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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Pandas (<font color='blue'>Pan</font>el <font color='blue'>D</font>ata <font color='blue'>A</font>nalysi<font color='blue'>s</font>)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image, HTML"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext load_style\n",
      "%load_style talk.css"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "\n",
        ".rendered_html {\n",
        "    font-family: \"proxima-nova\", helvetica;\n",
        "    font-size: 130%;\n",
        "    line-height: 1.5;\n",
        "}\n",
        "\n",
        ".rendered_html h1 {\n",
        "    margin: 0.25em 0em 0.5em;\n",
        "    color: #015C9C;\n",
        "    text-align: center;\n",
        "    line-height: 1.2; \n",
        "    page-break-before: always;\n",
        "}\n",
        "\n",
        ".rendered_html h2 {\n",
        "    margin: 1.1em 0em 0.5em;\n",
        "    color: #26465D;\n",
        "    line-height: 1.2;\n",
        "}\n",
        "\n",
        ".rendered_html h3 {\n",
        "    margin: 1.1em 0em 0.5em;\n",
        "    color: #002845;\n",
        "    line-height: 1.2;\n",
        "}\n",
        "\n",
        ".rendered_html li {\n",
        "    line-height: 1.5; \n",
        "}\n",
        "\n",
        "/*.prompt {\n",
        "    font-size: 120%; \n",
        "}*/\n",
        "\n",
        ".CodeMirror-lines {\n",
        "    font-size: 110%; \n",
        "}\n",
        "\n",
        "/*.output_area {\n",
        "    font-size: 120%; \n",
        "}*/\n",
        "\n",
        "/*#notebook {\n",
        "    background-image: url('files/images/witewall_3.png');\n",
        "}*/\n",
        "\n",
        "h1.bigtitle {\n",
        "    margin: 4cm 1cm 4cm 1cm;\n",
        "    font-size: 300%;\n",
        "}\n",
        "\n",
        "h3.point {\n",
        "    font-size: 200%;\n",
        "    text-align: center;\n",
        "    margin: 2em 0em 2em 0em;\n",
        "    #26465D\n",
        "}\n",
        "\n",
        ".logo {\n",
        "    margin: 20px 0 20px 0;\n",
        "}\n",
        "\n",
        "a.anchor-link {\n",
        "    display: none;\n",
        "}\n",
        "\n",
        "h1.title { \n",
        "    font-size: 250%;\n",
        "}\n",
        "\n",
        "</style>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x106af5a50>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=http://pandas.pydata.org/index.html width=900 height=350></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://pandas.pydata.org/index.html width=900 height=350></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.HTML at 0x1081d1710>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.set_option(\"line_width\", 80)\n",
      "# toggle the line below that if one doesnt want DataFrames displayed as HTML tables\n",
      "#pd.set_option(\"notebook_repr_html\", False) \n",
      "pd.set_option(\"notebook_repr_html\", True) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**pandas** is a Python package providing fast, flexible, and expressive data structures designed to work with *relational* or *labeled* data. It is a fundamental high-level building block for doing practical, real world data analysis in Python. \n",
      "\n",
      "pandas is well suited for:\n",
      "\n",
      "- Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet\n",
      "- Ordered and unordered (not necessarily fixed-frequency) time series data.\n",
      "- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels\n",
      "- Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure\n",
      "\n",
      "\n",
      "Key features:  \n",
      "    \n",
      "- Easy handling of **missing data**\n",
      "- **Size mutability**: columns can be inserted and deleted from DataFrame and higher dimensional objects\n",
      "- Automatic and explicit **data alignment**: objects can be explicitly aligned to a set of labels, or the data can be aligned automatically\n",
      "- Powerful, flexible **group by functionality** to perform split-apply-combine operations on data sets\n",
      "- Intelligent label-based **slicing, fancy indexing, and subsetting** of large data sets\n",
      "- Intuitive **merging and joining** data sets\n",
      "- Flexible **reshaping and pivoting** of data sets\n",
      "- **Hierarchical labeling** of axes\n",
      "- Robust **IO tools** for loading data from flat files, Excel files, databases, and HDF5\n",
      "- **Time series functionality**: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas's data structures and functionalities will be familiar to R users, there's a section on Pandas's website where \n",
      "Wes McKinney gives some translation of common idioms / operations between R and Pandas "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html#compare-with-r width=900 height=350></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html#compare-with-r width=900 height=350></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<IPython.core.display.HTML at 0x109b64150>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 1. Pandas data structures"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 1.1. Series\n",
      "\n",
      "A **Series** is a single vector of data values (think a NumPy array with shape N or (N,1)) with an **index** that labels each element in the vector."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = pd.Series(np.random.normal(0,1,(10,)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "0    0.515109\n",
        "1   -0.481021\n",
        "2   -0.859469\n",
        "3   -0.818741\n",
        "4    0.163780\n",
        "5   -0.639470\n",
        "6    0.762767\n",
        "7    2.322911\n",
        "8    1.129654\n",
        "9   -1.012590\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = b.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "0.10829310928343608"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "0    0.515109\n",
        "1   -0.481021\n",
        "2   -0.859469\n",
        "3   -0.818741\n",
        "4    0.163780\n",
        "5   -0.639470\n",
        "6    0.762767\n",
        "7    2.322911\n",
        "8    1.129654\n",
        "9   -1.012590\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([ 0.51510939, -0.48102095, -0.85946922, -0.81874054,  0.16377954,\n",
        "       -0.63946957,  0.76276735,  2.32291094,  1.12965396, -1.01258981])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### You can define your own **index**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = pd.Series(np.random.normal(0,1,(10,)), index=np.arange(1,11))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='int64')"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = pd.Series(np.random.normal(0,1,5), index=['a','b','c','d','e'], name='my series')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "a    0.489256\n",
        "b   -1.675855\n",
        "c    1.156451\n",
        "d    1.851782\n",
        "e    0.864339\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas objects expose some powerful, high level plotting functions (built on top of Matplotlib)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot = a.plot(kind='bar', rot=0, color='.8', title=a.name, hatch='///', edgecolor='k')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EHOTdOb5x40ZkZGS4G5eIqsnJyUFUVFSt/S7fEQQEBNSa7nGkY8eOaNCgAb7+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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105a44590>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots()\n",
      "bars = ax.bar(np.arange(len(a)), a.values, color='w', edgecolor='k', align='center', hatch='///')\n",
      "ax.set_xticks(np.arange(len(a)))\n",
      "ax.set_xlim(-0.5, len(a)-0.5)\n",
      "ax.set_xticklabels(a.index, fontsize=16)\n",
      "ax.set_title(a.name, fontsize=16)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<matplotlib.text.Text at 0x109d67c90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TPRZMBO2Y2p201pIAkbNiIrCT6GN+XEegDtHbhSMxFjLRY8FE0E5xHQER2YqJ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109b6fe90>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Selecting from a Series is easy, using the corresponding index key (like a dict)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a['c']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "1.1564513737482971"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "slices are permitted "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a['a':'c'] ### Note the difference with standard Python / Numpy positional, integer indexing"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "a    0.489256\n",
        "b   -1.675855\n",
        "c    1.156451\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a['c':]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "c    1.156451\n",
        "d    1.851782\n",
        "e    0.864339\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "deleting an element "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.drop('d')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "a    0.489256\n",
        "b   -1.675855\n",
        "c    1.156451\n",
        "e    0.864339\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Adding an element is (to my knowledge) not straightforward"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.append(pd.Series({'f':5}))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "a    0.489256\n",
        "b   -1.675855\n",
        "c    1.156451\n",
        "d    1.851782\n",
        "e    0.864339\n",
        "f    5.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Mathematical operations involving two series will perform operations by *aligning indices*.\n",
      "\n",
      "1. The union of all indices is created\n",
      "2. The mathematical operation is performed on matching indices. \n",
      "\n",
      "Indices that do not match are given the value NaN (not a number), and values are computed for all unique pairs of repeated indices."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1 = pd.Series(np.arange(1.0,4.0),index=['a','b','c'])\n",
      "s2 = pd.Series(np.arange(1.0,4.0),index=['b','c','d'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s3 = s1 + s2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "a   NaN\n",
        "b     3\n",
        "c     5\n",
        "d   NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "NaNs are ignored in all operations "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s3.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "4.0"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can drop them from the Series"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s4 = s3.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "b    3\n",
        "c    5\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or use the `fillna` method to replace them by a value"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s3.fillna(-999)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "a   -999\n",
        "b      3\n",
        "c      5\n",
        "d   -999\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s3.fillna(s3.mean())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "a    4\n",
        "b    3\n",
        "c    5\n",
        "d    4\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Series can have indexes representing dates / times "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "a    0.489256\n",
        "b   -1.675855\n",
        "c    1.156451\n",
        "d    1.851782\n",
        "e    0.864339\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.index = pd.date_range(start='2014-1-1', periods=len(a)) # default 'period' is daily"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "2014-01-01    0.489256\n",
        "2014-01-02   -1.675855\n",
        "2014-01-03    1.156451\n",
        "2014-01-04    1.851782\n",
        "2014-01-05    0.864339\n",
        "Freq: D, Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2014-01-01, ..., 2014-01-05]\n",
        "Length: 5, Freq: D, Timezone: None"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### a datetime index in Pandas has its own type\n",
      "a.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2014-01-01, ..., 2014-01-05]\n",
        "Length: 5, Freq: D, Timezone: None"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### but you can convert it to an numpy array of python datetime objects if you want\n",
      "py_datetimes = a.index.to_pydatetime()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py_datetimes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "array([datetime.datetime(2014, 1, 1, 0, 0),\n",
        "       datetime.datetime(2014, 1, 2, 0, 0),\n",
        "       datetime.datetime(2014, 1, 3, 0, 0),\n",
        "       datetime.datetime(2014, 1, 4, 0, 0),\n",
        "       datetime.datetime(2014, 1, 5, 0, 0)], dtype=object)"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And a number of useful methods for manipulation of time series is exposed"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### resample daily time-series to 5 minutes 'period', using forward filling method\n",
      "a.resample('5min',fill_method='ffill')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "2014-01-01 00:00:00    0.489256\n",
        "2014-01-01 00:05:00    0.489256\n",
        "2014-01-01 00:10:00    0.489256\n",
        "2014-01-01 00:15:00    0.489256\n",
        "2014-01-01 00:20:00    0.489256\n",
        "2014-01-01 00:25:00    0.489256\n",
        "2014-01-01 00:30:00    0.489256\n",
        "2014-01-01 00:35:00    0.489256\n",
        "2014-01-01 00:40:00    0.489256\n",
        "2014-01-01 00:45:00    0.489256\n",
        "2014-01-01 00:50:00    0.489256\n",
        "2014-01-01 00:55:00    0.489256\n",
        "2014-01-01 01:00:00    0.489256\n",
        "2014-01-01 01:05:00    0.489256\n",
        "2014-01-01 01:10:00    0.489256\n",
        "...\n",
        "2014-01-04 22:50:00    1.851782\n",
        "2014-01-04 22:55:00    1.851782\n",
        "2014-01-04 23:00:00    1.851782\n",
        "2014-01-04 23:05:00    1.851782\n",
        "2014-01-04 23:10:00    1.851782\n",
        "2014-01-04 23:15:00    1.851782\n",
        "2014-01-04 23:20:00    1.851782\n",
        "2014-01-04 23:25:00    1.851782\n",
        "2014-01-04 23:30:00    1.851782\n",
        "2014-01-04 23:35:00    1.851782\n",
        "2014-01-04 23:40:00    1.851782\n",
        "2014-01-04 23:45:00    1.851782\n",
        "2014-01-04 23:50:00    1.851782\n",
        "2014-01-04 23:55:00    1.851782\n",
        "2014-01-05 00:00:00    0.864339\n",
        "Freq: 5T, Name: my series, Length: 1153"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "2014-01-01    0.489256\n",
        "2014-01-02   -1.675855\n",
        "2014-01-03    1.156451\n",
        "2014-01-04    1.851782\n",
        "2014-01-05    0.864339\n",
        "Freq: D, Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### the ```shift``` method makes it easy e.g. to compare series with lead / lags \n",
      "a.shift(periods=-1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "2014-01-01   -1.675855\n",
        "2014-01-02    1.156451\n",
        "2014-01-03    1.851782\n",
        "2014-01-04    0.864339\n",
        "2014-01-05         NaN\n",
        "Freq: D, Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### and the ```truncate`` method allows easy selection of time-slices\n",
      "a.truncate(after='2014-1-2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "2014-01-01    0.489256\n",
        "2014-01-02   -1.675855\n",
        "Freq: D, Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 1.2. DataFrames\n",
      "\n",
      "DataFrames are IMHO one of the most powerful data structures in the Python / data analysis world. \n",
      "\n",
      "They can be viewed as a *collection* of Series. They feature two indexes, respectively for the rows and the columns, and can contain heteregoneous data types (although it must be consistent *within* each column). \n",
      "\n",
      "Note that a DataFrame index, either along the rows or the columns (or both !) can contain more than one level, they are called **hierarchical indexes** and allows the representation of complex data organisation. \n",
      "\n",
      "If the index along the rows of a DataFrame is of **datetime** type, all the methods exposed for the Series (re-sampling, shifting, truncating, etc) are available for the DataFrame."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import string # part of the standard library\n",
      "idx = list(string.lowercase[:10])\n",
      "print(idx)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(np.arange(100).reshape(10,10),columns=idx,index=np.arange(1,11))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "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",
        "      <th>f</th>\n",
        "      <th>g</th>\n",
        "      <th>h</th>\n",
        "      <th>i</th>\n",
        "      <th>j</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "      <td> 16</td>\n",
        "      <td> 17</td>\n",
        "      <td> 18</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 20</td>\n",
        "      <td> 21</td>\n",
        "      <td> 22</td>\n",
        "      <td> 23</td>\n",
        "      <td> 24</td>\n",
        "      <td> 25</td>\n",
        "      <td> 26</td>\n",
        "      <td> 27</td>\n",
        "      <td> 28</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 30</td>\n",
        "      <td> 31</td>\n",
        "      <td> 32</td>\n",
        "      <td> 33</td>\n",
        "      <td> 34</td>\n",
        "      <td> 35</td>\n",
        "      <td> 36</td>\n",
        "      <td> 37</td>\n",
        "      <td> 38</td>\n",
        "      <td> 39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 40</td>\n",
        "      <td> 41</td>\n",
        "      <td> 42</td>\n",
        "      <td> 43</td>\n",
        "      <td> 44</td>\n",
        "      <td> 45</td>\n",
        "      <td> 46</td>\n",
        "      <td> 47</td>\n",
        "      <td> 48</td>\n",
        "      <td> 49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 50</td>\n",
        "      <td> 51</td>\n",
        "      <td> 52</td>\n",
        "      <td> 53</td>\n",
        "      <td> 54</td>\n",
        "      <td> 55</td>\n",
        "      <td> 56</td>\n",
        "      <td> 57</td>\n",
        "      <td> 58</td>\n",
        "      <td> 59</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 60</td>\n",
        "      <td> 61</td>\n",
        "      <td> 62</td>\n",
        "      <td> 63</td>\n",
        "      <td> 64</td>\n",
        "      <td> 65</td>\n",
        "      <td> 66</td>\n",
        "      <td> 67</td>\n",
        "      <td> 68</td>\n",
        "      <td> 69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 70</td>\n",
        "      <td> 71</td>\n",
        "      <td> 72</td>\n",
        "      <td> 73</td>\n",
        "      <td> 74</td>\n",
        "      <td> 75</td>\n",
        "      <td> 76</td>\n",
        "      <td> 77</td>\n",
        "      <td> 78</td>\n",
        "      <td> 79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 80</td>\n",
        "      <td> 81</td>\n",
        "      <td> 82</td>\n",
        "      <td> 83</td>\n",
        "      <td> 84</td>\n",
        "      <td> 85</td>\n",
        "      <td> 86</td>\n",
        "      <td> 87</td>\n",
        "      <td> 88</td>\n",
        "      <td> 89</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 90</td>\n",
        "      <td> 91</td>\n",
        "      <td> 92</td>\n",
        "      <td> 93</td>\n",
        "      <td> 94</td>\n",
        "      <td> 95</td>\n",
        "      <td> 96</td>\n",
        "      <td> 97</td>\n",
        "      <td> 98</td>\n",
        "      <td> 99</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "     a   b   c   d   e   f   g   h   i   j\n",
        "1    0   1   2   3   4   5   6   7   8   9\n",
        "2   10  11  12  13  14  15  16  17  18  19\n",
        "3   20  21  22  23  24  25  26  27  28  29\n",
        "4   30  31  32  33  34  35  36  37  38  39\n",
        "5   40  41  42  43  44  45  46  47  48  49\n",
        "6   50  51  52  53  54  55  56  57  58  59\n",
        "7   60  61  62  63  64  65  66  67  68  69\n",
        "8   70  71  72  73  74  75  76  77  78  79\n",
        "9   80  81  82  83  84  85  86  87  88  89\n",
        "10  90  91  92  93  94  95  96  97  98  99\n",
        "\n",
        "[10 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### here I am creating a DataFrame from a dictionnary\n",
      "\n",
      "df = pd.DataFrame({'A' : np.random.random(5), 'B' : np.random.random(5), 'C': np.random.random(5)})\n",
      "print df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "          A         B         C\n",
        "0  0.576212  0.497153  0.155229\n",
        "1  0.338768  0.019293  0.991341\n",
        "2  0.277941  0.445321  0.593195\n",
        "3  0.178222  0.786506  0.855407\n",
        "4  0.695456  0.457891  0.296605\n",
        "\n",
        "[5 rows x 3 columns]\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "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",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.576212</td>\n",
        "      <td> 0.497153</td>\n",
        "      <td> 0.155229</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.338768</td>\n",
        "      <td> 0.019293</td>\n",
        "      <td> 0.991341</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.277941</td>\n",
        "      <td> 0.445321</td>\n",
        "      <td> 0.593195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.178222</td>\n",
        "      <td> 0.786506</td>\n",
        "      <td> 0.855407</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.695456</td>\n",
        "      <td> 0.457891</td>\n",
        "      <td> 0.296605</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "          A         B         C\n",
        "0  0.576212  0.497153  0.155229\n",
        "1  0.338768  0.019293  0.991341\n",
        "2  0.277941  0.445321  0.593195\n",
        "3  0.178222  0.786506  0.855407\n",
        "4  0.695456  0.457891  0.296605\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['A']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "0    0.576212\n",
        "1    0.338768\n",
        "2    0.277941\n",
        "3    0.178222\n",
        "4    0.695456\n",
        "Name: A, dtype: float64"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To access a particular *row* instead of a column, you use the *ix* method"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "A    0.178222\n",
        "B    0.786506\n",
        "C    0.855407\n",
        "Name: 3, dtype: float64"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And you can combine of course row (with ix) and column indexing, using the same convention for slices as we saw for the Series "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[3]['A':'B']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "A    0.178222\n",
        "B    0.786506\n",
        "Name: 3, dtype: float64"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[3][['A','C']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "A    0.178222\n",
        "C    0.855407\n",
        "Name: 3, dtype: float64"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Adding a column is easy "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "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",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.576212</td>\n",
        "      <td> 0.497153</td>\n",
        "      <td> 0.155229</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.338768</td>\n",
        "      <td> 0.019293</td>\n",
        "      <td> 0.991341</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.277941</td>\n",
        "      <td> 0.445321</td>\n",
        "      <td> 0.593195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.178222</td>\n",
        "      <td> 0.786506</td>\n",
        "      <td> 0.855407</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.695456</td>\n",
        "      <td> 0.457891</td>\n",
        "      <td> 0.296605</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "          A         B         C\n",
        "0  0.576212  0.497153  0.155229\n",
        "1  0.338768  0.019293  0.991341\n",
        "2  0.277941  0.445321  0.593195\n",
        "3  0.178222  0.786506  0.855407\n",
        "4  0.695456  0.457891  0.296605\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['D'] = np.random.random(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "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> 0.576212</td>\n",
        "      <td> 0.497153</td>\n",
        "      <td> 0.155229</td>\n",
        "      <td> 0.286158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.338768</td>\n",
        "      <td> 0.019293</td>\n",
        "      <td> 0.991341</td>\n",
        "      <td> 0.196536</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.277941</td>\n",
        "      <td> 0.445321</td>\n",
        "      <td> 0.593195</td>\n",
        "      <td> 0.382180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.178222</td>\n",
        "      <td> 0.786506</td>\n",
        "      <td> 0.855407</td>\n",
        "      <td> 0.954023</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.695456</td>\n",
        "      <td> 0.457891</td>\n",
        "      <td> 0.296605</td>\n",
        "      <td> 0.993321</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "          A         B         C         D\n",
        "0  0.576212  0.497153  0.155229  0.286158\n",
        "1  0.338768  0.019293  0.991341  0.196536\n",
        "2  0.277941  0.445321  0.593195  0.382180\n",
        "3  0.178222  0.786506  0.855407  0.954023\n",
        "4  0.695456  0.457891  0.296605  0.993321\n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following works because Pandas understands that a single value must be repeated over the row length"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['E'] = 2.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "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>0</th>\n",
        "      <td> 0.576212</td>\n",
        "      <td> 0.497153</td>\n",
        "      <td> 0.155229</td>\n",
        "      <td> 0.286158</td>\n",
        "      <td> 2.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.338768</td>\n",
        "      <td> 0.019293</td>\n",
        "      <td> 0.991341</td>\n",
        "      <td> 0.196536</td>\n",
        "      <td> 2.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.277941</td>\n",
        "      <td> 0.445321</td>\n",
        "      <td> 0.593195</td>\n",
        "      <td> 0.382180</td>\n",
        "      <td> 2.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.178222</td>\n",
        "      <td> 0.786506</td>\n",
        "      <td> 0.855407</td>\n",
        "      <td> 0.954023</td>\n",
        "      <td> 2.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.695456</td>\n",
        "      <td> 0.457891</td>\n",
        "      <td> 0.296605</td>\n",
        "      <td> 0.993321</td>\n",
        "      <td> 2.5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "          A         B         C         D    E\n",
        "0  0.576212  0.497153  0.155229  0.286158  2.5\n",
        "1  0.338768  0.019293  0.991341  0.196536  2.5\n",
        "2  0.277941  0.445321  0.593195  0.382180  2.5\n",
        "3  0.178222  0.786506  0.855407  0.954023  2.5\n",
        "4  0.695456  0.457891  0.296605  0.993321  2.5\n",
        "\n",
        "[5 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following doesn't work because there's no way to tell **where** to insert the missing value (1st or last index ?)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['F'] = np.random.random(4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "Length of values does not match length of index",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-69-3ee1a35781af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/nicolasfauchereau/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1885\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1886\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1887\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1888\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1889\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nicolasfauchereau/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1965\u001b[0m         \u001b[0mis_existing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1966\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1967\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1968\u001b[0m         \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nicolasfauchereau/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2015\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_is_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2016\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2017\u001b[0;31m                 raise ValueError('Length of values does not match length of '\n\u001b[0m\u001b[1;32m   2018\u001b[0m                                  'index')\n\u001b[1;32m   2019\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: Length of values does not match length of index"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unless we make a series out of it, with a index matching at least partly the DataFrame (row) index"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['F'] = pd.Series(np.random.random(4)) #"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "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",
        "      <th>F</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.576212</td>\n",
        "      <td> 0.497153</td>\n",
        "      <td> 0.155229</td>\n",
        "      <td> 0.286158</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.145339</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.338768</td>\n",
        "      <td> 0.019293</td>\n",
        "      <td> 0.991341</td>\n",
        "      <td> 0.196536</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.492748</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.277941</td>\n",
        "      <td> 0.445321</td>\n",
        "      <td> 0.593195</td>\n",
        "      <td> 0.382180</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.468712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.178222</td>\n",
        "      <td> 0.786506</td>\n",
        "      <td> 0.855407</td>\n",
        "      <td> 0.954023</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.943265</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.695456</td>\n",
        "      <td> 0.457891</td>\n",
        "      <td> 0.296605</td>\n",
        "      <td> 0.993321</td>\n",
        "      <td> 2.5</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "          A         B         C         D    E         F\n",
        "0  0.576212  0.497153  0.155229  0.286158  2.5  0.145339\n",
        "1  0.338768  0.019293  0.991341  0.196536  2.5  0.492748\n",
        "2  0.277941  0.445321  0.593195  0.382180  2.5  0.468712\n",
        "3  0.178222  0.786506  0.855407  0.954023  2.5  0.943265\n",
        "4  0.695456  0.457891  0.296605  0.993321  2.5       NaN\n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.apply(np.sqrt) # or np.sqrt(df)"
     ],
     "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",
        "      <th>F</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.759086</td>\n",
        "      <td> 0.705091</td>\n",
        "      <td> 0.393991</td>\n",
        "      <td> 0.534937</td>\n",
        "      <td> 1.581139</td>\n",
        "      <td> 0.381233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.582037</td>\n",
        "      <td> 0.138901</td>\n",
        "      <td> 0.995661</td>\n",
        "      <td> 0.443324</td>\n",
        "      <td> 1.581139</td>\n",
        "      <td> 0.701960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.527201</td>\n",
        "      <td> 0.667324</td>\n",
        "      <td> 0.770191</td>\n",
        "      <td> 0.618207</td>\n",
        "      <td> 1.581139</td>\n",
        "      <td> 0.684625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.422164</td>\n",
        "      <td> 0.886852</td>\n",
        "      <td> 0.924882</td>\n",
        "      <td> 0.976741</td>\n",
        "      <td> 1.581139</td>\n",
        "      <td> 0.971218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.833940</td>\n",
        "      <td> 0.676677</td>\n",
        "      <td> 0.544614</td>\n",
        "      <td> 0.996655</td>\n",
        "      <td> 1.581139</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "          A         B         C         D         E         F\n",
        "0  0.759086  0.705091  0.393991  0.534937  1.581139  0.381233\n",
        "1  0.582037  0.138901  0.995661  0.443324  1.581139  0.701960\n",
        "2  0.527201  0.667324  0.770191  0.618207  1.581139  0.684625\n",
        "3  0.422164  0.886852  0.924882  0.976741  1.581139  0.971218\n",
        "4  0.833940  0.676677  0.544614  0.996655  1.581139       NaN\n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.describe()"
     ],
     "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",
        "      <th>F</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 5.000000</td>\n",
        "      <td> 5.000000</td>\n",
        "      <td> 5.000000</td>\n",
        "      <td> 5.000000</td>\n",
        "      <td> 5.0</td>\n",
        "      <td> 4.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 0.413320</td>\n",
        "      <td> 0.441233</td>\n",
        "      <td> 0.578355</td>\n",
        "      <td> 0.562444</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.512516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 0.215227</td>\n",
        "      <td> 0.274166</td>\n",
        "      <td> 0.355652</td>\n",
        "      <td> 0.381349</td>\n",
        "      <td> 0.0</td>\n",
        "      <td> 0.327960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 0.178222</td>\n",
        "      <td> 0.019293</td>\n",
        "      <td> 0.155229</td>\n",
        "      <td> 0.196536</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.145339</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 0.277941</td>\n",
        "      <td> 0.445321</td>\n",
        "      <td> 0.296605</td>\n",
        "      <td> 0.286158</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.387869</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 0.338768</td>\n",
        "      <td> 0.457891</td>\n",
        "      <td> 0.593195</td>\n",
        "      <td> 0.382180</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.480730</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 0.576212</td>\n",
        "      <td> 0.497153</td>\n",
        "      <td> 0.855407</td>\n",
        "      <td> 0.954023</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.605377</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 0.695456</td>\n",
        "      <td> 0.786506</td>\n",
        "      <td> 0.991341</td>\n",
        "      <td> 0.993321</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.943265</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "              A         B         C         D    E         F\n",
        "count  5.000000  5.000000  5.000000  5.000000  5.0  4.000000\n",
        "mean   0.413320  0.441233  0.578355  0.562444  2.5  0.512516\n",
        "std    0.215227  0.274166  0.355652  0.381349  0.0  0.327960\n",
        "min    0.178222  0.019293  0.155229  0.196536  2.5  0.145339\n",
        "25%    0.277941  0.445321  0.296605  0.286158  2.5  0.387869\n",
        "50%    0.338768  0.457891  0.593195  0.382180  2.5  0.480730\n",
        "75%    0.576212  0.497153  0.855407  0.954023  2.5  0.605377\n",
        "max    0.695456  0.786506  0.991341  0.993321  2.5  0.943265\n",
        "\n",
        "[8 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot(ylim=[0,3]);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ekGULcY8KG+7QRoVsrZb3MrLYW1TEvLBQQmrTySzfQWi9O8l72wlxCemo509L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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109da5810>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[(df['A'] >= 0.5) & (df['B'] <= 0.5)]"
     ],
     "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",
        "      <th>F</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.576212</td>\n",
        "      <td> 0.497153</td>\n",
        "      <td> 0.155229</td>\n",
        "      <td> 0.286158</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.145339</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.695456</td>\n",
        "      <td> 0.457891</td>\n",
        "      <td> 0.296605</td>\n",
        "      <td> 0.993321</td>\n",
        "      <td> 2.5</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "          A         B         C         D    E         F\n",
        "0  0.576212  0.497153  0.155229  0.286158  2.5  0.145339\n",
        "4  0.695456  0.457891  0.296605  0.993321  2.5       NaN\n",
        "\n",
        "[2 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot(figsize=(10,8), subplots=True, sharex=True, kind='bar', rot=0); "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZEBgYCH9/f/zgB/Ic30REREQEdGNWYHR0NF566SXz9rYLMbddhBkAHn/8cTz+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\nAAAATEJiBQAAYBISKwAAAJP8Hz19LnMtsKg4AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109bc6410>"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 2. Input and Output in pandas\n",
      "\n",
      "Pandas has very powerful IO methods, allowing to load csv, excel, tab-delimited files very easily. Pandas DataFrames can also be \n",
      "saved also in csv, excel files. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.read_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI = pd.read_csv('./data/NIWA_SOI.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.head()"
     ],
     "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>Unnamed: 0</th>\n",
        "      <th>Jan</th>\n",
        "      <th>Feb</th>\n",
        "      <th>Mar</th>\n",
        "      <th>Apr</th>\n",
        "      <th>May</th>\n",
        "      <th>Jun</th>\n",
        "      <th>Jul</th>\n",
        "      <th>Aug</th>\n",
        "      <th>Sep</th>\n",
        "      <th>Oct</th>\n",
        "      <th>Nov</th>\n",
        "      <th>Dec</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1876</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1877</td>\n",
        "      <td>-1.044600</td>\n",
        "      <td>-0.834198</td>\n",
        "      <td>-0.759131</td>\n",
        "      <td>-1.103454</td>\n",
        "      <td> 0.349381</td>\n",
        "      <td>-1.901213</td>\n",
        "      <td>-1.002934</td>\n",
        "      <td>-1.044881</td>\n",
        "      <td>-1.839545</td>\n",
        "      <td>-1.745311</td>\n",
        "      <td>-1.453885</td>\n",
        "      <td>-1.541774</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1878</td>\n",
        "      <td>-0.940401</td>\n",
        "      <td>-2.339884</td>\n",
        "      <td>-1.981073</td>\n",
        "      <td>-1.009742</td>\n",
        "      <td> 0.189664</td>\n",
        "      <td>-0.448601</td>\n",
        "      <td> 1.568691</td>\n",
        "      <td> 1.216173</td>\n",
        "      <td> 1.734780</td>\n",
        "      <td> 1.002227</td>\n",
        "      <td> 1.460825</td>\n",
        "      <td> 1.744423</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1879</td>\n",
        "      <td> 1.404088</td>\n",
        "      <td> 1.302904</td>\n",
        "      <td> 1.257072</td>\n",
        "      <td> 1.426758</td>\n",
        "      <td> 0.189664</td>\n",
        "      <td> 1.602146</td>\n",
        "      <td> 2.147306</td>\n",
        "      <td> 2.243925</td>\n",
        "      <td> 1.858033</td>\n",
        "      <td> 1.439335</td>\n",
        "      <td> 0.905642</td>\n",
        "      <td>-0.774995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1880</td>\n",
        "      <td> 1.195689</td>\n",
        "      <td> 0.622917</td>\n",
        "      <td> 1.379266</td>\n",
        "      <td> 0.583354</td>\n",
        "      <td> 1.227823</td>\n",
        "      <td> 0.833116</td>\n",
        "      <td> 0.154297</td>\n",
        "      <td> 1.353207</td>\n",
        "      <td> 0.748760</td>\n",
        "      <td> 0.377786</td>\n",
        "      <td> 0.628051</td>\n",
        "      <td>-0.391605</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 13 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "   Unnamed: 0       Jan       Feb       Mar       Apr       May       Jun  \\\n",
        "0        1876       NaN       NaN       NaN       NaN       NaN       NaN   \n",
        "1        1877 -1.044600 -0.834198 -0.759131 -1.103454  0.349381 -1.901213   \n",
        "2        1878 -0.940401 -2.339884 -1.981073 -1.009742  0.189664 -0.448601   \n",
        "3        1879  1.404088  1.302904  1.257072  1.426758  0.189664  1.602146   \n",
        "4        1880  1.195689  0.622917  1.379266  0.583354  1.227823  0.833116   \n",
        "\n",
        "        Jul       Aug       Sep       Oct       Nov       Dec  \n",
        "0       NaN       NaN       NaN       NaN       NaN       NaN  \n",
        "1 -1.002934 -1.044881 -1.839545 -1.745311 -1.453885 -1.541774  \n",
        "2  1.568691  1.216173  1.734780  1.002227  1.460825  1.744423  \n",
        "3  2.147306  2.243925  1.858033  1.439335  0.905642 -0.774995  \n",
        "4  0.154297  1.353207  0.748760  0.377786  0.628051 -0.391605  \n",
        "\n",
        "[5 rows x 13 columns]"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.tail()"
     ],
     "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>Unnamed: 0</th>\n",
        "      <th>Jan</th>\n",
        "      <th>Feb</th>\n",
        "      <th>Mar</th>\n",
        "      <th>Apr</th>\n",
        "      <th>May</th>\n",
        "      <th>Jun</th>\n",
        "      <th>Jul</th>\n",
        "      <th>Aug</th>\n",
        "      <th>Sep</th>\n",
        "      <th>Oct</th>\n",
        "      <th>Nov</th>\n",
        "      <th>Dec</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>134</th>\n",
        "      <td> 2010</td>\n",
        "      <td>-1.096700</td>\n",
        "      <td>-1.659897</td>\n",
        "      <td>-1.431199</td>\n",
        "      <td> 1.707893</td>\n",
        "      <td> 0.988248</td>\n",
        "      <td> 0.064086</td>\n",
        "      <td> 2.018725</td>\n",
        "      <td> 1.832825</td>\n",
        "      <td> 2.474296</td>\n",
        "      <td> 1.751556</td>\n",
        "      <td> 1.599621</td>\n",
        "      <td> 2.730281</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>135</th>\n",
        "      <td> 2011</td>\n",
        "      <td> 2.185585</td>\n",
        "      <td> 2.128602</td>\n",
        "      <td> 2.173528</td>\n",
        "      <td> 2.832432</td>\n",
        "      <td> 0.189664</td>\n",
        "      <td>-0.106810</td>\n",
        "      <td> 1.054366</td>\n",
        "      <td> 0.051388</td>\n",
        "      <td> 1.118517</td>\n",
        "      <td> 0.627563</td>\n",
        "      <td> 1.322029</td>\n",
        "      <td> 2.292122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>136</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 1.039390</td>\n",
        "      <td> 0.088641</td>\n",
        "      <td> 0.096228</td>\n",
        "      <td>-0.822319</td>\n",
        "      <td>-0.289487</td>\n",
        "      <td>-1.217631</td>\n",
        "      <td>-0.167156</td>\n",
        "      <td>-0.702297</td>\n",
        "      <td> 0.194123</td>\n",
        "      <td> 0.128010</td>\n",
        "      <td> 0.281061</td>\n",
        "      <td>-0.829765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>137</th>\n",
        "      <td> 2013</td>\n",
        "      <td>-0.106804</td>\n",
        "      <td>-0.542775</td>\n",
        "      <td> 1.012684</td>\n",
        "      <td> 0.021085</td>\n",
        "      <td> 0.828531</td>\n",
        "      <td> 1.345802</td>\n",
        "      <td> 0.797204</td>\n",
        "      <td>-0.222680</td>\n",
        "      <td> 0.317375</td>\n",
        "      <td>-0.309098</td>\n",
        "      <td> 0.836244</td>\n",
        "      <td>-0.117755</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>138</th>\n",
        "      <td> 2014</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 13 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "     Unnamed: 0       Jan       Feb       Mar       Apr       May       Jun  \\\n",
        "134        2010 -1.096700 -1.659897 -1.431199  1.707893  0.988248  0.064086   \n",
        "135        2011  2.185585  2.128602  2.173528  2.832432  0.189664 -0.106810   \n",
        "136        2012  1.039390  0.088641  0.096228 -0.822319 -0.289487 -1.217631   \n",
        "137        2013 -0.106804 -0.542775  1.012684  0.021085  0.828531  1.345802   \n",
        "138        2014       NaN       NaN       NaN       NaN       NaN       NaN   \n",
        "\n",
        "          Jul       Aug       Sep       Oct       Nov       Dec  \n",
        "134  2.018725  1.832825  2.474296  1.751556  1.599621  2.730281  \n",
        "135  1.054366  0.051388  1.118517  0.627563  1.322029  2.292122  \n",
        "136 -0.167156 -0.702297  0.194123  0.128010  0.281061 -0.829765  \n",
        "137  0.797204 -0.222680  0.317375 -0.309098  0.836244 -0.117755  \n",
        "138       NaN       NaN       NaN       NaN       NaN       NaN  \n",
        "\n",
        "[5 rows x 13 columns]"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI = pd.read_csv('./data/NIWA_SOI.csv', index_col=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.head()"
     ],
     "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>Jan</th>\n",
        "      <th>Feb</th>\n",
        "      <th>Mar</th>\n",
        "      <th>Apr</th>\n",
        "      <th>May</th>\n",
        "      <th>Jun</th>\n",
        "      <th>Jul</th>\n",
        "      <th>Aug</th>\n",
        "      <th>Sep</th>\n",
        "      <th>Oct</th>\n",
        "      <th>Nov</th>\n",
        "      <th>Dec</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1876</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1877</th>\n",
        "      <td>-1.044600</td>\n",
        "      <td>-0.834198</td>\n",
        "      <td>-0.759131</td>\n",
        "      <td>-1.103454</td>\n",
        "      <td> 0.349381</td>\n",
        "      <td>-1.901213</td>\n",
        "      <td>-1.002934</td>\n",
        "      <td>-1.044881</td>\n",
        "      <td>-1.839545</td>\n",
        "      <td>-1.745311</td>\n",
        "      <td>-1.453885</td>\n",
        "      <td>-1.541774</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1878</th>\n",
        "      <td>-0.940401</td>\n",
        "      <td>-2.339884</td>\n",
        "      <td>-1.981073</td>\n",
        "      <td>-1.009742</td>\n",
        "      <td> 0.189664</td>\n",
        "      <td>-0.448601</td>\n",
        "      <td> 1.568691</td>\n",
        "      <td> 1.216173</td>\n",
        "      <td> 1.734780</td>\n",
        "      <td> 1.002227</td>\n",
        "      <td> 1.460825</td>\n",
        "      <td> 1.744423</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1879</th>\n",
        "      <td> 1.404088</td>\n",
        "      <td> 1.302904</td>\n",
        "      <td> 1.257072</td>\n",
        "      <td> 1.426758</td>\n",
        "      <td> 0.189664</td>\n",
        "      <td> 1.602146</td>\n",
        "      <td> 2.147306</td>\n",
        "      <td> 2.243925</td>\n",
        "      <td> 1.858033</td>\n",
        "      <td> 1.439335</td>\n",
        "      <td> 0.905642</td>\n",
        "      <td>-0.774995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1880</th>\n",
        "      <td> 1.195689</td>\n",
        "      <td> 0.622917</td>\n",
        "      <td> 1.379266</td>\n",
        "      <td> 0.583354</td>\n",
        "      <td> 1.227823</td>\n",
        "      <td> 0.833116</td>\n",
        "      <td> 0.154297</td>\n",
        "      <td> 1.353207</td>\n",
        "      <td> 0.748760</td>\n",
        "      <td> 0.377786</td>\n",
        "      <td> 0.628051</td>\n",
        "      <td>-0.391605</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "           Jan       Feb       Mar       Apr       May       Jun       Jul  \\\n",
        "1876       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
        "1877 -1.044600 -0.834198 -0.759131 -1.103454  0.349381 -1.901213 -1.002934   \n",
        "1878 -0.940401 -2.339884 -1.981073 -1.009742  0.189664 -0.448601  1.568691   \n",
        "1879  1.404088  1.302904  1.257072  1.426758  0.189664  1.602146  2.147306   \n",
        "1880  1.195689  0.622917  1.379266  0.583354  1.227823  0.833116  0.154297   \n",
        "\n",
        "           Aug       Sep       Oct       Nov       Dec  \n",
        "1876       NaN       NaN       NaN       NaN       NaN  \n",
        "1877 -1.044881 -1.839545 -1.745311 -1.453885 -1.541774  \n",
        "1878  1.216173  1.734780  1.002227  1.460825  1.744423  \n",
        "1879  2.243925  1.858033  1.439335  0.905642 -0.774995  \n",
        "1880  1.353207  0.748760  0.377786  0.628051 -0.391605  \n",
        "\n",
        "[5 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "Float64Index([1876.0, 1877.0, 1878.0, 1879.0, 1880.0, 1881.0, 1882.0, 1883.0, 1884.0, 1885.0, 1886.0, 1887.0, 1888.0, 1889.0, 1890.0, 1891.0, 1892.0, 1893.0, 1894.0, 1895.0, 1896.0, 1897.0, 1898.0, 1899.0, 1900.0, 1901.0, 1902.0, 1903.0, 1904.0, 1905.0, 1906.0, 1907.0, 1908.0, 1909.0, 1910.0, 1911.0, 1912.0, 1913.0, 1914.0, 1915.0, 1916.0, 1917.0, 1918.0, 1919.0, 1920.0, 1921.0, 1922.0, 1923.0, 1924.0, 1925.0, 1926.0, 1927.0, 1928.0, 1929.0, 1930.0, 1931.0, 1932.0, 1933.0, 1934.0, 1935.0, 1936.0, 1937.0, 1938.0, 1939.0, 1940.0, 1941.0, 1942.0, 1943.0, 1944.0, 1945.0, 1946.0, 1947.0, 1948.0, 1949.0, 1950.0, 1951.0, 1952.0, 1953.0, 1954.0, 1955.0, 1956.0, 1957.0, 1958.0, 1959.0, 1960.0, 1961.0, 1962.0, 1963.0, 1964.0, 1965.0, 1966.0, 1967.0, 1968.0, 1969.0, 1970.0, 1971.0, 1972.0, 1973.0, 1974.0, 1975.0, ...], dtype='object')"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI = SOI.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.head()"
     ],
     "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>Jan</th>\n",
        "      <th>Feb</th>\n",
        "      <th>Mar</th>\n",
        "      <th>Apr</th>\n",
        "      <th>May</th>\n",
        "      <th>Jun</th>\n",
        "      <th>Jul</th>\n",
        "      <th>Aug</th>\n",
        "      <th>Sep</th>\n",
        "      <th>Oct</th>\n",
        "      <th>Nov</th>\n",
        "      <th>Dec</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1877</th>\n",
        "      <td>-1.044600</td>\n",
        "      <td>-0.834198</td>\n",
        "      <td>-0.759131</td>\n",
        "      <td>-1.103454</td>\n",
        "      <td> 0.349381</td>\n",
        "      <td>-1.901213</td>\n",
        "      <td>-1.002934</td>\n",
        "      <td>-1.044881</td>\n",
        "      <td>-1.839545</td>\n",
        "      <td>-1.745311</td>\n",
        "      <td>-1.453885</td>\n",
        "      <td>-1.541774</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1878</th>\n",
        "      <td>-0.940401</td>\n",
        "      <td>-2.339884</td>\n",
        "      <td>-1.981073</td>\n",
        "      <td>-1.009742</td>\n",
        "      <td> 0.189664</td>\n",
        "      <td>-0.448601</td>\n",
        "      <td> 1.568691</td>\n",
        "      <td> 1.216173</td>\n",
        "      <td> 1.734780</td>\n",
        "      <td> 1.002227</td>\n",
        "      <td> 1.460825</td>\n",
        "      <td> 1.744423</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1879</th>\n",
        "      <td> 1.404088</td>\n",
        "      <td> 1.302904</td>\n",
        "      <td> 1.257072</td>\n",
        "      <td> 1.426758</td>\n",
        "      <td> 0.189664</td>\n",
        "      <td> 1.602146</td>\n",
        "      <td> 2.147306</td>\n",
        "      <td> 2.243925</td>\n",
        "      <td> 1.858033</td>\n",
        "      <td> 1.439335</td>\n",
        "      <td> 0.905642</td>\n",
        "      <td>-0.774995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1880</th>\n",
        "      <td> 1.195689</td>\n",
        "      <td> 0.622917</td>\n",
        "      <td> 1.379266</td>\n",
        "      <td> 0.583354</td>\n",
        "      <td> 1.227823</td>\n",
        "      <td> 0.833116</td>\n",
        "      <td> 0.154297</td>\n",
        "      <td> 1.353207</td>\n",
        "      <td> 0.748760</td>\n",
        "      <td> 0.377786</td>\n",
        "      <td> 0.628051</td>\n",
        "      <td>-0.391605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1881</th>\n",
        "      <td>-0.784101</td>\n",
        "      <td>-0.737057</td>\n",
        "      <td>-0.025966</td>\n",
        "      <td> 0.021085</td>\n",
        "      <td>-0.449204</td>\n",
        "      <td>-0.619496</td>\n",
        "      <td>-0.552899</td>\n",
        "      <td>-1.387465</td>\n",
        "      <td>-1.469787</td>\n",
        "      <td>-2.557084</td>\n",
        "      <td> 0.628051</td>\n",
        "      <td> 0.868104</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "           Jan       Feb       Mar       Apr       May       Jun       Jul  \\\n",
        "1877 -1.044600 -0.834198 -0.759131 -1.103454  0.349381 -1.901213 -1.002934   \n",
        "1878 -0.940401 -2.339884 -1.981073 -1.009742  0.189664 -0.448601  1.568691   \n",
        "1879  1.404088  1.302904  1.257072  1.426758  0.189664  1.602146  2.147306   \n",
        "1880  1.195689  0.622917  1.379266  0.583354  1.227823  0.833116  0.154297   \n",
        "1881 -0.784101 -0.737057 -0.025966  0.021085 -0.449204 -0.619496 -0.552899   \n",
        "\n",
        "           Aug       Sep       Oct       Nov       Dec  \n",
        "1877 -1.044881 -1.839545 -1.745311 -1.453885 -1.541774  \n",
        "1878  1.216173  1.734780  1.002227  1.460825  1.744423  \n",
        "1879  2.243925  1.858033  1.439335  0.905642 -0.774995  \n",
        "1880  1.353207  0.748760  0.377786  0.628051 -0.391605  \n",
        "1881 -1.387465 -1.469787 -2.557084  0.628051  0.868104  \n",
        "\n",
        "[5 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "Float64Index([1877.0, 1878.0, 1879.0, 1880.0, 1881.0, 1882.0, 1883.0, 1884.0, 1885.0, 1886.0, 1887.0, 1888.0, 1889.0, 1890.0, 1891.0, 1892.0, 1893.0, 1894.0, 1895.0, 1896.0, 1897.0, 1898.0, 1899.0, 1900.0, 1901.0, 1902.0, 1903.0, 1904.0, 1905.0, 1906.0, 1907.0, 1908.0, 1909.0, 1910.0, 1911.0, 1912.0, 1913.0, 1914.0, 1915.0, 1916.0, 1917.0, 1918.0, 1919.0, 1920.0, 1921.0, 1922.0, 1923.0, 1924.0, 1925.0, 1926.0, 1927.0, 1928.0, 1929.0, 1930.0, 1931.0, 1932.0, 1933.0, 1934.0, 1935.0, 1936.0, 1937.0, 1938.0, 1939.0, 1940.0, 1941.0, 1942.0, 1943.0, 1944.0, 1945.0, 1946.0, 1947.0, 1948.0, 1949.0, 1950.0, 1951.0, 1952.0, 1953.0, 1954.0, 1955.0, 1956.0, 1957.0, 1958.0, 1959.0, 1960.0, 1961.0, 1962.0, 1963.0, 1964.0, 1965.0, 1966.0, 1967.0, 1968.0, 1969.0, 1970.0, 1971.0, 1972.0, 1973.0, 1974.0, 1975.0, 1976.0, ...], dtype='object')"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.index = np.array(SOI.index.to_native_types(), dtype=np.int)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "Int64Index([1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, ...], dtype='int64')"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Stacking "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs = SOI.stack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "1877  Jan   -1.044600\n",
        "      Feb   -0.834198\n",
        "      Mar   -0.759131\n",
        "      Apr   -1.103454\n",
        "      May    0.349381\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "MultiIndex(levels=[[1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, ...], [u'Jan', u'Feb', u'Mar', u'Apr', u'May', u'Jun', u'Jul', u'Aug', u'Sep', u'Oct', u'Nov', u'Dec']],\n",
        "           labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, ...], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, ...]])"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from dateutil import parser"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dateindex = [parser.parse(\"-\".join(map(str,[x[0],x[1], 1]))) for x in SOIs.index]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dateindex"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "[datetime.datetime(1877, 1, 1, 0, 0),\n",
        " datetime.datetime(1877, 2, 1, 0, 0),\n",
        " datetime.datetime(1877, 3, 1, 0, 0),\n",
        " datetime.datetime(1877, 4, 1, 0, 0),\n",
        " datetime.datetime(1877, 5, 1, 0, 0),\n",
        " datetime.datetime(1877, 6, 1, 0, 0),\n",
        " datetime.datetime(1877, 7, 1, 0, 0),\n",
        " datetime.datetime(1877, 8, 1, 0, 0),\n",
        " datetime.datetime(1877, 9, 1, 0, 0),\n",
        " datetime.datetime(1877, 10, 1, 0, 0),\n",
        " datetime.datetime(1877, 11, 1, 0, 0),\n",
        " datetime.datetime(1877, 12, 1, 0, 0),\n",
        " datetime.datetime(1878, 1, 1, 0, 0),\n",
        " datetime.datetime(1878, 2, 1, 0, 0),\n",
        " datetime.datetime(1878, 3, 1, 0, 0),\n",
        " datetime.datetime(1878, 4, 1, 0, 0),\n",
        " datetime.datetime(1878, 5, 1, 0, 0),\n",
        " datetime.datetime(1878, 6, 1, 0, 0),\n",
        " datetime.datetime(1878, 7, 1, 0, 0),\n",
        " datetime.datetime(1878, 8, 1, 0, 0),\n",
        " datetime.datetime(1878, 9, 1, 0, 0),\n",
        " datetime.datetime(1878, 10, 1, 0, 0),\n",
        " datetime.datetime(1878, 11, 1, 0, 0),\n",
        " datetime.datetime(1878, 12, 1, 0, 0),\n",
        " datetime.datetime(1879, 1, 1, 0, 0),\n",
        " datetime.datetime(1879, 2, 1, 0, 0),\n",
        " datetime.datetime(1879, 3, 1, 0, 0),\n",
        " datetime.datetime(1879, 4, 1, 0, 0),\n",
        " datetime.datetime(1879, 5, 1, 0, 0),\n",
        " datetime.datetime(1879, 6, 1, 0, 0),\n",
        " datetime.datetime(1879, 7, 1, 0, 0),\n",
        " datetime.datetime(1879, 8, 1, 0, 0),\n",
        " datetime.datetime(1879, 9, 1, 0, 0),\n",
        " datetime.datetime(1879, 10, 1, 0, 0),\n",
        " datetime.datetime(1879, 11, 1, 0, 0),\n",
        " datetime.datetime(1879, 12, 1, 0, 0),\n",
        " datetime.datetime(1880, 1, 1, 0, 0),\n",
        " datetime.datetime(1880, 2, 1, 0, 0),\n",
        " datetime.datetime(1880, 3, 1, 0, 0),\n",
        " datetime.datetime(1880, 4, 1, 0, 0),\n",
        " datetime.datetime(1880, 5, 1, 0, 0),\n",
        " datetime.datetime(1880, 6, 1, 0, 0),\n",
        " datetime.datetime(1880, 7, 1, 0, 0),\n",
        " datetime.datetime(1880, 8, 1, 0, 0),\n",
        " datetime.datetime(1880, 9, 1, 0, 0),\n",
        " datetime.datetime(1880, 10, 1, 0, 0),\n",
        " datetime.datetime(1880, 11, 1, 0, 0),\n",
        " datetime.datetime(1880, 12, 1, 0, 0),\n",
        " datetime.datetime(1881, 1, 1, 0, 0),\n",
        " datetime.datetime(1881, 2, 1, 0, 0),\n",
        " datetime.datetime(1881, 3, 1, 0, 0),\n",
        " datetime.datetime(1881, 4, 1, 0, 0),\n",
        " datetime.datetime(1881, 5, 1, 0, 0),\n",
        " datetime.datetime(1881, 6, 1, 0, 0),\n",
        " datetime.datetime(1881, 7, 1, 0, 0),\n",
        " datetime.datetime(1881, 8, 1, 0, 0),\n",
        " datetime.datetime(1881, 9, 1, 0, 0),\n",
        " datetime.datetime(1881, 10, 1, 0, 0),\n",
        " datetime.datetime(1881, 11, 1, 0, 0),\n",
        " datetime.datetime(1881, 12, 1, 0, 0),\n",
        " datetime.datetime(1882, 1, 1, 0, 0),\n",
        " datetime.datetime(1882, 2, 1, 0, 0),\n",
        " datetime.datetime(1882, 3, 1, 0, 0),\n",
        " datetime.datetime(1882, 4, 1, 0, 0),\n",
        " datetime.datetime(1882, 5, 1, 0, 0),\n",
        " datetime.datetime(1882, 6, 1, 0, 0),\n",
        " datetime.datetime(1882, 7, 1, 0, 0),\n",
        " datetime.datetime(1882, 8, 1, 0, 0),\n",
        " datetime.datetime(1882, 9, 1, 0, 0),\n",
        " datetime.datetime(1882, 10, 1, 0, 0),\n",
        " datetime.datetime(1882, 11, 1, 0, 0),\n",
        " datetime.datetime(1882, 12, 1, 0, 0),\n",
        " datetime.datetime(1883, 1, 1, 0, 0),\n",
        " datetime.datetime(1883, 2, 1, 0, 0),\n",
        " datetime.datetime(1883, 3, 1, 0, 0),\n",
        " datetime.datetime(1883, 4, 1, 0, 0),\n",
        " datetime.datetime(1883, 5, 1, 0, 0),\n",
        " datetime.datetime(1883, 6, 1, 0, 0),\n",
        " datetime.datetime(1883, 7, 1, 0, 0),\n",
        " datetime.datetime(1883, 8, 1, 0, 0),\n",
        " datetime.datetime(1883, 9, 1, 0, 0),\n",
        " datetime.datetime(1883, 10, 1, 0, 0),\n",
        " datetime.datetime(1883, 11, 1, 0, 0),\n",
        " datetime.datetime(1883, 12, 1, 0, 0),\n",
        " datetime.datetime(1884, 1, 1, 0, 0),\n",
        " datetime.datetime(1884, 2, 1, 0, 0),\n",
        " datetime.datetime(1884, 3, 1, 0, 0),\n",
        " datetime.datetime(1884, 4, 1, 0, 0),\n",
        " datetime.datetime(1884, 5, 1, 0, 0),\n",
        " datetime.datetime(1884, 6, 1, 0, 0),\n",
        " datetime.datetime(1884, 7, 1, 0, 0),\n",
        " datetime.datetime(1884, 8, 1, 0, 0),\n",
        " datetime.datetime(1884, 9, 1, 0, 0),\n",
        " datetime.datetime(1884, 10, 1, 0, 0),\n",
        " datetime.datetime(1884, 11, 1, 0, 0),\n",
        " datetime.datetime(1884, 12, 1, 0, 0),\n",
        " datetime.datetime(1885, 1, 1, 0, 0),\n",
        " datetime.datetime(1885, 2, 1, 0, 0),\n",
        " datetime.datetime(1885, 3, 1, 0, 0),\n",
        " datetime.datetime(1885, 4, 1, 0, 0),\n",
        " datetime.datetime(1885, 5, 1, 0, 0),\n",
        " datetime.datetime(1885, 6, 1, 0, 0),\n",
        " datetime.datetime(1885, 7, 1, 0, 0),\n",
        " datetime.datetime(1885, 8, 1, 0, 0),\n",
        " datetime.datetime(1885, 9, 1, 0, 0),\n",
        " datetime.datetime(1885, 10, 1, 0, 0),\n",
        " datetime.datetime(1885, 11, 1, 0, 0),\n",
        " datetime.datetime(1885, 12, 1, 0, 0),\n",
        " datetime.datetime(1886, 1, 1, 0, 0),\n",
        " datetime.datetime(1886, 2, 1, 0, 0),\n",
        " datetime.datetime(1886, 3, 1, 0, 0),\n",
        " datetime.datetime(1886, 4, 1, 0, 0),\n",
        " datetime.datetime(1886, 5, 1, 0, 0),\n",
        " datetime.datetime(1886, 6, 1, 0, 0),\n",
        " datetime.datetime(1886, 7, 1, 0, 0),\n",
        " datetime.datetime(1886, 8, 1, 0, 0),\n",
        " datetime.datetime(1886, 9, 1, 0, 0),\n",
        " datetime.datetime(1886, 10, 1, 0, 0),\n",
        " datetime.datetime(1886, 11, 1, 0, 0),\n",
        " datetime.datetime(1886, 12, 1, 0, 0),\n",
        " datetime.datetime(1887, 1, 1, 0, 0),\n",
        " datetime.datetime(1887, 2, 1, 0, 0),\n",
        " datetime.datetime(1887, 3, 1, 0, 0),\n",
        " datetime.datetime(1887, 4, 1, 0, 0),\n",
        " datetime.datetime(1887, 5, 1, 0, 0),\n",
        " datetime.datetime(1887, 6, 1, 0, 0),\n",
        " datetime.datetime(1887, 7, 1, 0, 0),\n",
        " datetime.datetime(1887, 8, 1, 0, 0),\n",
        " datetime.datetime(1887, 9, 1, 0, 0),\n",
        " datetime.datetime(1887, 10, 1, 0, 0),\n",
        " datetime.datetime(1887, 11, 1, 0, 0),\n",
        " datetime.datetime(1887, 12, 1, 0, 0),\n",
        " datetime.datetime(1888, 1, 1, 0, 0),\n",
        " datetime.datetime(1888, 2, 1, 0, 0),\n",
        " datetime.datetime(1888, 3, 1, 0, 0),\n",
        " datetime.datetime(1888, 4, 1, 0, 0),\n",
        " datetime.datetime(1888, 5, 1, 0, 0),\n",
        " datetime.datetime(1888, 6, 1, 0, 0),\n",
        " datetime.datetime(1888, 7, 1, 0, 0),\n",
        " datetime.datetime(1888, 8, 1, 0, 0),\n",
        " datetime.datetime(1888, 9, 1, 0, 0),\n",
        " datetime.datetime(1888, 10, 1, 0, 0),\n",
        " datetime.datetime(1888, 11, 1, 0, 0),\n",
        " datetime.datetime(1888, 12, 1, 0, 0),\n",
        " datetime.datetime(1889, 1, 1, 0, 0),\n",
        " datetime.datetime(1889, 2, 1, 0, 0),\n",
        " datetime.datetime(1889, 3, 1, 0, 0),\n",
        " datetime.datetime(1889, 4, 1, 0, 0),\n",
        " datetime.datetime(1889, 5, 1, 0, 0),\n",
        " datetime.datetime(1889, 6, 1, 0, 0),\n",
        " datetime.datetime(1889, 7, 1, 0, 0),\n",
        " datetime.datetime(1889, 8, 1, 0, 0),\n",
        " datetime.datetime(1889, 9, 1, 0, 0),\n",
        " datetime.datetime(1889, 10, 1, 0, 0),\n",
        " datetime.datetime(1889, 11, 1, 0, 0),\n",
        " datetime.datetime(1889, 12, 1, 0, 0),\n",
        " datetime.datetime(1890, 1, 1, 0, 0),\n",
        " datetime.datetime(1890, 2, 1, 0, 0),\n",
        " datetime.datetime(1890, 3, 1, 0, 0),\n",
        " datetime.datetime(1890, 4, 1, 0, 0),\n",
        " datetime.datetime(1890, 5, 1, 0, 0),\n",
        " datetime.datetime(1890, 6, 1, 0, 0),\n",
        " datetime.datetime(1890, 7, 1, 0, 0),\n",
        " datetime.datetime(1890, 8, 1, 0, 0),\n",
        " datetime.datetime(1890, 9, 1, 0, 0),\n",
        " datetime.datetime(1890, 10, 1, 0, 0),\n",
        " datetime.datetime(1890, 11, 1, 0, 0),\n",
        " datetime.datetime(1890, 12, 1, 0, 0),\n",
        " datetime.datetime(1891, 1, 1, 0, 0),\n",
        " datetime.datetime(1891, 2, 1, 0, 0),\n",
        " datetime.datetime(1891, 3, 1, 0, 0),\n",
        " datetime.datetime(1891, 4, 1, 0, 0),\n",
        " datetime.datetime(1891, 5, 1, 0, 0),\n",
        " datetime.datetime(1891, 6, 1, 0, 0),\n",
        " datetime.datetime(1891, 7, 1, 0, 0),\n",
        " datetime.datetime(1891, 8, 1, 0, 0),\n",
        " datetime.datetime(1891, 9, 1, 0, 0),\n",
        " datetime.datetime(1891, 10, 1, 0, 0),\n",
        " datetime.datetime(1891, 11, 1, 0, 0),\n",
        " datetime.datetime(1891, 12, 1, 0, 0),\n",
        " datetime.datetime(1892, 1, 1, 0, 0),\n",
        " datetime.datetime(1892, 2, 1, 0, 0),\n",
        " datetime.datetime(1892, 3, 1, 0, 0),\n",
        " datetime.datetime(1892, 4, 1, 0, 0),\n",
        " datetime.datetime(1892, 5, 1, 0, 0),\n",
        " datetime.datetime(1892, 6, 1, 0, 0),\n",
        " datetime.datetime(1892, 7, 1, 0, 0),\n",
        " datetime.datetime(1892, 8, 1, 0, 0),\n",
        " datetime.datetime(1892, 9, 1, 0, 0),\n",
        " datetime.datetime(1892, 10, 1, 0, 0),\n",
        " datetime.datetime(1892, 11, 1, 0, 0),\n",
        " datetime.datetime(1892, 12, 1, 0, 0),\n",
        " datetime.datetime(1893, 1, 1, 0, 0),\n",
        " datetime.datetime(1893, 2, 1, 0, 0),\n",
        " datetime.datetime(1893, 3, 1, 0, 0),\n",
        " datetime.datetime(1893, 4, 1, 0, 0),\n",
        " datetime.datetime(1893, 5, 1, 0, 0),\n",
        " datetime.datetime(1893, 6, 1, 0, 0),\n",
        " datetime.datetime(1893, 7, 1, 0, 0),\n",
        " datetime.datetime(1893, 8, 1, 0, 0),\n",
        " datetime.datetime(1893, 9, 1, 0, 0),\n",
        " datetime.datetime(1893, 10, 1, 0, 0),\n",
        " datetime.datetime(1893, 11, 1, 0, 0),\n",
        " datetime.datetime(1893, 12, 1, 0, 0),\n",
        " datetime.datetime(1894, 1, 1, 0, 0),\n",
        " datetime.datetime(1894, 2, 1, 0, 0),\n",
        " datetime.datetime(1894, 3, 1, 0, 0),\n",
        " datetime.datetime(1894, 4, 1, 0, 0),\n",
        " datetime.datetime(1894, 5, 1, 0, 0),\n",
        " datetime.datetime(1894, 6, 1, 0, 0),\n",
        " datetime.datetime(1894, 7, 1, 0, 0),\n",
        " datetime.datetime(1894, 8, 1, 0, 0),\n",
        " datetime.datetime(1894, 9, 1, 0, 0),\n",
        " datetime.datetime(1894, 10, 1, 0, 0),\n",
        " datetime.datetime(1894, 11, 1, 0, 0),\n",
        " datetime.datetime(1894, 12, 1, 0, 0),\n",
        " datetime.datetime(1895, 1, 1, 0, 0),\n",
        " datetime.datetime(1895, 2, 1, 0, 0),\n",
        " datetime.datetime(1895, 3, 1, 0, 0),\n",
        " datetime.datetime(1895, 4, 1, 0, 0),\n",
        " datetime.datetime(1895, 5, 1, 0, 0),\n",
        " datetime.datetime(1895, 6, 1, 0, 0),\n",
        " datetime.datetime(1895, 7, 1, 0, 0),\n",
        " datetime.datetime(1895, 8, 1, 0, 0),\n",
        " datetime.datetime(1895, 9, 1, 0, 0),\n",
        " datetime.datetime(1895, 10, 1, 0, 0),\n",
        " datetime.datetime(1895, 11, 1, 0, 0),\n",
        " datetime.datetime(1895, 12, 1, 0, 0),\n",
        " datetime.datetime(1896, 1, 1, 0, 0),\n",
        " datetime.datetime(1896, 2, 1, 0, 0),\n",
        " datetime.datetime(1896, 3, 1, 0, 0),\n",
        " datetime.datetime(1896, 4, 1, 0, 0),\n",
        " datetime.datetime(1896, 5, 1, 0, 0),\n",
        " datetime.datetime(1896, 6, 1, 0, 0),\n",
        " datetime.datetime(1896, 7, 1, 0, 0),\n",
        " datetime.datetime(1896, 8, 1, 0, 0),\n",
        " datetime.datetime(1896, 9, 1, 0, 0),\n",
        " datetime.datetime(1896, 10, 1, 0, 0),\n",
        " datetime.datetime(1896, 11, 1, 0, 0),\n",
        " datetime.datetime(1896, 12, 1, 0, 0),\n",
        " datetime.datetime(1897, 1, 1, 0, 0),\n",
        " datetime.datetime(1897, 2, 1, 0, 0),\n",
        " datetime.datetime(1897, 3, 1, 0, 0),\n",
        " datetime.datetime(1897, 4, 1, 0, 0),\n",
        " datetime.datetime(1897, 5, 1, 0, 0),\n",
        " datetime.datetime(1897, 6, 1, 0, 0),\n",
        " datetime.datetime(1897, 7, 1, 0, 0),\n",
        " datetime.datetime(1897, 8, 1, 0, 0),\n",
        " datetime.datetime(1897, 9, 1, 0, 0),\n",
        " datetime.datetime(1897, 10, 1, 0, 0),\n",
        " datetime.datetime(1897, 11, 1, 0, 0),\n",
        " datetime.datetime(1897, 12, 1, 0, 0),\n",
        " datetime.datetime(1898, 1, 1, 0, 0),\n",
        " datetime.datetime(1898, 2, 1, 0, 0),\n",
        " datetime.datetime(1898, 3, 1, 0, 0),\n",
        " datetime.datetime(1898, 4, 1, 0, 0),\n",
        " datetime.datetime(1898, 5, 1, 0, 0),\n",
        " datetime.datetime(1898, 6, 1, 0, 0),\n",
        " datetime.datetime(1898, 7, 1, 0, 0),\n",
        " datetime.datetime(1898, 8, 1, 0, 0),\n",
        " datetime.datetime(1898, 9, 1, 0, 0),\n",
        " datetime.datetime(1898, 10, 1, 0, 0),\n",
        " datetime.datetime(1898, 11, 1, 0, 0),\n",
        " datetime.datetime(1898, 12, 1, 0, 0),\n",
        " datetime.datetime(1899, 1, 1, 0, 0),\n",
        " datetime.datetime(1899, 2, 1, 0, 0),\n",
        " datetime.datetime(1899, 3, 1, 0, 0),\n",
        " datetime.datetime(1899, 4, 1, 0, 0),\n",
        " datetime.datetime(1899, 5, 1, 0, 0),\n",
        " datetime.datetime(1899, 6, 1, 0, 0),\n",
        " datetime.datetime(1899, 7, 1, 0, 0),\n",
        " datetime.datetime(1899, 8, 1, 0, 0),\n",
        " datetime.datetime(1899, 9, 1, 0, 0),\n",
        " datetime.datetime(1899, 10, 1, 0, 0),\n",
        " datetime.datetime(1899, 11, 1, 0, 0),\n",
        " datetime.datetime(1899, 12, 1, 0, 0),\n",
        " datetime.datetime(1900, 1, 1, 0, 0),\n",
        " datetime.datetime(1900, 2, 1, 0, 0),\n",
        " datetime.datetime(1900, 3, 1, 0, 0),\n",
        " datetime.datetime(1900, 4, 1, 0, 0),\n",
        " datetime.datetime(1900, 5, 1, 0, 0),\n",
        " datetime.datetime(1900, 6, 1, 0, 0),\n",
        " datetime.datetime(1900, 7, 1, 0, 0),\n",
        " datetime.datetime(1900, 8, 1, 0, 0),\n",
        " datetime.datetime(1900, 9, 1, 0, 0),\n",
        " datetime.datetime(1900, 10, 1, 0, 0),\n",
        " datetime.datetime(1900, 11, 1, 0, 0),\n",
        " datetime.datetime(1900, 12, 1, 0, 0),\n",
        " datetime.datetime(1901, 1, 1, 0, 0),\n",
        " datetime.datetime(1901, 2, 1, 0, 0),\n",
        " datetime.datetime(1901, 3, 1, 0, 0),\n",
        " datetime.datetime(1901, 4, 1, 0, 0),\n",
        " datetime.datetime(1901, 5, 1, 0, 0),\n",
        " datetime.datetime(1901, 6, 1, 0, 0),\n",
        " datetime.datetime(1901, 7, 1, 0, 0),\n",
        " datetime.datetime(1901, 8, 1, 0, 0),\n",
        " datetime.datetime(1901, 9, 1, 0, 0),\n",
        " datetime.datetime(1901, 10, 1, 0, 0),\n",
        " datetime.datetime(1901, 11, 1, 0, 0),\n",
        " datetime.datetime(1901, 12, 1, 0, 0),\n",
        " datetime.datetime(1902, 1, 1, 0, 0),\n",
        " datetime.datetime(1902, 2, 1, 0, 0),\n",
        " datetime.datetime(1902, 3, 1, 0, 0),\n",
        " datetime.datetime(1902, 4, 1, 0, 0),\n",
        " datetime.datetime(1902, 5, 1, 0, 0),\n",
        " datetime.datetime(1902, 6, 1, 0, 0),\n",
        " datetime.datetime(1902, 7, 1, 0, 0),\n",
        " datetime.datetime(1902, 8, 1, 0, 0),\n",
        " datetime.datetime(1902, 9, 1, 0, 0),\n",
        " datetime.datetime(1902, 10, 1, 0, 0),\n",
        " datetime.datetime(1902, 11, 1, 0, 0),\n",
        " datetime.datetime(1902, 12, 1, 0, 0),\n",
        " datetime.datetime(1903, 1, 1, 0, 0),\n",
        " datetime.datetime(1903, 2, 1, 0, 0),\n",
        " datetime.datetime(1903, 3, 1, 0, 0),\n",
        " datetime.datetime(1903, 4, 1, 0, 0),\n",
        " datetime.datetime(1903, 5, 1, 0, 0),\n",
        " datetime.datetime(1903, 6, 1, 0, 0),\n",
        " datetime.datetime(1903, 7, 1, 0, 0),\n",
        " datetime.datetime(1903, 8, 1, 0, 0),\n",
        " datetime.datetime(1903, 9, 1, 0, 0),\n",
        " datetime.datetime(1903, 10, 1, 0, 0),\n",
        " datetime.datetime(1903, 11, 1, 0, 0),\n",
        " datetime.datetime(1903, 12, 1, 0, 0),\n",
        " datetime.datetime(1904, 1, 1, 0, 0),\n",
        " datetime.datetime(1904, 2, 1, 0, 0),\n",
        " datetime.datetime(1904, 3, 1, 0, 0),\n",
        " datetime.datetime(1904, 4, 1, 0, 0),\n",
        " datetime.datetime(1904, 5, 1, 0, 0),\n",
        " datetime.datetime(1904, 6, 1, 0, 0),\n",
        " datetime.datetime(1904, 7, 1, 0, 0),\n",
        " datetime.datetime(1904, 8, 1, 0, 0),\n",
        " datetime.datetime(1904, 9, 1, 0, 0),\n",
        " datetime.datetime(1904, 10, 1, 0, 0),\n",
        " datetime.datetime(1904, 11, 1, 0, 0),\n",
        " datetime.datetime(1904, 12, 1, 0, 0),\n",
        " datetime.datetime(1905, 1, 1, 0, 0),\n",
        " datetime.datetime(1905, 2, 1, 0, 0),\n",
        " datetime.datetime(1905, 3, 1, 0, 0),\n",
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        " datetime.datetime(1905, 5, 1, 0, 0),\n",
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        " datetime.datetime(1938, 5, 1, 0, 0),\n",
        " datetime.datetime(1938, 6, 1, 0, 0),\n",
        " datetime.datetime(1938, 7, 1, 0, 0),\n",
        " datetime.datetime(1938, 8, 1, 0, 0),\n",
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        " datetime.datetime(1940, 12, 1, 0, 0),\n",
        " datetime.datetime(1941, 1, 1, 0, 0),\n",
        " datetime.datetime(1941, 2, 1, 0, 0),\n",
        " datetime.datetime(1941, 3, 1, 0, 0),\n",
        " datetime.datetime(1941, 4, 1, 0, 0),\n",
        " datetime.datetime(1941, 5, 1, 0, 0),\n",
        " datetime.datetime(1941, 6, 1, 0, 0),\n",
        " datetime.datetime(1941, 7, 1, 0, 0),\n",
        " datetime.datetime(1941, 8, 1, 0, 0),\n",
        " datetime.datetime(1941, 9, 1, 0, 0),\n",
        " datetime.datetime(1941, 10, 1, 0, 0),\n",
        " datetime.datetime(1941, 11, 1, 0, 0),\n",
        " datetime.datetime(1941, 12, 1, 0, 0),\n",
        " datetime.datetime(1942, 1, 1, 0, 0),\n",
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        " datetime.datetime(1944, 4, 1, 0, 0),\n",
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        " datetime.datetime(1947, 11, 1, 0, 0),\n",
        " datetime.datetime(1947, 12, 1, 0, 0),\n",
        " datetime.datetime(1948, 1, 1, 0, 0),\n",
        " datetime.datetime(1948, 2, 1, 0, 0),\n",
        " datetime.datetime(1948, 3, 1, 0, 0),\n",
        " datetime.datetime(1948, 4, 1, 0, 0),\n",
        " datetime.datetime(1948, 5, 1, 0, 0),\n",
        " datetime.datetime(1948, 6, 1, 0, 0),\n",
        " datetime.datetime(1948, 7, 1, 0, 0),\n",
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        " datetime.datetime(1948, 12, 1, 0, 0),\n",
        " datetime.datetime(1949, 1, 1, 0, 0),\n",
        " datetime.datetime(1949, 2, 1, 0, 0),\n",
        " datetime.datetime(1949, 3, 1, 0, 0),\n",
        " datetime.datetime(1949, 4, 1, 0, 0),\n",
        " datetime.datetime(1949, 5, 1, 0, 0),\n",
        " datetime.datetime(1949, 6, 1, 0, 0),\n",
        " datetime.datetime(1949, 7, 1, 0, 0),\n",
        " datetime.datetime(1949, 8, 1, 0, 0),\n",
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        " datetime.datetime(1949, 11, 1, 0, 0),\n",
        " datetime.datetime(1949, 12, 1, 0, 0),\n",
        " datetime.datetime(1950, 1, 1, 0, 0),\n",
        " datetime.datetime(1950, 2, 1, 0, 0),\n",
        " datetime.datetime(1950, 3, 1, 0, 0),\n",
        " datetime.datetime(1950, 4, 1, 0, 0),\n",
        " datetime.datetime(1950, 5, 1, 0, 0),\n",
        " datetime.datetime(1950, 6, 1, 0, 0),\n",
        " datetime.datetime(1950, 7, 1, 0, 0),\n",
        " datetime.datetime(1950, 8, 1, 0, 0),\n",
        " datetime.datetime(1950, 9, 1, 0, 0),\n",
        " datetime.datetime(1950, 10, 1, 0, 0),\n",
        " datetime.datetime(1950, 11, 1, 0, 0),\n",
        " datetime.datetime(1950, 12, 1, 0, 0),\n",
        " datetime.datetime(1951, 1, 1, 0, 0),\n",
        " datetime.datetime(1951, 2, 1, 0, 0),\n",
        " datetime.datetime(1951, 3, 1, 0, 0),\n",
        " datetime.datetime(1951, 4, 1, 0, 0),\n",
        " datetime.datetime(1951, 5, 1, 0, 0),\n",
        " datetime.datetime(1951, 6, 1, 0, 0),\n",
        " datetime.datetime(1951, 7, 1, 0, 0),\n",
        " datetime.datetime(1951, 8, 1, 0, 0),\n",
        " datetime.datetime(1951, 9, 1, 0, 0),\n",
        " datetime.datetime(1951, 10, 1, 0, 0),\n",
        " datetime.datetime(1951, 11, 1, 0, 0),\n",
        " datetime.datetime(1951, 12, 1, 0, 0),\n",
        " datetime.datetime(1952, 1, 1, 0, 0),\n",
        " datetime.datetime(1952, 2, 1, 0, 0),\n",
        " datetime.datetime(1952, 3, 1, 0, 0),\n",
        " datetime.datetime(1952, 4, 1, 0, 0),\n",
        " datetime.datetime(1952, 5, 1, 0, 0),\n",
        " datetime.datetime(1952, 6, 1, 0, 0),\n",
        " datetime.datetime(1952, 7, 1, 0, 0),\n",
        " datetime.datetime(1952, 8, 1, 0, 0),\n",
        " datetime.datetime(1952, 9, 1, 0, 0),\n",
        " datetime.datetime(1952, 10, 1, 0, 0),\n",
        " datetime.datetime(1952, 11, 1, 0, 0),\n",
        " datetime.datetime(1952, 12, 1, 0, 0),\n",
        " datetime.datetime(1953, 1, 1, 0, 0),\n",
        " datetime.datetime(1953, 2, 1, 0, 0),\n",
        " datetime.datetime(1953, 3, 1, 0, 0),\n",
        " datetime.datetime(1953, 4, 1, 0, 0),\n",
        " datetime.datetime(1953, 5, 1, 0, 0),\n",
        " datetime.datetime(1953, 6, 1, 0, 0),\n",
        " datetime.datetime(1953, 7, 1, 0, 0),\n",
        " datetime.datetime(1953, 8, 1, 0, 0),\n",
        " datetime.datetime(1953, 9, 1, 0, 0),\n",
        " datetime.datetime(1953, 10, 1, 0, 0),\n",
        " datetime.datetime(1953, 11, 1, 0, 0),\n",
        " datetime.datetime(1953, 12, 1, 0, 0),\n",
        " datetime.datetime(1954, 1, 1, 0, 0),\n",
        " datetime.datetime(1954, 2, 1, 0, 0),\n",
        " datetime.datetime(1954, 3, 1, 0, 0),\n",
        " datetime.datetime(1954, 4, 1, 0, 0),\n",
        " datetime.datetime(1954, 5, 1, 0, 0),\n",
        " datetime.datetime(1954, 6, 1, 0, 0),\n",
        " datetime.datetime(1954, 7, 1, 0, 0),\n",
        " datetime.datetime(1954, 8, 1, 0, 0),\n",
        " datetime.datetime(1954, 9, 1, 0, 0),\n",
        " datetime.datetime(1954, 10, 1, 0, 0),\n",
        " datetime.datetime(1954, 11, 1, 0, 0),\n",
        " datetime.datetime(1954, 12, 1, 0, 0),\n",
        " datetime.datetime(1955, 1, 1, 0, 0),\n",
        " datetime.datetime(1955, 2, 1, 0, 0),\n",
        " datetime.datetime(1955, 3, 1, 0, 0),\n",
        " datetime.datetime(1955, 4, 1, 0, 0),\n",
        " datetime.datetime(1955, 5, 1, 0, 0),\n",
        " datetime.datetime(1955, 6, 1, 0, 0),\n",
        " datetime.datetime(1955, 7, 1, 0, 0),\n",
        " datetime.datetime(1955, 8, 1, 0, 0),\n",
        " datetime.datetime(1955, 9, 1, 0, 0),\n",
        " datetime.datetime(1955, 10, 1, 0, 0),\n",
        " datetime.datetime(1955, 11, 1, 0, 0),\n",
        " datetime.datetime(1955, 12, 1, 0, 0),\n",
        " datetime.datetime(1956, 1, 1, 0, 0),\n",
        " datetime.datetime(1956, 2, 1, 0, 0),\n",
        " datetime.datetime(1956, 3, 1, 0, 0),\n",
        " datetime.datetime(1956, 4, 1, 0, 0),\n",
        " datetime.datetime(1956, 5, 1, 0, 0),\n",
        " datetime.datetime(1956, 6, 1, 0, 0),\n",
        " datetime.datetime(1956, 7, 1, 0, 0),\n",
        " datetime.datetime(1956, 8, 1, 0, 0),\n",
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        " datetime.datetime(1956, 10, 1, 0, 0),\n",
        " datetime.datetime(1956, 11, 1, 0, 0),\n",
        " datetime.datetime(1956, 12, 1, 0, 0),\n",
        " datetime.datetime(1957, 1, 1, 0, 0),\n",
        " datetime.datetime(1957, 2, 1, 0, 0),\n",
        " datetime.datetime(1957, 3, 1, 0, 0),\n",
        " datetime.datetime(1957, 4, 1, 0, 0),\n",
        " datetime.datetime(1957, 5, 1, 0, 0),\n",
        " datetime.datetime(1957, 6, 1, 0, 0),\n",
        " datetime.datetime(1957, 7, 1, 0, 0),\n",
        " datetime.datetime(1957, 8, 1, 0, 0),\n",
        " datetime.datetime(1957, 9, 1, 0, 0),\n",
        " datetime.datetime(1957, 10, 1, 0, 0),\n",
        " datetime.datetime(1957, 11, 1, 0, 0),\n",
        " datetime.datetime(1957, 12, 1, 0, 0),\n",
        " datetime.datetime(1958, 1, 1, 0, 0),\n",
        " datetime.datetime(1958, 2, 1, 0, 0),\n",
        " datetime.datetime(1958, 3, 1, 0, 0),\n",
        " datetime.datetime(1958, 4, 1, 0, 0),\n",
        " datetime.datetime(1958, 5, 1, 0, 0),\n",
        " datetime.datetime(1958, 6, 1, 0, 0),\n",
        " datetime.datetime(1958, 7, 1, 0, 0),\n",
        " datetime.datetime(1958, 8, 1, 0, 0),\n",
        " datetime.datetime(1958, 9, 1, 0, 0),\n",
        " datetime.datetime(1958, 10, 1, 0, 0),\n",
        " datetime.datetime(1958, 11, 1, 0, 0),\n",
        " datetime.datetime(1958, 12, 1, 0, 0),\n",
        " datetime.datetime(1959, 1, 1, 0, 0),\n",
        " datetime.datetime(1959, 2, 1, 0, 0),\n",
        " datetime.datetime(1959, 3, 1, 0, 0),\n",
        " datetime.datetime(1959, 4, 1, 0, 0),\n",
        " datetime.datetime(1959, 5, 1, 0, 0),\n",
        " datetime.datetime(1959, 6, 1, 0, 0),\n",
        " datetime.datetime(1959, 7, 1, 0, 0),\n",
        " datetime.datetime(1959, 8, 1, 0, 0),\n",
        " datetime.datetime(1959, 9, 1, 0, 0),\n",
        " datetime.datetime(1959, 10, 1, 0, 0),\n",
        " datetime.datetime(1959, 11, 1, 0, 0),\n",
        " datetime.datetime(1959, 12, 1, 0, 0),\n",
        " datetime.datetime(1960, 1, 1, 0, 0),\n",
        " datetime.datetime(1960, 2, 1, 0, 0),\n",
        " datetime.datetime(1960, 3, 1, 0, 0),\n",
        " datetime.datetime(1960, 4, 1, 0, 0),\n",
        " ...]"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.index=dateindex"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "1877-01-01   -1.044600\n",
        "1877-02-01   -0.834198\n",
        "1877-03-01   -0.759131\n",
        "1877-04-01   -1.103454\n",
        "1877-05-01    0.349381\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.plot(figsize=(14,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a0f6f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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GH5Xfhr9KWK5mPL75CL7y3D4lrTtsn5mZuunstBxFzShQnQhpsHJDrrexzNYu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\n0MrMZ37sTRlXNxjALPelvlK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4DONsChWE8j4zgpigmBm+S7RvITaj55+kvnK3o6iYGf6N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       "text": [
        "<matplotlib.figure.Figure at 0x109bc5c90>"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 3. The GroupBy method"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The **groupby** method is a very powerful method of pandas DataFrames, in a nutschell it allows you to\n",
      "\n",
      "1. **split** your data according to unique values of a variable (or unique *combinations* of *N* variables)\n",
      "\n",
      "2. **apply** some operation to the groups thus defined, either an *aggregation* or *transformation* method \n",
      "\n",
      "3. **combine** the results into a DataFrame\n",
      "\n",
      "This process is illustrated below, where the operation is here calculating the mean of the groups's values\n",
      "\n",
      "A very nice explanation of the **groupby** method, with examples, is available from Pandas's documentation at:  \n",
      "\n",
      "[http://pandas.pydata.org/pandas-docs/stable/groupby.html](http://pandas.pydata.org/pandas-docs/stable/groupby.html)\n",
      "\n",
      "and a short tutorial on Wes McKinney's blog [here](http://wesmckinney.com/blog/?p=125)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename='images/split-apply-combine.png', width=800)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {
        "png": {
         "width": 800
        }
       },
       "output_type": "pyout",
       "png": 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1a0pud3Jyeu+998raG6A32x3nO2/ePKmmiRMnSl03dOTh4TFkyBDRptTUVKmZ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\nq9WiTatWrTLKCyQAFIaaL2C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W9MuRI0eWLl3q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\nRlesWHH69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S+nJCYmShIG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\nEFZVVdXHH3/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       "prompt_number": 98,
       "text": [
        "<IPython.core.display.Image at 0x10a7adb10>"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = \"ftp://ftp.cpc.ncep.noaa.gov/wd52dg/data/indices/ersst3b.nino.mth.81-10.ascii\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!wget -P ./data ftp://ftp.cpc.ncep.noaa.gov/wd52dg/data/indices/ersst3b.nino.mth.81-10.ascii"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_table(url, sep='\\s+')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.tail()"
     ],
     "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>YR</th>\n",
        "      <th>MON</th>\n",
        "      <th>NINO1+2</th>\n",
        "      <th>ANOM</th>\n",
        "      <th>NINO3</th>\n",
        "      <th>ANOM.1</th>\n",
        "      <th>NINO4</th>\n",
        "      <th>ANOM.2</th>\n",
        "      <th>NINO3.4</th>\n",
        "      <th>ANOM.3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>767</th>\n",
        "      <td> 2013</td>\n",
        "      <td> 12</td>\n",
        "      <td> 22.61</td>\n",
        "      <td>-0.40</td>\n",
        "      <td> 25.01</td>\n",
        "      <td>-0.30</td>\n",
        "      <td> 28.28</td>\n",
        "      <td>-0.26</td>\n",
        "      <td> 26.20</td>\n",
        "      <td>-0.49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>768</th>\n",
        "      <td> 2014</td>\n",
        "      <td>  1</td>\n",
        "      <td> 24.47</td>\n",
        "      <td>-0.22</td>\n",
        "      <td> 25.30</td>\n",
        "      <td>-0.47</td>\n",
        "      <td> 27.91</td>\n",
        "      <td>-0.42</td>\n",
        "      <td> 26.03</td>\n",
        "      <td>-0.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>769</th>\n",
        "      <td> 2014</td>\n",
        "      <td>  2</td>\n",
        "      <td> 25.29</td>\n",
        "      <td>-0.82</td>\n",
        "      <td> 25.85</td>\n",
        "      <td>-0.65</td>\n",
        "      <td> 28.04</td>\n",
        "      <td>-0.13</td>\n",
        "      <td> 26.05</td>\n",
        "      <td>-0.77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>770</th>\n",
        "      <td> 2014</td>\n",
        "      <td>  3</td>\n",
        "      <td> 25.97</td>\n",
        "      <td>-0.54</td>\n",
        "      <td> 27.04</td>\n",
        "      <td>-0.20</td>\n",
        "      <td> 28.26</td>\n",
        "      <td>-0.03</td>\n",
        "      <td> 26.84</td>\n",
        "      <td>-0.49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>771</th>\n",
        "      <td> 2014</td>\n",
        "      <td>  4</td>\n",
        "      <td> 25.17</td>\n",
        "      <td>-0.44</td>\n",
        "      <td> 27.53</td>\n",
        "      <td>-0.02</td>\n",
        "      <td> 28.76</td>\n",
        "      <td> 0.21</td>\n",
        "      <td> 27.62</td>\n",
        "      <td>-0.17</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "       YR  MON  NINO1+2  ANOM  NINO3  ANOM.1  NINO4  ANOM.2  NINO3.4  ANOM.3\n",
        "767  2013   12    22.61 -0.40  25.01   -0.30  28.28   -0.26    26.20   -0.49\n",
        "768  2014    1    24.47 -0.22  25.30   -0.47  27.91   -0.42    26.03   -0.64\n",
        "769  2014    2    25.29 -0.82  25.85   -0.65  28.04   -0.13    26.05   -0.77\n",
        "770  2014    3    25.97 -0.54  27.04   -0.20  28.26   -0.03    26.84   -0.49\n",
        "771  2014    4    25.17 -0.44  27.53   -0.02  28.76    0.21    27.62   -0.17\n",
        "\n",
        "[5 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I only keep the raw - monthly - values of NINO 3.4 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nino = data[['YR','MON','NINO3.4']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nino.head()"
     ],
     "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>YR</th>\n",
        "      <th>MON</th>\n",
        "      <th>NINO3.4</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 1</td>\n",
        "      <td> 24.83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 2</td>\n",
        "      <td> 25.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 3</td>\n",
        "      <td> 26.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 4</td>\n",
        "      <td> 26.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 5</td>\n",
        "      <td> 26.19</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "     YR  MON  NINO3.4\n",
        "0  1950    1    24.83\n",
        "1  1950    2    25.20\n",
        "2  1950    3    26.03\n",
        "3  1950    4    26.36\n",
        "4  1950    5    26.19\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now I want to calculate a climatology (over the whole period available)\n",
      "\n",
      "I first group by UNIQUE values of the variable months, I should get 12 groups"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "groups = nino.groupby('MON')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for month, group in groups:\n",
      "    print month\n",
      "    print group.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n",
        "      YR  MON  NINO3.4\n",
        "0   1950    1    24.83\n",
        "12  1951    1    25.46\n",
        "24  1952    1    26.85\n",
        "36  1953    1    26.85\n",
        "48  1954    1    27.03\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "2\n",
        "      YR  MON  NINO3.4\n",
        "1   1950    2    25.20\n",
        "13  1951    2    25.78\n",
        "25  1952    2    26.79\n",
        "37  1953    2    27.19\n",
        "49  1954    2    27.22\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "3\n",
        "      YR  MON  NINO3.4\n",
        "2   1950    3    26.03\n",
        "14  1951    3    26.72\n",
        "26  1952    3    27.32\n",
        "38  1953    3    27.68\n",
        "50  1954    3    27.21\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "4\n",
        "      YR  MON  NINO3.4\n",
        "3   1950    4    26.36\n",
        "15  1951    4    27.24\n",
        "27  1952    4    27.88\n",
        "39  1953    4    28.19\n",
        "51  1954    4    26.87\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "5\n",
        "      YR  MON  NINO3.4\n",
        "4   1950    5    26.19\n",
        "16  1951    5    27.68\n",
        "28  1952    5    27.99\n",
        "40  1953    5    28.29\n",
        "52  1954    5    27.07\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "6\n",
        "      YR  MON  NINO3.4\n",
        "5   1950    6    26.52\n",
        "17  1951    6    27.46\n",
        "29  1952    6    27.33\n",
        "41  1953    6    28.02\n",
        "53  1954    6    26.93\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "7\n",
        "      YR  MON  NINO3.4\n",
        "6   1950    7    26.42\n",
        "18  1951    7    27.72\n",
        "30  1952    7    26.72\n",
        "42  1953    7    27.52\n",
        "54  1954    7    26.37\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "8\n",
        "      YR  MON  NINO3.4\n",
        "7   1950    8    25.98\n",
        "19  1951    8    27.36\n",
        "31  1952    8    26.46\n",
        "43  1953    8    27.16\n",
        "55  1954    8    25.73\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "9\n",
        "      YR  MON  NINO3.4\n",
        "8   1950    9    25.78\n",
        "20  1951    9    27.51\n",
        "32  1952    9    26.54\n",
        "44  1953    9    27.13\n",
        "56  1954    9    25.38\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "10\n",
        "      YR  MON  NINO3.4\n",
        "9   1950   10    25.96\n",
        "21  1951   10    27.43\n",
        "33  1952   10    26.54\n",
        "45  1953   10    27.02\n",
        "57  1954   10    25.51\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "11\n",
        "      YR  MON  NINO3.4\n",
        "10  1950   11    25.64\n",
        "22  1951   11    27.48\n",
        "34  1952   11    26.36\n",
        "46  1953   11    26.96\n",
        "58  1954   11    25.67\n",
        "\n",
        "[5 rows x 3 columns]\n",
        "12\n",
        "      YR  MON  NINO3.4\n",
        "11  1950   12    25.50\n",
        "23  1951   12    27.12\n",
        "35  1952   12    26.53\n",
        "47  1953   12    26.99\n",
        "59  1954   12    25.37\n",
        "\n",
        "[5 rows x 3 columns]\n"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "climatology = groups.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 107
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Same as \n",
      "\n",
      "    climatology = groups.aggregate(np.mean)\n",
      "    \n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "climatology['NINO3.4'].head(12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "MON\n",
        "1      26.484615\n",
        "2      26.676923\n",
        "3      27.184154\n",
        "4      27.622923\n",
        "5      27.733281\n",
        "6      27.540469\n",
        "7      27.149844\n",
        "8      26.764531\n",
        "9      26.667344\n",
        "10     26.632656\n",
        "11     26.593750\n",
        "12     26.529062\n",
        "Name: NINO3.4, dtype: float64"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "climatology['NINO3.4'].plot(kind='bar',ylim=[26,28], rot=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a7bca10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PTw/mzp3rdJy2tjaH5aioKKxfvx7FxcU4cOAAdDodHnvsMcycOXNEO0FERMPnsgC89957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\nAIiIBMUCQEQkKBYAIiJB/R/7UMWIzIcm6wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109be4190>"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now suppose we want to apply a function that doesnt **aggregate** the values in the groups (such as sum, or mean) but rather want to apply a function to those values ... \n",
      "\n",
      "An example would be calculating the standardized anomalies per month (to each value subtract the mean of the corresponding month, then divide by the standard-deviation)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def zscore(x): \n",
      "    z = (x - x.mean()) / x.std()\n",
      "    return z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "transformed = groups.transform(zscore)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "transformed['NINO3.4'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 112,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a11d350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xwhAcoL5QZwyHJYalFvseLNtBxawt1CxW9WZbynUZlg3bcbCkO4MrV/fUnlvO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\nI8BgMBh9GGYEGAwGow/z/9qDKj8NHdCbAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a7b37d0>"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['ANOM.3'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a167a90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6KC3yKAcNDDdIB4UBW39C9+81B+QVHRlJCFTrkLea1Gh6uIy6UzMylvYk7M9h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\nESAIgghjyAgQBEGEMWQECIIgwpj/B0mKFxxXzufnAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a0be690>"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 4. Some more data manipulation with Pandas\n",
      "\n",
      "This is some real data provided by Dan Smale (Lauder). It is a summary file of daily QA/QC (data quality) diagnostics of raw GPS data collected at NIWA Lauder. The GPS data can be used to infer the amount of total column water vapour in the atmosphere (above lauder).\n",
      "\n",
      "Summary of data\n",
      "/data/gruan/gnss_processing/data/netr9_laud/rinex_211/LAUD0010.13S:SUM 13  1  1 00:00 13  1  1 23:59 24.00  30  25298  23597  93  0.57  0.43  11799\n",
      "\n",
      "+ 13  1  1 00:00 13  1  1 23:59: time period the QC/QA variables are calculated for.\n",
      "+ 24 = number of hourly entries\n",
      "+ 30 = dt\n",
      "+ 25298  = #expt = number of data points expected\n",
      "+ 23597  = #have = number of data points collected\n",
      "+ 93  =% #have/#expt*100\n",
      "+ 0.57 = mp1 = mean path 1\n",
      "+ 0.43  = mp2 = mean path 2\n",
      "+ 11799 =o/slps = number of time slips\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_table('./data/teqc_SUM.dat', sep='\\s+', header=None) ## the '\\s+' is a regular expression"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
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        "      <td> 25296</td>\n",
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        "      <td> 92</td>\n",
        "      <td> 0.61</td>\n",
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        "      <td>  5809</td>\n",
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        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
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        "<p>5 rows \u00d7 17 columns</p>\n",
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       "prompt_number": 115,
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        "3   1   4  23:59  24  30  25300  23939  95  0.59  0.45   7980  \n",
        "4   1   5  23:59  24  30  25301  24581  97  0.64  0.43  12291  \n",
        "\n",
        "[5 rows x 17 columns]"
       ]
      }
     ],
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    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_table('./data/teqc_SUM.dat', header=None, sep='\\s+', \\\n",
      "                     names=['path','year1','m1','d1','t1','year2','m2','d2','t2','nh','nd','c1','c2','c3','c4','c5','c6'])\n",
      "                                    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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        "      <th></th>\n",
        "      <th>path</th>\n",
        "      <th>year1</th>\n",
        "      <th>m1</th>\n",
        "      <th>d1</th>\n",
        "      <th>t1</th>\n",
        "      <th>year2</th>\n",
        "      <th>m2</th>\n",
        "      <th>d2</th>\n",
        "      <th>t2</th>\n",
        "      <th>nh</th>\n",
        "      <th>nd</th>\n",
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        "      <th>c3</th>\n",
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        "      <th>c6</th>\n",
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        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
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        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
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        "      <td> 22652</td>\n",
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        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
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        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25296</td>\n",
        "      <td> 23235</td>\n",
        "      <td> 92</td>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.43</td>\n",
        "      <td>  5809</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 00:00</td>\n",
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        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25300</td>\n",
        "      <td> 23939</td>\n",
        "      <td> 95</td>\n",
        "      <td> 0.59</td>\n",
        "      <td> 0.45</td>\n",
        "      <td>  7980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25301</td>\n",
        "      <td> 24581</td>\n",
        "      <td> 97</td>\n",
        "      <td> 0.64</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 12291</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 17 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 117,
       "text": [
        "                                                path  year1  m1  d1     t1  \\\n",
        "0  /data/gruan/gnss_processing/data/netr9_laud/ri...     13   1   1  00:00   \n",
        "1  /data/gruan/gnss_processing/data/netr9_laud/ri...     13   1   2  00:00   \n",
        "2  /data/gruan/gnss_processing/data/netr9_laud/ri...     13   1   3  00:00   \n",
        "3  /data/gruan/gnss_processing/data/netr9_laud/ri...     13   1   4  00:00   \n",
        "4  /data/gruan/gnss_processing/data/netr9_laud/ri...     13   1   5  00:00   \n",
        "\n",
        "   year2  m2  d2     t2  nh  nd     c1     c2  c3    c4    c5     c6  \n",
        "0     13   1   1  23:59  24  30  25298  23597  93  0.57  0.43  11799  \n",
        "1     13   1   2  23:59  24  30  24530  22652  92  0.58  0.42  22652  \n",
        "2     13   1   3  23:59  24  30  25296  23235  92  0.61  0.43   5809  \n",
        "3     13   1   4  23:59  24  30  25300  23939  95  0.59  0.45   7980  \n",
        "4     13   1   5  23:59  24  30  25301  24581  97  0.64  0.43  12291  \n",
        "\n",
        "[5 rows x 17 columns]"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We want the index (row wise) to properly represent datetime info "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.index = [datetime(y,m,d) for y,m,d in zip(data.year1+2000,data.m1,data.d1)]  # note that we are using list comprehension"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.head()"
     ],
     "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>path</th>\n",
        "      <th>year1</th>\n",
        "      <th>m1</th>\n",
        "      <th>d1</th>\n",
        "      <th>t1</th>\n",
        "      <th>year2</th>\n",
        "      <th>m2</th>\n",
        "      <th>d2</th>\n",
        "      <th>t2</th>\n",
        "      <th>nh</th>\n",
        "      <th>nd</th>\n",
        "      <th>c1</th>\n",
        "      <th>c2</th>\n",
        "      <th>c3</th>\n",
        "      <th>c4</th>\n",
        "      <th>c5</th>\n",
        "      <th>c6</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
        "      <td> 13</td>\n",
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        "      <td> 25298</td>\n",
        "      <td> 23597</td>\n",
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        "      <td> 0.57</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 11799</td>\n",
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        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 00:00</td>\n",
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        "      <td> 0.58</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 22652</td>\n",
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        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 00:00</td>\n",
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        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 23:59</td>\n",
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        "      <td> 30</td>\n",
        "      <td> 25296</td>\n",
        "      <td> 23235</td>\n",
        "      <td> 92</td>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.43</td>\n",
        "      <td>  5809</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25300</td>\n",
        "      <td> 23939</td>\n",
        "      <td> 95</td>\n",
        "      <td> 0.59</td>\n",
        "      <td> 0.45</td>\n",
        "      <td>  7980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td> /data/gruan/gnss_processing/data/netr9_laud/ri...</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
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        "      <td> 00:00</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25301</td>\n",
        "      <td> 24581</td>\n",
        "      <td> 97</td>\n",
        "      <td> 0.64</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 12291</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 17 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "                                                         path  year1  m1  d1  \\\n",
        "2013-01-01  /data/gruan/gnss_processing/data/netr9_laud/ri...     13   1   1   \n",
        "2013-01-02  /data/gruan/gnss_processing/data/netr9_laud/ri...     13   1   2   \n",
        "2013-01-03  /data/gruan/gnss_processing/data/netr9_laud/ri...     13   1   3   \n",
        "2013-01-04  /data/gruan/gnss_processing/data/netr9_laud/ri...     13   1   4   \n",
        "2013-01-05  /data/gruan/gnss_processing/data/netr9_laud/ri...     13   1   5   \n",
        "\n",
        "               t1  year2  m2  d2     t2  nh  nd     c1     c2  c3    c4    c5  \\\n",
        "2013-01-01  00:00     13   1   1  23:59  24  30  25298  23597  93  0.57  0.43   \n",
        "2013-01-02  00:00     13   1   2  23:59  24  30  24530  22652  92  0.58  0.42   \n",
        "2013-01-03  00:00     13   1   3  23:59  24  30  25296  23235  92  0.61  0.43   \n",
        "2013-01-04  00:00     13   1   4  23:59  24  30  25300  23939  95  0.59  0.45   \n",
        "2013-01-05  00:00     13   1   5  23:59  24  30  25301  24581  97  0.64  0.43   \n",
        "\n",
        "               c6  \n",
        "2013-01-01  11799  \n",
        "2013-01-02  22652  \n",
        "2013-01-03   5809  \n",
        "2013-01-04   7980  \n",
        "2013-01-05  12291  \n",
        "\n",
        "[5 rows x 17 columns]"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.pop('path') # we pop the 'path' variable, ve careful it operates in place"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 121,
       "text": [
        "2013-01-01    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-02    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-03    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-04    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-05    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-06    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-07    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-08    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-09    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-10    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-11    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-12    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-13    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-14    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2013-01-15    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "...\n",
        "2012-12-17    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-18    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-19    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-20    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-21    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-22    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-23    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-24    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-25    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-26    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-27    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-28    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-29    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-30    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "2012-12-31    /data/gruan/gnss_processing/data/netr9_laud/ri...\n",
        "Name: path, Length: 551"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.head()"
     ],
     "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>year1</th>\n",
        "      <th>m1</th>\n",
        "      <th>d1</th>\n",
        "      <th>t1</th>\n",
        "      <th>year2</th>\n",
        "      <th>m2</th>\n",
        "      <th>d2</th>\n",
        "      <th>t2</th>\n",
        "      <th>nh</th>\n",
        "      <th>nd</th>\n",
        "      <th>c1</th>\n",
        "      <th>c2</th>\n",
        "      <th>c3</th>\n",
        "      <th>c4</th>\n",
        "      <th>c5</th>\n",
        "      <th>c6</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25298</td>\n",
        "      <td> 23597</td>\n",
        "      <td> 93</td>\n",
        "      <td> 0.57</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 11799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 24530</td>\n",
        "      <td> 22652</td>\n",
        "      <td> 92</td>\n",
        "      <td> 0.58</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 22652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25296</td>\n",
        "      <td> 23235</td>\n",
        "      <td> 92</td>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.43</td>\n",
        "      <td>  5809</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25300</td>\n",
        "      <td> 23939</td>\n",
        "      <td> 95</td>\n",
        "      <td> 0.59</td>\n",
        "      <td> 0.45</td>\n",
        "      <td>  7980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25301</td>\n",
        "      <td> 24581</td>\n",
        "      <td> 97</td>\n",
        "      <td> 0.64</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 12291</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 16 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "            year1  m1  d1     t1  year2  m2  d2     t2  nh  nd     c1     c2  \\\n",
        "2013-01-01     13   1   1  00:00     13   1   1  23:59  24  30  25298  23597   \n",
        "2013-01-02     13   1   2  00:00     13   1   2  23:59  24  30  24530  22652   \n",
        "2013-01-03     13   1   3  00:00     13   1   3  23:59  24  30  25296  23235   \n",
        "2013-01-04     13   1   4  00:00     13   1   4  23:59  24  30  25300  23939   \n",
        "2013-01-05     13   1   5  00:00     13   1   5  23:59  24  30  25301  24581   \n",
        "\n",
        "            c3    c4    c5     c6  \n",
        "2013-01-01  93  0.57  0.43  11799  \n",
        "2013-01-02  92  0.58  0.42  22652  \n",
        "2013-01-03  92  0.61  0.43   5809  \n",
        "2013-01-04  95  0.59  0.45   7980  \n",
        "2013-01-05  97  0.64  0.43  12291  \n",
        "\n",
        "[5 rows x 16 columns]"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sort the dataframe according to the index values (rowwise)  \n",
      "\n",
      "Note that for plotting purposes, you don't even have to do that, pandas + matplotlib understand that the dates need to be in chronological order  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data2 = data.sort()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data2.head()"
     ],
     "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>year1</th>\n",
        "      <th>m1</th>\n",
        "      <th>d1</th>\n",
        "      <th>t1</th>\n",
        "      <th>year2</th>\n",
        "      <th>m2</th>\n",
        "      <th>d2</th>\n",
        "      <th>t2</th>\n",
        "      <th>nh</th>\n",
        "      <th>nd</th>\n",
        "      <th>c1</th>\n",
        "      <th>c2</th>\n",
        "      <th>c3</th>\n",
        "      <th>c4</th>\n",
        "      <th>c5</th>\n",
        "      <th>c6</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-04-28</th>\n",
        "      <td> 12</td>\n",
        "      <td> 4</td>\n",
        "      <td> 28</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 12</td>\n",
        "      <td> 4</td>\n",
        "      <td> 28</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25462</td>\n",
        "      <td> 25461</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0.55</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 25461</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-02</th>\n",
        "      <td> 12</td>\n",
        "      <td> 5</td>\n",
        "      <td>  2</td>\n",
        "      <td> 01:00</td>\n",
        "      <td> 12</td>\n",
        "      <td> 5</td>\n",
        "      <td>  2</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 23</td>\n",
        "      <td> 30</td>\n",
        "      <td> 24480</td>\n",
        "      <td> 24480</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0.56</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 24480</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-03</th>\n",
        "      <td> 12</td>\n",
        "      <td> 5</td>\n",
        "      <td>  3</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 12</td>\n",
        "      <td> 5</td>\n",
        "      <td>  3</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25455</td>\n",
        "      <td> 25455</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0.55</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 12728</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-04</th>\n",
        "      <td> 12</td>\n",
        "      <td> 5</td>\n",
        "      <td>  4</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 12</td>\n",
        "      <td> 5</td>\n",
        "      <td>  4</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25459</td>\n",
        "      <td> 25458</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0.55</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 25458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-05</th>\n",
        "      <td> 12</td>\n",
        "      <td> 5</td>\n",
        "      <td>  5</td>\n",
        "      <td> 00:00</td>\n",
        "      <td> 12</td>\n",
        "      <td> 5</td>\n",
        "      <td>  5</td>\n",
        "      <td> 23:59</td>\n",
        "      <td> 24</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25451</td>\n",
        "      <td> 25450</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0.54</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 12725</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 16 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 124,
       "text": [
        "            year1  m1  d1     t1  year2  m2  d2     t2  nh  nd     c1     c2  \\\n",
        "2012-04-28     12   4  28  00:00     12   4  28  23:59  24  30  25462  25461   \n",
        "2012-05-02     12   5   2  01:00     12   5   2  23:59  23  30  24480  24480   \n",
        "2012-05-03     12   5   3  00:00     12   5   3  23:59  24  30  25455  25455   \n",
        "2012-05-04     12   5   4  00:00     12   5   4  23:59  24  30  25459  25458   \n",
        "2012-05-05     12   5   5  00:00     12   5   5  23:59  24  30  25451  25450   \n",
        "\n",
        "             c3    c4    c5     c6  \n",
        "2012-04-28  100  0.55  0.42  25461  \n",
        "2012-05-02  100  0.56  0.42  24480  \n",
        "2012-05-03  100  0.55  0.43  12728  \n",
        "2012-05-04  100  0.55  0.42  25458  \n",
        "2012-05-05  100  0.54  0.42  12725  \n",
        "\n",
        "[5 rows x 16 columns]"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['c3'].plot(rot=90)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a0b9dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+8Q186lOfwi233IIvfelLOHhw9PY8rbaf/XC65mhx9jZPrVMc5JUDACd6egRn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\nQFH2CgoKCs0ARdkrKCgoNAP+H0TIK2/UhjP7AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a0ae250>"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(data2.c3.values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10c0e24d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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L4Fwf3kojzzkwi7gsFdRWdISK3qb/pwm55wgMyTkwJSgszoExSUibLq8GSa45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\nBMGEA4PBYDCCYMKBwWAwGEH8f6OXytvLxqxzAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ab669d0>"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "Index([u'year1', u'm1', u'd1', u't1', u'year2', u'm2', u'd2', u't2', u'nh', u'nd', u'c1', u'c2', u'c3', u'c4', u'c5', u'c6'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data2 = data[data.columns[-6:]] \n",
      "# same as data2 = data[['c1','c2','c3','c4','c5','c6']] or data2 = data.ix[:,10::]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data2.head()"
     ],
     "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>c1</th>\n",
        "      <th>c2</th>\n",
        "      <th>c3</th>\n",
        "      <th>c4</th>\n",
        "      <th>c5</th>\n",
        "      <th>c6</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 25298</td>\n",
        "      <td> 23597</td>\n",
        "      <td> 93</td>\n",
        "      <td> 0.57</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 11799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td> 24530</td>\n",
        "      <td> 22652</td>\n",
        "      <td> 92</td>\n",
        "      <td> 0.58</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 22652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td> 25296</td>\n",
        "      <td> 23235</td>\n",
        "      <td> 92</td>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.43</td>\n",
        "      <td>  5809</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td> 25300</td>\n",
        "      <td> 23939</td>\n",
        "      <td> 95</td>\n",
        "      <td> 0.59</td>\n",
        "      <td> 0.45</td>\n",
        "      <td>  7980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td> 25301</td>\n",
        "      <td> 24581</td>\n",
        "      <td> 97</td>\n",
        "      <td> 0.64</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 12291</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 129,
       "text": [
        "               c1     c2  c3    c4    c5     c6\n",
        "2013-01-01  25298  23597  93  0.57  0.43  11799\n",
        "2013-01-02  24530  22652  92  0.58  0.42  22652\n",
        "2013-01-03  25296  23235  92  0.61  0.43   5809\n",
        "2013-01-04  25300  23939  95  0.59  0.45   7980\n",
        "2013-01-05  25301  24581  97  0.64  0.43  12291\n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data2.plot(subplots=True, sharex=True, color='b', title='teqc_SUM', figsize=(14,13), rot=90)\n",
      "plt.savefig('teqc_SUM.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P+jrKyuDll20cP24C4PR08dTts00lTaaTP+5UdJ7q7q+Ip85f8X73F1bd83h7GyQkFBIY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\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QQwaYTBocO9aKzEwu4HP5m9/E4auvtKitbe5yQ/TychY335yAr782Y9Qof8Zw7twY9OvH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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ab66c50>"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(1,1, figsize=(7,7))\n",
      "data.groupby(data['m1']).mean()['c1'].plot(kind='bar', ylim=[24000,26000], color='steelblue',alpha=.7, ax=ax)\n",
      "ax.set_xticklabels(list('JFMAMJJASOND'), rotation=None, fontsize=15);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s606ePKkXX3xRd999twYMGHDe762srNTPfvYzzZ07V6tXr44YZIFAQImJicrO\nzravy8nJUffu3VVTU2OvSUtLi/iLYciQIWpra1Ntba2TzbgkJvcKbndqpue7vf/JN/fYNynfURlX\nXl6ujIyMiOG1bt06JScn69Zbbz3v940ePVp33XWXevXqpb1792rt2rX68ssvVVRUJElqampScnJy\nxPd4PB6lpKSoqanJXuP3+yPW9OzZU16v114DAMCliHoovvrqqwoEAnr66aftznDnzp364IMP9K//\n+q8Ra8PhcMTlcePG2f+fnp6uq666So8//rj27NmjjIyMqO/smbfrBpN7Bbc7NdPz3d7/5Jt77JuU\nH9VQXLlypTZv3qzi4mL169fPvn7Xrl3605/+pBkzZtjXhUIhrVmzRm+++aaWLl16ztsbOHCgvF6v\n9u/fr4yMDPn9fgWDwYg14XBYhw8fth8d+v3+s7rMI0eOKBQKnfUI8kwbN260/0E79RTYqcv1B/ar\nsbHR/qGfepjeUZfPzDvzckfnOc3nsruX3d7/5F+e+dLV7eb3zcwzOv/0sxrO5Alf4OFXeXm5qqqq\nVFxcfFZnGAwGI4ZZOBzWggULVFBQoFtuuUWpqannvM09e/Zo3rx5euqpp3Tttdeqrq5Oc+fOVUlJ\nif3UbE1NjebPn6+FCxcqNTVVH330kUpLS7V06dKIX7ilS5fqlVdeUWJi4jmzKioqlJ+ff97tq6kP\n6o3qfe39CCKcPkAv5M78q5UzIPm8X+/M7GjynTr9jws3XG75bu9/8r87+SZve2fkV1dX65Zbbjnn\n19p9pFhWVqYNGzbokUcekc/ns7u7xMREJSYmKjk5+awuMC4uTn6/3x6IX331lTZs2KD8/HwlJSWp\nrq5Oq1ev1sCBA5WTkyNJSktL09ChQ7V8+XI98MADkr4953HYsGH27eTl5Sk9PV2LFy/Wfffdp2Aw\nqNdee03jxo0770AEAMCJdofiu+++K0kqKSmJuH7ixImaMGFCdAHx8frkk0/01ltvqaWlRX369FF+\nfr4mTpwYcT7jww8/rBUrVmjBggWSpBEjRkScf+j1evXYY4+prKxMTzzxhBISEjRmzBgVFhZGt6Ud\nxORewe1OzfR8t/c/+eYe+ybltzsU/+M//sPxDS5ZsiTicp8+ffTkk09e8Pt69Oih2bNnt7vmyiuv\n1GOPPeb4PgEAEA3e+9Qhk89Vcvs8PdPz3d7/5Jt77JuUz1AEAMDCUHTI5F7B7U7N9Hy39z/55h77\nJuUzFAFWKqXhAAARgklEQVQAsDAUHTK5V3C7UzM93+39T765x75J+QxFAAAsDEWHTO4V3O7UTM93\ne/+Tb+6xb1I+QxEAAAtD0SGTewW3OzXT893e/+Sbe+yblM9QBADAwlB0yORewe1OzfR8t/c/+eYe\n+yblMxQBALAwFB0yuVdwu1MzPd/t/U++uce+SfkMRQAALO1+dBTOZnKv0NGd2sFgixqbW6Ne3zcz\nTzX1wajX905KUN/kjvsAajpF8k3MNi2foQjXNDa36o3qfZ12+3fmX92hQxHA5Y+nTx0yuVcwvVMz\nffvJN/fYNymfoQgAgIWh6JDJvYLpnZrp20++uce+SfkMRQAALAxFh0zuFUzv1EzffvLNPfZNymco\nAgBgYSg6ZHKvYHqnZvr2k2/usW9SPkMRAAALQ9Ehk3sF0zs107effHOPfZPyGYoAAFgYig6Z3CuY\n3qmZvv3km3vsm5TPUAQAwMJQdMjkXsH0Ts307Sff3GPfpHyGIgAAFj46yiGTe4XLrVP7rn2eo9v7\nn3xzj32T8hmKMBaf5wjgTDx96pDJvQKdGvnkm5dtWj5DEQAAC0PRIZN7hcutUySf/O9KvsnbHut8\nhiIAABaGokMm9wp0iuSTb162afkMRQAALAxFh0zuFegUySffvGzT8hmKAABYGIoOmdwr0CmST755\n2ablMxQBALAwFB0yuVegUySffPOyTctnKAIAYGEoOmRyr0CnSD755mWbls9QBADAwlB0yORegU6R\nfPLNyzYtn6EIAICFoeiQyb0CnSL55JuXbVo+QxEAAAtD0SGTewU6RfLJNy/btHyGIgAAFoaiQyb3\nCnSK5JNvXrZp+QxFAAAsDEWHTO4V6BTJJ9+8bNPyGYoAAFgYig6Z3CvQKZJPvnnZpuUzFAEAsDAU\nHTK5V6BTJJ9887JNy2coAgBgYSg6ZHKvQKdIPvnmZZuWz1AEAMDCUHTI5F6BTpF88s3LNi2foQgA\ngIWh6JDJvQKdIvnkm5dtWj5DEQAAC0PRIZN7BTpF8sk3L9u0fIYiAAAWhqJDJvcKdIrkk29etmn5\nDEUAACwMRYdM7hXoFMkn37xs0/IZigAAWBiKDpncK9Apkk++edmm5TMUAQCwMBQdMrlXoFMkn3zz\nsk3LZygCAGBhKDpkcq9Ap0g++eZlm5YfH7MkdDkHgy1qbG6Nev0JX1/V1AejXt87KUF9kxMv5q4B\ngCsYig5dTr1CY3Or3qje5+yb6qNff2f+1R06FE3qNcgnv6tkm5bP06cAAFgYig7RK5BPPvkmZZuW\nz1AEAMDCUHSIXoF88sk3Kdu0fIYiAAAWhqJD9Arkk0++Sdmm5TMUAQCwMBQdolcgn3zyTco2Lb/d\nk/fXr1+vLVu2qL6+Xt26dVNWVpYmT56s9PT0c65fvny5KioqVFhYqDvuuMO+/sSJE1q9erUqKyvV\n2tqq3NxcTZs2LWJDm5ubVV5erm3btkmShg8frqlTp8rn89lrGhoaVFZWpp07dyohIUEFBQWaMmWK\n4uN5DwIAwKVr95Hirl27NH78eC1YsEDz589XXFycSkpK1NzcfNbaqqoqffbZZ+rVq5c8Hk/E11au\nXKn/+q//0s9//nM9/fTTOn78uJ599lmFQiF7zUsvvaQ9e/aoqKhIjz/+uD7//HMtWrTI/nooFFJp\naam++eYblZSUaM6cOaqqqtKqVasu9WfgCL0C+eSTb1K2afntDsWioiKNHTtWaWlpuuaaazRr1iwF\ng0EFAoGIdQcPHtTKlSs1Z84cxcXFRXzt2LFj+uMf/6gpU6YoNzdXAwcO1KxZs/TFF1/o448/liTV\n1dVp+/btmjFjhrKyspSdna3p06erurpa+/fvlyRt375ddXV1mj17tjIyMpSXl6fCwkJVVFSopaWl\nI38mAABDOeoUjx8/rnA4rB49etjXnTx5Ui+++KLuvvtuDRgw4Kzvqa2t1cmTJzVkyBD7uj59+igt\nLc0eroFAQImJicrOzrbX5OTkqHv37qqpqbHXpKWlRTzlOmTIELW1tam2ttbJZlwSegXyySffpGzT\n8h0NxfLycmVkZEQMr3Xr1ik5OVm33nrrOb+nqalJXq9XPXv2jLg+JSVFTU1N9prk5OSIr3s8nrPW\n+P3+iDU9e/aU1+u11wAAcCmiHoqvvvqqAoGA5s6da3eGO3fu1AcffKCZM2dGrA2Hwxe8vWjWdMT3\ndDR6BfLJJ9+kbNPyo3rZ5sqVK7V582YVFxerX79+9vW7du3Sn/70J82YMcO+LhQKac2aNXrzzTe1\ndOlS+f1+hUIhHTlyJOLR4uHDhzV48GBJkt/vVzAY+Tl94XBYhw8fth8d+v3+s7rMI0eOKBQKnfUI\n8nR+v1/V1dXtbt+4s5/1Pb8BV0g6HtXSowc+VfWB9td0Vjb55JN/GeWbvO2dkN/ezLjgUCwvL1dV\nVZWKi4vP6gzHjx+vG2+80b4cDoe1YMECFRQU6JZbbpEkZWZmKi4uTtu3b1dBQYEk6dChQ9q3b59y\ncnIkSdnZ2WppaVEgELCfmg0EAvrmm2/sNTk5OXr99dfV2NhoP7+8Y8cOxcfHKzMz87z3f9iwYRfa\nRAAAJF1gKJaVlWnDhg165JFH5PP57O4uMTFRiYmJSk5OPqsLjIuLk9/vV2pqqiTJ5/Pp5ptv1po1\na5SSkqKkpCStWrVKGRkZys3NlSSlpaVp6NChWr58uR544AFJ357zOGzYMPt28vLylJ6ersWLF+u+\n++5TMBjUa6+9pnHjxikxkU93BwBcOk+4naLu3nvvPef1EydO1IQJE875tYceekg//OEP9eMf/9i+\nrq2tTatWrWr35P2jR49qxYoV2rp1qyRpxIgRFzx5f8yYMSosLOTkfQBAh2h3KAIAYBLe+xQAAAvP\nOzq0bt06/f73v9crr7wSs7zf/va3Z12fm5urf/7nf+703P79++vFF1886+sPP/ywvvrqK02YMEET\nJ07stPtxykMPPaSGhga9+OKL6t+/f6fndbXtP3WfYvm715XyY73/T3n//ff11ltv6cCBA/J6verX\nr5+uu+463XfffTHLf+edd1RXVyePx6OBAwfqxz/+sYYPH96puad+//Py8lRUVBTxteeee07Nzc0q\nLi7u9PxTfD6f+vfvryFDhui2225r99Wjl4qheBHOfG/Xzubz+c76xTy9a+0s3bp109dff63a2tqI\nV/h++umnOnjwoLp16xaTn0UgEFBDQ4MSEhJUWVmpu+++u9Mzpa6z/aeLdV5XyHdr/69fv17r1q3T\nnXfeqeuuu04nTpzQZ599po0bN8ZkKP7617/WH/7wB40fP16TJk3SyZMnVVlZqV/96leaPHmy7rzz\nzk6/Dzt27NBnn32mP//zP+/0rDOd/u/esWPHVFtbq3feeUfvvfeeHn/88XbPOrgUDMWLEOsa1uv1\natCgQTHNlKTu3bsrMzNTlZWVEb+AmzZt0ve+972Yvb3exo0b1b9/f/3FX/xFTP9R7Crbfzq3XwLg\nRr5b+//tt9/Wrbfeqp/+9Kf2dfn5+TF5ZmDLli167733NH36dI0bN86+fujQofL7/fr3f/935eXl\naeDAgZ12H5KSktS7d2+9/vrreuSRRzot53zO/HcvLy9PP/jBD1RcXKwXX3xRL7zwgrzejm8A6RTR\nrlGjRmnz5s325XA4rM2bN2v06NExyQ+FQtq8ebNuuOEGjRw5Uvv27dMXX3wRk2zJ/e03nZv7/9ix\nY0pJSYlJ1pneeust9e/f3z7f+3R33XWXrrjiCr399tudfj/uuusubd26VV9++WWnZ0XD5/Ppb/7m\nb3TgwAHt2LGjUzIYit8RoVBIJ0+etP+LBY/Ho5EjR+rw4cP6n//5H0nS7t27FQwGdcMNN8TkPnzy\nyScKBoMaOXKkcnNz5fP5VFlZGZPsrrD9pnNz/2dmZurtt9/WBx98oCNHjsQkU/r2QxYCgYCGDRt2\nzqerfT6frrvuOu3evbtT74fH49GNN96o1NRUvf76652a5cTgwYPl9Xr16aefdsrtMxS/A5qbmzVp\n0iRNnjzZ/u+TTz6JSbbP59OQIUPsf4g2bdqk66+/PiadpiRVVlaqd+/eGjRokOLj43X99dfH7B9F\nyf3tN52b+/9nP/uZEhMT9fLLL2vatGmaO3eu1q1bp+PHo3+7sYtx5MgRtbW1qW/fvuddc+WVV3b6\n+4GGw2F5PB799V//taqqquyP8XNbQkKCkpOTO+2DIBiK3wE+n0+lpaUR/8WiYzzVH40aNUpVVVVq\na2tTVVWVRo0a1enZ0rdv+rBlyxaNGDHCvm7kyJFqaGg4631wO4Pb2286t/f/NddcoxdeeEGPPvqo\nxo8fr3A4rN/+9rd67LHHjPoM1zFjxujKK6/U+vXr3b4rttM/oL6jMRS/A7xerzIzMyP+i+Vb2w0f\nPlwtLS1au3atvvnmm05/Ofgp//3f/61jx47pe9/7no4ePaqjR48qKytL8fHx2rhxY0zug+Te9puu\nK+z/+Ph4DRs2TFOnTtXzzz+vGTNm6MCBA/rDH/7QaZk9e/ZUfHy8Dh48eN41Bw8eVJ8+fTrtPpwu\nLi5OP/nJT7RhwwY1NDTEJLM9ra2tam5u7rTTMnj1KS4oMTFRw4YN05tvvqkbb7xRCQkJMck99TTZ\nc889d9bXqqqqdP/993fKq8/O5Nb2m66r7P/TnXof5/r6+k7LiIuLU3Z2tqqrqzVlypSzesVjx45p\n165dGjlyZKfdhzPdfPPNev311/W73/3O9dOCdu7cqVAoFPG5vh2JoYio/OAHP1BbW9t5P0y6o7W0\ntGjbtm0aPXp0xEvSJenzzz/XqlWr9MknnygvLy8m9yfW22+6rrD/Dx8+fNarT4PBoI4dO9apJ49L\n0o9+9CP927/9myoqKs7a/t/97ndqaWnRbbfd1qn34XTx8fG64447tHbtWmVmZrr2ftNHjx7VmjVr\n1L9/f/sDJToaQ/EiuP2XkhsGDx5sf/5lLGzdulWtra360Y9+dFZ/eupjxCorK2M2FGO9/efj9u9e\nrPK7wv7/p3/6J40YMUJ5eXlKTk5WQ0OD/vM//1Pdu3fXX/3VX3VarvTtByLceuuteuWVV1RXV6f8\n/HyFQiFt2rRJH3zwgSZPnqyMjIxOvQ9nuvXWW7V+/XoFAoGYHAuhUEj/+7//q3A4rJaWFvvk/RMn\nTujxxx/vtN9FhqJDra2tMf0ryePxuPIPoVu5p1RWVio1NfWcLyiKi4vTjTfeqMrKSk2fPr1T9ofb\n238usf7dczPf7f0vSRMmTNDWrVtVXl5ud1g5OTn6h3/4h3ZfGdpRpk2bpkGDBundd99VRUWF/dqC\nRx99tNM/J/Zcv/8JCQm6/fbb9Zvf/KZTs0/lHzt2zH4ry1Nv83bTTTfphz/8YaeeP8qnZDj03HPP\nqampSSUlJW7fFRjG7d89t/OBWODVp1Hau3ev3n77bVVXV0e8RBzobG7/7rmdD8QST59GacWKFfrq\nq690++23R3yAMtDZ3P7dczsfiCWePgUAwMLTpwAAWBiKAABYGIoAAFgYigAAWBiKAABYOCUDMMyy\nZcu0e/du7d+/X3//93+vsWPHun2XgC6DR4qAYTIyMjRt2jRlZmZ2ubeyA9zGI0XgMvDQQw9p/Pjx\n+vDDD/X111/rxhtv1KRJk/Tyyy+rpqZGgwYN0j/+4z+qR48eGj9+vCSpW7duLt9roOvhkSJwmdiy\nZYvmz5+vhQsXqrq6WqWlpZo8ebLKysoUDof11ltvuX0XgS6PR4rAZeK2225TcnKyJOnaa69VSkqK\n/fFCN9xwgz7++GMX7x3w3cAjReAycfoH3yYkJERc7tatm1paWty4W8B3CkMRuEzxtsaAcwxFwDBt\nbW1qbW1VOByO+H8AdIrAZev00y1O/yT1Z555Rrt375YkBQIBLV++XMXFxRo8eLAr9xPoSvjoKAAA\nLDx9CgCAhaEIAICFoQgAgIWhCACAhaEIAICFoQgAgIWhCACAhaEIAICFoQgAgOX/APco68brERDT\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c113d10>"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(1,1, figsize=(7,7))\n",
      "data.groupby(data.index.month).mean()['c1'].plot(kind='bar',\\\n",
      "                                                 ylim=[24000,26000], color='steelblue',alpha=.7, ax=ax)\n",
      "ax.set_xticklabels(list('JFMAMJJASOND'), rotation=None, fontsize=13);\n",
      "[f.set_fontsize(13) for f in ax.yaxis.get_ticklabels()] # list comprehension again"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 132,
       "text": [
        "[None, None, None, None, None]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10c67ba90>"
       ]
      }
     ],
     "prompt_number": 132
    }
   ],
   "metadata": {}
  }
 ]
}