{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('precip_yearly.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1987    17.095966\n",
       "1988    21.516704\n",
       "1989    26.196590\n",
       "1990    20.638263\n",
       "1991    21.751607\n",
       "1992    23.097024\n",
       "1993    40.622674\n",
       "1994    19.654803\n",
       "1995    49.356450\n",
       "1996    34.819709\n",
       "1997    35.984432\n",
       "1998    48.131613\n",
       "1999    27.392222\n",
       "2000    28.576508\n",
       "2001    20.214118\n",
       "2002    23.192717\n",
       "2003    30.315964\n",
       "2004    22.152847\n",
       "2005    37.733113\n",
       "2006    41.684191\n",
       "2007    18.828722\n",
       "2008    22.283302\n",
       "2009    23.027718\n",
       "2010    28.742391\n",
       "2011    38.864394\n",
       "2012    21.240446\n",
       "2013    20.997228\n",
       "2014    15.341224\n",
       "Name: precip, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('year').precip.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "years, precip = _.index.values, _.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,\n",
       "       1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,\n",
       "       2009, 2010, 2011, 2012, 2013, 2014])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 17.09596591,  21.51670391,  26.1965896 ,  20.63826347,\n",
       "        21.75160714,  23.09702381,  40.62267442,  19.65480263,\n",
       "        49.3564497 ,  34.8197093 ,  35.98443243,  48.1316129 ,\n",
       "        27.39222222,  28.57650794,  20.21411765,  23.19271676,\n",
       "        30.31596386,  22.15284672,  37.73311258,  41.68419118,\n",
       "        18.8287218 ,  22.28330189,  23.02771812,  28.7423913 ,\n",
       "        38.86439394,  21.24044643,  20.99722772,  15.34122449])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savez('mean_ca_precip.npz', years=years, precip=precip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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