notebooks/pandas.ipynb

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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext load_style\n",
      "%load_style talk.css\n",
      "from IPython.display import Image, HTML\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The load_style extension is already loaded. To reload it, use:\n",
        "  %reload_ext load_style\n"
       ]
      },
      {
       "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 0x10b9e6590>"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Pandas"
     ]
    },
    {
     "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": "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": 39,
       "text": [
        "<IPython.core.display.HTML at 0x10b17f750>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The conventional way to import pandas is ```import pandas as pd```"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "some options setting "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.set_option(\"display.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": 41
    },
    {
     "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": 42,
       "text": [
        "<IPython.core.display.HTML at 0x10b18a8d0>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Pandas data structures"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Series"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\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": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Series constructions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = pd.Series(np.random.normal(0,1,(10,)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "0    0.037009\n",
        "1    0.593607\n",
        "2   -1.623254\n",
        "3    0.294439\n",
        "4    2.162388\n",
        "5   -0.570866\n",
        "6    1.671018\n",
        "7    1.399286\n",
        "8   -0.573680\n",
        "9   -0.160413\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "array([ 0.03700874,  0.59360691, -1.6232544 ,  0.29443856,  2.16238755,\n",
        "       -0.57086554,  1.67101806,  1.39928642, -0.57367972, -0.16041259])"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "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": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "1     0.950278\n",
        "2    -0.206156\n",
        "3    -0.136064\n",
        "4     0.247603\n",
        "5    -0.228526\n",
        "6    -0.199513\n",
        "7    -0.071164\n",
        "8     1.546748\n",
        "9    -0.712044\n",
        "10    0.214061\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = pd.Series(np.random.normal(0,1,5), \\\n",
      "              index=['a','b','c','d','e'], name='my series')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "a   -0.448799\n",
        "b   -0.410785\n",
        "c   -1.607073\n",
        "d   -1.217413\n",
        "e    0.024734\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "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='w', hatch='///', title=a.name, width=0.9, grid='off')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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t5gPVff1AVeuh1q6RWisWtZtHt8wrgDuA+4HvA5c2zx8OXLxku1OAW5qumW0t+9uQE6HN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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b1966d0>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "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)\n",
      "ax.set_title(a.name)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<matplotlib.text.Text at 0x10b9342d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/IutmQjsbCndgG3DNlMeLsl24v9xw1zLYimg4uTxufX1zP3ZNW+aMWZOqQK6srDz4mHUC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\nZLhLUoEMd0kqkOEuSQX6f6oeMynUcSHMAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b6633d0>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Series indexing"
     ]
    },
    {
     "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"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "a   -0.448799\n",
        "b   -0.410785\n",
        "c   -1.607073\n",
        "d   -1.217413\n",
        "e    0.024734\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a['c']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "-1.607072752509346"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "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": 55,
       "text": [
        "a   -0.448799\n",
        "b   -0.410785\n",
        "c   -1.607073\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a['c':]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "c   -1.607073\n",
        "d   -1.217413\n",
        "e    0.024734\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "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": 57,
       "text": [
        "a   -0.448799\n",
        "b   -0.410785\n",
        "c   -1.607073\n",
        "e    0.024734\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Adding / modifying inplace an element to / of a Series: use the `loc` method"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "a   -0.448799\n",
        "b   -0.410785\n",
        "c   -1.607073\n",
        "d   -1.217413\n",
        "e    0.024734\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.loc['e'] = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "a   -0.448799\n",
        "b   -0.410785\n",
        "c   -1.607073\n",
        "d   -1.217413\n",
        "e    1.000000\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.loc['f'] = 5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "a   -0.448799\n",
        "b   -0.410785\n",
        "c   -1.607073\n",
        "d   -1.217413\n",
        "e    1.000000\n",
        "f    5.000000\n",
        "Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "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": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s3 = s1 + s2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "a   NaN\n",
        "b     3\n",
        "c     5\n",
        "d   NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "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": 27,
       "text": [
        "4.0"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "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": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "b    3\n",
        "c    5\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "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": 30,
       "text": [
        "a   -999\n",
        "b      3\n",
        "c      5\n",
        "d   -999\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s3.fillna(s3.mean())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "a    4\n",
        "b    3\n",
        "c    5\n",
        "d    4\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Series with a date / datetime index (timeseries)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Series can have indexes representing dates / times "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "2014-01-01    0.142453\n",
        "2014-01-02   -0.367365\n",
        "2014-01-03    1.160442\n",
        "2014-01-04    0.846886\n",
        "Freq: D, Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "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": 34,
       "text": [
        "2014-01-01 00:00:00    0.142453\n",
        "2014-01-01 00:05:00    0.142453\n",
        "2014-01-01 00:10:00    0.142453\n",
        "2014-01-01 00:15:00    0.142453\n",
        "2014-01-01 00:20:00    0.142453\n",
        "2014-01-01 00:25:00    0.142453\n",
        "2014-01-01 00:30:00    0.142453\n",
        "2014-01-01 00:35:00    0.142453\n",
        "2014-01-01 00:40:00    0.142453\n",
        "2014-01-01 00:45:00    0.142453\n",
        "2014-01-01 00:50:00    0.142453\n",
        "2014-01-01 00:55:00    0.142453\n",
        "2014-01-01 01:00:00    0.142453\n",
        "2014-01-01 01:05:00    0.142453\n",
        "2014-01-01 01:10:00    0.142453\n",
        "...\n",
        "2014-01-03 22:50:00    1.160442\n",
        "2014-01-03 22:55:00    1.160442\n",
        "2014-01-03 23:00:00    1.160442\n",
        "2014-01-03 23:05:00    1.160442\n",
        "2014-01-03 23:10:00    1.160442\n",
        "2014-01-03 23:15:00    1.160442\n",
        "2014-01-03 23:20:00    1.160442\n",
        "2014-01-03 23:25:00    1.160442\n",
        "2014-01-03 23:30:00    1.160442\n",
        "2014-01-03 23:35:00    1.160442\n",
        "2014-01-03 23:40:00    1.160442\n",
        "2014-01-03 23:45:00    1.160442\n",
        "2014-01-03 23:50:00    1.160442\n",
        "2014-01-03 23:55:00    1.160442\n",
        "2014-01-04 00:00:00    0.846886\n",
        "Freq: 5T, Name: my series, Length: 865"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": 35,
       "text": [
        "2014-01-01   -0.367365\n",
        "2014-01-02    1.160442\n",
        "2014-01-03    0.846886\n",
        "2014-01-04         NaN\n",
        "Freq: D, Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": 36,
       "text": [
        "2014-01-01    0.142453\n",
        "2014-01-02   -0.367365\n",
        "Freq: D, Name: my series, dtype: float64"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "DataFrames"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**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 named 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",
      "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": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "DataFrame constructions"
     ]
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(np.arange(100).reshape(10,10),\\\n",
      "                  columns=idx,index=np.arange(1,11))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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),\\\n",
      "                   'B' : np.random.random(5),\\\n",
      "                   'C': np.random.random(5)}, index=np.arange(1,6))\n",
      "print df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Indexing a DataFrame"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "different ways, return either a Series or a DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(df['A'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[['A']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(df['A']) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(df[['A']])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "access values per **positional indices** only"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.iloc[2,0:2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[3][['A','C']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "you can also use the `loc` method, giving it both row **AND** columns indexes \n",
      "(the indexing is based on the *label*, **NOT** on the *position*)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.loc[[3,5],['A','B']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "conditional indexing or subsetting of a DataFrame "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subset = df[df['A'] <= 0.7]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subset"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "queries"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "as from Pandas version 0.14, you can build complex *database-like* queries on DataFrames"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.query('A > B')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.query('(A > B) & (B > C)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Extending a DataFrame"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Adding a column is easy "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['D'] = np.random.random(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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'] = 5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following doesn't work because there's no way to tell **where** to insert the missing value (align to 1st or last index ?)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['F'] = np.random.random(4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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), index=np.arange(1,5)) #"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Useful DataFrames methods"
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "applying a numpy function"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.apply(np.sqrt) # or np.sqrt(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "summarizing data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.describe().T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "replacing values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['E'].replace(5, 0.0, inplace=True) \n",
      "# you can use dictionnary to multiple replacements"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "DataFrame high-level plotting functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.drop('E', axis=1).plot(figsize=(8,12), \\\n",
      "                          subplots=True, \\\n",
      "                          sharex=True, \\\n",
      "                          kind='bar', rot=0); "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "from sklearn.datasets import load_iris\n",
      " \n",
      "iris = load_iris()\n",
      " \n",
      "iris = pd.DataFrame(iris.data, columns=iris.feature_names)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iris.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iris.hist(); "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=http://pandas.pydata.org/pandas-docs/stable/visualization.html width=1000 height=350></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Input and Output in pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "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. \n",
      "\n",
      "Other supported file types are: \n",
      "\n",
      "* JSON (JavaScript Object Notation)\n",
      "* HDF5 \n",
      "* HTML (to e.g. read tables contained in HTML documents)\n",
      "* SQL (Pandas can **query directly from SQL databases** thanks to [SQLAlchemy](http://www.sqlalchemy.org/))\n",
      "* ..."
     ]
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI = pd.read_csv('../data/NIWA_SOI.csv', index_col=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOI.ix[1950:2000].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Stacking "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs = SOI.stack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from dateutil import parser"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.index=dateindex"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.plot(figsize=(12,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.rolling_mean(SOIs,12).plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Saving in csv, excel etc"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs = pd.DataFrame(SOIs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SOIs.to_excel('../data/SOI_time_series.xlsx', header=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!open ./data/SOI_time_series.xlsx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Reading Excel files"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "first example: an Excel file, containing one sheet, with an Excel `dates` column"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!open ../data/ISO_datetime.xls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_excel('../data/ISO_datetime.xls', sheetname='Sheet1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "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>date</th>\n",
        "      <th>value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2014-01-02</td>\n",
        "      <td> 0.786299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2014-01-03</td>\n",
        "      <td> 0.354398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2014-01-04</td>\n",
        "      <td> 0.747329</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2014-01-05</td>\n",
        "      <td> 0.461564</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2014-01-06</td>\n",
        "      <td> 0.626105</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "        date     value\n",
        "0 2014-01-02  0.786299\n",
        "1 2014-01-03  0.354398\n",
        "2 2014-01-04  0.747329\n",
        "3 2014-01-05  0.461564\n",
        "4 2014-01-06  0.626105"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.date"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_excel('../data/ISO_datetime.xls', \\\n",
      "                     sheetname='Sheet1', index_col=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2014-01-02, ..., 2014-01-11]\n",
        "Length: 10, Freq: None, Timezone: None"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "correctly parsing date(times) when date and time information are contained in different columns"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!open ../data/year_month_day.xlsx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ymd = pd.read_excel('../data/year_month_day.xlsx', sheetname='Sheet1')\n",
      "ymd.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ymd = pd.read_excel('../data/year_month_day.xlsx', sheetname='Sheet1', \\\n",
      "                    parse_dates = {'date': [0, 1, 2]}, \\\n",
      "                    index_col='date')\n",
      "ymd.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>value</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>date</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-01-02</th>\n",
        "      <td> 0.786299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-03</th>\n",
        "      <td> 0.354398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-04</th>\n",
        "      <td> 0.747329</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-05</th>\n",
        "      <td> 0.461564</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-06</th>\n",
        "      <td> 0.626105</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "               value\n",
        "date                \n",
        "2014-01-02  0.786299\n",
        "2014-01-03  0.354398\n",
        "2014-01-04  0.747329\n",
        "2014-01-05  0.461564\n",
        "2014-01-06  0.626105"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myd = pd.read_excel('../data/year_month_day.xlsx', sheetname='Sheet2'); myd.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myd = pd.read_excel('../data/year_month_day.xlsx', sheetname='Sheet2',\\\n",
      "                    parse_dates = {'date': [1, 0, 2]}, \\\n",
      "                    index_col='date'); \n",
      "myd.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>value</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>date</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-01-02</th>\n",
        "      <td> 0.478467</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-03</th>\n",
        "      <td> 0.833801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-04</th>\n",
        "      <td> 0.837646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-05</th>\n",
        "      <td> 0.453646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-06</th>\n",
        "      <td> 0.260355</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "               value\n",
        "date                \n",
        "2014-01-02  0.478467\n",
        "2014-01-03  0.833801\n",
        "2014-01-04  0.837646\n",
        "2014-01-05  0.453646\n",
        "2014-01-06  0.260355"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "groupby operations in Pandas "
     ]
    },
    {
     "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": 47,
       "text": [
        "<IPython.core.display.Image at 0x10809df90>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "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": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_table('../data/ersst3b.nino.mth.81-10.ascii', sep='\\s+') #the '\\s+' is a regular expression meaning any number of spaces"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# if the network confirguration allows it, you can read directly off the URL (ftp):\n",
      "\n",
      "#data = pd.read_table(url, sep='\\s+')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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>776</th>\n",
        "      <td> 2014</td>\n",
        "      <td>  9</td>\n",
        "      <td> 21.54</td>\n",
        "      <td> 0.66</td>\n",
        "      <td> 25.33</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 29.14</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 27.00</td>\n",
        "      <td> 0.17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>777</th>\n",
        "      <td> 2014</td>\n",
        "      <td> 10</td>\n",
        "      <td> 21.69</td>\n",
        "      <td> 0.49</td>\n",
        "      <td> 25.72</td>\n",
        "      <td> 0.65</td>\n",
        "      <td> 29.22</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 27.24</td>\n",
        "      <td> 0.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>778</th>\n",
        "      <td> 2014</td>\n",
        "      <td> 11</td>\n",
        "      <td> 22.44</td>\n",
        "      <td> 0.62</td>\n",
        "      <td> 26.05</td>\n",
        "      <td> 0.91</td>\n",
        "      <td> 29.38</td>\n",
        "      <td> 0.72</td>\n",
        "      <td> 27.56</td>\n",
        "      <td> 0.83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>779</th>\n",
        "      <td> 2014</td>\n",
        "      <td> 12</td>\n",
        "      <td> 23.41</td>\n",
        "      <td> 0.40</td>\n",
        "      <td> 26.09</td>\n",
        "      <td> 0.79</td>\n",
        "      <td> 29.19</td>\n",
        "      <td> 0.65</td>\n",
        "      <td> 27.35</td>\n",
        "      <td> 0.66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>780</th>\n",
        "      <td> 2015</td>\n",
        "      <td>  1</td>\n",
        "      <td> 24.83</td>\n",
        "      <td> 0.15</td>\n",
        "      <td> 26.28</td>\n",
        "      <td> 0.51</td>\n",
        "      <td> 28.97</td>\n",
        "      <td> 0.65</td>\n",
        "      <td> 27.21</td>\n",
        "      <td> 0.54</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "       YR  MON  NINO1+2  ANOM  NINO3  ANOM.1  NINO4  ANOM.2  NINO3.4  ANOM.3\n",
        "776  2014    9    21.54  0.66  25.33    0.31  29.14    0.42    27.00    0.17\n",
        "777  2014   10    21.69  0.49  25.72    0.65  29.22    0.50    27.24    0.46\n",
        "778  2014   11    22.44  0.62  26.05    0.91  29.38    0.72    27.56    0.83\n",
        "779  2014   12    23.41  0.40  26.09    0.79  29.19    0.65    27.35    0.66\n",
        "780  2015    1    24.83  0.15  26.28    0.51  28.97    0.65    27.21    0.54"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "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": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nino.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>NINO3.4</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>776</th>\n",
        "      <td> 2014</td>\n",
        "      <td>  9</td>\n",
        "      <td> 27.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>777</th>\n",
        "      <td> 2014</td>\n",
        "      <td> 10</td>\n",
        "      <td> 27.24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>778</th>\n",
        "      <td> 2014</td>\n",
        "      <td> 11</td>\n",
        "      <td> 27.56</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>779</th>\n",
        "      <td> 2014</td>\n",
        "      <td> 12</td>\n",
        "      <td> 27.35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>780</th>\n",
        "      <td> 2015</td>\n",
        "      <td>  1</td>\n",
        "      <td> 27.21</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "       YR  MON  NINO3.4\n",
        "776  2014    9    27.00\n",
        "777  2014   10    27.24\n",
        "778  2014   11    27.56\n",
        "779  2014   12    27.35\n",
        "780  2015    1    27.21"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "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": 52
    },
    {
     "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",
        "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",
        "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",
        "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",
        "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",
        "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"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\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",
        "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",
        "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",
        "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",
        "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",
        "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"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "climatology = groups.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "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": 55,
       "text": [
        "MON\n",
        "1      26.495606\n",
        "2      26.677077\n",
        "3      27.184308\n",
        "4      27.623692\n",
        "5      27.739692\n",
        "6      27.544462\n",
        "7      27.152000\n",
        "8      26.765385\n",
        "9      26.672462\n",
        "10     26.642000\n",
        "11     26.608615\n",
        "12     26.541692\n",
        "Name: NINO3.4, dtype: float64"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots()\n",
      "\n",
      "climatology['NINO3.4'].plot(ax=ax, kind='bar',ylim=[26,28], rot=0, width=0.9)\n",
      "\n",
      "ax.set_xticklabels(list('JFMAMJJASOND'));\n",
      "\n",
      "f.savefig('../outputs/climatology.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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si2aZZZZZg89qbLBLkuzYG+/lzDLLrDayVl1jxy5Jw9HYYF80yyyzzBp8VmODXZJkx954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\nGuNgl6TGONglqTEOdklqzN8BC6skznLOqPgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1080f5190>"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "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": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "transformed = nino.groupby(nino.MON).apply(zscore)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "transformed['NINO3.4'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10877be50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2x8H2KWA/kZ15pvuEE+p1EDZ7xCv9Now9Mn26P2kPD5Og6u+n+O67z/u7hSPt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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10810c050>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we want calculate (just) the anomalies WRT to the climatology, but with a ** *normal* established over 1980 - 2010**"
     ]
    },
    {
     "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",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "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"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "we can make use of the *query* method of Pandas DataFrames to select the climatological period"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nino.query('YR >= 1981 & YR <= 2000').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>372</th>\n",
        "      <td> 1981</td>\n",
        "      <td> 1</td>\n",
        "      <td> 26.17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>373</th>\n",
        "      <td> 1981</td>\n",
        "      <td> 2</td>\n",
        "      <td> 26.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>374</th>\n",
        "      <td> 1981</td>\n",
        "      <td> 3</td>\n",
        "      <td> 26.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>375</th>\n",
        "      <td> 1981</td>\n",
        "      <td> 4</td>\n",
        "      <td> 27.32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>376</th>\n",
        "      <td> 1981</td>\n",
        "      <td> 5</td>\n",
        "      <td> 27.41</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "       YR  MON  NINO3.4\n",
        "372  1981    1    26.17\n",
        "373  1981    2    26.13\n",
        "374  1981    3    26.75\n",
        "375  1981    4    27.32\n",
        "376  1981    5    27.41"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def demean(x): \n",
      "    z = x - x.query('YR >= 1981 & YR <= 2000').mean()\n",
      "    return z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anoms = nino.groupby([nino.MON]).apply(demean)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(figsize=(10,6))\n",
      "anoms['NINO3.4'].plot()\n",
      "data['ANOM.3'].plot() # Should be the same"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1088c2ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Sj507iZMEfV96zjYs4mx8NU0vXb9NOzR55MwDPPWsSxAAVouanf/+Hb0yES02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0lru1Ca1OFKtBXHQt8laeoHQ8z0HFHfLXRJo9FBaTq3epmwcpB8HmbyLUJi+TUK9VXCep4OwQQd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\nSWyg1qwM9Dv8P2hHUklFSQFb4Hg2hmIwJ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KxgBP1pAh+l11a/5YwZnjqXXEEX6xjMJqWr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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108838510>"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The file [Daily_clim_data_Auckland.csv](./data/Daily_clim_data_Auckland.csv) in the `data` directory contains \n",
      "daily values for rainfall, minimum and maximum temperatures for Auckland from 1972 to now.  \n",
      "\n",
      "1. Read the data in a Pandas DataFrame, correctly setting up the index as a datetime object \n",
      "2. calculate monthly means for the temperature data, monthly totals for rainfall \n",
      "3. calculate and plot a climatology over the period 1981 - 2000 for all parameters \n",
      "4. calculate the anomalies WRT this climatology for all parameters \n",
      "5. plots the time-series of minimum and maximum temperature \n",
      "6. dig into the Scipy documentation to find how to calculate the linear trend over the period for the temperature data \n",
      "7. calculate and plot the trend \n",
      "8. Contact the [New Zealand Climate Science Coalition](http://www.nzclimatescience.org/)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}