FrostRisk_Pre1980_post1979.ipynb

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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/Users/isaacpark/Dropbox/Herbarium_2017/Pheno_Assessed_3/FrostRisk/Data_ForSharing'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pylab import plot, show\n",
    "from scipy import stats\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import os\n",
    "import math\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as PLT\n",
    "from matplotlib import mlab as ML\n",
    "from scipy.stats.stats import pearsonr\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "% pylab inline\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "670908\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accepted_name_species</th>\n",
       "      <th>DOY</th>\n",
       "      <th>Year</th>\n",
       "      <th>bFFP_Annual</th>\n",
       "      <th>bFFP_Normal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Garrya elliptica</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>62.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anemone tuberosa</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>119.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Perityle emoryi</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>42.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monoptilon bellioides</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>89.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Plantago ovata</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accepted_name_species    DOY    Year  bFFP_Annual  bFFP_Normal\n",
       "0       Garrya elliptica   93.0  1920.0         76.0    62.333333\n",
       "1       Anemone tuberosa  105.0  1920.0        125.0   119.666667\n",
       "2        Perityle emoryi   74.0  1920.0         40.0    42.333333\n",
       "3  Monoptilon bellioides  110.0  1920.0        104.0    89.666667\n",
       "4         Plantago ovata   63.0  1920.0         51.0    45.666667"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in specimen Data\n",
    "rawData = pd.read_csv('specimenData.csv') #change name to match specimen input data\n",
    "\n",
    "####\n",
    "# Accepted_name_species = species name\n",
    "# DOY = Day of year on which specimen was collected\n",
    "# Year = Year on which specimen was collected\n",
    "# bFFP_Annual = Date of the beginning of the frost-free period at the location and year from which specimen was collected\n",
    "# bFFP_Normal = Normal Date of the beginning of the frost-free period at the location from which specimen was collected (based on 1901-1990 Normals)\n",
    "\n",
    "\n",
    "print len(rawData)\n",
    "rawData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "670908\n"
     ]
    }
   ],
   "source": [
    "#Create field for evaluating magnitude of annual deviations from bffp normals\n",
    "rawData['bFFP_ClimDiff'] = rawData['bFFP_Annual'] - rawData['bFFP_Normal']\n",
    "\n",
    "#Create field marking whether a specimen was collected prior to the onset of the frost-free period\n",
    "rawData['bFFP_Diff'] = rawData['DOY'] - rawData['bFFP_Annual']\n",
    "\n",
    "#Create field marking mean collection date for all specimens of each species\n",
    "bffP_Group = rawData.groupby(by = ['Accepted_name_species']).mean()\n",
    "bffP_Group = bffP_Group.reset_index()\n",
    "bffP_Group['DOY_SpecMean'] = bffP_Group['DOY']\n",
    "bffP_Group['bFFP_Diff_SpecMean'] = bffP_Group['bFFP_Diff']\n",
    "bffP_Group = bffP_Group.loc[:, ['Accepted_name_species', 'bFFP_Diff_SpecMean', 'DOY_SpecMean']]\n",
    "print len(rawData)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "670908\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accepted_name_species</th>\n",
       "      <th>DOY</th>\n",
       "      <th>Year</th>\n",
       "      <th>bFFP_Annual</th>\n",
       "      <th>bFFP_Normal</th>\n",
       "      <th>bFFP_ClimDiff</th>\n",
       "      <th>bFFP_Diff</th>\n",
       "      <th>bFFP_Diff_SpecMean</th>\n",
       "      <th>DOY_SpecMean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Garrya elliptica</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>62.333333</td>\n",
       "      <td>13.666667</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34.384615</td>\n",
       "      <td>86.723077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anemone tuberosa</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>119.666667</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>3.375000</td>\n",
       "      <td>87.464844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Perityle emoryi</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>-2.333333</td>\n",
       "      <td>34.0</td>\n",
       "      <td>45.504630</td>\n",
       "      <td>86.013889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monoptilon bellioides</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>89.666667</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.139535</td>\n",
       "      <td>84.048837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Plantago ovata</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.666667</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>12.0</td>\n",
       "      <td>37.656987</td>\n",
       "      <td>86.152450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accepted_name_species    DOY    Year  bFFP_Annual  bFFP_Normal  \\\n",
       "0       Garrya elliptica   93.0  1920.0         76.0    62.333333   \n",
       "1       Anemone tuberosa  105.0  1920.0        125.0   119.666667   \n",
       "2        Perityle emoryi   74.0  1920.0         40.0    42.333333   \n",
       "3  Monoptilon bellioides  110.0  1920.0        104.0    89.666667   \n",
       "4         Plantago ovata   63.0  1920.0         51.0    45.666667   \n",
       "\n",
       "   bFFP_ClimDiff  bFFP_Diff  bFFP_Diff_SpecMean  DOY_SpecMean  \n",
       "0      13.666667       17.0           34.384615     86.723077  \n",
       "1       5.333333      -20.0            3.375000     87.464844  \n",
       "2      -2.333333       34.0           45.504630     86.013889  \n",
       "3      14.333333        6.0           37.139535     84.048837  \n",
       "4       5.333333       12.0           37.656987     86.152450  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge mean collection dates back into main data\n",
    "rawData = pd.merge(rawData, bffP_Group, on = ['Accepted_name_species'], how = 'left')\n",
    "print len(rawData)\n",
    "rawData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def frostChecker_bffp(row):  #check if bFFP occurred after flowering onset\n",
    "    if row[\"bFFP_Annual\"] < row['DOY']:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "\n",
    "rawData[\"Frost_Suffered\"] = rawData.apply(frostChecker_bffp, axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accepted_name_species</th>\n",
       "      <th>DOY</th>\n",
       "      <th>Year</th>\n",
       "      <th>bFFP_Annual</th>\n",
       "      <th>bFFP_Normal</th>\n",
       "      <th>bFFP_ClimDiff</th>\n",
       "      <th>bFFP_Diff</th>\n",
       "      <th>bFFP_Diff_SpecMean</th>\n",
       "      <th>DOY_SpecMean</th>\n",
       "      <th>Frost_Suffered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Garrya elliptica</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>62.333333</td>\n",
       "      <td>13.666667</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34.384615</td>\n",
       "      <td>86.723077</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anemone tuberosa</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>119.666667</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>3.375000</td>\n",
       "      <td>87.464844</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Perityle emoryi</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>-2.333333</td>\n",
       "      <td>34.0</td>\n",
       "      <td>45.504630</td>\n",
       "      <td>86.013889</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monoptilon bellioides</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>89.666667</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.139535</td>\n",
       "      <td>84.048837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Plantago ovata</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.666667</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>12.0</td>\n",
       "      <td>37.656987</td>\n",
       "      <td>86.152450</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accepted_name_species    DOY    Year  bFFP_Annual  bFFP_Normal  \\\n",
       "0       Garrya elliptica   93.0  1920.0         76.0    62.333333   \n",
       "1       Anemone tuberosa  105.0  1920.0        125.0   119.666667   \n",
       "2        Perityle emoryi   74.0  1920.0         40.0    42.333333   \n",
       "3  Monoptilon bellioides  110.0  1920.0        104.0    89.666667   \n",
       "4         Plantago ovata   63.0  1920.0         51.0    45.666667   \n",
       "\n",
       "   bFFP_ClimDiff  bFFP_Diff  bFFP_Diff_SpecMean  DOY_SpecMean  Frost_Suffered  \n",
       "0      13.666667       17.0           34.384615     86.723077               0  \n",
       "1       5.333333      -20.0            3.375000     87.464844               1  \n",
       "2      -2.333333       34.0           45.504630     86.013889               0  \n",
       "3      14.333333        6.0           37.139535     84.048837               0  \n",
       "4       5.333333       12.0           37.656987     86.152450               0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def EightiesClasser(row):  #check if bFFP occurred after flowering onset\n",
    "    if row[\"Year\"] < 1980:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "rawData[\"Eighties\"] = rawData.apply(EightiesClasser, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accepted_name_species</th>\n",
       "      <th>DOY</th>\n",
       "      <th>Year</th>\n",
       "      <th>bFFP_Annual</th>\n",
       "      <th>bFFP_Normal</th>\n",
       "      <th>bFFP_ClimDiff</th>\n",
       "      <th>bFFP_Diff</th>\n",
       "      <th>bFFP_Diff_SpecMean</th>\n",
       "      <th>DOY_SpecMean</th>\n",
       "      <th>Frost_Suffered</th>\n",
       "      <th>Eighties</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Garrya elliptica</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>62.333333</td>\n",
       "      <td>13.666667</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34.384615</td>\n",
       "      <td>86.723077</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anemone tuberosa</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>119.666667</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>3.375000</td>\n",
       "      <td>87.464844</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Perityle emoryi</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>-2.333333</td>\n",
       "      <td>34.0</td>\n",
       "      <td>45.504630</td>\n",
       "      <td>86.013889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monoptilon bellioides</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>89.666667</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.139535</td>\n",
       "      <td>84.048837</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Plantago ovata</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.666667</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>12.0</td>\n",
       "      <td>37.656987</td>\n",
       "      <td>86.152450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accepted_name_species    DOY    Year  bFFP_Annual  bFFP_Normal  \\\n",
       "0       Garrya elliptica   93.0  1920.0         76.0    62.333333   \n",
       "1       Anemone tuberosa  105.0  1920.0        125.0   119.666667   \n",
       "2        Perityle emoryi   74.0  1920.0         40.0    42.333333   \n",
       "3  Monoptilon bellioides  110.0  1920.0        104.0    89.666667   \n",
       "4         Plantago ovata   63.0  1920.0         51.0    45.666667   \n",
       "\n",
       "   bFFP_ClimDiff  bFFP_Diff  bFFP_Diff_SpecMean  DOY_SpecMean  Frost_Suffered  \\\n",
       "0      13.666667       17.0           34.384615     86.723077               0   \n",
       "1       5.333333      -20.0            3.375000     87.464844               1   \n",
       "2      -2.333333       34.0           45.504630     86.013889               0   \n",
       "3      14.333333        6.0           37.139535     84.048837               0   \n",
       "4       5.333333       12.0           37.656987     86.152450               0   \n",
       "\n",
       "   Eighties  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Output Data\n",
    "rawData.to_csv('SpecimenData_prePost1980.csv')\n",
    "rawData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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></th>\n",
       "      <th>DOY</th>\n",
       "      <th>Year</th>\n",
       "      <th>bFFP_Annual</th>\n",
       "      <th>bFFP_Normal</th>\n",
       "      <th>bFFP_ClimDiff</th>\n",
       "      <th>bFFP_Diff</th>\n",
       "      <th>bFFP_Diff_SpecMean</th>\n",
       "      <th>DOY_SpecMean</th>\n",
       "      <th>Frost_Suffered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accepted_name_species</th>\n",
       "      <th>Eighties</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Abronia elliptica</th>\n",
       "      <th>0</th>\n",
       "      <td>145.409836</td>\n",
       "      <td>1956.229508</td>\n",
       "      <td>112.442623</td>\n",
       "      <td>114.672131</td>\n",
       "      <td>-2.229508</td>\n",
       "      <td>32.967213</td>\n",
       "      <td>32.044248</td>\n",
       "      <td>148.743363</td>\n",
       "      <td>0.114754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>152.653846</td>\n",
       "      <td>2000.038462</td>\n",
       "      <td>121.692308</td>\n",
       "      <td>128.076923</td>\n",
       "      <td>-6.384615</td>\n",
       "      <td>30.961538</td>\n",
       "      <td>32.044248</td>\n",
       "      <td>148.743363</td>\n",
       "      <td>0.134615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Abronia latifolia</th>\n",
       "      <th>0</th>\n",
       "      <td>174.487805</td>\n",
       "      <td>1943.463415</td>\n",
       "      <td>64.975610</td>\n",
       "      <td>66.487805</td>\n",
       "      <td>-1.512195</td>\n",
       "      <td>109.512195</td>\n",
       "      <td>118.380952</td>\n",
       "      <td>182.174603</td>\n",
       "      <td>0.024390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196.500000</td>\n",
       "      <td>1999.181818</td>\n",
       "      <td>61.590909</td>\n",
       "      <td>77.772727</td>\n",
       "      <td>-16.181818</td>\n",
       "      <td>134.909091</td>\n",
       "      <td>118.380952</td>\n",
       "      <td>182.174603</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abronia maritima</th>\n",
       "      <th>0</th>\n",
       "      <td>141.028571</td>\n",
       "      <td>1942.971429</td>\n",
       "      <td>15.971429</td>\n",
       "      <td>12.838095</td>\n",
       "      <td>3.133333</td>\n",
       "      <td>125.057143</td>\n",
       "      <td>120.240741</td>\n",
       "      <td>133.592593</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       DOY         Year  bFFP_Annual  \\\n",
       "Accepted_name_species Eighties                                         \n",
       "Abronia elliptica     0         145.409836  1956.229508   112.442623   \n",
       "                      1         152.653846  2000.038462   121.692308   \n",
       "Abronia latifolia     0         174.487805  1943.463415    64.975610   \n",
       "                      1         196.500000  1999.181818    61.590909   \n",
       "Abronia maritima      0         141.028571  1942.971429    15.971429   \n",
       "\n",
       "                                bFFP_Normal  bFFP_ClimDiff   bFFP_Diff  \\\n",
       "Accepted_name_species Eighties                                           \n",
       "Abronia elliptica     0          114.672131      -2.229508   32.967213   \n",
       "                      1          128.076923      -6.384615   30.961538   \n",
       "Abronia latifolia     0           66.487805      -1.512195  109.512195   \n",
       "                      1           77.772727     -16.181818  134.909091   \n",
       "Abronia maritima      0           12.838095       3.133333  125.057143   \n",
       "\n",
       "                                bFFP_Diff_SpecMean  DOY_SpecMean  \\\n",
       "Accepted_name_species Eighties                                     \n",
       "Abronia elliptica     0                  32.044248    148.743363   \n",
       "                      1                  32.044248    148.743363   \n",
       "Abronia latifolia     0                 118.380952    182.174603   \n",
       "                      1                 118.380952    182.174603   \n",
       "Abronia maritima      0                 120.240741    133.592593   \n",
       "\n",
       "                                Frost_Suffered  \n",
       "Accepted_name_species Eighties                  \n",
       "Abronia elliptica     0               0.114754  \n",
       "                      1               0.134615  \n",
       "Abronia latifolia     0               0.024390  \n",
       "                      1               0.000000  \n",
       "Abronia maritima      0               0.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Output species means for all fields\n",
    "groupData = rawData.groupby(by = ['Accepted_name_species', \"Eighties\"]).mean()\n",
    "groupData.to_csv('SpeciesMeans_Phylo_1980Split.csv')\n",
    "groupData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/isaacpark/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "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>Accepted_name_species</th>\n",
       "      <th>SampleCount_Pre1980</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abronia elliptica</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abronia latifolia</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abronia maritima</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abronia mellifera</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abronia pogonantha</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accepted_name_species  SampleCount_Pre1980\n",
       "0     Abronia elliptica                   61\n",
       "1     Abronia latifolia                   41\n",
       "2      Abronia maritima                   35\n",
       "3     Abronia mellifera                   29\n",
       "4    Abronia pogonantha                   39"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate number of samples of each species collected prior to 1980\n",
    "\n",
    "pre80Data = rawData[rawData['Eighties'] ==0]\n",
    "pre80Data['Dummy'] = 1\n",
    "pre80Data = pre80Data.loc[:,['Accepted_name_species', 'Dummy']]\n",
    "pre80Samples = pre80Data.groupby(by= ['Accepted_name_species']).sum()\n",
    "pre80Samples['SampleCount_Pre1980'] = pre80Samples['Dummy']\n",
    "del pre80Samples['Dummy']\n",
    "pre80Samples = pre80Samples.reset_index()\n",
    "pre80Samples.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Merge specieswide counts of pre-1980 collections into specimen data\n",
    "rawData = pd.merge(rawData, pre80Samples, on = ['Accepted_name_species'], how = 'left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/isaacpark/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "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>Accepted_name_species</th>\n",
       "      <th>SampleCount_Post1980</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abronia elliptica</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abronia latifolia</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abronia maritima</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abronia mellifera</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abronia pogonantha</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accepted_name_species  SampleCount_Post1980\n",
       "0     Abronia elliptica                    52\n",
       "1     Abronia latifolia                    22\n",
       "2      Abronia maritima                    19\n",
       "3     Abronia mellifera                    32\n",
       "4    Abronia pogonantha                    26"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate number of samples of each species collected after 1979\n",
    "\n",
    "post80Data = rawData[rawData['Eighties'] ==1]\n",
    "post80Data['Dummy'] = 1\n",
    "post80Data = post80Data.loc[:,['Accepted_name_species', 'Dummy']]\n",
    "post80Samples = post80Data.groupby(by= ['Accepted_name_species']).sum()\n",
    "post80Samples['SampleCount_Post1980'] = post80Samples['Dummy']\n",
    "del post80Samples['Dummy']\n",
    "post80Samples = post80Samples.reset_index()\n",
    "post80Samples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Merge specieswide counts of pre-1980 collections into specimen data\n",
    "rawData = pd.merge(rawData, post80Samples, on = ['Accepted_name_species'], how = 'left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accepted_name_species</th>\n",
       "      <th>DOY</th>\n",
       "      <th>Year</th>\n",
       "      <th>bFFP_Annual</th>\n",
       "      <th>bFFP_Normal</th>\n",
       "      <th>bFFP_ClimDiff</th>\n",
       "      <th>bFFP_Diff</th>\n",
       "      <th>bFFP_Diff_SpecMean</th>\n",
       "      <th>DOY_SpecMean</th>\n",
       "      <th>Frost_Suffered</th>\n",
       "      <th>Eighties</th>\n",
       "      <th>SampleCount_Pre1980</th>\n",
       "      <th>SampleCount_Post1980</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anemone tuberosa</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>119.666667</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>3.375000</td>\n",
       "      <td>87.464844</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Perityle emoryi</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>-2.333333</td>\n",
       "      <td>34.0</td>\n",
       "      <td>45.504630</td>\n",
       "      <td>86.013889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monoptilon bellioides</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>89.666667</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.139535</td>\n",
       "      <td>84.048837</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Plantago ovata</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.666667</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>12.0</td>\n",
       "      <td>37.656987</td>\n",
       "      <td>86.152450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lupinus arizonicus</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.258824</td>\n",
       "      <td>77.854902</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accepted_name_species    DOY    Year  bFFP_Annual  bFFP_Normal  \\\n",
       "1       Anemone tuberosa  105.0  1920.0        125.0   119.666667   \n",
       "2        Perityle emoryi   74.0  1920.0         40.0    42.333333   \n",
       "3  Monoptilon bellioides  110.0  1920.0        104.0    89.666667   \n",
       "4         Plantago ovata   63.0  1920.0         51.0    45.666667   \n",
       "5     Lupinus arizonicus   92.0  1920.0         53.0    46.333333   \n",
       "\n",
       "   bFFP_ClimDiff  bFFP_Diff  bFFP_Diff_SpecMean  DOY_SpecMean  Frost_Suffered  \\\n",
       "1       5.333333      -20.0            3.375000     87.464844               1   \n",
       "2      -2.333333       34.0           45.504630     86.013889               0   \n",
       "3      14.333333        6.0           37.139535     84.048837               0   \n",
       "4       5.333333       12.0           37.656987     86.152450               0   \n",
       "5       6.666667       39.0           39.258824     77.854902               0   \n",
       "\n",
       "   Eighties  SampleCount_Pre1980  SampleCount_Post1980  \n",
       "1         0                171.0                  85.0  \n",
       "2         0                197.0                 235.0  \n",
       "3         0                244.0                 186.0  \n",
       "4         0                277.0                 274.0  \n",
       "5         0                 97.0                 158.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Trim data to exclude specimens of species for which <50 specimens were collected either priot to 1980 or post-1979\n",
    "trimData = rawData[((rawData['SampleCount_Pre1980']>=50) & (rawData['SampleCount_Post1980']>=50))]\n",
    "trimData.to_csv('Frost_Data_Trimmed_50SampleMin_1980Split.csv')\n",
    "trimData.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#********************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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>Accepted_name_species</th>\n",
       "      <th>Eighties</th>\n",
       "      <th>DOY</th>\n",
       "      <th>Year</th>\n",
       "      <th>bFFP_Annual</th>\n",
       "      <th>bFFP_Normal</th>\n",
       "      <th>bFFP_ClimDiff</th>\n",
       "      <th>bFFP_Diff</th>\n",
       "      <th>bFFP_Diff_SpecMean</th>\n",
       "      <th>DOY_SpecMean</th>\n",
       "      <th>Frost_Suffered</th>\n",
       "      <th>SampleCount_Pre1980</th>\n",
       "      <th>SampleCount_Post1980</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abronia elliptica</td>\n",
       "      <td>0</td>\n",
       "      <td>145.409836</td>\n",
       "      <td>1956.229508</td>\n",
       "      <td>112.442623</td>\n",
       "      <td>114.672131</td>\n",
       "      <td>-2.229508</td>\n",
       "      <td>32.967213</td>\n",
       "      <td>32.044248</td>\n",
       "      <td>148.743363</td>\n",
       "      <td>0.114754</td>\n",
       "      <td>61.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abronia elliptica</td>\n",
       "      <td>1</td>\n",
       "      <td>152.653846</td>\n",
       "      <td>2000.038462</td>\n",
       "      <td>121.692308</td>\n",
       "      <td>128.076923</td>\n",
       "      <td>-6.384615</td>\n",
       "      <td>30.961538</td>\n",
       "      <td>32.044248</td>\n",
       "      <td>148.743363</td>\n",
       "      <td>0.134615</td>\n",
       "      <td>61.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abronia villosa</td>\n",
       "      <td>0</td>\n",
       "      <td>97.642857</td>\n",
       "      <td>1956.577922</td>\n",
       "      <td>55.350649</td>\n",
       "      <td>56.136364</td>\n",
       "      <td>-0.785714</td>\n",
       "      <td>42.292208</td>\n",
       "      <td>46.985251</td>\n",
       "      <td>94.011799</td>\n",
       "      <td>0.097403</td>\n",
       "      <td>154.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abronia villosa</td>\n",
       "      <td>1</td>\n",
       "      <td>90.989189</td>\n",
       "      <td>2001.427027</td>\n",
       "      <td>40.097297</td>\n",
       "      <td>56.246847</td>\n",
       "      <td>-16.149550</td>\n",
       "      <td>50.891892</td>\n",
       "      <td>46.985251</td>\n",
       "      <td>94.011799</td>\n",
       "      <td>0.070270</td>\n",
       "      <td>154.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acer glabrum</td>\n",
       "      <td>0</td>\n",
       "      <td>170.467066</td>\n",
       "      <td>1953.209581</td>\n",
       "      <td>151.502994</td>\n",
       "      <td>150.720559</td>\n",
       "      <td>0.782435</td>\n",
       "      <td>18.964072</td>\n",
       "      <td>17.924731</td>\n",
       "      <td>164.010753</td>\n",
       "      <td>0.443114</td>\n",
       "      <td>167.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accepted_name_species  Eighties         DOY         Year  bFFP_Annual  \\\n",
       "0     Abronia elliptica         0  145.409836  1956.229508   112.442623   \n",
       "1     Abronia elliptica         1  152.653846  2000.038462   121.692308   \n",
       "2       Abronia villosa         0   97.642857  1956.577922    55.350649   \n",
       "3       Abronia villosa         1   90.989189  2001.427027    40.097297   \n",
       "4          Acer glabrum         0  170.467066  1953.209581   151.502994   \n",
       "\n",
       "   bFFP_Normal  bFFP_ClimDiff  bFFP_Diff  bFFP_Diff_SpecMean  DOY_SpecMean  \\\n",
       "0   114.672131      -2.229508  32.967213           32.044248    148.743363   \n",
       "1   128.076923      -6.384615  30.961538           32.044248    148.743363   \n",
       "2    56.136364      -0.785714  42.292208           46.985251     94.011799   \n",
       "3    56.246847     -16.149550  50.891892           46.985251     94.011799   \n",
       "4   150.720559       0.782435  18.964072           17.924731    164.010753   \n",
       "\n",
       "   Frost_Suffered  SampleCount_Pre1980  SampleCount_Post1980  \n",
       "0        0.114754                 61.0                  52.0  \n",
       "1        0.134615                 61.0                  52.0  \n",
       "2        0.097403                154.0                 185.0  \n",
       "3        0.070270                154.0                 185.0  \n",
       "4        0.443114                167.0                 112.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#output species means, split between pre-1980 and post-1979 specimens\n",
    "groupData = trimData.groupby(by = ['Accepted_name_species', \"Eighties\"]).mean()\n",
    "groupData.reset_index(inplace = True)\n",
    "groupData.to_csv('Frost_Data_SpeciesMeans_50SampleMin_1980Split.csv')\n",
    "groupData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1653\n"
     ]
    },
    {
     "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>Accepted_name_species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anemone tuberosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Perityle emoryi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monoptilon bellioides</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Plantago ovata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lupinus arizonicus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accepted_name_species\n",
       "1       Anemone tuberosa\n",
       "2        Perityle emoryi\n",
       "3  Monoptilon bellioides\n",
       "4         Plantago ovata\n",
       "5     Lupinus arizonicus"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create list of species for which >=50 specimens were present both pre-1980 and post-1979\n",
    "speciesList = trimData.loc[:, ['Accepted_name_species']]\n",
    "speciesList = speciesList.drop_duplicates(subset = ['Accepted_name_species'])\n",
    "print len(speciesList)\n",
    "speciesList.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/isaacpark/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/Users/isaacpark/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/Users/isaacpark/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "/Users/isaacpark/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1653\n",
      "1653\n",
      "1653\n",
      "1653\n"
     ]
    },
    {
     "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>Accepted_name_species</th>\n",
       "      <th>bFFP_Pre80</th>\n",
       "      <th>Frost_Suffered_Pre80</th>\n",
       "      <th>DOY_SpecMean</th>\n",
       "      <th>bFFP_Diff_SpecMean</th>\n",
       "      <th>bFFP_Post79</th>\n",
       "      <th>Frost_Suffered_Post79</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abronia elliptica</td>\n",
       "      <td>114.672131</td>\n",
       "      <td>0.114754</td>\n",
       "      <td>148.743363</td>\n",
       "      <td>32.044248</td>\n",
       "      <td>128.076923</td>\n",
       "      <td>0.134615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abronia villosa</td>\n",
       "      <td>56.136364</td>\n",
       "      <td>0.097403</td>\n",
       "      <td>94.011799</td>\n",
       "      <td>46.985251</td>\n",
       "      <td>56.246847</td>\n",
       "      <td>0.070270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acer glabrum</td>\n",
       "      <td>150.720559</td>\n",
       "      <td>0.443114</td>\n",
       "      <td>164.010753</td>\n",
       "      <td>17.924731</td>\n",
       "      <td>145.410714</td>\n",
       "      <td>0.321429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acer macrophyllum</td>\n",
       "      <td>83.424779</td>\n",
       "      <td>0.070796</td>\n",
       "      <td>141.713415</td>\n",
       "      <td>59.993902</td>\n",
       "      <td>93.366013</td>\n",
       "      <td>0.156863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acer negundo</td>\n",
       "      <td>127.379310</td>\n",
       "      <td>0.528736</td>\n",
       "      <td>129.112426</td>\n",
       "      <td>8.195266</td>\n",
       "      <td>118.386179</td>\n",
       "      <td>0.585366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accepted_name_species  bFFP_Pre80  Frost_Suffered_Pre80  DOY_SpecMean  \\\n",
       "0     Abronia elliptica  114.672131              0.114754    148.743363   \n",
       "1       Abronia villosa   56.136364              0.097403     94.011799   \n",
       "2          Acer glabrum  150.720559              0.443114    164.010753   \n",
       "3     Acer macrophyllum   83.424779              0.070796    141.713415   \n",
       "4          Acer negundo  127.379310              0.528736    129.112426   \n",
       "\n",
       "   bFFP_Diff_SpecMean  bFFP_Post79  Frost_Suffered_Post79  \n",
       "0           32.044248   128.076923               0.134615  \n",
       "1           46.985251    56.246847               0.070270  \n",
       "2           17.924731   145.410714               0.321429  \n",
       "3           59.993902    93.366013               0.156863  \n",
       "4            8.195266   118.386179               0.585366  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert pre-1980 and post-1979 frost risk & bFFP dates to separate fields, place all data for each species on a single row\n",
    "\n",
    "Pre80_Data = groupData[groupData['Eighties'] == 0]\n",
    "Pre80_Data['bFFP_Pre80'] = Pre80_Data['bFFP_Normal']\n",
    "Pre80_Data['Frost_Suffered_Pre80'] = Pre80_Data['Frost_Suffered']\n",
    "Pre80_Data = Pre80_Data.loc[:, ['Accepted_name_species', 'bFFP_Pre80',  'Frost_Suffered_Pre80','DOY_SpecMean','bFFP_Diff_SpecMean']]\n",
    "\n",
    "Post80_Data = groupData[groupData['Eighties'] == 1]\n",
    "Post80_Data['bFFP_Post79'] = Post80_Data['bFFP_Normal']\n",
    "Post80_Data['Frost_Suffered_Post79'] = Post80_Data['Frost_Suffered']\n",
    "Post80_Data = Post80_Data.loc[:, ['Accepted_name_species', 'bFFP_Post79', 'Frost_Suffered_Post79']]\n",
    "\n",
    "\n",
    "print len(Pre80_Data)\n",
    "print len(Post80_Data)\n",
    "differentialData = pd.merge(Pre80_Data, Post80_Data, on = ['Accepted_name_species'],  how = 'inner')\n",
    "print len(differentialData)\n",
    "differentialData = pd.merge(differentialData, speciesList, on = ['Accepted_name_species'],  how = 'inner')\n",
    "print len(differentialData)\n",
    "differentialData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#calculate differences between pre-1980 and post-1979 bffp dates & frost risks for each species\n",
    "differentialData['bFFP_Diff'] = differentialData['bFFP_Post79'] - differentialData['bFFP_Pre80']\n",
    "differentialData['Frost_Suffered_Diff'] = differentialData['Frost_Suffered_Post79'] - differentialData['Frost_Suffered_Pre80']\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accepted_name_species</th>\n",
       "      <th>bFFP_Pre80</th>\n",
       "      <th>Frost_Suffered_Pre80</th>\n",
       "      <th>DOY_SpecMean</th>\n",
       "      <th>bFFP_Diff_SpecMean</th>\n",
       "      <th>bFFP_Post79</th>\n",
       "      <th>Frost_Suffered_Post79</th>\n",
       "      <th>bFFP_Diff</th>\n",
       "      <th>Frost_Suffered_Diff</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abronia elliptica</td>\n",
       "      <td>114.672131</td>\n",
       "      <td>0.114754</td>\n",
       "      <td>148.743363</td>\n",
       "      <td>32.044248</td>\n",
       "      <td>128.076923</td>\n",
       "      <td>0.134615</td>\n",
       "      <td>13.404792</td>\n",
       "      <td>0.019861</td>\n",
       "      <td>Abroniaelliptica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abronia villosa</td>\n",
       "      <td>56.136364</td>\n",
       "      <td>0.097403</td>\n",
       "      <td>94.011799</td>\n",
       "      <td>46.985251</td>\n",
       "      <td>56.246847</td>\n",
       "      <td>0.070270</td>\n",
       "      <td>0.110483</td>\n",
       "      <td>-0.027132</td>\n",
       "      <td>Abroniavillosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acer glabrum</td>\n",
       "      <td>150.720559</td>\n",
       "      <td>0.443114</td>\n",
       "      <td>164.010753</td>\n",
       "      <td>17.924731</td>\n",
       "      <td>145.410714</td>\n",
       "      <td>0.321429</td>\n",
       "      <td>-5.309845</td>\n",
       "      <td>-0.121685</td>\n",
       "      <td>Acerglabrum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acer macrophyllum</td>\n",
       "      <td>83.424779</td>\n",
       "      <td>0.070796</td>\n",
       "      <td>141.713415</td>\n",
       "      <td>59.993902</td>\n",
       "      <td>93.366013</td>\n",
       "      <td>0.156863</td>\n",
       "      <td>9.941234</td>\n",
       "      <td>0.086066</td>\n",
       "      <td>Acermacrophyllum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acer negundo</td>\n",
       "      <td>127.379310</td>\n",
       "      <td>0.528736</td>\n",
       "      <td>129.112426</td>\n",
       "      <td>8.195266</td>\n",
       "      <td>118.386179</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>-8.993131</td>\n",
       "      <td>0.056630</td>\n",
       "      <td>Acernegundo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accepted_name_species  bFFP_Pre80  Frost_Suffered_Pre80  DOY_SpecMean  \\\n",
       "0     Abronia elliptica  114.672131              0.114754    148.743363   \n",
       "1       Abronia villosa   56.136364              0.097403     94.011799   \n",
       "2          Acer glabrum  150.720559              0.443114    164.010753   \n",
       "3     Acer macrophyllum   83.424779              0.070796    141.713415   \n",
       "4          Acer negundo  127.379310              0.528736    129.112426   \n",
       "\n",
       "   bFFP_Diff_SpecMean  bFFP_Post79  Frost_Suffered_Post79  bFFP_Diff  \\\n",
       "0           32.044248   128.076923               0.134615  13.404792   \n",
       "1           46.985251    56.246847               0.070270   0.110483   \n",
       "2           17.924731   145.410714               0.321429  -5.309845   \n",
       "3           59.993902    93.366013               0.156863   9.941234   \n",
       "4            8.195266   118.386179               0.585366  -8.993131   \n",
       "\n",
       "   Frost_Suffered_Diff              name  \n",
       "0             0.019861  Abroniaelliptica  \n",
       "1            -0.027132    Abroniavillosa  \n",
       "2            -0.121685       Acerglabrum  \n",
       "3             0.086066  Acermacrophyllum  \n",
       "4             0.056630       Acernegundo  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create modified name field to match formatting of phylogenetic distance data\n",
    "def nameFormatter(row):\n",
    "    iterList = row['Accepted_name_species'].split(\" \")\n",
    "    return iterList[0] + iterList[1]\n",
    "\n",
    "differentialData['name'] = differentialData.apply(nameFormatter, axis=1)\n",
    "differentialData.to_csv('Pre_post_80_Differences_Phylo.csv', index = False)\n",
    "differentialData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accepted_name_species</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Biennial</th>\n",
       "      <th>Perennial</th>\n",
       "      <th>Forb</th>\n",
       "      <th>Graminoid</th>\n",
       "      <th>Vine</th>\n",
       "      <th>Subshrub</th>\n",
       "      <th>Shrub</th>\n",
       "      <th>Tree</th>\n",
       "      <th>AnnualSum</th>\n",
       "      <th>Invasive</th>\n",
       "      <th>Dummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Urtica dioica</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rubus idaeus</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Veronica serpyllifolia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vicia americana</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Castilleja exilis</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Accepted_name_species  Annual  Biennial  Perennial  Forb  Graminoid  Vine  \\\n",
       "0           Urtica dioica       0         0          3     3          0     0   \n",
       "1            Rubus idaeus       0         0          2     0          0     0   \n",
       "2  Veronica serpyllifolia       0         0          2     2          0     0   \n",
       "3         Vicia americana       0         0          2     2          0     2   \n",
       "4       Castilleja exilis       1         0          1     1          0     0   \n",
       "\n",
       "   Subshrub  Shrub  Tree  AnnualSum  Invasive  Dummy  \n",
       "0         0      0     0          3        -2      3  \n",
       "1         2      0     0          2        -2      2  \n",
       "2         0      0     0          2        -2      2  \n",
       "3         0      0     0          2         0      2  \n",
       "4         0      0     0          2         0      1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read in characteristics data taken from plants.usda.gov\n",
    "chars = pd.read_csv('Phylo_prep_Chars.csv')\n",
    "chars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1653\n",
      "1651\n"
     ]
    },
    {
     "data": {
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       "    }\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>Accepted_name_species</th>\n",
       "      <th>bFFP_Pre80</th>\n",
       "      <th>Frost_Suffered_Pre80</th>\n",
       "      <th>DOY_SpecMean</th>\n",
       "      <th>bFFP_Diff_SpecMean</th>\n",
       "      <th>bFFP_Post79</th>\n",
       "      <th>Frost_Suffered_Post79</th>\n",
       "      <th>bFFP_Diff</th>\n",
       "      <th>Frost_Suffered_Diff</th>\n",
       "      <th>name</th>\n",
       "      <th>...</th>\n",
       "      <th>Perennial</th>\n",
       "      <th>Forb</th>\n",
       "      <th>Graminoid</th>\n",
       "      <th>Vine</th>\n",
       "      <th>Subshrub</th>\n",
       "      <th>Shrub</th>\n",
       "      <th>Tree</th>\n",
       "      <th>AnnualSum</th>\n",
       "      <th>Invasive</th>\n",
       "      <th>Dummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abronia elliptica</td>\n",
       "      <td>114.672131</td>\n",
       "      <td>0.114754</td>\n",
       "      <td>148.743363</td>\n",
       "      <td>32.044248</td>\n",
       "      <td>128.076923</td>\n",
       "      <td>0.134615</td>\n",
       "      <td>13.404792</td>\n",
       "      <td>0.019861</td>\n",
       "      <td>Abroniaelliptica</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abronia villosa</td>\n",
       "      <td>56.136364</td>\n",
       "      <td>0.097403</td>\n",
       "      <td>94.011799</td>\n",
       "      <td>46.985251</td>\n",
       "      <td>56.246847</td>\n",
       "      <td>0.070270</td>\n",
       "      <td>0.110483</td>\n",
       "      <td>-0.027132</td>\n",
       "      <td>Abroniavillosa</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acer glabrum</td>\n",
       "      <td>150.720559</td>\n",
       "      <td>0.443114</td>\n",
       "      <td>164.010753</td>\n",
       "      <td>17.924731</td>\n",
       "      <td>145.410714</td>\n",
       "      <td>0.321429</td>\n",
       "      <td>-5.309845</td>\n",
       "      <td>-0.121685</td>\n",
       "      <td>Acerglabrum</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acer macrophyllum</td>\n",
       "      <td>83.424779</td>\n",
       "      <td>0.070796</td>\n",
       "      <td>141.713415</td>\n",
       "      <td>59.993902</td>\n",
       "      <td>93.366013</td>\n",
       "      <td>0.156863</td>\n",
       "      <td>9.941234</td>\n",
       "      <td>0.086066</td>\n",
       "      <td>Acermacrophyllum</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acer negundo</td>\n",
       "      <td>127.379310</td>\n",
       "      <td>0.528736</td>\n",
       "      <td>129.112426</td>\n",
       "      <td>8.195266</td>\n",
       "      <td>118.386179</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>-8.993131</td>\n",
       "      <td>0.056630</td>\n",
       "      <td>Acernegundo</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accepted_name_species  bFFP_Pre80  Frost_Suffered_Pre80  DOY_SpecMean  \\\n",
       "0     Abronia elliptica  114.672131              0.114754    148.743363   \n",
       "1       Abronia villosa   56.136364              0.097403     94.011799   \n",
       "2          Acer glabrum  150.720559              0.443114    164.010753   \n",
       "3     Acer macrophyllum   83.424779              0.070796    141.713415   \n",
       "4          Acer negundo  127.379310              0.528736    129.112426   \n",
       "\n",
       "   bFFP_Diff_SpecMean  bFFP_Post79  Frost_Suffered_Post79  bFFP_Diff  \\\n",
       "0           32.044248   128.076923               0.134615  13.404792   \n",
       "1           46.985251    56.246847               0.070270   0.110483   \n",
       "2           17.924731   145.410714               0.321429  -5.309845   \n",
       "3           59.993902    93.366013               0.156863   9.941234   \n",
       "4            8.195266   118.386179               0.585366  -8.993131   \n",
       "\n",
       "   Frost_Suffered_Diff              name  ...    Perennial  Forb  Graminoid  \\\n",
       "0             0.019861  Abroniaelliptica  ...            1     1          0   \n",
       "1            -0.027132    Abroniavillosa  ...            0     1          0   \n",
       "2            -0.121685       Acerglabrum  ...            2     0          0   \n",
       "3             0.086066  Acermacrophyllum  ...            1     0          0   \n",
       "4             0.056630       Acernegundo  ...            1     0          0   \n",
       "\n",
       "   Vine  Subshrub  Shrub  Tree  AnnualSum  Invasive  Dummy  \n",
       "0     0         0      0     0          1         0      1  \n",
       "1     0         0      0     0          1         0      1  \n",
       "2     0         0      1     2          2         0      2  \n",
       "3     0         0      0     1          1         0      1  \n",
       "4     0         0      0     1          1         0      1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge characteristics data with differential data\n",
    "print len(differentialData)\n",
    "merged = pd.merge(differentialData, chars, on = ['Accepted_name_species'], how = 'inner')\n",
    "print len(merged)\n",
    "merged.to_csv('Pre_post_80_Differences_Chars_Phylo.csv', index = False)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.12134726948599765, 0.087161042710505635]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112134490>"
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     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112134a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#calculate mean and standard deviations for pre-1980 and post-1979 frost risk among all species\n",
    "means = [merged['Frost_Suffered_Pre80'].mean(), merged['Frost_Suffered_Post79'].mean()]\n",
    "SEs = [merged['Frost_Suffered_Pre80'].sem(), merged['Frost_Suffered_Post79'].sem()]\n",
    "print means\n",
    "\n",
    "#create figure 2\n",
    "plt.clf()\n",
    "fig = plt.figure(figsize=(3, 3))\n",
    "\n",
    "#plt1 = plt.bar([1,2], means, width = 0.9,\n",
    "        #color = 'blue', alpha=0.6, yerr=SEs)\n",
    "#plt1 = plt.boxplot(merged['Frost_Suffered_Pre80'], 1)\n",
    "#plt2 = plt.boxplot(merged['Frost_Suffered_Post79'], 2)\n",
    "boxData = [merged['Frost_Suffered_Pre80'], merged['Frost_Suffered_Post79']]\n",
    "plt1 = plt.boxplot(boxData, whis = [5,95], \n",
    "                   flierprops=dict( markersize=1.5))\n",
    "\n",
    "\n",
    "\n",
    "#plt.ylim(0,.15)\n",
    "plt.xlabel('Time Period', fontweight = 'bold', fontsize = 14 )\n",
    "plt.ylabel('Frost Risk', fontweight = 'bold', fontsize = 14 )\n",
    "plt.yticks([0.00, 0.25, 0.50, 0.75, 1.0],fontweight = 'bold', fontsize = 10 )\n",
    "plt.xticks([1,2], ['Pre-1980', 'Post-1979'],fontweight = 'bold', fontsize = 10 )\n",
    "\n",
    "plt.text(0.55,0.999, \"a\", fontweight = 'bold', fontsize = 13, color = 'red', horizontalalignment= 'left', verticalalignment= 'top' )\n",
    "plt.text(1, 0.92, \"a\", fontweight = 'bold', fontsize = 12, horizontalalignment= 'center', verticalalignment= 'bottom' )\n",
    "plt.text(2, 0.85, \"b\", fontweight = 'bold', fontsize = 12, horizontalalignment= 'center', verticalalignment= 'bottom' )\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Fig_2a.jpg', dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137\n",
      "1503\n",
      "invas_pre80:  0.0877628080318\n",
      "invas_post79:  0.0679999336589\n",
      "native_pre80:  0.124776139299\n",
      "native_post79:  0.0891065437821\n"
     ]
    }
   ],
   "source": [
    "\n",
    "invasData = merged[merged['Invasive']== 1]\n",
    "nativeData = merged[merged['Invasive']== 0]\n",
    "\n",
    "print len(invasData)\n",
    "print len(nativeData)\n",
    "\n",
    "\n",
    "invas_Pre80Mean = invasData['Frost_Suffered_Pre80'].mean()\n",
    "invas_Post79Mean = invasData['Frost_Suffered_Post79'].mean()\n",
    "native_Pre80Mean = nativeData['Frost_Suffered_Pre80'].mean()\n",
    "native_Post79Mean = nativeData['Frost_Suffered_Post79'].mean()\n",
    "\n",
    "print 'invas_pre80: ', invas_Pre80Mean\n",
    "print 'invas_post79: ', invas_Post79Mean\n",
    "print 'native_pre80: ', native_Pre80Mean\n",
    "print 'native_post79: ', native_Post79Mean\n",
    "\n",
    "invas_Pre80SE = invasData['Frost_Suffered_Pre80'].std()/(len(invasData)**0.5)\n",
    "invas_Post79SE = invasData['Frost_Suffered_Post79'].std()/(len(invasData)**0.5)\n",
    "native_Pre80SE = nativeData['Frost_Suffered_Pre80'].std()/(len(invasData)**0.5)\n",
    "native_Post79SE = nativeData['Frost_Suffered_Post79'].std()/(len(invasData)**0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f30510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4N1wOSpiV4vKE8mIzOwI4kaAWOdzd+xDMVADBfGYQtMNWSjlrwUEBue9x9yeA\nv4ebfmZmHVJ8pD/wD3f/hbufy74mi15m1vqAfX9D0E7cFnjFzNrXJjZpWJSApaH5LUFTRXuCWQmW\nEbT/AhS5+78IuoW9BZRZMJ34UvbNcbcwXK4imFIH4Akzm29ml9Yylt+Fy+YEM+Em8/swlpVm9h7B\ndE8QtB9/fsC+XwGDCG7sdSBIwm1rGZc0EErA0qCEN8vOIajRNiGYBmc5cBtwRbhbGfAo8DFQQNBe\nu4qgqeCO8DhlBE0U64CjCNp1j61lLMXAa+HqKDM7JsmuzwDvELQf9yC4Ofg8cIFXMyC3u28gSMKf\nA92AmWGtXnKMBmQXEYmJasAiIjFRAhYRiYkSsIhITJSARURiogQsIhITJWARkZgoAYuIxEQJWEQk\nJv8faehRDxdYyhQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112387790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create Figure 2b\n",
    "plt.clf()\n",
    "fig = plt.figure(figsize=(5, 3))\n",
    "\n",
    "histbins=np.arange(0, 0.8, 0.02)\n",
    "\n",
    "\n",
    "plt.ylim(0,650)\n",
    "plt.xlim(-0.02,0.7)\n",
    "\n",
    "plt1 = plt.hist(merged['Frost_Suffered_Pre80'], bins = histbins, color = 'blue', alpha = 0.7, label = \"Pre-1980\")\n",
    "plt2 = plt.hist(merged['Frost_Suffered_Post79'], bins = histbins, color = 'red', alpha = 0.5, label = \"Post-1979\")\n",
    "\n",
    "pyplot.legend(loc='upper right')\n",
    "\n",
    "\n",
    "plt.xlabel('Frost Risk', fontweight = 'bold', fontsize = 14 )\n",
    "plt.ylabel('# Species', fontweight = 'bold', fontsize = 14 )\n",
    "\n",
    "plt.yticks([0,100,200,300,400,500, 600], ['0', '', '200', '', '400', '', '600'], fontweight = 'bold', fontsize = 10 )\n",
    "plt.xticks([0.0, 0.2, 0.4 ,0.6, 0.8], fontweight = 'bold', fontsize = 10 )\n",
    "\n",
    "plt.text(-0.015,645, \"b\", fontweight = 'bold', fontsize = 13, color = 'red', horizontalalignment= 'left', verticalalignment= 'top' )\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('Fig_2b.jpg', dpi = 600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113412090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11333da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig = plt.figure(figsize=(5, 3))\n",
    "\n",
    "histbins=np.arange(-0.4, 0.4, 0.02)\n",
    "\n",
    "#plt.ylim(0,380)\n",
    "plt.xlim(-0.405,0.405)\n",
    "\n",
    "\n",
    "lowerFrost = merged[merged['Frost_Suffered_Diff']<=0]\n",
    "higherFrost = merged[merged['Frost_Suffered_Diff']>0]\n",
    "\n",
    "plt1 = plt.hist(lowerFrost['Frost_Suffered_Diff'], bins = histbins, color = 'blue')\n",
    "plt2 = plt.hist(higherFrost['Frost_Suffered_Diff'], bins = histbins, color = 'red')\n",
    "\n",
    "\n",
    "plt.xlabel('$\\Delta$ Frost Risk', fontweight = 'bold', fontsize = 14 )\n",
    "plt.ylabel('# Species', fontweight = 'bold', fontsize = 14 )\n",
    "\n",
    "plt.yticks([0,100,200,300,400, 500], fontweight = 'bold', fontsize = 10 )\n",
    "plt.xticks([-0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4],['-0.4', '', '-0.2', '', '0.0', '', '0.2', '', '0.4'], fontweight = 'bold', fontsize = 10 )\n",
    "\n",
    "plt.text(-0.395,490, \"c\", fontweight = 'bold', fontsize = 13, color = 'red', horizontalalignment= 'left', verticalalignment= 'top' )\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Fig_2c.jpg', dpi = 600)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147cbed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147d24d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create Fig 4\n",
    "\n",
    "plt.clf()\n",
    "fig = plt.figure(figsize=(5, 3))\n",
    "\n",
    "\n",
    "\n",
    "#plt1 = plt.bar([1,4], [native_Pre80Mean, native_Post79Mean], width = 0.9,\n",
    "#        color = 'blue', alpha=0.7, yerr=[native_Pre80SE, native_Post79SE], label = \"Native\")\n",
    "#plt2 = plt.bar([2,5], [invas_Pre80Mean, invas_Post79Mean], width = 0.9,\n",
    "#        color = 'red', alpha=0.4, yerr=[invas_Pre80SE, invas_Post79SE], label = \"Exotic\")\n",
    "\n",
    "nativeboxData = [nativeData['Frost_Suffered_Pre80'], nativeData['Frost_Suffered_Post79']]\n",
    "\n",
    "invasiveboxData = [invasData['Frost_Suffered_Pre80'], invasData['Frost_Suffered_Post79']]\n",
    "\n",
    "plt1 = plt.boxplot(nativeboxData, positions = [1,3.5], whis = [5,95], \n",
    "                  patch_artist=True, flierprops=dict( markersize=1.5), boxprops=dict(facecolor=\"pink\"))\n",
    "\n",
    "plt2 = plt.boxplot(invasiveboxData, positions = [2,4.5], whis = [5,95], \n",
    "                  patch_artist=True, flierprops=dict( markersize=1.5), boxprops=dict(facecolor=\"lightblue\"))\n",
    "\n",
    "\n",
    "        \n",
    "legend((plt1[\"boxes\"][0], plt2[\"boxes\"][0]), ('Native', 'Exotic'))\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "plt.ylim(-0.05,1)\n",
    "plt.xlim(0.5, 5)\n",
    "\n",
    "plt.xticks([1.5, 4], ['Pre-1980', 'Post-1979'],fontweight = 'bold', fontsize = 10 )\n",
    "plt.yticks([0.0, 0.25, 0.50, 0.75, 1.0],fontweight = 'bold', fontsize = 10 )\n",
    "\n",
    "plt.xlabel('Species Type', fontweight = 'bold', fontsize = 14 )\n",
    "plt.ylabel('Frost Risk', fontweight = 'bold', fontsize = 14 )\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.text(1, 0.98, \"a\", fontweight = 'bold', color = 'black', fontsize = 11, horizontalalignment= 'center', verticalalignment= 'top' )\n",
    "plt.text(2, 0.9, \"b\", fontweight = 'bold', color = 'black', fontsize = 11, horizontalalignment= 'center', verticalalignment= 'top' )\n",
    "\n",
    "\n",
    "plt.text(3.5, 0.82, \"A\", fontweight = 'bold', color = 'black', fontsize = 11, horizontalalignment= 'center', verticalalignment= 'bottom' )\n",
    "plt.text(4.5, 0.65, \"B\", fontweight = 'bold', color = 'black', fontsize = 11, horizontalalignment= 'center', verticalalignment= 'bottom' )\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Fig_4.jpg', dpi = 600)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}