gistfile1.txt

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    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='intro'></a>\n# Data Analysis \n\n **In God we trust, all others bring data.** - *The Elements of Statistical Learning*\n\nBig Data, Data Analytics, Data Science etc are the common buzzwords of the data world. So much so that data is considered to be the \"new oil\". There are excellent data-specific programming tools like SAS, R, Hadoop. Using a more generic scripting language like Python for data analysis is helpful as it allows for combination of data tasks with scientific programming.\n\n\nOne major issue for statistical programmers using Python, in the past has been the lack of libraries implementing standard models and a cohesive framework for specifying models. **Pandas**, the data analysis library which has been in development since 2008, aims to bridge this gap.\n\nPandas derives its name from  **pan**el **da**tasets, which is a commonly used term for multi-dimensional datasets encountered in statistics and econometrics.\n\n\n<img src=\"http://radhakrishna.typepad.com/.a/6a00d83453b94569e20168e98eff44970c-pi\" width=\"500\" align = \"center\" />\n\n\nData analysis is only as good as its visualization. Today we will use a number of datasets in combination with the plotting library in Python; **matplotlib** to demonstrate our learnings. The notebook is structured as follows:\n\n## Contents\n- [Data Analysis](#intro)\n- [Matplotlib](#mpl)\n- [Data Analysis: pandas](#pandas)\n    - [Series](#series)\n    - [String methods](#smethods)\n    - [Reading from a csv](#csv)\n- [DataFrames](#df)\n    - [Exercise 1: DataFrames](#ex1)\n    - [Data Manipulation](#dm)\n    - [Exercise 2: Data Extraction](#ex2)\n    - [Plotting data](#plot)\n    - [Missing Data](#missing)\n    - [Excercise 3: DataFrame Methods](#ex3)\n    - [More Manipulations](#mm)\n- [Statistical Tests](#stats)\n    - [Regression](#regression)\n    - [T-Test](#ttest)\n    - [Time Series](#ts)\n- [Data Problem](#dp)\n    - [Data Cleaning](#dc)\n    - [Data Analysis](#da)\n- [Miscellaneous plots](#oplot)\n- [References](#refs)\n- [Credits](#credits)\n\n\n\n\n\n                   \n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from __future__ import division\nimport numpy as np\nimport pandas as pd\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\npd.set_option('display.mpl_style', 'default')\n#IPython magic command for inline plotting\n%matplotlib inline\n#a better plot shape for IPython\nmpl.rcParams['figure.figsize']=[15,3]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='mpl'></a>\n## Quick Overview of matplotlib\n\nMatplotlib is the primary plotting library in Python. We will have a separate notebook dedicated to its features in a subsequent session. For the purpose of plotting with **pandas** today, we will touch upon the very basic plotting in **matplotlib**."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "x = np.linspace(0, 1, 10001)\ny = np.cos(np.pi/x) * np.exp(-x**2)\n\nplt.plot(x, y)\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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lWwMxdzg47MK/PnMC/3hxN87rqi31cogkWI0iPnBuKx68aRUMAsMdjx7EI3uG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\ny5ALssriyBcAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x10c67d410>"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "* Plot the following equations over the domain $x \\in \\left[-1, 2\\right]$.\n    * $y = f(x) = x^2 \\exp(-x)$\n    * $y = f(x) = \\log x$\n    * $y = f(x) = 1 + x^x + 3 x^4$"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "x=np.linspace(-1, 2, 10001)\ny = x**2*np.exp(-x)\n\nplt.plot(x, y)\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ho2U+piEAeYeROExJxSGfbjhvhq4/t1Lbj3Tr2d3H9diWI7psTrE+ela53lMV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\nAPLI/wczXdwj88dNzwAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x10c69e890>"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='pandas'></a>\n\n## Data analysis:  pandas\n\nThe pandas data analysis module provides data structures and tools for data analysis. It focuses on data handling and manipulation as well as linear and panel regression. It is designed to let you carry out your entire data workflow in Python without having to switch to a domain-specific language such as R. Although largely compatible with NumPy/SciPy, there are some important differences in indexing, data organization, and features. The basic Pandas data type is not `ndarray`, but Series and DataFrame. These allow you to index data and align axes efficiently.\n\n<img src=\"https://docs.google.com/drawings/d/16Laq9U0qV3tglRiH4aHSq85_wmBaNQdF8wbzh8KFpMk/pub?w=939&h=541\" width=\"500\" align = \"center\" />\n\n"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='series'></a>\n## Series \n\nA `Series` object is a one-dimensional array which can hold any data type. Like a dictionary, it has a set of indices for access (like keys); unlike a dictionary, it is ordered. Data alignment is intrinsic and will not be broken unless you do it explicitly. It is very similar to ndarray from NumPy.\n\nAn arbitrary list of values can be used as the index, or a list of axis labels (so it can act something like a `dict`)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "s = pd.Series([1,5,float('NaN'),7.5,2.1,3])\nprint(s)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "0    1.0\n1    5.0\n2    NaN\n3    7.5\n4    2.1\n5    3.0\ndtype: float64\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dates = pd.date_range('20140201', periods=s.size)\ns.index = dates\nprint(s)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "2014-02-01    1.0\n2014-02-02    5.0\n2014-02-03    NaN\n2014-02-04    7.5\n2014-02-05    2.1\n2014-02-06    3.0\nFreq: D, dtype: float64\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "letters = ['A', 'B', 'Ch', '#', '#', '---']\ns.index = letters\nprint(s)\nprint('\\nAccess is like a dictionary key:\\ns[\\'---\\'] = '+str(s['---']))\nprint('\\nRepeat labels are possible:\\ns[\\'#\\']=\\n'+str(s['#']))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "A      1.0\nB      5.0\nCh     NaN\n#      7.5\n#      2.1\n---    3.0\ndtype: float64\n\nAccess is like a dictionary key:\ns['---'] = 3.0\n\nRepeat labels are possible:\ns['#']=\n#    7.5\n#    2.1\ndtype: float64\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "NumPy functions expecting an ndarray often do just fine with Series as well."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "t = np.exp(s)\nprint(t)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "A         2.718282\nB       148.413159\nCh             NaN\n#      1808.042414\n#         8.166170\n---      20.085537\ndtype: float64\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='smethods'></a>\n## String Methods\n\nSeries is equipped with a set of string processing methods that make it easy to operate on each element of the array. Perhaps most importantly, these methods exclude missing/NA values automatically. These are accessed via the Series\u2019s str attribute and generally have names matching the equivalent (scalar) built-in string methods:"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": " s.str.upper()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": "0       A\n1       B\n2       C\n3    AABA\n4    BACA\n5     NaN\n6    CABA\n7     DOG\n8     CAT\ndtype: object"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": " s.str.lower()\n    ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": "0       a\n1       b\n2       c\n3    aaba\n4    baca\n5     NaN\n6    caba\n7     dog\n8     cat\ndtype: object"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "s.str.len()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "0     1\n1     1\n2     1\n3     4\n4     4\n5   NaN\n6     4\n7     3\n8     3\ndtype: float64"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "s2 = pd.Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h'])\nprint s2\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "0    a_b_c\n1    c_d_e\n2      NaN\n3    f_g_h\ndtype: object\n"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "s2.str.split('_')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "0    [a, b, c]\n1    [c, d, e]\n2          NaN\n3    [f, g, h]\ndtype: object"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\n<table>\n<tr>\n<th>Method</th>\n<th>Description</th>\n</tr>\n<tr>\n<td>cat</td>\n<td>Concatenate strings</td>\n</tr>\n<tr>\n<td>split</td>\n<td>Split strings on delimiter</td>\n</tr>\n<tr>\n<td>get</td>\n<td>Index into each element (retrieve i-th element</td>\n</tr>\n<tr>\n<td>join</td>\n<td>Join strings in each element of the Series with passed separator</td>\n</tr>\n<tr>\n<td>contains</td>\n<td>Return boolean array if each string contains pattern/regex</td>\n</tr>\n<tr>\n<td>replace</td>\n<td>Replace occurrences of pattern/regex with some other string</td>\n</tr>\n<tr>\n<td>repeat</td>\n<td>Duplicate values (s.str.repeat(3) equivalent to x * 3)</td>\n</tr>\n<tr>\n<td>pad</td>\n<td>Add whitespace to left, right, or both sides of strings</td>\n</tr>\n<tr>\n<td>center</td>\n<td>Equivalent to pad(side='both')</td>\n</tr>\n<tr>\n<td>wrap</td>\n<td>Split long strings into lines with length less than a given width</td>\n</tr>\n<tr>\n<td>slice</td>\n<td>Slice each string in the Series</td>\n</tr>\n<tr>\n<td>slice_replace</td>\n<td>Replace slice in each string with passed value</td>\n</tr>\n<tr>\n<td>count</td>\n<td>Count occurrences of pattern</td>\n</tr>\n<tr>\n<td>startswith</td>\n<td>Equivalent to str.startswith(pat) for each element</td>\n</tr>\n<tr>\n<td>endswith</td>\n<td>Equivalent to str.endswith(pat) for each element</td>\n</tr>\n<tr>\n<td>findall </td>\n<td>Compute list of all occurrences of pattern/regex for each string</td>\n</tr>\n<tr>\n<td>match</td>\n<td>Call re.match on each element, returning matched groups as list</td>\n</tr>\n<tr>\n<td>extract</td>\n<td>Call re.match on each element, as match does, but return matched groups as strings for convenience.</td>\n</tr>\n<tr>\n<td>len</td>\n<td>Compute string lengths</td>\n</tr>\n<tr>\n<td>strip</td>\n<td>Equivalent to str.strip</td>\n</tr>\n<tr>\n<td>rstrip</td>\n<td>Equivalent to str.rstrip</td>\n</tr>\n<tr>\n<td>lstrip</td>\n<td>Equivalent to str.lstrip</td>\n</tr>\n<tr>\n<td>lower</td>\n<td>Equivalent to str.lower</td>\n</tr>\n<tr>\n<td>upper</td>\n<td>Equivalent to str.upper</td>\n</tr>\n</table>"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='csv'></a>\n## Reading from a csv\n\nIn most data scenarios, you will receive a comma separated file, on which you will need to perform your analysis. Reading a `csv` file into Python can be achieved by using the `read_csv` function. We will use the date from [this website](http://donnees.ville.montreal.qc.ca/dataset/velos-comptage), about how many people were on 7 different bike paths in Montreal, each day. Let's use the data from 2012."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "broken_df = pd.read_csv('2012.csv')\n\n#Look at the first 4 rows\nbroken_df[:4]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Berri 1</th>\n      <th>Br\u00e9beuf (donn\u00e9es non disponibles)</th>\n      <th>C\u00f4te-Sainte-Catherine</th>\n      <th>Maisonneuve 1</th>\n      <th>Maisonneuve 2</th>\n      <th>du Parc</th>\n      <th>Pierre-Dupuy</th>\n      <th>Rachel1</th>\n      <th>St-Urbain (donn\u00e9es non disponibles)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 01/01/2012</td>\n      <td>  35</td>\n      <td>NaN</td>\n      <td> 0</td>\n      <td>  38</td>\n      <td>  51</td>\n      <td>  26</td>\n      <td> 10</td>\n      <td> 16</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 02/01/2012</td>\n      <td>  83</td>\n      <td>NaN</td>\n      <td> 1</td>\n      <td>  68</td>\n      <td> 153</td>\n      <td>  53</td>\n      <td>  6</td>\n      <td> 43</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 03/01/2012</td>\n      <td> 135</td>\n      <td>NaN</td>\n      <td> 2</td>\n      <td> 104</td>\n      <td> 248</td>\n      <td>  89</td>\n      <td>  3</td>\n      <td> 58</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 04/01/2012</td>\n      <td> 144</td>\n      <td>NaN</td>\n      <td> 1</td>\n      <td> 116</td>\n      <td> 318</td>\n      <td> 111</td>\n      <td>  8</td>\n      <td> 61</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": "         Date  Berri 1  Br\u00e9beuf (donn\u00e9es non disponibles)  \\\n0  01/01/2012       35                                NaN   \n1  02/01/2012       83                                NaN   \n2  03/01/2012      135                                NaN   \n3  04/01/2012      144                                NaN   \n\n   C\u00f4te-Sainte-Catherine  Maisonneuve 1  Maisonneuve 2  du Parc  Pierre-Dupuy  \\\n0                      0             38             51       26            10   \n1                      1             68            153       53             6   \n2                      2            104            248       89             3   \n3                      1            116            318      111             8   \n\n   Rachel1  St-Urbain (donn\u00e9es non disponibles)  \n0       16                                  NaN  \n1       43                                  NaN  \n2       58                                  NaN  \n3       61                                  NaN  "
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fixed_df = pd.read_csv('2012.csv', index_col='Date')\nfixed_df[:3]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Berri 1</th>\n      <th>Br\u00e9beuf (donn\u00e9es non disponibles)</th>\n      <th>C\u00f4te-Sainte-Catherine</th>\n      <th>Maisonneuve 1</th>\n      <th>Maisonneuve 2</th>\n      <th>du Parc</th>\n      <th>Pierre-Dupuy</th>\n      <th>Rachel1</th>\n      <th>St-Urbain (donn\u00e9es non disponibles)</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>01/01/2012</th>\n      <td>  35</td>\n      <td>NaN</td>\n      <td> 0</td>\n      <td>  38</td>\n      <td>  51</td>\n      <td> 26</td>\n      <td> 10</td>\n      <td> 16</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>02/01/2012</th>\n      <td>  83</td>\n      <td>NaN</td>\n      <td> 1</td>\n      <td>  68</td>\n      <td> 153</td>\n      <td> 53</td>\n      <td>  6</td>\n      <td> 43</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>03/01/2012</th>\n      <td> 135</td>\n      <td>NaN</td>\n      <td> 2</td>\n      <td> 104</td>\n      <td> 248</td>\n      <td> 89</td>\n      <td>  3</td>\n      <td> 58</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": "            Berri 1  Br\u00e9beuf (donn\u00e9es non disponibles)  C\u00f4te-Sainte-Catherine  \\\nDate                                                                            \n01/01/2012       35                                NaN                      0   \n02/01/2012       83                                NaN                      1   \n03/01/2012      135                                NaN                      2   \n\n            Maisonneuve 1  Maisonneuve 2  du Parc  Pierre-Dupuy  Rachel1  \\\nDate                                                                       \n01/01/2012             38             51       26            10       16   \n02/01/2012             68            153       53             6       43   \n03/01/2012            104            248       89             3       58   \n\n            St-Urbain (donn\u00e9es non disponibles)  \nDate                                             \n01/01/2012                                  NaN  \n02/01/2012                                  NaN  \n03/01/2012                                  NaN  "
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='df'></a>\n##DataFrame\n\nWhat we did when we read the `csv` file into `broken_df`, we created a 2 Dimensional data structure called a `DataFrame`. The `DataFrame` object is similar to a table or a spreadsheet in Excel, i.e. a 2D Matrix-like object. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "s = pd.Series([1,5,float('NaN'),7.5,2.1,3])\ndf = pd.DataFrame(s, columns=['x'])\nprint(df)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "     x\n0  1.0\n1  5.0\n2  NaN\n3  7.5\n4  2.1\n5  3.0\n"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "t=np.exp(s)\ndf['exp(x)'] = t\ndf['exp(exp(x))'] = np.exp(t)\nprint(df)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "     x       exp(x)   exp(exp(x))\n0  1.0     2.718282  1.515426e+01\n1  5.0   148.413159  2.851124e+64\n2  NaN          NaN           NaN\n3  7.5  1808.042414           inf\n4  2.1     8.166170  3.519837e+03\n5  3.0    20.085537  5.284913e+08\n"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "There are a number of ways to access the elements of a `DataFrame`."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print(df['x'], '\\n')     #column\n#letters = ['A', 'B', 'Ch', '#', '#', '---']\n#df.index=letters\n#print(df.loc['#'], '\\n') #row by label\n#print(df.iloc[3], '\\n')  #row by number (note the transposition in output!)\nprint(df[1:4])     #row by slice",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "(0    1.0\n1    5.0\n2    NaN\n3    7.5\n4    2.1\n5    3.0\nName: x, dtype: float64, '\\n')\n     x       exp(x)   exp(exp(x))\n1  5.0   148.413159  2.851124e+64\n2  NaN          NaN           NaN\n3  7.5  1808.042414           inf\n"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='ex1'></a>\n## Exercise 1 : DataFrames"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df1=pd.DataFrame(np.random.randn(dates.size,4),index=dates,columns=list('ABCD'))\nprint df1",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                   A         B         C         D\n2014-02-01 -1.088830 -0.843649  0.923378 -0.857428\n2014-02-02 -0.170466 -0.381519  0.437727 -0.146664\n2014-02-03 -1.127080  0.098241  3.094603  0.250264\n2014-02-04 -0.475779  0.803705 -0.216043  0.305970\n2014-02-05  0.163283  1.145404  1.486144  0.894497\n2014-02-06 -0.329314  0.235182 -0.552184 -0.436983\n"
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Using the `DataFrame df1` created above, perform the following operations:\n\n    1. df1.head() and df1.tail()\n    2. df1.describe()\n    3. df1.T\n    4. df1.sort(columns='B')\n    5. df1.columns, df1.index, df1.values"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df1.sort(columns=list('B'))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-02-01</th>\n      <td>-1.088830</td>\n      <td>-0.843649</td>\n      <td> 0.923378</td>\n      <td>-0.857428</td>\n    </tr>\n    <tr>\n      <th>2014-02-02</th>\n      <td>-0.170466</td>\n      <td>-0.381519</td>\n      <td> 0.437727</td>\n      <td>-0.146664</td>\n    </tr>\n    <tr>\n      <th>2014-02-03</th>\n      <td>-1.127080</td>\n      <td> 0.098241</td>\n      <td> 3.094603</td>\n      <td> 0.250264</td>\n    </tr>\n    <tr>\n      <th>2014-02-06</th>\n      <td>-0.329314</td>\n      <td> 0.235182</td>\n      <td>-0.552184</td>\n      <td>-0.436983</td>\n    </tr>\n    <tr>\n      <th>2014-02-04</th>\n      <td>-0.475779</td>\n      <td> 0.803705</td>\n      <td>-0.216043</td>\n      <td> 0.305970</td>\n    </tr>\n    <tr>\n      <th>2014-02-05</th>\n      <td> 0.163283</td>\n      <td> 1.145404</td>\n      <td> 1.486144</td>\n      <td> 0.894497</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": "                   A         B         C         D\n2014-02-01 -1.088830 -0.843649  0.923378 -0.857428\n2014-02-02 -0.170466 -0.381519  0.437727 -0.146664\n2014-02-03 -1.127080  0.098241  3.094603  0.250264\n2014-02-06 -0.329314  0.235182 -0.552184 -0.436983\n2014-02-04 -0.475779  0.803705 -0.216043  0.305970\n2014-02-05  0.163283  1.145404  1.486144  0.894497"
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='dm'></a>\n## DataFrames: Data Manipulation\n\nNow let us look at the cyclist DataFrame we created.  To extract a column from the DataFrame, "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fixed_df['Berri 1']",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": "Date\n01/01/2012     35\n02/01/2012     83\n03/01/2012    135\n04/01/2012    144\n05/01/2012    197\n06/01/2012    146\n07/01/2012     98\n08/01/2012     95\n09/01/2012    244\n10/01/2012    397\n11/01/2012    273\n12/01/2012    157\n13/01/2012     75\n14/01/2012     32\n15/01/2012     54\n...\n22/10/2012    3650\n23/10/2012    4177\n24/10/2012    3744\n25/10/2012    3735\n26/10/2012    4290\n27/10/2012    1857\n28/10/2012    1310\n29/10/2012    2919\n30/10/2012    2887\n31/10/2012    2634\n01/11/2012    2405\n02/11/2012    1582\n03/11/2012     844\n04/11/2012     966\n05/11/2012    2247\nName: Berri 1, Length: 310, dtype: int64"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We can use **Boolean indexing** on columns to extract information satisfying our desired conditions. For example, if I wished to extract all data from the cyclist data set where the value in the column `Berri 1` is greater than 1000,"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fixed_df[fixed_df['Berri 1'] > 1000]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Berri 1</th>\n      <th>Br\u00e9beuf (donn\u00e9es non disponibles)</th>\n      <th>C\u00f4te-Sainte-Catherine</th>\n      <th>Maisonneuve 1</th>\n      <th>Maisonneuve 2</th>\n      <th>du Parc</th>\n      <th>Pierre-Dupuy</th>\n      <th>Rachel1</th>\n      <th>St-Urbain (donn\u00e9es non disponibles)</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>18/03/2012</th>\n      <td> 1940</td>\n      <td>NaN</td>\n      <td>  856</td>\n      <td> 1036</td>\n      <td> 1923</td>\n      <td> 1021</td>\n      <td> 1128</td>\n      <td> 2477</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>19/03/2012</th>\n      <td> 1821</td>\n      <td>NaN</td>\n      <td> 1024</td>\n      <td> 1278</td>\n      <td> 2581</td>\n      <td> 1609</td>\n      <td>  506</td>\n      <td> 2058</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>20/03/2012</th>\n      <td> 2481</td>\n      <td>NaN</td>\n      <td> 1261</td>\n      <td> 1709</td>\n      <td> 3130</td>\n      <td> 1955</td>\n      <td>  762</td>\n      <td> 2609</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>21/03/2012</th>\n      <td> 2829</td>\n      <td>NaN</td>\n      <td> 1558</td>\n      <td> 1893</td>\n      <td> 3510</td>\n      <td> 2225</td>\n      <td>  993</td>\n      <td> 2846</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22/03/2012</th>\n      <td> 2195</td>\n      <td>NaN</td>\n      <td> 1030</td>\n      <td> 1640</td>\n      <td> 2654</td>\n      <td> 1958</td>\n      <td>  548</td>\n      <td> 2254</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23/03/2012</th>\n      <td> 2115</td>\n      <td>NaN</td>\n      <td> 1143</td>\n      <td> 1512</td>\n      <td> 2955</td>\n      <td> 1791</td>\n      <td>  663</td>\n      <td> 2325</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27/03/2012</th>\n      <td> 1049</td>\n      <td>NaN</td>\n      <td>  517</td>\n      <td>  774</td>\n      <td> 1576</td>\n      <td>  972</td>\n      <td>  163</td>\n      <td> 1207</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>30/03/2012</th>\n      <td> 1157</td>\n      <td>NaN</td>\n      <td>  529</td>\n      <td>  910</td>\n      <td> 1596</td>\n      <td>  957</td>\n      <td>  196</td>\n      <td> 1288</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>02/04/2012</th>\n      <td> 1937</td>\n      <td>NaN</td>\n      <td>  967</td>\n      <td> 1537</td>\n      <td> 2853</td>\n      <td> 1614</td>\n      <td>  394</td>\n      <td> 2122</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>03/04/2012</th>\n      <td> 2416</td>\n      <td>NaN</td>\n      <td> 1078</td>\n      <td> 1791</td>\n      <td> 3556</td>\n      <td> 1880</td>\n      <td>  513</td>\n      <td> 2450</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>04/04/2012</th>\n      <td> 2211</td>\n      <td>NaN</td>\n      <td>  933</td>\n      <td> 1674</td>\n      <td> 2956</td>\n      <td> 1666</td>\n      <td>  274</td>\n      <td> 2242</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>05/04/2012</th>\n      <td> 2424</td>\n      <td>NaN</td>\n      <td> 1036</td>\n      <td> 1823</td>\n      <td> 3273</td>\n      <td> 1699</td>\n      <td>  355</td>\n      <td> 2463</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>06/04/2012</th>\n      <td> 1633</td>\n      <td>NaN</td>\n      <td>  650</td>\n      <td> 1045</td>\n      <td> 1913</td>\n      <td>  975</td>\n      <td>  621</td>\n      <td> 2138</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>07/04/2012</th>\n      <td> 1208</td>\n      <td>NaN</td>\n      <td>  494</td>\n      <td>  739</td>\n      <td> 1445</td>\n      <td>  709</td>\n      <td>  598</td>\n      <td> 1566</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>08/04/2012</th>\n      <td> 1164</td>\n      <td>NaN</td>\n      <td>  560</td>\n      <td>  621</td>\n      <td> 1333</td>\n      <td>  704</td>\n      <td>  792</td>\n      <td> 1533</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10/04/2012</th>\n      <td> 2183</td>\n      <td>NaN</td>\n      <td>  909</td>\n      <td> 1588</td>\n      <td> 2932</td>\n      <td> 1736</td>\n      <td>  252</td>\n      <td> 2108</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>11/04/2012</th>\n      <td> 2328</td>\n      <td>NaN</td>\n      <td> 1049</td>\n      <td> 1765</td>\n      <td> 3122</td>\n      <td> 1843</td>\n      <td>  330</td>\n      <td> 2311</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12/04/2012</th>\n      <td> 3064</td>\n      <td>NaN</td>\n      <td> 1483</td>\n      <td> 2306</td>\n      <td> 4076</td>\n      <td> 2280</td>\n      <td>  590</td>\n      <td> 3213</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>13/04/2012</th>\n      <td> 3341</td>\n      <td>NaN</td>\n      <td> 1505</td>\n      <td> 2565</td>\n      <td> 4465</td>\n      <td> 2358</td>\n      <td>  922</td>\n      <td> 3728</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14/04/2012</th>\n      <td> 2890</td>\n      <td>NaN</td>\n      <td> 1072</td>\n      <td> 1639</td>\n      <td> 2994</td>\n      <td> 1594</td>\n      <td> 1284</td>\n      <td> 3428</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15/04/2012</th>\n      <td> 2554</td>\n      <td>NaN</td>\n      <td> 1210</td>\n      <td> 1637</td>\n      <td> 2954</td>\n      <td> 1559</td>\n      <td> 1846</td>\n      <td> 3604</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16/04/2012</th>\n      <td> 3643</td>\n      <td>NaN</td>\n      <td> 1841</td>\n      <td> 2723</td>\n      <td> 4830</td>\n      <td> 2677</td>\n      <td> 1061</td>\n      <td> 3616</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>17/04/2012</th>\n      <td> 3539</td>\n      <td>NaN</td>\n      <td> 1616</td>\n      <td> 2636</td>\n      <td> 4592</td>\n      <td> 2450</td>\n      <td>  544</td>\n      <td> 3333</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>18/04/2012</th>\n      <td> 3570</td>\n      <td>NaN</td>\n      <td> 1751</td>\n      <td> 2759</td>\n      <td> 4655</td>\n      <td> 2534</td>\n      <td>  706</td>\n      <td> 3542</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>19/04/2012</th>\n      <td> 4231</td>\n      <td>NaN</td>\n      <td> 2010</td>\n      <td> 3235</td>\n      <td> 5311</td>\n      <td> 2877</td>\n      <td> 1206</td>\n      <td> 3929</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>20/04/2012</th>\n      <td> 2087</td>\n      <td>NaN</td>\n      <td>  800</td>\n      <td> 1529</td>\n      <td> 2922</td>\n      <td> 1531</td>\n      <td>  170</td>\n      <td> 2065</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22/04/2012</th>\n      <td> 1853</td>\n      <td>NaN</td>\n      <td>  487</td>\n      <td> 1224</td>\n      <td> 1331</td>\n      <td>  654</td>\n      <td>  198</td>\n      <td> 1779</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>24/04/2012</th>\n      <td> 1810</td>\n      <td>NaN</td>\n      <td>  720</td>\n      <td> 1355</td>\n      <td> 2379</td>\n      <td> 1286</td>\n      <td>  188</td>\n      <td> 1753</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25/04/2012</th>\n      <td> 2966</td>\n      <td>NaN</td>\n      <td> 1023</td>\n      <td> 2228</td>\n      <td> 3444</td>\n      <td> 1800</td>\n      <td>  445</td>\n      <td> 2454</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>26/04/2012</th>\n      <td> 2751</td>\n      <td>NaN</td>\n      <td> 1069</td>\n      <td> 2196</td>\n      <td> 3546</td>\n      <td> 1789</td>\n      <td>  381</td>\n      <td> 2438</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>04/10/2012</th>\n      <td> 4034</td>\n      <td>NaN</td>\n      <td> 2025</td>\n      <td> 2705</td>\n      <td> 4850</td>\n      <td> 3066</td>\n      <td>  555</td>\n      <td> 3418</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>05/10/2012</th>\n      <td> 4151</td>\n      <td>NaN</td>\n      <td> 1977</td>\n      <td> 2799</td>\n      <td> 4688</td>\n      <td> 2844</td>\n      <td> 1035</td>\n      <td> 4088</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>06/10/2012</th>\n      <td> 1304</td>\n      <td>NaN</td>\n      <td>  469</td>\n      <td>  933</td>\n      <td> 1589</td>\n      <td>  776</td>\n      <td>  236</td>\n      <td> 1775</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>07/10/2012</th>\n      <td> 1580</td>\n      <td>NaN</td>\n      <td>  660</td>\n      <td>  922</td>\n      <td> 1629</td>\n      <td>  860</td>\n      <td>  695</td>\n      <td> 2052</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>08/10/2012</th>\n      <td> 1854</td>\n      <td>NaN</td>\n      <td>  880</td>\n      <td>  987</td>\n      <td> 1818</td>\n      <td> 1040</td>\n      <td> 1115</td>\n      <td> 2502</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>09/10/2012</th>\n      <td> 4787</td>\n      <td>NaN</td>\n      <td> 2210</td>\n      <td> 3026</td>\n      <td> 5138</td>\n      <td> 3418</td>\n      <td>  927</td>\n      <td> 4078</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10/10/2012</th>\n      <td> 3115</td>\n      <td>NaN</td>\n      <td> 1537</td>\n      <td> 2081</td>\n      <td> 3681</td>\n      <td> 2608</td>\n      <td>  560</td>\n      <td> 2703</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>11/10/2012</th>\n      <td> 3746</td>\n      <td>NaN</td>\n      <td> 1857</td>\n      <td> 2569</td>\n      <td> 4694</td>\n      <td> 3034</td>\n      <td>  558</td>\n      <td> 3457</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12/10/2012</th>\n      <td> 3169</td>\n      <td>NaN</td>\n      <td> 1460</td>\n      <td> 2261</td>\n      <td> 4045</td>\n      <td> 2564</td>\n      <td>  448</td>\n      <td> 3224</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>13/10/2012</th>\n      <td> 1783</td>\n      <td>NaN</td>\n      <td>  802</td>\n      <td> 1205</td>\n      <td> 2113</td>\n      <td> 1183</td>\n      <td>  681</td>\n      <td> 2309</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15/10/2012</th>\n      <td> 3292</td>\n      <td>NaN</td>\n      <td> 1678</td>\n      <td> 2165</td>\n      <td> 4197</td>\n      <td> 2754</td>\n      <td>  560</td>\n      <td> 3183</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16/10/2012</th>\n      <td> 3739</td>\n      <td>NaN</td>\n      <td> 1858</td>\n      <td> 2684</td>\n      <td> 4681</td>\n      <td> 2997</td>\n      <td>  554</td>\n      <td> 3593</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>17/10/2012</th>\n      <td> 4098</td>\n      <td>NaN</td>\n      <td> 1964</td>\n      <td> 2645</td>\n      <td> 4836</td>\n      <td> 3063</td>\n      <td>  728</td>\n      <td> 3834</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>18/10/2012</th>\n      <td> 4671</td>\n      <td>NaN</td>\n      <td> 2292</td>\n      <td> 3129</td>\n      <td> 5542</td>\n      <td> 3477</td>\n      <td> 1108</td>\n      <td> 4245</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>19/10/2012</th>\n      <td> 1313</td>\n      <td>NaN</td>\n      <td>  597</td>\n      <td>  885</td>\n      <td> 1668</td>\n      <td> 1209</td>\n      <td>  111</td>\n      <td> 1486</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>20/10/2012</th>\n      <td> 2011</td>\n      <td>NaN</td>\n      <td>  748</td>\n      <td> 1323</td>\n      <td> 2266</td>\n      <td> 1213</td>\n      <td>  797</td>\n      <td> 2243</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>21/10/2012</th>\n      <td> 1277</td>\n      <td>NaN</td>\n      <td>  609</td>\n      <td>  869</td>\n      <td> 1777</td>\n      <td>  898</td>\n      <td>  242</td>\n      <td> 1648</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22/10/2012</th>\n      <td> 3650</td>\n      <td>NaN</td>\n      <td> 1819</td>\n      <td> 2495</td>\n      <td> 4800</td>\n      <td> 3023</td>\n      <td>  757</td>\n      <td> 3721</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23/10/2012</th>\n      <td> 4177</td>\n      <td>NaN</td>\n      <td> 1997</td>\n      <td> 2795</td>\n      <td> 5216</td>\n      <td> 3233</td>\n      <td>  795</td>\n      <td> 3554</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>24/10/2012</th>\n      <td> 3744</td>\n      <td>NaN</td>\n      <td> 1868</td>\n      <td> 2625</td>\n      <td> 4900</td>\n      <td> 3035</td>\n      <td>  649</td>\n      <td> 3622</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25/10/2012</th>\n      <td> 3735</td>\n      <td>NaN</td>\n      <td> 1815</td>\n      <td> 2528</td>\n      <td> 5010</td>\n      <td> 3017</td>\n      <td>  631</td>\n      <td> 3767</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>26/10/2012</th>\n      <td> 4290</td>\n      <td>NaN</td>\n      <td> 1987</td>\n      <td> 2754</td>\n      <td> 5246</td>\n      <td> 3000</td>\n      <td> 1456</td>\n      <td> 4578</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27/10/2012</th>\n      <td> 1857</td>\n      <td>NaN</td>\n      <td>  792</td>\n      <td> 1244</td>\n      <td> 2461</td>\n      <td> 1193</td>\n      <td>  618</td>\n      <td> 2471</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>28/10/2012</th>\n      <td> 1310</td>\n      <td>NaN</td>\n      <td>  697</td>\n      <td>  910</td>\n      <td> 1776</td>\n      <td>  955</td>\n      <td>  387</td>\n      <td> 1876</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>29/10/2012</th>\n      <td> 2919</td>\n      <td>NaN</td>\n      <td> 1458</td>\n      <td> 2071</td>\n      <td> 3768</td>\n      <td> 2440</td>\n      <td>  411</td>\n      <td> 2795</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>30/10/2012</th>\n      <td> 2887</td>\n      <td>NaN</td>\n      <td> 1251</td>\n      <td> 2007</td>\n      <td> 3516</td>\n      <td> 2255</td>\n      <td>  338</td>\n      <td> 2790</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>31/10/2012</th>\n      <td> 2634</td>\n      <td>NaN</td>\n      <td> 1294</td>\n      <td> 1835</td>\n      <td> 3453</td>\n      <td> 2220</td>\n      <td>  245</td>\n      <td> 2570</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>01/11/2012</th>\n      <td> 2405</td>\n      <td>NaN</td>\n      <td> 1208</td>\n      <td> 1701</td>\n      <td> 3082</td>\n      <td> 2076</td>\n      <td>  165</td>\n      <td> 2461</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>02/11/2012</th>\n      <td> 1582</td>\n      <td>NaN</td>\n      <td>  737</td>\n      <td> 1109</td>\n      <td> 2277</td>\n      <td> 1392</td>\n      <td>   97</td>\n      <td> 1888</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>05/11/2012</th>\n      <td> 2247</td>\n      <td>NaN</td>\n      <td> 1170</td>\n      <td> 1705</td>\n      <td> 3221</td>\n      <td> 2143</td>\n      <td>  179</td>\n      <td> 2430</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>218 rows \u00d7 9 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": "            Berri 1  Br\u00e9beuf (donn\u00e9es non disponibles)  C\u00f4te-Sainte-Catherine  \\\nDate                                                                            \n18/03/2012     1940                                NaN                    856   \n19/03/2012     1821                                NaN                   1024   \n20/03/2012     2481                                NaN                   1261   \n21/03/2012     2829                                NaN                   1558   \n22/03/2012     2195                                NaN                   1030   \n23/03/2012     2115                                NaN                   1143   \n27/03/2012     1049                                NaN                    517   \n30/03/2012     1157                                NaN                    529   \n02/04/2012     1937                                NaN                    967   \n03/04/2012     2416                                NaN                   1078   \n04/04/2012     2211                                NaN                    933   \n05/04/2012     2424                                NaN                   1036   \n06/04/2012     1633                                NaN                    650   \n07/04/2012     1208                                NaN                    494   \n08/04/2012     1164                                NaN                    560   \n10/04/2012     2183                                NaN                    909   \n11/04/2012     2328                                NaN                   1049   \n12/04/2012     3064                                NaN                   1483   \n13/04/2012     3341                                NaN                   1505   \n14/04/2012     2890                                NaN                   1072   \n15/04/2012     2554                                NaN                   1210   \n16/04/2012     3643                                NaN                   1841   \n17/04/2012     3539                                NaN                   1616   \n18/04/2012     3570                                NaN                   1751   \n19/04/2012     4231                                NaN                   2010   \n20/04/2012     2087                                NaN                    800   \n22/04/2012     1853                                NaN                    487   \n24/04/2012     1810                                NaN                    720   \n25/04/2012     2966                                NaN                   1023   \n26/04/2012     2751                                NaN                   1069   \n...             ...                                ...                    ...   \n04/10/2012     4034                                NaN                   2025   \n05/10/2012     4151                                NaN                   1977   \n06/10/2012     1304                                NaN                    469   \n07/10/2012     1580                                NaN                    660   \n08/10/2012     1854                                NaN                    880   \n09/10/2012     4787                                NaN                   2210   \n10/10/2012     3115                                NaN                   1537   \n11/10/2012     3746                                NaN                   1857   \n12/10/2012     3169                                NaN                   1460   \n13/10/2012     1783                                NaN                    802   \n15/10/2012     3292                                NaN                   1678   \n16/10/2012     3739                                NaN                   1858   \n17/10/2012     4098                                NaN                   1964   \n18/10/2012     4671                                NaN                   2292   \n19/10/2012     1313                                NaN                    597   \n20/10/2012     2011                                NaN                    748   \n21/10/2012     1277                                NaN                    609   \n22/10/2012     3650                                NaN                   1819   \n23/10/2012     4177                                NaN                   1997   \n24/10/2012     3744                                NaN                   1868   \n25/10/2012     3735                                NaN                   1815   \n26/10/2012     4290                                NaN                   1987   \n27/10/2012     1857                                NaN                    792   \n28/10/2012     1310                                NaN                    697   \n29/10/2012     2919                                NaN                   1458   \n30/10/2012     2887                                NaN                   1251   \n31/10/2012     2634                                NaN                   1294   \n01/11/2012     2405                                NaN                   1208   \n02/11/2012     1582                                NaN                    737   \n05/11/2012     2247                                NaN                   1170   \n\n            Maisonneuve 1  Maisonneuve 2  du Parc  Pierre-Dupuy  Rachel1  \\\nDate                                                                       \n18/03/2012           1036           1923     1021          1128     2477   \n19/03/2012           1278           2581     1609           506     2058   \n20/03/2012           1709           3130     1955           762     2609   \n21/03/2012           1893           3510     2225           993     2846   \n22/03/2012           1640           2654     1958           548     2254   \n23/03/2012           1512           2955     1791           663     2325   \n27/03/2012            774           1576      972           163     1207   \n30/03/2012            910           1596      957           196     1288   \n02/04/2012           1537           2853     1614           394     2122   \n03/04/2012           1791           3556     1880           513     2450   \n04/04/2012           1674           2956     1666           274     2242   \n05/04/2012           1823           3273     1699           355     2463   \n06/04/2012           1045           1913      975           621     2138   \n07/04/2012            739           1445      709           598     1566   \n08/04/2012            621           1333      704           792     1533   \n10/04/2012           1588           2932     1736           252     2108   \n11/04/2012           1765           3122     1843           330     2311   \n12/04/2012           2306           4076     2280           590     3213   \n13/04/2012           2565           4465     2358           922     3728   \n14/04/2012           1639           2994     1594          1284     3428   \n15/04/2012           1637           2954     1559          1846     3604   \n16/04/2012           2723           4830     2677          1061     3616   \n17/04/2012           2636           4592     2450           544     3333   \n18/04/2012           2759           4655     2534           706     3542   \n19/04/2012           3235           5311     2877          1206     3929   \n20/04/2012           1529           2922     1531           170     2065   \n22/04/2012           1224           1331      654           198     1779   \n24/04/2012           1355           2379     1286           188     1753   \n25/04/2012           2228           3444     1800           445     2454   \n26/04/2012           2196           3546     1789           381     2438   \n...                   ...            ...      ...           ...      ...   \n04/10/2012           2705           4850     3066           555     3418   \n05/10/2012           2799           4688     2844          1035     4088   \n06/10/2012            933           1589      776           236     1775   \n07/10/2012            922           1629      860           695     2052   \n08/10/2012            987           1818     1040          1115     2502   \n09/10/2012           3026           5138     3418           927     4078   \n10/10/2012           2081           3681     2608           560     2703   \n11/10/2012           2569           4694     3034           558     3457   \n12/10/2012           2261           4045     2564           448     3224   \n13/10/2012           1205           2113     1183           681     2309   \n15/10/2012           2165           4197     2754           560     3183   \n16/10/2012           2684           4681     2997           554     3593   \n17/10/2012           2645           4836     3063           728     3834   \n18/10/2012           3129           5542     3477          1108     4245   \n19/10/2012            885           1668     1209           111     1486   \n20/10/2012           1323           2266     1213           797     2243   \n21/10/2012            869           1777      898           242     1648   \n22/10/2012           2495           4800     3023           757     3721   \n23/10/2012           2795           5216     3233           795     3554   \n24/10/2012           2625           4900     3035           649     3622   \n25/10/2012           2528           5010     3017           631     3767   \n26/10/2012           2754           5246     3000          1456     4578   \n27/10/2012           1244           2461     1193           618     2471   \n28/10/2012            910           1776      955           387     1876   \n29/10/2012           2071           3768     2440           411     2795   \n30/10/2012           2007           3516     2255           338     2790   \n31/10/2012           1835           3453     2220           245     2570   \n01/11/2012           1701           3082     2076           165     2461   \n02/11/2012           1109           2277     1392            97     1888   \n05/11/2012           1705           3221     2143           179     2430   \n\n            St-Urbain (donn\u00e9es non disponibles)  \nDate                                             \n18/03/2012                                  NaN  \n19/03/2012                                  NaN  \n20/03/2012                                  NaN  \n21/03/2012                                  NaN  \n22/03/2012                                  NaN  \n23/03/2012                                  NaN  \n27/03/2012                                  NaN  \n30/03/2012                                  NaN  \n02/04/2012                                  NaN  \n03/04/2012                                  NaN  \n04/04/2012                                  NaN  \n05/04/2012                                  NaN  \n06/04/2012                                  NaN  \n07/04/2012                                  NaN  \n08/04/2012                                  NaN  \n10/04/2012                                  NaN  \n11/04/2012                                  NaN  \n12/04/2012                                  NaN  \n13/04/2012                                  NaN  \n14/04/2012                                  NaN  \n15/04/2012                                  NaN  \n16/04/2012                                  NaN  \n17/04/2012                                  NaN  \n18/04/2012                                  NaN  \n19/04/2012                                  NaN  \n20/04/2012                                  NaN  \n22/04/2012                                  NaN  \n24/04/2012                                  NaN  \n25/04/2012                                  NaN  \n26/04/2012                                  NaN  \n...                                         ...  \n04/10/2012                                  NaN  \n05/10/2012                                  NaN  \n06/10/2012                                  NaN  \n07/10/2012                                  NaN  \n08/10/2012                                  NaN  \n09/10/2012                                  NaN  \n10/10/2012                                  NaN  \n11/10/2012                                  NaN  \n12/10/2012                                  NaN  \n13/10/2012                                  NaN  \n15/10/2012                                  NaN  \n16/10/2012                                  NaN  \n17/10/2012                                  NaN  \n18/10/2012                                  NaN  \n19/10/2012                                  NaN  \n20/10/2012                                  NaN  \n21/10/2012                                  NaN  \n22/10/2012                                  NaN  \n23/10/2012                                  NaN  \n24/10/2012                                  NaN  \n25/10/2012                                  NaN  \n26/10/2012                                  NaN  \n27/10/2012                                  NaN  \n28/10/2012                                  NaN  \n29/10/2012                                  NaN  \n30/10/2012                                  NaN  \n31/10/2012                                  NaN  \n01/11/2012                                  NaN  \n02/11/2012                                  NaN  \n05/11/2012                                  NaN  \n\n[218 rows x 9 columns]"
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from pandas.util.testing import rands\ndf=pd.DataFrame(np.random.randn(dates.size,4),index=dates,columns=list('ABCD'))\nprint df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                   A         B         C         D\n2014-02-01 -1.054596  1.121003 -0.320041 -0.692536\n2014-02-02  0.714781 -0.604180  1.067904 -1.194036\n2014-02-03 -0.009586  0.361285  1.257356  2.206935\n2014-02-04  0.280065  0.011517  0.602386  0.275055\n2014-02-05  2.100321  1.131649 -0.251465  0.250192\n2014-02-06  0.808438 -0.122169 -2.123913 -1.208994\n"
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df[df.B>0]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-02-01</th>\n      <td>-1.054596</td>\n      <td> 1.121003</td>\n      <td>-0.320041</td>\n      <td>-0.692536</td>\n    </tr>\n    <tr>\n      <th>2014-02-03</th>\n      <td>-0.009586</td>\n      <td> 0.361285</td>\n      <td> 1.257356</td>\n      <td> 2.206935</td>\n    </tr>\n    <tr>\n      <th>2014-02-04</th>\n      <td> 0.280065</td>\n      <td> 0.011517</td>\n      <td> 0.602386</td>\n      <td> 0.275055</td>\n    </tr>\n    <tr>\n      <th>2014-02-05</th>\n      <td> 2.100321</td>\n      <td> 1.131649</td>\n      <td>-0.251465</td>\n      <td> 0.250192</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": "                   A         B         C         D\n2014-02-01 -1.054596  1.121003 -0.320041 -0.692536\n2014-02-03 -0.009586  0.361285  1.257356  2.206935\n2014-02-04  0.280065  0.011517  0.602386  0.275055\n2014-02-05  2.100321  1.131649 -0.251465  0.250192"
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df[df > 0]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-02-01</th>\n      <td>      NaN</td>\n      <td> 1.121003</td>\n      <td>      NaN</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>2014-02-02</th>\n      <td> 0.714781</td>\n      <td>      NaN</td>\n      <td> 1.067904</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>2014-02-03</th>\n      <td>      NaN</td>\n      <td> 0.361285</td>\n      <td> 1.257356</td>\n      <td> 2.206935</td>\n    </tr>\n    <tr>\n      <th>2014-02-04</th>\n      <td> 0.280065</td>\n      <td> 0.011517</td>\n      <td> 0.602386</td>\n      <td> 0.275055</td>\n    </tr>\n    <tr>\n      <th>2014-02-05</th>\n      <td> 2.100321</td>\n      <td> 1.131649</td>\n      <td>      NaN</td>\n      <td> 0.250192</td>\n    </tr>\n    <tr>\n      <th>2014-02-06</th>\n      <td> 0.808438</td>\n      <td>      NaN</td>\n      <td>      NaN</td>\n      <td>      NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": "                   A         B         C         D\n2014-02-01       NaN  1.121003       NaN       NaN\n2014-02-02  0.714781       NaN  1.067904       NaN\n2014-02-03       NaN  0.361285  1.257356  2.206935\n2014-02-04  0.280065  0.011517  0.602386  0.275055\n2014-02-05  2.100321  1.131649       NaN  0.250192\n2014-02-06  0.808438       NaN       NaN       NaN"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df2 = df.copy()\ndf2['E']=['one', 'one','two','three','four','three']\nprint df2",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                   A         B         C         D      E\n2014-02-01 -1.054596  1.121003 -0.320041 -0.692536    one\n2014-02-02  0.714781 -0.604180  1.067904 -1.194036    one\n2014-02-03 -0.009586  0.361285  1.257356  2.206935    two\n2014-02-04  0.280065  0.011517  0.602386  0.275055  three\n2014-02-05  2.100321  1.131649 -0.251465  0.250192   four\n2014-02-06  0.808438 -0.122169 -2.123913 -1.208994  three\n"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df2[df2['E'].isin(['one'])]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-02-01</th>\n      <td>-1.054596</td>\n      <td> 1.121003</td>\n      <td>-0.320041</td>\n      <td>-0.692536</td>\n      <td> one</td>\n    </tr>\n    <tr>\n      <th>2014-02-02</th>\n      <td> 0.714781</td>\n      <td>-0.604180</td>\n      <td> 1.067904</td>\n      <td>-1.194036</td>\n      <td> one</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": "                   A         B         C         D    E\n2014-02-01 -1.054596  1.121003 -0.320041 -0.692536  one\n2014-02-02  0.714781 -0.604180  1.067904 -1.194036  one"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.at[dates[0],'A'] = 0\nprint df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                   A         B         C         D\n2014-02-01  0.000000  1.121003 -0.320041 -0.692536\n2014-02-02  0.714781 -0.604180  1.067904 -1.194036\n2014-02-03 -0.009586  0.361285  1.257356  2.206935\n2014-02-04  0.280065  0.011517  0.602386  0.275055\n2014-02-05  2.100321  1.131649 -0.251465  0.250192\n2014-02-06  0.808438 -0.122169 -2.123913 -1.208994\n"
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.iat[0,1] = 0\nprint df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                   A         B         C         D\n2014-02-01  0.000000  0.000000 -0.320041 -0.692536\n2014-02-02  0.714781 -0.604180  1.067904 -1.194036\n2014-02-03 -0.009586  0.361285  1.257356  2.206935\n2014-02-04  0.280065  0.011517  0.602386  0.275055\n2014-02-05  2.100321  1.131649 -0.251465  0.250192\n2014-02-06  0.808438 -0.122169 -2.123913 -1.208994\n"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='ex2'></a>\n## Exercise 2 : Conditional data extraction"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from random import randint\ndf = pd.DataFrame({'A': [randint(1, 9) for x in xrange(10)],\n                   'B': [randint(1, 9)*10 for x in xrange(10)],\n                   'C': [randint(1, 9)*100 for x in xrange(10)]})\nprint df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "   A   B    C\n0  8  30  600\n1  8  10  300\n2  8  80  600\n3  6  80  300\n4  3  10  600\n5  4  90  700\n6  2  50  300\n7  4  40  700\n8  8  40  100\n9  7  70  100\n"
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Find the entries from *A* for which corresponding values for *B* will be greater than 50, and those in *C*   equal to 900"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='plot'></a>\n### Plotting Data"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fixed_df['Berri 1'].plot()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": "<matplotlib.axes.AxesSubplot at 0x10c685110>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dqsmI5DBIdQKnV+hKNvNWyky+KCOsUx1btIDOYAIOaHV0lQFdsiDC52Ectbpg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\n/QcjzNM3zOMWjQAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x10c7513d0>"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='missing'></a>\n## Missing Data\n\nMany data sets simply arrive with missing data, either because it exists and was not collected or it never existed. For example, in a collection of financial time series, some of the time series might start on different dates. Thus, values prior to the start date would generally be marked as missing.\n\nAs data comes in many shapes and forms, `pandas` aims to be flexible with regard to handling missing data. While NaN is the default missing value marker for reasons of computational speed and convenience, we need to be able to easily detect this value with data of different types: floating point, integer, boolean, and general object. In many cases, however, the Python None will arise and we wish to also consider that \u201cmissing\u201d or \u201cnull\u201d."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df= pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\nprint df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                   A         B         C         D\n2014-02-01 -1.059138 -0.196474  0.239179  1.191028\n2014-02-02 -0.641067  1.734050 -0.359996 -0.126219\n2014-02-03  0.357383 -0.664820 -0.601961 -0.964749\n2014-02-04 -0.340011  1.304989  0.717388 -0.268375\n2014-02-05 -0.431061 -0.681776  0.147624  0.209896\n2014-02-06 -0.326568  0.577446  0.682139  0.210614\n"
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df1 = df.reindex(index=dates[0:4],columns=list(df.columns) + ['E'])\nprint df1",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                   A         B         C         D   E\n2014-02-01 -1.059138 -0.196474  0.239179  1.191028 NaN\n2014-02-02 -0.641067  1.734050 -0.359996 -0.126219 NaN\n2014-02-03  0.357383 -0.664820 -0.601961 -0.964749 NaN\n2014-02-04 -0.340011  1.304989  0.717388 -0.268375 NaN\n"
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df1.loc[dates[0]:dates[1],'E'] = 1\nprint df1",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                   A         B         C         D   E\n2014-02-01 -1.059138 -0.196474  0.239179  1.191028   1\n2014-02-02 -0.641067  1.734050 -0.359996 -0.126219   1\n2014-02-03  0.357383 -0.664820 -0.601961 -0.964749 NaN\n2014-02-04 -0.340011  1.304989  0.717388 -0.268375 NaN\n"
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df1.dropna(how='all') #any",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-02-01</th>\n      <td>-1.059138</td>\n      <td>-0.196474</td>\n      <td> 0.239179</td>\n      <td> 1.191028</td>\n      <td>  1</td>\n    </tr>\n    <tr>\n      <th>2014-02-02</th>\n      <td>-0.641067</td>\n      <td> 1.734050</td>\n      <td>-0.359996</td>\n      <td>-0.126219</td>\n      <td>  1</td>\n    </tr>\n    <tr>\n      <th>2014-02-03</th>\n      <td> 0.357383</td>\n      <td>-0.664820</td>\n      <td>-0.601961</td>\n      <td>-0.964749</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-02-04</th>\n      <td>-0.340011</td>\n      <td> 1.304989</td>\n      <td> 0.717388</td>\n      <td>-0.268375</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": "                   A         B         C         D   E\n2014-02-01 -1.059138 -0.196474  0.239179  1.191028   1\n2014-02-02 -0.641067  1.734050 -0.359996 -0.126219   1\n2014-02-03  0.357383 -0.664820 -0.601961 -0.964749 NaN\n2014-02-04 -0.340011  1.304989  0.717388 -0.268375 NaN"
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df1.fillna(value=15)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-02-01</th>\n      <td>-1.059138</td>\n      <td>-0.196474</td>\n      <td> 0.239179</td>\n      <td> 1.191028</td>\n      <td>  1</td>\n    </tr>\n    <tr>\n      <th>2014-02-02</th>\n      <td>-0.641067</td>\n      <td> 1.734050</td>\n      <td>-0.359996</td>\n      <td>-0.126219</td>\n      <td>  1</td>\n    </tr>\n    <tr>\n      <th>2014-02-03</th>\n      <td> 0.357383</td>\n      <td>-0.664820</td>\n      <td>-0.601961</td>\n      <td>-0.964749</td>\n      <td> 15</td>\n    </tr>\n    <tr>\n      <th>2014-02-04</th>\n      <td>-0.340011</td>\n      <td> 1.304989</td>\n      <td> 0.717388</td>\n      <td>-0.268375</td>\n      <td> 15</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": "                   A         B         C         D   E\n2014-02-01 -1.059138 -0.196474  0.239179  1.191028   1\n2014-02-02 -0.641067  1.734050 -0.359996 -0.126219   1\n2014-02-03  0.357383 -0.664820 -0.601961 -0.964749  15\n2014-02-04 -0.340011  1.304989  0.717388 -0.268375  15"
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "pd.isnull(df1)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-02-01</th>\n      <td> False</td>\n      <td> False</td>\n      <td> False</td>\n      <td> False</td>\n      <td> False</td>\n    </tr>\n    <tr>\n      <th>2014-02-02</th>\n      <td> False</td>\n      <td> False</td>\n      <td> False</td>\n      <td> False</td>\n      <td> False</td>\n    </tr>\n    <tr>\n      <th>2014-02-03</th>\n      <td> False</td>\n      <td> False</td>\n      <td> False</td>\n      <td> False</td>\n      <td>  True</td>\n    </tr>\n    <tr>\n      <th>2014-02-04</th>\n      <td> False</td>\n      <td> False</td>\n      <td> False</td>\n      <td> False</td>\n      <td>  True</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": "                A      B      C      D      E\n2014-02-01  False  False  False  False  False\n2014-02-02  False  False  False  False  False\n2014-02-03  False  False  False  False   True\n2014-02-04  False  False  False  False   True"
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Missing values propogate through arithmetic operations."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df2=pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\nprint df2\ndf2.loc[dates[0]:dates[2],'B']=float('NaN')\nprint df2\nprint df1+df2",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                   A         B         C         D\n2014-02-01 -1.654418  0.697809  0.991626  0.511788\n2014-02-02 -1.163772  0.239813 -0.945453  0.192696\n2014-02-03 -0.048510 -1.188224  1.718062  1.868163\n2014-02-04 -0.277427 -1.509794  0.360021  2.071887\n2014-02-05  0.008847 -2.179037 -0.074886  0.649411\n2014-02-06 -0.019938  0.121653  1.180238 -1.312769\n                   A         B         C         D\n2014-02-01 -1.654418       NaN  0.991626  0.511788\n2014-02-02 -1.163772       NaN -0.945453  0.192696\n2014-02-03 -0.048510       NaN  1.718062  1.868163\n2014-02-04 -0.277427 -1.509794  0.360021  2.071887\n2014-02-05  0.008847 -2.179037 -0.074886  0.649411\n2014-02-06 -0.019938  0.121653  1.180238 -1.312769\n                   A         B         C         D   E\n2014-02-01 -2.713556       NaN  1.230805  1.702816 NaN\n2014-02-02 -1.804838       NaN -1.305449  0.066476 NaN\n2014-02-03  0.308873       NaN  1.116101  0.903414 NaN\n2014-02-04 -0.617438 -0.204804  1.077410  1.803513 NaN\n2014-02-05       NaN       NaN       NaN       NaN NaN\n2014-02-06       NaN       NaN       NaN       NaN NaN\n"
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "But this can be avoided by using built-in methods, that exclude missing values."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df1['A'].sum()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": "-1.6828330115428896"
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df1.mean(1)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": "2014-02-01    0.234919\n2014-02-02    0.321353\n2014-02-03   -0.468537\n2014-02-04    0.353498\nFreq: D, dtype: float64"
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df2.cumsum()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-02-01</th>\n      <td>-1.654418</td>\n      <td>      NaN</td>\n      <td> 0.991626</td>\n      <td> 0.511788</td>\n    </tr>\n    <tr>\n      <th>2014-02-02</th>\n      <td>-2.818190</td>\n      <td>      NaN</td>\n      <td> 0.046173</td>\n      <td> 0.704484</td>\n    </tr>\n    <tr>\n      <th>2014-02-03</th>\n      <td>-2.866700</td>\n      <td>      NaN</td>\n      <td> 1.764235</td>\n      <td> 2.572647</td>\n    </tr>\n    <tr>\n      <th>2014-02-04</th>\n      <td>-3.144127</td>\n      <td>-1.509794</td>\n      <td> 2.124256</td>\n      <td> 4.644534</td>\n    </tr>\n    <tr>\n      <th>2014-02-05</th>\n      <td>-3.135281</td>\n      <td>-3.688830</td>\n      <td> 2.049370</td>\n      <td> 5.293945</td>\n    </tr>\n    <tr>\n      <th>2014-02-06</th>\n      <td>-3.155219</td>\n      <td>-3.567178</td>\n      <td> 3.229609</td>\n      <td> 3.981177</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": "                   A         B         C         D\n2014-02-01 -1.654418       NaN  0.991626  0.511788\n2014-02-02 -2.818190       NaN  0.046173  0.704484\n2014-02-03 -2.866700       NaN  1.764235  2.572647\n2014-02-04 -3.144127 -1.509794  2.124256  4.644534\n2014-02-05 -3.135281 -3.688830  2.049370  5.293945\n2014-02-06 -3.155219 -3.567178  3.229609  3.981177"
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Gaussian numbers histogram\nfrom numpy.random import normal\nn = 1000\nx = pd.Series(normal(size=n))\n#print x\navg = x.mean()\nstd = x.std()\n\nx_avg  = pd.Series(np.ones(n)* avg)\nx_stdl = pd.Series(np.ones(n)*(avg-std))\nx_stdh = pd.Series(np.ones(n)*(avg+std))\n\ndf_gauss=pd.DataFrame({'A':x_stdl,'B':x_stdh,'x':x})\n\ndf_gauss.plot(style=['rx','rx','bx'])\nplt.figure()\ndf_gauss['x'].diff().hist(color='g', bins=50)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": "<matplotlib.axes.AxesSubplot at 0x10c893b10>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZI7WSgYGEHo4b50JDg0HwLjGOlIxpoOGOgeg7udA/Eq4KgLs64emnbairE0Ys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\nZOWgnNcszptUdlpTddw80SEzM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       "text": "<matplotlib.figure.Figure at 0x10c68afd0>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x10cd39910>"
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='ex3'></a>\n## Exercise 3: DataFrame methods"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df=pd.DataFrame(np.random.randn(5,5), columns=list('ABCDE'))\nprint df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "          A         B         C         D         E\n0 -1.353547 -0.059735 -0.597045 -0.299746  1.335253\n1 -0.621872 -0.592243  0.060789 -0.366381  0.186925\n2  0.382292  0.201983  0.828402 -0.869741 -0.448232\n3 -2.099593 -0.471666 -0.422174 -1.474813 -0.173611\n4  1.540184 -0.721423 -0.135882 -0.793001 -0.629852\n"
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Try the following with `df` as defined above:\n\n    1. df.mean()\n    2. df.apply(np.cumsum)\n    3. df.apply(lambda x: x.max() - x.min())\n    4. Plot a histogram"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.apply(lambda x: x.max() - x.min())\n#What does lambda do?",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": "A    3.639777\nB    0.923406\nC    1.425447\nD    1.175067\nE    1.965105\ndtype: float64"
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def f(x):\n...     return x*2\ng = lambda x: x*2 \n\nprint g(3)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "6\n"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='mm'></a>\n### More manipulations"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from pandas import read_csv\nfrom urllib import urlopen\npage = urlopen(\"http://econpy.pythonanywhere.com/ex/NFL_1979.csv\")\ndf = read_csv(page)\nprint df[:3]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "         Date          Visitor  Visitor Score             Home Team  \\\n0  09/01/1979    Detroit Lions             16  Tampa Bay Buccaneers   \n1  09/02/1979  Atlanta Falcons             40    New Orleans Saints   \n2  09/02/1979  Baltimore Colts              0    Kansas City Chiefs   \n\n   Home Score  Line  Total Line  \n0          31     3          30  \n1          34     5          32  \n2          14     1          37  \n"
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df1=df[0:10]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print df1",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "         Date             Visitor  Visitor Score             Home Team  \\\n0  09/01/1979       Detroit Lions             16  Tampa Bay Buccaneers   \n1  09/02/1979     Atlanta Falcons             40    New Orleans Saints   \n2  09/02/1979     Baltimore Colts              0    Kansas City Chiefs   \n3  09/02/1979  Cincinnati Bengals              0        Denver Broncos   \n4  09/02/1979    Cleveland Browns             25         New York Jets   \n5  09/02/1979      Dallas Cowboys             22    St Louis Cardinals   \n6  09/02/1979   Green Bay Packers              3         Chicago Bears   \n7  09/02/1979      Houston Oilers             29   Washington Redskins   \n8  09/02/1979      Miami Dolphins              9         Buffalo Bills   \n9  09/02/1979     New York Giants             17   Philadelphia Eagles   \n\n   Home Score  Line  Total Line  \n0          31     3        30.0  \n1          34     5        32.0  \n2          14     1        37.0  \n3          10     3        31.5  \n4          22     2        41.0  \n5          21    -4        37.0  \n6           6     3        31.0  \n7          27    -4        33.0  \n8           7    -5        39.0  \n9          23     7        31.5  \n"
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "A=df1[:3]\nB=df1[3:7]\nC=df1[7:10]\nprint A,B,C",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "         Date          Visitor  Visitor Score             Home Team  \\\n0  09/01/1979    Detroit Lions             16  Tampa Bay Buccaneers   \n1  09/02/1979  Atlanta Falcons             40    New Orleans Saints   \n2  09/02/1979  Baltimore Colts              0    Kansas City Chiefs   \n\n   Home Score  Line  Total Line  \n0          31     3          30  \n1          34     5          32  \n2          14     1          37            Date             Visitor  Visitor Score           Home Team  \\\n3  09/02/1979  Cincinnati Bengals              0      Denver Broncos   \n4  09/02/1979    Cleveland Browns             25       New York Jets   \n5  09/02/1979      Dallas Cowboys             22  St Louis Cardinals   \n6  09/02/1979   Green Bay Packers              3       Chicago Bears   \n\n   Home Score  Line  Total Line  \n3          10     3        31.5  \n4          22     2        41.0  \n5          21    -4        37.0  \n6           6     3        31.0            Date          Visitor  Visitor Score            Home Team  \\\n7  09/02/1979   Houston Oilers             29  Washington Redskins   \n8  09/02/1979   Miami Dolphins              9        Buffalo Bills   \n9  09/02/1979  New York Giants             17  Philadelphia Eagles   \n\n   Home Score  Line  Total Line  \n7          27    -4        33.0  \n8           7    -5        39.0  \n9          23     7        31.5  \n"
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "parts=[A,B,C]\ndf2=pd.concat(parts)\nprint df2",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "         Date             Visitor  Visitor Score             Home Team  \\\n0  09/01/1979       Detroit Lions             16  Tampa Bay Buccaneers   \n1  09/02/1979     Atlanta Falcons             40    New Orleans Saints   \n2  09/02/1979     Baltimore Colts              0    Kansas City Chiefs   \n3  09/02/1979  Cincinnati Bengals              0        Denver Broncos   \n4  09/02/1979    Cleveland Browns             25         New York Jets   \n5  09/02/1979      Dallas Cowboys             22    St Louis Cardinals   \n6  09/02/1979   Green Bay Packers              3         Chicago Bears   \n7  09/02/1979      Houston Oilers             29   Washington Redskins   \n8  09/02/1979      Miami Dolphins              9         Buffalo Bills   \n9  09/02/1979     New York Giants             17   Philadelphia Eagles   \n\n   Home Score  Line  Total Line  \n0          31     3        30.0  \n1          34     5        32.0  \n2          14     1        37.0  \n3          10     3        31.5  \n4          22     2        41.0  \n5          21    -4        37.0  \n6           6     3        31.0  \n7          27    -4        33.0  \n8           7    -5        39.0  \n9          23     7        31.5  \n"
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})\nright= pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})\nprint left\nprint right ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "   key  lval\n0  foo     1\n1  foo     2\n   key  rval\n0  foo     4\n1  foo     5\n"
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "pd.merge(left, right, on='key')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>key</th>\n      <th>lval</th>\n      <th>rval</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> foo</td>\n      <td> 1</td>\n      <td> 4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> foo</td>\n      <td> 1</td>\n      <td> 5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> foo</td>\n      <td> 2</td>\n      <td> 4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> foo</td>\n      <td> 2</td>\n      <td> 5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": "   key  lval  rval\n0  foo     1     4\n1  foo     1     5\n2  foo     2     4\n3  foo     2     5"
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rowadd=df.iloc[3]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print rowadd,df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "A    0.029758\nB   -1.297145\nC    0.322048\nD   -2.251859\nName: 3, dtype: float64           A         B         C         D\n0  0.096170 -0.100691 -0.083139  0.157201\n1 -1.167492 -1.671596 -1.636372  0.895365\n2  0.704329 -0.995257 -0.027827  1.043940\n3  0.029758 -1.297145  0.322048 -2.251859\n4 -0.086081 -0.144922  0.673781 -0.468736\n5 -0.196530 -1.329766 -0.142091  0.161405\n6  0.870045  1.492161 -1.159585  0.299559\n7  1.599507  0.218105  0.400768 -0.442777\n"
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.append(rowadd,ignore_index=True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 0.096170</td>\n      <td>-0.100691</td>\n      <td>-0.083139</td>\n      <td> 0.157201</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-1.167492</td>\n      <td>-1.671596</td>\n      <td>-1.636372</td>\n      <td> 0.895365</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 0.704329</td>\n      <td>-0.995257</td>\n      <td>-0.027827</td>\n      <td> 1.043940</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 0.029758</td>\n      <td>-1.297145</td>\n      <td> 0.322048</td>\n      <td>-2.251859</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.086081</td>\n      <td>-0.144922</td>\n      <td> 0.673781</td>\n      <td>-0.468736</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-0.196530</td>\n      <td>-1.329766</td>\n      <td>-0.142091</td>\n      <td> 0.161405</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td> 0.870045</td>\n      <td> 1.492161</td>\n      <td>-1.159585</td>\n      <td> 0.299559</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> 1.599507</td>\n      <td> 0.218105</td>\n      <td> 0.400768</td>\n      <td>-0.442777</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td> 0.029758</td>\n      <td>-1.297145</td>\n      <td> 0.322048</td>\n      <td>-2.251859</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": "          A         B         C         D\n0  0.096170 -0.100691 -0.083139  0.157201\n1 -1.167492 -1.671596 -1.636372  0.895365\n2  0.704329 -0.995257 -0.027827  1.043940\n3  0.029758 -1.297145  0.322048 -2.251859\n4 -0.086081 -0.144922  0.673781 -0.468736\n5 -0.196530 -1.329766 -0.142091  0.161405\n6  0.870045  1.492161 -1.159585  0.299559\n7  1.599507  0.218105  0.400768 -0.442777\n8  0.029758 -1.297145  0.322048 -2.251859"
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n   ....:                          'foo', 'bar', 'foo', 'foo'],\n   ....:                    'B' : ['one', 'one', 'two', 'three',\n   ....:                          'two', 'two', 'one', 'three'],\n   ....:                    'C' : np.random.randn(8),\n   ....:                    'D' : np.random.randn(8)})\nprint df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "     A      B         C         D\n0  foo    one -0.508353 -1.725045\n1  bar    one -0.014625  1.706129\n2  foo    two  0.041269  1.253173\n3  bar  three  1.302055 -1.497082\n4  foo    two  0.116896  0.007921\n5  bar    two -0.009417 -0.083856\n6  foo    one -1.478390 -0.921723\n7  foo  three  0.451666 -0.119239\n"
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.groupby('A').sum()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n    <tr>\n      <th>A</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>bar</th>\n      <td> 1.278014</td>\n      <td> 0.125191</td>\n    </tr>\n    <tr>\n      <th>foo</th>\n      <td>-1.376912</td>\n      <td>-1.504914</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": "            C         D\nA                      \nbar  1.278014  0.125191\nfoo -1.376912 -1.504914"
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.groupby(['A','B']).sum()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n    <tr>\n      <th>A</th>\n      <th>B</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">bar</th>\n      <th>one</th>\n      <td>-0.014625</td>\n      <td> 1.706129</td>\n    </tr>\n    <tr>\n      <th>three</th>\n      <td> 1.302055</td>\n      <td>-1.497082</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>-0.009417</td>\n      <td>-0.083856</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">foo</th>\n      <th>one</th>\n      <td>-1.986743</td>\n      <td>-2.646768</td>\n    </tr>\n    <tr>\n      <th>three</th>\n      <td> 0.451666</td>\n      <td>-0.119239</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td> 0.158165</td>\n      <td> 1.261093</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": "                  C         D\nA   B                        \nbar one   -0.014625  1.706129\n    three  1.302055 -1.497082\n    two   -0.009417 -0.083856\nfoo one   -1.986743 -2.646768\n    three  0.451666 -0.119239\n    two    0.158165  1.261093"
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='stats'></a>\n## Statistical Tests\n\n`pandas` allows for using some built-in statistical methods to compare, fit or interpolate data. \n\n<a id='regression'></a>\n### Regression\n\nRegression analysis refers to the process of estimating relationships between variables. Linear regression is equivalent to fitting a line between to sets of data points (x,y)\n\n$$y_i(x) = a_0 + a_1x_i $$"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "\nimport statsmodels.formula.api as sm\nimport matplotlib.pyplot as plt\nurl = \"http://vincentarelbundock.github.com/Rdatasets/csv/HistData/Guerry.csv\"\ndf = pd.read_csv(url)\n#print df\ndf = df[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()\ndf.head()\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Lottery</th>\n      <th>Literacy</th>\n      <th>Wealth</th>\n      <th>Region</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 41</td>\n      <td> 37</td>\n      <td> 73</td>\n      <td> E</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 38</td>\n      <td> 51</td>\n      <td> 22</td>\n      <td> N</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 66</td>\n      <td> 13</td>\n      <td> 61</td>\n      <td> C</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 80</td>\n      <td> 46</td>\n      <td> 76</td>\n      <td> E</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 79</td>\n      <td> 69</td>\n      <td> 83</td>\n      <td> E</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": "   Lottery  Literacy  Wealth Region\n0       41        37      73      E\n1       38        51      22      N\n2       66        13      61      C\n3       80        46      76      E\n4       79        69      83      E"
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "\nmod = sm.ols(formula='Lottery ~ Literacy ', data=df)\nres = mod.fit()\nprint res.summary()\nintercept, slope =res.params",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                Lottery   R-squared:                       0.146\nModel:                            OLS   Adj. R-squared:                  0.135\nMethod:                 Least Squares   F-statistic:                     14.16\nDate:                Mon, 09 Feb 2015   Prob (F-statistic):           0.000312\nTime:                        14:29:19   Log-Likelihood:                -386.13\nNo. Observations:                  85   AIC:                             776.3\nDf Residuals:                      83   BIC:                             781.2\nDf Model:                           1                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n------------------------------------------------------------------------------\nIntercept     64.2389      6.163     10.423      0.000        51.981    76.497\nLiteracy      -0.5417      0.144     -3.763      0.000        -0.828    -0.255\n==============================================================================\nOmnibus:                        7.455   Durbin-Watson:                   2.010\nProb(Omnibus):                  0.024   Jarque-Bera (JB):                2.936\nSkew:                           0.061   Prob(JB):                        0.230\nKurtosis:                       2.098   Cond. No.                         106.\n==============================================================================\n"
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "xtest=np.linspace(1,100,100)\nytest=intercept+slope*xtest",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.plot(df['Literacy'],df['Lottery'],'kx')\nplt.plot(xtest,ytest,'r')\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P3r18nZHh8HOZSCwM2Cjl9HglTw9z3Xnl37z0MD6FJ8uQLl6MXkN34zbf6Cis\nK1ZAWrYM1wsL0drVhS8+9hgybrllso5uyRLAYkn1kQiFY5NExbFJItOkho1oJqwFST7WHhgLr3an\ngMUCZckS+Jcsgf/uuyPubm1txf9etQrZXi8sPT1I83jw5UWL4D1yBI6xsclpl1euQHY6Y2fpiouB\njIwUHBwREZkFM2yUVMwIJYceM5Y0M/5NdWp0FJZz5yJXuwxk6fr6oDgcUadbBrY0QFZWqo+CiIhS\njFMiSQisuUoeZmOMia+RSYYa334/pPPnwwK56QukKFZrRFYutK3k53PaJRGRwTFgo5TTY/aAc90p\nFeaahTby+NTj+0XcFAXS5cuRC6OErnZ59epUEDc9U+dyQXY6gfT0lB5GaJAdGJu6DbLJsIz8vkn6\nxxo2Srn29vawk61ATRs/zImmsC5xkqlqXiUJSk4O/Dk58K9dG/1nRkbCg7lz55D2619PtS9cgJKX\nFzHVMjTAQ2amqodRWloaDKqB8KCbiIjUxQwbzYmhpjARpYCpskpzxJrXOZqYgKWvb8YsnbJwYeQ+\ndCFBnZKXB0hSQt3glF4iovhxSiSpjiebRInhRY9wPPlPIkWBNDgYe2GUnh5Io6ORq1yGfl9UBKTN\nPumGQTYRUXwYsJEmjHaCxbnuJDIjj09eAEqB4eGIQC50cRRpYAByYWF4IBca4Llc8E5MYM+ePSgr\nK8Px48cN+/fixRX9MvL7JumfqjVsP/3pT3H69GlkZWVh69atyM7OhsfjQVNTExRFQUVFBVwuV1xP\nTvpit9tRXV0dvLpqxA9qIlIfa15TIDMT8qpVkFetin7/+Dgsvb1hWbq0Dz+E5fXXgwFemiSh4ZZb\ncOmjj/B/V6/GW488gi9VVmLhbbdNrnaZk5PwtEsRhNbrTb+gQESUCnPOsHV0dKCrqwuPPPII9u7d\ni23btgEAGhsbsXPnzpiPY4bNOIyWYSOiSMwuUDStLS34PytXwn7lSjCAm/jsM1z96CMUjo5OZukm\nJqLW0fkDdXRFRYDVmupDmRN+3hFRsqm+SuTExAQ++ugjFBUVwefzwWq1wuFwBO8fGxtDeoqXHCZ1\nTZ+yFFjhjR9iRMbC7AJFs2nzZgCAH4D/rruCt9sAXA00hoYi9qFb8NFHyLgxFVMaHIS8ZElkDV3I\nv1i0SOtDi4ozSohIJHPKsP3d3/0d0tPT8b3vfQ99fX1oa2tD2o3i5ImJCWzcuBHLly+P+lhm2IzB\niFfdOdcJ9QKRAAAT60lEQVSdRJbK8cnsQvLwvTOEzwfLuXORq10GbuvthZKVFXVz8eAm43a7JtMu\n+RrQJ36uk8g0WXTkxIkTeO+997Bt2za8+OKLqKmpgaIoqKurw9NPPx0zw9bW1oaRkZHgC8jtdgMA\n22ynvB34XpT+sM22SOMzsBpgY2MjHnrooZT/PvTaHh4eRltbG2pra9HZ2RnRTnX/4mkHbkv6///u\nu8i4fBllTicsPT04+957WNTfj+KJicmtC/7wB0iKAmnZMsglJTiXloaRggIsXb8essuF9vPnMepw\nYP2GDQn1Z82aNdizZw/Ky8uRmZkZ0U7175/t2O3Ozk5UVVUJ0x+22Q5t22w29QO2s2fP4s0338Q3\nv/lNvPDCC6iqqoIsy9i/fz927doV83HMsBER6QuzC8mlh9+nXjKBktcbvnXBtK0MpCtXIDudkatc\nBr6Ki4GMjBmfQy+/CyLSF1UzbP/2b/+Gy5cvIzs7G1u2bEFubi66u7vR3NwMSZJmXSWSARsRkX5w\nyX11iL5/mWH+7qOjU1Mspwd2Hg8sfX1QcnKiT7l0ueAvKQGyslJ9FERkQNyHjSgObjfnupO4UjU+\n9ZJd0Es/AX1k2IC591PX751+P6Tz54MBnHV6ps7jgWK1hi+MMi1Lp+TnAxZLqo+EotD12CTDU32V\nSCKaOz2dSBJNF22M2u124cauXlaz1NMKu6ZYGdFqhVJcDH9xMfwAxqffryiQLl+OmG6ZduJEcNNx\naXh4MkMXbcqlywXZ6QS4cjYRJREzbERJZpipRUSC00PmSk8XcPTw+xTCyEj0VS4DXxcuQMnLi9iH\nLnQLA2RmpvooiEhjnBJJJBie+EzS08kq6ZPotWF6wQtNSTQxAUtfX+SiKIHplx4PlIULYy+M4nJB\nycvTZPsCItIOAzaiOKg9150nkjwJTARrMWbHCyPJM5+LKxybCVIUSIODkcFcSKZOGh2NDOZCvy8q\nAtJY1TIdxyaJjDVsRAJpbW3FqlWrUF9fj46ODtTX12PHjh04ffq06bJKoTU7PKmmZNJTbZge6KV2\n0RAkCUpeHvx5efD/yZ9E/5nh4bCMnKWnBwuOHp0K6AYGIBcWhgdy04I72GzaHhcRqYYZNqIk6+np\nwSOPPIJXXnkFJSUlEW0zYraRko3TbcnUxsdh6e2NOeXS4vFAycyMGsgFV7t0ODjtkkhDzLARCeT0\n6dN45ZVXsG/fvmBW6ZVXXsHp06d1F7Al46TY6/WGZRuZAaFkYEaITG3BAsjLlkFetiz6/bIMqb8/\nPJA7cwZp7747laXz+yOyc6ELpChLlgBWq7bHRURRMcNGpsUattklWoPGGrb4sRaDRMWxaRBDQ1N7\n0UXJ0kmXLkFesiR6Dd2N+josXJjqowjDsUkiY4aNSDBGySolWoPW3t4e9vOB/4/T1ohoOk5z1VhW\nFuTVqyGvXh39fp8vfMsCjwdpx4/D0tw82e7thZKdHb2GLjDtUoefe0QiYoaNKMmMmFUyQraQiMRm\nxPdOQ5NlSBcuhGfmenomM3Y3vockQXa5JqdaTsvOySUlUAoLAYsl1UdCpAlVl/U/cOAAenp6sHjx\nYjzxxBNwOBzweDxoamqCoiioqKiAy+WK+XgGbGQ2RrtKzKXTiUgreni/Mdp7vGoUBZLXGzndMuRf\naWgIstMZfU86lwtycTGQkZHqIyFKCk32YXv//ffxhz/8ARUVFdi7dy+2bdsGAGhsbMTOnTtjPo4B\nG4mKc91nxyveqcPxSaIye/0v3xeT6Pr18GmX04O68+eh5ORE3WA8sEAKsrKC/x3fN0lkmtSwZWZm\nwu/3w+fzwWq1wuFwBO8bGxtDenp6XB0gInGxBk1bvHJPZqeH+l/uL5lEixZBXrkS8sqV0e/3+yH1\n9U0tjuLxwPrxx1jw9tvBoE5JSwsGcX9stSLj5MnwOrr8fE67JN2bc4atsbERX/nKVzA2Noa2tjak\npU3GehMTE9i4cSOWL18e9XHMsBERzY0ertwzqCS16GH8hxI9E2gKigLp0qWprFyULJ00PAy5uDj6\nwiguF2SnE2DSgTSgeobtxIkTKC4uRnFxMXw+HwYHB1FTUwNFUVBXVwen0xnXkxMR0RQ9XLkvLS2N\neVJNlAg9ZfT1kAk0BUmCkpsLf24u/GvXRv+Za9fCp1l6PEg7diy4QIp08SKUvLyIqZahAR4yM7U9\nLqJpZg3YPvvsM3R1deHRRx8FAGRkZECWZYyMjECWZciyPOt0yNA5xW63GwDYZjvl7cD3ovSHbbYD\n7erqaqxbtw6NjY3o7OxMeX+mtwNBZVlZGV599VU0NDTAbrcL0z+21W0Hbkv2/2+z2aKO90CwJsrx\nr1mzBnv27EF5eTnOnj0bfD2Ul5cjMzMz5f0LtOvq6rBq1Sps3rw5eP/w8DAsFgs2bdqU8v6p0e7s\n7ERVVVXE/fJtt+Hd/n5g5Uqsr6wMv7+sDJa+PvzujTewqL8fq2w2pJ06Be+//ztsFy8i69IlKAsX\n4qrDgev5+cheuxZySQk+vnYNI/n5uOMv/gJKXh7cv/lNyo+fbbHbNpsN8Zp1SuRTTz2F3NxcWCwW\nLF26FFu3bkV3dzeam5shSRJXiSTdcrtZnEziCWSsysrKcPz4cWGv3HM6mHmZ/b1TL9OC9TbFNBlU\nGZuKAmlwMOrm4oG2NDoafZXLwPdFRcCNUiIyL01WiYwXAzYiorlJ9ARLqxNJPSy9TkR8rWpmeDgi\nkLOGBHjSwADkwsLwQG5acIcEsi+kDwzYiIgMINGAS4sr6ma8ak+kZ8yGC2B8HJbe3vAsXWiA5/FA\nycyMGsgFV7t0OABJSvWRUAIYsBHFwezTekhs8Y5Pta+o62U6mJlo/Tfhe6d+mC3DptuxKcuQ+vuj\nbi4ezNL5/RHZudAFUpQlSwCrNdVHQjPQZB82IiISn91uDy5a0tHRkfSTs2gBgN1uZ7CWQly5k6KZ\nnv0OLI5i9KBNlywWKIWF8BcWwn/XXdF/Zmhoaj+6G1/pnZ3BDJ106RLkJUsis3Mh/2LhQm2Pi5KG\nGTYig2ImxJzMdkWdJvHvTtPxM8BkfD5Yzp2LvThKby+U7OzoNXSBaZd8z1AVp0QSUQTWGpkP/+bm\nxlolIopJliFduBCxubg15HtIEmSXa3KqZZRMnVJYCFgsqT4S3WLARhQH3c51nwdeddeveMYnr6ib\nl5av9XjfOzk+SW1m+FxXjaJA8nojArrQ76WhIchO51QgNz1LV1wMzLI3s5mxho2IolK7nonEwvoy\nc9JLrRJr7YgEJklQsrPhz86Gf82a6D9z/XrEtMs0t3uqfeEClNxcyMXFYYGcPyTAQ1aWtsdlEMyw\nERkYM2xE8dNLRkgv/QT4nkRkaH4/pL6+8MVRpmXqlAULIrJyYXV0+fmG3b6AUyKJdEaLEyzWMxEl\nhq8hdbDWjsikFAXSpUsRWxaEbmUgXbs2mY2bFsgFv4qKgAULUn0kcWHARhSHVM511+JEUE9X3SkS\nazHEwIxQpETGJn+fpCa+bxrAtWuRe9GFLJAiXbwIJT9/aqpllHo6LF6c6qOIijVsRDoTWmei1omL\nCPVMDBpJ71gHmjx6qbUjohR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       "text": "<matplotlib.figure.Figure at 0x10f577ed0>"
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='ttest'></a>\n### T-Test\n\nThe t-test assesses whether the means of two groups are statistically different from each other."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "\ntown1_heights = pd.Series([5, 6, 7, 6, 7.1, 6, 4])\ntown2_heights = pd.Series([5.5, 6.5, 7, 6, 7.1, 6])\n\ntown1_mean = town1_heights.mean()\ntown2_mean = town2_heights.mean()\n\nprint \"Town 1 avg. height\", town1_mean\nprint \"Town 2 avg. height\", town2_mean\n\nprint \"Effect size: \", abs(town1_mean - town2_mean)\n\ndf=pd.DataFrame({'T1':town1_heights,'T2':town2_heights})\nb=df.boxplot()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Town 1 avg. height 5.87142857143\nTown 2 avg. height 6.35\nEffect size:  0.478571428571\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "/Users/lrao/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/tools/plotting.py:2380: FutureWarning: \nThe default value for 'return_type' will change to 'axes' in a future release.\n To use the future behavior now, set return_type='axes'.\n To keep the previous behavior and silence this warning, set return_type='dict'.\n  warnings.warn(msg, FutureWarning)\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x10f5ab290>"
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from scipy import stats\n\nprint \"Town 1 Shapiro-Wilks p-value\", stats.shapiro(town1_heights)[1]\n\nprint \" T-Test p-value:\", stats.ttest_ind(town1_heights, town2_heights,equal_var = False)[1]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Town 1 Shapiro-Wilks p-value 0.380458295345\n T-Test p-value: 0.347028503558\n"
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='ts'></a>\n### Time Series\n\nA time series is a sequence of data points, measured typically at successive points in time spaced at uniform time intervals. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rng = pd.date_range('1/1/2012', periods=100, freq='S')\nprint rng",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "<class 'pandas.tseries.index.DatetimeIndex'>\n[2012-01-01 00:00:00, ..., 2012-01-01 00:01:39]\nLength: 100, Freq: S, Timezone: None\n"
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)\nts.plot()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": "<matplotlib.axes.AxesSubplot at 0x10f5b9ed0>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cJKFH2lFp32u1kq8g4HZonqM0EHSjyHK23E2dS5VxSCFIpJ6Iz4VyVbD1mAGC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\n",
       "text": "<matplotlib.figure.Figure at 0x10cdd5a90>"
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\nts=ts.cumsum()\nts.plot()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": "<matplotlib.axes.AxesSubplot at 0x10f7e3490>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+KjfMjx1jdb3t3rGNObHBo9pkGboaqMnhfefndSuot+Ps9HGxydJngZK5sSE8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\nhBBCCCGEEGKY+f8R4+r+BRZTRgAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x10cd3f050>"
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Bar plot\n\nts = pd.DataFrame(np.random.randn(1000,5), index=pd.date_range('1/1/2000', periods=1000))\nts=ts.cumsum()\nprint ts.ix[5]\nts.ix[5].plot(kind='bar'); plt.axhline(0, color='k')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "0   -4.948548\n1   -1.926755\n2    2.653564\n3    0.093642\n4   -3.095961\nName: 2000-01-06 00:00:00, dtype: float64\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": "<matplotlib.lines.Line2D at 0x10f7aee10>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x10f7d4a50>"
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='dp'></a>\n## Data Problem\n\nImagine yourself to be a sales analyst at an apparel company. Your boss asks you to look at weather data from the past year to understand the weather data over the months, so that you can have the right apparel on display at the appropriate time.\n\nYou can get the data from [here](http://climate.weather.gc.ca/index_e.html) (so your company is Canadian). The template for downloading the data is:\n\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "url_template = \"http://climate.weather.gc.ca/climateData/bulkdata_e.html?format=csv&stationID=5415&Year={year}&Month={month}&timeframe=1&submit=Download+Data\"",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Usually in data tasks, there are no specified objectives. One needs to play around with the data in order to derive inferences. While this might seem like a vague and daunting task, it simply requires a start and once you get familiar with the data, you will eventually find some patterns and will be able to make an initial set of conclusions.\n\nHere let's start with the data for March 2012 (there seems to be less data for the more recent years).\n\n<a id='dc'></a>\n\n### Data Cleaning"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "url = url_template.format(month=3, year=2012)\nweather_mar2012 = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True, encoding='latin1')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "weather_mar2012",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>Day</th>\n      <th>Time</th>\n      <th>Data Quality</th>\n      <th>Temp (\u00c2\u00b0C)</th>\n      <th>Temp Flag</th>\n      <th>Dew Point Temp (\u00c2\u00b0C)</th>\n      <th>Dew Point Temp Flag</th>\n      <th>Rel Hum (%)</th>\n      <th>...</th>\n      <th>Wind Spd Flag</th>\n      <th>Visibility (km)</th>\n      <th>Visibility Flag</th>\n      <th>Stn Press (kPa)</th>\n      <th>Stn Press Flag</th>\n      <th>Hmdx</th>\n      <th>Hmdx Flag</th>\n      <th>Wind Chill</th>\n      <th>Wind Chill Flag</th>\n      <th>Weather</th>\n    </tr>\n    <tr>\n      <th>Date/Time</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-03-01 00:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 00:00</td>\n      <td>  </td>\n      <td>-5.5</td>\n      <td>NaN</td>\n      <td>-9.7</td>\n      <td>NaN</td>\n      <td> 72</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  4.0</td>\n      <td>NaN</td>\n      <td> 100.97</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-13</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 01:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 01:00</td>\n      <td>  </td>\n      <td>-5.7</td>\n      <td>NaN</td>\n      <td>-8.7</td>\n      <td>NaN</td>\n      <td> 79</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  2.4</td>\n      <td>NaN</td>\n      <td> 100.87</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-13</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 02:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 02:00</td>\n      <td>  </td>\n      <td>-5.4</td>\n      <td>NaN</td>\n      <td>-8.3</td>\n      <td>NaN</td>\n      <td> 80</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  4.8</td>\n      <td>NaN</td>\n      <td> 100.80</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-13</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 03:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 03:00</td>\n      <td>  </td>\n      <td>-4.7</td>\n      <td>NaN</td>\n      <td>-7.7</td>\n      <td>NaN</td>\n      <td> 79</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  4.0</td>\n      <td>NaN</td>\n      <td> 100.69</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-12</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 04:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 04:00</td>\n      <td>  </td>\n      <td>-5.4</td>\n      <td>NaN</td>\n      <td>-7.8</td>\n      <td>NaN</td>\n      <td> 83</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  1.6</td>\n      <td>NaN</td>\n      <td> 100.62</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-14</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 05:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 05:00</td>\n      <td>  </td>\n      <td>-5.3</td>\n      <td>NaN</td>\n      <td>-7.9</td>\n      <td>NaN</td>\n      <td> 82</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  2.4</td>\n      <td>NaN</td>\n      <td> 100.58</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-14</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 06:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 06:00</td>\n      <td>  </td>\n      <td>-5.2</td>\n      <td>NaN</td>\n      <td>-7.8</td>\n      <td>NaN</td>\n      <td> 82</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  4.0</td>\n      <td>NaN</td>\n      <td> 100.57</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-14</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 07:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 07:00</td>\n      <td>  </td>\n      <td>-4.9</td>\n      <td>NaN</td>\n      <td>-7.4</td>\n      <td>NaN</td>\n      <td> 83</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  1.6</td>\n      <td>NaN</td>\n      <td> 100.59</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-13</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 08:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 08:00</td>\n      <td>  </td>\n      <td>-5.0</td>\n      <td>NaN</td>\n      <td>-7.5</td>\n      <td>NaN</td>\n      <td> 83</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  1.2</td>\n      <td>NaN</td>\n      <td> 100.59</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-13</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 09:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 09:00</td>\n      <td>  </td>\n      <td>-4.9</td>\n      <td>NaN</td>\n      <td>-7.5</td>\n      <td>NaN</td>\n      <td> 82</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  1.6</td>\n      <td>NaN</td>\n      <td> 100.60</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-13</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 10:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 10:00</td>\n      <td>  </td>\n      <td>-4.7</td>\n      <td>NaN</td>\n      <td>-7.3</td>\n      <td>NaN</td>\n      <td> 82</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  1.2</td>\n      <td>NaN</td>\n      <td> 100.62</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-13</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 11:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 11:00</td>\n      <td>  </td>\n      <td>-4.4</td>\n      <td>NaN</td>\n      <td>-6.8</td>\n      <td>NaN</td>\n      <td> 83</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  1.0</td>\n      <td>NaN</td>\n      <td> 100.66</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-12</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 12:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 12:00</td>\n      <td>  </td>\n      <td>-4.3</td>\n      <td>NaN</td>\n      <td>-6.8</td>\n      <td>NaN</td>\n      <td> 83</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  1.2</td>\n      <td>NaN</td>\n      <td> 100.66</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-12</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 13:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 13:00</td>\n      <td>  </td>\n      <td>-4.3</td>\n      <td>NaN</td>\n      <td>-6.9</td>\n      <td>NaN</td>\n      <td> 82</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  1.2</td>\n      <td>NaN</td>\n      <td> 100.65</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-12</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 14:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 14:00</td>\n      <td>  </td>\n      <td>-3.9</td>\n      <td>NaN</td>\n      <td>-6.6</td>\n      <td>NaN</td>\n      <td> 81</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  1.2</td>\n      <td>NaN</td>\n      <td> 100.67</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-11</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 15:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 15:00</td>\n      <td>  </td>\n      <td>-3.3</td>\n      <td>NaN</td>\n      <td>-6.2</td>\n      <td>NaN</td>\n      <td> 80</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  1.6</td>\n      <td>NaN</td>\n      <td> 100.71</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-10</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 16:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 16:00</td>\n      <td>  </td>\n      <td>-2.7</td>\n      <td>NaN</td>\n      <td>-5.7</td>\n      <td>NaN</td>\n      <td> 80</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  2.4</td>\n      <td>NaN</td>\n      <td> 100.74</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> -8</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 17:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 17:00</td>\n      <td>  </td>\n      <td>-2.9</td>\n      <td>NaN</td>\n      <td>-5.9</td>\n      <td>NaN</td>\n      <td> 80</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  4.0</td>\n      <td>NaN</td>\n      <td> 100.80</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> -9</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 18:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 18:00</td>\n      <td>  </td>\n      <td>-3.0</td>\n      <td>NaN</td>\n      <td>-6.0</td>\n      <td>NaN</td>\n      <td> 80</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  4.0</td>\n      <td>NaN</td>\n      <td> 100.87</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> -9</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 19:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 19:00</td>\n      <td>  </td>\n      <td>-3.6</td>\n      <td>NaN</td>\n      <td>-6.4</td>\n      <td>NaN</td>\n      <td> 81</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  3.2</td>\n      <td>NaN</td>\n      <td> 100.93</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> -9</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 20:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 20:00</td>\n      <td>  </td>\n      <td>-3.7</td>\n      <td>NaN</td>\n      <td>-6.4</td>\n      <td>NaN</td>\n      <td> 81</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  4.8</td>\n      <td>NaN</td>\n      <td> 100.95</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-10</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 21:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 21:00</td>\n      <td>  </td>\n      <td>-3.9</td>\n      <td>NaN</td>\n      <td>-6.7</td>\n      <td>NaN</td>\n      <td> 81</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  6.4</td>\n      <td>NaN</td>\n      <td> 100.98</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-10</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 22:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 22:00</td>\n      <td>  </td>\n      <td>-4.3</td>\n      <td>NaN</td>\n      <td>-6.9</td>\n      <td>NaN</td>\n      <td> 82</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  2.4</td>\n      <td>NaN</td>\n      <td> 101.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-11</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 23:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 23:00</td>\n      <td>  </td>\n      <td>-4.3</td>\n      <td>NaN</td>\n      <td>-7.1</td>\n      <td>NaN</td>\n      <td> 81</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  4.8</td>\n      <td>NaN</td>\n      <td> 101.04</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-11</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 00:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 00:00</td>\n      <td>  </td>\n      <td>-4.8</td>\n      <td>NaN</td>\n      <td>-7.3</td>\n      <td>NaN</td>\n      <td> 83</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  3.2</td>\n      <td>NaN</td>\n      <td> 101.04</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-12</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 01:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 01:00</td>\n      <td>  </td>\n      <td>-5.3</td>\n      <td>NaN</td>\n      <td>-7.9</td>\n      <td>NaN</td>\n      <td> 82</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  4.8</td>\n      <td>NaN</td>\n      <td> 101.09</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-12</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 02:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 02:00</td>\n      <td>  </td>\n      <td>-5.2</td>\n      <td>NaN</td>\n      <td>-7.8</td>\n      <td>NaN</td>\n      <td> 82</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  6.4</td>\n      <td>NaN</td>\n      <td> 101.11</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-12</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 03:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 03:00</td>\n      <td>  </td>\n      <td>-5.5</td>\n      <td>NaN</td>\n      <td>-7.9</td>\n      <td>NaN</td>\n      <td> 83</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  4.8</td>\n      <td>NaN</td>\n      <td> 101.15</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-12</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 04:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 04:00</td>\n      <td>  </td>\n      <td>-5.6</td>\n      <td>NaN</td>\n      <td>-8.2</td>\n      <td>NaN</td>\n      <td> 82</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td>  6.4</td>\n      <td>NaN</td>\n      <td> 101.15</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-13</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 05:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 05:00</td>\n      <td>  </td>\n      <td>-5.5</td>\n      <td>NaN</td>\n      <td>-8.3</td>\n      <td>NaN</td>\n      <td> 81</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 12.9</td>\n      <td>NaN</td>\n      <td> 101.15</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-12</td>\n      <td> NaN</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 18:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 18:00</td>\n      <td>  </td>\n      <td> 3.9</td>\n      <td>NaN</td>\n      <td>-7.9</td>\n      <td>NaN</td>\n      <td> 42</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 24.1</td>\n      <td>NaN</td>\n      <td> 101.26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 19:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 19:00</td>\n      <td>  </td>\n      <td> 3.1</td>\n      <td>NaN</td>\n      <td>-6.7</td>\n      <td>NaN</td>\n      <td> 49</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.29</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 20:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 20:00</td>\n      <td>  </td>\n      <td> 3.0</td>\n      <td>NaN</td>\n      <td>-8.4</td>\n      <td>NaN</td>\n      <td> 43</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.30</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 21:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 21:00</td>\n      <td>  </td>\n      <td> 1.7</td>\n      <td>NaN</td>\n      <td>-9.0</td>\n      <td>NaN</td>\n      <td> 45</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.32</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 22:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 22:00</td>\n      <td>  </td>\n      <td> 0.4</td>\n      <td>NaN</td>\n      <td>-8.1</td>\n      <td>NaN</td>\n      <td> 53</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.30</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 23:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 23:00</td>\n      <td>  </td>\n      <td> 1.4</td>\n      <td>NaN</td>\n      <td>-7.7</td>\n      <td>NaN</td>\n      <td> 51</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.34</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 00:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 00:00</td>\n      <td>  </td>\n      <td> 1.5</td>\n      <td>NaN</td>\n      <td>-8.6</td>\n      <td>NaN</td>\n      <td> 47</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.33</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 01:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 01:00</td>\n      <td>  </td>\n      <td> 1.3</td>\n      <td>NaN</td>\n      <td>-9.6</td>\n      <td>NaN</td>\n      <td> 44</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.31</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 02:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 02:00</td>\n      <td>  </td>\n      <td> 1.3</td>\n      <td>NaN</td>\n      <td>-9.7</td>\n      <td>NaN</td>\n      <td> 44</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.29</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 03:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 03:00</td>\n      <td>  </td>\n      <td> 0.7</td>\n      <td>NaN</td>\n      <td>-8.8</td>\n      <td>NaN</td>\n      <td> 49</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.30</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 04:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 04:00</td>\n      <td>  </td>\n      <td>-0.9</td>\n      <td>NaN</td>\n      <td>-8.5</td>\n      <td>NaN</td>\n      <td> 56</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.32</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> -5</td>\n      <td> NaN</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 05:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 05:00</td>\n      <td>  </td>\n      <td>-0.6</td>\n      <td>NaN</td>\n      <td>-9.2</td>\n      <td>NaN</td>\n      <td> 52</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 101.30</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> -5</td>\n      <td> NaN</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 06:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 06:00</td>\n      <td>  </td>\n      <td>-0.5</td>\n      <td>NaN</td>\n      <td>-9.2</td>\n      <td>NaN</td>\n      <td> 52</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 101.32</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> -5</td>\n      <td> NaN</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 07:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 07:00</td>\n      <td>  </td>\n      <td>-0.3</td>\n      <td>NaN</td>\n      <td>-9.2</td>\n      <td>NaN</td>\n      <td> 51</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 101.32</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> -5</td>\n      <td> NaN</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 08:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 08:00</td>\n      <td>  </td>\n      <td> 0.7</td>\n      <td>NaN</td>\n      <td>-8.5</td>\n      <td>NaN</td>\n      <td> 50</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 101.33</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 09:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 09:00</td>\n      <td>  </td>\n      <td> 1.5</td>\n      <td>NaN</td>\n      <td>-7.8</td>\n      <td>NaN</td>\n      <td> 50</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 101.34</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 10:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 10:00</td>\n      <td>  </td>\n      <td> 2.9</td>\n      <td>NaN</td>\n      <td>-8.1</td>\n      <td>NaN</td>\n      <td> 44</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 101.30</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 11:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 11:00</td>\n      <td>  </td>\n      <td> 4.6</td>\n      <td>NaN</td>\n      <td>-9.7</td>\n      <td>NaN</td>\n      <td> 35</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 101.24</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 12:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 12:00</td>\n      <td>  </td>\n      <td> 6.4</td>\n      <td>NaN</td>\n      <td>-7.1</td>\n      <td>NaN</td>\n      <td> 37</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 101.16</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 13:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 13:00</td>\n      <td>  </td>\n      <td> 6.5</td>\n      <td>NaN</td>\n      <td>-9.7</td>\n      <td>NaN</td>\n      <td> 30</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 101.08</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 14:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 14:00</td>\n      <td>  </td>\n      <td> 7.7</td>\n      <td>NaN</td>\n      <td>-8.5</td>\n      <td>NaN</td>\n      <td> 31</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 101.01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 15:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 15:00</td>\n      <td>  </td>\n      <td> 7.7</td>\n      <td>NaN</td>\n      <td>-8.6</td>\n      <td>NaN</td>\n      <td> 30</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 100.94</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 16:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 16:00</td>\n      <td>  </td>\n      <td> 8.4</td>\n      <td>NaN</td>\n      <td>-7.7</td>\n      <td>NaN</td>\n      <td> 31</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 100.89</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 17:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 17:00</td>\n      <td>  </td>\n      <td> 7.9</td>\n      <td>NaN</td>\n      <td>-8.1</td>\n      <td>NaN</td>\n      <td> 31</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 100.88</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 18:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 18:00</td>\n      <td>  </td>\n      <td> 7.0</td>\n      <td>NaN</td>\n      <td>-8.2</td>\n      <td>NaN</td>\n      <td> 33</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 48.3</td>\n      <td>NaN</td>\n      <td> 100.87</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 19:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 19:00</td>\n      <td>  </td>\n      <td> 5.9</td>\n      <td>NaN</td>\n      <td>-8.0</td>\n      <td>NaN</td>\n      <td> 36</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 100.88</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 20:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 20:00</td>\n      <td>  </td>\n      <td> 4.4</td>\n      <td>NaN</td>\n      <td>-7.2</td>\n      <td>NaN</td>\n      <td> 43</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 100.85</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 21:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 21:00</td>\n      <td>  </td>\n      <td> 2.6</td>\n      <td>NaN</td>\n      <td>-6.3</td>\n      <td>NaN</td>\n      <td> 52</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 100.86</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 22:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 22:00</td>\n      <td>  </td>\n      <td> 2.7</td>\n      <td>NaN</td>\n      <td>-6.7</td>\n      <td>NaN</td>\n      <td> 50</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 100.82</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 23:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 23:00</td>\n      <td>  </td>\n      <td> 1.5</td>\n      <td>NaN</td>\n      <td>-6.9</td>\n      <td>NaN</td>\n      <td> 54</td>\n      <td>...</td>\n      <td> NaN</td>\n      <td> 25.0</td>\n      <td>NaN</td>\n      <td> 100.79</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td>         Clear</td>\n    </tr>\n  </tbody>\n</table>\n<p>744 rows \u00d7 24 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": "                     Year  Month  Day   Time Data Quality  Temp (\u00c2\u00b0C)  \\\nDate/Time                                                               \n2012-03-01 00:00:00  2012      3    1  00:00                     -5.5   \n2012-03-01 01:00:00  2012      3    1  01:00                     -5.7   \n2012-03-01 02:00:00  2012      3    1  02:00                     -5.4   \n2012-03-01 03:00:00  2012      3    1  03:00                     -4.7   \n2012-03-01 04:00:00  2012      3    1  04:00                     -5.4   \n2012-03-01 05:00:00  2012      3    1  05:00                     -5.3   \n2012-03-01 06:00:00  2012      3    1  06:00                     -5.2   \n2012-03-01 07:00:00  2012      3    1  07:00                     -4.9   \n2012-03-01 08:00:00  2012      3    1  08:00                     -5.0   \n2012-03-01 09:00:00  2012      3    1  09:00                     -4.9   \n2012-03-01 10:00:00  2012      3    1  10:00                     -4.7   \n2012-03-01 11:00:00  2012      3    1  11:00                     -4.4   \n2012-03-01 12:00:00  2012      3    1  12:00                     -4.3   \n2012-03-01 13:00:00  2012      3    1  13:00                     -4.3   \n2012-03-01 14:00:00  2012      3    1  14:00                     -3.9   \n2012-03-01 15:00:00  2012      3    1  15:00                     -3.3   \n2012-03-01 16:00:00  2012      3    1  16:00                     -2.7   \n2012-03-01 17:00:00  2012      3    1  17:00                     -2.9   \n2012-03-01 18:00:00  2012      3    1  18:00                     -3.0   \n2012-03-01 19:00:00  2012      3    1  19:00                     -3.6   \n2012-03-01 20:00:00  2012      3    1  20:00                     -3.7   \n2012-03-01 21:00:00  2012      3    1  21:00                     -3.9   \n2012-03-01 22:00:00  2012      3    1  22:00                     -4.3   \n2012-03-01 23:00:00  2012      3    1  23:00                     -4.3   \n2012-03-02 00:00:00  2012      3    2  00:00                     -4.8   \n2012-03-02 01:00:00  2012      3    2  01:00                     -5.3   \n2012-03-02 02:00:00  2012      3    2  02:00                     -5.2   \n2012-03-02 03:00:00  2012      3    2  03:00                     -5.5   \n2012-03-02 04:00:00  2012      3    2  04:00                     -5.6   \n2012-03-02 05:00:00  2012      3    2  05:00                     -5.5   \n...                   ...    ...  ...    ...          ...         ...   \n2012-03-30 18:00:00  2012      3   30  18:00                      3.9   \n2012-03-30 19:00:00  2012      3   30  19:00                      3.1   \n2012-03-30 20:00:00  2012      3   30  20:00                      3.0   \n2012-03-30 21:00:00  2012      3   30  21:00                      1.7   \n2012-03-30 22:00:00  2012      3   30  22:00                      0.4   \n2012-03-30 23:00:00  2012      3   30  23:00                      1.4   \n2012-03-31 00:00:00  2012      3   31  00:00                      1.5   \n2012-03-31 01:00:00  2012      3   31  01:00                      1.3   \n2012-03-31 02:00:00  2012      3   31  02:00                      1.3   \n2012-03-31 03:00:00  2012      3   31  03:00                      0.7   \n2012-03-31 04:00:00  2012      3   31  04:00                     -0.9   \n2012-03-31 05:00:00  2012      3   31  05:00                     -0.6   \n2012-03-31 06:00:00  2012      3   31  06:00                     -0.5   \n2012-03-31 07:00:00  2012      3   31  07:00                     -0.3   \n2012-03-31 08:00:00  2012      3   31  08:00                      0.7   \n2012-03-31 09:00:00  2012      3   31  09:00                      1.5   \n2012-03-31 10:00:00  2012      3   31  10:00                      2.9   \n2012-03-31 11:00:00  2012      3   31  11:00                      4.6   \n2012-03-31 12:00:00  2012      3   31  12:00                      6.4   \n2012-03-31 13:00:00  2012      3   31  13:00                      6.5   \n2012-03-31 14:00:00  2012      3   31  14:00                      7.7   \n2012-03-31 15:00:00  2012      3   31  15:00                      7.7   \n2012-03-31 16:00:00  2012      3   31  16:00                      8.4   \n2012-03-31 17:00:00  2012      3   31  17:00                      7.9   \n2012-03-31 18:00:00  2012      3   31  18:00                      7.0   \n2012-03-31 19:00:00  2012      3   31  19:00                      5.9   \n2012-03-31 20:00:00  2012      3   31  20:00                      4.4   \n2012-03-31 21:00:00  2012      3   31  21:00                      2.6   \n2012-03-31 22:00:00  2012      3   31  22:00                      2.7   \n2012-03-31 23:00:00  2012      3   31  23:00                      1.5   \n\n                     Temp Flag  Dew Point Temp (\u00c2\u00b0C)  Dew Point Temp Flag  \\\nDate/Time                                                                   \n2012-03-01 00:00:00        NaN                  -9.7                  NaN   \n2012-03-01 01:00:00        NaN                  -8.7                  NaN   \n2012-03-01 02:00:00        NaN                  -8.3                  NaN   \n2012-03-01 03:00:00        NaN                  -7.7                  NaN   \n2012-03-01 04:00:00        NaN                  -7.8                  NaN   \n2012-03-01 05:00:00        NaN                  -7.9                  NaN   \n2012-03-01 06:00:00        NaN                  -7.8                  NaN   \n2012-03-01 07:00:00        NaN                  -7.4                  NaN   \n2012-03-01 08:00:00        NaN                  -7.5                  NaN   \n2012-03-01 09:00:00        NaN                  -7.5                  NaN   \n2012-03-01 10:00:00        NaN                  -7.3                  NaN   \n2012-03-01 11:00:00        NaN                  -6.8                  NaN   \n2012-03-01 12:00:00        NaN                  -6.8                  NaN   \n2012-03-01 13:00:00        NaN                  -6.9                  NaN   \n2012-03-01 14:00:00        NaN                  -6.6                  NaN   \n2012-03-01 15:00:00        NaN                  -6.2                  NaN   \n2012-03-01 16:00:00        NaN                  -5.7                  NaN   \n2012-03-01 17:00:00        NaN                  -5.9                  NaN   \n2012-03-01 18:00:00        NaN                  -6.0                  NaN   \n2012-03-01 19:00:00        NaN                  -6.4                  NaN   \n2012-03-01 20:00:00        NaN                  -6.4                  NaN   \n2012-03-01 21:00:00        NaN                  -6.7                  NaN   \n2012-03-01 22:00:00        NaN                  -6.9                  NaN   \n2012-03-01 23:00:00        NaN                  -7.1                  NaN   \n2012-03-02 00:00:00        NaN                  -7.3                  NaN   \n2012-03-02 01:00:00        NaN                  -7.9                  NaN   \n2012-03-02 02:00:00        NaN                  -7.8                  NaN   \n2012-03-02 03:00:00        NaN                  -7.9                  NaN   \n2012-03-02 04:00:00        NaN                  -8.2                  NaN   \n2012-03-02 05:00:00        NaN                  -8.3                  NaN   \n...                        ...                   ...                  ...   \n2012-03-30 18:00:00        NaN                  -7.9                  NaN   \n2012-03-30 19:00:00        NaN                  -6.7                  NaN   \n2012-03-30 20:00:00        NaN                  -8.4                  NaN   \n2012-03-30 21:00:00        NaN                  -9.0                  NaN   \n2012-03-30 22:00:00        NaN                  -8.1                  NaN   \n2012-03-30 23:00:00        NaN                  -7.7                  NaN   \n2012-03-31 00:00:00        NaN                  -8.6                  NaN   \n2012-03-31 01:00:00        NaN                  -9.6                  NaN   \n2012-03-31 02:00:00        NaN                  -9.7                  NaN   \n2012-03-31 03:00:00        NaN                  -8.8                  NaN   \n2012-03-31 04:00:00        NaN                  -8.5                  NaN   \n2012-03-31 05:00:00        NaN                  -9.2                  NaN   \n2012-03-31 06:00:00        NaN                  -9.2                  NaN   \n2012-03-31 07:00:00        NaN                  -9.2                  NaN   \n2012-03-31 08:00:00        NaN                  -8.5                  NaN   \n2012-03-31 09:00:00        NaN                  -7.8                  NaN   \n2012-03-31 10:00:00        NaN                  -8.1                  NaN   \n2012-03-31 11:00:00        NaN                  -9.7                  NaN   \n2012-03-31 12:00:00        NaN                  -7.1                  NaN   \n2012-03-31 13:00:00        NaN                  -9.7                  NaN   \n2012-03-31 14:00:00        NaN                  -8.5                  NaN   \n2012-03-31 15:00:00        NaN                  -8.6                  NaN   \n2012-03-31 16:00:00        NaN                  -7.7                  NaN   \n2012-03-31 17:00:00        NaN                  -8.1                  NaN   \n2012-03-31 18:00:00        NaN                  -8.2                  NaN   \n2012-03-31 19:00:00        NaN                  -8.0                  NaN   \n2012-03-31 20:00:00        NaN                  -7.2                  NaN   \n2012-03-31 21:00:00        NaN                  -6.3                  NaN   \n2012-03-31 22:00:00        NaN                  -6.7                  NaN   \n2012-03-31 23:00:00        NaN                  -6.9                  NaN   \n\n                     Rel Hum (%)     ...       Wind Spd Flag  Visibility (km)  \\\nDate/Time                            ...                                        \n2012-03-01 00:00:00           72     ...                 NaN              4.0   \n2012-03-01 01:00:00           79     ...                 NaN              2.4   \n2012-03-01 02:00:00           80     ...                 NaN              4.8   \n2012-03-01 03:00:00           79     ...                 NaN              4.0   \n2012-03-01 04:00:00           83     ...                 NaN              1.6   \n2012-03-01 05:00:00           82     ...                 NaN              2.4   \n2012-03-01 06:00:00           82     ...                 NaN              4.0   \n2012-03-01 07:00:00           83     ...                 NaN              1.6   \n2012-03-01 08:00:00           83     ...                 NaN              1.2   \n2012-03-01 09:00:00           82     ...                 NaN              1.6   \n2012-03-01 10:00:00           82     ...                 NaN              1.2   \n2012-03-01 11:00:00           83     ...                 NaN              1.0   \n2012-03-01 12:00:00           83     ...                 NaN              1.2   \n2012-03-01 13:00:00           82     ...                 NaN              1.2   \n2012-03-01 14:00:00           81     ...                 NaN              1.2   \n2012-03-01 15:00:00           80     ...                 NaN              1.6   \n2012-03-01 16:00:00           80     ...                 NaN              2.4   \n2012-03-01 17:00:00           80     ...                 NaN              4.0   \n2012-03-01 18:00:00           80     ...                 NaN              4.0   \n2012-03-01 19:00:00           81     ...                 NaN              3.2   \n2012-03-01 20:00:00           81     ...                 NaN              4.8   \n2012-03-01 21:00:00           81     ...                 NaN              6.4   \n2012-03-01 22:00:00           82     ...                 NaN              2.4   \n2012-03-01 23:00:00           81     ...                 NaN              4.8   \n2012-03-02 00:00:00           83     ...                 NaN              3.2   \n2012-03-02 01:00:00           82     ...                 NaN              4.8   \n2012-03-02 02:00:00           82     ...                 NaN              6.4   \n2012-03-02 03:00:00           83     ...                 NaN              4.8   \n2012-03-02 04:00:00           82     ...                 NaN              6.4   \n2012-03-02 05:00:00           81     ...                 NaN             12.9   \n...                          ...     ...                 ...              ...   \n2012-03-30 18:00:00           42     ...                 NaN             24.1   \n2012-03-30 19:00:00           49     ...                 NaN             25.0   \n2012-03-30 20:00:00           43     ...                 NaN             25.0   \n2012-03-30 21:00:00           45     ...                 NaN             25.0   \n2012-03-30 22:00:00           53     ...                 NaN             25.0   \n2012-03-30 23:00:00           51     ...                 NaN             25.0   \n2012-03-31 00:00:00           47     ...                 NaN             25.0   \n2012-03-31 01:00:00           44     ...                 NaN             25.0   \n2012-03-31 02:00:00           44     ...                 NaN             25.0   \n2012-03-31 03:00:00           49     ...                 NaN             25.0   \n2012-03-31 04:00:00           56     ...                 NaN             25.0   \n2012-03-31 05:00:00           52     ...                 NaN             25.0   \n2012-03-31 06:00:00           52     ...                 NaN             48.3   \n2012-03-31 07:00:00           51     ...                 NaN             48.3   \n2012-03-31 08:00:00           50     ...                 NaN             48.3   \n2012-03-31 09:00:00           50     ...                 NaN             48.3   \n2012-03-31 10:00:00           44     ...                 NaN             48.3   \n2012-03-31 11:00:00           35     ...                 NaN             48.3   \n2012-03-31 12:00:00           37     ...                 NaN             48.3   \n2012-03-31 13:00:00           30     ...                 NaN             48.3   \n2012-03-31 14:00:00           31     ...                 NaN             48.3   \n2012-03-31 15:00:00           30     ...                 NaN             48.3   \n2012-03-31 16:00:00           31     ...                 NaN             48.3   \n2012-03-31 17:00:00           31     ...                 NaN             48.3   \n2012-03-31 18:00:00           33     ...                 NaN             48.3   \n2012-03-31 19:00:00           36     ...                 NaN             25.0   \n2012-03-31 20:00:00           43     ...                 NaN             25.0   \n2012-03-31 21:00:00           52     ...                 NaN             25.0   \n2012-03-31 22:00:00           50     ...                 NaN             25.0   \n2012-03-31 23:00:00           54     ...                 NaN             25.0   \n\n                     Visibility Flag  Stn Press (kPa) Stn Press Flag  Hmdx  \\\nDate/Time                                                                    \n2012-03-01 00:00:00              NaN           100.97            NaN   NaN   \n2012-03-01 01:00:00              NaN           100.87            NaN   NaN   \n2012-03-01 02:00:00              NaN           100.80            NaN   NaN   \n2012-03-01 03:00:00              NaN           100.69            NaN   NaN   \n2012-03-01 04:00:00              NaN           100.62            NaN   NaN   \n2012-03-01 05:00:00              NaN           100.58            NaN   NaN   \n2012-03-01 06:00:00              NaN           100.57            NaN   NaN   \n2012-03-01 07:00:00              NaN           100.59            NaN   NaN   \n2012-03-01 08:00:00              NaN           100.59            NaN   NaN   \n2012-03-01 09:00:00              NaN           100.60            NaN   NaN   \n2012-03-01 10:00:00              NaN           100.62            NaN   NaN   \n2012-03-01 11:00:00              NaN           100.66            NaN   NaN   \n2012-03-01 12:00:00              NaN           100.66            NaN   NaN   \n2012-03-01 13:00:00              NaN           100.65            NaN   NaN   \n2012-03-01 14:00:00              NaN           100.67            NaN   NaN   \n2012-03-01 15:00:00              NaN           100.71            NaN   NaN   \n2012-03-01 16:00:00              NaN           100.74            NaN   NaN   \n2012-03-01 17:00:00              NaN           100.80            NaN   NaN   \n2012-03-01 18:00:00              NaN           100.87            NaN   NaN   \n2012-03-01 19:00:00              NaN           100.93            NaN   NaN   \n2012-03-01 20:00:00              NaN           100.95            NaN   NaN   \n2012-03-01 21:00:00              NaN           100.98            NaN   NaN   \n2012-03-01 22:00:00              NaN           101.00            NaN   NaN   \n2012-03-01 23:00:00              NaN           101.04            NaN   NaN   \n2012-03-02 00:00:00              NaN           101.04            NaN   NaN   \n2012-03-02 01:00:00              NaN           101.09            NaN   NaN   \n2012-03-02 02:00:00              NaN           101.11            NaN   NaN   \n2012-03-02 03:00:00              NaN           101.15            NaN   NaN   \n2012-03-02 04:00:00              NaN           101.15            NaN   NaN   \n2012-03-02 05:00:00              NaN           101.15            NaN   NaN   \n...                              ...              ...            ...   ...   \n2012-03-30 18:00:00              NaN           101.26            NaN   NaN   \n2012-03-30 19:00:00              NaN           101.29            NaN   NaN   \n2012-03-30 20:00:00              NaN           101.30            NaN   NaN   \n2012-03-30 21:00:00              NaN           101.32            NaN   NaN   \n2012-03-30 22:00:00              NaN           101.30            NaN   NaN   \n2012-03-30 23:00:00              NaN           101.34            NaN   NaN   \n2012-03-31 00:00:00              NaN           101.33            NaN   NaN   \n2012-03-31 01:00:00              NaN           101.31            NaN   NaN   \n2012-03-31 02:00:00              NaN           101.29            NaN   NaN   \n2012-03-31 03:00:00              NaN           101.30            NaN   NaN   \n2012-03-31 04:00:00              NaN           101.32            NaN   NaN   \n2012-03-31 05:00:00              NaN           101.30            NaN   NaN   \n2012-03-31 06:00:00              NaN           101.32            NaN   NaN   \n2012-03-31 07:00:00              NaN           101.32            NaN   NaN   \n2012-03-31 08:00:00              NaN           101.33            NaN   NaN   \n2012-03-31 09:00:00              NaN           101.34            NaN   NaN   \n2012-03-31 10:00:00              NaN           101.30            NaN   NaN   \n2012-03-31 11:00:00              NaN           101.24            NaN   NaN   \n2012-03-31 12:00:00              NaN           101.16            NaN   NaN   \n2012-03-31 13:00:00              NaN           101.08            NaN   NaN   \n2012-03-31 14:00:00              NaN           101.01            NaN   NaN   \n2012-03-31 15:00:00              NaN           100.94            NaN   NaN   \n2012-03-31 16:00:00              NaN           100.89            NaN   NaN   \n2012-03-31 17:00:00              NaN           100.88            NaN   NaN   \n2012-03-31 18:00:00              NaN           100.87            NaN   NaN   \n2012-03-31 19:00:00              NaN           100.88            NaN   NaN   \n2012-03-31 20:00:00              NaN           100.85            NaN   NaN   \n2012-03-31 21:00:00              NaN           100.86            NaN   NaN   \n2012-03-31 22:00:00              NaN           100.82            NaN   NaN   \n2012-03-31 23:00:00              NaN           100.79            NaN   NaN   \n\n                     Hmdx Flag  Wind Chill  Wind Chill Flag        Weather  \nDate/Time                                                                   \n2012-03-01 00:00:00        NaN         -13              NaN           Snow  \n2012-03-01 01:00:00        NaN         -13              NaN           Snow  \n2012-03-01 02:00:00        NaN         -13              NaN           Snow  \n2012-03-01 03:00:00        NaN         -12              NaN           Snow  \n2012-03-01 04:00:00        NaN         -14              NaN           Snow  \n2012-03-01 05:00:00        NaN         -14              NaN           Snow  \n2012-03-01 06:00:00        NaN         -14              NaN           Snow  \n2012-03-01 07:00:00        NaN         -13              NaN           Snow  \n2012-03-01 08:00:00        NaN         -13              NaN           Snow  \n2012-03-01 09:00:00        NaN         -13              NaN           Snow  \n2012-03-01 10:00:00        NaN         -13              NaN           Snow  \n2012-03-01 11:00:00        NaN         -12              NaN           Snow  \n2012-03-01 12:00:00        NaN         -12              NaN           Snow  \n2012-03-01 13:00:00        NaN         -12              NaN           Snow  \n2012-03-01 14:00:00        NaN         -11              NaN           Snow  \n2012-03-01 15:00:00        NaN         -10              NaN           Snow  \n2012-03-01 16:00:00        NaN          -8              NaN           Snow  \n2012-03-01 17:00:00        NaN          -9              NaN           Snow  \n2012-03-01 18:00:00        NaN          -9              NaN           Snow  \n2012-03-01 19:00:00        NaN          -9              NaN           Snow  \n2012-03-01 20:00:00        NaN         -10              NaN           Snow  \n2012-03-01 21:00:00        NaN         -10              NaN           Snow  \n2012-03-01 22:00:00        NaN         -11              NaN           Snow  \n2012-03-01 23:00:00        NaN         -11              NaN           Snow  \n2012-03-02 00:00:00        NaN         -12              NaN           Snow  \n2012-03-02 01:00:00        NaN         -12              NaN           Snow  \n2012-03-02 02:00:00        NaN         -12              NaN           Snow  \n2012-03-02 03:00:00        NaN         -12              NaN           Snow  \n2012-03-02 04:00:00        NaN         -13              NaN           Snow  \n2012-03-02 05:00:00        NaN         -12              NaN           Snow  \n...                        ...         ...              ...            ...  \n2012-03-30 18:00:00        NaN         NaN              NaN  Mostly Cloudy  \n2012-03-30 19:00:00        NaN         NaN              NaN  Mostly Cloudy  \n2012-03-30 20:00:00        NaN         NaN              NaN  Mostly Cloudy  \n2012-03-30 21:00:00        NaN         NaN              NaN         Cloudy  \n2012-03-30 22:00:00        NaN         NaN              NaN  Mostly Cloudy  \n2012-03-30 23:00:00        NaN         NaN              NaN   Mainly Clear  \n2012-03-31 00:00:00        NaN         NaN              NaN  Mostly Cloudy  \n2012-03-31 01:00:00        NaN         NaN              NaN  Mostly Cloudy  \n2012-03-31 02:00:00        NaN         NaN              NaN         Cloudy  \n2012-03-31 03:00:00        NaN         NaN              NaN         Cloudy  \n2012-03-31 04:00:00        NaN          -5              NaN         Cloudy  \n2012-03-31 05:00:00        NaN          -5              NaN         Cloudy  \n2012-03-31 06:00:00        NaN          -5              NaN         Cloudy  \n2012-03-31 07:00:00        NaN          -5              NaN         Cloudy  \n2012-03-31 08:00:00        NaN         NaN              NaN         Cloudy  \n2012-03-31 09:00:00        NaN         NaN              NaN  Mostly Cloudy  \n2012-03-31 10:00:00        NaN         NaN              NaN   Mainly Clear  \n2012-03-31 11:00:00        NaN         NaN              NaN          Clear  \n2012-03-31 12:00:00        NaN         NaN              NaN          Clear  \n2012-03-31 13:00:00        NaN         NaN              NaN          Clear  \n2012-03-31 14:00:00        NaN         NaN              NaN   Mainly Clear  \n2012-03-31 15:00:00        NaN         NaN              NaN   Mainly Clear  \n2012-03-31 16:00:00        NaN         NaN              NaN   Mainly Clear  \n2012-03-31 17:00:00        NaN         NaN              NaN   Mainly Clear  \n2012-03-31 18:00:00        NaN         NaN              NaN   Mainly Clear  \n2012-03-31 19:00:00        NaN         NaN              NaN          Clear  \n2012-03-31 20:00:00        NaN         NaN              NaN          Clear  \n2012-03-31 21:00:00        NaN         NaN              NaN          Clear  \n2012-03-31 22:00:00        NaN         NaN              NaN          Clear  \n2012-03-31 23:00:00        NaN         NaN              NaN          Clear  \n\n[744 rows x 24 columns]"
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We are only interested in the temperatures, so let's go ahead and plot the column for the month of March,2012. But we also see that the Temp column has some special characters, which might be painful to reuse. So, let's fix that first!\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "weather_mar2012.columns = [\n    u'Year', u'Month', u'Day', u'Time', u'Data Quality', u'Temp (C)', \n    u'Temp Flag', u'Dew Point Temp (C)', u'Dew Point Temp Flag', \n    u'Rel Hum (%)', u'Rel Hum Flag', u'Wind Dir (10s deg)', u'Wind Dir Flag', \n    u'Wind Spd (km/h)', u'Wind Spd Flag', u'Visibility (km)', u'Visibility Flag',\n    u'Stn Press (kPa)', u'Stn Press Flag', u'Hmdx', u'Hmdx Flag', u'Wind Chill', \n    u'Wind Chill Flag', u'Weather']",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "weather_mar2012[u'Temp (C)'].plot(figsize=(15, 5))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": "<matplotlib.axes.AxesSubplot at 0x10fe28250>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XD4fw3Q+vUn19kizjIw/uxJMqHoN8n/nNHtx31Vq0OAoX2IcDSdzz0gB+/sl1\nqq+bEFIZ6lEjhBBCGkA4JcCroNeq2Ih+JSbjAloVTGBscZhU9YkVE0mLcFqMqgoUA8Ogp8mKC3qb\nIEoSJuKlzyDTc+ojUHhF7cn9gTnDRZQyMAwcZlbTCiWfkRDjMvCV2NrZ7bFiJMohI9HkR0KqSUMU\navW6n5WyKIuyKIuyKCtfMCkq2sJnNc7toVKSlZFkhNNiyTf9OZ1uC0aixbfTKb1v4ZSoqPgspsNp\nwfNvvFP0clmWEUmrG1ZSSqEeteEIh7E4h3N7PJqv11mgT03JYzgZF9DsMMFQ4jgFq9EAn81U8lDt\nan7dUxZl1XpWMQ1RqBFCCCH1TpZlhFOCooLDZjIgyomIqjybK5IW4TSzMCpY3ep2WzBcolBTKqTw\nPhXT7jIjLBR/u5PgMzCzDMysfm+JZvcAvjcex8ZOl6Ztizla+9QmEjzaimx5zNfTZMFQmYEihJCF\nRT1qhBBCSB1I8Bls/u/38Ie/KX8+2p3PH8GxqcOXv/nBFYoz+vxJ/PDV47j/6pPK/uzrA2E8fSCA\n/6/EeWtKvNAfxLbBKO54/zJNv/+rHaPISDJuPKur4OXDEQ5ff6Yfv/zrk7XfyFkO+ZP40avH8R+f\nWAtRknH/G0NY4bPhEye3ar7O//VUPz69oQ1nLXGr+r1nDwWwazSO2y/pLflzP3l9EB0uCz55qrKz\n5wgh+ijVo6bfoSGEEEIIWTRK+9MAwGpiMRThEEmLkGS55La4fIGkgGaFY+z1W1FTN/FxtnanGe+O\nxIpeHkmL8FRw/YVke9QkPHMwgFePhhFMCZr703LclsIj+suZiPNoc5R/ztpdFoyX6eUjhCyshtj6\nWK/7WSmLsiiLsiiLsnKCKeUDMWxGAzpdZnisRhwNphRnqSnUOqfe+BcbUKH0vkUq3PrY4TLj0LC/\n+PXrXKid6FHLYDLB492RGMZjPFb4Kpt+6bSwiM/a+qjkMVRynAIAdDjNGKceNcqirEXJKqYhCjVC\nao0sy9g3nljsm0EIqSEhFStqXpsRl63yYWOnEztHyp8zlhNICIoGiQCA2WiA12aseJUmlBLRVMFE\nxg6XBWGh+IrhfK6ohZIiNnQ4sbHTWVF/GgA4LUbENUx9HI/ziqZ0drjMGI/TWWqEVBPqUSOkCh0O\nJHHzYwfx++tPVXR4LSGE/GHvJI6H0/jKhT1lf1aWZcgAXj4SwstHwviWwj61H209jlXNdsXb+P7X\nU3341KlGG1efAAAgAElEQVTtOLtHXV9Vvn995jA+tr4F5y3VNjExI8n4+JZdeOxvNhQcGPK7XeOI\npkX83bndmm/jbLIs44oHd+LMbjeuXNeCc5e6FW8vLebhXeMIp0V8QeXtvP6hvbjnivJn5sU4Edc/\ntBeP3bABTIW3lRCiHJ2jRkiNeeVIGADQH0gt8i0hWnCihP/ZM4Hn+gKLfVNIA1GzosYwDAwMg26P\nVdWKVyChfOsjkB3RPxqrbJUmmFK+ilcIa2DQ7DAV3dYXSYvw6DSaP4dhGFiNBozGODTZjBUXaYC2\nqY+iJCOYFNDmLP/4Oc0sAGjqgyOEzI+GKNTqdT8rZdVnlizLePloGCe3O3DIn5zXLD1Q1lxbto/g\npSMhbNk+CiWbFmrlflFWdWeFUiK8KguaZrsJgalDqfXuUQOAlrzrn03pfQuqzCyk3ZDA20PRgpfN\nR48aANhMLEajnKJz7ZRwWYyIqzxHbSLOw2c3waTg6AGGYdBRYqBItb7uKYuy6iGrmIYo1AipJSNR\nDkJGwkdPakHfpLZCjSyuN45H8U+bemA1GnCAnkOyQNSsqOU0WY2IcSLEIgM/ZlNbNLU4zPAnCxdq\nSoiSjBiXqWjqIwCc7MpM71SYbT561IDsWXUZGRUd1p3PY2URSqlbURuJcuh0l+9Py2l3lR4oQghZ\nWA1RqG3atImyKKtmssZiPLo9FqxpsWteUavG+9UoWcORNNJiBit8NlyywouXDofmLUsLyqrfrGBS\n+dTHHNbAoMlmQigllM3KSDKiXEbVBMYWhwn+IitqSu5bKCXAYzVWPIjjusvPw1AkjYkCq0V6F2q5\n+2U3sXCYWZiN+rzV6vXaMBBOz1ilL/cYjsV4dLpK96bl63CaMVZkRa1aX/eURVn1kFVMQxRqhNSS\nQFJAi92Ebo8FkbSIPn8S/+eNIXzlDwfx49cGi24jItVh22AU5yzxgGEYfHCNDy8dCeHVo4U/ySdE\nT+MKx7DP1lxie2K+UEqA28KqKpqa7SYEKlhRy06ZrLyIMhoYvH+lF3/YOznnski6snPairGZDLqt\npgGAx2qE1WhQ1VM4qnJFrafJioEQ9UYTUi0aolCr1/2slFUdWRlJVrRtSGmWPyGg2WEGa2Bwy0VL\n8U9/OoSBUBo3n7cERpbB157uBy9KumTpgbJmemc4hrOWuABkz5H6xgeW4/43h+YlSwvKqs+slJBB\nWpQ0FQa5YqpcViCpfqhHdkVNe8+T2p64YrZu3YprNrbjmUMBhFIzC8dIWoR7XnrUDKpXOMtZ7rPi\naDA9J6uY0RiHLhUrauva7EWPhqnG1z1lUVa9ZBXTEIUaIfPpif1+/HzbsG7X508IaHVk/+d+yQov\n/uPja3Dn5Suwvt2BL57bjZ4mC37z7phueUQ/GUnGe2NxbOxyTX9vVbMdkZSoaKgIIVqNxXh0OM2a\nxqr77MaSq17hlICtR8MIJkXVRZPTzCIjA0kN538B+gwSyWl1mHHR8iY8c/DENFZelCBmZNhN+r8d\nsplYXVfUAGClz4Y+fxJPH/Ar+psyGuPR4VZeqC3z2hBICoim1fXCEULmR0MUavW6n5WyqiNrKMJh\nMJwu+3NKs/xJfsYbk5XNdlimehwYhsGNZ3Xh+f5gyf9J19pjWC9Zff4k2p3mGf0uFqMBBgODlFB8\nFbTa7xdlVX/WaIxDh0v9tkfgxIra7Kzf7RrH43snsXMkjv/aMZpd3XKoK5oYhkGL3VRwoIiS+6bX\nilou69weD3aNnjjgOzA1+l/Pc8NO9Kjpv6K2otmGR/ZM4N6tgxiL8WUfw5DKx481MFjTaseBybmr\natX4uqcsyqqXrGIaolAjZD5NxHmMRPWbkuVPCGgp8Waox2NBRpYxSpO5NOFECcdD5QtrLXaNxrGh\n0zXn+x4ri4jK848IUWMsxqNDxRa3fM12E4IFCql3R2Lo9ycxHucxEuUwmeA1FU0tDmU9cIXoVajl\nnNLhwP6JBIRM9oOTyXjpv7eVaLKZ0K7g/DI11rTYYTUacHK7A/smCm9RzJHl7MRM19T5aEqtb3Ng\n10i8/A8SQuZdQxRq9bqflbKqI2siwWM8xiFTpk9NaVZ2mEjxT8YZhsHGTteMT4W1Zumh1rJ+u3MM\nNz9+AG8PFj5TSWsWL0p49WgYG7uccy5zW4yIpYtv/aq1x5Cyqi8rW6hpXFFznOhR+8nrQzgaTEGW\nZfT5kxiJcRiP8eAzMg5NJjUdPN1sN8GfnPvBktIetUoOu56d5bIY0e224ODUsRmTCX56q7leclmf\nPb0DV5/Sput1d3us+O21J+PCZU3YN54o+RimRQkGA6N66uQlK7x46UgIdz5/FFxeP3Q1vu4pi7Lq\nJasYzYXa/v37cccdd+BXv/rV9PeGhoZw77334oc//CGGhko3zxNSLybiPEysAZNFGubVEDJS9syg\nMn0Np3U6sWskVnFeo4mmRTyx348vn78EP3ljUNfr/t7LA+hwmXH+Us+cyzxWIyLU80FmKffhjhqj\n0cq2Pk4mBKQywFMH/Ljj6X7sGo2DEyWMRXmMxTkYGGDveELT6lZriRH95QR1KtTybex0YufU389s\nT7C2x60co4Gp+FiBQkxsdkVtf5kVtRiXgcuibjUNAJb7bPjFNethYhl854WjWm8mIUQHmgs1QRBw\n9dVXz/jeL3/5S9x444246aab8Nvf/rbiG6eXet3PSlmLn5XkM+BFCatb7GW3IirJCiZFNNnKnxm0\nrt0x/Ymw1iy91FLWC/1BnLvUg8tW+zAZF0q+UVabtX0oin/c1FPwuXOXKdRq6TGkLH2ydo/G8Ne/\n2TNnAqHWrLG4uvOy8i3xWDEe42HvPQVr2+y44qQWfO/lAZze5UKEEzEY5rC21Y60KGkq1Lo9Vhwv\n0Mer5L6F06IuAznys85d6sHrAxEA2RU1vbc+LsRrcWWzDYMRDt3rzyz6MzFOhFtDoQYAZtaA2y/p\nxYHJ5PTZc7X2b4yyKKuWsorRXKht2LABTueJLT7pdBosy8Lr9cLr9QIAeJ56aEh9m0jwaHWa0eU2\nYyzKab6eR3eP49lDAfiTPFoUvBFyWYxICtqmqDWyA5NJnNbphJk1wGVlKzrfKR8nShAkGc4ivSAe\nqxFR6lEjU9KihB+8chydbgse3T1R8fXJslzR1keL0YA1LXY8vncSq1vs+OQprUgJEk5qc6DdacZ4\nnMfpU5NMtRRqK5ptOBJQfzaXLMuIpjNwW/SdnHhqhxOBpIDhCIfJuIBWDWfPLTYza8Dfn9uNf36y\nD4f8hT+0i3EZOM3aHzvWwODMbhe2ldkmTgiZP7r1qI2OjqKlpQVbtmzBli1b4PP5MDIyotfVV6Re\n97NS1uJnTcR5tDnN6HBZMFJmRS2XxWck/MuTfTNGRA+E0xiJcAinxLLbHgHAZjQgXeIstVp6DBcy\nq8+fxOoWOwCg3Wme/qS40qxwKntgbrHJceVW1GrpMaSsyrN2j8bQ4jDjGx9YjqcOBvC3j+7HoSIr\n5EqywikRFpaBXeXQiHwbu5zYNRrHmhY7nBYjvva+Xly2yosOV3aK6cpmOwwMZkw0VWpZkxXDUQ58\nZubfrHL3LSlIMLHqe6wKyc9iDQw2LWvCK0dDmEzwaNN56+NCvRavXNeCldY0+koUalq2PuY7p8c9\n3c9bS//GKIuyai2rGN0Kta6uLgQCAWzevBnXXnst/H4/urq6Sv5O/gOwdetW+lrl13v27FmwvD17\n9iz6/a3GryfiAtqdZkRHjmLPkeGSP597vkJJEYfGY7jvtYHpSWvHRibQPzCEpJCBw8yWzd/+1htI\nCxlIUyP66fkq//ULL29FIClgaZMVW7duhSEVxfhUoVbpv6+X3twOYyZd9HL/0DEcOjZU9HJ6vhrr\n7+EL7xyAkw+i1WHGLz69DhusEXzr6f3TB9mrvb5nX3sbToZX/POFvmb82V6kNS12bN26FcLxPehw\nWdDltsABDpNH9qLZbgJrYFRf/7Y3X4eHzUwfY6L096NpEW6LcV5eH82JQTzXF8RkQsCRve9W1etD\nzdc+s4xt+w4XvDzOiXBV+PidtcSNd4bCePlV+vdMXxf/mp6vyr4uhZErOIV179692LFjB66//noA\nwN13342bb74ZkiThpz/9Kb7+9a8X/d0XXngBZ5xxhtZoQqrCf24bhtXE4qRWOx7dM4F7rlhV9ncO\nTCTw49cHkeAl3PWhFej2WHHLnw6h02XGmlYHhiJpfPmCnrLX87Etu/DwZ0+BzVTZJ6aNYtdIDL/Y\nPooffXwNAOCBbcOwmVhsPr2j4ut+83gET+z34zsfWlnw8pePhPDykTC+8YHlFWeR2nfn80dw0fIm\nvH+lD0B2i983nzuCM7rduOrkVtXX9+LhIF4/FsG/Xqb99cVnJHzvpQF8/dJlMOStDD+6ZwIHJhL4\n10uXYTyu/QiAu188hjO7Xbh8TbPi3zkwkcB9rw/hvqvWasosRZZlfOF/DmA4yuGJmzbOuM+15JWj\nIfylP4RvfXDFnMse3jWOSFrE353bXVHG9Q/txT1XrEK3R9tzTwgpbceOHbjssssKXqZ5Re3xxx/H\nI488gnfeeQc/+9nPAACbN2/GAw88gC1btuCGG27QetWE1IS0KOH5viDOX+rObkUscaBxvnBaRJPV\nBJvJMH0IcjQtIiFISPAZOBQWXlYVmQQ46E9iTat9+uu2qd4bPYSmtj4W47YaEaWpj2RKfyCFlc0n\nXosMw+CvTmnDkwf8JQ+yL2Y0qr0/LcfMGvC/L1s+p2C5fLUPN53VCYZhNBdpALDCZ8PhoLo+tSgn\nwm2dnw+iGIbBR05qRovDVLNFGgB0uy0YLtIfHeNEuHR4/DrdZozGtPdgE0K001yoXXXVVfjWt76F\nH/3oR/jCF74AAOjt7cVtt92GW2+9FUuWLNHtRlaq3LIiZVGWFk/s92NdmwMrm+2wmgxIi6WHe+Sy\nwqnsFDOb0YDU1FanKJdBks9kCzWFfSY2U/E+tVp5DBcy69WjYZy15MRh1O0uPXvUhJK9hR6LseSB\n17XyGFJW5VlxTkQ4JaLbPbPo2djpREaSsW985sh1JVljMR4d7spXOwplua1GdHusFV/3+nYH9sw6\n+7HcfYukRU09cYUUyvrw2mbcfJ7+71UW8rU4sHcHRqPc9Db4fNEKh4nkdLosGIvxNfNvjLIoqxaz\nimmIA68J0VtKyODR3eO4/oxOAIDNxE6vjpUTmnpTbzUZkBIykGUZMU5Egs8gKWQUDwSwGQ2KMxtd\nvz+JUErAmd3u6e+167iiFk6LaLIVn4bnsRoRm1pR++mbQzgemjuqnDSGw4EUVvhsc45xyK7wtODJ\nA37V1zkW59BR5ZML17c5MBbjVU1ajczDxMd8NhOL83vnnntYS8wGwGlh4U8I2DYYwe/fOzFFNMZl\nNI/nz9fpNmOkgqnGi+mhXWN4Y+ooBkJqUUMUavV65gJlLV7WE/v9WN/uxIpmGwBlRVMuK/emPlfc\nJfgMJDk74Sy7oqbsn6XNxCJVZBWvFh7Dhcx69lAAH17bMuPNcdvU1MdiW83UZIXLbH10WVlEuQwO\nB5L4/XuT2HosrDmrUpS1uFl9gRRWtdgKXnb5ah/eOB6dsU1WSVZ262PlK2rz+RiyBgZndLuwfejE\nqPdyedG0CLdOK2q18vrQktXtzp5T99M3h/HykdD0ZbGpYSKVyq6ocTX3GMqyjD/t8+OHrx5HqMQH\nBLV2vyirPrOKaYhCjRC9vdAfxCdPPdH0bzWd2MZYTu5Nfa64i3IZWIwG1VsfLbSipthAOI11ef1p\nQLbQtZlYhFOV946FU0LJQ3nNrAGXrfLia08fRq/Xil2jsYozSW06HEjO6E/L57YacW6PG8/3BxVf\nX0aSEUwKaHPqe2jzfDinx63qTC49tz7Ws7OWuPDdvxyDmTVgIJSe/vApzlc+nh8AOt0WjERr71zc\nsRiPjCzjY+ta8I3njiBOZ1mSGtQQhVq97melrMXLiqQzaMvbamQxGiBkpIJ9ArOzcm/qbVNbH6Np\nER0uMxJCBkleUjxMhHrUlAulRPgKHNTb5jRhrMj2RzVZIQXn333xvCXocJnxzxcvxYHJ5IwzpWrh\nMaQsfbL6Aymsai68ogYAF/R6sCuvl6tcVowTYTMZYGL1PWtsPqxpteNY3kARJfdNr2EitfL60JJ1\n7WkduOcjq/CNDyyH1WjAZCK7eqTfipoZYzEOr75aW4/hztE4Nna6cP0ZHVjhs+HB7aPzlqUUZVGW\nWg1RqBGitxgnwpm38mVgGJhZAzgFq2q5N/VWE4u0KCHKiWixmyDL2W2RinvUTDT1UalQsvCwj3KH\nXiuVHRBTekXDYWbx40+sxdpWB5Z4LDhY5IBjUr84UcJolEOvt/hwjpXNdvQXOcC4kKQgVXTQ9ULK\n9YUqnWwZSWfgmccetXqypsWOLrcFvV4rBqZ6YGNcBk4dVtRcFiNYA4Nk6XlZVWf3aAwbO51gGAbv\nW+nFsZC6qaOE6GEiziNSwdTnhijU6nU/K2UtThYvSpDk7Hj8fNYyWxGne9Sm3tRPb31MZ+C2GmE3\nGeBP8MqnPhpZpATqUStHlGQk+MJDCUqN6FealZGyw2DUbNE6t8eDV46c6FOr9seQsvTJOhpMoafJ\nCnOJ1a9OtxkJPjPdp1YuKyVkYNfpLMX5fgxz241DKWX3jXrU1Gf1em0YCKWQFiWIkgy7SZ+3ed1u\nC7pOOk2X61Ki0sdQlmXsGsmuqAHZ2z8S4SDLMsZmHTXQKK8NylqcrJ+8PoT/+9aw5t9viEKNED3F\n+AycZhbMrLN38s9FKyb/TX3u56OcCLfFCLuZBZ+RFRdqavriGlk4JcBjNc6ZsgdMfcIfq2xFLVji\n+ov58Npm/OVwsOjWVVKfDgdTWOkrvu0RyK7Or2i2oT+gbFUtwUuwKxxAVA06XMqnrVKPmnq9XisG\nwmlMxnm0Okxz/j+l1YpmG44EamdFKtdT1+XOtii0OExI8BnsHInjS48dREZSf14hIWrxGQm7RmN4\nYyCi+SzV2vnrXoF63c9KWYuTFefEgg3atjJnqW3duhVRToRzahtJdutjZupTY3a6QLMp/AS01IHX\n1f4YLmRWKCXCW6A/DSh9lprSLH9CQKvK0ehtTjPWtznwytSEtmp/DClLn6zJuLKDqVc329E/9aa4\nXFZSyCjuay1nIR7Ddqd5ekWjVJ40dWyJHsMwymXpbTGzepuyWx/9CQGtDv2ObFjhs+H1fUd1u75y\nKn0Md43GsLHLOV2oGhgGnW4LnusLIM5nsH/ixHmFjfLaoKyFz9ozGscyrw3n9LjxgoohUfkaolAj\nRE8xLlOwQdtqZMv2jOWPcc+f+ui2GGE3sbCbDDAo/AS01DARckKoxERGPc5Sy31yrdY5PW7snXW4\nMalvSleIVjbbcFjh6kWSV372YjXocJkxpmAVO85lYDOxugxJaSTdbguGoxwmE9r+LhWz0mfDOFc7\nz8Wu0RPbHnO63Ra8eiyCTpdZ1fRRQrR47VgYj+yZwNk9bly0rAnvDGub9lw7/+oqUGv7WSmrurOK\nNWjnb0X82tP9c5a5N23alC3UpoqG6a2PUytqdpNB1Ruu7Dls1KNWTqjEoI9Sww2UZk0kBLRo+OS6\np8mKwUgaQkZC9/ozVf++VtX+fNVzVvbff/k3z6ua7eibGihSLispSLr1IS3EY5j/4UipvLCCSapq\n1MLrQ4+sJpsRGUnG4WBK1xW1ZT4bAqJpwbYMVvIYPrHfj7cHozityznj+90eCzhRwvVndM4o1Brl\ntUFZC5v1H68NorfJig+u9mFduwP7JxIlJ4MX0xCFGiF6ivMzJz7m5FbIknwGO4Zj2D0Wn/MzoZQw\ns1ATMwgkBTTbzXCYWcX9aUD54SUkK5gU4Cvyhs9pMYJBtvjWyq/xk+ueJisGwxzeGIjgh68eL/gz\nrw+Ep7dHktqndEVtqdeKyThf9IOYfElev2EiC6HdpawvNEz9aZowDIMutwW7RuJo0XFFzWFm4bUZ\nMRzlyv/wIhqLcdiyfQT3fGTVnEPgu90WNFmNeP9KL0aiHBJ8BoGEoOnNMyGlRNMiOFHCF8/rRpvT\njGa7CXYTi6GI+n8/DVGo1dJ+Vsqq/qx4ka2PuR613KfFu0ZmFmpbt25FOH1idcdqym6V9CcEtDhM\nsJtZVb0mVjpHTZFyqxhem6ng6FylWZMae0F8NiOEjIQ3B6MYC80t6gFg+1AM7+m8PbLan696zgqn\nla0SGQ0Mer02HAmmymYlBP22Pi7EY9jpsuBIMIXDgWTJvHBamN4mrodaeH3oldXtyT7Gantny/HI\nyQUbKKL1Mdw3nsCGTifWtMw9VP6UDieuOrkVrIFBr9eKI8EUvvZMP/77uTcqvbmKLfZrg7IWJutY\nKI1er3XGMJ/17Q7s0/D/84Yo1AjRU7ZHrdDWRxYpQcJYjIfPZsSu0bn7kUOzetSSgoRgSkCz3QSH\nmVU1vc1GK2qKBFMCfPbib/g8VqPmaUzAVI+aU/0n1wzDoKfJilePhpHMFO5LHI/xiPM1dngRKSq/\nR7UcpX1qSV6/rY8LYYnHgmtP68BtT/QhXeKlHUmJ8Oi49bGRdLuzK0l69qgBQLtFwpFgdU9+3D+R\nwLo2R8HLepqs2Hx6B4DscJTdo3EMhNJIivpMxiQkZyCUQm/TzAm/69scM4bYKFU7f90rUCv7WSmr\nNrKK9qhNTWEcj/M4r9eDiTiPcEqYkRXOG2xhMxkwmeBhNRpgMRpgNxlUbX20TU2NLKTaH8OFzAol\nS6+oua0sItzcQk1pViXT1Xo8FggZCWAMBbe5jcWy23P0VO3PV71mCRkJKUH5AcSrmm3o8ycV9Khl\nVP3dKGUhHkOGYXDVya1Y5rWhdfXGoj8XTisvapWo9teHnlndnlyhpu+K2vtOX4ujC1SoaX0M944n\nsL5IoZZvZbMNzxwMAAC6VqzWlKXFYr82KGthsgbC2RW1fGtaT/Qeq9EQhRoheiraozY1TGQsxqHL\nbcEpHc45fWr52/BsJhZCRkbL1Oh4r80En4JBAznUo1aeLMs4Hk5Pf8JciMdqRKTUR/slZCQZ4bSI\n5iLj/8vpabJiZbMNXrtxzvZLWZYxHueRpBW1uhBNZ+CxGhVPdV3ZbMfRYLrsz9Vaj1rO6pbSb1ro\nDDXtut1WWFhGt6MNclb6bDg8D4WaKMl4YyCCtwejFQ0rSQkZDEY4rC6w7XG2FT4bxuM8DAwq2lFB\nSCEDobmF2jKvFYPhNESVr/GGKNRqYT8rANz/xhBCeSsw85mlFmWdULRHzZhdFRmP8ehwmrGx04nd\noycKtVyPWq5HxWrM/vNrntqe8sHVPvzdud2Kb0ep8fzV/hguVNZYjAdrYEpuAXJbCm99VJIVSGb7\naNQcdp3v3B4PPnVqG4xiGuHUidvw23fHsHs0Dj4jl11Re+t4BC+rGDhSzc9XPWeF04KqwsNnNyKc\nFhSdo6bXgdcL+RiuabXhtf2Fh+gAyidkKlXtrw89s5b7slv89DrsOqd/19tI8hndC5vfvDuGB7eP\n4L92jOIL/7MfKSGj6THcO57ASp8NZmP5fw/Lpw6eP6Xdif2Hi78O9bbYrw3Kmv+st45H0B9IYbl3\n5tZHm4lFq9OMwXD5D+DyNUShVgtCKQGP7Z3EgQn1y6JkYZXqUUuL2a2PHS4LNna6pgeKSLIMSZ45\n9ZE1MDCzDFrs5umvjSre8FtNtKJWzr6JBNa3O0q+YcmuqGl74xFICtOFthYrmm14/0of7Gx2ZS7n\n5SMhPLRrHC4LW7ZQ2z4UxStHw5pvA1kYasfNuyxGRdNIs+P5a29FbU2LHaPp4m9BIjpvfWwkNhOL\na0/r0P16GSZb4OjVp5aRZDxzMIAn9/txzxWr8B8fX4MWhwk7NJ43tW0winN63Ip+1mFmccXaZpzf\n60GKNi0QnYRSAu5+8Rhuv6S34HsDLf9+GqJQq4X9rO8MZf8wHQspfwJr4X7VY1aME4v2qOWGibS7\nzFjZbIM/KSCUFPDkfj/ewdI5wwRsJlbzCGU6R628fePFG8tzXFYjYnk9akORNP58KIBzz7+g7PUH\nk8UP01ZjeVfbjGLRnxTwznAMK3y2ssNExmI8BkLKP6Gr5uernrPUjpu3mwzgRQnnXXBhyZ9L6Hjg\n9UI+hks8VqRQfJAPnaNWnVkrm23oV9lnk5FkPNcXgCxnC7OfvzWM//vmED73yD680B/EnZevQLPd\nBIZhcE6PB9sGo5rul5pCDQBuuWgputwWWD3NqrO0qufXBmVlB4B1eyw4v9dT8PIVPpvqyakNUajV\ngm2DEZzc7lD1hossjjifgctceDx/bknbbWHBGhic0u7Ae+MJHA2l8crRMAwMA1vep99Wo0FzoWZh\nGViMBoSSyrfLNpr9Ewmc3F66UPNY2RlF0tMHAvjPbSP4zgvHyl5/KCXCp7E/LV+T1Ti99ZETJaSn\nDjFe4bMhwWcKHsidMx7nMTx1cDapXpG0uq18DMPAaZn5IUIhSSFTU1Mfc1gDg/OWevDkAX/By+kc\ntep0UqsD+yfVFWojUQ7ff/k4vvfyAH7z7hg8ViO8NhP++ZJefP+jq3FS3odp5/S48fZQFG8ej+AP\neydxcFLZlLzhSBppMYOVzbbyP5zHbWErOkeTkHwTCR5tJYb4rKAVtcKqfT+rKMl4ZziGT53ahgEV\ne1er/X7VY5Ysy4gXmfpoMxnQH0jhfSu801vtVvhsOBpMYTjCIcGLc1ZfbCbthRrDMFhRpLm7mh/D\nhcrKDRJZNquhdzaPxYho3jCR/RMJfOXCHrwzFAZfpvjJHqZdeaEWGhuaLhb9CQE+uwnn9LjR67XC\nZGCK9iLKsoyxGA+Pzaj4IM1qfb7qPSucUl94uCwsXn59W8mfyY7nr51z1PKtk4bx+/cm52zvzUgy\nYpy+hVq1vz5qJevkqbOgSn14NNtwlEOPx4KtxyK49eKluGZjO67Z2I5TO5xzfnaJxwILa8D9r/Rh\nIDuy5oEAACAASURBVJzGnc8fxa93jJbN2DYYxdlL3Kr78lxWI8ZC2rZaalHPrw3KAibjQsnzC5d5\nrRiMUI9azdk5EsMSjwVndLswFE5XNPWIzC8uIwMMYCnQrJxbKfvouhPbKHq9VgyE0xiJcljrzMzZ\nynP5ah9WN5efUFWMlk9nGkWCz8BomLmCWYg7r0dNyEjoD6RwRrcLLWYJB8t8chxICrqsqDmM8vRR\nDoEkj1aHCf98cS8+tKYZDnO2T63QFrEol72P69scOBZKlV19IYtHy2vFZWGRKnLGHpAtaPiMBGsN\nrqgBQItFxmmdTvz5UGDG92OcCIeZ1Tykh8yfDpcZGUnGZEL5To7hCIczut149LpTcXqXq+TPMgyD\nn1y1Fn/bm8ZXL+zBD65cjcf3ToIr8mFVzttDUZzTU3i7WSnuMv/GCFEju6JW/O98m9OMUFJUtQOm\nNv+6q1TN+1mB7OCAi5d7YTOxaLKZMBZT9sl4td+vesyKcWLRkcftTjOuWNuMlXmFV6/Xin5/EsGU\ngH+4dD3OnvU/kk9taIe3gjf6K5oL73eu5sdwobKCSWXbEj1WI6JTBc7hQApdbjMcZhYXrO7ErpHS\nn7SGyhymrdTZG9ZNDxPxJ7IDSsxGA1gDA7uZxZvHo/ja0/1zfm88xqPDZUav14b/88Ywvvz4wbKf\ndFfr81XvWWNTz5UaLosRy086uejlKSEDq9GgeOR/OQv5GObyrlzXgqcOBGa8bvU+Qy2XtVDqOYth\nGKxvd2DvuPKDe4ejHLo9loIfcBZiN7O46KLs/epwWbCm1Y5XjhafbJsWJewdT+CM7tJFYCEuixGc\nzCzYB+T1/NqgrPIraqyBQYvDhPE4r/g6G6JQq2aiJOP1gQguWt4EAFjbasdrxyKLfKtIMXGucH8a\nAPjsJtxy0dIZ3+vxWDEez+5ZPqnNgetO13cSF62oFRdMCfAq2JaYW7HKSDL25g0fOa3LiV2j8ZK/\nG0yKijLKabKapnvU/Elh+my93O07OJnAsdDcPrSxOId2pxkbOp1Y12YHwzBlVwHJ4hiLcRoKNRYx\nTsRf+oMF30gmBUm3QSKLZUOnExlZxn2vD2HP1LmTEZ1H8xN9rW9zYP+EikItwpU8y7KcK9a24Pm+\n4oXarpEY1rTYNR38zhqY6f8HEFKpyQSPthKFGgB0ui0YjVKhNkM172cdCKXQZDVOP7GfO7sLD+8e\nV3TOQjXfr3rNihXpTyvGbDSgy21Bt8cyL/ert8mKkSg3p5eqmh/DhcoKJpWtdrEGBi5Ldvvjkwf8\n2LQs+6FJ/Oge9PmTJc8MCiYFzYdd5zu8912Mx3nEOBGBhIDmvGZkh5lFfyAFUcr23OXLTRg9vcuF\nOy9fifev9JYd1V+tz1c9Z/EZCeGUWLLJvBCXxYgd+/rxvZcHCvai5rYI6mWhe9S2bt0KhmFw+yW9\n8FiNuPP5o3hnKDovg0Sq+fVRa1nrp/rUlBqZWlHTkgUAZ3S7cGAyUbRn+OBkEuvLDI0qxSQJ07sq\n5lu9vzYaPWsiXnqYCAB0uswYVbhzDmiQQq2aDYTSWOY7MaWoy23Bx9e3Fp2ERRZXnBfhVPnGaJnX\nWtGniaWYjQZ0uCwYCiv/R98ogiomMrosLH61YxQeq3F6+4zZAJy5xI3XBgqvcEuyPOMA80q4jDIu\nXenFT14fwmRCmHFAt9PMYiCUhoVl5qyejkWzZ/blXLy8Ca/SmWpVZzLOo9lhUt1z5bKwGE0bIMnA\n/gJvjCfiAjrKfHpbC05qc+CGMzvx9+d24w/7JqcmZNLEx2q1usWOgXC66JCjHF6UcDSYQjAloL2C\n16nDzKLHYy16zuxgJI0eT+mhUaXYWFnzWZqk9ozGOGwbjBQ93kgrPiMhzs2dRTBbdkWNCrUZqnk/\n60Aojd6mmX9gzu/NniOid1YlKCsrzmXgUvlJ76WrfDi/1zNv96vHY8HQrClC1fwYLlSWmomMl670\nYiIu4IvndU9PDdu0aRMuWd6El48U3nITTYuwmQwws5X/Gd20aRM+d3YXjgRTeGswMmfroyjJOGuJ\ne04/4uFgEivyPuhZ5rUiLUqYKLH/vVqfr3rOGtXQnwZki/SAbIeByR7ePttIlJtRqFdqMXrU8i31\nWjGZEOacNzkfWfOp3rMsRgOWea04VGab9YtHQvj73x9Au9Os+kOK2fdrY6cTu0cL9wwPhjksbdJe\nqHW3emdM/p1P9f7aqPYsTpRw65/68MC2Efz0zWFds3ITm8u91jtdFozEaOtjzThWYHz4yubs2Ukj\nKipusjCiXAYulStqm5Y14bQyk64q0dNkxaDC0eyNROnWRwC47oxO3PXhlVjbOnP7zNk9buwejRcc\n0KF0WIlSNhOL731kFc7tcaMn701HbmvbhcuaZqyoiZKMI8E0VuWdG8QwjOr+ETL/xmI8OjUUVC6L\nEeNxHqd3uQpuNRuLcehy1/6KWk6r3QR/QtBtpZrMHyV/Z/r9Kdx4Zifu+tDKivM2djmxc2Ruz7Ak\nyxiOcliicmtlPk/e5F9S35464MeaVju+/9HVeO1YWNUWxHIm4zxaneXfE3S5zRijFbWZqnk/60Ao\njWXemQc0GhgGZy9xl11Vq+b7Va9ZcU5U1aNWSZZSSzyWOT2N1fwYLlRWSOEwkVJZNhMLE8sgKczd\n4hNMCfDp9GYyd7+abCZ84wMr4M5bTbCbWdhNBpze7UJ/IDVdNA6EUmhzmOYMk1jf7ii4+jI7ayHU\nWpYoyfhLfxBSmcmZarO0DBIBALc1+9yeu9SDpJBBYNZI9JFZW18rtRg9avk8NiOSQgaTcZ561Ko8\na327A++NlR62dDSYwkltdnRq2Po/+36d2uFEXyCJ5KyhH5NxAU4zW9FQnWRgDMGk8uMGKlHtr42M\nJOP+N4Zw1S93lezP1iNLq0qy/rjPj2s3tsNtNeLKdS14ZPeEblnlDrvO6XBZMBrjFZ9F2BCFWrXi\nRAn+BI+uAp8EndfrwWvHqNek2sT5jOoetfmWXVFTd4BiIwgmRV0GfTjNLOLc3G0xk3EerSqHQ2jN\n73Rb0Gw3wWYyYHjqk7g+fwprWueewbeuTV2jPznh7cEo7nlpAD9+bVDVgb7laBnND2RX1IDshzEX\n9HrwxKze5dEYh846WlEzMAxa7Cb0BZK6b30k+trQ4cTe8UTRsfayLONIMDVja3YlbCYW69oceHfW\nkSmDkTR6mir7sMJllBFYoEKt2r0zHMWu0Ri8NlPJLfR6GItx+OLvD+DgZAK7R+NzinC9caKEiQSP\nVS3Z/29eua4FLx8J6ZZbbjR/jsPMwsBk308q0RCFWrXund03kUC32wJjgf2sZy9xoz+QKvkpT7Xe\nr3rOinGZ6TdP852lVLZHjZvxxrKaH8OFygqmKjuMOpflNLOI83M/WZxMKPujrCarEI/VOL2tJ78I\nO+RPYnXL3EJtTasdx0LpogfEVuvzVciRQAoPbCvfR1Bp1p6xOH7x9gheORrC587uxP6JJJ7rC05f\n/uQBP7ZsH5n+N6Y2K1uoadn6mP1QqNttwebTOvDHfZPTn3JnJBnjcX1X1Ba7Rw0AWhxmBJMiPDpv\nfayl130tZHntJvjspoLTSIHs30ezkdF8zEKh+3VOz9ydRoPhygaJAMDZp560YIVatb82ntjvxydO\nbkOX2wK/isdEbZYky/jBK8exxGPBrU/04Z4Xj+GOZ/oVHZOg9TEciXLodJ14z93iMOPUDideLNKH\nrjar3GHX+VodZvgVHhrfEIVaNeJECfe9NohrTyt8rpbFaMA5PW5spVW1qlLqwOvF4rQYYTMa6BPB\nPLwoISVIujxXToux8IpagkeLwj/KlbhoeRNu2ZQ9n299W3ZbIy9KePN4BBs7nXN+3mo0oLfJij5/\n7Z+n9vRBPx7ZMzHvn+z++VAAv9s9jlePhnH56mbcfkkvfvbWMP7t2cP4751j+MXbI3hrMIr//6UB\nvNAfxM/eUlY85oxq3fpoMcJkYNDmNKPTbcFZS9x4aepNRSApwGVhYVV4iHCtyE08pRW16rex04ld\nI3MHfKSE/8fefYe3WZ77A/9KsiRr2NbytuMVO7azF2GEhBkgaVmBFMIo4wAthVMoPRzo6aC0lFNK\nWf1BGacQRhkl7FE2JCQEsmMnsWPHKx6yZcmStff7+8Ox8dDWq31/rit/2Jb1fV9JkfW8z3Pfjwd7\n+oyszaaNW16Wi119UwdqHTobqqLMUYr5cVv6mMx0VhcODVlwWrUMKgk/5IFEJN5v0cLp8eLu0yvx\n2sZ5eOnyuSjNFeKFveqYZY4N6qde2FpTp/DbMCxcoc6oAUC+lI9hS2h/19LrHd6PZFw7+9nREZTk\nCnFajdzvbc6uVeDtQ8N+r4wn43mle5Y5zH3UoskKR2leNvomNRRJ5scwHlm9o3aU5grB5YTXacxX\nllTIg8nHVb7pbfSjEei8sricifqL8f2LPmjVokYhQo1y5owacHzmzU+dWjI+X754vAy+7jJgSWkO\nPjqii1mWl2Gwq9eIO1bNwjl1SijEfFQrRfjz2tk4u06BFo0FN64oxYPralEgFeCjIzq8d2hoRr2Y\nPxanBy4PE9HAIzc7CzdUWCa6iJ1ckYddvUYwDIPvjo1G1KAkkETXqAFAvlQADhDxyoVwsmIlU7IW\nlkhnLEUEgL9904fn96px4qw81rKAsSXAVqdnSu3UAbUZC3xcsApH5+H9cbvQmcyvjUNDZswrlELE\n50Ep5kMb4kAi3Cy1yYEX9qjxy1UV4HE5kAqzwOVwcP0JpfisfSTocxHpY3hs1IGyad1BFxbn4Miw\n1e8efbGoUQPGZtQ0ZppRS2o7e404PcAgDQCWleWiRiHCs7sG4nRUJBiz04McQfJd6S2Uxn49eSrp\n0NlQrWTnaq5UwPO5HENrccWlRm2yGqUYZocHL+4dxNVLi/3errFQnNJ1ap06G57+rh9yMR83nFCK\nfx/R+a2FiVaHzgaxgIeza5W49ZTyie/XKMVYVSXHvWtqsKZOCYmAh+uWl+Av62pRKfbggJ9W4dON\nNxLhRHjRQCX8/ryXlOagedCMJ3b04d0WLa5d5v81kKryJXzkZmeF3c6dxN+y0ly0a23o0X+//NHj\nHbvw8dj5c3B+Yz6reRwOB+Wy7y9Kqk0OuDzeGbMk4ZLyGOht7qBNhNJd+7AVtcfrnvMl/JgNXl/a\nO4jzG/OndDcGxmY219ar8N8fHvU5UxstXzNqwfboC0eoXR8BQCWhGbUpkm1NsNPjxYEBE5aV5Qa9\n7S2nlGN3nxH/3DcYURZbKGuMKYoZtVieV75UMGWglsyPYTyy2Chin6hRE/Jgmrb0kWEYDFucKIhD\njdpkWVwOXt44D29evcBnfdq4xgIpWjQWnw0xkvH5mu6tQxoMGB245aQyVClEKJQK8O0x3xuPR5u1\nq9eI5eXB34snO2NeBQ6oA3e8GxfpHmrjJp9XjjAL1UoRtnQa8OC6WiwoZnfbj2SoUcuXCGKy7DEV\nXvepliUW8LB+fj6e262G+/iFlDatFTJRVtTvjf7Oa3LzrKbjs2mRXgQZd9qqlRDzuXFp0Z/Mr402\nrRV1qrG/m8owlz6GmtU3asfOXiPWzy/w+fPrlhXj8kWFeDRAQ6dIH8Neg33G4BA4voTXz4W3ULMs\nTg8YIORmc1SjluQODppRKRdNacHtT152Fv6yrhYftmqxuy/4JtgkdhiGiao9fywVSgUYohm1CWx2\nG8sR8GB2fP8H/KsO/cRSPDE/Od9CC6R8cDmcKcthU0nXiB2XLSrE3KKxJU3rGpT4YFrHQ7YcUJuw\nOMx9DhcW54Q8UIt0DzV/Ni4qwq/PrGK9fX2yqC8Q44K57M7EkNi5oDEfNpcHN2xugcnhxq5eI04I\n4SJ0pMqPb0fTqbPhnUPDrO1Rmul1agzDoF1rm7gAqBILwmomEqpP20ewplYxsT/odBwOB2fUyMHj\nckJ+jw0Fc3y/PV+zr4tKcvBZ+0jQ7SYC0RzvAh3qRYN8mlGbKpnWBDMMg381abC6WhbyfSrEfPzi\n1Fl46Otj2DNpsJZM55UJWTaXF1k8LgS8yP7bxPK8CqRT1zsn62MYjyyGYdDJwtLH8SzJtKWP/z6i\nxVPf9Yf1phxqFls4HA5WV8vwyaTOhbHKCiTSfXx6DFP3l1xVJUeHzoYuPx3mIs1yerxoHbZiflF4\nNS79h/cAYILO8gHAUISNRMZNP69lZblR1+SEmhVrvvLkIj5+0KCKS1asZFKWiM/Dn9fWYl6RBC/s\nUePfR3RYFcbnm3CyAKA8LxudIzbc/dFRnFWrwJo6JStZyhgu9ZuexSaGYVjpY6A2OSHicyf2HlVJ\n+CHX4YaTtavXiJMqAtcucjgc/KBehQ/9XJyL5DHU29zgH6+Hm25ZWQ6uWFyEX3/cMWMWL9SsYYsz\nrJr1fIkAw1Sjlpw+ahuBxekJe+320rJc/OTEUvx16zHs62d/7S4Jbqw+Lflm0wCgQCKgGrXjRqxu\ncDgc1jajzhFmTSx9HP9gLxNlsdZIJFbW1qvwSZsOLj9F0slq0OSALDtryhVXYRYXl8wvwEs+loBH\n48iwFeV52X6v7vrD4QC/OHUWHt3WG7SdtDrC1vyEpJKNi4vwfosWp1bJMCdfErOccpkQe/pMmCXL\nxsXzCnxubxQJhYgPnTX2Sx/ZsK3bgCd29OGrDj3u/7Ib171+GGpjdKsn9g2Y0Fjw/fOWI+TB6fHC\n5mJvbzOdxYUhsxMNBcFfH6uqZdjVZ5pYUhutsT0nfb8PczgcrKlTQsCLvHv2gNEZ1sbu410f/9U0\nFPRvSEYM1JJpTfBHR7T48dLiiAqlV1XJsXFxEd5r0YaUxSbKGmvNH82yx9jWqI39p490n6doJFvW\n2N5S0c92Ta5RG9+YcvyD/Y8WFLLaejoWj+EsWTZKcoUzlo8k2/M1XdeIHZXymXUEP2zMR8uQBV92\n+G6lHEnWgQFTRLNTK1euxILiHMySCdEUZHnOgDG6TamT/flKlTzKim1WcY4Q9583G9ctL4lpVnGu\nEBzO2IUotqxcuRJKMR+6MLocRpMVDY+XwaPbeiHLzsLbh4bh8jC4aF4Bfvtp54zZoHCyPmzVTpmd\n5HA4YbXoDyVrV58RS0pyQvr8KxfxUZorxCEfyxEjeQzVRieKg6xsKJdlo9cwdcAbalb/qAOlYQzU\nRHweSnKFeL1JE/RvSEYM1JLFqN2NHr09qmUrp9fIsX/ARHtmJUA0rfljTcQf20/JYEuNK4KxZHS4\nWa3fkQp4E/uojX+wP69ehRtWlLKWESs1ShF6DfZEH0ZYuvU2VPoYBGdncXHfuTV46rs+PP5N75QW\n3ZHa02/CktLIa1zmFo41bfHH4vRAa3FFvSEvIalgUUlOzPf1E/C4+PnKWVhZGXnrf19UEn5MarLY\ndkBtQqFUgI2Li/DI+XX43dnVWD8vHwwDNEdYY3Vk2AKj3YOlZVPfC5eW5uL1Jg0bhw1grD/DwjBq\nCn1tcB6pQDNq48plwolGNeHqN9pRGmb30afXN+Cs2XL0GPwv6QcyZKCW6DXc43b3GbGwOCfiGidg\nrF5mUYkUTWpT0pxXpmSZHJ6o9vaJ9XkVSAXQHL8imKyPYTyyjHZ3SI16Qs0an1FjGAbbug04IcwO\ngeFksa0oR4hB09SrxJFmjdrdeOrbvrCaekSS1aW3o8rHjBoAVClE+PuF9bA4Pfjr18dgdXomOrWF\nm2VyuNE1YsOCMOvTJmc1FEgCDtQ6dFZUK0RRtZpPtv9fqZpHWemTdd4cJfhRfI7ylaUKowtftFnR\n2NJpmFEDyOFwsK5eifdbpr43h5KlMTvxpy+6ccXiohn7jl6/vAT7BkwhDQBDyWrTWlGX779b8XTL\ny3OxvWd0xvLHSB5DdQhNncrzZs6ohZo1YAxvRm1chVyEHn3gwWFGDNSSxc4I2kD7UigNvQiRsMeU\nxDVqwNhAjTo/jg0o8lic+ZQe7/rYorHC7mZi1sghFopzBBiIsnZh3HO7B9BjsOPFPWrW6gZ86Rqx\noSrAslK5mI/bTp2FQaMDG185iF+81wbn8WJ6f5uW+rKnz4T5RVIIopgBaCgQo01r9bvHW5vWhloV\ne0tkCSGxkS8Jb4PnRDA53NjebcBp1TP34D1ztgLfHjOGvd/kq/uHcGqVDOfOmdmURSzgYV2DEls7\nfS83D4fN5YHa6PC5rN2f+nwxSnOFeGV/9LXJaqMDJUGWoEc6o+b2Mhi2uCJqGlUhz6aBGpAca7hN\nDjd29hpZma7PlwowbHElxXllUla0rfljfV5leUL0Hb8alKyPYTyy2JpR+75GLQtmpwfvHh7Gunrl\njKuObIjVY1iSO3NGLdKso1obrlhchJI8IXb0BO92GEmWw+2FxuxEWZAlJAIeF384pwb/78I5qJCL\ncO/nXfjMVoL1LzYHLcwe9+2x0YhnRye/NgokAnT66UbZrrUG3O8unKx4oBo1ysrUrLENiGN/ATya\n83qjWYOTKvJ87lGXm50FhTgL/ZMuzIWS1WOwY2mp//fBE8rysLPX6HdPs1CzOkdsqJCLwlpRxuFw\ncPup5Xj70PCUDpQR1aiFsvQxL3tGqUAoWYMmB1RifkSzvBWymZnTZcRALRl81j6C5WU5kImi7xQX\nzo7mhD1jNWrJu3dRhTwbPSlWjxQLo3YPqzVqQh4HDAMcUJt9XnVMZkU5AgyaHPAG+SM7nZdhsH/g\n++6y4y3zq+QinN+Qjxf2qGOyOewxgx0lucKQ/uAVSAUoy8vGbSvLsbgkByvKc1GjEOHQUPBlOoeH\nLNg/YMIqH1emw1Wt9L90pW04vKU+hJDEyMvOgt3thd1Pq/tEc3q8eK9Fi42Li/zeplohQqcucL3T\ndGObQPsfwFQpsuHyMFHvydk2bEVdBBetVBIBTq2S4aM2XcTZdrcXZocHSnHgz98FUgFG7e6wO132\njzrCrk8bJxbwgo4LMmKglgzrqj9u02EdS12K8iUCDFucSXFemZRlckS39DHW5zW21tkWl6zJki1r\n1MFujRqHw0G+VICfryyPqkYxlCy2ifg8iAU8dI/YJ65IhpK1t9+EOz88OnGlr3/UAaU4C2IBD6ur\nZTipIg+/+2Rml7Hpwj2vYMsefcnNzsL6+QXI0bZiWVkODgwEHqh5vAz+urUHN59cFvGAfvJ5FecI\noDbN/BCjNjpgdnqibiSSbP+/UjWPsigrUBaHw4Eqgs6PDMOgRWMJeYAX6XkdHDSjLE8YsM6qWiGa\nMrsfLMvkcMPh8QYcwHA4HKyYlYu3Dg0HfL8PltU6bEVthBet1tWr8O8j2ollneE+hkc0FlTKRUFX\nw/C4HJTmCtE/6kCHzorfftKBP7/zXdD77zXYI6pPG1cRZDloRgzU4sntxYz/sEMmJ3RWN+azVNtS\nEMZGeYQ9JqcbOUna9REYa8feP+oIe416ujHa2e36CADPrK/HibPY7TIWL8U5Qtz90VHc9e+jIddw\nvd+iRUmuYGLD0Y4R28R2BBwOB9csK4be5sKRYSurx9qt992aP1QLS3JmbEcw3RcdI5CJ+Di1MvpN\neYGxduG+9jDa2mXAqZWyqBqJEELiRyURhLX80eNl8PN32/CnL7pxzWuHsLlpKKw62XDs6jVieXng\nv0HVSpHfZdi+9I06UJ6XHXQrm2uXlaBVY8Et7xzB/V92oyuMDGBshcbefhMWl0T2GbhWJUauMAtN\nEXa13NlrDHmZe7ksG72jdjyyrRd1KjGaRrNw6ztH8NZB/90vDw5ZQtobzp/bT50V8OcZMVCL11rn\nUbsbr2pV+Oe0TVl39RmxrCyHtdoWmWisZuaEk05m5f5Ckc5r00MVbXv+WJ9XdhYXCjEfA0ZH0j6G\n8cgy2t3IZWFAPTmLzQ5jwbLYVpwrQGmeEGV5Qvxz72DQrCGTE82DZvz2zGp8dlQPp8eLzkkDNQDg\ncjhYW68K2gEy3POKZEZtctacfDF6R+144KtubO3STyz51Fqc+OvWHvz5q278Y9cArl5SFNU+e5PP\na2xGbeZV+C2d+hnd2aLNijWqUaOsTM4KZ98wAGjRWOD0ePHCjxpx37k12NJlwEdHAi/Ri/S8Qhls\nVCtE6NDZMGp3w2h345RTTgl4+16DPWg9MDC2auHBdbW4aUUpapUi3PnhUezoGZ1SDxzovNq1Vsiy\ns1AUpOtiIKur5RNNTcJ9DHf2hd7Ir1yWjc4RO7r1dqyfX4CnL1uEjYuK8NK+Qbh8DMK9DIPmMLcd\nmC7YksyMGKjFy2Pbe8EwDAanXV3dFcZoPhQ8LgcKcdaU4koSe2ZndO354yGUDkLpzujwsLL0MV1c\nu6wEvzurGj87uQzvt2oxEmCvIIZh8Mi2Y1g/rwDVShFKcgU4OGhGq8YyoynGmjoFtnWPwuxgp1bN\n6fGiRWNBYxRXJgU8Lu45uxpzi6R4ed8gntutBgC8eXAYdpcXS0tz8YtTZ0X1R3U6XzNqx/R2jFhd\nmB9B639CSGLkh1n/v7PXiBXleeBwOKhRirGmVoGj2vBmm0IxZHLC5PBgtjLwRaxCqQA5Qh6uf/0w\nLn/5IHb1Bd6DbKw+LbQVDGIBDwuKc3DJgkLcc3YVntjRh8tePoh/h7BdCxsdz1dVybCtezTsFUMa\nsxMGmzvk+riyPCG2dRlQJBVAxOdBIuDhpIo8lOdlY9+kuu1xXSM25GVnBR1sRSMjBmrxWOuss7iw\nr9+EEyWGKVPnVqcHTYPmgF11IpEvEeDzb/ewep+BpPPa9FCZHB5Ik7hGDQAq5WNLH5L1MYx1lsfL\nwORwI5eFAXUynVc0CqQC5GVnQSUR4MzZCtzz7n58cXQEx/R2XPf6Yfzwuf34/aed6NBZ8eLeQZgc\nHvxoYSEAYHl5Hj5uG0GHzobF0wY3chEfy0pz8PlR/62bwzmvZrUZlXJRxIPs8azFJTlYV6/C/efN\nxidtOuzoGcWn7SO4ZlkJzqpV4IQgy4fCyQLGroaanZ4pS94/OKLFOXVKVpY9psvrMNF5lEVZqGfb\nEQAAIABJREFUwbLC3Utt+ixXjVIcdOlhJOd1QG3CwhJp0FVZHA4HT69vwOarFuDsOgW+2d/i97Ze\nhsF+tRlVivCXms8tlOLFy+biyYvm4KV9g/igVevzvJxuL949PIx3D2txSkV077vFuUIUSgVoUpvD\negw7R8a2SAn1vbhclo1+o2Oinm48a3W1DFs6DTNuf0BtxqJi9i78+cL6QK2vrw8PP/wwHnroIfT1\n9bF990nrozYdVlXLkC/wTrki80WHHouKpaxf4c+X8GFyUe1DPJkcyV2jBgD1BWK0DvvfgDfdWZwe\nSAQ8qgvyY+OiQuTyGXx2dAQ3vNGCCxrz8crGeZhfLMX/fNyBr7sM+MOa6onH74TyXHzZoccJ5bk+\n9xtb26DC+63aoE1FQhHO8pRQyEV83Lm6Ao/v6MVspSjirlzBcDkcFEoFE7NqDrcXn7eP4Lz61OoQ\nSkimC2fp44DRgWGLE/WTVgBUKbLRo7exXie+X23GwjAHAwoRHya3/7+Dbx8aRhaXgxVRXLgqzcvG\nA2tr8fK+QXwwKIDG7ITZ4cZLe9W4YXMLNr5yELt6jfjDmrFVDtE6cVYudvaGtjXMOI3ZiUIf2xn4\nU37878T0GbglpTlo0cz8bHV4yIK5RZGvAgkF6+uDnn/+edx8880AgGeeeQZ33nkn2xFhi/VaZ53F\nhbcPDeOBtbNRlleGJ55vgsfLgMsBPmzV4rrlJaxnFuYIIZRXsX6//qT72vRgvAwDizO69vzxOK+G\nAgke3HIMfzwn8Np0NiXT8zVqZ2c2LZQsNsUrSybi44/rTwAwtgGpiD924eHieQX4QYMKXA4HWZMG\nubOVIihEWVjtp439omIpJHweHt/Rh5tPKptxxTec89rVa8Tdp1eGd0JBspaW5eK5SxtZ36B7elbx\n8f3qqhQifHtsFLNV4qjqMQJlxRLVqFFWJmeFs/Txlf2DOL8xf8pFQRGfB6VEgL5ROyrkvpcphnte\nDMOgSW3C5cdXOYRKIeZDpyr2e5//ahrC/efOjvqiZmmeEI9fOAebmzX46VutAIAVs/Lwy9WzIBfx\nfe75FqkTyvPw56+6cdOloT+GGrMzrGMQ8XnIl/AnBmrjz1dhjhAasxNehpnyd65Na8WPl/p+nNnC\n6oya3W4Hj8eDXC6HXD72h93pTP/9vh7f0YsfNqhQpRCBz+MiV8jDiM2FzhEbTA4PlpSyPy1anidE\nH+2ZFTc2lxfCLO6UD7HJSC7iIzebF3QDxXQVi46P6Wp8kDZOwJv5+uZyOHj8onqcOMv3TBeHw8F9\n59agR2/HTW+0ht0NbNyA0QGr04OaIDUYkeDzuDPOlW01ChH29o/VL2zpNOA0FvZnI4TEl0oigC5A\nDe+4QZMDO3pGcfG8/Bk/m94iP1qDZifcXiakph+TKcRZfuuR+0Yd4HE4UXXYnUwm4uM/TijFc5c2\n4u8X1ePO1RWYky9hdZAGALNVIpgcHp/bofgzFOZADQAeXFeLhoKpM2rZWVyI+Tzord/XZBuPN22J\n1WqNcawO1NRqNVQqFTZt2oRNmzZBoVBgYGCAzYiIxHKtM3O87eiFc/MnsvKlY+3zx/5gy1jr9jhZ\nuSwbrf3BizjZku5r04MxOdxR1aeFkxWthgIJ/u+LZuzqDVxIzJZker4Mdjdys9n5UJ5M55XILKWY\nH7BDokTAwwNrZ+P8RhX+98vuKZ2xQs3adbzYPJr3ykQ+hufPzccXHSPoG7Vjb78RJ0dZjxEoK5ao\nRo2yMjlLlp0Fk8Pjs7vfZN8dM+KkijyfzcVmK0VoD9BQJNzz6jXYUSUXhd2lViHi49iw7/rhA+qx\nLoXRdL6dbtu2bcjNzmJ9cDYZl8PBktIcvL5lX8i/M2x2hbX0ERhbITH+2Ex+vopyBBg0fz9IbNNa\nUasSx+Qz/mSsDtRKSkqg0+mwceNGXH755dBqtSgp8b/sb/IDsG3btpT82ny8PemBXd9O/DxfwsfX\ne5rw8WE1Vh2/ssp2fl/LPmhsmKgNifX5Njc3J8Xjnaivv/5uD7hue8S/v23bNjQ3N8fleBeV5GDv\nCAePfNket8cnWb7WWV1QiQWs3F+8ni8g9f9/bd++HTLdERTnCvG37X34+uuZt//6621oHjRDbXTM\n+P2Pm7qRYx7we//J/ny17P0OCyQ23PRGK5aU5qJp97cxzaevU+v1kQznn2pfJ+L54nE5kIuy8PGW\nHQFv/3lzN0Qm3+9X84ul+KZdzdrxDVtc8JpHwv79jkP7YXZzfP7886YuiE1qVh/PeD1fs5UiHO4P\n/fHQmJ3oaTnASn5hjgBDJufE1+3HB2psnF8gHIaNKvBJ7r//fvz0pz+F1+vFk08+iV/96lc+b/f5\n559jyZIlbEYnxFGtFQ9u7cGTFzdMfO+JHX0YMjvRo7fhuUsbWb1qMdmGl5rxxEVzoJLE7goGGbO7\nz4jXmzT489rZiT6UoBiGgdPDYP2LTXjr6gUx3wMsmTy7awDCLC6uWFyU6EPJSFanB7/+uAP1BRLc\nuKJ04vt7+ox4fo8aJocHJocbcjEfi4pz8JMTS9E3asfP323DS5fNjaoGNNEYhoHV5UV2Fpea2RCS\nom57tw3/cUIJ5vlpfuFlGGx4qRl/v7ge+T4+ezk9Xlz6UjNevnweJFGuwgGATbsHwONycNWS8Oqg\nXB4vLni+Ce9fu3DKjI+XYXDZPw/isQvqWKujjafdfUa8dmAIf1lXG/S244/Be9csZOU9+R87+yHi\n87BxcRGcbi9++UE71s8v8FvDHY69e/fizDPP9Pkz1v8qbty4Ef/4xz/A4XBw9dVXs333SWfQ5ESh\ndOqLPV/Cx9uHhvGbM6tiNkgDju+gbnDQQC0ODDY3ZKLU+BDJ4XAgzOKgQCpAv9GBSj9FzelIa3Vh\nYTHtXZUoYgEP95xdjZvebEVjgQQV8uyxixzNGtx4QilOrZLB7vZi0OTAk9/24+fvtmHY4sQtJ5en\n9CANGPt/x8YHM0JI4qgk/ClbLE3XPWKHVJjlc5AGjNX61ueL0Txoxomzol8CrbW4IuqYyOdxIeZz\nYbS7IRN9v8dXh84GqZCXkoM0AKhSiNA1YgPDMEE/X2utLijEWaxdOCvMEaJda8VRrRVPftuPQqkA\np1TKWLnvQFi/1F5RUYE77rgDv/jFL1BWVsb23Uck2LRiNAbNThTmfP8fdtu2bZhXJMX6efk4tSq2\nTyDfrkfvaHyaRsTyMUyFLIPdDVmUTSrifV4Vsug2vz6mt+Oz9pGQsuIlWJbW4mRt48lkOq9UysrN\nzsIdq2bhud0DuPPdQ/iyQ48H19XitBo5eNyxwUyNUow/nlODHy8txp/OrcFZtYqoc9PpMcyErHjn\nURZlhZI11qLffxO8lmEL5hUGbse+sDgHB3xsjjw9KxTDFifyJZH9TRMyLoxMan4BzNz7jS3xer4U\noiy43G6M2NxBb6sxOVEQ5UTG5PMqyhFg/4AJd/37KE6pzMNdp1fGpcFc5qyJipEhkxNFOVNfCA0F\nEtx0YuwHqSqBF91RfBAnoRtNwW6CFfLsqF4ff/+2D49t78Xbh4ZZPKrY0lpcEf9RI+xZVpaLf1za\niJ9V2/DYBXNQkjvz6q0wi4vl5bmoUYp93AMhhMRfsE2vB0YdQbv8LS7Nwe5+3wO1cA1bXBEPNqRZ\nDEZsU89lV68Ry8vYH6jFC4fDQYHQG1KH4cEIOj4GUigVQG1y4sYVpbhoXkHclrizXqMWqnSpUfvN\nxx04d44yLtOf0zWpTfi/nQN47II5cc/ONH/d2oOGAgnW1qsSfSgh+7JDj6+79PjtWdVh/d7T3/Wj\nQ2fDgNGBX59ZiT990Y3nfzQ3RkfJrgueP8BabQAhhJDMsqVTjy2dBvz2LN/71N77WRdWVclwWo3/\nuiSPl8FlLx/E/7tgzpQVV+FiGAbnbzqA166YD3EEf9P+/FU3WjRWnF2rwBWLizBideG61w/jX1fO\nhyCFa9cf/6YPhVI+LlkQeG+5p7/rR46Qh8sXsVOz7vEy2Nqlx2nVctbLmgLVqKXuM5UkhswzZ9Ti\npUYpRpfezvpmrmSmUXvq1KiNq4xgRs3kcOPfR3RYUpqD/zylHLOVYozY3LAc726azCxODxgGEPPp\nbY0QQkj4yvKE6Nb7n61Rmxw+VwhMxuNysKwsB7v6otsix+TwIIvHjWiQBgBn1ChwRo0cH7Ro4WUY\nfNymw+pqeUoP0gCgSpGNrhA+2/To7ahgaa84YOx5Pb1GEdPeE76k9rMVolitnWUYBkNm55Q9GuK5\nrnrfzh0okPDRE+BNhS2ZsDY9EIMt+qWP8T6v0lwhhsxOeMIYyH/WPoLlZTn40cJCLC/PBY87tilm\nd4BlBsnwfLk8XgxbnFBJAu/5xUZWLFAWZWVaVrzzKIuyQsmqUogwandD52P5I8MwUBsdIV2cX16W\ni529owGzgommPg0AHD1NuHppMaRCHg4OWvBhqw7rYrQqKJ7Pl7G3LaSljz0GGypk0TVTi/d7oi8Z\nMVCLlb5RB3KFWQntVlarEgfcXJGwY9Tuhiw7tWqfBFlcKER8DJocwW983MdtIzhv2ht5tUKEjhDe\nFKOlMTvxabsurN8ZX7n9t+19uPezLqioPo0QQkiEuBwO5hdJcUA9s8bM6PCAw+EgRxh8hmthcQ4O\nDVngjaK6aNjiYuVv2qpqOX77SQdKcoWoy0/9muB8gRe9BnvAi9BWpwejNnfCVryxiWrUImBzedCu\ntUFtcmBPnxG/OsP3WuZ4ePOgBr0GO36+clbCjiETpGrt068+OooLGvOxIoQ2wb0GO/7rw3b887J5\nU4pk3zk0jC69DbfF8DWms7pw+3tt0NvceH5DIxTTOjc+t2tsL5mrl36/lwzDMLjtvTZctaQYf/qi\nG2IBFwuKpLjztMqYHSchhJD09tZBDbr1dtx+6tS/ea0aCx7b3osnLqoP6X6uevUQ7ju3BrNkkS2/\ne3nfIMxOz5Q9KSNhtLvRorFgeXnulD3VUtnVrx3CfefUoNzPYxvuc5VoVKPGsk/bR/DbTzqwt9+E\nxsLE7tl00qw8fN1lQP9o6LMmJDwOtxduD5OStU+ludnoC/G1sbXLgFMr5TM6GVUrRejUjc2o7ewd\nxX++cwTfHZu5pCMaX3XosaBIijNq5Hhu9wBuf69tYunJwUEz/tU0hNZhy5Tf2a82o1Nnw/1fdqNK\nIcIfz6nBDxvzWT0uQgghmWVBsRQHB80zvq82OVAcpD5tssZCCVo0luA39OOwxoLGgsBbAYQiNzsL\nK2blpc0gDQCq5CJ0BSj76THYUclifVoipd4nzwiwvcZ0S6cB2VlcfNWhn/GfKN7rqotzhdi4uAiP\n7+iNeVa8JFvWeGv+aGufEnFeZXnCkAfxX3cZsKp6ZvfS2UoRjhns0Fqc+NMX3VhZJcPD247BaHfD\n42Xw6Zboz+uwxoJFJTlY16DCp+0j8DIMXmsags3lwYNbj+GaZSUYMDqmPIYftmhx3fISyEV8rK6W\noVIuQgMLf9TGJdvrkLIoK52y4p1HWZQValbJ8fru6QvO+o1OlISxlK6hQILDQ1MHaqGel5dh0KKx\noCHInm2BpPPzNVY777+hSKfOhkpFdPVp41mJlhEDNTbpLC5062249ZRyCHgcVCujfyFEa129CgcH\nLbC5kr8zXyoypGDHx3FlecKQNkU3OdxQmxw+r96J+DwsLMnBI9t60VAgwYYFhTi1UoYX96rxwh41\nXu6N/qpVy5AFjYUS1KnEePnyebjnrGp8fnQE//3hUTQWiHHxvHwMm13wHP+76fR48W2vEWfXKvDw\nD2vxg4bU2TaBEEJI8hLxecjicmByTP1MdVRrRXUY+z42Fs4cqIWqb9QBMZ8HpZjqrn2pVATuat2m\ntaJOlfr1eECGDNRWrlzJ2n29cVCDlZUynFyRh79fXD9jV3I2s4IZzxJmcVGrEuNQhG8I4WTFQ7Jl\nGWwuVja7TsR5leVlhzSj1qoZe1Pzt4HjadUy7Ow1YnX12N4xGxcV4YsOPd5v1cLKEaJDZ434WDVm\nJ1xeBsXHr1QqxHzIxXzcf95sXLKgALeeUg4+jwulhI/ZC5YBAI5qbSjPE0IqzEKOMCsmSzqS7XVI\nWZSVTlnxzqMsygonK18igM46tfPjkWEr6gtC//BfoxDB6HBP+Rsc6nm1aMYuXkYj0Y9hLLNmybL9\nXoT2eBl0jthQy8JALd7vib6k9ECtTWuF1uKMW97BQTO+ODqCa5cVg8PhoCwveda/LiyW4sDAzC5F\nJHoGmxvyFL2qlS/lw+RwB51tPayxBFw2uKI8DxXybJxcMdaURC7m44LGfKypVeCHDSp80BJet8Zx\nf/+2D3e8347GAsmMpaV1KjFWVckh4o81cCnNFaLfOPYH7/CQmdVljoQQQsg4pYQP7aQW/cMWJ9xe\nBkXS0Jc+8rgcnFolw9Yufdj5fWlUYxULpXnZUBsdPjs/9o7aoRDxU675mz8pPVD7y5Ye3P5eO3oN\ngZd2sbHGtE1rxb2fdeEXq2ZBJvL/oT1Ra4IXFkuxXz2z+DUWWbGWbFk6qwsKFpY+JuK8uBwOSnKF\nGDAGnlU7PBT46p1YwMPTF9cjd9LM4tVLi3HjilJI9Z3Y3R/+xp5uL4PP20ewfn4BLllQEPT2pXlC\nfL2vZex4NdaorzYGk2yvQ8qirHTKinceZVFWOFkqMR/aSTNqRzRWzMkXh12rvqpKji2d3w/UQj0v\njcWFfEl0reUT/RjGMis7iwu5iI8h88zJmrZhK2vbEFCNWhS69TZYnB5ctqgQt7/XhrcPDccsy+Nl\n8JevevDTk8pwQnnwNueJUF8gQeeILazNjUloRqzuGe3iU0lpXjZ6Df4Hah4vgyPDgWfUAPj8A8Xh\ncKAQMDDY3LA4w6uR3D9gQmmeEBfOzcf8ouDdU0tzhRhxcsEwDA5rzKx0wyKEEEKmG5tR+34QcGTY\ngvoIPvzPLZRgxOqGOsjF0umi3ew6E5TLhD4natq1NtQmQf8ItqTsPmov7FHD6vLgJyeWoVNnw28+\n6cBLl82NujOfL1926PHWQQ0ePb8uJvfPlitfPYi/rK0Nq30sCe6Pn3fhlEoZTq+RJ/pQIvLsrgEI\nsri4cnGRz58fHDTjiR19Ue03cus7R3DTilLMC2HANe6hrccwS56NS+YHn00DgA6dFXd+eBR52VnI\nFWbh4R/WJvX/R0IIIanp/RYt2rXWib3U/uuDdmxYUIjl5blh39dftvRgTr4Y54exfcxVrx7Cn9fO\nRgl9nvPriR19KJDwccmCwinf/+8P23HpgkIsKwv/uUqUtNtHjWEYbO0yTDQ2qFJkQ8TnoXU48oYG\ngbxzaBgbFxcl/YfCyTU8hD0jVheU4tTs+giMt+j3vzx4Z68Ry6N8Q6tWiNA54n9PE18OqE1YXpYT\n8u1rlGI8d2kjfn5KOR6iQRohhJAYUYr5E81EPF4G7dqxpY+RWF6Wi529oZcHeBkGI1YXVCm8kice\nyvOE6PXRLK3X4EB5EvWQiFZKDtS69XbY3Z6JaWgOh4NVVTJ82Kr1ufQvmjWmVqcHnSM2LCkJ7QNl\nItcEl+aG1uGPjaxYileW1uLEO19sD3q7EZsb8gB1iaFK1GNYlhd40+udvUacEMFVwslZNUoROnSh\nD9T0VhdMDg/KZeG9mTbt/hYLS3LisnFnOr7mKYuykiUr3nmURVnhZKkmNRM5ZrBDJuJPqdEOx9Ky\nHDQPmuFwe0M6L4PNDbGAB0FWdB/RE/0Yxjprliwb3dM2vbY6PTA5PciXsjPIpRq1CG3tMmBVlXzK\nFfW19Ur06O249Z0j0E9rqRqNg0NmzMkXR/0fJh5K8oQBP5CTqf7VpMFLx7JhDVJbNWJ1pXSN2vim\n175WOffobdBanKiPst4r3Bm1wxoL6gvEcRlwEUIIIeFQifnQmJ3Y02dEq8YS8WwaAOQIs1CjEKEp\nxIZvVJ8WmjkFEnSN2Kd0te4bdaA0V5hWny1SrkbNyzC47vUW/PdpFTOaHzAMg5f2DWJrpwGPXzQH\nAl70g6tnvutHNp+Lq5YUR31fsbajZxTvtQzjT+fOTvShpIRb3zkCj5fBopIc3LiidMbPP2nTIS87\nC3/8ohvv/nhBSi+1u/SlZlQrRPjZyWWYdXwWy+NlcNt7bWMt9sNYO++LzeXBNf86jN+dVR20G6PB\n5sLrTRqIBDy/dXOEEEJIongZBn/Z0oN27djFzB8vLcZF80Krp/bllf2DGLG68bOTy4LedluXAZ+2\nj+D3a6ojzssUv3ivDVcsLsLS4+Ubn7WP4LveUfzPGVUJPrLwpFWN2tZOA3KFPJ/ddzgcDq5aUowC\nqQAfHYlsX6fp9qtNWFgceh1NIpWG0IadjHG4vejW23HNsmK0Ds/cKNzkcOOJHX14fo8aClFWSg/S\nAOCBtbMxWynCc7sGJr63o2cUXA6wrkEV9f2L+Dz87KQyPLi1J2DnUYZhcPVrh/HWoWE0hrFxKCGE\nEBIvXA4H/31aJf52QR1OnJUXdWOKE8pzsbN31OfKlumGLU7Wlu6luwXFUhyYNFPZN2pPq/o0IIUG\nasf0dry0V43ndg/gqiXFAT84X7WkCC/vH8Q/9w3C7HBHvMa0R2+DzuIKa7+mRK4JLsoVYNjsgsvj\njXlWLMUjq01rRaU8G4PtB6dsajnuzYPDmF8kxVGdjbVlj4l8DKsUIly9tBgtwxZ06Maa7uzsNeK0\nannUSwTGs1ZVy2F1eSYKsH0xOTzgcTn4w5rqiC6ApNvrkLIoK1Oz4p1HWZQVSZaIz8Ndp1eGXU89\nXbVCBKeHwbtffhP0tsMWF1QsLH1MlscwllkLS3JwQG2a+PqYwRH1c+UvK1FSYqD2ZYced3zQDqvL\ne7zlZuAPePUFEly/vAT7B0x4r0Ubce4HrTqcM0eJLG5qzKYIeFzUqsRhdRfKVC1DY/uG5WYx0Flc\n8E67yrWjZxQbFxdhtlLESiORZCDM4uK8OSp8cVQPhmGws280qiYivhRJhT43oByns7qgFPOxtCwX\nvBT5f0UIIYREg8PhYHlZLo6aeUFve1RnRaU8ffYBi6XGSXVqNpcHB9QmzCtKrz1WE16j5vR4YbC5\nUSD1vQO7zuLCT95qxX3n1qBOFd5SqTatFX/4rAubNjSG/aHQ6fbi8lcO4vEL56AoJ3X2sfi0XYcv\nO/RUpxbEre8cwZWLi7BiVh4ufakZT11cPzFz5nB7ccmLTXjj6gV4df/QxH596aBFY8FDXx/DXadV\n4A+fd2PThkZW7//+L7uxvCwXZ9UqfP58b78Rrx4YwgNra1nNJYQQQpLZti4DPmjV4v7z/H8+c3q8\nuOTFZryycR4kguCDOgLc8X47LltYCK3VhR09Bty7pibRhxS2pK5R+7R9BPd82jnleyaHG16Ggd7m\nwq8+OoqL5+WHPUgDgDqVGLnZPOzpD3+GaU+/CZVyUUoN0gBgVZUcbcNWaALMamQCh9uLzU1D+LeP\nWsUOnRUjVtfEmvP8SW14AaBzxIZyWTYEPC4uW1SIa5aVxO24Y61OJYbB5saT3/bHZAPvIqkAgyb/\ndZIjVna2OiCEEEJSyeLSHBzWWKZ0KZyuVWPFLFk2DdLCsLBYin0DJrx7eBjr6qOvuU82CR+o7ew1\n4qjONlHXore5cN3rLbjpjVbc+EYrVlbJcNnCwiD34t8P6lV4cXtb2L+3pVOP1dWysH8v0WuChVlc\nLC7JCbkNbDRZscJG1uPf9OGjthF82j5zoPZ+ixbn1avA43Kwbds25EsE0Fi+H9i2a62oPX5hQMDj\nIpulrRmS4THkcTlYVpaDEasrqv9X/rKKcgQBlz6O2MaWPrKRFWuURVmUlT55lEVZic6SCHgo5Luw\nt9/k9zYH1CYsLJaykpeOj6GvrIXFUrxzaBhiPg/LWS7nyPgaNafHiwMDYy/K3X1GGO1uPLT1GNbU\nKvCzk8vwyA9rgzYOCea0Gjl6rDxs7dSjeTC0wYvd7cXOXiNWVoY/UEsGDYUSHB6a2ckwUwwYHfim\nx4C7T6/AoHHqoEFrcWJrlwHr5ignvpcvHZtRG7W78WGrFrv7jKiLYs+UZHftshLcd24NhDHYG7Aw\nR4BBU4CBmtUFhSiyTUMJIYSQVLYoz403Dmr8dn9sHjRjAUsDtUzRUCBBlUKEX66alVb7p41LaI0a\np7AWm/YMYF29Ci/vH4TJ4cEpFTLccnIZqxtMP7GjD98eG4XN5cUTF81BvsR3Pdy4p77tw4jNjbtP\nr2TtGOKpVWPBI9t68eTF9Yk+lIR4+OtjUIr5uGJxEc5//gDevGoB3jk0jI/bdBDxeVhYLMUNk/ZN\ne/XAII5qbTigNqM+X4z9ajMeO78OVQoq5g1X/6gDd390FC/8aK7Pn//piy6smJWHM2f7rmEjhBBC\n0pXHy+CGN1pw68nlWFw6tTGel2Gw/sVmPHdpA2RUIpBRAtWoJfTS9gt71ThrtgInVeShb9SB8+qV\nKI5BTdhNK0rxkxNL8fK+Qfzuk06cMVuBPX1GnF4jx5mzFVMajRwZtuDLTj2evriB9eOIlxqlCANG\nByxOz4x1zjaXB3/ZcgxXLC4En8vFa01DWFkpw0kVeQk6WnZZnB583WXA/13SAB6XgwKJAN/0GPDO\n4WH8cnUF9vabcMn8qZtW5ksEeHaXGpcvKsS1y0rg8TLUkTBC+VI+dBaX38dQb3Oztt0BIYQQkkp4\nXA4umV+A91q0MwZqaqMDEgGXBmlkioQufeRyOFjXoEKOMAvXLS+JySANAHZ8sx1cDgeXLyrChXPz\n0a614vQaOT5tH8G1rx/G7z7pRKtmbKngpt1qXLm4GLnZkY1hk2FNMJ/HxWylyGeb/md3qaG1OPFf\nHxzFHR+0Q2N2Yndf8GYryXBeofj86AiWlOZMDAaKcwX4/KgeS0pzsLgkB9cvL0HepOd227ZtKJQK\nIOZzsX7e2AAuVoO0VHkMo8kS8LjIE2X5bWaji3LpYyY8hpRFWZmQFe88yqKsZMlaWSkCBfKMAAAd\nVElEQVTD3n7jjKYibVpbRI3zAmXFC2XFTkJn1H53VlVc15PyuBysqVNiTd1YfdKaOiWODFuwq8+E\nx3f04bplJegdteOcutRflnXd8hL8/rMuFEj4mFs0tt5ZY3bii44RbNrQCIPNDaWYj4NDZrx1cDjB\nR8ueLzv0uHzR900yinOE+KBVi5+fUu73d+YWSvD3i+sjHpyTqRoLJGgeNKM4d+aFlxGri2bUCCGE\nZKzc7CzMLZTi22PGKd2XJzcyI2RcwvdRSwZehsFNb7Zi2OzEr8+smmjbnuq+6tBjc7MGf7ugDhwO\nBx+0atGsNuOu0ysnbtM/asfdH3X4rSlKJU6PF+tfbMa/rpgHEX9syefmZg2e/q4fT6+vpw0k4+ST\nNh129hrx6zOrpnzf4vRgwz+b8f41C6NqEEQIIYSksk/adPi6y4A/nPP9nl//9UE7frSwMG0+g5LQ\nJfU+asmAy+Hgl6tm4Q/n1KTVf5BV1TK4vV78c/8QOnRW7Ow1zmhdWpgjhM7qgsvjTdBRsqdDZ0NZ\nnnBikAYAxTkCSAQ8zJJlJ/DIMsuyslzs7TfB7f3+GpDT7cW9n3Xh9Go5DdIIIYRktFOrZDissUwp\nE+jW21FNTczINBkxUAtljemcfAnmF0XfEjWZ1s5yORz85ymzcExvw13/7sC+ftOMgWgWl4N8iQDq\nAC3VQ8liU6RZh4csaCiQTPleY6EEP15a7HeJbSqcV6plKcR8FOUIcGDg+71iPj86AoDB7afOYjUr\nliiLsigrffIoi7KSKUvE5+H0Gjk+OjK216vZ4YbT44Wcxe1r0v0xTLcsfzJioJbJGgsl+NUZVbh3\nTTV+0KCa0khjXGmuEP2jjgQcHbsOayxonDZQk4v4uHBufoKOKHNdMDcfrx4Ymvj6g1Yd1s8voG6a\nhBBCCIA1tUps6dQDAPqNDpTmCmnFCZmBatQIntjRhwKpYEbb+lRz5asH8efzZqM0j5Y5JprHy+D6\nzYfx85WzIMri4r4vurFpQyMN1AghhBCM/Z28+MUmvPijudjdZ8SOnlH8z7TabpIZknYfNZIcyvKE\n6NDZEn0YUbE4PTA5PD47DZL443E5+I8TSvHYtl6I+FxsXFRIgzRCCCHkOB6Xgzn5YrQOW9BvdKAk\njz6/kJkyYuljuq5nZStrtlKMozprXLJCEUlW94gNs2TZYW/3kOznlcpZKytlqMsXQy7i49w5yphm\nxQJlURZlpU8eZVFWMmY1FEhwaMiC/tGxpY+xzIolyoodmlEjqFaKcExvh9PjhYCXmmP3Lr0dlXJa\n8phs7lxdAQagdfeEEELINI0FEmxu1sDu9uKHjapEHw5JQlSjRgAAN73RgjtWV6AuRTdb/Nv2XpTm\nCXHxvNSusyOEEEJIZrA4Pbj5rVYMmpx49Yp5kIv4iT4kkgBUo0aCqssXo23YmrIDtS69DSsrZYk+\nDEIIIYSQkEgEPPzfJQ04MmylQRrxKTXXuYUpXdezsplVqxJjS6ce+/pNPn+ezOfFMAy6R+yoVIS/\n9DGZz4uyKIuyKCtds+KdR1mUlaxZfB4X81jYxzeUrFihrNjJiIEaCe7EWXmokGfj9591wubyJPpw\nwqK1upDF5dDVKEIIIYQQkjaoRo1M8auPjmJNrRKn1cgTfSgh29k7ijeaNfjz2tpEHwohhBBCCCEh\nC1SjRjNqZIrV1XJs7dIn+jDCMrbsUZTowyCEEEIIIYQ1GTFQS9f1rLHIOrkiD/sGzDDa3THP8ifc\nrC69DVXyyAZqyXxelEVZlEVZ6ZoV7zzKoizKoqxkzvInIwZqJHQ5wiycUJ6Lz4+OJPpQQtY1YkdV\nBI1ECCGEEEIISVZUo0ZmaFKb8ei2Y/i/SxqSfqNit5fBhc8fwOtXzoeIz0v04RBCCCGEEBIyqlEj\nYZlfJIHd7UW/0ZHoQwmq12BHvkRAgzRCCCGEEJJWIhqotbS04O6778aLL7445ft9fX14+OGH8dBD\nD6Gvr4+VA2RDuq5njVUWh8PBbKUYXSP2mGf5Ek5Wu9aKuvzIN+lO1vOiLMqiLMpK56x451EWZVEW\nZSVzlj8RDdRcLhcuuuiiGd9//vnncc011+Daa6/Fyy+/HPXBkcSpUmSja8TG+v1qzE4cHDSzdn/t\nWitqldTxkRBCCCGEpJeIa9QOHz6MPXv24KqrrgIA2O12PPLII7jrrrsAAA888ABuu+02CAQCn79P\nNWrJbUunHl916PG7s6tZvd/ffdqJAwMmPL2+AQVS36+NcPz83SO4fnkJFhTnsHB0hBBCCCGExE+g\nGrWsQL/Y1NSEd955Z8r3rr76alRUVMy4rVqthkqlwqZNmwAACoUCAwMDqKysjOyoSUJVKUR4bvcA\nq/fZqrGgbdiKC+bm4+Gvj+FP59ZE1azE42XQOWJHjTLypY+EEEIIIYQko4BLHxcsWIDf/OY3U/75\nGqQBQElJCXQ6HTZu3IjLL78cWq0WJSUlMTnocKXretZYZpXmCqGzumFzeVjJer9Fi//5uAM3rijB\n1UuKYXJ48OERnc/bhpp1zGBHvoQPiSDyRiLp8nxRFmVRFmWlUla88yiLsiiLspI5y5+Iuz5OXzEp\nFArh9XphtVphsVjg9Xr9LnscN/kB2LZtG30d5tfNzc0xu/8d32yHgudCt36soUhzc3NU9/fKrh5c\nUmjG6TUK8LgcnJGjxdM7jsHkcEd8vB9/ux9VClFMzj/Vnq/pX0f7fNHX9Hyl2tf0fNHXgb6m10dq\nfU3PV2p9Tc9XdF8HElGN2ttvv439+/fDYDCgsbERN954IwCgp6cHmzdvBofDwYYNG1BWVub3PqhG\nLfk9uKUHjYUSrK1XRXU/epsL173egs1XzgeP+/1Sx79u7YFSzMc1yyKbeX1hjxpehon49wkhhBBC\nCEmkiGvU/Lnwwgtx4YUXzvh+RUUF7rjjjkjukiShSoWIlc6PzWoz5hVKpgzSAGDj4iLc8vYRXLqg\nMKLli72jdqwoz4v6+AghhBBCCEk2GbHhdbBpRcryrVqRPbGXWjRZB9RmLCyWzvh+cY4QC4ul+LJD\nP+X7oWb1GhyYJcuO+LjCyWIDZVEWZVEWZSUmj7Ioi7IoK5mz/MmIgRqJTJVchC69bUY9Yjg8XgY7\njo1iaVmuz5+vrVfh/RZt2BlehkG/0YGyPGHEx0YIIYQQQkiyingftWhRjVpq2PBSMx6/aA7yJZHt\nebajZxSv7B/EYxfM8flzL8Pgx68dxm/PqkKtKvQ2+0MmJ25/rw0vb5wX0XERQgghhBCSaIFq1GhG\njQRUpciOqk7tw1Yt1jX4b0bC5XBwWo0cWzv1fm/jS++oHeUymk0jhBBCCCHpKSMGaum6njUeWVUK\nEbpG7BFlOT1e7FebsapKFvB2q6tk2NJlmFj+GErWgNGB4tzoB2rp9nxRFmVRFmWlQla88yiLsiiL\nspI5y5+MGKiRyFVH0fnxqNaG8jwhRPzAHR1rlCJwORx06ELPGba4UBDhckxCCCGEEEKSHdWokYDa\ntFb8dUsPnlrfEPbvbm4awqDZiVtOLg9624e/PoYapQjnN+aHdN/3f9mNZWU5OLtWGfZxEUIIIYQQ\nkgyoRo1ErEKWjX6jAy6PN+zfPayxoqFAEtJta1VitGutId/3sMVJM2qEEEIIISRtZcRALV3Xs8Yj\nS5jFRYFUgHe/3BHW7zEMg8MaMxoLQxuo1eWL0TY8NlAL5byGzS7kS6MfqKXb80VZlEVZlJUKWfHO\noyzKoizKSuYsfzJioEaiU60QQeMI76Wit7nh8jAoCnEwVSnPxoDRAbs7+Mydx8tgxOqCSsIP65gI\nIYQQQghJFVSjRoJ6ad8gHC4Prj+hNOTf2ddvwov71HjoB3Uh/87P3m7Fz04qDzoLp7U48bO3j+C1\nK+aHfN+EEEIIIYQkG6pRI1GplGWjW28P63e69TZUykRh/c6cfAkOayxBb6cxu1DAwrJHQgghhBBC\nklVGDNTSdT1rvLIq5Nk4MmgI63d6DHZUyLPD+p0FRVI0qU1Bz2vY4kQ+S41E0vH5oizKoizKSvas\neOdRFmVRFmUlc5Y/GTFQI9EpyRXC7ObA5vKE/Ds9+vAHaguLpTg4aIE3yGJcjdmJfCnVpxFCCCGE\nkPRFNWokJD95swW/OLUCdfnioLdlGAbrX2zGPy5tgFwU3oDqhs0t+K/TKlCn8p/zxI4+FEoFWD+/\nIKz7JoQQQgghJJlQjRqJWoVchB6DLaTbjljd4HE5YQ/SAGBhiRR/2dKDN5o1fm9DM2qEEEIIISTd\nZcRALV3Xs8Z17ezoELpHQmso0q6zokYZXiORcVctKcbJEgNebx5C86DZ523Y3Ow6XZ8vyqIsyqKs\nZM6Kdx5lURZlUVYyZ/mTEQM1Er3ibC/atNaQbts2bA24dDGQvOws1Eo9uGlFGZ7bNeDzNhqWNrsm\nhBBCCCEkWVGNGgmJyeHGla8ewptXLQCPywl429983IFz6pRYWSWLKu+KVw7h7R8vAJfzfZ7D7cXF\nLzThvWsXTvk+IYQQQgghqYZq1EjUcoRZKJAI0DkSuE6NYRi0a62ojXBGbXJeXnYW1EbHlO9rLU4o\nJXwapBFCCCGEkLSWEQO1dF3PGu+shgIJWoJsSK2zuuBhgIIomn2Mn1e1QoSOaQNDjcXFWn3a5Kx4\noCzKoizKoqzE5FEWZVEWZSVzlj8ZMVAj7GgolOCgnwYf4w4PWTAnXwwOCzNe1UoR2rU2vLRvEN36\nsQHbMHV8JIQQQgghGYBq1EjIdBYXbnijBS9dNhdiAc/nbf66tQc1SjEunJsfdd7XXQb871fdKM8T\nYtjiwsM/rMPWLgNcbi+uXV4S9f0TQgghhBCSSFSjRlihlPCxsFiKLzr0Pn/uZRjs6jVieVkuK3l1\nK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AAA+BlW1AAAf1RJSYkcDofWrFmjzMxMSVJCQoKKioqUm5ur8PBwxcTEyG63T+mXmpqq\n69evKzg4WOvXr9fmzZslSQcOHNDDhw+Vk5OjRYsWKSEhQUlJSZKk2tpaJScn/+ucfrYa97NnE20z\nPUtMTJTL5VJhYaEkKTQ0VFlZWf86BwDAwsb1/AAAAABgMGx9BAAAAACDIagBAAAAgMEQ1AAAAADA\nYAhqAAAAAGAwBDUAAAAAMBiCGgAAAAAYDEENAAAAAAyGoAYAAAAABvM/w0GrrXnQu50AAAAASUVO\nRK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x10f7d4090>"
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "There are also many columns with NA values. We cannot use them in any of our analyses, so we can go ahead and drop them."
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": "weather_mar2012 = weather_mar2012.dropna(axis=1, how='any')\nweather_mar2012",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>Day</th>\n      <th>Time</th>\n      <th>Data Quality</th>\n      <th>Temp (C)</th>\n      <th>Dew Point Temp (C)</th>\n      <th>Rel Hum (%)</th>\n      <th>Wind Spd (km/h)</th>\n      <th>Visibility (km)</th>\n      <th>Stn Press (kPa)</th>\n      <th>Weather</th>\n    </tr>\n    <tr>\n      <th>Date/Time</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-03-01 00:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 00:00</td>\n      <td>  </td>\n      <td>-5.5</td>\n      <td>-9.7</td>\n      <td> 72</td>\n      <td> 24</td>\n      <td>  4.0</td>\n      <td> 100.97</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 01:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 01:00</td>\n      <td>  </td>\n      <td>-5.7</td>\n      <td>-8.7</td>\n      <td> 79</td>\n      <td> 26</td>\n      <td>  2.4</td>\n      <td> 100.87</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 02:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 02:00</td>\n      <td>  </td>\n      <td>-5.4</td>\n      <td>-8.3</td>\n      <td> 80</td>\n      <td> 28</td>\n      <td>  4.8</td>\n      <td> 100.80</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 03:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 03:00</td>\n      <td>  </td>\n      <td>-4.7</td>\n      <td>-7.7</td>\n      <td> 79</td>\n      <td> 28</td>\n      <td>  4.0</td>\n      <td> 100.69</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 04:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 04:00</td>\n      <td>  </td>\n      <td>-5.4</td>\n      <td>-7.8</td>\n      <td> 83</td>\n      <td> 35</td>\n      <td>  1.6</td>\n      <td> 100.62</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 05:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 05:00</td>\n      <td>  </td>\n      <td>-5.3</td>\n      <td>-7.9</td>\n      <td> 82</td>\n      <td> 33</td>\n      <td>  2.4</td>\n      <td> 100.58</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 06:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 06:00</td>\n      <td>  </td>\n      <td>-5.2</td>\n      <td>-7.8</td>\n      <td> 82</td>\n      <td> 33</td>\n      <td>  4.0</td>\n      <td> 100.57</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 07:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 07:00</td>\n      <td>  </td>\n      <td>-4.9</td>\n      <td>-7.4</td>\n      <td> 83</td>\n      <td> 30</td>\n      <td>  1.6</td>\n      <td> 100.59</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 08:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 08:00</td>\n      <td>  </td>\n      <td>-5.0</td>\n      <td>-7.5</td>\n      <td> 83</td>\n      <td> 32</td>\n      <td>  1.2</td>\n      <td> 100.59</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 09:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 09:00</td>\n      <td>  </td>\n      <td>-4.9</td>\n      <td>-7.5</td>\n      <td> 82</td>\n      <td> 32</td>\n      <td>  1.6</td>\n      <td> 100.60</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 10:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 10:00</td>\n      <td>  </td>\n      <td>-4.7</td>\n      <td>-7.3</td>\n      <td> 82</td>\n      <td> 32</td>\n      <td>  1.2</td>\n      <td> 100.62</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 11:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 11:00</td>\n      <td>  </td>\n      <td>-4.4</td>\n      <td>-6.8</td>\n      <td> 83</td>\n      <td> 28</td>\n      <td>  1.0</td>\n      <td> 100.66</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 12:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 12:00</td>\n      <td>  </td>\n      <td>-4.3</td>\n      <td>-6.8</td>\n      <td> 83</td>\n      <td> 30</td>\n      <td>  1.2</td>\n      <td> 100.66</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 13:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 13:00</td>\n      <td>  </td>\n      <td>-4.3</td>\n      <td>-6.9</td>\n      <td> 82</td>\n      <td> 28</td>\n      <td>  1.2</td>\n      <td> 100.65</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 14:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 14:00</td>\n      <td>  </td>\n      <td>-3.9</td>\n      <td>-6.6</td>\n      <td> 81</td>\n      <td> 28</td>\n      <td>  1.2</td>\n      <td> 100.67</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 15:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 15:00</td>\n      <td>  </td>\n      <td>-3.3</td>\n      <td>-6.2</td>\n      <td> 80</td>\n      <td> 24</td>\n      <td>  1.6</td>\n      <td> 100.71</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 16:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 16:00</td>\n      <td>  </td>\n      <td>-2.7</td>\n      <td>-5.7</td>\n      <td> 80</td>\n      <td> 19</td>\n      <td>  2.4</td>\n      <td> 100.74</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 17:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 17:00</td>\n      <td>  </td>\n      <td>-2.9</td>\n      <td>-5.9</td>\n      <td> 80</td>\n      <td> 20</td>\n      <td>  4.0</td>\n      <td> 100.80</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 18:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 18:00</td>\n      <td>  </td>\n      <td>-3.0</td>\n      <td>-6.0</td>\n      <td> 80</td>\n      <td> 19</td>\n      <td>  4.0</td>\n      <td> 100.87</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 19:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 19:00</td>\n      <td>  </td>\n      <td>-3.6</td>\n      <td>-6.4</td>\n      <td> 81</td>\n      <td> 17</td>\n      <td>  3.2</td>\n      <td> 100.93</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 20:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 20:00</td>\n      <td>  </td>\n      <td>-3.7</td>\n      <td>-6.4</td>\n      <td> 81</td>\n      <td> 20</td>\n      <td>  4.8</td>\n      <td> 100.95</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 21:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 21:00</td>\n      <td>  </td>\n      <td>-3.9</td>\n      <td>-6.7</td>\n      <td> 81</td>\n      <td> 22</td>\n      <td>  6.4</td>\n      <td> 100.98</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 22:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 22:00</td>\n      <td>  </td>\n      <td>-4.3</td>\n      <td>-6.9</td>\n      <td> 82</td>\n      <td> 22</td>\n      <td>  2.4</td>\n      <td> 101.00</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-01 23:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  1</td>\n      <td> 23:00</td>\n      <td>  </td>\n      <td>-4.3</td>\n      <td>-7.1</td>\n      <td> 81</td>\n      <td> 22</td>\n      <td>  4.8</td>\n      <td> 101.04</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 00:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 00:00</td>\n      <td>  </td>\n      <td>-4.8</td>\n      <td>-7.3</td>\n      <td> 83</td>\n      <td> 22</td>\n      <td>  3.2</td>\n      <td> 101.04</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 01:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 01:00</td>\n      <td>  </td>\n      <td>-5.3</td>\n      <td>-7.9</td>\n      <td> 82</td>\n      <td> 20</td>\n      <td>  4.8</td>\n      <td> 101.09</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 02:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 02:00</td>\n      <td>  </td>\n      <td>-5.2</td>\n      <td>-7.8</td>\n      <td> 82</td>\n      <td> 19</td>\n      <td>  6.4</td>\n      <td> 101.11</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 03:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 03:00</td>\n      <td>  </td>\n      <td>-5.5</td>\n      <td>-7.9</td>\n      <td> 83</td>\n      <td> 19</td>\n      <td>  4.8</td>\n      <td> 101.15</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 04:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 04:00</td>\n      <td>  </td>\n      <td>-5.6</td>\n      <td>-8.2</td>\n      <td> 82</td>\n      <td> 24</td>\n      <td>  6.4</td>\n      <td> 101.15</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>2012-03-02 05:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 05:00</td>\n      <td>  </td>\n      <td>-5.5</td>\n      <td>-8.3</td>\n      <td> 81</td>\n      <td> 19</td>\n      <td> 12.9</td>\n      <td> 101.15</td>\n      <td>          Snow</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 18:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 18:00</td>\n      <td>  </td>\n      <td> 3.9</td>\n      <td>-7.9</td>\n      <td> 42</td>\n      <td> 11</td>\n      <td> 24.1</td>\n      <td> 101.26</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 19:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 19:00</td>\n      <td>  </td>\n      <td> 3.1</td>\n      <td>-6.7</td>\n      <td> 49</td>\n      <td>  7</td>\n      <td> 25.0</td>\n      <td> 101.29</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 20:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 20:00</td>\n      <td>  </td>\n      <td> 3.0</td>\n      <td>-8.4</td>\n      <td> 43</td>\n      <td>  7</td>\n      <td> 25.0</td>\n      <td> 101.30</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 21:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 21:00</td>\n      <td>  </td>\n      <td> 1.7</td>\n      <td>-9.0</td>\n      <td> 45</td>\n      <td>  4</td>\n      <td> 25.0</td>\n      <td> 101.32</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 22:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 22:00</td>\n      <td>  </td>\n      <td> 0.4</td>\n      <td>-8.1</td>\n      <td> 53</td>\n      <td>  0</td>\n      <td> 25.0</td>\n      <td> 101.30</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-30 23:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 30</td>\n      <td> 23:00</td>\n      <td>  </td>\n      <td> 1.4</td>\n      <td>-7.7</td>\n      <td> 51</td>\n      <td>  6</td>\n      <td> 25.0</td>\n      <td> 101.34</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 00:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 00:00</td>\n      <td>  </td>\n      <td> 1.5</td>\n      <td>-8.6</td>\n      <td> 47</td>\n      <td> 13</td>\n      <td> 25.0</td>\n      <td> 101.33</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 01:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 01:00</td>\n      <td>  </td>\n      <td> 1.3</td>\n      <td>-9.6</td>\n      <td> 44</td>\n      <td> 13</td>\n      <td> 25.0</td>\n      <td> 101.31</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 02:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 02:00</td>\n      <td>  </td>\n      <td> 1.3</td>\n      <td>-9.7</td>\n      <td> 44</td>\n      <td> 11</td>\n      <td> 25.0</td>\n      <td> 101.29</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 03:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 03:00</td>\n      <td>  </td>\n      <td> 0.7</td>\n      <td>-8.8</td>\n      <td> 49</td>\n      <td> 13</td>\n      <td> 25.0</td>\n      <td> 101.30</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 04:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 04:00</td>\n      <td>  </td>\n      <td>-0.9</td>\n      <td>-8.5</td>\n      <td> 56</td>\n      <td> 13</td>\n      <td> 25.0</td>\n      <td> 101.32</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 05:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 05:00</td>\n      <td>  </td>\n      <td>-0.6</td>\n      <td>-9.2</td>\n      <td> 52</td>\n      <td> 13</td>\n      <td> 25.0</td>\n      <td> 101.30</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 06:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 06:00</td>\n      <td>  </td>\n      <td>-0.5</td>\n      <td>-9.2</td>\n      <td> 52</td>\n      <td> 15</td>\n      <td> 48.3</td>\n      <td> 101.32</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 07:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 07:00</td>\n      <td>  </td>\n      <td>-0.3</td>\n      <td>-9.2</td>\n      <td> 51</td>\n      <td> 19</td>\n      <td> 48.3</td>\n      <td> 101.32</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 08:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 08:00</td>\n      <td>  </td>\n      <td> 0.7</td>\n      <td>-8.5</td>\n      <td> 50</td>\n      <td> 17</td>\n      <td> 48.3</td>\n      <td> 101.33</td>\n      <td>        Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 09:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 09:00</td>\n      <td>  </td>\n      <td> 1.5</td>\n      <td>-7.8</td>\n      <td> 50</td>\n      <td> 17</td>\n      <td> 48.3</td>\n      <td> 101.34</td>\n      <td> Mostly Cloudy</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 10:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 10:00</td>\n      <td>  </td>\n      <td> 2.9</td>\n      <td>-8.1</td>\n      <td> 44</td>\n      <td> 15</td>\n      <td> 48.3</td>\n      <td> 101.30</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 11:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 11:00</td>\n      <td>  </td>\n      <td> 4.6</td>\n      <td>-9.7</td>\n      <td> 35</td>\n      <td>  7</td>\n      <td> 48.3</td>\n      <td> 101.24</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 12:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 12:00</td>\n      <td>  </td>\n      <td> 6.4</td>\n      <td>-7.1</td>\n      <td> 37</td>\n      <td> 11</td>\n      <td> 48.3</td>\n      <td> 101.16</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 13:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 13:00</td>\n      <td>  </td>\n      <td> 6.5</td>\n      <td>-9.7</td>\n      <td> 30</td>\n      <td>  9</td>\n      <td> 48.3</td>\n      <td> 101.08</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 14:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 14:00</td>\n      <td>  </td>\n      <td> 7.7</td>\n      <td>-8.5</td>\n      <td> 31</td>\n      <td>  4</td>\n      <td> 48.3</td>\n      <td> 101.01</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 15:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 15:00</td>\n      <td>  </td>\n      <td> 7.7</td>\n      <td>-8.6</td>\n      <td> 30</td>\n      <td>  6</td>\n      <td> 48.3</td>\n      <td> 100.94</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 16:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 16:00</td>\n      <td>  </td>\n      <td> 8.4</td>\n      <td>-7.7</td>\n      <td> 31</td>\n      <td>  4</td>\n      <td> 48.3</td>\n      <td> 100.89</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 17:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 17:00</td>\n      <td>  </td>\n      <td> 7.9</td>\n      <td>-8.1</td>\n      <td> 31</td>\n      <td>  6</td>\n      <td> 48.3</td>\n      <td> 100.88</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 18:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 18:00</td>\n      <td>  </td>\n      <td> 7.0</td>\n      <td>-8.2</td>\n      <td> 33</td>\n      <td>  7</td>\n      <td> 48.3</td>\n      <td> 100.87</td>\n      <td>  Mainly Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 19:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 19:00</td>\n      <td>  </td>\n      <td> 5.9</td>\n      <td>-8.0</td>\n      <td> 36</td>\n      <td>  4</td>\n      <td> 25.0</td>\n      <td> 100.88</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 20:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 20:00</td>\n      <td>  </td>\n      <td> 4.4</td>\n      <td>-7.2</td>\n      <td> 43</td>\n      <td>  9</td>\n      <td> 25.0</td>\n      <td> 100.85</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 21:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 21:00</td>\n      <td>  </td>\n      <td> 2.6</td>\n      <td>-6.3</td>\n      <td> 52</td>\n      <td>  7</td>\n      <td> 25.0</td>\n      <td> 100.86</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 22:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 22:00</td>\n      <td>  </td>\n      <td> 2.7</td>\n      <td>-6.7</td>\n      <td> 50</td>\n      <td>  0</td>\n      <td> 25.0</td>\n      <td> 100.82</td>\n      <td>         Clear</td>\n    </tr>\n    <tr>\n      <th>2012-03-31 23:00:00</th>\n      <td> 2012</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 23:00</td>\n      <td>  </td>\n      <td> 1.5</td>\n      <td>-6.9</td>\n      <td> 54</td>\n      <td>  0</td>\n      <td> 25.0</td>\n      <td> 100.79</td>\n      <td>         Clear</td>\n    </tr>\n  </tbody>\n</table>\n<p>744 rows \u00d7 12 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": "                     Year  Month  Day   Time Data Quality  Temp (C)  \\\nDate/Time                                                             \n2012-03-01 00:00:00  2012      3    1  00:00                   -5.5   \n2012-03-01 01:00:00  2012      3    1  01:00                   -5.7   \n2012-03-01 02:00:00  2012      3    1  02:00                   -5.4   \n2012-03-01 03:00:00  2012      3    1  03:00                   -4.7   \n2012-03-01 04:00:00  2012      3    1  04:00                   -5.4   \n2012-03-01 05:00:00  2012      3    1  05:00                   -5.3   \n2012-03-01 06:00:00  2012      3    1  06:00                   -5.2   \n2012-03-01 07:00:00  2012      3    1  07:00                   -4.9   \n2012-03-01 08:00:00  2012      3    1  08:00                   -5.0   \n2012-03-01 09:00:00  2012      3    1  09:00                   -4.9   \n2012-03-01 10:00:00  2012      3    1  10:00                   -4.7   \n2012-03-01 11:00:00  2012      3    1  11:00                   -4.4   \n2012-03-01 12:00:00  2012      3    1  12:00                   -4.3   \n2012-03-01 13:00:00  2012      3    1  13:00                   -4.3   \n2012-03-01 14:00:00  2012      3    1  14:00                   -3.9   \n2012-03-01 15:00:00  2012      3    1  15:00                   -3.3   \n2012-03-01 16:00:00  2012      3    1  16:00                   -2.7   \n2012-03-01 17:00:00  2012      3    1  17:00                   -2.9   \n2012-03-01 18:00:00  2012      3    1  18:00                   -3.0   \n2012-03-01 19:00:00  2012      3    1  19:00                   -3.6   \n2012-03-01 20:00:00  2012      3    1  20:00                   -3.7   \n2012-03-01 21:00:00  2012      3    1  21:00                   -3.9   \n2012-03-01 22:00:00  2012      3    1  22:00                   -4.3   \n2012-03-01 23:00:00  2012      3    1  23:00                   -4.3   \n2012-03-02 00:00:00  2012      3    2  00:00                   -4.8   \n2012-03-02 01:00:00  2012      3    2  01:00                   -5.3   \n2012-03-02 02:00:00  2012      3    2  02:00                   -5.2   \n2012-03-02 03:00:00  2012      3    2  03:00                   -5.5   \n2012-03-02 04:00:00  2012      3    2  04:00                   -5.6   \n2012-03-02 05:00:00  2012      3    2  05:00                   -5.5   \n...                   ...    ...  ...    ...          ...       ...   \n2012-03-30 18:00:00  2012      3   30  18:00                    3.9   \n2012-03-30 19:00:00  2012      3   30  19:00                    3.1   \n2012-03-30 20:00:00  2012      3   30  20:00                    3.0   \n2012-03-30 21:00:00  2012      3   30  21:00                    1.7   \n2012-03-30 22:00:00  2012      3   30  22:00                    0.4   \n2012-03-30 23:00:00  2012      3   30  23:00                    1.4   \n2012-03-31 00:00:00  2012      3   31  00:00                    1.5   \n2012-03-31 01:00:00  2012      3   31  01:00                    1.3   \n2012-03-31 02:00:00  2012      3   31  02:00                    1.3   \n2012-03-31 03:00:00  2012      3   31  03:00                    0.7   \n2012-03-31 04:00:00  2012      3   31  04:00                   -0.9   \n2012-03-31 05:00:00  2012      3   31  05:00                   -0.6   \n2012-03-31 06:00:00  2012      3   31  06:00                   -0.5   \n2012-03-31 07:00:00  2012      3   31  07:00                   -0.3   \n2012-03-31 08:00:00  2012      3   31  08:00                    0.7   \n2012-03-31 09:00:00  2012      3   31  09:00                    1.5   \n2012-03-31 10:00:00  2012      3   31  10:00                    2.9   \n2012-03-31 11:00:00  2012      3   31  11:00                    4.6   \n2012-03-31 12:00:00  2012      3   31  12:00                    6.4   \n2012-03-31 13:00:00  2012      3   31  13:00                    6.5   \n2012-03-31 14:00:00  2012      3   31  14:00                    7.7   \n2012-03-31 15:00:00  2012      3   31  15:00                    7.7   \n2012-03-31 16:00:00  2012      3   31  16:00                    8.4   \n2012-03-31 17:00:00  2012      3   31  17:00                    7.9   \n2012-03-31 18:00:00  2012      3   31  18:00                    7.0   \n2012-03-31 19:00:00  2012      3   31  19:00                    5.9   \n2012-03-31 20:00:00  2012      3   31  20:00                    4.4   \n2012-03-31 21:00:00  2012      3   31  21:00                    2.6   \n2012-03-31 22:00:00  2012      3   31  22:00                    2.7   \n2012-03-31 23:00:00  2012      3   31  23:00                    1.5   \n\n                     Dew Point Temp (C)  Rel Hum (%)  Wind Spd (km/h)  \\\nDate/Time                                                               \n2012-03-01 00:00:00                -9.7           72               24   \n2012-03-01 01:00:00                -8.7           79               26   \n2012-03-01 02:00:00                -8.3           80               28   \n2012-03-01 03:00:00                -7.7           79               28   \n2012-03-01 04:00:00                -7.8           83               35   \n2012-03-01 05:00:00                -7.9           82               33   \n2012-03-01 06:00:00                -7.8           82               33   \n2012-03-01 07:00:00                -7.4           83               30   \n2012-03-01 08:00:00                -7.5           83               32   \n2012-03-01 09:00:00                -7.5           82               32   \n2012-03-01 10:00:00                -7.3           82               32   \n2012-03-01 11:00:00                -6.8           83               28   \n2012-03-01 12:00:00                -6.8           83               30   \n2012-03-01 13:00:00                -6.9           82               28   \n2012-03-01 14:00:00                -6.6           81               28   \n2012-03-01 15:00:00                -6.2           80               24   \n2012-03-01 16:00:00                -5.7           80               19   \n2012-03-01 17:00:00                -5.9           80               20   \n2012-03-01 18:00:00                -6.0           80               19   \n2012-03-01 19:00:00                -6.4           81               17   \n2012-03-01 20:00:00                -6.4           81               20   \n2012-03-01 21:00:00                -6.7           81               22   \n2012-03-01 22:00:00                -6.9           82               22   \n2012-03-01 23:00:00                -7.1           81               22   \n2012-03-02 00:00:00                -7.3           83               22   \n2012-03-02 01:00:00                -7.9           82               20   \n2012-03-02 02:00:00                -7.8           82               19   \n2012-03-02 03:00:00                -7.9           83               19   \n2012-03-02 04:00:00                -8.2           82               24   \n2012-03-02 05:00:00                -8.3           81               19   \n...                                 ...          ...              ...   \n2012-03-30 18:00:00                -7.9           42               11   \n2012-03-30 19:00:00                -6.7           49                7   \n2012-03-30 20:00:00                -8.4           43                7   \n2012-03-30 21:00:00                -9.0           45                4   \n2012-03-30 22:00:00                -8.1           53                0   \n2012-03-30 23:00:00                -7.7           51                6   \n2012-03-31 00:00:00                -8.6           47               13   \n2012-03-31 01:00:00                -9.6           44               13   \n2012-03-31 02:00:00                -9.7           44               11   \n2012-03-31 03:00:00                -8.8           49               13   \n2012-03-31 04:00:00                -8.5           56               13   \n2012-03-31 05:00:00                -9.2           52               13   \n2012-03-31 06:00:00                -9.2           52               15   \n2012-03-31 07:00:00                -9.2           51               19   \n2012-03-31 08:00:00                -8.5           50               17   \n2012-03-31 09:00:00                -7.8           50               17   \n2012-03-31 10:00:00                -8.1           44               15   \n2012-03-31 11:00:00                -9.7           35                7   \n2012-03-31 12:00:00                -7.1           37               11   \n2012-03-31 13:00:00                -9.7           30                9   \n2012-03-31 14:00:00                -8.5           31                4   \n2012-03-31 15:00:00                -8.6           30                6   \n2012-03-31 16:00:00                -7.7           31                4   \n2012-03-31 17:00:00                -8.1           31                6   \n2012-03-31 18:00:00                -8.2           33                7   \n2012-03-31 19:00:00                -8.0           36                4   \n2012-03-31 20:00:00                -7.2           43                9   \n2012-03-31 21:00:00                -6.3           52                7   \n2012-03-31 22:00:00                -6.7           50                0   \n2012-03-31 23:00:00                -6.9           54                0   \n\n                     Visibility (km)  Stn Press (kPa)        Weather  \nDate/Time                                                             \n2012-03-01 00:00:00              4.0           100.97           Snow  \n2012-03-01 01:00:00              2.4           100.87           Snow  \n2012-03-01 02:00:00              4.8           100.80           Snow  \n2012-03-01 03:00:00              4.0           100.69           Snow  \n2012-03-01 04:00:00              1.6           100.62           Snow  \n2012-03-01 05:00:00              2.4           100.58           Snow  \n2012-03-01 06:00:00              4.0           100.57           Snow  \n2012-03-01 07:00:00              1.6           100.59           Snow  \n2012-03-01 08:00:00              1.2           100.59           Snow  \n2012-03-01 09:00:00              1.6           100.60           Snow  \n2012-03-01 10:00:00              1.2           100.62           Snow  \n2012-03-01 11:00:00              1.0           100.66           Snow  \n2012-03-01 12:00:00              1.2           100.66           Snow  \n2012-03-01 13:00:00              1.2           100.65           Snow  \n2012-03-01 14:00:00              1.2           100.67           Snow  \n2012-03-01 15:00:00              1.6           100.71           Snow  \n2012-03-01 16:00:00              2.4           100.74           Snow  \n2012-03-01 17:00:00              4.0           100.80           Snow  \n2012-03-01 18:00:00              4.0           100.87           Snow  \n2012-03-01 19:00:00              3.2           100.93           Snow  \n2012-03-01 20:00:00              4.8           100.95           Snow  \n2012-03-01 21:00:00              6.4           100.98           Snow  \n2012-03-01 22:00:00              2.4           101.00           Snow  \n2012-03-01 23:00:00              4.8           101.04           Snow  \n2012-03-02 00:00:00              3.2           101.04           Snow  \n2012-03-02 01:00:00              4.8           101.09           Snow  \n2012-03-02 02:00:00              6.4           101.11           Snow  \n2012-03-02 03:00:00              4.8           101.15           Snow  \n2012-03-02 04:00:00              6.4           101.15           Snow  \n2012-03-02 05:00:00             12.9           101.15           Snow  \n...                              ...              ...            ...  \n2012-03-30 18:00:00             24.1           101.26  Mostly Cloudy  \n2012-03-30 19:00:00             25.0           101.29  Mostly Cloudy  \n2012-03-30 20:00:00             25.0           101.30  Mostly Cloudy  \n2012-03-30 21:00:00             25.0           101.32         Cloudy  \n2012-03-30 22:00:00             25.0           101.30  Mostly Cloudy  \n2012-03-30 23:00:00             25.0           101.34   Mainly Clear  \n2012-03-31 00:00:00             25.0           101.33  Mostly Cloudy  \n2012-03-31 01:00:00             25.0           101.31  Mostly Cloudy  \n2012-03-31 02:00:00             25.0           101.29         Cloudy  \n2012-03-31 03:00:00             25.0           101.30         Cloudy  \n2012-03-31 04:00:00             25.0           101.32         Cloudy  \n2012-03-31 05:00:00             25.0           101.30         Cloudy  \n2012-03-31 06:00:00             48.3           101.32         Cloudy  \n2012-03-31 07:00:00             48.3           101.32         Cloudy  \n2012-03-31 08:00:00             48.3           101.33         Cloudy  \n2012-03-31 09:00:00             48.3           101.34  Mostly Cloudy  \n2012-03-31 10:00:00             48.3           101.30   Mainly Clear  \n2012-03-31 11:00:00             48.3           101.24          Clear  \n2012-03-31 12:00:00             48.3           101.16          Clear  \n2012-03-31 13:00:00             48.3           101.08          Clear  \n2012-03-31 14:00:00             48.3           101.01   Mainly Clear  \n2012-03-31 15:00:00             48.3           100.94   Mainly Clear  \n2012-03-31 16:00:00             48.3           100.89   Mainly Clear  \n2012-03-31 17:00:00             48.3           100.88   Mainly Clear  \n2012-03-31 18:00:00             48.3           100.87   Mainly Clear  \n2012-03-31 19:00:00             25.0           100.88          Clear  \n2012-03-31 20:00:00             25.0           100.85          Clear  \n2012-03-31 21:00:00             25.0           100.86          Clear  \n2012-03-31 22:00:00             25.0           100.82          Clear  \n2012-03-31 23:00:00             25.0           100.79          Clear  \n\n[744 rows x 12 columns]"
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We managed to clean up some of the data for March 2012. That's great, but we are also interested in the entire year's data. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "##Pandas cookbook\ndef download_weather_month(year, month):\n    if month == 1:\n        year += 1\n    url = url_template.format(year=year, month=month)\n    weather_data = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True)\n    weather_data = weather_data.dropna(axis=1)\n    weather_data.columns = [col.replace('\\xb0', '') for col in weather_data.columns]\n    weather_data = weather_data.drop(['Year', 'Day', 'Month', 'Time', 'Data Quality'], axis=1)\n    return weather_data",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "data_by_month = [download_weather_month(2012, i) for i in range(1, 13)]\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Saving to a csv\nweather_2012 = pd.concat(data_by_month)\nweather_2012.to_csv('weather_2012.csv')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 96
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='da'></a>\n### Data Analysis\n\n`pandas` provides vectorized string functions, to make it easy to operate on columns containing text."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "weather_description = weather_2012['Weather']\nis_snowing = weather_description.str.contains('Snow')\nis_snowing.plot()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": "<matplotlib.axes.AxesSubplot at 0x1101e9850>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0fH9TNeaPy8FPt5/Cr++dpPT/irPT8dQtpfjptlMYlJqEyoZ2AMAzt43BB9Ut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\nAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x110bf6450>"
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Let's now try to find the month where it snowed the most, so that your company can have extra stock of those down jackets for this month. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "weather_2012['Temp (C)'].resample('M', how=np.median).plot(kind='bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 98,
       "text": "<matplotlib.axes.AxesSubplot at 0x110c540d0>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bt271EomE19HR4a1du9bnlfmLLja6uGhio4uNLja6uGhio4uNLja6uMLeJFKH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\n",
       "text": "<matplotlib.figure.Figure at 0x110c4d2d0>"
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "is_snowing.astype(float).resample('M', how=np.mean)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 99,
       "text": "Date/Time\n2012-02-29    0.162356\n2012-03-31    0.087366\n2012-04-30    0.015278\n2012-05-31    0.000000\n2012-06-30    0.000000\n2012-07-31    0.000000\n2012-08-31    0.000000\n2012-09-30    0.000000\n2012-10-31    0.000000\n2012-11-30    0.038889\n2012-12-31    0.251344\n2013-01-31    0.197581\nFreq: M, Name: Weather, dtype: float64"
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "is_snowing.astype(float).resample('M', how=np.mean).plot(kind='bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": "<matplotlib.axes.AxesSubplot at 0x110bb17d0>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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09+7dikajKi0tVWdnpzKZjKqrq1VRUeH38nxDFxtdXDSx0cVGFxtdXDSx0cVG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\n",
       "text": "<matplotlib.figure.Figure at 0x110c48b10>"
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='oplot'></a>\n## Other kinds of Plotting \n### Scatter plots"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])\nprint df\n#df.plot(kind='bar')\n#df.plot(kind='bar', stacked=True)\n#df.plot(kind='barh', stacked=True)\n#print pd.__version__\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "          a         b         c         d\n0  0.025943  0.770927  0.248177  0.814551\n1  0.288738  0.097600  0.760169  0.612650\n2  0.573980  0.047479  0.563015  0.187549\n3  0.772887  0.152365  0.956512  0.201353\n4  0.205687  0.157111  0.307448  0.514710\n5  0.387677  0.512868  0.302177  0.391439\n6  0.106364  0.453050  0.390067  0.932892\n7  0.588174  0.528449  0.281689  0.425967\n8  0.645613  0.104521  0.660080  0.838605\n9  0.200220  0.986742  0.184672  0.943926\n"
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from pandas.tools.plotting import scatter_matrix\ndf = pd.DataFrame(np.random.randn(100, 4), columns=['a', 'b', 'c', 'd'])\nscatter_matrix(df, figsize=(7, 7), diagonal='kde')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": "array([[<matplotlib.axes.AxesSubplot object at 0x11147a0d0>,\n        <matplotlib.axes.AxesSubplot object at 0x1114af6d0>,\n        <matplotlib.axes.AxesSubplot object at 0x111620510>,\n        <matplotlib.axes.AxesSubplot object at 0x11163dd90>],\n       [<matplotlib.axes.AxesSubplot object at 0x1117a3610>,\n        <matplotlib.axes.AxesSubplot object at 0x111648450>,\n        <matplotlib.axes.AxesSubplot object at 0x111666690>,\n        <matplotlib.axes.AxesSubplot object at 0x111689d10>],\n       [<matplotlib.axes.AxesSubplot object at 0x11164c890>,\n        <matplotlib.axes.AxesSubplot object at 0x112420290>,\n        <matplotlib.axes.AxesSubplot object at 0x1124419d0>,\n        <matplotlib.axes.AxesSubplot object at 0x11245e650>],\n       [<matplotlib.axes.AxesSubplot object at 0x112485210>,\n        <matplotlib.axes.AxesSubplot object at 0x11246abd0>,\n        <matplotlib.axes.AxesSubplot object at 0x1124c7bd0>,\n        <matplotlib.axes.AxesSubplot object at 0x112d1d0d0>]], dtype=object)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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r3T5srTRhZ01m2pjtHiNqrLr5yc4DrQ6QJBbcC4KkgKGIjN6NvT4OSUlBR4UJ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iRrgZax6WkcaLagN4XpfoUbebwPmq0sDrdYYNVTuDIVRP9sOKXVYz16hkre001WPT63pxa9tQl1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\nFgAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x111486810>"
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "### Parallel Coordinates"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from pandas import read_csv\nfrom urllib import urlopen\nfrom pandas.tools.plotting import andrews_curves \n\npage = urlopen(\"https://raw.githubusercontent.com/pydata/pandas/master/pandas/tests/data/iris.csv\")\ndf = read_csv(page)\nandrews_curves(df, 'Name')\n\nfrom pandas.tools.plotting import parallel_coordinates\n\n#parallel_coordinates(df,'Name')\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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H4VyEx32p4mpfquj6vEReJpKHEXkJdyFfRmxPQCSPkedRHe+++dfTcaWEf/Ui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ToJ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\nH7S5tzWjGZHCAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x11147dd90>"
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "### Lag plots"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from pandas.tools.plotting import lag_plot\ndata = pd.Series(0.1 * np.random.rand(1000) + 0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))\nlag_plot(data)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": "<matplotlib.axes.AxesSubplot at 0x113ab0d10>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XcASXSUcwkc5VTNQopCe+X2I3EEqkWV7j4ubHW8mquRWRTy0rz+dLxdNZCqyT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\ncsiICJdZ4TPnVXMgl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       "text": "<matplotlib.figure.Figure at 0x112d180d0>"
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<a id='refs'></a>\n## References\n- [Pandas cookbook](https://github.com/jvns/pandas-cookbook)\n- [Pandas Data Visualization](http://pandas.pydata.org/pandas-docs/stable/visualization.html/). \n- [Pandas documentation](http://pandas.pydata.org/pandas-docs/stable/index.html/).\n- [On Andrew's curves](http://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/mvahtmlnode9.html)\n- [Parallel coordinates](http://en.wikipedia.org/wiki/Parallel_coordinates)"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "---\n<a id='credits'></a>\n## Credits\n\nNeal Davis and Lakshmi Rao developed these materials for [Computational Science and Engineering](http://cse.illinois.edu/) at the University of Illinois at Urbana\u2013Champaign.\n\n<img src=\"http://i.creativecommons.org/l/by/3.0/88x31.png\" align=\"left\">\nThis content is available under a [Creative Commons Attribution 3.0 Unported License](https://creativecommons.org/licenses/by/3.0/).\n\n[![](https://bytebucket.org/davis68/resources/raw/f7c98d2b95e961fae257707e22a58fa1a2c36bec/logos/baseline_cse_wdmk.png?token=be4cc41d4b2afe594f5b1570a3c5aad96a65f0d6)](http://cse.illinois.edu/)"
    }
   ],
   "metadata": {}
  }
 ]
}