parliament_seats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from scipy.stats import rankdata\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def position_angles(rows, sumdelegates):\n",
    "    \n",
    "    Totals = [4, 15, 33, 61, 95] # Threshold for new row. For instance, more than 15 seats means 3 rows\n",
    "    \n",
    "    # Maximum radius of spot is 0.5/rows; leave a bit of space.\n",
    "    radius = 0.4/rows\n",
    "\n",
    "    # Create list of centre spots\n",
    "    poslist_24 = []\n",
    "\n",
    "    for i in range(1, rows):\n",
    "        \"\"\" The relevant math is borrowed from David Richfield (slashme on GitHub)\"\"\"\n",
    "        \n",
    "        # Each row can contain pi/(2asin(2/(3n+4i-2))) spots, where n is the number of rows and i is the number\n",
    "        # of the current row. Fill each row proportionally to the \"fullness\" of the diagram, up to the second-last row.\n",
    "        \n",
    "        J = int(float(sumdelegates) / Totals[rows-1] * math.pi/(2*math.asin(2.0/(3.0*rows+4.0*i-2.0))))\n",
    "        \n",
    "        # The radius of the ith row in an N-row diagram (Ri) is (3*N+4*i-2)/(4*N)\n",
    "        \n",
    "        R = (3.0*rows+4.0*i-2.0)/(4.0*rows)\n",
    "        \n",
    "        if J == 1:\n",
    "            poslist_24.append([math.pi/2.0, 1.75*R, R])\n",
    "        else:\n",
    "            for j in range(J):\n",
    "                # The angle to a spot is n.(pi-2sin(r/Ri))/(Ni-1)+sin(r/Ri) where Ni is the number in the arc\n",
    "                # x=R.cos(theta) + 1.75\n",
    "                # y=R.sin(theta)\n",
    "                \n",
    "                angle = float(j) * (math.pi-2.0*math.sin(radius/R)) / (float(J)-1.0)+math.sin(radius/R)   \n",
    "                poslist_24.append([angle, R*math.cos(angle)+1.75, R*math.sin(angle)])\n",
    "\n",
    "                \n",
    "    # Now whatever seats are left go into the outside row:\n",
    "    J_2 = sumdelegates-len(poslist_24)\n",
    "    R = (7.0*rows-2.0)/(4.0*rows)\n",
    "    if J_2 == 1:\n",
    "        poslist_24.append([math.pi/2.0, 1.75*R, R])\n",
    "    else:\n",
    "        for j in range(J_2):\n",
    "            angle = float(j) * (math.pi-2.0*math.sin(radius/R)) / (float(J_2)-1.0)+math.sin(radius/R)\n",
    "            poslist_24.append([angle, R*math.cos(angle)+1.75, R*math.sin(angle)])\n",
    "    poslist_24.sort(reverse = True)\n",
    "    poslist_data = pd.DataFrame(data = poslist_24, columns = ['angle', 'x', 'y'])\n",
    "    \n",
    "    return poslist_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "\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>angle</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3.080093</td>\n",
       "      <td>0.128072</td>\n",
       "      <td>0.099874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3.068929</td>\n",
       "      <td>0.378628</td>\n",
       "      <td>0.099824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.052821</td>\n",
       "      <td>0.629430</td>\n",
       "      <td>0.099737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.027556</td>\n",
       "      <td>0.880683</td>\n",
       "      <td>0.099566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.828544</td>\n",
       "      <td>0.203977</td>\n",
       "      <td>0.500437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.250162</td>\n",
       "      <td>3.324418</td>\n",
       "      <td>0.402286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.114037</td>\n",
       "      <td>2.619317</td>\n",
       "      <td>0.099566</td>\n",
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       "      <td>47</td>\n",
       "      <td>0.088772</td>\n",
       "      <td>2.870570</td>\n",
       "      <td>0.099737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.072663</td>\n",
       "      <td>3.121372</td>\n",
       "      <td>0.099824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.061500</td>\n",
       "      <td>3.371928</td>\n",
       "      <td>0.099874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1802 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       angle         x         y\n",
       "0   3.080093  0.128072  0.099874\n",
       "1   3.068929  0.378628  0.099824\n",
       "2   3.052821  0.629430  0.099737\n",
       "3   3.027556  0.880683  0.099566\n",
       "4   2.828544  0.203977  0.500437\n",
       "..       ...       ...       ...\n",
       "45  0.250162  3.324418  0.402286\n",
       "46  0.114037  2.619317  0.099566\n",
       "47  0.088772  2.870570  0.099737\n",
       "48  0.072663  3.121372  0.099824\n",
       "49  0.061500  3.371928  0.099874\n",
       "\n",
       "[1802 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.read_excel(r'C:\\Users\\mariu\\Documents\\Project Local Elections\\seat_allocation.xlsx')\n",
    "\n",
    "poslist_final = pd.DataFrame(columns = ['angle', 'x', 'y'])\n",
    "\n",
    "dataframe['rows'] = 3\n",
    "dataframe.loc[dataframe['council_seats'] >= 33,  'rows'] = 4\n",
    "dataframe.loc[dataframe['council_seats'] >= 61,  'rows'] = 5\n",
    "\n",
    "for i in range(len(dataframe.city.unique())):\n",
    "    \n",
    "    city_now = dataframe.city.unique()[i]\n",
    "    dataframe_1 = dataframe[(dataframe.city == city_now)].reset_index()\n",
    "    row = dataframe_1.rows[0]\n",
    "    no_seats = dataframe_1.council_seats[0]\n",
    "    \n",
    "    poslist_final = pd.concat([poslist_final, position_angles(row, no_seats)])\n",
    "\n",
    "poslist_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_graph = dataframe.join(poslist_final.reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_graph['color'] = '#999999'\n",
    "\n",
    "data_graph.loc[data_graph['party'] == 'BP', 'color'] = '#98F5FF'\n",
    "data_graph.loc[data_graph['party'] == 'Partei', 'color'] = '#B5152B'\n",
    "data_graph.loc[data_graph['party'] == 'AfD', 'color'] = '#87CEFF'\n",
    "data_graph.loc[data_graph['party'] == 'ödp', 'color'] = '#698B22'\n",
    "data_graph.loc[data_graph['party'] == 'FW', 'color'] = '#FFA500'\n",
    "data_graph.loc[data_graph['party'] == 'Linke', 'color'] = '#A020F0'\n",
    "data_graph.loc[data_graph['party'] == 'BIA', 'color'] = '#8B4513'\n",
    "data_graph.loc[(data_graph['party'] != 'ödp') & (data_graph['party'] != 'Partei') & (data_graph['left_index'] == 4)\n",
    "               , 'color'] = '#EEE5DE'\n",
    "data_graph.loc[(data_graph['party'] != 'Linke') & (data_graph['left_index'] == 1), 'color'] = '#DDA0DD'\n",
    "data_graph.loc[data_graph['party'] == 'FDP', 'color'] = '#FFFF00'\n",
    "data_graph.loc[data_graph['party'] == 'Grüne', 'color'] = '#00AA00'\n",
    "data_graph.loc[data_graph['party'] == 'SPD', 'color'] = '#FF0000'\n",
    "data_graph.loc[data_graph['party'] == 'CSU', 'color'] = '#000000'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_graph_1 = data_graph[:896].reset_index()\n",
    "data_graph_2 = data_graph[896:].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.offline as pyo\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "import chart_studio\n",
    "\n",
    "chart_studio.tools.set_credentials_file(username='marius92', api_key='4naxu3XiGZTGniWO02z1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create individual graphs\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "list_parties= data_graph_2.party.unique()\n",
    "list_cities = data_graph_2.city.unique()\n",
    "\n",
    "for j in range(len(list_cities)):\n",
    "    \n",
    "    city_now = list_cities[j]\n",
    "    data_trace = data_graph_2[data_graph_2['city'].str.match(city_now)]\n",
    "    list_parties = data_trace.party.unique()\n",
    "    \n",
    "    if j == 0:\n",
    "        for i in range(len(list_parties)):\n",
    "            \n",
    "            party_now = list_parties[i]\n",
    "            \n",
    "            fig.add_trace(go.Scatter(\n",
    "                x= data_trace[data_trace['party'].str.match(party_now)]['x'], \n",
    "                y= data_trace[data_trace['party'].str.match(party_now)]['y'],\n",
    "                mode='markers',\n",
    "                name = party_now,\n",
    "                marker = dict(\n",
    "                    size = 24,\n",
    "                    color = data_trace[data_trace['party'].str.match(list_parties[i])]['color']),\n",
    "                hoverinfo = 'text',\n",
    "                hovertext = party_now,\n",
    "                visible = False, # Change to forecasted politician/or just number of seat for party ?\n",
    "            )\n",
    "            )\n",
    "    \n",
    "    \n",
    "    if j != 0:       \n",
    "    # Generate a scatter of a certain color for every party\n",
    "        for i in range(len(list_parties)):\n",
    "            party_now = list_parties[i]\n",
    "    \n",
    "            fig.add_trace(go.Scatter(\n",
    "                x= data_trace[data_trace['party'].str.match(party_now)]['x'], \n",
    "                y= data_trace[data_trace['party'].str.match(party_now)]['y'],\n",
    "                mode='markers',\n",
    "                name = party_now,\n",
    "                marker = dict(\n",
    "                    size = 24,\n",
    "                    color = data_trace[data_trace['party'].str.match(list_parties[i])]['color']),\n",
    "                hoverinfo = 'text',\n",
    "                hovertext = party_now,\n",
    "                visible = False, # Change to forecasted politician/or just number of seat for party ?\n",
    "            )\n",
    "            )\n",
    "        \n",
    "    x_annot = (data_trace['x'].iloc[-1] / 2) + data_trace['x'].iloc[0] # x center alignment for text\n",
    "    y_max = data_trace['y'].max()\n",
    "        \n",
    "    fig.layout.update(\n",
    "        legend = dict(orientation='v', # Put in a legend\n",
    "                        font = dict(family = \"Old Standard TT\",\n",
    "                                    size = 24,\n",
    "                                    color = 'black',\n",
    "                                    ),\n",
    "                        x = -0.5019,\n",
    "                        y = data_graph_2['y'].iloc[0] ,\n",
    "                ),\n",
    "    )\n",
    "        \n",
    "    fig.layout.update(\n",
    "    xaxis = dict(showgrid=False, zeroline=False, visible = False), # Hide anything related to the x-axis\n",
    "    yaxis = dict(showgrid=False, zeroline=False, visible = False), # Hide anything related to the y-axis\n",
    "    plot_bgcolor = 'white',\n",
    "    title = dict(font = dict(family = 'Old Standard TT',\n",
    "                             size = 32,\n",
    "                             color = 'black',\n",
    "                        ),\n",
    "                 x = 0.64,\n",
    "            ),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "button_data = data_graph_2.groupby(['city', 'party'], as_index = False).mean()\n",
    "data_button.to_excel(r'C:\\Users\\mariu\\Documents\\Project Local Elections\\weird_3.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "button_data = pd.read_excel(r'C:\\Users\\mariu\\Documents\\Project Local Elections\\weird_3.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "button_data_2 = pd.DataFrame()\n",
    "\n",
    "for i in range(len(button_data.city.unique())):\n",
    "    \n",
    "    name = list_cities[i]\n",
    "    button_data[name] = False\n",
    "    button_data.loc[button_data['city'] == name, name] = True\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "buttons=[]\n",
    "\n",
    "for i in range(len(button_data.city.unique())):\n",
    "    \n",
    "    name = button_data.city.unique()[i]\n",
    "    buttons.append(dict(method='update',\n",
    "                        label= '<b>' +  button_data.city.unique()[i] + '</b>',\n",
    "                        args=[{'visible': button_data[name].values,\n",
    "                               'title': \" <b>\" + button_data.city.unique()[i] + \"</b>\"}, ],\n",
    "                        )\n",
    "                  )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
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       "            width=\"1000px\"\n",
       "            height=\"500px\"\n",
       "            src=\"https://plot.ly/~marius92/269.embed\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
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   "source": [
    "fig.layout.update(\n",
    "    updatemenus=[\n",
    "        go.layout.Updatemenu(\n",
    "            buttons=buttons,\n",
    "            x = data_graph['x'].iloc[0] - 0.4,\n",
    "            y = 1.24,\n",
    "            bgcolor = '#efefef',\n",
    "            font = dict(family = 'Old Standard TT',\n",
    "                        size = 18,\n",
    "                       ),\n",
    "            borderwidth = 1.2,\n",
    "        )\n",
    "    ])\n",
    "\n",
    "fig['layout'].update(width = 1000, height = 500)\n",
    "filename = 'Parliament_seats_2'\n",
    "py.iplot(fig, filename = filename)"
   ]
  }
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