Trees-Project_Update_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import operator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Update 2 \n",
    "by Jacob Paul and Jack Sandberg\n",
    "\n",
    "### Setup (same from last update)\n",
    "\n",
    "The code below was used to get our basic data extracted and structured in a way that is useful for our analysis. We will be using many of these structures in this update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FID</th>\n",
       "      <th>ID</th>\n",
       "      <th>UNIQUEID</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>SUFFIX</th>\n",
       "      <th>STREET</th>\n",
       "      <th>ONSTR</th>\n",
       "      <th>FROMSTR</th>\n",
       "      <th>TOSTR</th>\n",
       "      <th>SIDE</th>\n",
       "      <th>...</th>\n",
       "      <th>GUARDSZ</th>\n",
       "      <th>EAB</th>\n",
       "      <th>INV_DATE</th>\n",
       "      <th>INV_TIME</th>\n",
       "      <th>INSPECT_DT</th>\n",
       "      <th>INSPECT_TM</th>\n",
       "      <th>NOTES</th>\n",
       "      <th>ACTIVE</th>\n",
       "      <th>COMMONNAME</th>\n",
       "      <th>GENUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>59084.0</td>\n",
       "      <td>JS 20150203131235</td>\n",
       "      <td>601</td>\n",
       "      <td></td>\n",
       "      <td>10TH ST</td>\n",
       "      <td>10TH ST</td>\n",
       "      <td>BASELINE RD</td>\n",
       "      <td>CUL-DE-SAC</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>2015-02-03 00:00:00</td>\n",
       "      <td></td>\n",
       "      <td>2015-07-10 00:00:00</td>\n",
       "      <td>11:40:29</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>Blue Spruce</td>\n",
       "      <td>Picea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>59085.0</td>\n",
       "      <td>JS 20150203131431</td>\n",
       "      <td>601</td>\n",
       "      <td></td>\n",
       "      <td>10TH ST</td>\n",
       "      <td>10TH ST</td>\n",
       "      <td>BASELINE RD</td>\n",
       "      <td>CUL-DE-SAC</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>2015-02-03 00:00:00</td>\n",
       "      <td></td>\n",
       "      <td>2015-07-10 00:00:00</td>\n",
       "      <td>11:40:29</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>Blue Spruce</td>\n",
       "      <td>Picea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>86480.0</td>\n",
       "      <td>DROID 201607141607903829845</td>\n",
       "      <td>601</td>\n",
       "      <td></td>\n",
       "      <td>10TH ST</td>\n",
       "      <td>10TH ST</td>\n",
       "      <td>BASELINE RD</td>\n",
       "      <td>CUL-DE-SAC</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>2016-07-14 00:00:00</td>\n",
       "      <td></td>\n",
       "      <td>2017-03-09 00:00:00</td>\n",
       "      <td>12:46:32</td>\n",
       "      <td>TC np.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Siberian Elm</td>\n",
       "      <td>Ulmus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>59087.0</td>\n",
       "      <td>JS 20150203131448</td>\n",
       "      <td>601</td>\n",
       "      <td></td>\n",
       "      <td>10TH ST</td>\n",
       "      <td>10TH ST</td>\n",
       "      <td>BASELINE RD</td>\n",
       "      <td>CUL-DE-SAC</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>2015-02-03 00:00:00</td>\n",
       "      <td></td>\n",
       "      <td>2015-07-10 00:00:00</td>\n",
       "      <td>11:40:30</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>Norway Maple</td>\n",
       "      <td>Acer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>59088.0</td>\n",
       "      <td>JS 20150203131959</td>\n",
       "      <td>690</td>\n",
       "      <td></td>\n",
       "      <td>10TH ST</td>\n",
       "      <td>10TH ST</td>\n",
       "      <td>CUL-DE-SAC</td>\n",
       "      <td>BASELINE RD</td>\n",
       "      <td>Front</td>\n",
       "      <td>...</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>2015-02-03 00:00:00</td>\n",
       "      <td></td>\n",
       "      <td>2015-07-10 00:00:00</td>\n",
       "      <td>11:40:30</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>Norway Maple</td>\n",
       "      <td>Acer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FID       ID                     UNIQUEID  ADDRESS SUFFIX   STREET  \\\n",
       "0    0  59084.0            JS 20150203131235      601         10TH ST   \n",
       "1    1  59085.0            JS 20150203131431      601         10TH ST   \n",
       "2    2  86480.0  DROID 201607141607903829845      601         10TH ST   \n",
       "3    3  59087.0            JS 20150203131448      601         10TH ST   \n",
       "4    4  59088.0            JS 20150203131959      690         10TH ST   \n",
       "\n",
       "     ONSTR      FROMSTR        TOSTR   SIDE  ...       GUARDSZ         EAB  \\\n",
       "0  10TH ST  BASELINE RD   CUL-DE-SAC  Front  ...    Unassigned  Unassigned   \n",
       "1  10TH ST  BASELINE RD   CUL-DE-SAC  Front  ...    Unassigned  Unassigned   \n",
       "2  10TH ST  BASELINE RD   CUL-DE-SAC  Front  ...    Unassigned  Unassigned   \n",
       "3  10TH ST  BASELINE RD   CUL-DE-SAC  Front  ...    Unassigned  Unassigned   \n",
       "4  10TH ST   CUL-DE-SAC  BASELINE RD  Front  ...    Unassigned  Unassigned   \n",
       "\n",
       "              INV_DATE INV_TIME           INSPECT_DT INSPECT_TM   NOTES  \\\n",
       "0  2015-02-03 00:00:00           2015-07-10 00:00:00   11:40:29           \n",
       "1  2015-02-03 00:00:00           2015-07-10 00:00:00   11:40:29           \n",
       "2  2016-07-14 00:00:00           2017-03-09 00:00:00   12:46:32  TC np.   \n",
       "3  2015-02-03 00:00:00           2015-07-10 00:00:00   11:40:30           \n",
       "4  2015-02-03 00:00:00           2015-07-10 00:00:00   11:40:30           \n",
       "\n",
       "  ACTIVE    COMMONNAME  GENUS  \n",
       "0    1.0   Blue Spruce  Picea  \n",
       "1    1.0   Blue Spruce  Picea  \n",
       "2    1.0  Siberian Elm  Ulmus  \n",
       "3    1.0  Norway Maple   Acer  \n",
       "4    1.0  Norway Maple   Acer  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Trees_Public.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dic(c):\n",
    "    d = {}\n",
    "    for i in c:\n",
    "        try:\n",
    "            d[i]+=1\n",
    "        except:\n",
    "            d[i]=1\n",
    "    import operator\n",
    "    d = sorted(d.items(), key=operator.itemgetter(1),reverse = True)\n",
    "    return d\n",
    "def dic2(c):\n",
    "    a = {}\n",
    "    for i in c:\n",
    "        try:\n",
    "            a[i]+=1\n",
    "        except:\n",
    "            a[i]=1\n",
    "    import operator\n",
    "    a = sorted(a.items(), key=operator.itemgetter(1),reverse = True)\n",
    "    t=0\n",
    "    for i in a:\n",
    "        t+=i[1]\n",
    "    d = []\n",
    "    for i in a:\n",
    "        d.append([i[0],float(i[1])/t])\n",
    "    return d\n",
    "def dic3(c):\n",
    "    d={}\n",
    "    for i in c:\n",
    "        try:\n",
    "            d[df[df['STREET']==i[0]]['HOOD'].iloc[0]]+=i[1]\n",
    "        except:\n",
    "            d[df[df['STREET']==i[0]]['HOOD'].iloc[0]]=i[1]\n",
    "    for i in df['HOOD']:\n",
    "        try:\n",
    "            d[i]\n",
    "        except:\n",
    "            if i == i:\n",
    "                d[i]=0\n",
    "    d = sorted(d.items(), key=operator.itemgetter(1),reverse = True)\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jacobpaul/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/Users/jacobpaul/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "ash = df[df['COMMONNAME']=='Green Ash'].append(df[df['COMMONNAME']=='White Ash']) #Subset of tree dataset that only has ash trees\n",
    "mt_dic = dic(ash['MT'])  #dictionary of sorted values of Maintenance columns\n",
    "strs = df['STREET'].append(df['TOSTR']).append(df['FROMSTR']).append(df['ONSTR']).unique()\n",
    "\n",
    "strs_tree_counts={}\n",
    "for i in df['STREET'].unique():\n",
    "    e={}\n",
    "    for j in df[df['STREET']==i]['COMMONNAME']:\n",
    "        #e['Ash']=0\n",
    "        if j !=\"White Ash\" and j!=\"Green Ash\":\n",
    "            try:\n",
    "                e[j]+=1\n",
    "            except:\n",
    "                e[j]=1\n",
    "        else:\n",
    "            try:\n",
    "                e['Ash']+=1\n",
    "            except:\n",
    "                e['Ash']=1\n",
    "    strs_tree_counts[i]=[np.sum([values for key,values in e.items()]),e]\n",
    "for key,value in strs_tree_counts.items():\n",
    "    for a,b in value[1].items():\n",
    "        strs_tree_counts[key][1][a] = [b,float(b)/value[0]]\n",
    "\n",
    "bad_ash = ash[ash['MT']!='No Specific Maintenance Need']\n",
    "good_ash = ash[ash['MT']=='No Specific Maintenance Need']\n",
    "\n",
    "boulder_trees = dic(df['COMMONNAME'])\n",
    "\n",
    "bad_ash_strs =  [i for i in bad_ash['STREET'].unique()]\n",
    "bad_ash_strs_counts = sorted([[i,len(bad_ash[bad_ash['STREET'] ==i])] for i in bad_ash_strs], key = lambda num:num[1], reverse = True)\n",
    "good_ash_strs = [i for i in good_ash['STREET'].unique()]\n",
    "only_good_ash_strs = [i for i in good_ash['STREET'].unique() if i not in bad_ash_strs]\n",
    "only_bad_ash_strs = [i for i in bad_ash['STREET'].unique() if i not in good_ash_strs]\n",
    "both_ash_strs = [i for i in good_ash['STREET'].unique() if i in bad_ash['STREET'].unique()]\n",
    "both_ash_strs_percent = len(both_ash_strs)/len(strs)\n",
    "\n",
    "bad_strs_percent = len(bad_ash_strs)/len(strs)\n",
    "good_strs_percent = len(good_ash_strs)/len(strs)\n",
    "\n",
    "bad_ash_tofro_strs = bad_ash['TOSTR'].append(bad_ash['FROMSTR']).unique()\n",
    "\n",
    "\n",
    "at_risk_strs = [i for i in only_good_ash_strs if i in bad_ash_tofro_strs]\n",
    "\n",
    "at_risk_strs_counts = []\n",
    "for i in at_risk_strs:\n",
    "    at_risk_strs_counts.append([i,len(ash[ash['STREET']==i])])\n",
    "    \n",
    "at_risk_strs_sorted = sorted(at_risk_strs_counts, key=lambda num:num[1], reverse = True)\n",
    "\n",
    "at_risk_strs_d = {}\n",
    "for i in at_risk_strs_sorted:\n",
    "    at_risk_strs_d[i[0]]=i[1]\n",
    "    \n",
    "at_risk_strs_ash_dic = {}\n",
    "for i in at_risk_strs_sorted:\n",
    "    at_risk_strs_ash_dic[i[0]]=strs_tree_counts[i[0]][1]['Ash']\n",
    "    \n",
    "at_risk_hoods=dic3(at_risk_strs_sorted)\n",
    "bad_ash_hoods_counts = dic3(bad_ash_strs_counts)\n",
    "\n",
    "\n",
    "strs_trees_percent_dic = {}\n",
    "for i in df['COMMONNAME'].unique():\n",
    "    strs_trees_percent_dic[i]=np.mean([value[1][i][1] for key,value in strs_tree_counts.items() if i in value[1]])\n",
    "t=[value[1]['Ash'][1] for key,value in strs_tree_counts.items() if 'Ash'in value[1]]\n",
    "strs_trees_percent_dic['Ash']=np.mean(t)\n",
    "strs_trees_percent_dic_sorted = sorted(strs_trees_percent_dic.items(), key=operator.itemgetter(1),reverse = True)\n",
    "\n",
    "strs_trees_percent_dic_2 = {}\n",
    "for i in df['COMMONNAME'].unique():\n",
    "    t = []\n",
    "    for key,value in strs_tree_counts.items():\n",
    "        if i in value[1]:\n",
    "            t.append(np.mean(value[1][i][1]))\n",
    "        else:\n",
    "            t.append(0)\n",
    "    strs_trees_percent_dic_2[i]=np.mean(t)\n",
    "t=[]\n",
    "for key,value in strs_tree_counts.items():\n",
    "    if 'Ash' in value[1]:\n",
    "        t.append(value[1]['Ash'][1])\n",
    "    else:\n",
    "        t.append(0)\n",
    "strs_trees_percent_dic_2['Ash']=np.mean(t)\n",
    "strs_trees_percent_dic_sorted_2 = sorted(strs_trees_percent_dic_2.items(), key=operator.itemgetter(1),reverse = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "help_vars = {'ash': 'filtered Data frame that only includes ash trees',\n",
    "'mt_dic': 'sorted list of maintenance values for ash trees',\n",
    "'boulder_trees': 'quantities of each tree in boulder',\n",
    "'strs': 'list of all streets in boulder',\n",
    "\n",
    "'bad_ash': 'data frame of all ash trees that need maintenance',\n",
    "'bad_ash_strs': 'list of streets that have ash trees that need maintenance',\n",
    "'only_bad_ash_strs': 'list of streets that have ash trees and all of those trees need maintenance',\n",
    "'bad_ash_strs_counts': 'sorted list of streets and the amounts of bad ash trees they have',\n",
    "'bad_strs_percent': 'percent of streets in Boulder that have bad trees',\n",
    "'bad_ash_tofro_strs': 'list of streets that have another street touching it that have bad ash trees on it',\n",
    "'bad_ash_hoods_counts': 'sorted list of hoods and the amount of bad ash trees that it has',\n",
    "\n",
    "'good_ash': 'data frame of all ash trees that dont need maintenance',\n",
    "'good_ash_strs': 'list of all streets that have good ash trees on it',\n",
    "'only_good_ash_strs': 'list of streets that have ash trees and none of those trees need maintenance',\n",
    "'good_strs_percent': 'percent of streets in Boulder that have good ash trees',\n",
    "\n",
    "'both_ash_strs': 'list of streets that have some ash trees that need maintenance and some that dont',\n",
    "'both_ash_strs_percent': 'percent of streets in boulder that have both good & bad ash trees',\n",
    "\n",
    "'at_risk_strs': 'streets that have an ajoining street that has a tree that needs maintenance',\n",
    "'at_risk_strs_d': 'alphabetized dictionary of streets that have no bad trees and the amount of bad trees on ajoining streets',\n",
    "'at_risk_strs_sorted': 'list of streets that have no bad trees, sorted by how many bad trees are on ajoining streets',\n",
    "'at_risk_strs_ash_dic': 'dictionary of streets that have neighboring streets with bad ash trees, and the amount of ash trees and the percentage of ash tree makeup of that street',\n",
    "'at_risk_hoods': 'list of hoods, sorted by the amount of at risk streets they have',\n",
    "\n",
    "'strs_trees_percent_dic': 'alphabetized dictionary of each tree, and the average percentage that the tree makes up for streets that have that tree',\n",
    "'strs_trees_percent_dic_sorted': 'list of each tree, sorted by the average percentage that the tree makes up for streets that have that tree',\n",
    "\n",
    "'strs_trees_percent_dic_2': 'alphabetized dictionary of each tree, and the average percentage of trees that that tree makes up for every street',\n",
    "'strs_trees_percent_dic_sorted_2': 'list of each tree, sorted by average percentage of trees that that tree makes up for every street',\n",
    "\n",
    "'strs_tree_counts': 'Dictionary of each street. values are counts of trees on that street, and another dictionary that has each type of tree on that street, and the count of that tree and the percentage makeup of that tree on that street'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ash_weighted_streets_trees = {}\n",
    "ash_weighted_streets = {}\n",
    "for i in strs:\n",
    "    try:\n",
    "        ash_weighted_streets[i]=strs_tree_counts[i][0]*strs_tree_counts[i][1]['Ash'][1]\n",
    "    except:\n",
    "        pass\n",
    "ash_weighted_streets = sorted(ash_weighted_streets.items(),key=operator.itemgetter(1),reverse=True)\n",
    "for i in ash_weighted_streets:\n",
    "    if strs_tree_counts[i[0]][1]['Ash'][1]>.08: #### grab all streets that have ash percentages of over .08\n",
    "        #ash_weighted_streets_trees[i[0]]=[i[1],sorted(strs_tree_counts[i[0]][1].items(),key=operator.itemgetter(1),reverse=True)[:10]]\n",
    "        ash_weighted_streets_trees[i[0]]=i[1],strs_tree_counts[i[0]][1]\n",
    "len(ash_weighted_streets_trees['WHITE PL'][1]) #this is a dictionary that has the grabbed streets, weighted value calculated above, and top 10 tree percentages on that street"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating Diversity\n",
    "\n",
    "Areas with high percentages of ash trees and low amounts of diversity are at the biggest risk of impact from EAB. In the event that treatments for EAB do not work, these trees will likely need to be replaced. This could be seen as an opportunity to increase tree diversity on streets impacted by EAB. From a statistical point of view, what should we replace these trees with?\n",
    "\n",
    "To find this, we need to identify the streets with the *least* diversity and with the highest composition of ash trees. To measure \"diversity\" we used the variance of the percent makeup of various trees on a given street."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "rcParams.update({'figure.autolayout': True})\n",
    "import altair as alt\n",
    "alt.renderers.enable('notebook')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Creating a dataset with the \"diversity\" calculation\n",
    "diversity = []\n",
    "for street in ash_weighted_streets_trees:\n",
    "    percents = [ash_weighted_streets_trees[street][1][i][1] for i in ash_weighted_streets_trees[street][1]]\n",
    "    diversity.append([street,ash_weighted_streets_trees[street][0],ash_weighted_streets_trees[street][1][\"Ash\"][1],np.std(percents),len(ash_weighted_streets_trees[street][1])])\n",
    "    #creating dataset with street, number of trees, percent of ash trees, std deviation of percentage of trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of this diversity figure follows a mostly normal distribution, which means that we'll be looking at the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118ba0080>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jacobpaul/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py:2362: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot([i[2] for i in diversity])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create diversity dataframe\n",
    "diversity_df = pd.DataFrame(diversity, columns=['street','num_trees','percent_ash_trees','std_dev_of_tree_makeup', 'num_species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sort the dataframe and sort to get the \"least diverse\" streets\n",
    "least_diverse_high_ash = diversity_df[diversity_df['num_trees'] > 20][:100].sort_values(['std_dev_of_tree_makeup','num_trees'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>street</th>\n",
       "      <th>num_trees</th>\n",
       "      <th>percent_ash_trees</th>\n",
       "      <th>std_dev_of_tree_makeup</th>\n",
       "      <th>num_species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>WHITE PL</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.281325</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>FLATIRON PY</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>0.224814</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>LODGE CT</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.221523</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CENTRAL AV</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.510417</td>\n",
       "      <td>0.136041</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LINDEN PARK DR</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0.639485</td>\n",
       "      <td>0.133155</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>LODGE LN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.103780</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>GREENBRIAR BV</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>0.103665</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ROXWOOD LN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.114537</td>\n",
       "      <td>0.091489</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>26TH ST</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.151111</td>\n",
       "      <td>0.079239</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>RESERVOIR RD</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.084677</td>\n",
       "      <td>0.075894</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>MEADOW GLEN DR</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.343750</td>\n",
       "      <td>0.074065</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>FRANKLIN DR</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.066960</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>FOLSOM ST</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.262136</td>\n",
       "      <td>0.062193</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LINDEN AV</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.316129</td>\n",
       "      <td>0.060147</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>QUINCE AV</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.056975</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>55TH ST</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.311828</td>\n",
       "      <td>0.056095</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>75TH ST</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.168950</td>\n",
       "      <td>0.052548</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>BUTTE MILL RD</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.140940</td>\n",
       "      <td>0.052499</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>MOORHEAD AV</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.210938</td>\n",
       "      <td>0.050965</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>NORWOOD AV</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.107317</td>\n",
       "      <td>0.050748</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>JUNIPER AV</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.261111</td>\n",
       "      <td>0.046861</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>WONDERLAND HILL AV</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.177966</td>\n",
       "      <td>0.044674</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>S BROADWAY</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.192000</td>\n",
       "      <td>0.043684</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>KINGS RIDGE BV</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.242604</td>\n",
       "      <td>0.043319</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>34TH ST</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.178771</td>\n",
       "      <td>0.040110</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>SPINE RD</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.143791</td>\n",
       "      <td>0.039659</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47TH ST</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.148148</td>\n",
       "      <td>0.038912</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19TH ST</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.093544</td>\n",
       "      <td>0.036603</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>HAWTHORN AV</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.137725</td>\n",
       "      <td>0.035573</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PENNSYLVANIA AV</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.213270</td>\n",
       "      <td>0.035053</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ARAPAHOE AV</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.085017</td>\n",
       "      <td>0.031149</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PEARL ST</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.159184</td>\n",
       "      <td>0.030851</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>PLEASANT ST</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.128906</td>\n",
       "      <td>0.030394</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>MAXWELL AV</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.148810</td>\n",
       "      <td>0.030205</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WALNUT ST</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.185629</td>\n",
       "      <td>0.029423</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MAPLETON AV</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.127517</td>\n",
       "      <td>0.028291</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>BLUFF ST</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.167235</td>\n",
       "      <td>0.028052</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9TH ST</td>\n",
       "      <td>245.0</td>\n",
       "      <td>0.229401</td>\n",
       "      <td>0.027655</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ALPINE AV</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.146853</td>\n",
       "      <td>0.027571</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KALMIA AV</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.179159</td>\n",
       "      <td>0.027184</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11TH ST</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.127219</td>\n",
       "      <td>0.026530</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>UNIVERSITY AV</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.117886</td>\n",
       "      <td>0.025630</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CANYON BV</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.094901</td>\n",
       "      <td>0.025106</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13TH ST</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.114530</td>\n",
       "      <td>0.024953</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16TH ST</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.122016</td>\n",
       "      <td>0.024770</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>28TH ST</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.089400</td>\n",
       "      <td>0.024636</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>18TH ST</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.138060</td>\n",
       "      <td>0.024171</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>IRIS AV</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.112957</td>\n",
       "      <td>0.024100</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PINE ST</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.099548</td>\n",
       "      <td>0.023859</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>POPLAR AV</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>0.023798</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>17TH ST</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.142487</td>\n",
       "      <td>0.023207</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SPRUCE ST</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.149573</td>\n",
       "      <td>0.022896</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30TH ST</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.109914</td>\n",
       "      <td>0.022280</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>7TH ST</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.086093</td>\n",
       "      <td>0.021159</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARAPAHOE  AV</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.132321</td>\n",
       "      <td>0.021022</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SIOUX DR</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.104723</td>\n",
       "      <td>0.020827</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8TH ST</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.119122</td>\n",
       "      <td>0.020704</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4TH ST</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.094092</td>\n",
       "      <td>0.019873</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BASELINE RD</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.107466</td>\n",
       "      <td>0.018197</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BROADWAY</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.101370</td>\n",
       "      <td>0.017829</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                street  num_trees  percent_ash_trees  std_dev_of_tree_makeup  \\\n",
       "36            WHITE PL       35.0           0.795455                0.281325   \n",
       "55         FLATIRON PY       26.0           0.634146                0.224814   \n",
       "66            LODGE CT       21.0           0.636364                0.221523   \n",
       "22          CENTRAL AV       49.0           0.510417                0.136041   \n",
       "2       LINDEN PARK DR      149.0           0.639485                0.133155   \n",
       "67            LODGE LN       21.0           0.344262                0.103780   \n",
       "50       GREENBRIAR BV       27.0           0.281250                0.103665   \n",
       "56          ROXWOOD LN       26.0           0.114537                0.091489   \n",
       "37             26TH ST       34.0           0.151111                0.079239   \n",
       "68        RESERVOIR RD       21.0           0.084677                0.075894   \n",
       "61      MEADOW GLEN DR       22.0           0.343750                0.074065   \n",
       "40         FRANKLIN DR       33.0           0.305556                0.066960   \n",
       "49           FOLSOM ST       27.0           0.262136                0.062193   \n",
       "23           LINDEN AV       49.0           0.316129                0.060147   \n",
       "60           QUINCE AV       23.0           0.083333                0.056975   \n",
       "17             55TH ST       58.0           0.311828                0.056095   \n",
       "34             75TH ST       37.0           0.168950                0.052548   \n",
       "65       BUTTE MILL RD       21.0           0.140940                0.052499   \n",
       "51         MOORHEAD AV       27.0           0.210938                0.050965   \n",
       "62          NORWOOD AV       22.0           0.107317                0.050748   \n",
       "25          JUNIPER AV       47.0           0.261111                0.046861   \n",
       "69  WONDERLAND HILL AV       21.0           0.177966                0.044674   \n",
       "24          S BROADWAY       48.0           0.192000                0.043684   \n",
       "30      KINGS RIDGE BV       41.0           0.242604                0.043319   \n",
       "42             34TH ST       32.0           0.178771                0.040110   \n",
       "63            SPINE RD       22.0           0.143791                0.039659   \n",
       "47             47TH ST       28.0           0.148148                0.038912   \n",
       "12             19TH ST       71.0           0.093544                0.036603   \n",
       "59         HAWTHORN AV       23.0           0.137725                0.035573   \n",
       "27     PENNSYLVANIA AV       45.0           0.213270                0.035053   \n",
       "..                 ...        ...                ...                     ...   \n",
       "5          ARAPAHOE AV      122.0           0.085017                0.031149   \n",
       "1             PEARL ST      156.0           0.159184                0.030851   \n",
       "41         PLEASANT ST       33.0           0.128906                0.030394   \n",
       "57          MAXWELL AV       25.0           0.148810                0.030205   \n",
       "10           WALNUT ST       93.0           0.185629                0.029423   \n",
       "6          MAPLETON AV      114.0           0.127517                0.028291   \n",
       "21            BLUFF ST       49.0           0.167235                0.028052   \n",
       "0               9TH ST      245.0           0.229401                0.027655   \n",
       "64           ALPINE AV       21.0           0.146853                0.027571   \n",
       "8            KALMIA AV       98.0           0.179159                0.027184   \n",
       "28             11TH ST       43.0           0.127219                0.026530   \n",
       "46       UNIVERSITY AV       29.0           0.117886                0.025630   \n",
       "15           CANYON BV       67.0           0.094901                0.025106   \n",
       "14             13TH ST       67.0           0.114530                0.024953   \n",
       "26             16TH ST       46.0           0.122016                0.024770   \n",
       "13             28TH ST       70.0           0.089400                0.024636   \n",
       "33             18TH ST       37.0           0.138060                0.024171   \n",
       "38             IRIS AV       34.0           0.112957                0.024100   \n",
       "16             PINE ST       66.0           0.099548                0.023859   \n",
       "11           POPLAR AV       76.0           0.114286                0.023798   \n",
       "18             17TH ST       55.0           0.142487                0.023207   \n",
       "7            SPRUCE ST      105.0           0.149573                0.022896   \n",
       "19             30TH ST       51.0           0.109914                0.022280   \n",
       "53              7TH ST       26.0           0.086093                0.021159   \n",
       "4         ARAPAHOE  AV      122.0           0.132321                0.021022   \n",
       "20            SIOUX DR       51.0           0.104723                0.020827   \n",
       "32              8TH ST       38.0           0.119122                0.020704   \n",
       "29              4TH ST       43.0           0.094092                0.019873   \n",
       "9          BASELINE RD       95.0           0.107466                0.018197   \n",
       "3             BROADWAY      148.0           0.101370                0.017829   \n",
       "\n",
       "    num_species  \n",
       "36            6  \n",
       "55            5  \n",
       "66            5  \n",
       "22           11  \n",
       "2            21  \n",
       "67           12  \n",
       "50           10  \n",
       "56           19  \n",
       "37           31  \n",
       "68           23  \n",
       "61           18  \n",
       "40           25  \n",
       "49           22  \n",
       "23           29  \n",
       "60           34  \n",
       "17           29  \n",
       "34           28  \n",
       "65           31  \n",
       "51           24  \n",
       "62           33  \n",
       "25           34  \n",
       "69           27  \n",
       "24           29  \n",
       "30           37  \n",
       "42           34  \n",
       "63           24  \n",
       "47           31  \n",
       "12           71  \n",
       "59           38  \n",
       "27           45  \n",
       "..          ...  \n",
       "5            71  \n",
       "1            76  \n",
       "41           49  \n",
       "57           48  \n",
       "10           59  \n",
       "6            69  \n",
       "21           59  \n",
       "0            80  \n",
       "64           40  \n",
       "8            65  \n",
       "28           61  \n",
       "46           51  \n",
       "15           62  \n",
       "14           67  \n",
       "26           54  \n",
       "13           48  \n",
       "33           57  \n",
       "38           47  \n",
       "16           71  \n",
       "11           70  \n",
       "18           69  \n",
       "7            82  \n",
       "19           64  \n",
       "53           63  \n",
       "4            73  \n",
       "20           49  \n",
       "32           61  \n",
       "29           78  \n",
       "9            83  \n",
       "3            94  \n",
       "\n",
       "[70 rows x 5 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "least_diverse_high_ash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "alt.Chart(least_diverse_high_ash).mark_point().encode(\n",
    "    y=alt.Y(\"std_dev_of_tree_makeup\",title=\"Diversity (higher = less diverse)\"),\n",
    "    x=alt.X(\"num_species\",title=\"Number of Species\")\n",
    ").save(\"diversity_vs_num_species.png\", scale_factor=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By plotting the percent of ash trees against the variation (higher variation means less diversity), we should focus on the right side of the chart. These streets have high percentage of ash trees and low diversity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 6.79999999999999, 'Percent of Ash Trees')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.lmplot(data=least_diverse_high_ash, x = \"percent_ash_trees\", y = \"std_dev_of_tree_makeup\")\n",
    "plt.ylabel(\"Diversity(variance in percent composition) - higher = less diversity\")\n",
    "plt.xlabel(\"Percent of Ash Trees\")\n",
    "sb.savefig(\"scatter_percent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.corrcoef(least_diverse_high_ash)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "strs_trees_percent_dic_3 = {}\n",
    "for i in df['COMMONNAME'].unique():\n",
    "    t = []\n",
    "    for key,value in strs_tree_counts.items():\n",
    "        if key == \"Ash\":\n",
    "            continue\n",
    "        if i in value[1]:\n",
    "            t.append(np.mean(value[1][i][1]))\n",
    "        else:\n",
    "            t.append(0)\n",
    "    strs_trees_percent_dic_3[i]=np.mean(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8671042035780248"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(strs_trees_percent_dic_3.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5709"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ash)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "alt.Chart(df[df[\"STREET\"] == \"WHITE PL\"]).mark_bar().encode(\n",
    "    x=\"GENUS\",\n",
    "    y=\"count()\",\n",
    ").save('white_pl_diversity.png', scale_factor=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "alt.Chart(df[df[\"STREET\"] == \"9TH ST\"]).mark_bar().encode(\n",
    "    x=\"GENUS\",\n",
    "    y=\"count()\",\n",
    ").save('9thst_diversity.png', scale_factor=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preview for the next update\n",
    "\n",
    "In the next update, we want to tackle the question: if all Ash trees on a street die, what trees should replace them? Below is a preliminary approach at caclulating what trees should replace the ash trees. In this case, we use the \"standard street\" - the average percent composition of tree species across all streets in Boulder, excluding ash. By multiplying these percentages by the amount of Ash trees on a street, we can get a rough idea of what trees and how many of them should replace the ash trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linden_replacements = {}\n",
    "num_ash = ash_weighted_streets_trees[\"LINDEN PARK DR\"][1][\"Ash\"][0]\n",
    "for key,value in ash_weighted_streets_trees[\"LINDEN PARK DR\"][1].items():\n",
    "    if key != \"Ash\":\n",
    "        linden_replacements[key] = num_ash*value[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linden_replacements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}