Puma_Map_Wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import os\n",
    "from os.path import join\n",
    "from shapely.geometry import Polygon\n",
    "from matplotlib import pyplot\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\karan\\\\Python Notebooks\\\\MPI\\\\Data'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir('Data/')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>STATEFP10</th>\n",
       "      <th>PUMACE10</th>\n",
       "      <th>NAMELSAD10</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>3803</td>\n",
       "      <td>NYC-Manhattan Community District 10--Central H...</td>\n",
       "      <td>POLYGON ((-73.95150599999999 40.812008, -73.95...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>3707</td>\n",
       "      <td>NYC-Bronx Community District 5--Morris Heights...</td>\n",
       "      <td>POLYGON ((-73.90980999999999 40.857348, -73.90...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>3201</td>\n",
       "      <td>Nassau County (Northwest)--North Hempstead Tow...</td>\n",
       "      <td>POLYGON ((-73.628102 40.816038, -73.627775 40....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>3301</td>\n",
       "      <td>Suffolk County (Northwest)--Huntington Town (N...</td>\n",
       "      <td>POLYGON ((-73.36815199999999 40.863641, -73.36...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>3303</td>\n",
       "      <td>Suffolk County (Northwest)--Smithtown Town PUMA</td>\n",
       "      <td>POLYGON ((-73.232677 40.913281, -73.151922 40....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP10  PUMACE10                                         NAMELSAD10  \\\n",
       "0        36      3803  NYC-Manhattan Community District 10--Central H...   \n",
       "1        36      3707  NYC-Bronx Community District 5--Morris Heights...   \n",
       "2        36      3201  Nassau County (Northwest)--North Hempstead Tow...   \n",
       "3        36      3301  Suffolk County (Northwest)--Huntington Town (N...   \n",
       "4        36      3303    Suffolk County (Northwest)--Smithtown Town PUMA   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-73.95150599999999 40.812008, -73.95...  \n",
       "1  POLYGON ((-73.90980999999999 40.857348, -73.90...  \n",
       "2  POLYGON ((-73.628102 40.816038, -73.627775 40....  \n",
       "3  POLYGON ((-73.36815199999999 40.863641, -73.36...  \n",
       "4  POLYGON ((-73.232677 40.913281, -73.151922 40....  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_shp = gpd.read_file('puma.geojson')\n",
    "all_shp['PUMACE10'] = all_shp['PUMACE10'].astype(int)\n",
    "all_shp = all_shp[['STATEFP10','PUMACE10','NAMELSAD10','geometry']]\n",
    "all_shp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>origin_puma</th>\n",
       "      <th>dest_puma</th>\n",
       "      <th>od_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>17413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>102</td>\n",
       "      <td>4828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>103</td>\n",
       "      <td>3755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>104</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101</td>\n",
       "      <td>105</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   origin_puma  dest_puma  od_counts\n",
       "0          101        101      17413\n",
       "1          101        102       4828\n",
       "2          101        103       3755\n",
       "3          101        104        149\n",
       "4          101        105        421"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('sf_puma.csv')\n",
    "df.drop(columns=['Unnamed: 0'],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>STATEFP10</th>\n",
       "      <th>PUMACE10</th>\n",
       "      <th>NAMELSAD10</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>34</td>\n",
       "      <td>101</td>\n",
       "      <td>Atlantic County (East)--Atlantic City &amp; Ventno...</td>\n",
       "      <td>POLYGON ((-74.508858 39.309347, -74.51181 39.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>06</td>\n",
       "      <td>101</td>\n",
       "      <td>Alameda County (North)--Berkeley &amp; Albany Citi...</td>\n",
       "      <td>POLYGON ((-122.245314 37.869774, -122.245313 3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    STATEFP10  PUMACE10                                         NAMELSAD10  \\\n",
       "191        34       101  Atlantic County (East)--Atlantic City & Ventno...   \n",
       "386        06       101  Alameda County (North)--Berkeley & Albany Citi...   \n",
       "\n",
       "                                              geometry  \n",
       "191  POLYGON ((-74.508858 39.309347, -74.51181 39.3...  \n",
       "386  POLYGON ((-122.245314 37.869774, -122.245313 3...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_shp[all_shp['PUMACE10']==101]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(571, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_shp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['36', '34', '17', '06'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_shp['STATEFP10'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_poly(geom):\n",
    "    if(geom.type=='Polygon'):\n",
    "        return geom\n",
    "    else:\n",
    "        return geom[0]\n",
    "    \n",
    "all_shp['geometry'] = all_shp['geometry'].map(convert_to_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\karan\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\ipykernel_launcher.py:41: 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"
     ]
    }
   ],
   "source": [
    "file_dict = {'ny':'nyc_puma.csv','la':'la_puma.csv','sf':'sf_puma.csv','chic':'chic_puma.csv'}\n",
    "city_list = ['ny','la','sf','chic']\n",
    "#state_dict = {'ny':['34','36'],'la':['06'],'sf':['06'],'chic':['17']}\n",
    "state_dict = {'ny':['36'],'la':['06'],'sf':['06'],'chic':['17']}\n",
    "bin_dict = {\n",
    "    'ny':[    0,   5000,   10000,  25000,  50000,  100000, 723000],\n",
    "    'la':[    0,   5000,   10000,  25000,  50000,  100000, 533000],\n",
    "    'sf':[    0,   5000,   10000,  25000,  50000,  100000, 294000],\n",
    "    'chic':[    0,   5000,   10000,  25000,  50000,  100000, 249000]\n",
    "    }\n",
    "df_list = []\n",
    "colors = ['darkred','red','orange','yellow','lime','green']\n",
    "colormap = {i: colors[i] for i in range(6)}\n",
    "\n",
    "def map_puma_id(puma,city):\n",
    "    try:\n",
    "        shp_match = all_shp[(all_shp['PUMACE10']==puma)&(all_shp['STATEFP10'].isin(state_dict[city]))]\n",
    "        puma_id = list(shp_match['PUMACE10'])[0]\n",
    "        state_id = list(shp_match['STATEFP10'])[0]\n",
    "        return str(state_id) + '_' + str(puma_id)\n",
    "    except IndexError:\n",
    "        return '0'\n",
    "\n",
    "for city in city_list:\n",
    "    df = pd.read_csv(file_dict[city])\n",
    "    bins = bin_dict[city]\n",
    "    labels = np.arange(1,len(bins))\n",
    "    \n",
    "    df['binned'] = pd.cut(df['od_counts'], bins=bins, labels=labels)\n",
    "    df['color'] = df['binned'].map(lambda x: colormap[5-(x-1)])    \n",
    "    df.drop(columns=['Unnamed: 0','binned'],inplace=True)  \n",
    "    \n",
    "    df['origin_puma'] = df['origin_puma'].map(lambda x: map_puma_id(int(x),city)) \n",
    "    df['dest_puma'] = df['dest_puma'].map(lambda x: map_puma_id(int(x),city))\n",
    "    df = df[(df['origin_puma']!='0')&(df['dest_puma']!='0')]\n",
    "    \n",
    "    df_list.append(df)\n",
    "    df.to_csv('d3_puma_'+city+'.csv')\n",
    "    \n",
    "    shp = all_shp[all_shp['STATEFP10'].isin(state_dict[city])]\n",
    "    shp['puma_id'] = shp.apply(lambda row: str(row['STATEFP10'])+'_'+str(row['PUMACE10']),axis=1)\n",
    "    shp = shp[shp['puma_id'].isin(list(df['origin_puma'].unique())+list(df['dest_puma'].unique()))]\n",
    "    shp.drop(columns=['STATEFP10','PUMACE10'],inplace=True)\n",
    "    shp.to_file('d3_puma_'+city+'.geojson',driver='GeoJSON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722318\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAD8CAYAAAC/1zkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFZ1JREFUeJzt3X/QpWV93/H3R1YUUNlFFkt3SRaSHSLJxLBuEEtqUzH80ERMR9p1nLJDSbaT0FabzkRI2pBqnNFMKsikUamQgFUR0chWsXQFTKadCiw/FBDJrkJhA8qaRTAaf6z59o9zPXBYnh/nefZ6nvMceL9mzpz7vu7rvq/v2XN4Ptw/zn1SVUiS1MNzxl2AJOmZw1CRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqZsW4C1hqhx9+eK1bt27cZUjSxLj11lu/WVWrR+n7rAuVdevWsX379nGXIUkTI8n/G7Wvh78kSd0sWqgkuSzJI0nuGmo7LMm2JDva86rWniQXJ9mZ5EtJNgyts7n135Fk81D7y5Pc2da5OEkW67VIkkazmHsqfwactk/becD1VbUeuL7NA5wOrG+PLcD7YBBCwAXAK4ATgAumgqj12TK03r5jSZKW2KKFSlX9JbBnn+YzgMvb9OXAG4bar6iBLwArkxwJnApsq6o9VfUosA04rS17UVX93xrcu/+KoW1JksZkqc+pvKSqHgZoz0e09jXAg0P9drW22dp3TdM+rSRbkmxPsn337t37/SIkSdNbLifqpzsfUgton1ZVXVJVG6tq4+rVI10VJ0lagKUOlW+0Q1e050da+y7gqKF+a4GH5mhfO027JGmMljpUtgJTV3BtBq4Zaj+rXQV2IvBYOzx2HXBKklXtBP0pwHVt2beTnNiu+jpraFuSpDFZtC8/Jvko8IvA4Ul2MbiK613AVUnOAR4AzmzdrwVeC+wEvgucDVBVe5K8A7il9Xt7VU2d/P8NBleYHQR8tj0kSWOUwcVTzx4bN26shX6jft15n3li+v53va5XSZK0rCW5tao2jtJ3uZyolyQ9AxgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3YwlVJL8+yR3J7kryUeTPD/J0UluSrIjyceSHNj6Pq/N72zL1w1t5/zWfm+SU8fxWiRJT1ryUEmyBvh3wMaq+hngAGAT8G7gwqpaDzwKnNNWOQd4tKp+Eriw9SPJcW29nwZOA/4kyQFL+VokSU81rsNfK4CDkqwADgYeBl4NXN2WXw68oU2f0eZpy09OktZ+ZVV9v6ruA3YCJyxR/ZKkaSx5qFTVXwN/BDzAIEweA24FvlVVe1u3XcCaNr0GeLCtu7f1f/Fw+zTrSJLGYByHv1Yx2Ms4GviHwCHA6dN0ralVZlg2U/t0Y25Jsj3J9t27d8+/aEnSSMZx+Os1wH1Vtbuqfgh8EvhHwMp2OAxgLfBQm94FHAXQlh8K7Blun2adp6iqS6pqY1VtXL16de/XI0lqxhEqDwAnJjm4nRs5GfgycCPwxtZnM3BNm97a5mnLb6iqau2b2tVhRwPrgZuX6DVIkqaxYu4ufVXVTUmuBm4D9gK3A5cAnwGuTPIHre3StsqlwIeS7GSwh7KpbefuJFcxCKS9wLlV9aMlfTGSpKdY8lABqKoLgAv2af4a01y9VVXfA86cYTvvBN7ZvUBJ0oL4jXpJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3YwUKkl+ZrELkSRNvlH3VN6f5OYkv5lk5aJWJEmaWCOFSlX9AvBm4Chge5KPJPmlhQ6aZGWSq5N8Jck9SV6Z5LAk25LsaM+rWt8kuTjJziRfSrJhaDubW/8dSTYvtB5JUh8jn1Opqh3AfwTeBvwT4OIWCv9sAeO+F/ifVfVTwMuAe4DzgOuraj1wfZsHOB1Y3x5bgPcBJDkMuAB4BXACcMFUEEmSxmPUcyo/m+RCBn/8Xw38SlW9tE1fOJ8Bk7wIeBVwKUBV/aCqvgWcAVzeul0OvKFNnwFcUQNfAFYmORI4FdhWVXuq6lFgG3DafGqRJPU16p7KHwO3AS+rqnOr6jaAqnqIwd7LfBwD7Ab+NMntST6Y5BDgJVX1cNvuw8ARrf8a4MGh9Xe1tpnaJUljMmqovBb4SFX9HUCS5yQ5GKCqPjTPMVcAG4D3VdXxwHd48lDXdDJNW83S/vQNJFuSbE+yfffu3fMsV5I0qlFD5XPAQUPzB7e2hdgF7Kqqm9r81QxC5hvtsBbt+ZGh/kcNrb8WeGiW9qepqkuqamNVbVy9evUCy5YkzWXUUHl+Vf3t1EybPnghA1bV14EHkxzbmk4GvgxsBaau4NoMXNOmtwJntavATgQea4fHrgNOSbKqnaA/pbVJksZkxYj9vpNkw9S5lCQvB/5uP8b9t8CHkxwIfA04m0HAXZXkHOAB4MzW91oGh992At9tfamqPUneAdzS+r29qvbsR02SpP00aqi8Ffh4kqnDS0cC/2Khg1bVHcDGaRadPE3fAs6dYTuXAZcttA5JUl8jhUpV3ZLkp4BjGZwg/0pV/XBRK5MkTZxR91QAfh5Y19Y5PglVdcWiVCVJmkgjhUqSDwE/AdwB/Kg1F2CoSJKeMOqeykbguHZ+Q5KkaY16SfFdwD9YzEIkSZNv1D2Vw4EvJ7kZ+P5UY1W9flGqkiRNpFFD5fcXswhJ0jPDqJcU/0WSHwfWV9Xn2n2/Dljc0iRJk2bUW9//OoN7dH2gNa0BPrVYRUmSJtOoJ+rPBU4CHocnfrDriFnXkCQ964waKt+vqh9MzSRZwQy3mZckPXuNGip/keR3gIPab9N/HPgfi1eWJGkSjRoq5zH4tcY7gX/N4M7B8/3FR0nSM9yoV3/9PfDf2kOSpGmNeu+v+5jmHEpVHdO9IknSxJrPvb+mPJ/BD2gd1r8cSdIkG+mcSlX9zdDjr6vqIuDVi1ybJGnCjHr4a8PQ7HMY7Lm8cFEqkiRNrFEPf/2Xoem9wP3AP+9ejSRpoo169dc/XexCJEmTb9TDX7812/Kqek+fciRJk2w+V3/9PLC1zf8K8JfAg4tRlCRpMs3nR7o2VNW3AZL8PvDxqvq1xSpMkjR5Rr1Ny48BPxia/wGwrns1kqSJNuqeyoeAm5P8OYNv1v8qcMWiVSVJmkijXv31ziSfBf5xazq7qm5fvLIkSZNo1MNfAAcDj1fVe4FdSY5epJokSRNq1J8TvgB4G3B+a3ou8N8XqyhJ0mQadU/lV4HXA98BqKqH8DYtkqR9jBoqP6iqot3+Pskhi1eSJGlSjRoqVyX5ALAyya8Dn8Mf7JIk7WPUq7/+qP02/ePAscDvVdW2Ra1MkjRx5gyVJAcA11XVawCDRJI0ozkPf1XVj4DvJjm058BJDkhye5JPt/mjk9yUZEeSjyU5sLU/r83vbMvXDW3j/NZ+b5JTe9YnSZq/Uc+pfA+4M8mlSS6eeuzn2G8B7hmafzdwYVWtBx4Fzmnt5wCPVtVPAhe2fiQ5DtgE/DRwGvAnba9KkjQmo4bKZ4D/xODOxLcOPRYkyVrgdcAH23wY/Dzx1a3L5cAb2vQZbZ62/OTW/wzgyqr6flXdB+wETlhoTZKk/TfrOZUkP1ZVD1TV5bP1W4CLgN/mye+6vBj4VlXtbfO7gDVteg3tFvtVtTfJY63/GuALQ9scXkeSNAZz7al8amoiySd6DJjkl4FHqmp4TyfTdK05ls22zr5jbkmyPcn23bt3z6teSdLo5gqV4T/cx3Qa8yTg9UnuB65kcNjrIgbfgZnac1oLPNSmdwFHAbTlhwJ7htunWecpquqSqtpYVRtXr17d6WVIkvY1V6jUDNMLVlXnV9XaqlrH4ET7DVX1ZuBG4I2t22bgmja9tc3Tlt/Qvt2/FdjUrg47GlgP3NyjRknSwsz1PZWXJXmcwR7LQW2aNl9V9aKOtbwNuDLJHwC3A5e29kuBDyXZyWAPZRODwe9OchXwZWAvcG67/FmSNCazhkpVLeolulX1eeDzbfprTHP1VlV9DzhzhvXfCbxz8SqUJM3HfH5PRZKkWRkqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuljxUkhyV5MYk9yS5O8lbWvthSbYl2dGeV7X2JLk4yc4kX0qyYWhbm1v/HUk2L/VrkSQ91Tj2VPYC/6GqXgqcCJyb5DjgPOD6qloPXN/mAU4H1rfHFuB9MAgh4ALgFcAJwAVTQSRJGo8lD5WqeriqbmvT3wbuAdYAZwCXt26XA29o02cAV9TAF4CVSY4ETgW2VdWeqnoU2AactoQvRZK0j7GeU0myDjgeuAl4SVU9DIPgAY5o3dYADw6ttqu1zdQ+3ThbkmxPsn337t09X4IkacjYQiXJC4BPAG+tqsdn6zpNW83S/vTGqkuqamNVbVy9evX8i5UkjWQsoZLkuQwC5cNV9cnW/I12WIv2/Ehr3wUcNbT6WuChWdolSWMyjqu/AlwK3FNV7xlatBWYuoJrM3DNUPtZ7SqwE4HH2uGx64BTkqxqJ+hPaW2SpDFZMYYxTwL+JXBnkjta2+8A7wKuSnIO8ABwZlt2LfBaYCfwXeBsgKrak+QdwC2t39uras/SvARJ0nSWPFSq6n8z/fkQgJOn6V/AuTNs6zLgsn7VSZL2h9+olyR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3Sz5b9Q/U6w77zNPTN//rteNsRJJWj7cU5EkdWOoSJK6MVQkSd0YKpKkbgwVSVI3hookqRtDRZLUjd9T6cDvrEjSgHsqkqRuDBVJUjeGiiSpG8+pdOb5FUnPZu6pSJK6mfg9lSSnAe8FDgA+WFXvGnNJT3CvRdKzzUSHSpIDgP8K/BKwC7glydaq+vJ4K3s6A0bSs8FEhwpwArCzqr4GkORK4Axg2YXKsOGAmYnBI2kSTXqorAEeHJrfBbxiTLV0NUrwLJbhQHMPS9J8THqoZJq2elqnZAuwpc3+bZJ7Fzje4cA3F7juUtqvOvPu+bXvh2fFv+cSss7+JqXWxa7zx0ftOOmhsgs4amh+LfDQvp2q6hLgkv0dLMn2qtq4v9tZbNbZl3X2NSl1wuTUupzqnPRLim8B1ic5OsmBwCZg65hrkqRnrYneU6mqvUn+DXAdg0uKL6uqu8dcliQ9a010qABU1bXAtUs03H4fQlsi1tmXdfY1KXXC5NS6bOpM1dPOa0uStCCTfk5FkrSMGCojSHJaknuT7Exy3iKOc1mSR5LcNdR2WJJtSXa051WtPUkubjV9KcmGoXU2t/47kmwean95kjvbOhcnyWxjzFLnUUluTHJPkruTvGU51prk+UluTvLFVud/bu1HJ7mpbeNj7SIPkjyvze9sy9cNbev81n5vklOH2qf9bMw0xhz/rgckuT3Jp5drnUnub+/LHUm2z/aejPMz2tZZmeTqJF9pn9VXLrdakxzb/i2nHo8neetyq3NeqsrHLA8GFwB8FTgGOBD4InDcIo31KmADcNdQ2x8C57Xp84B3t+nXAp9l8F2dE4GbWvthwNfa86o2vaotuxl4ZVvns8Dps40xS51HAhva9AuBvwKOW261tnVf0KafC9zUxr8K2NTa3w/8Rpv+TeD9bXoT8LE2fVx7358HHN0+DwfM9tmYaYw5/l1/C/gI8OnZtjHOOoH7gcP3aVtW7/tQXZcDv9amDwRWLtdah/7WfJ3Bd0KWbZ1zvo4eG3kmP9qbcd3Q/PnA+Ys43jqeGir3Ake26SOBe9v0B4A37dsPeBPwgaH2D7S2I4GvDLU/0W+mMeZR8zUM7r+2bGsFDgZuY3DHhW8CK/Z9fxlcRfjKNr2i9cu+7/lUv5k+G22daceYpb61wPXAq4FPz7aNMdd5P08PlWX3vgMvAu6jnTdezrUObeMU4P8s9zrnenj4a27T3QpmzRKO/5KqehigPR8xR12zte+apn22MebUDr0cz2AvYNnV2g4p3QE8Amxj8H/s36qqvdNs+4l62vLHgBcvoP4XzzLGTC4Cfhv4+zY/2zbGWWcB/yvJrRncqQKW4fvOYK9sN/CnGRxS/GCSQ5ZprVM2AR+dYxvLoc5ZGSpzG+lWMGMwU13zbV94AckLgE8Ab62qx2frOs+autVaVT+qqp9jsCdwAvDSWbbdq8551Z/kl4FHqurW4eblVmdzUlVtAE4Hzk3yqln6jvMzuoLBoeT3VdXxwHcYHOKZyVj/e2rnsl4PfHyurvOsZ8n/fhkqcxvpVjCL6BtJjgRoz4/MUdds7WunaZ9tjBkleS6DQPlwVX1yOdcKUFXfAj7P4Dj0yiRT39Ea3vYT9bTlhwJ7FlD/N2cZYzonAa9Pcj9wJYNDYBctwzqpqofa8yPAnzMI6uX4vu8CdlXVTW3+agYhsxxrhUFI31ZV35hjG+Ouc06GytzGfSuYrcDmNr2ZwfmLqfaz2tUgJwKPtV3Y64BTkqxqV3OcwuA4+cPAt5Oc2K7+OGufbU03xrTa+pcC91TVe5ZrrUlWJ1nZpg8CXgPcA9wIvHGGOqe2/UbghhoccN4KbMrgqqujgfUMTn5O+9lo68w0xtNU1flVtbaq1rVt3FBVb15udSY5JMkLp6YZvF93scze9/Zv+nXgwSTHtqaTGfwkxrKrtXkTTx76mm0b465zbj1OzDzTHwyuuPgrBsfjf3cRx/ko8DDwQwb/h3EOg+Pe1wM72vNhrW8Y/EDZV4E7gY1D2/lXwM72OHuofSODPwJfBf6YJ7/8Ou0Ys9T5Cwx2ob8E3NEer11utQI/C9ze6rwL+L3WfgyDP7Y7GRxueF5rf36b39mWHzO0rd9ttdxLu3pmts/GTGOM8Bn4RZ68+mtZ1dn6frE97p7aznJ734e29XPA9vb+f4rBVVHLrlYGF5H8DXDoUNuyq3PUh9+olyR14+EvSVI3hookqRtDRZLUjaEiSerGUJEkdWOoSJK6MVQkSd0YKpKkbv4/OAphEndVf7oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_list[0].od_counts.plot(kind='hist',bins=100)\n",
    "print(df_list[0]['od_counts'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "532918\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": [
    "df_list[1].od_counts.plot(kind='hist',bins=100)\n",
    "print(df_list[1]['od_counts'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293001\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": [
    "df_list[2].od_counts.plot(kind='hist',bins=100)\n",
    "print(df_list[2]['od_counts'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "248292\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": [
    "df_list[3].od_counts.plot(kind='hist',bins=100)\n",
    "print(df_list[3]['od_counts'].max())"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}