{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Timeseries Analysis using Pandas\n",
    "-------------------------------------------------------------------\n",
    "\n",
    "Kushal Keshavamurthy Raviprakash\n",
    "\n",
    "kushalkr2992@gmail.com\n",
    "\n",
    "This notebook is a part of the [Python for Earth and Atmospheric Sciences](https://github.com/Kushalkr/Python_for_Earth_and_Atmospheric_Sciences) workshop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "-------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/pandas.png\" style=\"float: right\" />**`Pandas`** is a python package which provides fast, flexible and expressive data structures and is a package designed to make working with \"relational\" or \"labelled\" data easy and intuitive.\n",
    "\n",
    "I find pandas very useful for timeseries analysis.\n",
    "\n",
    "The pandas package is generally imported as :\n",
    "\n",
    "```py\n",
    "import pandas as pd\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most common data structures in `pandas` are:\n",
    "* `Series` (1-Dimensional, labeled, homogeneous array)\n",
    "* `DataFrame` (2-Dimensional, labeled with potentially heterogenous columns)\n",
    "* `Panel` (3-Dimensional, size mutable array) [**DEPRECATED** and will be removed in the future]\n",
    "\n",
    "In this lecture, we will look at the `Series` and `DataFrame` data structures in some detail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us first import the necessary packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.20.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import necessary packages\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the [Arctic Oscillation (AO)](http://en.wikipedia.org/wiki/Arctic_oscillation) and [North Atlantic Oscillation (NAO)](http://en.wikipedia.org/wiki/North_Atlantic_oscillation) datasets as an example.\n",
    "\n",
    "You can get the data from [here](http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii). But, as a sample, you will find it being present in the `data/` directory of my repository already."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pandas` has some very good I/O facilities. But, for now we will stick to what we have learnt so far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data\n",
    "-------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.95000000e+03   1.00000000e+00  -6.03100000e-02]\n",
      " [  1.95000000e+03   2.00000000e+00   6.26810000e-01]]\n"
     ]
    }
   ],
   "source": [
    "ao = np.loadtxt('data/monthly.ao.index.b50.current.ascii')\n",
    "print(ao[0:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has three columns: year, month and the AO index value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeseries\n",
    "-------------------------------------------------------------------\n",
    "\n",
    "Timeseries of a single variable are usually put into the `Series` data structure provided by `pandas`.\n",
    "\n",
    "To create a timeseries, we need to first create a time range for which we have the data and later use it to index the data. This is done with the help of the `date_range` function defined in the `pandas` module.\n",
    "\n",
    "The data we have loaded ranges from January-1950 until May-2017. You may have to change the range if you obtain a newer version of the data. We use `freq = 'M'` which means the data is available every month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1950-01-31', '1950-02-28', '1950-03-31', '1950-04-30',\n",
       "               '1950-05-31', '1950-06-30', '1950-07-31', '1950-08-31',\n",
       "               '1950-09-30', '1950-10-31',\n",
       "               ...\n",
       "               '2016-03-31', '2016-04-30', '2016-05-31', '2016-06-30',\n",
       "               '2016-07-31', '2016-08-31', '2016-09-30', '2016-10-31',\n",
       "               '2016-11-30', '2016-12-31'],\n",
       "              dtype='datetime64[ns]', length=804, freq='M')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('1950-01','2017-01', freq='M') # Actual data is until 2017-06\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(804,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the data only until Dec-2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1950-01-31   -0.060310\n",
       "1950-02-28    0.626810\n",
       "1950-03-31   -0.008127\n",
       "1950-04-30    0.555100\n",
       "1950-05-31    0.071577\n",
       "1950-06-30    0.538570\n",
       "1950-07-31   -0.802480\n",
       "1950-08-31   -0.851010\n",
       "1950-09-30    0.357970\n",
       "1950-10-31   -0.378900\n",
       "1950-11-30   -0.515110\n",
       "1950-12-31   -1.928100\n",
       "1951-01-31   -0.084969\n",
       "1951-02-28   -0.399930\n",
       "1951-03-31   -1.934100\n",
       "1951-04-30   -0.776480\n",
       "1951-05-31   -0.862780\n",
       "1951-06-30   -0.917860\n",
       "1951-07-31    0.090023\n",
       "1951-08-31   -0.377410\n",
       "1951-09-30   -0.817780\n",
       "1951-10-31   -0.212910\n",
       "1951-11-30   -0.068519\n",
       "1951-12-31    1.987200\n",
       "1952-01-31    0.368250\n",
       "1952-02-29   -1.747200\n",
       "1952-03-31   -1.859500\n",
       "1952-04-30    0.538520\n",
       "1952-05-31   -0.773510\n",
       "1952-06-30   -0.440930\n",
       "                ...   \n",
       "2014-07-31   -0.488940\n",
       "2014-08-31   -0.371540\n",
       "2014-09-30    0.101910\n",
       "2014-10-31   -1.134400\n",
       "2014-11-30   -0.530300\n",
       "2014-12-31    0.412920\n",
       "2015-01-31    1.091600\n",
       "2015-02-28    1.042600\n",
       "2015-03-31    1.837400\n",
       "2015-04-30    1.215700\n",
       "2015-05-31    0.762760\n",
       "2015-06-30    0.427040\n",
       "2015-07-31   -1.107900\n",
       "2015-08-31   -0.689020\n",
       "2015-09-30   -0.164510\n",
       "2015-10-31   -0.250060\n",
       "2015-11-30    1.945000\n",
       "2015-12-31    1.444100\n",
       "2016-01-31   -1.448700\n",
       "2016-02-29   -0.023521\n",
       "2016-03-31    0.280240\n",
       "2016-04-30   -1.051100\n",
       "2016-05-31   -0.035739\n",
       "2016-06-30    0.312880\n",
       "2016-07-31    0.084758\n",
       "2016-08-31    0.472380\n",
       "2016-09-30    0.780970\n",
       "2016-10-31   -1.917300\n",
       "2016-11-30   -0.610910\n",
       "2016-12-31    1.786400\n",
       "Freq: M, Length: 804, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AO = pd.Series(ao[:804,2], index=dates)\n",
    "AO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the data looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1128e4908>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F37LWmLbx5E9bE9rDswqHRn7OijBgqrGx+zRaIOP6xxfF5yuW1BMaB8OD6Wz7ynNT2IQ9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11283d470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AO.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11295a470>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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opXj/55/C8Yub+IN33oyJZIQr9l5VxRgGxROXNnHT7lEo4VDXHvtmsQKD1quOusC+g3z2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\nqDTJE2TvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11297c780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AO['1980':'1990'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114c26f98>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H+MIv9lDj7OF/Hir2y56eSik2FOdQVumksnnsW7P5yq6KFk41drFpRWj3hTGD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\nEWL+P6Gd0VX6Bf1nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c41898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AO['1992-01':'1993-08'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing data is very intuituve. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1992-01-31    0.55003\n",
       "1992-02-29    1.12170\n",
       "1992-03-31    0.98423\n",
       "1992-04-30   -0.52053\n",
       "1992-05-31    1.34140\n",
       "1992-06-30   -0.30196\n",
       "1992-07-31    0.19113\n",
       "1992-08-31    0.53534\n",
       "1992-09-30   -0.64030\n",
       "1992-10-31   -0.36589\n",
       "1992-11-30    0.71699\n",
       "1992-12-31    1.62670\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AO['1992']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-07-31    0.084758\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AO['2016-07']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use fancy indexing as with arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1950-05-31    0.071577\n",
       "1951-07-31    0.090023\n",
       "1953-06-30    0.022535\n",
       "1953-08-31    0.084960\n",
       "1955-04-30    0.194230\n",
       "1955-05-31    0.241610\n",
       "1955-10-31    0.099053\n",
       "1956-06-30    0.280140\n",
       "1956-12-31    0.000891\n",
       "1957-04-30    0.237660\n",
       "1957-08-31    0.096898\n",
       "1959-04-30    0.119240\n",
       "1959-07-31    0.104720\n",
       "1960-06-30    0.054978\n",
       "1961-05-31    0.157480\n",
       "1961-08-31    0.012693\n",
       "1961-10-31    0.203080\n",
       "1962-05-31    0.067997\n",
       "1962-06-30    0.287070\n",
       "1962-08-31    0.152070\n",
       "1963-09-30    0.083110\n",
       "1964-06-30    0.141590\n",
       "1965-06-30    0.037800\n",
       "1965-12-31    0.162970\n",
       "1966-07-31    0.010944\n",
       "1966-09-30    0.011063\n",
       "1966-11-30    0.110560\n",
       "1967-05-31    0.126510\n",
       "1967-07-31    0.259260\n",
       "1967-09-30    0.132930\n",
       "                ...   \n",
       "1996-11-30    0.136370\n",
       "1997-08-31    0.120620\n",
       "1997-09-30    0.194510\n",
       "1998-10-31    0.294330\n",
       "1999-01-31    0.110350\n",
       "1999-04-30    0.284450\n",
       "1999-05-31    0.225870\n",
       "1999-09-30    0.059078\n",
       "2000-08-31    0.143890\n",
       "2003-02-28    0.127800\n",
       "2003-07-31    0.075316\n",
       "2003-12-31    0.265210\n",
       "2005-08-31    0.026141\n",
       "2005-10-31    0.029768\n",
       "2005-11-30    0.227690\n",
       "2006-04-30    0.138330\n",
       "2006-05-31    0.155810\n",
       "2006-07-31    0.102700\n",
       "2007-09-30    0.178920\n",
       "2008-11-30    0.092205\n",
       "2009-03-31    0.121340\n",
       "2012-05-31    0.168410\n",
       "2012-07-31    0.167790\n",
       "2012-08-31    0.013999\n",
       "2013-08-31    0.154250\n",
       "2013-10-31    0.262760\n",
       "2014-02-28    0.043775\n",
       "2014-09-30    0.101910\n",
       "2016-03-31    0.280240\n",
       "2016-07-31    0.084758\n",
       "Length: 106, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AO[np.logical_and((AO < 0.3),(AO > 0))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame\n",
    "-------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2-Dimensional data are handled well by the data structure called `Dataframe`. We will use the [North Atlantic Oscillation (NAO)](http://en.wikipedia.org/wiki/North_Atlantic_oscillation) data as an example. This data is available [here](http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a `Series` the same way as the previously done for the AO data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "nao = np.loadtxt('data/norm.nao.monthly.b5001.current.ascii.txt')\n",
    "dates_nao = pd.date_range('1950-01', '2017-01',freq='M')\n",
    "print(dates_nao.dtype)\n",
    "NAO = pd.Series(nao[:804,2],index=dates_nao)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aonao = pd.DataFrame({'AO' : AO, 'NAO' : NAO})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114e158d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qHNvvoeOxoOvTGEG57pryG1b22GUnlbGHShrdtJd2Ua4NVHjf+N3ewB/fDwBS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\n0ybTRtQz4UyHUbR3/AEOfwTYOk+TjDy9tf1eANZZMdUuij2RuyAfPHD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+Vz9QX1vJD740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\naEoMrdg1Go2mxNCKXaPRaEqM/w8Cu09jxOiBWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114de9b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aonao.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AO</th>\n",
       "      <th>NAO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-31</th>\n",
       "      <td>-0.060310</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-28</th>\n",
       "      <td>0.626810</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-31</th>\n",
       "      <td>-0.008127</td>\n",
       "      <td>-0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-30</th>\n",
       "      <td>0.555100</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-31</th>\n",
       "      <td>0.071577</td>\n",
       "      <td>-0.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AO   NAO\n",
       "1950-01-31 -0.060310  0.92\n",
       "1950-02-28  0.626810  0.40\n",
       "1950-03-31 -0.008127 -0.36\n",
       "1950-04-30  0.555100  0.73\n",
       "1950-05-31  0.071577 -0.59"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aonao.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing works for both data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AO</th>\n",
       "      <th>NAO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-01-31</th>\n",
       "      <td>-2.065700</td>\n",
       "      <td>-0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-02-29</th>\n",
       "      <td>-0.933720</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-03-31</th>\n",
       "      <td>-1.433300</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-04-30</th>\n",
       "      <td>-0.419130</td>\n",
       "      <td>1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-05-31</th>\n",
       "      <td>-1.154800</td>\n",
       "      <td>-1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-06-30</th>\n",
       "      <td>0.721490</td>\n",
       "      <td>-0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-07-31</th>\n",
       "      <td>-0.622210</td>\n",
       "      <td>-0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-08-31</th>\n",
       "      <td>-0.185190</td>\n",
       "      <td>-2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-09-30</th>\n",
       "      <td>0.312620</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-10-31</th>\n",
       "      <td>-0.521210</td>\n",
       "      <td>-1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-11-30</th>\n",
       "      <td>-1.361000</td>\n",
       "      <td>-0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-31</th>\n",
       "      <td>-0.057300</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-31</th>\n",
       "      <td>-0.116340</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-28</th>\n",
       "      <td>-0.331580</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-03-31</th>\n",
       "      <td>-1.644700</td>\n",
       "      <td>-1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-04-30</th>\n",
       "      <td>0.430410</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-05-31</th>\n",
       "      <td>0.179560</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-06-30</th>\n",
       "      <td>-0.437930</td>\n",
       "      <td>-0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-07-31</th>\n",
       "      <td>0.560550</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-08-31</th>\n",
       "      <td>-0.244110</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-09-30</th>\n",
       "      <td>-1.040100</td>\n",
       "      <td>-1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-10-31</th>\n",
       "      <td>-1.167500</td>\n",
       "      <td>-1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-11-30</th>\n",
       "      <td>-0.187670</td>\n",
       "      <td>-0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-12-31</th>\n",
       "      <td>-1.215700</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-31</th>\n",
       "      <td>-0.883410</td>\n",
       "      <td>-0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-02-28</th>\n",
       "      <td>0.973910</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-03-31</th>\n",
       "      <td>1.074100</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-30</th>\n",
       "      <td>1.453800</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-05-31</th>\n",
       "      <td>-0.208700</td>\n",
       "      <td>-0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-06-30</th>\n",
       "      <td>-1.180100</td>\n",
       "      <td>-1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-07-31</th>\n",
       "      <td>0.130550</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-08-31</th>\n",
       "      <td>1.097800</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-09-30</th>\n",
       "      <td>0.166890</td>\n",
       "      <td>-1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-10-31</th>\n",
       "      <td>1.368900</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-11-30</th>\n",
       "      <td>-0.687930</td>\n",
       "      <td>-0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-12-31</th>\n",
       "      <td>0.186250</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-01-31</th>\n",
       "      <td>0.905040</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-02-29</th>\n",
       "      <td>-0.302720</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-03-31</th>\n",
       "      <td>-2.386000</td>\n",
       "      <td>-0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-04-30</th>\n",
       "      <td>-0.283590</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-05-31</th>\n",
       "      <td>0.479180</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-06-30</th>\n",
       "      <td>0.007289</td>\n",
       "      <td>-0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-07-31</th>\n",
       "      <td>0.018871</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-08-31</th>\n",
       "      <td>0.465690</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-09-30</th>\n",
       "      <td>-0.412760</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-10-31</th>\n",
       "      <td>-0.270260</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-11-30</th>\n",
       "      <td>-0.965870</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-12-31</th>\n",
       "      <td>0.446000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-31</th>\n",
       "      <td>-2.805700</td>\n",
       "      <td>-1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-02-28</th>\n",
       "      <td>-1.439800</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-03-31</th>\n",
       "      <td>0.551440</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-04-30</th>\n",
       "      <td>0.652420</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-05-31</th>\n",
       "      <td>-0.432210</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-06-30</th>\n",
       "      <td>-0.346560</td>\n",
       "      <td>-0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-07-31</th>\n",
       "      <td>-0.389580</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-08-31</th>\n",
       "      <td>-0.001429</td>\n",
       "      <td>-0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-09-30</th>\n",
       "      <td>0.114410</td>\n",
       "      <td>-0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-10-31</th>\n",
       "      <td>1.035100</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-11-30</th>\n",
       "      <td>-1.217500</td>\n",
       "      <td>-0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-12-31</th>\n",
       "      <td>-1.947600</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AO   NAO\n",
       "1980-01-31 -2.065700 -0.75\n",
       "1980-02-29 -0.933720  0.05\n",
       "1980-03-31 -1.433300 -0.31\n",
       "1980-04-30 -0.419130  1.29\n",
       "1980-05-31 -1.154800 -1.50\n",
       "1980-06-30  0.721490 -0.37\n",
       "1980-07-31 -0.622210 -0.42\n",
       "1980-08-31 -0.185190 -2.24\n",
       "1980-09-30  0.312620  0.66\n",
       "1980-10-31 -0.521210 -1.77\n",
       "1980-11-30 -1.361000 -0.37\n",
       "1980-12-31 -0.057300  0.78\n",
       "1981-01-31 -0.116340  0.37\n",
       "1981-02-28 -0.331580  0.92\n",
       "1981-03-31 -1.644700 -1.19\n",
       "1981-04-30  0.430410  0.36\n",
       "1981-05-31  0.179560  0.20\n",
       "1981-06-30 -0.437930 -0.45\n",
       "1981-07-31  0.560550  0.05\n",
       "1981-08-31 -0.244110  0.39\n",
       "1981-09-30 -1.040100 -1.45\n",
       "1981-10-31 -1.167500 -1.35\n",
       "1981-11-30 -0.187670 -0.38\n",
       "1981-12-31 -1.215700 -0.02\n",
       "1982-01-31 -0.883410 -0.89\n",
       "1982-02-28  0.973910  1.15\n",
       "1982-03-31  1.074100  1.15\n",
       "1982-04-30  1.453800  0.10\n",
       "1982-05-31 -0.208700 -0.53\n",
       "1982-06-30 -1.180100 -1.63\n",
       "...              ...   ...\n",
       "1983-07-31  0.130550  1.19\n",
       "1983-08-31  1.097800  1.61\n",
       "1983-09-30  0.166890 -1.12\n",
       "1983-10-31  1.368900  0.65\n",
       "1983-11-30 -0.687930 -0.98\n",
       "1983-12-31  0.186250  0.29\n",
       "1984-01-31  0.905040  1.66\n",
       "1984-02-29 -0.302720  0.72\n",
       "1984-03-31 -2.386000 -0.37\n",
       "1984-04-30 -0.283590 -0.28\n",
       "1984-05-31  0.479180  0.54\n",
       "1984-06-30  0.007289 -0.42\n",
       "1984-07-31  0.018871 -0.07\n",
       "1984-08-31  0.465690  1.15\n",
       "1984-09-30 -0.412760  0.17\n",
       "1984-10-31 -0.270260 -0.07\n",
       "1984-11-30 -0.965870 -0.06\n",
       "1984-12-31  0.446000  0.00\n",
       "1985-01-31 -2.805700 -1.61\n",
       "1985-02-28 -1.439800 -0.49\n",
       "1985-03-31  0.551440  0.20\n",
       "1985-04-30  0.652420  0.32\n",
       "1985-05-31 -0.432210 -0.49\n",
       "1985-06-30 -0.346560 -0.80\n",
       "1985-07-31 -0.389580  1.22\n",
       "1985-08-31 -0.001429 -0.48\n",
       "1985-09-30  0.114410 -0.52\n",
       "1985-10-31  1.035100  0.90\n",
       "1985-11-30 -1.217500 -0.67\n",
       "1985-12-31 -1.947600  0.22\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aonao['1980':'1985']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add more complexity to the slicing this way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114f2b748>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZBfwsXzOzo6Sqh7XbB4NXqGZmZhVwQTUzM6tAO18wfq+kHknbGtqnSNoo6WlJ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\nzCrggmpmZlYBF1QzM7MKuKCamZlV4P8Hb/vB2ovXu/0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e32550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "aonao.loc[(aonao.AO > 0) & (aonao.NAO < 0)\n",
    "         & (aonao.index > datetime.datetime(1980,1,1))\n",
    "         & (aonao.index < datetime.datetime(1989,1,1)), 'NAO'].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I have used the [DataFrame.loc](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.loc.html?highlight=loc#pandas.DataFrame.loc) advanced indexing attribute. We choose all the necessary constraints such as, AO > 0, NAO < 0, date range between years 1980-1989 (I have used the datetime module for this) and then, voila! you have your sliced data according to your requirements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics with Pandas\n",
    "-------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AO    -4.2657\n",
       "NAO   -3.1800\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aonao.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AO     3.4953\n",
       "NAO    3.0400\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aonao.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AO    -0.116709\n",
       "NAO   -0.018718\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aonao.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AO    -0.044346\n",
       "NAO    0.025000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aonao.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AO    -0.116709\n",
      "NAO   -0.018718\n",
      "dtype: float64\n",
      "\n",
      " Now let's see the mean of both indices:\n",
      "\n",
      "1950-01-31    0.429845\n",
      "1950-02-28    0.513405\n",
      "1950-03-31   -0.184064\n",
      "1950-04-30    0.642550\n",
      "1950-05-31   -0.259211\n",
      "1950-06-30    0.239285\n",
      "1950-07-31   -1.031240\n",
      "1950-08-31   -0.450505\n",
      "1950-09-30    0.303985\n",
      "1950-10-31    0.235550\n",
      "1950-11-30   -0.887555\n",
      "1950-12-31   -1.474050\n",
      "1951-01-31   -0.002485\n",
      "1951-02-28    0.150035\n",
      "1951-03-31   -1.477050\n",
      "1951-04-30   -0.498240\n",
      "1951-05-31   -0.726390\n",
      "1951-06-30   -1.278930\n",
      "1951-07-31    0.730012\n",
      "1951-08-31   -0.298705\n",
      "1951-09-30   -1.088890\n",
      "1951-10-31    0.828545\n",
      "1951-11-30   -0.229260\n",
      "1951-12-31    1.653600\n",
      "1952-01-31    0.649125\n",
      "1952-02-29   -1.288600\n",
      "1952-03-31   -1.674750\n",
      "1952-04-30    0.774260\n",
      "1952-05-31   -0.946755\n",
      "1952-06-30   -0.420465\n",
      "                ...   \n",
      "2014-07-31   -0.156775\n",
      "2014-08-31   -1.026520\n",
      "2014-09-30    0.859105\n",
      "2014-10-31   -1.202500\n",
      "2014-11-30    0.073835\n",
      "2014-12-31    1.135210\n",
      "2015-01-31    1.440150\n",
      "2015-02-28    1.182700\n",
      "2015-03-31    1.643550\n",
      "2015-04-30    0.970525\n",
      "2015-05-31    0.454140\n",
      "2015-06-30    0.180109\n",
      "2015-07-31   -2.143450\n",
      "2015-08-31   -0.724690\n",
      "2015-09-30   -0.406485\n",
      "2015-10-31    0.093585\n",
      "2015-11-30    1.844400\n",
      "2015-12-31    1.843850\n",
      "2016-01-31   -0.666090\n",
      "2016-02-29    0.778390\n",
      "2016-03-31    0.507320\n",
      "2016-04-30   -0.337980\n",
      "2016-05-31   -0.404660\n",
      "2016-06-30   -0.059815\n",
      "2016-07-31   -0.837771\n",
      "2016-08-31   -0.586460\n",
      "2016-09-30    0.695965\n",
      "2016-10-31   -0.753025\n",
      "2016-11-30   -0.387285\n",
      "2016-12-31    1.132365\n",
      "Freq: M, Length: 804, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(aonao.mean(0))# This is the regular mean (column-wise)\n",
    "print(\"\\n Now let's see the mean of both indices:\\n\")\n",
    "print(aonao.mean(1)) # Mean along row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can get all necessary statistics in one command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AO</th>\n",
       "      <th>NAO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>804.000000</td>\n",
       "      <td>804.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.116709</td>\n",
       "      <td>-0.018718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.007779</td>\n",
       "      <td>1.011623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.265700</td>\n",
       "      <td>-3.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.662915</td>\n",
       "      <td>-0.760090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.044346</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.475240</td>\n",
       "      <td>0.681750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.495300</td>\n",
       "      <td>3.040000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AO         NAO\n",
       "count  804.000000  804.000000\n",
       "mean    -0.116709   -0.018718\n",
       "std      1.007779    1.011623\n",
       "min     -4.265700   -3.180000\n",
       "25%     -0.662915   -0.760090\n",
       "50%     -0.044346    0.025000\n",
       "75%      0.475240    0.681750\n",
       "max      3.495300    3.040000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aonao.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting correlation coefficients is also very easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AO</th>\n",
       "      <th>NAO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AO</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.605191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAO</th>\n",
       "      <td>0.605191</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AO       NAO\n",
       "AO   1.000000  0.605191\n",
       "NAO  0.605191  1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aonao.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try out basic pandas on some irregular data like the Bloomington station temperature and dew point temperature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1158e88d0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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87W/M+FTYrGyACWE2nfQic9Pp8jhcpDC1OdKGVlDTycgJ0b0mOgY/pa8BAPx+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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1158e26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "data = pd.read_csv('data/KBMG-2013.csv')\n",
    "\n",
    "data['date'] = data.apply(lambda x: dt.datetime(x['yyyy'], x['mm'],x['dd'],x['hh'], x['mm.1']), axis=1)\n",
    "data.set_index(data['date'], inplace=True)\n",
    "\n",
    "T = pd.Series(data['temp'], index=data.index)\n",
    "TD = pd.Series(data['dwpt'], index=data.index)\n",
    "\n",
    "TTD = pd.DataFrame({'$T$': T, '$T_d$' : TD})\n",
    "\n",
    "TTD.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading\n",
    "-------------------------------------------------------------------\n",
    "* [Pandas Tutorials](http://pandas.pydata.org/pandas-docs/stable/tutorials.html) - This link has links to multiple tutorials at the end apart from the official pandas tutorial."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}