fecundity_calculation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import collections\n",
    "import networkx as nx\n",
    "import functools\n",
    "import operator\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_values=['N','0000-00-00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dhananjay/genealogy/.env/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3051: DtypeWarning: Columns (7,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "people=pd.read_csv('./neurotree/people.tsv', sep='\\t', encoding='utf-8',  escapechar='\\\\', \n",
    "                   na_values = missing_values)\n",
    "connect=pd.read_csv('./neurotree/connect.tsv', sep='\\t', encoding='latin-1',  escapechar='\\\\', \n",
    "                    na_values = missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "connect_remove=connect[~(connect['pid2']==0)].copy()\n",
    "connect_remove['startdate'].fillna('0000', inplace=True)\n",
    "connect_remove['stopdate'].fillna('0000', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_att=['startdate','stopdate']\n",
    "connect_remove[date_att]=connect_remove[date_att].applymap(lambda x : int(x.split('-')[0].strip()) if len(x) > 1 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "people1=people[people['majorarea'].str.contains('computerscience')]\n",
    "pid=people1['pid']\n",
    "conn=connect_remove[(connect_remove['pid1'].isin(pid.values))|(connect_remove['pid2'].isin(pid.values))]\n",
    "con_uni_pid=pd.unique(conn[['pid1', 'pid2']].values.ravel('K'))\n",
    "people_mod=people[people['pid'].isin(con_uni_pid)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fecundity_value(pid, connection, time=10):\n",
    "    temp=connection[connection['pid2']==pid].sort_values(by=['startdate', 'stopdate'])\n",
    "    tmp_list=list(filter(lambda a: a != 0, temp['startdate'].values))\n",
    "    if len(tmp_list) > 0:\n",
    "        min_year=min(tmp_list)\n",
    "        max_year=min_year+time\n",
    "        fecun=len(temp[(temp['startdate'] >= min_year) & (temp['startdate'] <=max_year)])\n",
    "    else:\n",
    "        fecun=0\n",
    "    #print(pid, fecun)\n",
    "    return fecun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fecun_val=[]\n",
    "for pid in people_mod['pid'].values:\n",
    "    fecun_val.append(fecundity_value(pid, conn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f89407bc630>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "pndas=pd.DataFrame(fecun_val, columns=['pid', 'fecun'])\n",
    "pndas['fecun'].hist(bins=[0,5,10,15,20,25,30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f893f912e10>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "pndas['fecun'].value_counts().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fecun2(pid, connection):\n",
    "    temp=connection[connection['pid2']==pid].sort_values(by=['startdate', 'stopdate'])\n",
    "    fecun=len(temp)\n",
    "    return fecun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fecun3 = functools.partial(fecun2,connection=conn[['pid1','pid2','startdate','stopdate']])\n",
    "people_mod['fecun']=people_mod['pid'].apply(fecun3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8940029d68>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "people_mod['fecun'].hist(bins=[0,5,10,15,20,25,30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f893f853390>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "people_mod['fecun'].value_counts().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "people_mod.locid=people_mod.locid.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#people_mod.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=people_mod.groupby(['locid','location'])['fecun'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "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>locid</th>\n",
       "      <th>location</th>\n",
       "      <th>fecun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>ETH Zürich</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>Arizona State University</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>Bar-Ilan University</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>Brandeis University</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   locid                  location  fecun\n",
       "0      3                ETH Zürich      1\n",
       "1     12  Arizona State University    109\n",
       "2     18       Bar-Ilan University      1\n",
       "3     27         Boston University     92\n",
       "4     29       Brandeis University     28"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "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>locid</th>\n",
       "      <th>location</th>\n",
       "      <th>fecun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>312</td>\n",
       "      <td>University of California, Berkeley</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>995</td>\n",
       "      <td>Georgia Institute of Technology</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>388</td>\n",
       "      <td>University of Illinois, Urbana-Champaign</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>38</td>\n",
       "      <td>Carnegie Mellon University</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>277</td>\n",
       "      <td>Stanford University</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>315</td>\n",
       "      <td>University of California, Los Angeles</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>398</td>\n",
       "      <td>University of Maryland, College Park</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>1938</td>\n",
       "      <td>Capella University</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>3762</td>\n",
       "      <td>North Carolina State University</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>405</td>\n",
       "      <td>University of Michigan, Ann Arbor</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     locid                                  location  fecun\n",
       "58     312        University of California, Berkeley    479\n",
       "156    995           Georgia Institute of Technology    474\n",
       "88     388  University of Illinois, Urbana-Champaign    395\n",
       "8       38                Carnegie Mellon University    391\n",
       "51     277                       Stanford University    371\n",
       "61     315     University of California, Los Angeles    363\n",
       "92     398      University of Maryland, College Park    316\n",
       "202   1938                        Capella University    300\n",
       "251   3762           North Carolina State University    297\n",
       "94     405         University of Michigan, Ann Arbor    286"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.sort_values(by=['fecun'],ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fecun4 = functools.partial(fecun2,connection=connect_remove[['pid1','pid2','startdate','stopdate']])\n",
    "people_mod['fecun1']=people_mod['pid'].apply(fecun3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8940231160>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "people_mod['fecun1'].hist(bins=[0,5,10,15,20,25,30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f893f8ac710>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "people_mod['fecun1'].value_counts().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_connect_remove=connect_remove[connect_remove['startdate']!=0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "con_uni_pid1=pd.unique(mod_connect_remove[['pid1', 'pid2']].values.ravel('K'))\n",
    "mod_people=people[people['pid'].isin(con_uni_pid1)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_fecun_val=[]\n",
    "for pid in mod_people['pid'].values:\n",
    "    mod_fecun_val.append(fecundity_value(pid, mod_connect_remove))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f893f976f98>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD4CAYAAAAO9oqkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAWH0lEQVR4nO3db6xc9X3n8fenODSWG2ITslfIZtfsxkpEYUPgChw1qu4GxVzYVU2lFIF2i4NYXCmkSrRIW9InbqFI6WppGlDKrndxbVc0BJFQWy3UtQijbh+YYBKCAzTrGxpkW4C3MX96g5rI6XcfzM/J1LnXHl/fP3PH75c0mnO+53fO/L4zcD/MmTNDqgpJ0pnt5xZ6ApKkhWcYSJIMA0mSYSBJwjCQJAFLFnoCM3XeeefV6tWrZ7TvD37wA5YtWza7E1ogw9LLsPQB9jKIhqUPOL1ennnmmb+vqvdOtW3RhsHq1avZu3fvjPbtdDqMjY3N7oQWyLD0Mix9gL0MomHpA06vlyQvT7fN00SSJMNAkmQYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgSWIRfwP5dOw79CafuOMvFnoas2Lr+HB8xV7SwvKdgSTJMJAkGQaSJAwDSRKGgSQJw0CShGEgSaKPMEjy/iTP9tzeSvKZJOcm2Z1kf7tf0cYnyb1JJpI8l+SynmNtaOP3J9nQU788yb62z71JMjftSpKmctIwqKrvVNWlVXUpcDnwNvAocAfwRFWtAZ5o6wDXAGvabSNwP0CSc4FNwJXAFcCmYwHSxtzas9/4rHQnSerLqZ4mugr4blW9DKwHtrX6NuC6trwe2F5de4DlSc4HrgZ2V9WRqnod2A2Mt23nVNWeqipge8+xJEnz4FR/juIG4EtteaSqXmnLrwIjbXklcKBnn4OtdqL6wSnqPyPJRrrvNhgZGaHT6Zzi9NvEl8Ltlxyd0b6DZnJycsbPwyAZlj7AXgbRsPQBc9dL32GQ5GzgV4DPHr+tqipJzebEplJVm4HNAKOjozU2Njaj49z34A7u2TccP8u0dXwZM30eBkmn0xmKPsBeBtGw9AFz18upnCa6BvhGVb3W1l9rp3ho94db/RBwQc9+q1rtRPVVU9QlSfPkVMLgRn56ighgJ3DsiqANwI6e+k3tqqK1wJvtdNIuYF2SFe2D43XArrbtrSRr21VEN/UcS5I0D/o6V5JkGfAx4Dd6yp8DHk5yC/AycH2rPwZcC0zQvfLoZoCqOpLkLuDpNu7OqjrSlj8JbAWWAo+3myRpnvQVBlX1A+A9x9W+T/fqouPHFnDbNMfZAmyZor4XuLifuUiSZp/fQJYkGQaSJMNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CSRJ9hkGR5kkeS/G2SF5N8OMm5SXYn2d/uV7SxSXJvkokkzyW5rOc4G9r4/Uk29NQvT7Kv7XNvksx+q5Kk6fT7zuALwF9W1QeADwIvAncAT1TVGuCJtg5wDbCm3TYC9wMkORfYBFwJXAFsOhYgbcytPfuNn15bkqRTcdIwSPJu4JeBBwCq6kdV9QawHtjWhm0DrmvL64Ht1bUHWJ7kfOBqYHdVHamq14HdwHjbdk5V7amqArb3HEuSNA+W9DHmQuD/AX+c5IPAM8CngZGqeqWNeRUYacsrgQM9+x9stRPVD05R/xlJNtJ9t8HIyAidTqeP6f+skaVw+yVHZ7TvoJmcnJzx8zBIhqUPsJdBNCx9wNz10k8YLAEuA36zqp5K8gV+ekoIgKqqJDXrsztOVW0GNgOMjo7W2NjYjI5z34M7uGdfP60Pvq3jy5jp8zBIOp3OUPQB9jKIhqUPmLte+vnM4CBwsKqeauuP0A2H19opHtr94bb9EHBBz/6rWu1E9VVT1CVJ8+SkYVBVrwIHkry/la4CXgB2AseuCNoA7GjLO4Gb2lVFa4E32+mkXcC6JCvaB8frgF1t21tJ1rariG7qOZYkaR70e67kN4EHk5wNvATcTDdIHk5yC/AycH0b+xhwLTABvN3GUlVHktwFPN3G3VlVR9ryJ4GtwFLg8XaTJM2TvsKgqp4FRqfYdNUUYwu4bZrjbAG2TFHfC1zcz1wkSbPPbyBLkgwDSZJhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgSaLPMEjyvST7kjybZG+rnZtkd5L97X5FqyfJvUkmkjyX5LKe42xo4/cn2dBTv7wdf6Ltm9luVJI0vVN5Z/DvqurSqhpt63cAT1TVGuCJtg5wDbCm3TYC90M3PIBNwJXAFcCmYwHSxtzas9/4jDuSJJ2y0zlNtB7Y1pa3Adf11LdX1x5geZLzgauB3VV1pKpeB3YD423bOVW1p6oK2N5zLEnSPFjS57gC/ipJAf+zqjYDI1X1Stv+KjDSllcCB3r2PdhqJ6ofnKL+M5JspPtug5GRETqdTp/T/+dGlsLtlxyd0b6DZnJycsbPwyAZlj7AXgbRsPQBc9dLv2Hwkao6lORfALuT/G3vxqqqFhRzqoXQZoDR0dEaGxub0XHue3AH9+zrt/XBtnV8GTN9HgZJp9MZij7AXgbRsPQBc9dLX6eJqupQuz8MPEr3nP9r7RQP7f5wG34IuKBn91WtdqL6qinqkqR5ctIwSLIsybuOLQPrgG8DO4FjVwRtAHa05Z3ATe2qorXAm+100i5gXZIV7YPjdcCutu2tJGvbVUQ39RxLkjQP+jlXMgI82q72XAL8aVX9ZZKngYeT3AK8DFzfxj8GXAtMAG8DNwNU1ZEkdwFPt3F3VtWRtvxJYCuwFHi83SRJ8+SkYVBVLwEfnKL+feCqKeoF3DbNsbYAW6ao7wUu7mO+kqQ54DeQJUmGgSTJMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRxCmGQ5Kwk30zy5239wiRPJZlI8uUkZ7f6z7f1ibZ9dc8xPtvq30lydU99vNUmktwxe+1JkvpxKu8MPg282LP++8Dnq+p9wOvALa1+C/B6q3++jSPJRcANwC8C48AftYA5C/gicA1wEXBjGytJmid9hUGSVcC/B/53Ww/wUeCRNmQbcF1bXt/WaduvauPXAw9V1Q+r6u+ACeCKdpuoqpeq6kfAQ22sJGmeLOlz3B8C/xV4V1t/D/BGVR1t6weBlW15JXAAoKqOJnmzjV8J7Ok5Zu8+B46rXznVJJJsBDYCjIyM0Ol0+pz+PzeyFG6/5OjJBy4Ck5OTM34eBsmw9AH2MoiGpQ+Yu15OGgZJ/gNwuKqeSTI26zM4BVW1GdgMMDo6WmNjM5vOfQ/u4J59/ebgYNs6voyZPg+DpNPpDEUfYC+DaFj6gLnrpZ+/iL8E/EqSa4F3AucAXwCWJ1nS3h2sAg618YeAC4CDSZYA7wa+31M/pnef6eqSpHlw0s8MquqzVbWqqlbT/QD4a1X1H4EngY+3YRuAHW15Z1unbf9aVVWr39CuNroQWAN8HXgaWNOuTjq7PcbOWelOktSX0zlX8lvAQ0l+D/gm8ECrPwD8SZIJ4AjdP+5U1fNJHgZeAI4Ct1XVjwGSfArYBZwFbKmq509jXpKkU3RKYVBVHaDTll+ieyXQ8WP+Efi1afa/G7h7ivpjwGOnMhdJ0uzxG8iSJMNAkmQYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkugjDJK8M8nXk3wryfNJfrfVL0zyVJKJJF9Ocnar/3xbn2jbV/cc67Ot/p0kV/fUx1ttIskds9+mJOlE+nln8EPgo1X1QeBSYDzJWuD3gc9X1fuA14Fb2vhbgNdb/fNtHEkuAm4AfhEYB/4oyVlJzgK+CFwDXATc2MZKkubJScOguibb6jvarYCPAo+0+jbgura8vq3Ttl+VJK3+UFX9sKr+DpgArmi3iap6qap+BDzUxkqS5smSfga1/3p/Bngf3f+K/y7wRlUdbUMOAivb8krgAEBVHU3yJvCeVt/Tc9jefQ4cV79ymnlsBDYCjIyM0Ol0+pn+zxhZCrdfcvTkAxeBycnJGT8Pg2RY+gB7GUTD0gfMXS99hUFV/Ri4NMly4FHgA7M+k/7msRnYDDA6OlpjY2MzOs59D+7gnn19tT7wto4vY6bPwyDpdDpD0QfYyyAalj5g7no5pauJquoN4Engw8DyJMf+oq4CDrXlQ8AFAG37u4Hv99aP22e6uiRpnvRzNdF72zsCkiwFPga8SDcUPt6GbQB2tOWdbZ22/WtVVa1+Q7va6EJgDfB14GlgTbs66Wy6HzLvnI3mJEn96edcyfnAtva5wc8BD1fVnyd5AXgoye8B3wQeaOMfAP4kyQRwhO4fd6rq+SQPAy8AR4Hb2uknknwK2AWcBWypqudnrUNJ0kmdNAyq6jngQ1PUX6J7JdDx9X8Efm2aY90N3D1F/THgsT7mK0maA34DWZJkGEiSDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kSfYRBkguSPJnkhSTPJ/l0q5+bZHeS/e1+Rasnyb1JJpI8l+SynmNtaOP3J9nQU788yb62z71JMhfNSpKm1s87g6PA7VV1EbAWuC3JRcAdwBNVtQZ4oq0DXAOsabeNwP3QDQ9gE3AlcAWw6ViAtDG39uw3fvqtSZL6ddIwqKpXquobbfkfgBeBlcB6YFsbtg24ri2vB7ZX1x5geZLzgauB3VV1pKpeB3YD423bOVW1p6oK2N5zLEnSPFhyKoOTrAY+BDwFjFTVK23Tq8BIW14JHOjZ7WCrnah+cIr6VI+/ke67DUZGRuh0Oqcy/Z8YWQq3X3J0RvsOmsnJyRk/D4NkWPoAexlEw9IHzF0vfYdBkl8AvgJ8pqre6j2tX1WVpGZ9dsepqs3AZoDR0dEaGxub0XHue3AH9+w7pRwcWFvHlzHT52GQdDqdoegD7GUQDUsfMHe99HU1UZJ30A2CB6vqq638WjvFQ7s/3OqHgAt6dl/Vaieqr5qiLkmaJ/1cTRTgAeDFqvqDnk07gWNXBG0AdvTUb2pXFa0F3mynk3YB65KsaB8crwN2tW1vJVnbHuumnmNJkuZBP+dKfgn4dWBfkmdb7beBzwEPJ7kFeBm4vm17DLgWmADeBm4GqKojSe4Cnm7j7qyqI235k8BWYCnweLtJkubJScOgqv4GmO66/6umGF/AbdMcawuwZYr6XuDik81FkjQ3/AayJMkwkCQZBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiT6CIMkW5IcTvLtntq5SXYn2d/uV7R6ktybZCLJc0ku69lnQxu/P8mGnvrlSfa1fe5NMt3/b1mSNEf6eWewFRg/rnYH8ERVrQGeaOsA1wBr2m0jcD90wwPYBFwJXAFsOhYgbcytPfsd/1iSpDl20jCoqr8GjhxXXg9sa8vbgOt66turaw+wPMn5wNXA7qo6UlWvA7uB8bbtnKraU1UFbO85liRpniyZ4X4jVfVKW34VGGnLK4EDPeMOttqJ6genqE8pyUa67zgYGRmh0+nMbPJL4fZLjs5o30EzOTk54+dhkAxLH2Avg2hY+oC562WmYfATVVVJajYm08djbQY2A4yOjtbY2NiMjnPfgzu4Z99ptz4Qto4vY6bPwyDpdDpD0QfYyyAalj5g7nqZ6dVEr7VTPLT7w61+CLigZ9yqVjtRfdUUdUnSPJppGOwEjl0RtAHY0VO/qV1VtBZ4s51O2gWsS7KifXC8DtjVtr2VZG27iuimnmNJkubJSc+VJPkSMAacl+Qg3auCPgc8nOQW4GXg+jb8MeBaYAJ4G7gZoKqOJLkLeLqNu7Oqjn0o/Um6VywtBR5vN0nSPDppGFTVjdNsumqKsQXcNs1xtgBbpqjvBS4+2TwkSXPHbyBLkgwDSZJhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRJ9/M9tNNj2HXqTT9zxFws9jdO2dXzZQk9BOqP5zkCSZBhIkgYoDJKMJ/lOkokkdyz0fCTpTDIQYZDkLOCLwDXARcCNSS5a2FlJ0pljUD5AvgKYqKqXAJI8BKwHXljQWWneDMsH4QC3X3J0aHrxg/0zR6pqoedAko8D41X1n9v6rwNXVtWnjhu3EdjYVt8PfGeGD3ke8Pcz3HfQDEsvw9IH2MsgGpY+4PR6+VdV9d6pNgzKO4O+VNVmYPPpHifJ3qoanYUpLbhh6WVY+gB7GUTD0gfMXS8D8ZkBcAi4oGd9VatJkubBoITB08CaJBcmORu4Adi5wHOSpDPGQJwmqqqjST4F7ALOArZU1fNz+JCnfappgAxLL8PSB9jLIBqWPmCOehmID5AlSQtrUE4TSZIWkGEgSTqzwmCYfvIiyfeS7EvybJK9Cz2fU5FkS5LDSb7dUzs3ye4k+9v9ioWcY7+m6eV3khxqr82zSa5dyDn2I8kFSZ5M8kKS55N8utUX3etygl4W4+vyziRfT/Kt1svvtvqFSZ5qf8u+3C68Ob3HOlM+M2g/efF/gY8BB+lewXRjVS3Kbzkn+R4wWlWL7os0SX4ZmAS2V9XFrfbfgCNV9bkW1Cuq6rcWcp79mKaX3wEmq+q/L+TcTkWS84Hzq+obSd4FPANcB3yCRfa6nKCX61l8r0uAZVU1meQdwN8Anwb+C/DVqnooyf8AvlVV95/OY51J7wx+8pMXVfUj4NhPXmieVdVfA0eOK68HtrXlbXT/5R140/Sy6FTVK1X1jbb8D8CLwEoW4etygl4WneqabKvvaLcCPgo80uqz8rqcSWGwEjjQs36QRfoPSFPAXyV5pv1Mx2I3UlWvtOVXgZGFnMws+FSS59pppIE/tdIryWrgQ8BTLPLX5bheYBG+LknOSvIscBjYDXwXeKOqjrYhs/K37EwKg2Hzkaq6jO4vvd7WTlcMheqeu1zM5y/vB/4NcCnwCnDPwk6nf0l+AfgK8Jmqeqt322J7XaboZVG+LlX146q6lO4vM1wBfGAuHudMCoOh+smLqjrU7g8Dj9L9h2Qxe62d6z12zvfwAs9nxqrqtfYv8D8B/4tF8tq0c9JfAR6sqq+28qJ8XabqZbG+LsdU1RvAk8CHgeVJjn1peFb+lp1JYTA0P3mRZFn7YIwky4B1wLdPvNfA2wlsaMsbgB0LOJfTcuyPZ/OrLILXpn1Q+QDwYlX9Qc+mRfe6TNfLIn1d3ptkeVteSvcCmBfphsLH27BZeV3OmKuJANqlZH/IT3/y4u4FntKMJPnXdN8NQPcnRf50MfWS5EvAGN2f4n0N2AT8GfAw8C+Bl4Hrq2rgP5idppcxuqciCvge8Bs9590HUpKPAP8H2Af8Uyv/Nt1z7YvqdTlBLzey+F6Xf0v3A+Kz6P7H+8NVdWf7G/AQcC7wTeA/VdUPT+uxzqQwkCRN7Uw6TSRJmoZhIEkyDCRJhoEkCcNAkoRhIEnCMJAkAf8fG08vXY7hlAcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pndas1=pd.DataFrame(mod_fecun_val, columns=['pid', 'fecun'])\n",
    "pndas1['fecun'].hist(bins=[0,5,10,15,20,25,30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f893f8ec278>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "pndas1['fecun'].value_counts().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    76890.000000\n",
       "mean         0.852959\n",
       "std          1.863334\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max         69.000000\n",
       "Name: fecun, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pndas1['fecun'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 28s, sys: 29.6 ms, total: 2min 28s\n",
      "Wall time: 2min 28s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "fecun5 = functools.partial(fecundity_value,connection=mod_connect_remove[['pid1','pid2','startdate','stopdate']],time=30)\n",
    "mod_people['fecun20']=mod_people['pid'].apply(fecun5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32919.000000\n",
       "mean         2.393906\n",
       "std          3.587548\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          2.000000\n",
       "max        175.000000\n",
       "Name: fecun20, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_people[mod_people['fecun20']!=0]['fecun20'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8937285c18>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "mod_people[mod_people['fecun20']!=0]['fecun20'].value_counts().plot()"
   ]
  },
  {
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}