               55.700001\n",
       "Albania                   51.400002\n",
       "Algeria                   50.500000\n",
       "Angola                    75.699997\n",
       "Argentina                 58.400002\n",
       "Armenia                   40.099998\n",
       "Australia                 61.500000\n",
       "Austria                   57.099998\n",
       "Azerbaijan                60.900002\n",
       "Bahamas                   66.599998\n",
       "Bahrain                   60.400002\n",
       "Bangladesh                68.099998\n",
       "Barbados                  66.900002\n",
       "Belarus                   53.400002\n",
       "Belgium                   48.599998\n",
       "Belize                    56.799999\n",
       "Benin                     71.599998\n",
       "Bhutan                    58.400002\n",
       "Bolivia                   70.400002\n",
       "Bosnia and Herzegovina    41.200001\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexing pairs list given to 'index' to respective values in the series\n",
    "employment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.70000076\n",
      "55.70000076\n"
     ]
    }
   ],
   "source": [
    "print employment[0]\n",
    "print employment.iloc[0]    # iloc helps to refer the values using positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71.59999847\n",
      "71.59999847\n"
     ]
    }
   ],
   "source": [
    "print employment['Benin']       # index helps to use pd.Series as a dictionary\n",
    "print employment.loc['Benin']   # loc helps to refer values using index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20.000000\n",
       "mean     58.685000\n",
       "std       9.580862\n",
       "min      40.099998\n",
       "25%      52.900002\n",
       "50%      58.400002\n",
       "75%      66.674999\n",
       "max      75.699997\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employment.describe()   # pd.Series supports describe method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Angola', 75.699996949999999)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def max_employment(employment):\n",
    "    '''\n",
    "    Fill in this function to return the name of the country\n",
    "    with the highest employment in the given employment\n",
    "    data, and the employment in that country.\n",
    "    \n",
    "    The input will be a Pandas series where the values\n",
    "    are employment and the index is country names.\n",
    "    \n",
    "    Try using the Pandas argmax() function. Documention is\n",
    "    here: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.argmax.html\n",
    "    '''   \n",
    "    max_country = employment.argmax()\n",
    "    max_value = employment[max_country]\n",
    "\n",
    "    return (max_country, max_value)\n",
    "\n",
    "max_employment(employment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorized operations and Series index\n",
    "\n",
    "Operations on 2 pd series are carried out based on indices.  \n",
    "In `s1 + s2` values with same index are added. If one of them is not available, `NaN` is reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    11\n",
      "b    22\n",
      "c    33\n",
      "d    44\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Addition when indexes are the same\n",
    "if True:\n",
    "    s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n",
    "    s2 = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])\n",
    "    print s1 + s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    31\n",
      "b    12\n",
      "c    43\n",
      "d    24\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Indexes have same elements in a different order\n",
    "if True:\n",
    "    s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n",
    "    s2 = pd.Series([10, 20, 30, 40], index=['b', 'd', 'a', 'c'])\n",
    "    print s1 + s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a     NaN\n",
      "b     NaN\n",
      "c    13.0\n",
      "d    24.0\n",
      "e     NaN\n",
      "f     NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Indexes overlap, but do not have exactly the same elements\n",
    "if True:\n",
    "    s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n",
    "    s2 = pd.Series([10, 20, 30, 40], index=['c', 'd', 'e', 'f'])\n",
    "    print s1 + s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a   NaN\n",
      "b   NaN\n",
      "c   NaN\n",
      "d   NaN\n",
      "e   NaN\n",
      "f   NaN\n",
      "g   NaN\n",
      "h   NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Indexes do not overlap\n",
    "if True:\n",
    "    s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n",
    "    s2 = pd.Series([10, 20, 30, 40], index=['e', 'f', 'g', 'h'])\n",
    "    print s1 + s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n",
    "s2 = pd.Series([10, 20, 30, 40], index=['c', 'd', 'e', 'f'])\n",
    "\n",
    "# Try to write code that will add the 2 previous series together,\n",
    "# but treating missing values from either series as 0. The result\n",
    "# when printed out should be similar to the following line:\n",
    "# print pd.Series([1, 2, 13, 24, 30, 40], index=['a', 'b', 'c', 'd', 'e', 'f'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a     NaN\n",
      "b     NaN\n",
      "c    13.0\n",
      "d    24.0\n",
      "e     NaN\n",
      "f     NaN\n",
      "dtype: float64\n",
      "--------------------\n",
      "c    13.0\n",
      "d    24.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# To drop NaN from the series\n",
    "result = s1 + s2\n",
    "print result\n",
    "print '-'*20\n",
    "print result[~result.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a     1.0\n",
      "b     2.0\n",
      "c    13.0\n",
      "d    24.0\n",
      "e    30.0\n",
      "f    40.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Alternate solution: To treat NaN as 0 before addition\n",
    "print s1.add(s2, fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying custom functions on pd.Series\n",
    "\n",
    "`pd.apply()` - It is similar to python `map()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    5\n",
       "2    6\n",
       "3    7\n",
       "4    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add3(num):\n",
    "    return num+3\n",
    "\n",
    "a = pd.Series([1,2,3,4,5])\n",
    "a.apply(add3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Plotting in Pandas\n",
    "\n",
    "If the variable data is a NumPy array or a Pandas Series, just like if it is a list, the code\n",
    "\n",
    "```py\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(data)\n",
    "```\n",
    "\n",
    "will create a histogram of the data.\n",
    "\n",
    "Pandas also has built-in plotting that uses matplotlib behind the scenes, so if data is a Series, you can create a histogram using data.hist().\n",
    "\n",
    "There's no difference between these two in this case, but sometimes the Pandas wrapper can be more convenient. For example, you can make a line plot of a series using data.plot(). The index of the Series will be used for the x-axis and the values for the y-axis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy and Pandas to analyse 2D data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Representing 2D data**\n",
    "\n",
    "**Python** : list of lists  \n",
    "**NumPy** : 2D array  \n",
    "**Pandas** : DataFrame  \n",
    "\n",
    "NumPy 2D arrays, compared to Python list of lists -   \n",
    "\n",
    "* Memory efficient\n",
    "* Accessing is different - `a[1,3]` instead of `a[1][3]`. Rows and columns are 0 index based.  \n",
    "  Rows and columns both support slicing.\n",
    "* Methods like `mean()`, `std()` operate on the entire array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Try 2D array\n",
    "import numpy as np\n",
    "\n",
    "# Subway ridership for 5 stations on 10 different days\n",
    "ridership = np.array([\n",
    "    [   0,    0,    2,    5,    0],\n",
    "    [1478, 3877, 3674, 2328, 2539],\n",
    "    [1613, 4088, 3991, 6461, 2691],\n",
    "    [1560, 3392, 3826, 4787, 2613],\n",
    "    [1608, 4802, 3932, 4477, 2705],\n",
    "    [1576, 3933, 3909, 4979, 2685],\n",
    "    [  95,  229,  255,  496,  201],\n",
    "    [   2,    0,    1,   27,    0],\n",
    "    [1438, 3785, 3589, 4174, 2215],\n",
    "    [1342, 4043, 4009, 4665, 3033]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2328\n",
      "[[2328 2539]\n",
      " [6461 2691]]\n",
      "[1478 3877 3674 2328 2539]\n"
     ]
    }
   ],
   "source": [
    "# Accessing elements\n",
    "if True:\n",
    "    print ridership[1, 3]\n",
    "    print ridership[1:3, 3:5]\n",
    "    print ridership[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1478 3877 3676 2333 2539]\n",
      "[   0 5355 5701 4952 6410 5509  324    2 5223 5385]\n"
     ]
    }
   ],
   "source": [
    "# Vectorized operations on rows or columns\n",
    "if True:\n",
    "    print ridership[0, :] + ridership[1, :]\n",
    "    print ridership[:, 0] + ridership[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  3  4]\n",
      " [ 6  7  8]\n",
      " [10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "# Vectorized operations on entire arrays\n",
    "if True:\n",
    "    a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "    b = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])\n",
    "    print a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2342.5999999999999, 3239.9000000000001)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mean_riders_for_max_station(ridership):\n",
    "    '''\n",
    "    Fill in this function to find the station with the maximum riders on the\n",
    "    first day, then return the mean riders per day for that station. Also\n",
    "    return the mean ridership overall for comparsion.\n",
    "    \n",
    "    Hint: NumPy's argmax() function might be useful:\n",
    "    http://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html\n",
    "    '''\n",
    "    overall_mean = ridership.mean()\n",
    "    max_on_first_day = ridership[0, :].argmax()\n",
    "    mean_for_max = ridership[:, max_on_first_day].mean()\n",
    "    \n",
    "    return (overall_mean, mean_for_max)\n",
    "\n",
    "mean_riders_for_max_station(ridership)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2342.6\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print ridership.mean()\n",
    "print ridership[1, :].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2779.1999999999998"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridership[ridership[1, :].argmax() , :].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**NumPy axis argument**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n",
      "[12 15 18]\n",
      "[ 6 15 24]\n"
     ]
    }
   ],
   "source": [
    "# NumPy axis argument\n",
    "if True:\n",
    "    a = np.array([\n",
    "        [1, 2, 3],\n",
    "        [4, 5, 6],\n",
    "        [7, 8, 9]\n",
    "    ])\n",
    "    \n",
    "    print a.sum()\n",
    "    print a.sum(axis=0)\n",
    "    print a.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3239.9000000000001, 1071.2)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def min_and_max_riders_per_day(ridership):\n",
    "    '''\n",
    "    Fill in this function. First, for each subway station, calculate the\n",
    "    mean ridership per day. Then, out of all the subway stations, return the\n",
    "    maximum and minimum of these values. That is, find the maximum\n",
    "    mean-ridership-per-day and the minimum mean-ridership-per-day for any\n",
    "    subway station.\n",
    "    '''\n",
    "    station_riders = ridership.mean(axis=0)\n",
    "    max_daily_ridership = station_riders.max()\n",
    "    min_daily_ridership = station_riders.min()\n",
    "    \n",
    "    return (max_daily_ridership, min_daily_ridership)\n",
    "\n",
    "min_and_max_riders_per_day(ridership)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Dataframes\n",
    "\n",
    "Dataframes are 2D arrays where each column can have a different datatype.  \n",
    "It has index - each column has a name; and each row has a numerical index value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R003</th>\n",
       "      <th>R004</th>\n",
       "      <th>R005</th>\n",
       "      <th>R006</th>\n",
       "      <th>R007</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>05-01-11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-02-11</th>\n",
       "      <td>1478</td>\n",
       "      <td>3877</td>\n",
       "      <td>3674</td>\n",
       "      <td>2328</td>\n",
       "      <td>2539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-03-11</th>\n",
       "      <td>1613</td>\n",
       "      <td>4088</td>\n",
       "      <td>3991</td>\n",
       "      <td>6461</td>\n",
       "      <td>2691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-04-11</th>\n",
       "      <td>1560</td>\n",
       "      <td>3392</td>\n",
       "      <td>3826</td>\n",
       "      <td>4787</td>\n",
       "      <td>2613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-05-11</th>\n",
       "      <td>1608</td>\n",
       "      <td>4802</td>\n",
       "      <td>3932</td>\n",
       "      <td>4477</td>\n",
       "      <td>2705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-06-11</th>\n",
       "      <td>1576</td>\n",
       "      <td>3933</td>\n",
       "      <td>3909</td>\n",
       "      <td>4979</td>\n",
       "      <td>2685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-07-11</th>\n",
       "      <td>95</td>\n",
       "      <td>229</td>\n",
       "      <td>255</td>\n",
       "      <td>496</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-08-11</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-09-11</th>\n",
       "      <td>1438</td>\n",
       "      <td>3785</td>\n",
       "      <td>3589</td>\n",
       "      <td>4174</td>\n",
       "      <td>2215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-10-11</th>\n",
       "      <td>1342</td>\n",
       "      <td>4043</td>\n",
       "      <td>4009</td>\n",
       "      <td>4665</td>\n",
       "      <td>3033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          R003  R004  R005  R006  R007\n",
       "05-01-11     0     0     2     5     0\n",
       "05-02-11  1478  3877  3674  2328  2539\n",
       "05-03-11  1613  4088  3991  6461  2691\n",
       "05-04-11  1560  3392  3826  4787  2613\n",
       "05-05-11  1608  4802  3932  4477  2705\n",
       "05-06-11  1576  3933  3909  4979  2685\n",
       "05-07-11    95   229   255   496   201\n",
       "05-08-11     2     0     1    27     0\n",
       "05-09-11  1438  3785  3589  4174  2215\n",
       "05-10-11  1342  4043  4009  4665  3033"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Subway ridership for 5 stations on 10 different days\n",
    "ridership_df = pd.DataFrame(\n",
    "    data=[[   0,    0,    2,    5,    0],\n",
    "          [1478, 3877, 3674, 2328, 2539],\n",
    "          [1613, 4088, 3991, 6461, 2691],\n",
    "          [1560, 3392, 3826, 4787, 2613],\n",
    "          [1608, 4802, 3932, 4477, 2705],\n",
    "          [1576, 3933, 3909, 4979, 2685],\n",
    "          [  95,  229,  255,  496,  201],\n",
    "          [   2,    0,    1,   27,    0],\n",
    "          [1438, 3785, 3589, 4174, 2215],\n",
    "          [1342, 4043, 4009, 4665, 3033]],\n",
    "    index=['05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11',\n",
    "           '05-06-11', '05-07-11', '05-08-11', '05-09-11', '05-10-11'],\n",
    "    columns=['R003', 'R004', 'R005', 'R006', 'R007']\n",
    "    )\n",
    "\n",
    "ridership_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B\n",
      "0  0  3\n",
      "1  1  4\n",
      "2  2  5\n",
      "   A  B  C\n",
      "0  0  1  2\n",
      "1  3  4  5\n"
     ]
    }
   ],
   "source": [
    "# DataFrame creation\n",
    "if True:\n",
    "    # You can create a DataFrame out of a dictionary mapping column names to values\n",
    "    df_1 = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})\n",
    "    print df_1\n",
    "\n",
    "    # You can also use a list of lists or a 2D NumPy array\n",
    "    df_2 = pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=['A', 'B', 'C'])\n",
    "    print df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R003    0\n",
      "R004    0\n",
      "R005    2\n",
      "R006    5\n",
      "R007    0\n",
      "Name: 05-01-11, dtype: int64\n",
      "--------------------\n",
      "R003    1608\n",
      "R004    4802\n",
      "R005    3932\n",
      "R006    4477\n",
      "R007    2705\n",
      "Name: 05-05-11, dtype: int64\n",
      "--------------------\n",
      "05-01-11       0\n",
      "05-02-11    1478\n",
      "05-03-11    1613\n",
      "05-04-11    1560\n",
      "05-05-11    1608\n",
      "05-06-11    1576\n",
      "05-07-11      95\n",
      "05-08-11       2\n",
      "05-09-11    1438\n",
      "05-10-11    1342\n",
      "Name: R003, dtype: int64\n",
      "--------------------\n",
      "2328\n"
     ]
    }
   ],
   "source": [
    "# Accessing elements\n",
    "if True:\n",
    "    print ridership_df.iloc[0]   # returns 0th row\n",
    "    print '-'*20\n",
    "    print ridership_df.loc['05-05-11']    # returns row with label '05-05-11'\n",
    "    print '-'*20\n",
    "    print ridership_df['R003']   # returns column with label 'R003'\n",
    "    print '-'*20\n",
    "    print ridership_df.iloc[1, 3]  # returns a single element"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **`iloc` and `loc` both return a row of a data-frame.**  \n",
    "> `loc` works on labels in the index.   \n",
    "> `iloc` works on the positions in the index (so it only takes integers).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          R003  R004  R005  R006  R007\n",
      "05-02-11  1478  3877  3674  2328  2539\n",
      "05-03-11  1613  4088  3991  6461  2691\n",
      "05-04-11  1560  3392  3826  4787  2613\n"
     ]
    }
   ],
   "source": [
    "# Accessing multiple rows\n",
    "if True:\n",
    "    print ridership_df.iloc[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          R003  R005\n",
      "05-01-11     0     2\n",
      "05-02-11  1478  3674\n",
      "05-03-11  1613  3991\n",
      "05-04-11  1560  3826\n",
      "05-05-11  1608  3932\n",
      "05-06-11  1576  3909\n",
      "05-07-11    95   255\n",
      "05-08-11     2     1\n",
      "05-09-11  1438  3589\n",
      "05-10-11  1342  4009\n"
     ]
    }
   ],
   "source": [
    "# Accessing multiple columns\n",
    "if True:\n",
    "    print ridership_df[['R003', 'R005']]   # Notice double brackets, [[]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B\n",
      "0  0  3\n",
      "1  1  4\n",
      "2  2  5\n",
      "--------------------\n",
      "A     3\n",
      "B    12\n",
      "dtype: int64\n",
      "--------------------\n",
      "0    3\n",
      "1    5\n",
      "2    7\n",
      "dtype: int64\n",
      "--------------------\n",
      "[[0 3]\n",
      " [1 4]\n",
      " [2 5]]\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "# Pandas axis\n",
    "if True:\n",
    "    df = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})\n",
    "    print df\n",
    "    print '-'*20\n",
    "    print df.sum()         # sum of each column\n",
    "    print '-'*20\n",
    "    print df.sum(axis=1)   # sum of each row\n",
    "    print '-'*20\n",
    "    print df.values        # returns a data frame\n",
    "    print df.values.sum()  # sum of all values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2342.5999999999999, 3239.9000000000001)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mean_riders_for_max_station(ridership):\n",
    "    '''\n",
    "    Fill in this function to find the station with the maximum riders on the\n",
    "    first day, then return the mean riders per day for that station. Also\n",
    "    return the mean ridership overall for comparsion.\n",
    "    \n",
    "    This is the same as a previous exercise, but this time the\n",
    "    input is a Pandas DataFrame rather than a 2D NumPy array.\n",
    "    '''\n",
    "    max_station = ridership.iloc[0].argmax()\n",
    "    overall_mean = ridership.values.mean()\n",
    "    mean_for_max = ridership[max_station].mean()\n",
    "    \n",
    "    return (overall_mean, mean_for_max)\n",
    "\n",
    "mean_riders_for_max_station(ridership_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DFs are great data structures to represent CSVs !**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "subway_df = pd.read_csv('nyc_subway_weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UNIT</th>\n",
       "      <th>DATEn</th>\n",
       "      <th>TIMEn</th>\n",
       "      <th>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "      <th>ENTRIESn_hourly</th>\n",
       "      <th>EXITSn_hourly</th>\n",
       "      <th>datetime</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_week</th>\n",
       "      <th>...</th>\n",
       "      <th>pressurei</th>\n",
       "      <th>rain</th>\n",
       "      <th>tempi</th>\n",
       "      <th>wspdi</th>\n",
       "      <th>meanprecipi</th>\n",
       "      <th>meanpressurei</th>\n",
       "      <th>meantempi</th>\n",
       "      <th>meanwspdi</th>\n",
       "      <th>weather_lat</th>\n",
       "      <th>weather_lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.22</td>\n",
       "      <td>0</td>\n",
       "      <td>55.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 04:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.25</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 12:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.28</td>\n",
       "      <td>0</td>\n",
       "      <td>62.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 16:00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.26</td>\n",
       "      <td>0</td>\n",
       "      <td>57.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 20:00:00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.28</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNIT     DATEn     TIMEn  ENTRIESn   EXITSn  ENTRIESn_hourly  \\\n",
       "0  R003  05-01-11  00:00:00   4388333  2911002              0.0   \n",
       "1  R003  05-01-11  04:00:00   4388333  2911002              0.0   \n",
       "2  R003  05-01-11  12:00:00   4388333  2911002              0.0   \n",
       "3  R003  05-01-11  16:00:00   4388333  2911002              0.0   \n",
       "4  R003  05-01-11  20:00:00   4388333  2911002              0.0   \n",
       "\n",
       "   EXITSn_hourly             datetime  hour  day_week     ...       pressurei  \\\n",
       "0            0.0  2011-05-01 00:00:00     0         6     ...           30.22   \n",
       "1            0.0  2011-05-01 04:00:00     4         6     ...           30.25   \n",
       "2            0.0  2011-05-01 12:00:00    12         6     ...           30.28   \n",
       "3            0.0  2011-05-01 16:00:00    16         6     ...           30.26   \n",
       "4            0.0  2011-05-01 20:00:00    20         6     ...           30.28   \n",
       "\n",
       "  rain  tempi  wspdi meanprecipi  meanpressurei  meantempi  meanwspdi  \\\n",
       "0    0   55.9    3.5         0.0         30.258      55.98       7.86   \n",
       "1    0   52.0    3.5         0.0         30.258      55.98       7.86   \n",
       "2    0   62.1    6.9         0.0         30.258      55.98       7.86   \n",
       "3    0   57.9   15.0         0.0         30.258      55.98       7.86   \n",
       "4    0   52.0   10.4         0.0         30.258      55.98       7.86   \n",
       "\n",
       "   weather_lat  weather_lon  \n",
       "0    40.700348   -73.887177  \n",
       "1    40.700348   -73.887177  \n",
       "2    40.700348   -73.887177  \n",
       "3    40.700348   -73.887177  \n",
       "4    40.700348   -73.887177  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway_df.head()  # returns DF with first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'UNIT', u'DATEn', u'TIMEn', u'ENTRIESn', u'EXITSn', u'ENTRIESn_hourly',\n",
       "       u'EXITSn_hourly', u'datetime', u'hour', u'day_week', u'weekday',\n",
       "       u'station', u'latitude', u'longitude', u'conds', u'fog', u'precipi',\n",
       "       u'pressurei', u'rain', u'tempi', u'wspdi', u'meanprecipi',\n",
       "       u'meanpressurei', u'meantempi', u'meanwspdi', u'weather_lat',\n",
       "       u'weather_lon'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "      <th>ENTRIESn_hourly</th>\n",
       "      <th>EXITSn_hourly</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_week</th>\n",
       "      <th>weekday</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>fog</th>\n",
       "      <th>...</th>\n",
       "      <th>pressurei</th>\n",
       "      <th>rain</th>\n",
       "      <th>tempi</th>\n",
       "      <th>wspdi</th>\n",
       "      <th>meanprecipi</th>\n",
       "      <th>meanpressurei</th>\n",
       "      <th>meantempi</th>\n",
       "      <th>meanwspdi</th>\n",
       "      <th>weather_lat</th>\n",
       "      <th>weather_lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.264900e+04</td>\n",
       "      <td>4.264900e+04</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.812486e+07</td>\n",
       "      <td>1.986993e+07</td>\n",
       "      <td>1886.589955</td>\n",
       "      <td>1361.487866</td>\n",
       "      <td>10.046754</td>\n",
       "      <td>2.905719</td>\n",
       "      <td>0.714436</td>\n",
       "      <td>40.724647</td>\n",
       "      <td>-73.940364</td>\n",
       "      <td>0.009824</td>\n",
       "      <td>...</td>\n",
       "      <td>29.971096</td>\n",
       "      <td>0.224741</td>\n",
       "      <td>63.103780</td>\n",
       "      <td>6.927872</td>\n",
       "      <td>0.004618</td>\n",
       "      <td>29.971096</td>\n",
       "      <td>63.103780</td>\n",
       "      <td>6.927872</td>\n",
       "      <td>40.728555</td>\n",
       "      <td>-73.938693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.043607e+07</td>\n",
       "      <td>2.028986e+07</td>\n",
       "      <td>2952.385585</td>\n",
       "      <td>2183.845409</td>\n",
       "      <td>6.938928</td>\n",
       "      <td>2.079231</td>\n",
       "      <td>0.451688</td>\n",
       "      <td>0.071650</td>\n",
       "      <td>0.059713</td>\n",
       "      <td>0.098631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137942</td>\n",
       "      <td>0.417417</td>\n",
       "      <td>8.455597</td>\n",
       "      <td>4.510178</td>\n",
       "      <td>0.016344</td>\n",
       "      <td>0.131158</td>\n",
       "      <td>6.939011</td>\n",
       "      <td>3.179832</td>\n",
       "      <td>0.065420</td>\n",
       "      <td>0.059582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.576152</td>\n",
       "      <td>-74.073622</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.550000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.590000</td>\n",
       "      <td>49.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.600204</td>\n",
       "      <td>-74.014870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.039762e+07</td>\n",
       "      <td>7.613712e+06</td>\n",
       "      <td>274.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.677107</td>\n",
       "      <td>-73.987342</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.890000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.913333</td>\n",
       "      <td>58.283333</td>\n",
       "      <td>4.816667</td>\n",
       "      <td>40.688591</td>\n",
       "      <td>-73.985130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.818389e+07</td>\n",
       "      <td>1.331609e+07</td>\n",
       "      <td>905.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.717241</td>\n",
       "      <td>-73.953459</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.958000</td>\n",
       "      <td>60.950000</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>40.720570</td>\n",
       "      <td>-73.949150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.263049e+07</td>\n",
       "      <td>2.393771e+07</td>\n",
       "      <td>2255.000000</td>\n",
       "      <td>1537.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.759123</td>\n",
       "      <td>-73.907733</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.060000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.100000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.060000</td>\n",
       "      <td>67.466667</td>\n",
       "      <td>8.850000</td>\n",
       "      <td>40.755226</td>\n",
       "      <td>-73.912033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.357746e+08</td>\n",
       "      <td>1.493782e+08</td>\n",
       "      <td>32814.000000</td>\n",
       "      <td>34828.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.889185</td>\n",
       "      <td>-73.755383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.320000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.157500</td>\n",
       "      <td>30.293333</td>\n",
       "      <td>79.800000</td>\n",
       "      <td>17.083333</td>\n",
       "      <td>40.862064</td>\n",
       "      <td>-73.694176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ENTRIESn        EXITSn  ENTRIESn_hourly  EXITSn_hourly  \\\n",
       "count  4.264900e+04  4.264900e+04     42649.000000   42649.000000   \n",
       "mean   2.812486e+07  1.986993e+07      1886.589955    1361.487866   \n",
       "std    3.043607e+07  2.028986e+07      2952.385585    2183.845409   \n",
       "min    0.000000e+00  0.000000e+00         0.000000       0.000000   \n",
       "25%    1.039762e+07  7.613712e+06       274.000000     237.000000   \n",
       "50%    1.818389e+07  1.331609e+07       905.000000     664.000000   \n",
       "75%    3.263049e+07  2.393771e+07      2255.000000    1537.000000   \n",
       "max    2.357746e+08  1.493782e+08     32814.000000   34828.000000   \n",
       "\n",
       "               hour      day_week       weekday      latitude     longitude  \\\n",
       "count  42649.000000  42649.000000  42649.000000  42649.000000  42649.000000   \n",
       "mean      10.046754      2.905719      0.714436     40.724647    -73.940364   \n",
       "std        6.938928      2.079231      0.451688      0.071650      0.059713   \n",
       "min        0.000000      0.000000      0.000000     40.576152    -74.073622   \n",
       "25%        4.000000      1.000000      0.000000     40.677107    -73.987342   \n",
       "50%       12.000000      3.000000      1.000000     40.717241    -73.953459   \n",
       "75%       16.000000      5.000000      1.000000     40.759123    -73.907733   \n",
       "max       20.000000      6.000000      1.000000     40.889185    -73.755383   \n",
       "\n",
       "                fog      ...          pressurei          rain         tempi  \\\n",
       "count  42649.000000      ...       42649.000000  42649.000000  42649.000000   \n",
       "mean       0.009824      ...          29.971096      0.224741     63.103780   \n",
       "std        0.098631      ...           0.137942      0.417417      8.455597   \n",
       "min        0.000000      ...          29.550000      0.000000     46.900000   \n",
       "25%        0.000000      ...          29.890000      0.000000     57.000000   \n",
       "50%        0.000000      ...          29.960000      0.000000     61.000000   \n",
       "75%        0.000000      ...          30.060000      0.000000     69.100000   \n",
       "max        1.000000      ...          30.320000      1.000000     86.000000   \n",
       "\n",
       "              wspdi   meanprecipi  meanpressurei     meantempi     meanwspdi  \\\n",
       "count  42649.000000  42649.000000   42649.000000  42649.000000  42649.000000   \n",
       "mean       6.927872      0.004618      29.971096     63.103780      6.927872   \n",
       "std        4.510178      0.016344       0.131158      6.939011      3.179832   \n",
       "min        0.000000      0.000000      29.590000     49.400000      0.000000   \n",
       "25%        4.600000      0.000000      29.913333     58.283333      4.816667   \n",
       "50%        6.900000      0.000000      29.958000     60.950000      6.166667   \n",
       "75%        9.200000      0.000000      30.060000     67.466667      8.850000   \n",
       "max       23.000000      0.157500      30.293333     79.800000     17.083333   \n",
       "\n",
       "        weather_lat   weather_lon  \n",
       "count  42649.000000  42649.000000  \n",
       "mean      40.728555    -73.938693  \n",
       "std        0.065420      0.059582  \n",
       "min       40.600204    -74.014870  \n",
       "25%       40.688591    -73.985130  \n",
       "50%       40.720570    -73.949150  \n",
       "75%       40.755226    -73.912033  \n",
       "max       40.862064    -73.694176  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway_df.describe()   # gives stats for each **numerical** column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating correlation\n",
    "\n",
    "**Perason Coefficient** or **Correlation Coefficient**  \n",
    "\n",
    "The Pearson product-moment correlation coefficient is a measure of the strength of the linear relationship between two variables. It is referred to as Pearson's correlation or simply as the correlation coefficient. If the relationship between the variables is not linear, then the correlation coefficient does not adequately represent the strength of the relationship between the variables.  \n",
    "\n",
    "The symbol for Pearson's correlation is $\\rho$ when it is measured in the population and $r$ when it is measured in a sample.  \n",
    "\n",
    "$r \\in [-1, 1]$  \n",
    "$r = -1$ : a perfect negative linear relationship between variables  \n",
    "$r = 0$ : no linear relationship between variables,\n",
    "$r = 1$ : a perfect positive linear relationship between variables.\n",
    "\n",
    "Calculating $r$:  \n",
    "\n",
    "Let the numbers be:  \n",
    "$x_1, x_2, x_3, x_4, ...$  \n",
    "$y_1, y_2, y_3, y_4, ...$  \n",
    "\n",
    "1. Standardize each variable(multiples of std. dev.)\n",
    "1. $r = average of (x in std-dev.) \\times (y in std-dev.)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0356485157722\n",
      "-0.0266933483216\n",
      "-0.229034323408\n",
      "0.585895470766\n"
     ]
    }
   ],
   "source": [
    "def correlation(x, y):\n",
    "    '''\n",
    "    Fill in this function to compute the correlation between the two\n",
    "    input variables. Each input is either a NumPy array or a Pandas\n",
    "    Series.\n",
    "    \n",
    "    correlation = average of (x in standard units) times (y in standard units)\n",
    "    \n",
    "    Remember to pass the argument \"ddof=0\" to the Pandas std() function!\n",
    "    '''\n",
    "    x = pd.Series(x)\n",
    "    y = pd.Series(y)\n",
    "    \n",
    "    std_x = (x-x.mean())/x.std(ddof=0)\n",
    "    std_y = (y-y.mean())/y.std(ddof=0)\n",
    "    \n",
    "    return (std_x * std_y).mean()\n",
    "\n",
    "entries = subway_df['ENTRIESn_hourly']\n",
    "cum_entries = subway_df['ENTRIESn']\n",
    "rain = subway_df['meanprecipi']\n",
    "temp = subway_df['meantempi']\n",
    "\n",
    "print correlation(entries, rain)\n",
    "print correlation(entries, temp)\n",
    "print correlation(rain, temp)\n",
    "print correlation(entries, cum_entries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "ENTRIESn_hourly and ENTRIESn are strongly correlated. This is expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorized operations on DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    a   b   c\n",
      "0  11  44  77\n",
      "1  22  55  88\n",
      "2  33  66  99\n"
     ]
    }
   ],
   "source": [
    "# Adding DataFrames with the column names\n",
    "if True:\n",
    "    df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})\n",
    "    df2 = pd.DataFrame({'a': [10, 20, 30], 'b': [40, 50, 60], 'c': [70, 80, 90]})\n",
    "    print df1 + df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    a   b   c   d\n",
      "0 NaN  74  47 NaN\n",
      "1 NaN  85  58 NaN\n",
      "2 NaN  96  69 NaN\n"
     ]
    }
   ],
   "source": [
    "# Adding DataFrames with overlapping column names \n",
    "if True:\n",
    "    df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})\n",
    "    df2 = pd.DataFrame({'d': [10, 20, 30], 'c': [40, 50, 60], 'b': [70, 80, 90]})\n",
    "    print df1 + df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         a     b     c\n",
      "row1   NaN   NaN   NaN\n",
      "row2  32.0  65.0  98.0\n",
      "row3  23.0  56.0  89.0\n",
      "row4   NaN   NaN   NaN\n"
     ]
    }
   ],
   "source": [
    "# Adding DataFrames with overlapping row indexes\n",
    "if True:\n",
    "    df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]},\n",
    "                       index=['row1', 'row2', 'row3'])\n",
    "    df2 = pd.DataFrame({'a': [10, 20, 30], 'b': [40, 50, 60], 'c': [70, 80, 90]},\n",
    "                       index=['row4', 'row3', 'row2'])\n",
    "    print df1 + df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Cumulative entries and exits for one station for a few hours.\n",
    "entries_and_exits = pd.DataFrame({\n",
    "    'ENTRIESn': [3144312, 3144335, 3144353, 3144424, 3144594,\n",
    "                 3144808, 3144895, 3144905, 3144941, 3145094],\n",
    "    'EXITSn': [1088151, 1088159, 1088177, 1088231, 1088275,\n",
    "               1088317, 1088328, 1088331, 1088420, 1088753]\n",
    "})\n",
    "\n",
    "def get_hourly_entries_and_exits(entries_and_exits):\n",
    "    '''\n",
    "    Fill in this function to take a DataFrame with cumulative entries\n",
    "    and exits (entries in the first column, exits in the second) and\n",
    "    return a DataFrame with hourly entries and exits (entries in the\n",
    "    first column, exits in the second).\n",
    "    '''\n",
    "    \n",
    "    return entries_and_exits - entries_and_exits.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>170.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>214.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>87.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>153.0</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENTRIESn  EXITSn\n",
       "0       NaN     NaN\n",
       "1      23.0     8.0\n",
       "2      18.0    18.0\n",
       "3      71.0    54.0\n",
       "4     170.0    44.0\n",
       "5     214.0    42.0\n",
       "6      87.0    11.0\n",
       "7      10.0     3.0\n",
       "8      36.0    89.0\n",
       "9     153.0   333.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entries_and_exits - entries_and_exits.shift(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DataFrame.applymap(fn)**  \n",
    "Apply function `fn` to each element of dataframe and returns a new dataframme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a   b   c\n",
      "0  2  11   6\n",
      "1  3  21  11\n",
      "2  4  31  16\n"
     ]
    }
   ],
   "source": [
    "# DataFrame applymap()\n",
    "if True:\n",
    "    df = pd.DataFrame({\n",
    "        'a': [1, 2, 3],\n",
    "        'b': [10, 20, 30],\n",
    "        'c': [5, 10, 15]\n",
    "    })\n",
    "    \n",
    "    def add_one(x):\n",
    "        return x + 1\n",
    "        \n",
    "    print df.applymap(add_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DataFrame.apply(fn)**:  \n",
    "\n",
    "Applies function `fn` to each **column** of df.  \n",
    "\n",
    "When is such an operation needed ?  \n",
    "\n",
    "In cases where the transformation of an element depends on the other values in the column too. For eg. consider a df having subjects as columns and students as indices. To calculate the relative grades of each subject, we need to consider all the marks of a subject, when calculating the grade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a     3\n",
      "b    30\n",
      "c    15\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "if True:\n",
    "    df = pd.DataFrame({\n",
    "        'a': [1, 2, 3],\n",
    "        'b': [10, 20, 30],\n",
    "        'c': [5, 10, 15]\n",
    "    })\n",
    "    \n",
    "    print df.apply(np.max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a DataFrame and a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0   1    2    3\n",
      "0  10  50   90  130\n",
      "1  20  60  100  140\n",
      "2  30  70  110  150\n",
      "3  40  80  120  160\n",
      "\n",
      "    0   1    2    3\n",
      "0  11  52   93  134\n",
      "1  21  62  103  144\n",
      "2  31  72  113  154\n",
      "3  41  82  123  164\n"
     ]
    }
   ],
   "source": [
    "# Adding a Series to a square DataFrame\n",
    "if True:\n",
    "    s = pd.Series([1, 2, 3, 4])\n",
    "    df = pd.DataFrame({\n",
    "        0: [10, 20, 30, 40],\n",
    "        1: [50, 60, 70, 80],\n",
    "        2: [90, 100, 110, 120],\n",
    "        3: [130, 140, 150, 160]\n",
    "    })\n",
    "    \n",
    "    print df\n",
    "    print '' # Create a blank line between outputs\n",
    "    print df + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0   1   2   3\n",
      "0  10  20  30  40\n",
      "\n",
      "    0   1   2   3\n",
      "0  11  22  33  44\n"
     ]
    }
   ],
   "source": [
    "# Adding a Series to a one-row DataFrame \n",
    "if True:\n",
    "    s = pd.Series([1, 2, 3, 4])\n",
    "    df = pd.DataFrame({0: [10], 1: [20], 2: [30], 3: [40]})\n",
    "    \n",
    "    print df\n",
    "    print '' # Create a blank line between outputs\n",
    "    print df + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0\n",
      "0  10\n",
      "1  20\n",
      "2  30\n",
      "3  40\n",
      "\n",
      "    0   1   2   3\n",
      "0  11 NaN NaN NaN\n",
      "1  21 NaN NaN NaN\n",
      "2  31 NaN NaN NaN\n",
      "3  41 NaN NaN NaN\n"
     ]
    }
   ],
   "source": [
    "# Adding a Series to a one-column DataFrame\n",
    "if True:\n",
    "    s = pd.Series([1, 2, 3, 4])\n",
    "    df = pd.DataFrame({0: [10, 20, 30, 40]})\n",
    "    \n",
    "    print df\n",
    "    print '' # Create a blank line between outputs\n",
    "    print df + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    a   b    c    d\n",
      "0  10  50   90  130\n",
      "1  20  60  100  140\n",
      "2  30  70  110  150\n",
      "3  40  80  120  160\n",
      "\n",
      "    a   b    c    d\n",
      "0  11  52   93  134\n",
      "1  21  62  103  144\n",
      "2  31  72  113  154\n",
      "3  41  82  123  164\n"
     ]
    }
   ],
   "source": [
    "# Adding when DataFrame column names match Series index\n",
    "if True:\n",
    "    s = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n",
    "    df = pd.DataFrame({\n",
    "        'a': [10, 20, 30, 40],\n",
    "        'b': [50, 60, 70, 80],\n",
    "        'c': [90, 100, 110, 120],\n",
    "        'd': [130, 140, 150, 160]\n",
    "    })\n",
    "    \n",
    "    print df\n",
    "    print '' # Create a blank line between outputs\n",
    "    print df + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    a   b    c    d\n",
      "0  10  50   90  130\n",
      "1  20  60  100  140\n",
      "2  30  70  110  150\n",
      "3  40  80  120  160\n",
      "\n",
      "    0   1   2   3   a   b   c   d\n",
      "0 NaN NaN NaN NaN NaN NaN NaN NaN\n",
      "1 NaN NaN NaN NaN NaN NaN NaN NaN\n",
      "2 NaN NaN NaN NaN NaN NaN NaN NaN\n",
      "3 NaN NaN NaN NaN NaN NaN NaN NaN\n"
     ]
    }
   ],
   "source": [
    "# Adding when DataFrame column names don't match Series index\n",
    "if True:\n",
    "    s = pd.Series([1, 2, 3, 4])\n",
    "    df = pd.DataFrame({\n",
    "        'a': [10, 20, 30, 40],\n",
    "        'b': [50, 60, 70, 80],\n",
    "        'c': [90, 100, 110, 120],\n",
    "        'd': [130, 140, 150, 160]\n",
    "    })\n",
    "    \n",
    "    print df\n",
    "    print '' # Create a blank line between outputs\n",
    "    print df + s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A series is considered as a row while operating with a df. Hence in `df + s` the s[0] is added to the 0th column of df i.e. a number is added to a column => the number is added to each element of the column **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas `groupby()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>above_three</th>\n",
       "      <th>even</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  above_three   even  value\n",
       "a       False  False      1\n",
       "b       False  False      3\n",
       "c       False   True      2\n",
       "d        True   True      4\n",
       "e       False  False      1\n",
       "f        True   True      6\n",
       "g        True   True      4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "values = np.array([1, 3, 2, 4, 1, 6, 4])\n",
    "example_df = pd.DataFrame({\n",
    "    'value': values,\n",
    "    'even': values % 2 == 0,\n",
    "    'above_three': values > 3 \n",
    "}, index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])\n",
    "\n",
    "example_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pandas.core.groupby.DataFrameGroupBy object at 0x7fb35f0f0450>\n",
      "{False: Index([u'a', u'b', u'e'], dtype='object'), True: Index([u'c', u'd', u'f', u'g'], dtype='object')}\n"
     ]
    }
   ],
   "source": [
    "# Examine groups\n",
    "if True:\n",
    "    grouped_data = example_df.groupby('even')\n",
    "    # The groups attribute is a dictionary mapping keys to lists of row indexes\n",
    "    print grouped_data\n",
    "    print grouped_data.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(True, False): Index([u'c'], dtype='object'), (False, False): Index([u'a', u'b', u'e'], dtype='object'), (True, True): Index([u'd', u'f', u'g'], dtype='object')}\n"
     ]
    }
   ],
   "source": [
    "# Group by multiple columns\n",
    "if True:\n",
    "    grouped_data = example_df.groupby(['even', 'above_three'])\n",
    "    print grouped_data.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       above_three  value\n",
      "even                     \n",
      "False          0.0      5\n",
      "True           3.0     16\n"
     ]
    }
   ],
   "source": [
    "# Get sum of each group\n",
    "# The output is a DF where the keys are row indices and (initial columns - group columns) are the columns.\n",
    "if True:\n",
    "    grouped_data = example_df.groupby('even')\n",
    "    print grouped_data.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   value\n",
      "even  above_three       \n",
      "False False            5\n",
      "True  False            2\n",
      "      True            14\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "    grouped_data = example_df.groupby(['even', 'above_three'])\n",
    "    print grouped_data.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "even\n",
      "False     5\n",
      "True     16\n",
      "Name: value, dtype: int64\n",
      "\n",
      "\n",
      "even\n",
      "False     5\n",
      "True     16\n",
      "Name: value, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Limit columns in result\n",
    "if True:\n",
    "    grouped_data = example_df.groupby('even')\n",
    "    \n",
    "    # You can take one or more columns from the result DataFrame\n",
    "    print grouped_data.sum()['value']\n",
    "    \n",
    "    print '\\n' # Blank line to separate results\n",
    "    \n",
    "    # You can also take a subset of columns from the grouped data before \n",
    "    # collapsing to a DataFrame. In this case, the result is the same.\n",
    "    print grouped_data['value'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joining two DFs\n",
    "\n",
    "**DataFrame.merge()**  \n",
    "\n",
    "This is similar to a join in SQL. Hence, there should be option to Left join, Right join and Inner join while joining DFs also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "subway_df = pd.DataFrame({\n",
    "    'UNIT': ['R003', 'R003', 'R003', 'R003', 'R003', 'R004', 'R004', 'R004',\n",
    "             'R004', 'R004'],\n",
    "    'DATEn': ['05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11',\n",
    "              '05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11'],\n",
    "    'hour': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    'ENTRIESn': [ 4388333,  4388348,  4389885,  4391507,  4393043, 14656120,\n",
    "                 14656174, 14660126, 14664247, 14668301],\n",
    "    'EXITSn': [ 2911002,  2911036,  2912127,  2913223,  2914284, 14451774,\n",
    "               14451851, 14454734, 14457780, 14460818],\n",
    "    'latitude': [ 40.689945,  40.689945,  40.689945,  40.689945,  40.689945,\n",
    "                  40.69132 ,  40.69132 ,  40.69132 ,  40.69132 ,  40.69132 ],\n",
    "    'longitude': [-73.872564, -73.872564, -73.872564, -73.872564, -73.872564,\n",
    "                  -73.867135, -73.867135, -73.867135, -73.867135, -73.867135]\n",
    "})\n",
    "\n",
    "weather_df = pd.DataFrame({\n",
    "    'DATEn': ['05-01-11', '05-01-11', '05-02-11', '05-02-11', '05-03-11',\n",
    "              '05-03-11', '05-04-11', '05-04-11', '05-05-11', '05-05-11'],\n",
    "    'hour': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    'latitude': [ 40.689945,  40.69132 ,  40.689945,  40.69132 ,  40.689945,\n",
    "                  40.69132 ,  40.689945,  40.69132 ,  40.689945,  40.69132 ],\n",
    "    'longitude': [-73.872564, -73.867135, -73.872564, -73.867135, -73.872564,\n",
    "                  -73.867135, -73.872564, -73.867135, -73.872564, -73.867135],\n",
    "    'pressurei': [ 30.24,  30.24,  30.32,  30.32,  30.14,  30.14,  29.98,  29.98,\n",
    "                   30.01,  30.01],\n",
    "    'fog': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    'rain': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    'tempi': [ 52. ,  52. ,  48.9,  48.9,  54. ,  54. ,  57.2,  57.2,  48.9,  48.9],\n",
    "    'wspdi': [  8.1,   8.1,   6.9,   6.9,   3.5,   3.5,  15. ,  15. ,  15. ,  15. ]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATEn</th>\n",
       "      <th>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>hour</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05-01-11</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05-02-11</td>\n",
       "      <td>4388348</td>\n",
       "      <td>2911036</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05-03-11</td>\n",
       "      <td>4389885</td>\n",
       "      <td>2912127</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05-04-11</td>\n",
       "      <td>4391507</td>\n",
       "      <td>2913223</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-05-11</td>\n",
       "      <td>4393043</td>\n",
       "      <td>2914284</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>05-01-11</td>\n",
       "      <td>14656120</td>\n",
       "      <td>14451774</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>05-02-11</td>\n",
       "      <td>14656174</td>\n",
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       "      <td>R004</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>05-03-11</td>\n",
       "      <td>14660126</td>\n",
       "      <td>14454734</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>05-04-11</td>\n",
       "      <td>14664247</td>\n",
       "      <td>14457780</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>05-05-11</td>\n",
       "      <td>14668301</td>\n",
       "      <td>14460818</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DATEn  ENTRIESn    EXITSn  UNIT  hour   latitude  longitude\n",
       "0  05-01-11   4388333   2911002  R003     0  40.689945 -73.872564\n",
       "1  05-02-11   4388348   2911036  R003     0  40.689945 -73.872564\n",
       "2  05-03-11   4389885   2912127  R003     0  40.689945 -73.872564\n",
       "3  05-04-11   4391507   2913223  R003     0  40.689945 -73.872564\n",
       "4  05-05-11   4393043   2914284  R003     0  40.689945 -73.872564\n",
       "5  05-01-11  14656120  14451774  R004     0  40.691320 -73.867135\n",
       "6  05-02-11  14656174  14451851  R004     0  40.691320 -73.867135\n",
       "7  05-03-11  14660126  14454734  R004     0  40.691320 -73.867135\n",
       "8  05-04-11  14664247  14457780  R004     0  40.691320 -73.867135\n",
       "9  05-05-11  14668301  14460818  R004     0  40.691320 -73.867135"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATEn</th>\n",
       "      <th>fog</th>\n",
       "      <th>hour</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>pressurei</th>\n",
       "      <th>rain</th>\n",
       "      <th>tempi</th>\n",
       "      <th>wspdi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05-01-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>30.24</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05-01-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>30.24</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>30.32</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>30.32</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-03-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>30.14</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>05-03-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>30.14</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>05-04-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>29.98</td>\n",
       "      <td>0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>05-04-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>29.98</td>\n",
       "      <td>0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>05-05-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>30.01</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>05-05-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>30.01</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DATEn  fog  hour   latitude  longitude  pressurei  rain  tempi  wspdi\n",
       "0  05-01-11    0     0  40.689945 -73.872564      30.24     0   52.0    8.1\n",
       "1  05-01-11    0     0  40.691320 -73.867135      30.24     0   52.0    8.1\n",
       "2  05-02-11    0     0  40.689945 -73.872564      30.32     0   48.9    6.9\n",
       "3  05-02-11    0     0  40.691320 -73.867135      30.32     0   48.9    6.9\n",
       "4  05-03-11    0     0  40.689945 -73.872564      30.14     0   54.0    3.5\n",
       "5  05-03-11    0     0  40.691320 -73.867135      30.14     0   54.0    3.5\n",
       "6  05-04-11    0     0  40.689945 -73.872564      29.98     0   57.2   15.0\n",
       "7  05-04-11    0     0  40.691320 -73.867135      29.98     0   57.2   15.0\n",
       "8  05-05-11    0     0  40.689945 -73.872564      30.01     0   48.9   15.0\n",
       "9  05-05-11    0     0  40.691320 -73.867135      30.01     0   48.9   15.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATEn</th>\n",
       "      <th>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>hour</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>fog</th>\n",
       "      <th>pressurei</th>\n",
       "      <th>rain</th>\n",
       "      <th>tempi</th>\n",
       "      <th>wspdi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05-01-11</td>\n",
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       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
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       "      <td>0</td>\n",
       "      <td>30.24</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05-02-11</td>\n",
       "      <td>4388348</td>\n",
       "      <td>2911036</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
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       "      <td>6.9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05-03-11</td>\n",
       "      <td>4389885</td>\n",
       "      <td>2912127</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
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       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05-04-11</td>\n",
       "      <td>4391507</td>\n",
       "      <td>2913223</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>0</td>\n",
       "      <td>29.98</td>\n",
       "      <td>0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-05-11</td>\n",
       "      <td>4393043</td>\n",
       "      <td>2914284</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>0</td>\n",
       "      <td>30.01</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>15.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>05-01-11</td>\n",
       "      <td>14656120</td>\n",
       "      <td>14451774</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>0</td>\n",
       "      <td>30.24</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>05-02-11</td>\n",
       "      <td>14656174</td>\n",
       "      <td>14451851</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>0</td>\n",
       "      <td>30.32</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>05-03-11</td>\n",
       "      <td>14660126</td>\n",
       "      <td>14454734</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>0</td>\n",
       "      <td>30.14</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>05-04-11</td>\n",
       "      <td>14664247</td>\n",
       "      <td>14457780</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>0</td>\n",
       "      <td>29.98</td>\n",
       "      <td>0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>05-05-11</td>\n",
       "      <td>14668301</td>\n",
       "      <td>14460818</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>0</td>\n",
       "      <td>30.01</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DATEn  ENTRIESn    EXITSn  UNIT  hour   latitude  longitude  fog  \\\n",
       "0  05-01-11   4388333   2911002  R003     0  40.689945 -73.872564    0   \n",
       "1  05-02-11   4388348   2911036  R003     0  40.689945 -73.872564    0   \n",
       "2  05-03-11   4389885   2912127  R003     0  40.689945 -73.872564    0   \n",
       "3  05-04-11   4391507   2913223  R003     0  40.689945 -73.872564    0   \n",
       "4  05-05-11   4393043   2914284  R003     0  40.689945 -73.872564    0   \n",
       "5  05-01-11  14656120  14451774  R004     0  40.691320 -73.867135    0   \n",
       "6  05-02-11  14656174  14451851  R004     0  40.691320 -73.867135    0   \n",
       "7  05-03-11  14660126  14454734  R004     0  40.691320 -73.867135    0   \n",
       "8  05-04-11  14664247  14457780  R004     0  40.691320 -73.867135    0   \n",
       "9  05-05-11  14668301  14460818  R004     0  40.691320 -73.867135    0   \n",
       "\n",
       "   pressurei  rain  tempi  wspdi  \n",
       "0      30.24     0   52.0    8.1  \n",
       "1      30.32     0   48.9    6.9  \n",
       "2      30.14     0   54.0    3.5  \n",
       "3      29.98     0   57.2   15.0  \n",
       "4      30.01     0   48.9   15.0  \n",
       "5      30.24     0   52.0    8.1  \n",
       "6      30.32     0   48.9    6.9  \n",
       "7      30.14     0   54.0    3.5  \n",
       "8      29.98     0   57.2   15.0  \n",
       "9      30.01     0   48.9   15.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def combine_dfs(subway_df, weather_df):\n",
    "    '''\n",
    "    Fill in this function to take 2 DataFrames, one with subway data and one with weather data,\n",
    "    and return a single dataframe with one row for each date, hour, and location. Only include\n",
    "    times and locations that have both subway data and weather data available.\n",
    "    '''\n",
    "    return subway_df.merge(weather_df, how='inner', on=['DATEn', 'hour', 'latitude', 'longitude'])\n",
    "\n",
    "combine_dfs(subway_df, weather_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting DFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "values = np.array([1, 3, 2, 4, 1, 6, 4])\n",
    "example_df = pd.DataFrame({\n",
    "    'value': values,\n",
    "    'even': values % 2 == 0,\n",
    "    'above_three': values > 3 \n",
    "}, index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# groupby() without as_index\n",
    "if False:\n",
    "    first_even = example_df.groupby('even').first()\n",
    "    print first_even\n",
    "    print first_even['even'] # Causes an error. 'even' is no longer a column in the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    even above_three  value\n",
      "0  False       False      1\n",
      "1   True       False      2\n",
      "0    False\n",
      "1     True\n",
      "Name: even, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# groupby() with as_index=False\n",
    "# columns mentioned in group_by() will not be used as index, but will be columns\n",
    "if True:\n",
    "    first_even = example_df.groupby('even', as_index=False).first()\n",
    "    print first_even\n",
    "    print first_even['even'] # Now 'even' is still a column in the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make a scatter plot between lat and long; and ridership size as bubble size**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "subway_df = pd.read_csv('nyc_subway_weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UNIT</th>\n",
       "      <th>DATEn</th>\n",
       "      <th>TIMEn</th>\n",
       "      <th>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "      <th>ENTRIESn_hourly</th>\n",
       "      <th>EXITSn_hourly</th>\n",
       "      <th>datetime</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_week</th>\n",
       "      <th>...</th>\n",
       "      <th>pressurei</th>\n",
       "      <th>rain</th>\n",
       "      <th>tempi</th>\n",
       "      <th>wspdi</th>\n",
       "      <th>meanprecipi</th>\n",
       "      <th>meanpressurei</th>\n",
       "      <th>meantempi</th>\n",
       "      <th>meanwspdi</th>\n",
       "      <th>weather_lat</th>\n",
       "      <th>weather_lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.22</td>\n",
       "      <td>0</td>\n",
       "      <td>55.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 04:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.25</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 12:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.28</td>\n",
       "      <td>0</td>\n",
       "      <td>62.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNIT     DATEn     TIMEn  ENTRIESn   EXITSn  ENTRIESn_hourly  \\\n",
       "0  R003  05-01-11  00:00:00   4388333  2911002              0.0   \n",
       "1  R003  05-01-11  04:00:00   4388333  2911002              0.0   \n",
       "2  R003  05-01-11  12:00:00   4388333  2911002              0.0   \n",
       "\n",
       "   EXITSn_hourly             datetime  hour  day_week     ...       pressurei  \\\n",
       "0            0.0  2011-05-01 00:00:00     0         6     ...           30.22   \n",
       "1            0.0  2011-05-01 04:00:00     4         6     ...           30.25   \n",
       "2            0.0  2011-05-01 12:00:00    12         6     ...           30.28   \n",
       "\n",
       "  rain  tempi  wspdi meanprecipi  meanpressurei  meantempi  meanwspdi  \\\n",
       "0    0   55.9    3.5         0.0         30.258      55.98       7.86   \n",
       "1    0   52.0    3.5         0.0         30.258      55.98       7.86   \n",
       "2    0   62.1    6.9         0.0         30.258      55.98       7.86   \n",
       "\n",
       "   weather_lat  weather_lon  \n",
       "0    40.700348   -73.887177  \n",
       "1    40.700348   -73.887177  \n",
       "2    40.700348   -73.887177  \n",
       "\n",
       "[3 rows x 27 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'UNIT', u'DATEn', u'TIMEn', u'ENTRIESn', u'EXITSn', u'ENTRIESn_hourly',\n",
       "       u'EXITSn_hourly', u'datetime', u'hour', u'day_week', u'weekday',\n",
       "       u'station', u'latitude', u'longitude', u'conds', u'fog', u'precipi',\n",
       "       u'pressurei', u'rain', u'tempi', u'wspdi', u'meanprecipi',\n",
       "       u'meanpressurei', u'meantempi', u'meanwspdi', u'weather_lat',\n",
       "       u'weather_lon'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_by_location = subway_df.groupby(['latitude', 'longitude'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    40.576152\n",
       "1    40.576298\n",
       "2    40.577961\n",
       "3    40.589547\n",
       "4    40.590867\n",
       "Name: latitude, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_by_location.head()['latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb35cdc3910>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NlJMNFrrKXbrEdrD87kqmTJnFm950KOcbx0o5PDjTuc7UHZXt95hPmYtxnOUY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\nkIiISEEUSEREpCD/H6H9BwRB3JqWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb35cef2710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Lat')\n",
    "plt.ylabel('Long')\n",
    "plt.scatter(data_by_location['latitude'], data_by_location['longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb35d1b7a90>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CHGRirgBO0Lz553Ttephu3dwUFdUwZEhrRo8eVKsqZlVVFT/72ccYc2uYIzZF\noo9uQFa2axEfxKvA9QQLgoPYl3z2RuiEs21XI1m24c7JHzdiNrnZbmACKUF8BdGwgPAmuRpE2ExH\nvu9AViFVZUHCUK+I4rifIolvS5BQ2zcJTmxcja90d6rIAcrJzMymUaNUmQHrFyoclLiprq5mzpwP\nWLToJGvW9OD4cbvVdA6QQ2npCDZtgk2b5N3MzD0MGLCESy+Fu+66iLy8fP74x7dYu/baCPvqiaxa\n9yKmjbHISnQ/8BjwI2oLCCfCYRRSH+iyMJ/PRjQGJ1bZJdg7qhPNBqKbTBcRfmXudaLfSmjBALL6\nb+M3PpqM9U8QDcdbNr0V8hsV+o3JIhWF94LpRaNGH5CTk0qNr/4Qk3CwLOt+RO/y3gmFxphelmVN\nBO5DPEv7gZuNMRV+2zVBdP5CpHjKbcaY/fFdglJXVFRU8Kc/LePtt7P44otRxOssrK7uwIYN17Fh\nQxUvvvgOvXtv4eOP/SeOUHyF3IJfULv1ZzniP3iB2n0AKrHPLchDbs9QvIuUtw43UfqzBxGEiW1O\nE5ly5FqdTqarkfMbhCTwHaF2mO8SxNwTLm/jW6SAXywsQcpvrMDn0O+H+Ge8wuE0yatKa8c5NGmy\n4Izt7xCTcDDGzABmAFiWdQu+u/9HwFhjzAnLsp5Fli+v+G16F7DNGHOtZVmXAL8H7o715JXk4na7\nKS4uYefOAxw/XoHb7SY/P4cuXQrYu/ckDz/8DevXX0Pio2+y2LPnUvbsOYKvx0AoDiImpdsITpr6\nFDFvlCEmpgsQLcNpLaYeBEfdfOfZPjBRLBzv48wnkUjeJXzXtkC2IMLE61A/D8ls9r7ejUwR4Sqo\ngnzPXs2oCudaw1eIEKr0bOe9hwITETdh7/9JFplkZ595vgYvcZmVLMvKREJERgAYY8Z43s9CvFOB\nZS57Au94xr5vWdZT8RxfSSyVlZXMmfMBmzaVUVycwY4dWeza1Z2Kin74Vm/HyMiYB1xATc1NSTyb\nN5HEqnB8i/gXfkHoVfJRxOzTBMmmXYMkc3XFWbjrVs84r3DwhmpOC7tFbb5CbvdUrzq/xpkZqwr5\n/m73ey8f0RzA5yuxu95d+ARCMdDdwbGrESE0ldC1sWr8/r8PXzG+1JORUVddAuueeH0OU4Alxph/\nxnpZlnUr8BCwwBjzfsD4zUig9zzLsoaT+hAEJQQ7d+7hqac2sGJFLiLfAxvae9kLvEdNzfdJbqy+\nG1lRhnvD/uKfAAAgAElEQVQwtyMJb6FqBdUgNvTA2v1DkcStJ7C3x28EzkXMT+uQ2kDeGH6nk/0n\niJM8lezAmbkLxAF9fYj3myHf0weE97l4KUEqnno5TviyF/4soXbF1UBtowU+31DdmnQKC5PVlrX+\nYyscLMuaDtyJPLHe1lC/NcYsQ8IX7vIfb4x53rKsF4EXLMu6wRjjb1Z6BhhgWdZKJEPHvvsG0qg+\nnamr69uzZz//9m/LWbq0G4cP29XeKUFMCE5XzvGwGrHrh6IYCV29MsRnO5HV7pWefQSSh5Ta2I2Y\njUJRhqy+veagj5FWom5qO0kj8QWpN4V4HcdOjrsFWZeFEr6jEId7ayLnZNQgAsTfz1SOs0XDaXx+\nn3YEC4dhSDXXKcSXsxI/+fmN037+CYetcDDGPINM6rXwOJc7GmN2el7nAKOMMW8ZY2osy1qAFHR/\nxW9fFXh8DJZldUCMxbYcOBCqeUh6UFCQl/Lrc7vdvPjiSv7610yKi2/AfnW2C7H9Oi2iFi/fEboq\n5x7PeYQyN21FJvVbkdINoXpDb/Vs3zjEZ14WU/s6RyBO7Z/ZnXTAca6OYnwieAIRqHbO4RpEyN8Y\n5vNsZCFgp/W8gZiv1gZsa7fe+4Ta5btbEGzm86/0munZZ90EVmZmVjTY+SdeoRbPN96f2rWSq4An\nLcsabIz5DjEUGv8NLMsaBwwxxvwOeYoXxXF8JQYOHjzML36xnKVLL6eysoODLQ4AH5GcRjSnkRh5\n/wzUI4R2+B7zjA01aX2G2Ka92kQpwRFCqxAheB3hb7sDiJPUvyDeaaRngVPzxjHCm+WSRTEiFI4A\nl9iMXYVkMoejBsnkjuRY3ous/BtTW1NojH1V213UdvJnUbs0h5dLEQHUF9F0oi0lnhiKimrsB6Up\n8ZTPaI+EqwJgjKkGvg/M95iNugBPAViWNc8z7D1goGVZ3tz+/4jj+EqU7Ny5l5tuep833rjdoWBw\nI/bhRK+CtyFJZO8hJoSJfn/NCV0mYRGhI38+RSZF/widMmprB/MRk5DdxPkewaUw1oY5n0j7GBnF\n+EQwE4nMcmEvxA4S2Vz0AaLwR2I18p1kUXvV3w0RVNHQjNDFDvPxlbGIdp+JopQ+fVJRUr1+ErPm\nYIyZizzh/u+9jTRqDRw72fNvGaGNxUqS2bXrO269dSNffBGNz2AZks6SKKdgKWK26U14p/CeEMdb\njTiUA9cyW5DV/piA991+/85GJjv/2v+hVoM7kNW3/7G/xFdYzik1pKbiqpfNSMmPDOx/px3YZ04f\nwJftHApv5rjL86+/1uekH0bgORYg91koLkLKm9dNOGnbtusYPrxuNJb6gBbeOwM4ePAwd975EV98\nESo6JRz7PP8mqqHKaqSv8I2EbrQDkuEcWGupFFntBoZIFiO+hUDBAKI1lCGCYRTB1xBqEv2Y4FpE\nXxKdY3k/zqJ1EskinGdgbyB0xVMvTjrk+WtX3vgUf+wcyIHfvZ22MwkRgEcijEkOl1xyiPz8ZHUM\nrP+ocDgDuO++d9m4MdqcBK/WkAi8Zp2JhL/lPkJs9YFZ1osJVjarkLyFcDV88hB79XBCm1BaUHuy\nOYaYMfw5HOJc7PiI1Mbkn0DO2754oWg0dtrFp9iX2j5J4n0qkQIEmgI/AOZFGJN4srN3cO21iVoY\nNUxUOKQ5s2at4q23ojUNfYpkJifCnDQXyU4OFzoKYnP25kv6r9TDmZMWEjlyqgQxjYR7uM9DrtFL\nKF/DSuxt74FUk9qompeInEHuzzakHHYkjhBZIIaqmxR4j/RCNJRI+wikI5H9CgWIbyJcSZPEM3To\nSkaOjKYnRfqhwiGN2bfvEI895qayslOUW24n8mTulKXIRGyX6zgbmey/w1eqoQqJigk0J20FOhA+\nQe4Lz2eRfAV5iP0dZLIKnNRriL6AnDcNKJUcRiKp/M8hHF8hUUiRsIvMCXVftMFnggQRQGsJT6hz\nvIDwfgcvVyOJe8mnbdsP+e1v+56xNZW8qHBIY373u1V880005ZNBVvGJsLPuQCbLUPkG/ryBRPcE\n3oqhagRVIXHy4VbLB5AVsl0VzZWIYxUkAzrQFLSa0HkWkdhJ9GWy48FNcAHBDCKX3bYTdnbx/FsJ\n1j6G4O2a5uMk4ftAh8KFaCwVEcZkIpnqa6LYbyyUcsstO+nbNxGLo4aNCoc0Ze/efaxYUUT0q9kV\nxB+K6UYmfTufRQ0yqfb0vPY/1+ME+wEWEN6c5A27nRjmcy9rEc2hH1KfaQ/Bms1Boncsf4FEYaWK\nHQSbgM5GNIRYiFSuxEuoooWZiObgr3WcjZjqQpFF7QgnL+MQM1kkeiKBBltsxsVKGVOmzObnP091\nifX6iQqHNOXvf/+YQ4fs4vpDEa05JRRv4Kyu0P8RerLfSrAJxCDtKcNNYO/ga1MZjs3I9Z2PmDIC\nS39B7OahKlIbwrqe4HIePZAs8Vg4SbAwDiTc99KR2t+lN9T1cIixI5CotECaIpqXCfGZPyMRge60\nlatTjjN58os89tjVZGTotAgqHNKSqqoq3nuvEdFPciex73XghJ3YR/qUev71LwfttUdvJjgjdjPh\nI4G8K9dIpaXLkdBUb7hqBrLaD+x1HOrYTki1fXoHwSHBGYS26TsRXF9gX7Qv3DXmI/4h77HPRSb6\nN0KMbUr4XIiRSJqUne9jtGcfCxyMtSc390N++MOFPPHEdWRnp1LA129UOKQhCxZ8yJdfOml6H8in\nhM9BcMr7BEf+hGIutWvsgJgcjhCsHRwksrDxdoCLRCiTVDXBgmA7wQKjPlKDZBA7oQz7RLLtxK4x\nupHv/03P6yJEeA1D/DuBDCJ0YUSQcmt/wz5fYggiTF5GBFssHKZlyz/z2msZPPjgRNUYAtBvIw35\n7LOTOIt9D8SutIITPkbCGSPhRkwDgY7j3kidxtEB768ifFjpJkSgRVq5f+Y5p0YB7wdmRGOzn0ik\nunpouOqnzZG2nf5UYh9im03oGkdOyEByR5oiWqML0Va6Itro3oDxRZ5zLCWYfCQy6Wnsv9N84CbP\n8eYgPqfTNtvUIKG2r9Gv31/YuHEqQ4ak0lfUcFDhkIYUF9flz+ok0mkNMnEETsRdkNW8/8TnNRuE\nu6YtiBkjHOWIHTsw07mKYDOKN1GsIRBOiA1DhKk/OUSOBAJnRfPCmXDOQjSPEYimUI2vF/R4xDkd\nKLAmIMmRoQRAIaLl/Y3QzutAzkUKQ16EXPsCJBdmkd/fQs/7iwEXEyceZd26+2nS5Mxt5mNH3dTB\nVZKG2+2mpCTWCS5eu7md+cfLJkLfei6CJ+yVhK/1swb7JLD5SA+HQHYT7NCN1aRUFzkO4SZ7r9/B\nX9DlYr+izoqwTy85hO7Z0Av5nnsiNbNmISv6BZ7XNwJ/Bvw7Cmcgpde9Y0PVXJoOvI7kujjJPM/D\nrkFRp07vcNddJdx99/Xk5DgRmmcuDWWZpDhk377v2Lkz2qQ3L/FOcH9AYtEjcRIxX7QI8dkJgkNI\nDxNcftvLbiLnFnhzDwLNSSCmhUDBYrDPIg5FXfQbiDTZB2oPkfIfoqEvoaOEvGU5qhHhPhGZ9HMQ\njcEF/BSJTvvGb7sWiGbxAqGvpxEiWJogYa4lMZ9506abGDfueebO7cS//MuYMz7BzQkqHNKMPXsO\ncvq0045liaQEmRzsSoEvRmzQPUN85i3b4WU/4UtgGOwzfj8m/IrzBMECKlQcvxPqYqKJVI+oDcFm\nHDtqsL+ODoivKBTD8TmfWyDa2j58PaIzEEGwltpRRq2QXtJvIAmOoeiLCIl9iCaxAnsTGMBRunRZ\nzC23vMyCBVU8//wUioq0M7FT1KyUZhw7dprIE0ey8MbdR5pgjiKTQQ6h8xUOUTsC5zPCZyp/TuQG\nRBX44u1DESpkMda1krdbWapwkotiId9ff89ru4k/17PfUsL7jbyO5lB4fQwVyIo/D+nn9Q9kQTAB\niVL6GglEmIWEFfdAfotrkKijOUg+y5CAc3Z53huCaJOrEQFx0POZt/lQGdJQqIKRI90899wd6leI\nEdUc0gxRl1MdOeOdrOzWGiuQ5LO2DsaCmBpCCbpTYd73ZznBUU/+JPLWT/V3vgf7Xs19EO3K69C1\n8zv0R76TjTb7bYvUwArFVdSuf5SJ+A2q8GkVk5CQ15uQcid/x1cSozciJDohVVi9juUNSFmUr5FF\nyPvIPVCNVOa9AynTUonkupwF/JSRI3uqYIgDFQ5pRl5eLrGHJMaaUFSCOA17ErmmjguJLhpI6CgU\np+aZVdiX+DhF5KSuUBNl3Tazd84xxGG732ac/2TdDwkECEch4g8KldXsz0WEz1HIQe6BzwLen4Ro\nB8sRoT4Z8TMMAu5CfqdHgBfxFVac4jn/Kz3nVoEImfaIT+NKRACuAv6IhG5P8ZxfFe3bv88NN6Sy\nfHr6oWalNKNTp0KaNt3DyZOpLAK3Bamj1BkpZBeqR7E3euYA4lfYHmKMU+Fwmsh1gDZjX+cosMhc\nvGGsqfQ7VCHXt5bItaRykEgib3LjaiJH/VQhk+wRwufJuPCZ0UJNHwOQQnx51K6oeyGSof4yIiyu\nR0xI/RHB1c+zz9cQraGJ5y8H33db4xnTHDFdfYX4I37ld5y1wPcYNmwVLVqECnpQnKLCIc1o27Yt\nXbt+xpdfxrJ1Ds6yaQOpRlaGm/GVwg7E26KyBDFNRCrrDOJ8DFX8zonTeBuR+z0QYh8HCR8VVd/I\nRSZJJ/0N+iEl0Xshj7u3zWco+iAa3QpCh/96GYUIgHAdf8chDuZqagcenOt5PR/5rq9HzFizEXNj\nN8TxHAk3Ut57HXA3tZM2pd90bm4bpk7tYrMfxQ41K6UhXbvGah6yMz2EIwOZzE8QfuI+hqz4nK6w\n/Z2p/nyMfbis3W39BcE2+xPE1+EslSapjojT1WngwWTEMTwMKYUejl6IYK0hsokxH4lIKokw5gok\n1HgZtb+bbOBaZMU/37OPQYhwnov4GD5AgheqEXPSLs/Yp4HfIffSrwnO5n8DGM+gQZ8wdGg07V2V\nUKjmkIZ07x7rRNUBWZFFi4WYCH6AVEc9SnCYaLiicOEoI/Tkt4/IiW9OzENbCRZi8eYqNCJ0glgy\naIJ8P15HuJ3AzUZqFv0VyUKPRKFn/yuIXCPrEmCmZ3/hvu8RSNjyTCQ5zV8TbIP4CKoRYb0X32+y\nA3FSlyPXVug53sfA9xGHdSDFQAtcrhomTIhW81VCoZpDGjJwYB4ul52zMhyxJEvlICtBF+Iofj3E\nmEJkAjjged0Ue+d1LNh1PDtO6Mqz1cT3OPRFzGqpZBChy46HIhcxw3xE5PO8GJmsj2HfsGcCvjyG\ncLQHpnmO+TrBSXSZiMY6EXFAT0QimX6BaAeTEf/HLs/7oQRDFeJPuYSRIxdx++3x9iNRQIVDWjJ+\n/FD69l0R49a9iL6ZyjpkZQei2mcQbJZoizihveUKhhG+IUw8fE3kFqdrPMcOjNfPIr4s4g74+mCn\ngiwkJyRS7+VAmgE/QaKCImlxVyC/4SKb/bVAfAihKq/640K0kKsRLWYuEkW1CBHmpUiQQSny+y3y\nfD7Xc55XE75XRw2imVxNhw4r+P3vB2n2c4KISY+2LOt+JDzFG+BeaIzp5ff53cB9xphuAdtlIcbP\nrsjTebsxpiSmM1fCkpGRwahR1WzaFEvGb29khRepB3MgmciDu8azXTvkAb8qYNxufBVbs5FbwN8s\nYucrOYx9tVk3kdc8XnNVZcD7WcQeAgy+8hGpYhRiwuuG5B2EyyQPpDPiMH4eMTWFohUieHKBD4nc\ndrU3ktm8ivA1sPzphe8eqEaEwWf4THJtkExqJ/dtNV7B4HKd5s47D9Kz50C7jRSHxKQ5GGNmGGNG\nGmNGAc8AT3k/syyrANEFQy1NpgJHjDGXADOA/4zl+Io9d901lMLCFTFu3RRnkTAg/oWWiAliH2LS\nyUechf7hqpXI6joPXxjp+Yht2ynbCV12IxYChUMrJIQzHrJD7DdZ5CIT6sVIqepoGIE8ntsijLkI\nmXy3ELq0tj/nI/dAYC9pOzIRQTEMWWsOQyKanAiGU0iuxDVAM0aOXMi999r19FCiIS6zkmVZmcA9\nwON+bz+CGAtDMRoJYgZZ9lwUz/GV8LRp05pRo3YTW2LbKJxPON4Y+kXUjrm/EAlX9ZqR3kXCSwfg\nS5LagmgTTh3V4cJb/bEzKXiPFTiJ52M/CdpxIb5s31RwAWJrzyd6reV2JFM50nc/DtFI/oF9Vde+\nSHTZi/j8SsliPSKIbgaa0rfva/zpT5eoOSnBxOtzmAIsMcaUA1iWNRw4boxZT+intB2eO8cY4wZq\nPKYmJQk89NAoLGuB/cAgGiHhkk76ER9DyjQMIfh2ugapoVONlHzIQ1bo3smjDOkg5l0vtCO4MYw/\nNcTX39rf/FJB7YkxERNLK6IveBcPXRBtZyDRr9pBSmi/aDPmCmTS/zP25a07IM7nDYTuEx0vp5Dq\nrE0RP0QWlrWAJ5/sQ4cOdosGJVpsJ2bLsqYDd+IzDruB3xpjliGFU+7yjMsGHsQ++8gfR8KpoCCe\n+PP6T7Kur6Agj3//9xJuu62YsrJu9hvUYigysXfHfkLeSejcgxzgOuBZavsKMhCTSBaiCXyDCIV+\niO26PcnJG9iCRPiArLa3E9l5HQtZpC6kFSRi6CVE6O3BviquPy2Q6LIFBPuH/BmBmH9mAL8kcna6\nC7jccy5zkIl8NKHLpjtlL+L7yEbuJ5m2+vWbz0sv9ad372jvbR/pPrfEg61wMMY8g/gVamFZVhOg\nozFmp+et85A7823LslxAO8uyXjLGTPXbbA9yF3/u1RiMMbblLA8cCCx1kD4UFOQl9fpGjerPlVfO\n4bXXuhK9ongVYi6KJO8PE9kP0BQRMP6TsLf37wWe1z0QK+M0fE5hb6G4RFaY9a+31AHJd/A/r0QI\npNHIqnl8AvblhAxk8t6ARH9NJTotqDOiESwifMYzyGP7G0SDuN6zXSQ6IJrjSeBtRGB2QRYATvIQ\nDiLZ08c8x56C77oqGDToZZ54Yiht27aJ+flJ9rNX18Qr+OIx6fRHni4AjDHr8AtxsSyrOEAwACxF\n0iOXIQbqZMQyKgH88Y+Xs2vX86xdexvRTRzNEPNSuGxlEMd1pGgWkOQl/4SqfETbuNnzOhNZAb+C\nb1Xa33Nc/4S3eE0//glyTbC3o8dCsvYbiU7I5LsWebSidcyehazs5yDmmnDfcybwc0T4bUSEid1v\n0hSf0NmLhL2W4zNE+G/vL5xbI/dV7eKJ+fmbuO66z3jwwfHk5mqyWzKJRzi0J3JZyH/+0pZlzTPG\nTAZeBcZYlvU+skS8LY7jKw5p1qwZzz47imnTXuCTT24hukn2AsSZvBVfCKI/HRzsr0mIMT2oba5q\niQiQp5DVdyGSsJUseiFrFX8Spa30w+eoTxVnIWaXF5EaSdGYl0A0gSaIKXEksigIx2jEbzQL+c2c\nHqu95y8WKhgw4HUeeKALw4dHY7lWYsXldtf7MsXudFf9UnV9Bw8e5o47lrN27a1E79hdikwg/vkP\nx5DIkUhlFkDKNtwb8J6/GcP//98iK9NbkezbiWG2CUekMYsRB6vXdfYHpH2lV1v5DvFDJCKI7hXg\nhgTsJ1oqkBLWvyR2v8e7yOr+cuwF/3IknHkkzvqHR0sVHTsuY/z4Q/zmN5clVFs4A8xKcanamiF9\nBtGmTSteemkc11//Ao0bR4pxD8VYJJTUv5Wjk9VxGfYKqn+4bSfP+FCLFicLmUhjcvD1kXAhvpAP\n/D5vR/hGNtHyPewrzyaDRsB9wJPE3p1uFKIxzsI+A3s04pP6FDFLbYjxmLXJzNzNwIGv8qtfvc6q\nVf14+OGr1IyUYjSM9AyjWbNmPPbYNYwZs5b//u/P2LJlMs7XCCOQSWA2MiEcwX61eITQK1g34YvG\nXYKU5Ag088Trc+iGaAbeXg+NCe5FnKhHoggxiwW2u0wF2UiBuhcRv04s19QGCRBYjywIWiNJaqHu\nlUx82mMxkmGfjfzu/XBmSjrhOc5R8vOLefzx3lx22TjNXahDVDicoUyc+D1GjDjG7343i+XLC9m7\ndxTObocBiL3+BZxNet7KmoF0RpzSXfHlB3j7R3dBhMMwZMKIxsyTja+PcSBnIWYnr3DIQ9xmgZVc\n423842U0EqlzeQL2FS2NEbPWC0jBulhNTIPwldSe73mvD+IzCvUddfP8gWiNm5CABDeiyRxH8l4a\nISXca5CifH2QRUFrxo2bzeWXaxe3ukaFwxlMfn4+f/rTJEpLS3nqqbm88w58+ukl1NTYrfRykdSX\nGQ6O4p2sA+mNhK92RaKgVyNlnb14a/X7m3maIKGRkdp/9kGqip4X4rPAXs8XIavcNfgE0KCA1/HQ\nBnnE9hK7IzYeGiOaw8vId1sYx768JbZrkOCEN/BpfxlIvkpjfHWryhBfhL9NvzVSQK+55/Vx5Pv/\nMRLBBnCCCy9MVY6IEgkVDgrNmzfn5z8fx7/9m5ulS9exfv0HlJRkeP5aUlpaiM+0c5rmzfdRVHSE\ngwfz2G1biLQ5kgsRWASwEZL2AhIyG+gYvAQxX7XDp1V0QhzWVoTjdUJMX6GEA9TWYnKQ69rn915H\nwvdIjoVLEfPONFJvXgIRzjcjIa4uwlc3dUoGUv/oXL/3qpHopXLEDJiD/O49Cd9AaQ3yvd+M/33R\nvv0qrroqUr8OJVWocFD+icvl4rLLhnCZ3wL+6NEjHD58iGPHSgE3+fm5tGrVnRYtWvLTny7lpZfs\n9toMmYy2Uruv8+fIZOIVGj2QQnDepLQMRAhkIFnTkxGz0NtEFg6B2kEgGdQWVJlIGK1/dnE82byh\nuAypY3RFgvfrFBcSUPAd4mQeQ3xaRCCZOK8Kewwpl3IRkoVfmwEDTtKkSaQMbCVVqHBQItKiRUta\ntAhdJrtZM6fF3lpT2xFcgZh+piJJUaMQX8Yr1M5YPs/zXiZiznDac6Ga8M7u8xF/hndiuhjRNNYg\nCWAglWUjJf5FS1tk8txIeI0mFbRD/A/LkO9zLKkLWHQjYa8lSNG/UKHUZVx4oQZQ1hf0l1BipkeP\nLOyLsYHYk/3HzcfX4euw3/vnE5z4NglZbTrteAYSIRSuOmoHfOYsEGf4UcQUctLz3rmI8EokAxHf\nQyobAoXCq0VcgPwOcxFnc7I4gdRuegr5He8kXI5Nt25vc8stl4T8TEk9KhyUmBk50iI310lrzAuQ\nSWInUvyuJ77omYH4cg3ORiZP/14SuYjQ8OZlOGmq04HI1V0LqJ3c3wUxVfmXKW+F854WThmP9K9I\n9H5joTXiYJ6Er4WnN6ooEXyJ5D28j/z+Xod2eEaMOEnjxomspaXEgwoHJWa6dOlM584lDkYWIWGM\nHyOThr9ppRuiPXh7KVyFOKL9BcB5yKr+Y2SiWefgmIWET2i7mNrJbwORkMs8fJrMKCQiJ9HcgEya\nqa6/FA5v4b6rEaG5AMlMX4BkSjtpgHQCEQL+29Yghfe+hwjEyRH3kJf3Cbff3jviGCW1qM9BiRmX\ny0Xv3hV87aTtA2chfodQ1TwnII7SaYjJ4XokPv8WfCaIu5ByF7/CWRbzRYjJ5OoQn2Xg00C8LU6b\nIYJnGVIWupHns3C+i1jJRK5zpuc49cn5WuT583IM8cccoXaRPK924UKEQDPEZ9Sa2hxC+kzchN13\nOHSooVevCTGfuZJ4VHNQ4uLqqwvIyvrWwcjhSPbs6BCfZSB28Lme102RifMFZIICMS/9GL+OtDa4\nkIk4XAmJkdQ2I41BHKZF+JocjSA52kMWEsI5h9o+l/pGPpKIeBVS4+pKz99Ev9dXIb9poGD4FhG0\n9oIhN3cbt98ebaFAJdmocFDiYuzYCzjvPCd5ARlItFI4ZbUQaRj0OrIybYoU31uBNHoBmYAuQExM\nxsExRyITVChaIDkOXgdxBmJW6YyYraoRO3kZsdcoikQmIiBW4Ff5Pk34BAlVvgEnWtfIkWsZPfr8\nZJ+UEiUqHJS4cLlcjBnjwlfQLhxlBK8uA+mMJL/NRHwQGcgKtRAJaf0cqfFfg5g77GiOCJpw0Tij\nELu610wyDMmpuAqxnYOUvoil1aoTXIiT9qDnuOnAEuT7HOdodF7ep/z4x5HyVpS6QoWDEjf33DOK\ngQPn2IzahBRhs6MtYopYic9pfBayCm2EaBZ5nv05ceqOo7b5KNLnLiSMdRtS7uIrxJ6e5fBYsXKx\n53gvE1wIsKFwFBHqvREHvzPGjt3EwIGh+oQodY0KByVucnJy+PWvz6JFi40RRkVTX8irMXRE7PLL\nETOPhTiYr0RyGeY52JcLMS+9G+bzNkhYrbfbbV9EKJyHmEeOIwLEybHioSfSJPEtpBJqQ+J9z99U\n7NuH+ujU6R1+9atQvceV+oAKByUhXHRRb667bjPhzUuxRPx0Q8Ihz0OqqS5AJs89iIA4Ru1eEOHo\n6DmvSOaltX6fT0ISxK5DhFMWUtDv4xiuIRqyEOHXFDGjJaq3RLIoQbSdzsjv4Xw6ycg4yF13HaOo\nKFLHOaUuUeGgJIzf/GYcw4a9QOgJO55w0FbIhO2NjDmECIt8fBFOdlxOZPPStUjdplLEfHUOUu7i\nauA1xCRWjL1vJRGcg4TzGs+xSyMPTzn7kY6/+4AbqR3+6ozRoxdx992XJva0lISiwkFJGDk5OTz7\n7HgGD/4Hicu0DaQRoklMRMwYLqREtB3eiqThnMsuz/7eQCqM9kMmv+NIhNTbiIByKozixYWE/05B\nSoHMAXak6Njh2Oo5j82IVhVbz4VOnZbx8MNDtZFPPUeFg5JQ8vPzmTVrLBdf/CzJCQENxM7h7E87\nxKfwVpjPXchKeCXilB4PvIf4SjohDvL+1M6uTjaZiNZzNbJifx3xwaRCgwFxkC9BhEIlYuYbRaya\nYG5uMT/+cbmakxoAmiGtJJzmzZsza9aVPPzwa8yadR4nT/YieZpEE0SbOICvQVAkuiMT6zIk8S0Q\nF3/0zRgAABDlSURBVDIBfohvhfwykq1dg88P8BVSCypVuBAN5gIkMmg5UswwC1nBO7l2p+xGnOI1\nyPd7MRK1FS8nmDZtFbfdFrnGklI/cLndyXpoE4b7wIHARjDpQ0FBHul6fQUFecycuYI//nE/mzdn\nY1dfJ3bcSBjlzVFssxX4BtEOwnEM8W30Quz/NyKFA3d4PrscX1ezuqISSdo7hE8A5yDaTgcktySU\ngcDboGcvIvC8VXNdiIY1kHDVU2OjhrFjn+b5568jMzOR+42ddH72AAoK8uKy26lwqGPS+Qb1XtvJ\nkye5++6nWbr0RyRvMt2DTNyhynOEowQJV51MZDPJh4hwaIT4JXYhoZtlwG0kdhJNBOWIv2QPEoEV\n6hn3tvZsjyQZZif1jPr3f4nXXx9Ofn6+/eAUkc7PHtSRcLAs635EJ3cjT0ahMaaX3+d3A/cZY7qF\n2HYEEupwuzHmTQeHU+HQQPG/ttLSo1xwwTaOHh2ZxCMuRUJOo6nTcxCxqY9EQl7DUYHkOuwDfoTY\n4mcit/8dqPsuPGefvYBnnjkLy+pa16dSi3R+9iB+4RDTHW2MmWGMGWmMGQU8g181NMuyCpClWJDU\nsSzrLKR6WrrUClAc0rx5C4qKnJR/joexiC2+LIpt2iBVUg3SyjPcYqkREl56M/BXRCjchZhw/hZh\nuzObHj0W87e/da13gkGxJ67ljmVZmcA9wON+bz8C/DrMJt8aY6ZQP7qdKClm4MAykj+J3oAkkEV7\nnFFI4b9ZRO7W1hK4G4ka2oMIi77Af+MsIe/M4eyzF/G3v3WkT5+z6vpUlBiIVxeeAiwxxpQDWJY1\nHDhujFlPCCOud5xyZnLnnf1p3txJo554yEaydWPJR/DXIuYiOQ7hjnETUvzvE6RY4G3AH0ldiGl9\nxk3v3q/x9NNF9OvXw364Ui+xDWW1LGs60vjV2/XEDfzWGLMMmI7o1liWlQ08iGQKJZSCgrxE77Je\nkc7X539tBQW9GTnydebPjy15yjmtkeYzb+G0OmhtRiFRQO8hSq6FFJQL5AqkAOCryG3/c+AJJCfh\nTI3jL2Ps2Fd58cUJtG1rV4W37knnZy9eYo5WsiyrCfCRMaav5/Vg4Hl8baMGAPOMMVNDbPsc8Jo6\npNPbKRbq2hYuXMM995xLZWWnFJzBV0i46uVx7mcr8AVS82gU4n/wpxzJvLaQJLnFSBSQ8+qk6UB2\n9m6uv/5t/vjHSWRnJzf6KRGk87MH8Tuk40mC649flxJjzDqkKAwAlmUVhxIMfmju/BnIxIlDWbBg\nDosW3Ubyb4GzEcvpIsTUFCu9PH8nkOQ5b3/rTKTEdxGSLOfVIkYguRBvIHkU6X+rt279ET/84bfc\ne++1dX0qSoKIx+fQHsnnD8c/VRLLsuZ5/p1sWdbnSGGcxy3LSnaZS6UeMmPGCLp1c6I0JoIewPmI\noznech7NEFOSt03mWMQv4a0YW4J0l5uJFO3r5TnunjiPW585xdChLzJrVhb33ju2rk9GSSCaBFfH\npLNqG+naZs5cya9+NSBF5iWQZj2zEQ2iVQqOV43kXZxEopjygctIJy2ideuPuPnmb/jlLy8nK6vh\nVeJJ52cP6ijPQVHiZdq04Vx//dtIGYpU0Bipj7QKiTBKNpmIM/xqxOy0F/gL0uq0YZOVtZsRI2Yy\nc2Ym998/oUEKBsUe/VWVOuPRR6+hrGw2c+bciDh7k40LiSraTE7OXykvnw7kpuCYgz1/B5H+DMuB\nCYjJqyFxmMGDl3DHHa2YPHmiltxOc1Q4KHVGRkYGjz12DdnZr/DKK5Nxu1Nh7oFzz/2Kv/xlCPPn\nv87LL3fn8OGhKTmu5FHc4/n/SsS5PZrUVneNHpfrIP36vcvUqTnceusVZGSoweFMQH0OdUw62z2d\nXpvb7ebPf17Cc891YN++i5N2PhkZ+7n00sX84Q+X0LlzOwC2bPmGP//5c5YtO4+TJ0PlMiSbjUi4\nbT4SJltfivi5ad58PUOHbmfSpOZMmnRh2gmFdH72QKuyNnjS+QaN9tq++OIbfvvbT1m1ahKJrt7a\nufMy7r77BN///uiQ5pA1azbz+OPb+eCD8zl9+tyEHtsZRxBtwg10Rbrdpd5sk529nbPP3siQIRX8\n8pcX0qpV/U9ki5V0fvZAhUODJ51v0FiuraamhsceW8bChW4+/3wUku0cKzW0bfsel1yyh/vuG0zX\nrvbVWjds2MLMmdtZubIF3357KckuZR2Mm/z8ebRr9yW5uYXs3duNAwe+R2Ka7YSigvz89fTps4fz\nzqtm/PguDBrUG5fLldb3JqT3swcqHBo86XyDxnNt1dXVzJ27moULj7NmTReOHRtEcGZyKNy4XDvp\n0+cjRo2q5vvf/x5t27aJ+vgnThzn6adXs3YtbNrUioMHL3F4/FiooVmz9fTuvYOBA6u4447z6dJF\nBNmxY6UsX/4ZX355ipKSDL75JoOdO1tRWtoNacrj9Jyqgf00abKLzp2/pajITbduNfTokcWYMX1o\n375d0BbpfG/CGXF9KhwaMul8gybq2vbs2cuyZZv5+usqduzIZMeODE6dclFZCZmZ0KgRFBbWUFRU\nTbduboYMacfgwf0S1nHs6NEjvPbax6xfX8O2bVns3Nma0tLziX01X0Fu7ud06bKD7t2rGTDAzdVX\n96VrV2c5H0ePHmH79m/56qt9HDhQxdGjUFrqoqLCRU2NzAcZGZCZWUPz5tC8uZvWrV306NGGHj06\n0rZtoaNIo3S+N+GMuD4VDg2ZdL5Bk31tbre7TsIpDx8+xIoVX1BSUs3OnZUcOeLiyBE4ehROn86g\n2lNdQwSXmxYt3LRs6aZFCxetWlXTo0cWI0f2olOnjvU6HDSd7004I66vzmorKUqdUlcTa6tWrZky\nZVjaTy7KmU16xaYpiqIoCUGFg6IoihKECgdFURQlCBUOiqIoShAqHBRFUZQgVDgoiqIoQahwUBRF\nUYJQ4aAoiqIEocJBURRFCUKFg6IoihKECgdFURQlCBUOiqIoShAqHBRFUZQgYqrKalnW/cAYpKdh\nJlBojOnl9/ndwH3GmG4B22UCzwBnebb7uTHmwxjPXVEURUkSMQkHY8wMYAaAZVm3AAXezyzLKgAm\nI4IjkJuBU8aYSyzLOhd4DhgSyzkoiqIoySMus5JHE7gHeNzv7UeAX4fZZCbwM8//DwCt4jm+oiiK\nkhzibfYzBVhijCkHsCxrOHDcGLPesqygTizGmCqgyvPyp8BLcR5fURRFSQK2wsGyrOnAnYiZyOX5\n97fGmGXAdOAuz7hs4EFgkoN93gucB1zp5CQLCvKcDGuwpPP1pfO1gV5fQyfdry8eYu4hbVlWE+Aj\nY0xfz+vBwPPAEUSIDADmGWOmBmw3HbgauMoYU+ngUNpDuoGSztcGen0NnTPg+uqsh3R/YKv3hTFm\nHXCO97VlWcUhBEN34G5gmEPBoCiKotQB8Tik2wP7I3z+T5XEsqx5nv9OR5zQb1qW9Z5lWe9alhWv\n30NRFEVJMDGblVKImpUaKOl8baDX19A5A64vLrOSZkgriqIoQahwUBRFUYJQ4aAoiqIEocJBURRF\nCUKFg6IoihKECgdFURQlCBUOiqIoShAqHBRFUZQgVDgoiqIoQahwUBRFUYJQ4aAoiqIEocJBURRF\nCUKFg6IoihKECgdFURQlCBUOiqIoShAqHBRFUZQgVDgoiqIoQahwUBRFUYJQ4aAoiqIEocJBURRF\nCUKFg6IoihKECgdFURQliKxYNrIs635gDOAGMoFCY0wvv8/vBu4zxnQL2K4AeB7IBbKBnxljPo7x\n3BVFUZQkEZNwMMbMAGYAWJZ1C1Dg/cwjACYjgiOQacALxphXLMsaBvwHcFks56AoiqIkj5iEgxfL\nsjKBe4ARfm8/AvwamB043hjzZ7+XXYBd8RxfURRFSQ5xCQdgCrDEGFMOYFnWcOC4MWa9ZVmuUBtY\nllUILAKaAaPiPL6iKIqSBGyFg2VZ04E7ETORy/Pvb40xy4DpwF2ecdnAg8CkSPszxuwDBluWdTni\nf1CzkqIoSj3D5XaHcg3YY1lWE+AjY0xfz+vByGR/BBEiA4B5xpipftsMBzYZY454Xh8wxhQE7VxR\nFEWpU+IxK/UHtnpfGGPWAed4X1uWVewvGDxMRoTGXyzL6gvsjOP4iqIoSpKIJ8+hPbA/wuf/VEks\ny5rn+e/vgTGWZa0A/o44sxVFUZR6RsxmJUVRFCV90QxpRVEUJQgVDoqiKEoQKhwURVGUIOJNgosb\ny7Jygc3AQ8C7wIuI0NoL3GyMqYy0jTHmhRSebtREc32eUN/XPONdSNjvT1J+0lEQ7e9nWdZNwC+A\nSuBBY8xbqT3j6Ijy97sDuBlfTtBAY0x+yk/aIVFeW1PgBaAl0Ah59pam/KSjIMrrc/3/9u4nVMoq\njOP499bCxY0wk6hUsujyg4gW1SJ0YZnQokDBgijCigSjomgZ5L21aFEh/aEooizozyIhC4IyKK/0\nx6JaZAt/mVqKtzRbZVIR2eK8U/c6c2fmPff6zhGez+YOw3tmnofzznneM2fec4HngIuBP4F1tr9r\nPOgajh8HJd0LPAbMtX20w/EbgCuAf4D7bH/Z7fVLmDk8CPxaPX4YeNr2MmA3cHsfbUpXN7+ttpfb\nvqr0wlDpOz9J80g3Si4BrgNWNhhnrr7zs/1S1W/LgVHSfT8lq3Nu3grsrHK7AXiyqSBnoE5+K4HT\nbS8F1gIbGosy33/5VXvcnQEc6HRgtZfdhbaXkG5qfqrXiw+0OEgSIOBd0pXWMtLWGlR/V/RoU7Sc\n/KrjTgoZ+a0APrB91PZB2+saCzZDZv+1rCf9dLtIGbkdAs6sHs8DfmkgzGwZ+Y0AXwDY3g1cMN0W\nQCXoMA5usj3WpcnVwGYA2zuBuZJO6/Yeg545PA7cz/8D4vCkqd4h0r0UvdqULCe/iyRtlrRNUrfB\npwR181sMDEt6W9K4pNL31srpPyRdDuyz3e0+oEGrlZvtN4FFknYBH1VtS1a3774FrpF0SjXwLgLm\nNxJpnin5dfoa6ThnM7WgH66em9bAioOkW4Bx29PdJd02+HdoU2yByMkP2AWM2V5Fmsa/KGng60Kd\nZOY3RLrqXAXcBmw8QeHNWGZ+LXcAL896ULMk87N3M7Df9gjpqvuZExjijOTkV619fQ1sI+0Z91On\n40owS+NgzzaDHHiuBc6XtBpYAPwFHJE0p9rldQEw0aXNQuAPSfttf9hk4H2qnZ/tCdKCNLb3SPq5\nOu7HRiPvT07/HQQ+tX0M2CPpN0nzbR9uNPL+5OTXciVwdyNR5snJbSnwPoDtbyQtlDRU9WVpsvrO\n9gMA1QXZmoJnft3Gwen6Y4KpM4VzSQVwWgMrDrZvbD2WtB74gbRQeT3wGrAaeK9Lm1Fgb6GFISs/\nSTcBI7YfknQW6Z8odVxgGrSc/IAtwEZJj5JmEMOFFobc/JB0Dmnb+r+bibS+zNy+J/3S5S1J5wFH\nCi0MuZ+9S4B7bK8lLbhvbSjc2nqMg0N0nhVsAcaAFyRdChyw/Xu39xn0mkNLK5lRYI2kcdLK+ysA\nkl6XNGdQwc2CXvm9UeX3DnCZpI9Ji0d3ljzITNJXftXMaBOwnbSQVvLV9WR1zs9ee46Vpt9z83lg\ncbUv2qtUW/WfBPrNbwdwqqTtpD3fSl9TaTkGIOkRSTtI59/nkp6tnm999j4DvpL0CfAEcFevF469\nlUIIIbQpZeYQQgihIFEcQgghtIniEEIIoU0UhxBCCG2iOIQQQmgTxSGEEEKbKA4hhBDaRHEIIYTQ\n5l/QD4lVILi6EQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb35cc859d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the bubble size\n",
    "plt.scatter(data_by_location['latitude'], data_by_location['longitude'], \n",
    "           s=data_by_location['ENTRIESn_hourly'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb35d326350>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HmDVrvV1jsIVz585mm/PHzc2NdesGER4eRmBgIAsWtKdt28IN5G7Y8Ae9e0fw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb35ca48f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaled_entries = data_by_location['ENTRIESn_hourly'] / data_by_location['ENTRIESn_hourly'].mean()\n",
    "plt.scatter(data_by_location['latitude'], data_by_location['longitude'], \n",
    "           s=scaled_entries*4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Findings**: One can notice the stations where there is high volume of ridership."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": 1
}