{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#so this workbook tries to figure out what the strength of the NDA and UPA political alliances in the Rajya Sabha\n",
    "    #will be given what we know about the strength of the parties in the different state assemblies\n",
    "    #the alliances they are loyal to in those states and at the centre, when the terms of different state assemblies\n",
    "    #are up, when different batches of Rajya Sabha MPs are retiring etc.\n",
    "\n",
    "#have put in relevant (and not so relevant!) comments and sources for code snippets below\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>term_begins</th>\n",
       "      <th>term_ends</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>19-06-2014</td>\n",
       "      <td>2019-06-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>02-06-2014</td>\n",
       "      <td>2019-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Assam</td>\n",
       "      <td>02-06-2016</td>\n",
       "      <td>2021-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>30-11-2015</td>\n",
       "      <td>2020-11-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>06-01-2014</td>\n",
       "      <td>2019-01-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               state term_begins   term_ends\n",
       "0     Andhra Pradesh  19-06-2014  2019-06-18\n",
       "1  Arunachal Pradesh  02-06-2014  2019-06-01\n",
       "2              Assam  02-06-2016  2021-06-01\n",
       "3              Bihar  30-11-2015  2020-11-29\n",
       "4       Chhattisgarh  06-01-2014  2019-01-05"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#just want to give a preview of the data files we're using\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df_assembly_dates = pd.read_csv('assembly_end_dates.csv')\n",
    "    #this dataframe has the dates for when the terms for each state assembly will begin and end\n",
    "      #wasn't aware of this earlier, but it seems the Jammu & Kashmir assembly has a six-year term\n",
    "\n",
    "df_assembly_dates['term_ends'] = pd.to_datetime(df_assembly_dates['term_ends'], format=\"%d-%m-%Y\").dt.date\n",
    "    #got code above from https://stackoverflow.com/questions/17134716/convert-dataframe-column-type-from-string-to-datetime\n",
    "    #and from https://stackoverflow.com/a/34277514\n",
    "       \n",
    "df_assembly_dates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>party</th>\n",
       "      <th>full_form</th>\n",
       "      <th>alliance</th>\n",
       "      <th>seats</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INC</td>\n",
       "      <td>Indian National Congress</td>\n",
       "      <td>UPA</td>\n",
       "      <td>1</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BJP</td>\n",
       "      <td>Bharatiya Janta Party</td>\n",
       "      <td>NDA</td>\n",
       "      <td>47</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PPA</td>\n",
       "      <td>Peoples Party Of Arunachal</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IND</td>\n",
       "      <td>Independent</td>\n",
       "      <td>NDA</td>\n",
       "      <td>2</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vacant</td>\n",
       "      <td>Vacant</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    party                   full_form alliance  seats              state\n",
       "0     INC    Indian National Congress      UPA      1  Arunachal Pradesh\n",
       "1     BJP       Bharatiya Janta Party      NDA     47  Arunachal Pradesh\n",
       "2     PPA  Peoples Party Of Arunachal        0      5  Arunachal Pradesh\n",
       "3     IND                 Independent      NDA      2  Arunachal Pradesh\n",
       "4  Vacant                      Vacant        0      1  Arunachal Pradesh"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_assembly_data = pd.read_csv('assembly_alliances_strength.csv')\n",
    "    #this has details for how many seats each party holds in each state assembly, and which alliance the party is loyal to\n",
    "\n",
    "df_assembly_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seat_number</th>\n",
       "      <th>state</th>\n",
       "      <th>notification_date</th>\n",
       "      <th>retirement_date</th>\n",
       "      <th>diff_dates</th>\n",
       "      <th>party_short</th>\n",
       "      <th>party_full</th>\n",
       "      <th>alliance</th>\n",
       "      <th>name</th>\n",
       "      <th>cycle_category</th>\n",
       "      <th>notes</th>\n",
       "      <th>poll_notification_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>19-08-2017</td>\n",
       "      <td>2023-08-18</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>BJP</td>\n",
       "      <td>Bharatiya Janta Party</td>\n",
       "      <td>NDA</td>\n",
       "      <td>Irani, Smt. Smriti Zubin</td>\n",
       "      <td>A</td>\n",
       "      <td>3 out of 3</td>\n",
       "      <td>http://eci.nic.in/eci_main1/current/PN59_14112...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>19-08-2017</td>\n",
       "      <td>2023-08-18</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>BJP</td>\n",
       "      <td>Bharatiya Janta Party</td>\n",
       "      <td>NDA</td>\n",
       "      <td>Shah, Amit</td>\n",
       "      <td>A</td>\n",
       "      <td>3 out of 3</td>\n",
       "      <td>http://eci.nic.in/eci_main1/current/PN59_14112...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>19-08-2017</td>\n",
       "      <td>2023-08-18</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>INC</td>\n",
       "      <td>Indian National Congress</td>\n",
       "      <td>UPA</td>\n",
       "      <td>Patel, Shri Ahmed</td>\n",
       "      <td>A</td>\n",
       "      <td>3 out of 3</td>\n",
       "      <td>http://eci.nic.in/eci_main1/current/PN59_14112...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>19-08-2017</td>\n",
       "      <td>2023-08-18</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>INC</td>\n",
       "      <td>Indian National Congress</td>\n",
       "      <td>UPA</td>\n",
       "      <td>Bhattacharya, Shri P.</td>\n",
       "      <td>A</td>\n",
       "      <td>6 elected, 5 retiring</td>\n",
       "      <td>http://eci.nic.in/eci_main1/current/PN59_14112...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>19-08-2017</td>\n",
       "      <td>2023-08-18</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>AITC</td>\n",
       "      <td>All India Trinamool Congress</td>\n",
       "      <td>0</td>\n",
       "      <td>Sen, Ms. Dola</td>\n",
       "      <td>A</td>\n",
       "      <td>6 elected, 5 retiring</td>\n",
       "      <td>http://eci.nic.in/eci_main1/current/PN59_14112...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seat_number        state notification_date retirement_date  diff_dates  \\\n",
       "0            1      Gujarat        19-08-2017      2023-08-18      2191.0   \n",
       "1            2      Gujarat        19-08-2017      2023-08-18      2191.0   \n",
       "2            3      Gujarat        19-08-2017      2023-08-18      2191.0   \n",
       "3            4  West Bengal        19-08-2017      2023-08-18      2191.0   \n",
       "4            5  West Bengal        19-08-2017      2023-08-18      2191.0   \n",
       "\n",
       "  party_short                    party_full alliance  \\\n",
       "0         BJP         Bharatiya Janta Party      NDA   \n",
       "1         BJP         Bharatiya Janta Party      NDA   \n",
       "2         INC      Indian National Congress      UPA   \n",
       "3         INC      Indian National Congress      UPA   \n",
       "4        AITC  All India Trinamool Congress        0   \n",
       "\n",
       "                       name cycle_category                  notes  \\\n",
       "0  Irani, Smt. Smriti Zubin              A             3 out of 3   \n",
       "1                Shah, Amit              A             3 out of 3   \n",
       "2         Patel, Shri Ahmed              A             3 out of 3   \n",
       "3     Bhattacharya, Shri P.              A  6 elected, 5 retiring   \n",
       "4             Sen, Ms. Dola              A  6 elected, 5 retiring   \n",
       "\n",
       "                               poll_notification_url  \n",
       "0  http://eci.nic.in/eci_main1/current/PN59_14112...  \n",
       "1  http://eci.nic.in/eci_main1/current/PN59_14112...  \n",
       "2  http://eci.nic.in/eci_main1/current/PN59_14112...  \n",
       "3  http://eci.nic.in/eci_main1/current/PN59_14112...  \n",
       "4  http://eci.nic.in/eci_main1/current/PN59_14112...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rs_data = pd.read_csv('rajya_sabha_alliances_strength.csv')\n",
    "    #this has info for each rajya sabha seat, who holds it, when their term ends, which alliance do they belong to?\n",
    "\n",
    "df_rs_data['retirement_date'] = pd.to_datetime(df_rs_data['retirement_date'], format=\"%d-%m-%Y\").dt.date\n",
    "\n",
    "df_rs_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2024-06-27\n",
      "2024-06-28\n",
      "2024-06-29\n",
      "2024-06-30\n",
      "program done\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import datetime\n",
    "import collections\n",
    "import math\n",
    "\n",
    "headings = ['date', 'NDA', 'UPA', 'Others']\n",
    "\n",
    "with open(\"alliances_over_time_realistic.csv\", \"wb\") as filex:   #after this change mode to ab for append\n",
    "    wr = csv.writer(filex, delimiter = ',' , quotechar = '\"' )\n",
    "    wr.writerow(headings)\n",
    "    #seems you have to use the write mode as 'wb' instead of 'w', it's some Windows quirk - https://stackoverflow.com/a/3191811\n",
    "\n",
    "#creating a range of dates from Aug 19, 2017 to July 1, 2024\n",
    "#Aug 19 is when all the new Rajya Sabha MPs from Gujarat and West Bengal take their seats\n",
    "\n",
    "#got the code below from https://stackoverflow.com/a/24637447\n",
    "\n",
    "start = datetime.datetime.strptime(\"19-08-2017\", \"%d-%m-%Y\")\n",
    "end = datetime.datetime.strptime(\"01-07-2024\", \"%d-%m-%Y\")\n",
    "date_generated = [start + datetime.timedelta(days=x) for x in range(0, (end-start).days)]\n",
    "\n",
    "for this_date_raw in date_generated:\n",
    "    this_date = this_date_raw.date()\n",
    "    #print type(this_date)\n",
    "    print this_date\n",
    "    NDA = 0\n",
    "    UPA = 0\n",
    "    \n",
    "    #Initialise variables NDA = 0, UPA = 0\n",
    "\n",
    "    #for each row in the rs_data csv, ie. for each of the 245 seats in the Rajya Sabha\n",
    "    \n",
    "    for index, row in df_rs_data.iterrows():\n",
    "\n",
    "        #rs_seat_retirement_date_raw = row['retirement_date']\n",
    "        #rs_seat_retirement_date = datetime.datetime.strptime(rs_seat_retirement_date_raw, \"%d-%m-%Y\")\n",
    "        rs_seat_retirement_date = row['retirement_date']\n",
    "        \n",
    "           \n",
    "        #the line above is is about making python recognise the object as a date and not as a text string\n",
    "   \n",
    "        #the stackoverflow link below clarifies why it's datetime.datetime and not just datetime: \n",
    "        #https://stackoverflow.com/a/12070240\n",
    "                \n",
    "        alliance = row['alliance']\n",
    "        \n",
    "        state = row['state']\n",
    "        \n",
    "        rs_mp_name = row['name'] #note this could be marked 'VACANT' too, have contingency plans for that\n",
    "        \n",
    "        #assembly_end_date_raw = str(df_assembly_dates[df_assembly_dates['state'] == state]['term_ends']).strip('\\n')\n",
    "        \n",
    "        if state != 'Nominated':\n",
    "            #assembly_end_date_raw = df_assembly_dates[df_assembly_dates['state'] == state]['term_ends'].values[0]\n",
    "            #assembly_end_date = datetime.datetime.strptime(assembly_end_date_raw, \"%d-%m-%Y\")\n",
    "            #code above from https://stackoverflow.com/a/36684095\n",
    "            assembly_end_date = df_assembly_dates[df_assembly_dates['state'] == state]['term_ends'].values[0]\n",
    "        \n",
    "            if this_date <= rs_seat_retirement_date: \n",
    "                if rs_mp_name != 'VACANT':\n",
    "\n",
    "                    if alliance == 'NDA':\n",
    "                        NDA += 1\n",
    "                    elif alliance == 'UPA':\n",
    "                        UPA += 1\n",
    "\n",
    "                else:  # ie. if seat is marked 'vacant'\n",
    "\n",
    "                    if this_date <= assembly_end_date:\n",
    "\n",
    "                        diff_bw_this_date_retirement_date = rs_seat_retirement_date - this_date\n",
    "\n",
    "                        if diff_bw_this_date_retirement_date.days > 365:\n",
    "\n",
    "                            #want to calculate what the earliest retirement date is for any seat in the Rajya Sabha\n",
    "\n",
    "                            #am assuming that the election commission will hold the by-poll for \n",
    "                                #a vacant RS seat when there are elections for other RS seats going on\n",
    "                                #they won't be grouped together though\n",
    "                                #the elections could be held sooner too, but it won't be held later than that\n",
    "                                #this is all about calculating the most recent date by which these vacancies are guaranteed \n",
    "                                #to be filled\n",
    "\n",
    "                            next_any_state_retirement_date = df_rs_data[(df_rs_data['retirement_date'] >= this_date) &\\\n",
    "                                               (df_rs_data['retirement_date'] < rs_seat_retirement_date)].min()[\"retirement_date\"]\n",
    "\n",
    "                            date_difference_any_state = rs_seat_retirement_date - next_any_state_retirement_date\n",
    "\n",
    "                            #am assuming that if there's less than a year for the term for the seat to end, the Election \n",
    "                                #commission would rather leave the seat vacant till the term is up and not bother with holding \n",
    "                                #two elections for the same seat within a year\n",
    "                                #but that if there's more than a year left, the commission might organise the election\n",
    "\n",
    "                            if date_difference_any_state.days > 365:\n",
    "\n",
    "                                if this_date > next_any_state_retirement_date:\n",
    "\n",
    "                                    seats_vacant_now_state2 = len(df_rs_data[(df_rs_data['state'] == state) &\\\n",
    "                                                      (df_rs_data['retirement_date'] == rs_seat_retirement_date) &\\\n",
    "                                                      (rs_mp_name == 'VACANT')])\n",
    "\n",
    "                                    assembly_seats_total_v2 = df_assembly_data[df_assembly_data['state'] == state].sum()[\"seats\"]\n",
    "\n",
    "                                    droop_quota_v2 = math.floor((assembly_seats_total_v2 * 100)/(seats_vacant_now_state2 + 1)) + 1\n",
    "\n",
    "                                    #now i've seen the droop quota calculated in some places as \n",
    "                                        #math.floor((assembly_seats_total_v2)/(seats_vacant_now_state2 + 1)) + 1\n",
    "                                        #that's certainly how most of the media have been doing it in the runup to \n",
    "                                        #the Gujarat Rajya Sabha polls\n",
    "                                        #I will do it the way the election commission has done it in their \n",
    "                                        #handbook for election officials, the link is http://bit.ly/2vy4cyX\n",
    "\n",
    "                                    nda_strength_v2 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "                                                   (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "                                    #code above from https://stackoverflow.com/a/20995313\n",
    "\n",
    "                                    upa_strength_v2 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "                                                   (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                                    #below I'm calculating the number of seats any party will get in an RS election for a \n",
    "                                        #specific number of vacancies\n",
    "\n",
    "                                    nda_seats_v2 = math.floor((nda_strength_v2 * 100)/droop_quota_v2)\n",
    "\n",
    "                                    upa_seats_v2 = math.floor((upa_strength_v2 * 100)/droop_quota_v2)\n",
    "\n",
    "                                    #I'll be rounding down the result, so am using math.floor function here, \n",
    "                                        \n",
    "\n",
    "                                    NDA += nda_seats_v2/seats_vacant_now_state2\n",
    "\n",
    "                                    UPA += upa_seats_v2/seats_vacant_now_state2\n",
    "            \n",
    "            else: # ie. this_date > rs_seat_retirement_date\n",
    "\n",
    "                if this_date <= assembly_end_date:\n",
    "\n",
    "                    if state not in ['Delhi', 'Punjab']:\n",
    "\n",
    "                    #no else statement if this_date > assembly_end_date, it becomes part of OTHERS grouping\n",
    "                    #we're just calculating min. guaranteed UPA and NDA seats here\n",
    "\n",
    "                        #find number of seats from the state which share the same retirement date\n",
    "\n",
    "                        vacancies_number_v3 = len(df_rs_data[(df_rs_data['state'] == state) &\\\n",
    "                                                          (df_rs_data['retirement_date'] == rs_seat_retirement_date)])\n",
    "                        #code above from https://stackoverflow.com/a/17322585 \n",
    "\n",
    "                        #calculate droop quota , crudely put, it's the no. of seats a party or alliance should\n",
    "                            #have in a state legislature to guarantee a minimum of one Rajya Sabha seat from the RS vacancies\n",
    "                            #being contested\n",
    "\n",
    "                        assembly_seats_total_v3 = df_assembly_data[df_assembly_data['state'] == state].sum()[\"seats\"]\n",
    "\n",
    "                        droop_quota_v3 = math.floor((assembly_seats_total_v3 * 100)/(vacancies_number_v3 + 1)) + 1\n",
    "\n",
    "                        nda_strength_v3 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "                                       (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "                        #code above from https://stackoverflow.com/a/20995313\n",
    "\n",
    "                        upa_strength_v3 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "                                       (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                        #below I'm calculating the number of seats any party will get in an RS election for a \n",
    "                            #specific number of vacancies\n",
    "\n",
    "                        nda_seats_v3 = math.floor((nda_strength_v3 * 100)/droop_quota_v3)\n",
    "\n",
    "                        upa_seats_v3 = math.floor((upa_strength_v3 * 100)/droop_quota_v3)\n",
    "\n",
    "                        #I'll be rounding down the result, so am using math.floor function here, \n",
    "                            \n",
    "\n",
    "                        NDA += nda_seats_v3/vacancies_number_v3\n",
    "\n",
    "                        UPA += upa_seats_v3/vacancies_number_v3\n",
    "\n",
    "                        #realised later that I'm double or triple counting the no. of seats an alliance would get\n",
    "                        #basically by dividing it by the number of vacancies, I'm ensuring that when everything is summed up later\n",
    "                        #you get back the actual no. of minimum guaranteed nda_seats and upa_seats\n",
    "\n",
    "                    elif state == 'Delhi':\n",
    "\n",
    "                        vacancies_number_d = 1 \n",
    "\n",
    "                        #so delhi has 3 RS seats, all with terms ending on the same day\n",
    "                        #but the vacancies will not be counted together\n",
    "                        #there will be separate elections for each of the three seats\n",
    "                        #below is the link to the Election commission notification explaining why:\n",
    "                            #http://eci.nic.in/eci_main1/current/PN19122011.pdf \n",
    "                            #and an official press release about it: http://pib.nic.in/newsite/PrintRelease.aspx?relid=79013\n",
    "\n",
    "                        assembly_seats_total_d = df_assembly_data[df_assembly_data['state'] == 'Delhi'].sum()[\"seats\"]\n",
    "\n",
    "                        droop_quota_d = math.floor((assembly_seats_total_d * 100)/(vacancies_number_d + 1)) + 1\n",
    "\n",
    "                        nda_strength_d = df_assembly_data[(df_assembly_data['state'] == 'Delhi') &\\\n",
    "                                       (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "                        #code above from https://stackoverflow.com/a/20995313\n",
    "\n",
    "                        upa_strength_d = df_assembly_data[(df_assembly_data['state'] == 'Delhi') &\\\n",
    "                                       (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                        #below I'm calculating the number of seats any party will get in an RS election for a \n",
    "                            #specific number of vacancies\n",
    "\n",
    "                        nda_seats_d = math.floor((nda_strength_d * 100)/droop_quota_d)\n",
    "\n",
    "                        upa_seats_d = math.floor((upa_strength_d * 100)/droop_quota_d)\n",
    "\n",
    "                        #I'll be rounding down the result, so am using math.floor function here, \n",
    "                            \n",
    "\n",
    "                        NDA += nda_seats_d #Do I need to divide by vacancies_number_d here? No\n",
    "\n",
    "                        UPA += upa_seats_d \n",
    "\n",
    "                    elif state == 'Punjab':\n",
    "\n",
    "                        punjab_dates_list = df_rs_data[df_rs_data['state'] == 'Punjab']['retirement_date'].tolist()\n",
    "\n",
    "                        #the line above gives us a list of retirement dates for RS MPs from Punjab\n",
    "\n",
    "                        punjab_dates_freq = dict(collections.Counter(punjab_dates_list))\n",
    "\n",
    "                        #the line above gives us a frequency table of those dates\n",
    "\n",
    "                        #got the code above from https://stackoverflow.com/a/23240989\n",
    "\n",
    "                        #so there are five seats (out of 7) with the same retirement date and another group of 2\n",
    "\n",
    "                        #from that first group of five, there are two separate elections, one for 3 seats and one for 2 seats\n",
    "                        \n",
    "                        #why that is the case, I don't know, it just is! Here is the link to the Election commission notification:\n",
    "                        #http://eci.nic.in/eci_main1/current/PN15_24022016.pdf\n",
    "\n",
    "\n",
    "                        #from the frequency table, we can identify those 5 seats with the same retirement date\n",
    "\n",
    "\n",
    "                        if punjab_dates_freq[rs_seat_retirement_date] == 5: #if the retirement date is the one for the five seats\n",
    "\n",
    "                            vacancies_number_p_g2 = 2 #g2 for group of two\n",
    "\n",
    "                            vacancies_number_p_g3 = 3 #g3 for group of three\n",
    "\n",
    "                            assembly_seats_total_p = df_assembly_data[df_assembly_data['state'] == 'Punjab'].sum()[\"seats\"]                    \n",
    "\n",
    "                            droop_quota_p_g2 = math.floor((assembly_seats_total_p * 100)/(vacancies_number_p_g2 + 1)) + 1\n",
    "\n",
    "                            droop_quota_p_g3 = math.floor((assembly_seats_total_p * 100)/(vacancies_number_p_g3 + 1)) + 1\n",
    "\n",
    "                            nda_strength_p = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "                                           (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "                            #code above from https://stackoverflow.com/a/20995313\n",
    "\n",
    "                            upa_strength_p = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "                                           (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                            nda_seats_p_g2 = math.floor((nda_strength_p * 100)/droop_quota_p_g2)\n",
    "\n",
    "                            upa_seats_p_g2 = math.floor((upa_strength_p * 100)/droop_quota_p_g2)\n",
    "\n",
    "                            nda_seats_p_g3 = math.floor((nda_strength_p * 100)/droop_quota_p_g3)\n",
    "\n",
    "                            upa_seats_p_g3 = math.floor((upa_strength_p * 100)/droop_quota_p_g3)\n",
    "\n",
    "                            #I'll be rounding down the result, so am using math.floor function here, \n",
    "                                \n",
    "\n",
    "                            NDA += (nda_seats_p_g2 + nda_seats_p_g3)/5  #Is dividing by 5 the right thing here?\n",
    "                                                                        #guess so, since the number of seats NDA & UPA seats\n",
    "                                                                        #will be counted five times\n",
    "\n",
    "                            UPA += (upa_seats_p_g2 + upa_seats_p_g3)/5 \n",
    "\n",
    "\n",
    "                        else: #if it's the group of two RS seats from Punjab that share the same retirement date\n",
    "\n",
    "                            vacancies_number_p2 = len(df_rs_data[(df_rs_data['state'] == 'Punjab') &\\\n",
    "                                          (df_rs_data['retirement_date'] == rs_seat_retirement_date)])\n",
    "\n",
    "                            assembly_seats_total_p2 = df_assembly_data[df_assembly_data['state'] == 'Punjab'].sum()[\"seats\"]\n",
    "\n",
    "                            droop_quota_p2 = math.floor((assembly_seats_total_p2 * 100)/(vacancies_number_p2 + 1)) + 1\n",
    "\n",
    "                            nda_strength_p2 = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "                                           (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "                            #code above from https://stackoverflow.com/a/20995313\n",
    "\n",
    "                            upa_strength_p2 = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "                                           (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                            #below I'm calculating the number of seats any party will get in an RS election for a \n",
    "                                #specific number of vacancies\n",
    "\n",
    "                            nda_seats_p2 = math.floor((nda_strength_p2 * 100)/droop_quota_p2)\n",
    "\n",
    "                            upa_seats_p2 = math.floor((upa_strength_p2 * 100)/droop_quota_p2)\n",
    "\n",
    "                            #I'll be rounding down the result, so am using math.floor function here, \n",
    "                                \n",
    "\n",
    "                            NDA += nda_seats_p2/vacancies_number_p2\n",
    "\n",
    "                            UPA += upa_seats_p2/vacancies_number_p2 \n",
    "        \n",
    "        else: #if it is a nominated seat, the 'state' is marked 'nominated' in the data\n",
    "            if this_date <= rs_seat_retirement_date:\n",
    "                if alliance == 'NDA':\n",
    "                    NDA += 1\n",
    "                elif alliance == 'UPA':\n",
    "                    UPA += 1\n",
    "                #and if there is no alliance affiliation recorded against that MP, do nothing\n",
    "                #and if the date is after the retirement date, we can't say anything anyway\n",
    "                #note that here I know there are no vacancies among nominated seats, so I didn't provide an if/else for that\n",
    "\n",
    "    OTHERS = 245 - (NDA + UPA)\n",
    "    \n",
    "    csv_row = list()\n",
    "    csv_row.append(this_date)\n",
    "    csv_row.append(NDA)\n",
    "    csv_row.append(UPA)\n",
    "    csv_row.append(OTHERS)\n",
    "     \n",
    "    with open(\"alliances_over_time_realistic.csv\", \"ab\") as filez:\n",
    "        wrz = csv.writer(filez, delimiter = ',' , quotechar = '\"' )\n",
    "        wrz.writerow(csv_row)\n",
    "\n",
    "filez.close()\n",
    "\n",
    "print \"program done\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2024-03-25\n",
      "2024-03-26\n",
      "2024-03-27\n",
      "2024-03-28\n",
      "2024-03-29\n",
      "2024-03-30\n",
      "2024-03-31\n",
      "2024-04-01\n",
      "2024-04-02\n",
      "2024-04-03\n",
      "2024-04-04\n",
      "2024-04-05\n",
      "2024-04-06\n",
      "2024-04-07\n",
      "2024-04-08\n",
      "2024-04-09\n",
      "2024-04-10\n",
      "2024-04-11\n",
      "2024-04-12\n",
      "2024-04-13\n",
      "2024-04-14\n",
      "2024-04-15\n",
      "2024-04-16\n",
      "2024-04-17\n",
      "2024-04-18\n",
      "2024-04-19\n",
      "2024-04-20\n",
      "2024-04-21\n",
      "2024-04-22\n",
      "2024-04-23\n",
      "2024-04-24\n",
      "2024-04-25\n",
      "2024-04-26\n",
      "2024-04-27\n",
      "2024-04-28\n",
      "2024-04-29\n",
      "2024-04-30\n",
      "2024-05-01\n",
      "2024-05-02\n",
      "2024-05-03\n",
      "2024-05-04\n",
      "2024-05-05\n",
      "2024-05-06\n",
      "2024-05-07\n",
      "2024-05-08\n",
      "2024-05-09\n",
      "2024-05-10\n",
      "2024-05-11\n",
      "2024-05-12\n",
      "2024-05-13\n",
      "2024-05-14\n",
      "2024-05-15\n",
      "2024-05-16\n",
      "2024-05-17\n",
      "2024-05-18\n",
      "2024-05-19\n",
      "2024-05-20\n",
      "2024-05-21\n",
      "2024-05-22\n",
      "2024-05-23\n",
      "2024-05-24\n",
      "2024-05-25\n",
      "2024-05-26\n",
      "2024-05-27\n",
      "2024-05-28\n",
      "2024-05-29\n",
      "2024-05-30\n",
      "2024-05-31\n",
      "2024-06-01\n",
      "2024-06-02\n",
      "2024-06-03\n",
      "2024-06-04\n",
      "2024-06-05\n",
      "2024-06-06\n",
      "2024-06-07\n",
      "2024-06-08\n",
      "2024-06-09\n",
      "2024-06-10\n",
      "2024-06-11\n",
      "2024-06-12\n",
      "2024-06-13\n",
      "2024-06-14\n",
      "2024-06-15\n",
      "2024-06-16\n",
      "2024-06-17\n",
      "2024-06-18\n",
      "2024-06-19\n",
      "2024-06-20\n",
      "2024-06-21\n",
      "2024-06-22\n",
      "2024-06-23\n",
      "2024-06-24\n",
      "2024-06-25\n",
      "2024-06-26\n",
      "2024-06-27\n",
      "2024-06-28\n",
      "2024-06-29\n",
      "2024-06-30\n",
      "program done\n"
     ]
    }
   ],
   "source": [
    "#just a little experiment\n",
    "\n",
    "#in this we assume that party positions remain the same during the next assembly term of each state\n",
    "#and we try and see how alliance strength in the RS changes as a result\n",
    "\n",
    "#so here there won't be the issue that we can't say something about a RS seat election because it is being contested\n",
    "    #after the term of the state assembly is over\n",
    "\n",
    "import csv\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import collections\n",
    "import math\n",
    "\n",
    "df_assembly_dates = pd.read_csv('assembly_end_dates.csv')\n",
    "\n",
    "df_assembly_dates['term_ends'] = pd.to_datetime(df_assembly_dates['term_ends'], format=\"%d-%m-%Y\").dt.date\n",
    "\n",
    "df_assembly_data = pd.read_csv('assembly_alliances_strength.csv')\n",
    "\n",
    "df_rs_data = pd.read_csv('rajya_sabha_alliances_strength.csv')\n",
    "\n",
    "df_rs_data['retirement_date'] = pd.to_datetime(df_rs_data['retirement_date'], format=\"%d-%m-%Y\").dt.date\n",
    "\n",
    "headings = ['date', 'NDA', 'UPA', 'Others']\n",
    "\n",
    "with open(\"alliances_over_time_experiment.csv\", \"wb\") as filex:  \n",
    "    wr = csv.writer(filex, delimiter = ',' , quotechar = '\"' )\n",
    "    wr.writerow(headings)\n",
    "\n",
    "start = datetime.datetime.strptime(\"19-08-2017\", \"%d-%m-%Y\")\n",
    "end = datetime.datetime.strptime(\"01-07-2024\", \"%d-%m-%Y\")\n",
    "date_generated = [start + datetime.timedelta(days=x) for x in range(0, (end-start).days)]\n",
    "\n",
    "for this_date_raw in date_generated:\n",
    "    this_date = this_date_raw.date()\n",
    "    print this_date\n",
    "    NDA = 0\n",
    "    UPA = 0\n",
    "    \n",
    "    for index, row in df_rs_data.iterrows():\n",
    "\n",
    "        rs_seat_retirement_date = row['retirement_date']\n",
    "        \n",
    "        alliance = row['alliance']\n",
    "        \n",
    "        state = row['state']\n",
    "        \n",
    "        rs_mp_name = row['name'] #note this could be marked 'VACANT' too, have contingency plans for that\n",
    "        \n",
    "        if state != 'Nominated':\n",
    "        \n",
    "            assembly_end_date = df_assembly_dates[df_assembly_dates['state'] == state]['term_ends'].values[0]\n",
    "        \n",
    "            #assembly_end_dates only matter for how seats marked VACANT are filled before their terms are over\n",
    "            #otherwise in this experiment party positions are permanent\n",
    "\n",
    "\n",
    "            if this_date <= rs_seat_retirement_date:\n",
    "                if rs_mp_name != 'VACANT':\n",
    "\n",
    "                    if alliance == 'NDA':\n",
    "                        NDA += 1\n",
    "                    elif alliance == 'UPA':\n",
    "                        UPA += 1\n",
    "\n",
    "                else: #if seat is vacant\n",
    "\n",
    "                    if this_date <= assembly_end_date: #you need to only check assembly end date for seats VACANT now\n",
    "\n",
    "                        diff_bw_this_date_retirement_date = rs_seat_retirement_date - this_date\n",
    "\n",
    "                        if diff_bw_this_date_retirement_date.days > 365:  #am I doubling up conditions?\n",
    "\n",
    "\n",
    "                            next_any_state_retirement_date = df_rs_data[(df_rs_data['retirement_date'] >= this_date) &\\\n",
    "                                           (df_rs_data['retirement_date'] < rs_seat_retirement_date)].min()[\"retirement_date\"]\n",
    "\n",
    "                            date_difference_any_state = rs_seat_retirement_date - next_any_state_retirement_date\n",
    "\n",
    "                            if date_difference_any_state.days > 365:\n",
    "\n",
    "                                if this_date > next_any_state_retirement_date:\n",
    "\n",
    "                                    seats_vacant_now_state_v2 = len(df_rs_data[(df_rs_data['state'] == state) &\\\n",
    "                                                      (df_rs_data['retirement_date'] == rs_seat_retirement_date) &\\\n",
    "                                                      (rs_mp_name == 'VACANT')])\n",
    "\n",
    "                                    assembly_seats_total_v2 = df_assembly_data[df_assembly_data['state'] == state].sum()[\"seats\"]\n",
    "\n",
    "                                    droop_quota_v2 = math.floor((assembly_seats_total_v2 * 100)/(seats_vacant_now_state_v2 + 1)) + 1\n",
    "\n",
    "                                    nda_strength_v2 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "                                                   (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "\n",
    "                                    upa_strength_v2 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "                                                   (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                                    nda_seats_v2 = math.floor((nda_strength_v2 * 100)/droop_quota_v2)\n",
    "\n",
    "                                    upa_seats_v2 = math.floor((upa_strength_v2 * 100)/droop_quota_v2)\n",
    "\n",
    "                                    NDA += nda_seats_v2/seats_vacant_now_state_v2\n",
    "\n",
    "                                    UPA += upa_seats_v2/seats_vacant_now_state_v2\n",
    "            \n",
    "            else: # ie. this_date > rs_seat_retirement_date\n",
    "\n",
    "                if state not in ['Delhi', 'Punjab']:\n",
    "\n",
    "                    vacancies_number_v3 = len(df_rs_data[(df_rs_data['state'] == state) &\\\n",
    "                                                      (df_rs_data['retirement_date'] == rs_seat_retirement_date)])\n",
    "\n",
    "                    assembly_seats_total_v3 = df_assembly_data[df_assembly_data['state'] == state].sum()[\"seats\"]\n",
    "\n",
    "                    droop_quota_v3 = math.floor((assembly_seats_total_v3 * 100)/(vacancies_number_v3 + 1)) + 1\n",
    "\n",
    "                    nda_strength_v3 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "                                   (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "\n",
    "                    upa_strength_v3 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "                                   (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                    nda_seats_v3 = math.floor((nda_strength_v3 * 100)/droop_quota_v3)\n",
    "\n",
    "                    upa_seats_v3 = math.floor((upa_strength_v3 * 100)/droop_quota_v3)\n",
    "\n",
    "                    NDA += nda_seats_v3/vacancies_number_v3\n",
    "\n",
    "                    UPA += upa_seats_v3/vacancies_number_v3\n",
    "\n",
    "                elif state == 'Delhi':\n",
    "\n",
    "                    vacancies_number_d = 1 \n",
    "\n",
    "                    assembly_seats_total_d = df_assembly_data[df_assembly_data['state'] == 'Delhi'].sum()[\"seats\"]\n",
    "\n",
    "                    droop_quota_d = math.floor((assembly_seats_total_d * 100)/(vacancies_number_d + 1)) + 1\n",
    "\n",
    "                    nda_strength_d = df_assembly_data[(df_assembly_data['state'] == 'Delhi') &\\\n",
    "                                   (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "\n",
    "                    upa_strength_d = df_assembly_data[(df_assembly_data['state'] == 'Delhi') &\\\n",
    "                                   (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                    nda_seats_d = math.floor((nda_strength_d * 100)/droop_quota_d)\n",
    "\n",
    "                    upa_seats_d = math.floor((upa_strength_d * 100)/droop_quota_d)\n",
    "\n",
    "                    NDA += nda_seats_d \n",
    "\n",
    "                    UPA += upa_seats_d \n",
    "\n",
    "                elif state == 'Punjab':\n",
    "\n",
    "                    punjab_dates_list = df_rs_data[df_rs_data['state'] == 'Punjab']['retirement_date'].tolist()\n",
    "\n",
    "                    punjab_dates_freq = dict(collections.Counter(punjab_dates_list))\n",
    "\n",
    "                    if punjab_dates_freq[rs_seat_retirement_date] == 5: \n",
    "\n",
    "                        vacancies_number_p_g2 = 2\n",
    "\n",
    "                        vacancies_number_p_g3 = 3\n",
    "\n",
    "                        assembly_seats_total_p = df_assembly_data[df_assembly_data['state'] == 'Punjab'].sum()[\"seats\"]                    \n",
    "\n",
    "                        droop_quota_p_g2 = math.floor((assembly_seats_total_p * 100)/(vacancies_number_p_g2 + 1)) + 1\n",
    "\n",
    "                        droop_quota_p_g3 = math.floor((assembly_seats_total_p * 100)/(vacancies_number_p_g3 + 1)) + 1\n",
    "\n",
    "                        nda_strength_p = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "                                       (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "\n",
    "                        upa_strength_p = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "                                       (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                        nda_seats_p_g2 = math.floor((nda_strength_p * 100)/droop_quota_p_g2)\n",
    "\n",
    "                        upa_seats_p_g2 = math.floor((upa_strength_p * 100)/droop_quota_p_g2)\n",
    "\n",
    "                        nda_seats_p_g3 = math.floor((nda_strength_p * 100)/droop_quota_p_g3)\n",
    "\n",
    "                        upa_seats_p_g3 = math.floor((upa_strength_p * 100)/droop_quota_p_g3)\n",
    "\n",
    "                        NDA += (nda_seats_p_g2 + nda_seats_p_g3)/5 \n",
    "\n",
    "                        UPA += (upa_seats_p_g2 + upa_seats_p_g3)/5 \n",
    "\n",
    "\n",
    "                    else:\n",
    "\n",
    "                        vacancies_number_p2 = len(df_rs_data[(df_rs_data['state'] == 'Punjab') &\\\n",
    "                                      (df_rs_data['retirement_date'] == rs_seat_retirement_date)])\n",
    "\n",
    "                        assembly_seats_total_p = df_assembly_data[df_assembly_data['state'] == 'Punjab'].sum()[\"seats\"]\n",
    "\n",
    "                        droop_quota_p2 = math.floor((assembly_seats_total_p * 100)/(vacancies_number_p2 + 1)) + 1\n",
    "\n",
    "                        nda_strength_p = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "                                       (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "\n",
    "                        upa_strength_p = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "                                       (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                        nda_seats_p2 = math.floor((nda_strength_p * 100)/droop_quota_p2)\n",
    "\n",
    "                        upa_seats_p2 = math.floor((upa_strength_p * 100)/droop_quota_p2)\n",
    "\n",
    "                        NDA += nda_seats_p2/vacancies_number_p2 \n",
    "\n",
    "                        UPA += upa_seats_p2/vacancies_number_p2 \n",
    "        \n",
    "        else: #if MP is nominated\n",
    "            if this_date <= rs_seat_retirement_date:\n",
    "                if alliance == 'NDA':\n",
    "                    NDA += 1\n",
    "                elif alliance == 'UPA':\n",
    "                    UPA += 1\n",
    "            #no else condition here, if after retirement date, we can't say anything about who will get nominated\n",
    "            #and if they are otherwise politically affiliated or not\n",
    "\n",
    "    OTHERS = 245 - (NDA + UPA)\n",
    "    \n",
    "    csv_row = list()\n",
    "    csv_row.append(this_date)\n",
    "    csv_row.append(NDA)\n",
    "    csv_row.append(UPA)\n",
    "    csv_row.append(OTHERS)\n",
    "     \n",
    "    with open(\"alliances_over_time_experiment.csv\", \"ab\") as filez:\n",
    "        wrz = csv.writer(filez, delimiter = ',' , quotechar = '\"' )\n",
    "        wrz.writerow(csv_row)\n",
    "\n",
    "filez.close()\n",
    "\n",
    "print \"program done\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>NDA</th>\n",
       "      <th>UPA</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   NDA   UPA  Others\n",
       "0  2017-08-19  83.0  63.0    99.0\n",
       "1  2017-08-20  83.0  63.0    99.0\n",
       "2  2017-08-21  83.0  63.0    99.0\n",
       "3  2017-08-22  83.0  63.0    99.0\n",
       "4  2017-08-23  83.0  63.0    99.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_real = pd.read_csv('alliances_over_time_realistic.csv')\n",
    "df_exp = pd.read_csv('alliances_over_time_experiment.csv')\n",
    "\n",
    "df_real.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>NDA</th>\n",
       "      <th>UPA</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   NDA   UPA  Others\n",
       "0  2017-08-19  83.0  63.0    99.0\n",
       "1  2017-08-20  83.0  63.0    99.0\n",
       "2  2017-08-21  83.0  63.0    99.0\n",
       "3  2017-08-22  83.0  63.0    99.0\n",
       "4  2017-08-23  83.0  63.0    99.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>nda</th>\n",
       "      <th>upa</th>\n",
       "      <th>oth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   nda   upa   oth\n",
       "0  2017-08-19  83.0  63.0  99.0\n",
       "1  2017-08-20  83.0  63.0  99.0\n",
       "2  2017-08-21  83.0  63.0  99.0\n",
       "3  2017-08-22  83.0  63.0  99.0\n",
       "4  2017-08-23  83.0  63.0  99.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change all column names to lower case, seems d3 doesn't deal well with column names that are all upper case or\n",
    "        #has underscores in them or is more than 4 letters long. Since when?? I must be doing sthing wrong elsewhere\n",
    "\n",
    "df_real = df_real.rename(columns={'NDA': 'nda', 'UPA': 'upa', 'Others': 'oth' })\n",
    "df_real.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>ndax</th>\n",
       "      <th>upax</th>\n",
       "      <th>othx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  ndax  upax  othx\n",
       "0  2017-08-19  83.0  63.0  99.0\n",
       "1  2017-08-20  83.0  63.0  99.0\n",
       "2  2017-08-21  83.0  63.0  99.0\n",
       "3  2017-08-22  83.0  63.0  99.0\n",
       "4  2017-08-23  83.0  63.0  99.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exp = df_exp.rename(columns={'NDA': 'ndax', 'UPA': 'upax', 'Others': 'othx' })\n",
    "df_exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>nda</th>\n",
       "      <th>upa</th>\n",
       "      <th>oth</th>\n",
       "      <th>ndax</th>\n",
       "      <th>upax</th>\n",
       "      <th>othx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   nda   upa   oth  ndax  upax  othx\n",
       "0  2017-08-19  83.0  63.0  99.0  83.0  63.0  99.0\n",
       "1  2017-08-20  83.0  63.0  99.0  83.0  63.0  99.0\n",
       "2  2017-08-21  83.0  63.0  99.0  83.0  63.0  99.0\n",
       "3  2017-08-22  83.0  63.0  99.0  83.0  63.0  99.0\n",
       "4  2017-08-23  83.0  63.0  99.0  83.0  63.0  99.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = pd.merge(df_real, df_exp, on=['date'])\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined.to_csv('alliances_strength_combined.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-04-04\n",
      "The no. of seats added for NDA is 31.0.\n",
      "The no. of seats added for UPA is 6.0.\n",
      "The no. of vacancies is 60.\n",
      "The no. of vacancies before assembly end is 60.\n",
      "program done\n"
     ]
    }
   ],
   "source": [
    "#this code block isn't important, used it to check and test subsets of my data\n",
    "\n",
    "import csv\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import collections\n",
    "import math\n",
    "\n",
    "df_assembly_dates = pd.read_csv('assembly_end_dates.csv')\n",
    "\n",
    "df_assembly_dates['term_ends'] = pd.to_datetime(df_assembly_dates['term_ends'], format=\"%d-%m-%Y\").dt.date\n",
    "        \n",
    "df_assembly_data = pd.read_csv('assembly_alliances_strength.csv')\n",
    "\n",
    "df_rs_data = pd.read_csv('rajya_sabha_alliances_strength.csv')\n",
    "\n",
    "df_rs_data['retirement_date'] = pd.to_datetime(df_rs_data['retirement_date'], format=\"%d-%m-%Y\").dt.date\n",
    "\n",
    "\n",
    "headings = ['date', 'NDA', 'UPA', 'Others']\n",
    "\n",
    "start = datetime.datetime.strptime(\"04-04-2018\", \"%d-%m-%Y\")\n",
    "end = datetime.datetime.strptime(\"05-04-2018\", \"%d-%m-%Y\")\n",
    "date_generated = [start + datetime.timedelta(days=x) for x in range(0, (end-start).days)]\n",
    "\n",
    "for this_date_raw in date_generated:\n",
    "    this_date = this_date_raw.date()\n",
    "    print this_date\n",
    "    NDA = 0\n",
    "    UPA = 0\n",
    "    NOM = 0\n",
    "    seats_post_retirement = 0\n",
    "    seats_post_retirement_before_assembly_end = 0\n",
    "    \n",
    "    for index, row in df_rs_data.iterrows():\n",
    "        rs_seat_retirement_date = row['retirement_date']\n",
    "\n",
    "        alliance = row['alliance']\n",
    "        \n",
    "        state = row['state']\n",
    "        \n",
    "        rs_mp_name = row['name'] #note this could be marked 'VACANT' too, have contingency plans for that\n",
    "        \n",
    "        if state != 'Nominated':\n",
    "            \n",
    "\n",
    "            assembly_end_date = df_assembly_dates[df_assembly_dates['state'] == state]['term_ends'].values[0]\n",
    "        \n",
    "#             if this_date <= rs_seat_retirement_date: \n",
    "#                 if rs_mp_name != 'VACANT':\n",
    "\n",
    "#                     if alliance == 'NDA':\n",
    "#                         NDA += 1\n",
    "#                     elif alliance == 'UPA':\n",
    "#                         UPA += 1\n",
    "\n",
    "#                 else:  # ie. if seat is marked 'vacant'\n",
    "\n",
    "#                     if this_date <= assembly_end_date:\n",
    "\n",
    "#                         diff_bw_this_date_retirement_date = rs_seat_retirement_date - this_date\n",
    "\n",
    "#                         if diff_bw_this_date_retirement_date.days > 365:\n",
    "\n",
    "#                             next_any_state_retirement_date = df_rs_data[(df_rs_data['retirement_date'] >= this_date) &\\\n",
    "#                                                (df_rs_data['retirement_date'] < rs_seat_retirement_date)].min()[\"retirement_date\"]\n",
    "\n",
    "#                             date_difference_any_state = rs_seat_retirement_date - next_any_state_retirement_date\n",
    "\n",
    "#                             if date_difference_any_state.days > 365:\n",
    "\n",
    "#                                 if this_date > next_any_state_retirement_date:\n",
    "\n",
    "#                                     seats_vacant_now_state2 = len(df_rs_data[(df_rs_data['state'] == state) &\\\n",
    "#                                                       (df_rs_data['retirement_date'] == rs_seat_retirement_date) &\\\n",
    "#                                                       (rs_mp_name == 'VACANT')])\n",
    "\n",
    "#                                     assembly_seats_total_v2 = df_assembly_data[df_assembly_data['state'] == state].sum()[\"seats\"]\n",
    "\n",
    "#                                     droop_quota_v2 = math.floor((assembly_seats_total_v2 * 100)/(seats_vacant_now_state2 + 1)) + 1\n",
    "\n",
    "#                                     nda_strength_v2 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "#                                                    (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "#                                     #code above from https://stackoverflow.com/a/20995313\n",
    "\n",
    "#                                     upa_strength_v2 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "#                                                    (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "#                                     nda_seats_v2 = math.floor((nda_strength_v2 * 100)/droop_quota_v2)\n",
    "\n",
    "#                                     upa_seats_v2 = math.floor((upa_strength_v2 * 100)/droop_quota_v2)\n",
    "\n",
    "#                                     NDA += nda_seats_v2/seats_vacant_now_state2\n",
    "\n",
    "#                                     UPA += upa_seats_v2/seats_vacant_now_state2\n",
    "\n",
    "#             else: # ie. this_date > rs_seat_retirement_date\n",
    "\n",
    "            if this_date > rs_seat_retirement_date:\n",
    "                \n",
    "                seats_post_retirement += 1\n",
    "            \n",
    "#                 if this_date <= assembly_end_date:\n",
    "    #                    if state =='Uttar Pradesh':\n",
    "\n",
    "                seats_post_retirement_before_assembly_end += 1\n",
    "\n",
    "#                     if state not in ['Delhi', 'Punjab']:\n",
    "\n",
    "                vacancies_number_v3 = len(df_rs_data[(df_rs_data['state'] == state) &\\\n",
    "                                                  (df_rs_data['retirement_date'] == rs_seat_retirement_date)])\n",
    "\n",
    "                #print \"there are {} vacancies.\".format(vacancies_number_v3)\n",
    "\n",
    "                assembly_seats_total_v3 = df_assembly_data[df_assembly_data['state'] == state].sum()[\"seats\"]\n",
    "\n",
    "                #print \"there are {} assembly seats.\".format(assembly_seats_total_v3)\n",
    "\n",
    "                droop_quota_v3 = math.floor((assembly_seats_total_v3 * 100)/(vacancies_number_v3 + 1)) + 1\n",
    "\n",
    "                #print \"the droop quota is {}.\".format(droop_quota_v3)\n",
    "\n",
    "                nda_strength_v3 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "                               (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "                #code above from https://stackoverflow.com/a/20995313\n",
    "\n",
    "                #print \"the nda_strength is {}.\".format(nda_strength_v3)\n",
    "\n",
    "                upa_strength_v3 = df_assembly_data[(df_assembly_data['state'] == state) &\\\n",
    "                               (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "                #print \"the upa_strength is {}.\".format(upa_strength_v3)\n",
    "\n",
    "                nda_seats_v3 = math.floor((nda_strength_v3 * 100)/droop_quota_v3)\n",
    "\n",
    "                #print \"the nda seats are {}.\".format(nda_seats_v3)\n",
    "\n",
    "                upa_seats_v3 = math.floor((upa_strength_v3 * 100)/droop_quota_v3)\n",
    "\n",
    "                #print \"the upa seats are {}.\".format(upa_seats_v3)\n",
    "\n",
    "                NDA += nda_seats_v3/vacancies_number_v3\n",
    "\n",
    "                #print \"what will get added to NDA variable is {}.\".format(NDA)\n",
    "\n",
    "                UPA += upa_seats_v3/vacancies_number_v3\n",
    "\n",
    "                    #print \"what will get added to UPA variable is {}.\".format(UPA)\n",
    "\n",
    "#                     elif state == 'Delhi':\n",
    "\n",
    "#                         vacancies_number_d = 1 \n",
    "\n",
    "#                         assembly_seats_total_d = df_assembly_data[df_assembly_data['state'] == 'Delhi'].sum()[\"seats\"]\n",
    "\n",
    "#                         droop_quota_d = math.floor((assembly_seats_total_d * 100)/(vacancies_number_d + 1)) + 1\n",
    "\n",
    "#                         nda_strength_d = df_assembly_data[(df_assembly_data['state'] == 'Delhi') &\\\n",
    "#                                        (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "#                         #code above from https://stackoverflow.com/a/20995313\n",
    "\n",
    "#                         upa_strength_d = df_assembly_data[(df_assembly_data['state'] == 'Delhi') &\\\n",
    "#                                        (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "#                         nda_seats_d = math.floor((nda_strength_d * 100)/droop_quota_d)\n",
    "\n",
    "#                         upa_seats_d = math.floor((upa_strength_d * 100)/droop_quota_d)\n",
    "\n",
    "#                         NDA += nda_seats_d #Do I need to divide by vacancies_number_d here? No\n",
    "\n",
    "#                         UPA += upa_seats_d \n",
    "\n",
    "#                     elif state == 'Punjab':\n",
    "\n",
    "#                         punjab_dates_list = df_rs_data[df_rs_data['state'] == 'Punjab']['retirement_date'].tolist()\n",
    "\n",
    "#                         punjab_dates_freq = dict(collections.Counter(punjab_dates_list))\n",
    "\n",
    "#                         if punjab_dates_freq[rs_seat_retirement_date] == 5: #if the retirement date is the one for the five seats\n",
    "\n",
    "#                             vacancies_number_p_g2 = 2 #g2 for group of two\n",
    "\n",
    "#                             vacancies_number_p_g3 = 3 #g3 for group of three\n",
    "\n",
    "#                             assembly_seats_total_p = df_assembly_data[df_assembly_data['state'] == 'Punjab'].sum()[\"seats\"]                    \n",
    "\n",
    "#                             droop_quota_p_g2 = math.floor((assembly_seats_total_p * 100)/(vacancies_number_p_g2 + 1)) + 1\n",
    "\n",
    "#                             droop_quota_p_g3 = math.floor((assembly_seats_total_p * 100)/(vacancies_number_p_g3 + 1)) + 1\n",
    "\n",
    "#                             nda_strength_p = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "#                                            (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "#                             #code above from https://stackoverflow.com/a/20995313\n",
    "\n",
    "#                             upa_strength_p = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "#                                            (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "#                             nda_seats_p_g2 = math.floor((nda_strength_p * 100)/droop_quota_p_g2)\n",
    "\n",
    "#                             upa_seats_p_g2 = math.floor((upa_strength_p * 100)/droop_quota_p_g2)\n",
    "\n",
    "#                             nda_seats_p_g3 = math.floor((nda_strength_p * 100)/droop_quota_p_g3)\n",
    "\n",
    "#                             upa_seats_p_g3 = math.floor((upa_strength_p * 100)/droop_quota_p_g3)\n",
    "\n",
    "#                             NDA += (nda_seats_p_g2 + nda_seats_p_g3)/5  #Is dividing by 5 the right thing here?\n",
    "\n",
    "#                             UPA += (upa_seats_p_g2 + upa_seats_p_g3)/5 \n",
    "\n",
    "\n",
    "#                         else: #if it's the group of two RS seats from Punjab that share the same retirement date\n",
    "\n",
    "#                             vacancies_number_p2 = len(df_rs_data[(df_rs_data['state'] == 'Punjab') &\\\n",
    "#                                           (df_rs_data['retirement_date'] == rs_seat_retirement_date)])\n",
    "\n",
    "#                             assembly_seats_total_p2 = df_assembly_data[df_assembly_data['state'] == 'Punjab'].sum()[\"seats\"]\n",
    "\n",
    "#                             droop_quota_p2 = math.floor((assembly_seats_total_p2 * 100)/(vacancies_number_p2 + 1)) + 1\n",
    "\n",
    "#                             nda_strength_p2 = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "#                                            (df_assembly_data['alliance'] == \"NDA\")].sum()[\"seats\"]\n",
    "#                             #code above from https://stackoverflow.com/a/20995313\n",
    "\n",
    "#                             upa_strength_p2 = df_assembly_data[(df_assembly_data['state'] == 'Punjab') &\\\n",
    "#                                            (df_assembly_data['alliance'] == \"UPA\")].sum()[\"seats\"]\n",
    "\n",
    "#                             #below I'm calculating the number of seats any party will get in an RS election for a \n",
    "#                                 #specific number of vacancies\n",
    "\n",
    "#                             nda_seats_p2 = math.floor((nda_strength_p2 * 100)/droop_quota_p2)\n",
    "\n",
    "#                             upa_seats_p2 = math.floor((upa_strength_p2 * 100)/droop_quota_p2)\n",
    "\n",
    "#                             NDA += nda_seats_p2/vacancies_number_p2 #Do I need to divide by vacancies_number_p here?\n",
    "\n",
    "#                             UPA += upa_seats_p2/vacancies_number_p2 \n",
    "\n",
    "#         else: #if it is a nominated seat, the 'state' is marked 'nominated' in the data\n",
    "#             if this_date <= rs_seat_retirement_date:\n",
    "#                 if alliance == 'NDA':\n",
    "#                     NDA += 1\n",
    "#                 elif alliance == 'UPA':\n",
    "#                     UPA += 1\n",
    "#                 #and if there is no alliance affiliation recorded against that MP, do nothing\n",
    "#                 #and if the date is after the retirement date, we can't say anything anyway\n",
    "#             if this_date > rs_seat_retirement_date:\n",
    "#                 seats_post_retirement += 1\n",
    "#                 NOM += 1\n",
    "\n",
    "    print \"The no. of seats added for NDA is {}.\".format(NDA)\n",
    "    print \"The no. of seats added for UPA is {}.\".format(UPA)\n",
    "    #print NOM\n",
    "    print \"The no. of vacancies is {}.\".format(seats_post_retirement)\n",
    "    print \"The no. of vacancies before assembly end is {}.\".format(seats_post_retirement_before_assembly_end)\n",
    "    #OTHERS = 245 - (NDA + UPA)\n",
    "    #print OTHERS\n",
    "    \n",
    "#    csv_row = list()\n",
    "#    csv_row.append(this_date)\n",
    "#    csv_row.append(NDA)\n",
    "#    csv_row.append(UPA)\n",
    "#    csv_row.append(OTHERS)\n",
    "     \n",
    " #   with open(\"alliances_over_time_realistic.csv\", \"ab\") as filez:\n",
    "  #      wrz = csv.writer(filez, delimiter = ',' , quotechar = '\"' )\n",
    "   #     wrz.writerow(csv_row)\n",
    "\n",
    "#filez.close()\n",
    "\n",
    "print \"program done\""
   ]
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