{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import re\n",
"import json\n",
"import numpy as np\n",
"import timeit \n",
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Cufflinks wrapper on plotly\n",
"import cufflinks\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = 'all'\n",
"\n",
"from plotly.offline import iplot\n",
"cufflinks.go_offline()\n",
"\n",
"# Set global theme\n",
"\n",
"import plotly.figure_factory as ff\n",
"\n",
"import plotly.graph_objects as go\n",
"import plotly.express as px"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import plotly.express as px\n",
"from shapely.geometry import Polygon, MultiPolygon\n",
"import geopandas as gpd\n",
"import matplotlib.pyplot as plt\n",
"import geojsoncontour\n",
"\n",
"from scipy.interpolate import griddata\n",
"from numpy import linspace"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#Selecting a central city point to center all graphs around - Swietokrzyska Subway \n",
"center_coors=52.235176, 21.008393"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project overwiev"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook is 3rd part of a Warsaw Real Estate market analysis project. In this part we build initial model for real estate benchmarking with focus on Feature Selection and Error analysis based on spatial data, which allows to determine if we can improve model accuracy with data enrichment - especially with data desribing building location, which is the key driver of price and is not present in the offer itself. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data input and initial transformation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"## Reading data from GitHub\n",
"df = pd.read_excel(r\"https://raw.githubusercontent.com/Jan-Majewski/Project_Portfolio/master/03_Real_Estate_pricing_in_Warsaw/RE_models_input_enriched.xlsx\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Id | \n",
" offer_date | \n",
" Area | \n",
" Price | \n",
" latitude | \n",
" longitude | \n",
" build_year | \n",
" building_floors_num | \n",
" rooms_num | \n",
" City | \n",
" ... | \n",
" time_return_transit_5PM | \n",
" distance_return_driving_5PM | \n",
" time_return_driving_5PM | \n",
" price_decrease_from_20k | \n",
" price_decrease_per_10min | \n",
" restaurant_price_level | \n",
" restaurant_mean_rating | \n",
" restaurant_mean_popularity | \n",
" restaurant_count | \n",
" restaurant_ratings_count | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 54693264 | \n",
" 2020-07-07 | \n",
" 59.31 | \n",
" 414576 | \n",
" 52.344113 | \n",
" 20.944478 | \n",
" 2020 | \n",
" 3 | \n",
" 3 | \n",
" warszawa | \n",
" ... | \n",
" 48.183333 | \n",
" 15.96 | \n",
" 25.65 | \n",
" 12215.632653 | \n",
" 2446.388382 | \n",
" 1.75 | \n",
" 4.325 | \n",
" 233.8 | \n",
" 10 | \n",
" 2338 | \n",
"
\n",
" \n",
" 1 | \n",
" 60139185 | \n",
" 2020-07-07 | \n",
" 80.00 | \n",
" 618000 | \n",
" 52.343998 | \n",
" 20.944001 | \n",
" 1998 | \n",
" 4 | \n",
" 3 | \n",
" warszawa | \n",
" ... | \n",
" 48.183333 | \n",
" 15.96 | \n",
" 25.65 | \n",
" 12215.632653 | \n",
" 2446.388382 | \n",
" 1.75 | \n",
" 4.325 | \n",
" 233.8 | \n",
" 10 | \n",
" 2338 | \n",
"
\n",
" \n",
" 2 | \n",
" 60654016 | \n",
" 2020-07-07 | \n",
" 65.12 | \n",
" 620000 | \n",
" 52.345895 | \n",
" 20.943476 | \n",
" 2012 | \n",
" 4 | \n",
" 3 | \n",
" warszawa | \n",
" ... | \n",
" 48.183333 | \n",
" 15.96 | \n",
" 25.65 | \n",
" 12215.632653 | \n",
" 2446.388382 | \n",
" 1.75 | \n",
" 4.325 | \n",
" 233.8 | \n",
" 10 | \n",
" 2338 | \n",
"
\n",
" \n",
" 3 | \n",
" 60647324 | \n",
" 2020-07-07 | \n",
" 102.56 | \n",
" 614334 | \n",
" 52.342630 | \n",
" 20.942790 | \n",
" 2020 | \n",
" 3 | \n",
" 4 | \n",
" warszawa | \n",
" ... | \n",
" 48.183333 | \n",
" 15.96 | \n",
" 25.65 | \n",
" 12215.632653 | \n",
" 2446.388382 | \n",
" 1.75 | \n",
" 4.325 | \n",
" 233.8 | \n",
" 10 | \n",
" 2338 | \n",
"
\n",
" \n",
" 4 | \n",
" 59470914 | \n",
" 2020-07-07 | \n",
" 52.00 | \n",
" 398000 | \n",
" 52.344822 | \n",
" 20.941277 | \n",
" 2021 | \n",
" 5 | \n",
" 3 | \n",
" warszawa | \n",
" ... | \n",
" 48.183333 | \n",
" 15.96 | \n",
" 25.65 | \n",
" 12215.632653 | \n",
" 2446.388382 | \n",
" 1.75 | \n",
" 4.325 | \n",
" 233.8 | \n",
" 10 | \n",
" 2338 | \n",
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\n",
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\n",
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5 rows × 75 columns
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"
],
"text/plain": [
" Id offer_date Area Price latitude longitude build_year \\\n",
"0 54693264 2020-07-07 59.31 414576 52.344113 20.944478 2020 \n",
"1 60139185 2020-07-07 80.00 618000 52.343998 20.944001 1998 \n",
"2 60654016 2020-07-07 65.12 620000 52.345895 20.943476 2012 \n",
"3 60647324 2020-07-07 102.56 614334 52.342630 20.942790 2020 \n",
"4 59470914 2020-07-07 52.00 398000 52.344822 20.941277 2021 \n",
"\n",
" building_floors_num rooms_num City ... time_return_transit_5PM \\\n",
"0 3 3 warszawa ... 48.183333 \n",
"1 4 3 warszawa ... 48.183333 \n",
"2 4 3 warszawa ... 48.183333 \n",
"3 3 4 warszawa ... 48.183333 \n",
"4 5 3 warszawa ... 48.183333 \n",
"\n",
" distance_return_driving_5PM time_return_driving_5PM price_decrease_from_20k \\\n",
"0 15.96 25.65 12215.632653 \n",
"1 15.96 25.65 12215.632653 \n",
"2 15.96 25.65 12215.632653 \n",
"3 15.96 25.65 12215.632653 \n",
"4 15.96 25.65 12215.632653 \n",
"\n",
" price_decrease_per_10min restaurant_price_level restaurant_mean_rating \\\n",
"0 2446.388382 1.75 4.325 \n",
"1 2446.388382 1.75 4.325 \n",
"2 2446.388382 1.75 4.325 \n",
"3 2446.388382 1.75 4.325 \n",
"4 2446.388382 1.75 4.325 \n",
"\n",
" restaurant_mean_popularity restaurant_count restaurant_ratings_count \n",
"0 233.8 10 2338 \n",
"1 233.8 10 2338 \n",
"2 233.8 10 2338 \n",
"3 233.8 10 2338 \n",
"4 233.8 10 2338 \n",
"\n",
"[5 rows x 75 columns]"
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},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(25240, 75)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df['rooms_num']=np.where(df['rooms_num']==\"more\",10,df['rooms_num'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"## Removing columns not needed in this project\n",
"df.drop(columns=['floor_no',\"City\",\"district_old\",'price_decrease_from_20k','price_decrease_per_10min'],inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Id', 'offer_date', 'Area', 'Price', 'latitude', 'longitude',\n",
" 'build_year', 'building_floors_num', 'rooms_num', 'subdistrict',\n",
" 'market', 'Building_material', 'Building_ownership', 'Building_type',\n",
" 'Construction_status', 'Heating', 'Windows_type',\n",
" 'Equipment_types_dishwasher', 'Equipment_types_fridge',\n",
" 'Equipment_types_furniture', 'Equipment_types_oven',\n",
" 'Equipment_types_stove', 'Equipment_types_tv',\n",
" 'Equipment_types_washing_machine', 'Extras_types_air_conditioning',\n",
" 'Extras_types_attic', 'Extras_types_balcony', 'Extras_types_basement',\n",
" 'Extras_types_garage', 'Extras_types_garden', 'Extras_types_lift',\n",
" 'Extras_types_separate_kitchen', 'Extras_types_terrace',\n",
" 'Extras_types_two_storey', 'Extras_types_usable_room',\n",
" 'Media_types_cable-television', 'Media_types_cable_television',\n",
" 'Media_types_electricity', 'Media_types_internet', 'Media_types_phone',\n",
" 'Media_types_sewage', 'Media_types_water', 'Security_types_alarm',\n",
" 'Security_types_anti_burglary_door', 'Security_types_closed_area',\n",
" 'Security_types_entryphone', 'Security_types_monitoring',\n",
" 'Security_types_roller_shutters', 'floor_num', 'district', 'unit_price',\n",
" 'lon_mod', 'lat_mod', 'index', 'grid_price', 'sample_size', 'geo_Id',\n",
" 'distance_transit_8AM', 'time_transit_8AM', 'distance_driving_8AM',\n",
" 'time_driving_8AM', 'distance_return_transit_5PM',\n",
" 'time_return_transit_5PM', 'distance_return_driving_5PM',\n",
" 'time_return_driving_5PM', 'restaurant_price_level',\n",
" 'restaurant_mean_rating', 'restaurant_mean_popularity',\n",
" 'restaurant_count', 'restaurant_ratings_count'],\n",
" dtype='object')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Averaging distance and transport time from morning and evening\n",
"df[\"distance_driving\"]=(df.distance_driving_8AM+df.distance_return_driving_5PM)/2\n",
"df[\"distance_transit\"]=(df.distance_transit_8AM+df.distance_return_transit_5PM)/2\n",
"df[\"time_driving\"]=(df.time_driving_8AM+df.time_return_driving_5PM)/2\n",
"df[\"time_transit\"]=(df.time_return_transit_5PM+df.time_transit_8AM)/2"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Removing morning and evening transport data to leave only averages\n",
"df.drop(columns=['distance_transit_8AM', 'time_transit_8AM', 'distance_driving_8AM',\n",
" 'time_driving_8AM', 'distance_return_transit_5PM',\n",
" 'time_return_transit_5PM', 'distance_return_driving_5PM',\n",
" 'time_return_driving_5PM'],inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#Mapping districts between East and West banks of Vistula River\n",
"\n",
"east_bank_dict={\n",
" 'Wola':0,\n",
" 'Subburbs':0,\n",
" 'Downtown':0,\n",
" 'Ochota':0,\n",
" 'Mokotow':0,\n",
" 'Bialoleka':1,\n",
" 'Bielany':0,\n",
" 'Bemowo':0,\n",
" 'Southern Praga':1,\n",
" 'Wilanow':0,\n",
" 'Praga':1,\n",
" 'Ursynow':0,\n",
" 'Zoliborz':0,\n",
" 'Wlochy':0,\n",
" 'Targowek':1,\n",
" 'Wawer':1,\n",
" \"Other\":0}\n",
"\n",
"df[\"east_bank\"]=df.district.apply(lambda x: east_bank_dict[x])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data exploration"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Category composition\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's investigate categorical data composition to see how complete and diverse each category looks. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#Unique features with key property characteristics\n",
"unique_features=[\"district\",\"market\",\"Building_material\",\"Building_ownership\",\"Building_type\",\"Construction_status\",\"Heating\",\n",
"\"Windows_type\"]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": false
},
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"name": "stdout",
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"source": [
"# Iteraring through each category allows us to create 8 graphs with 10 lines of code\n",
"\n",
"for feature in unique_features:\n",
" feature_list=[\"Id\"]\n",
" feature_list.append(feature)\n",
"\n",
" df_temp=df[feature_list].groupby(feature, as_index=False).count()\n",
" df_temp.rename(columns={\"Id\":\"share\"},inplace=True)\n",
" df_temp.sort_values(by=\"share\",inplace=True, ascending=False)\n",
" df_temp[\"share\"]=df_temp[\"share\"]/df.shape[0]\n",
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" df_temp[\"colour\"]=np.where(df_temp[feature]==\"not_specified\",\"missing_data\",\"valid_data\")\n",
" \n",
" fig = px.bar(df_temp, x=feature, y='share', color=\"colour\",\n",
" color_discrete_sequence=[\"blue\", \"red\"],\n",
" category_orders={\"colour\": [\"valid_data\", \"missing_data\"]},)\n",
" print(\"\\n Feature summary for {} - Share of category within whole sample\".format(feature))\n",
" fig.show()\n",
" print(\"----------------------------------------------------------------------------------------------------------------------------\\n\\n\\n\")\n",
"\n",
" "
]
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"source": [
"## Average unit price by feature "
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Are there any clear relations between unit price and categorical features? Let's investigate using box plots."
]
},
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"cell_type": "code",
"execution_count": 17,
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"text": [
"\n",
" Unit price distribution in split by district\n"
]
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"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Downtown",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Downtown",
"Downtown",
"Downtown",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Downtown",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Downtown",
"Downtown",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Downtown",
"Downtown",
"Mokotow",
"Downtown",
"Downtown",
"Mokotow",
"Mokotow",
"Mokotow",
"Downtown",
"Mokotow",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Wlochy",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Wlochy",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Mokotow",
"Mokotow",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Mokotow",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Mokotow",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
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"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Subburbs",
"Wlochy",
"Wlochy",
"Wlochy",
"Subburbs",
"Wlochy",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Wlochy",
"Subburbs",
"Subburbs",
"Subburbs",
"Wlochy",
"Subburbs",
"Subburbs",
"Wlochy",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Wlochy",
"Subburbs",
"Subburbs",
"Subburbs",
"Wlochy",
"Subburbs",
"Subburbs",
"Subburbs",
"Wlochy",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Wlochy",
"Wlochy",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Wlochy",
"Subburbs",
"Subburbs",
"Wlochy",
"Subburbs",
"Subburbs",
"Wlochy",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Southern Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Southern Praga",
"Praga",
"Southern Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Southern Praga",
"Praga",
"Praga",
"Praga",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Targowek",
"Praga",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Praga",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ochota",
"Downtown",
"Ochota",
"Ochota",
"Downtown",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Downtown",
"Downtown",
"Downtown",
"Ochota",
"Downtown",
"Downtown",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ochota",
"Ochota",
"Ochota",
"Wlochy",
"Ochota",
"Wlochy",
"Wlochy",
"Wlochy",
"Ochota",
"Wlochy",
"Ochota",
"Wlochy",
"Wlochy",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Wlochy",
"Wlochy",
"Wlochy",
"Ochota",
"Ochota",
"Subburbs",
"Ochota",
"Ochota",
"Ochota",
"Wlochy",
"Ochota",
"Ochota",
"Ochota",
"Wlochy",
"Wlochy",
"Wlochy",
"Subburbs",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Subburbs",
"Subburbs",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Subburbs",
"Ochota",
"Ochota",
"Subburbs",
"Ochota",
"Wlochy",
"Subburbs",
"Ochota",
"Subburbs",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Mokotow",
"Wilanow",
"Mokotow",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Downtown",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Downtown",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Downtown",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Downtown",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Downtown",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Downtown",
"Wola",
"Wola",
"Wola",
"Downtown",
"Wola",
"Downtown",
"Downtown",
"Wola",
"Wola",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Wola",
"Wola",
"Wola",
"Wola",
"Downtown",
"Wola",
"Wola",
"Wola",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Wola",
"Downtown",
"Wola",
"Wola",
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"Downtown",
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"Wola",
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"Wola",
"Wola",
"Wola",
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"Downtown",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Downtown",
"Wola",
"Mokotow",
"Mokotow",
"Mokotow",
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"Mokotow",
"Mokotow",
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"Mokotow",
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"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
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"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wola",
"Wola",
"Southern Praga",
"Wola",
"Southern Praga",
"Wola",
"Wola",
"Wola",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Ochota",
"Ochota",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ursynow",
"Ursynow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Ursynow",
"Ursynow",
"Ursynow",
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"Ursynow",
"Ursynow",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Ursynow",
"Ursynow",
"Mokotow",
"Mokotow",
"Ursynow",
"Ursynow",
"Ursynow",
"Mokotow",
"Mokotow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Targowek",
"Targowek",
"Targowek",
"Southern Praga",
"Targowek",
"Targowek",
"Southern Praga",
"Targowek",
"Targowek",
"Targowek",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Downtown",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Mokotow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Mokotow",
"Ursynow",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Wawer",
"Wawer",
"Southern Praga",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Wawer",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ursynow",
"Ursynow",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Ursynow",
"Subburbs",
"Subburbs",
"Ursynow",
"Subburbs",
"Ursynow",
"Ursynow",
"Subburbs",
"Subburbs",
"Subburbs",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Mokotow",
"Ursynow",
"Ursynow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ursynow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Mokotow",
"Ursynow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ursynow",
"Mokotow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Mokotow",
"Mokotow",
"Mokotow",
"Ursynow",
"Ursynow",
"Mokotow",
"Ursynow",
"Mokotow",
"Ursynow",
"Mokotow",
"Mokotow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Mokotow",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
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"Bielany",
"Bielany",
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"Zoliborz",
"Zoliborz",
"Zoliborz",
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"Wola",
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"Southern Praga",
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"Southern Praga",
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"Southern Praga",
"Southern Praga",
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"Southern Praga",
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"Southern Praga",
"Southern Praga",
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"Southern Praga",
"Southern Praga",
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"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Bemowo",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Wawer",
"Wawer",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Subburbs",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Subburbs",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Subburbs",
"Subburbs",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Praga",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Subburbs",
"Wlochy",
"Other",
"Wlochy",
"Wlochy",
"Other",
"Other",
"Other",
"Other",
"Other",
"Other",
"Subburbs",
"Wlochy",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Zoliborz",
"Bielany",
"Bielany",
"Zoliborz",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Zoliborz",
"Zoliborz",
"Bielany",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Bielany",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Bielany",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Bielany",
"Zoliborz",
"Zoliborz",
"Bialoleka",
"Bialoleka",
"Targowek",
"Targowek",
"Bialoleka",
"Targowek",
"Bialoleka",
"Targowek",
"Bialoleka",
"Bialoleka",
"Targowek",
"Bialoleka",
"Targowek",
"Targowek",
"Bialoleka",
"Targowek",
"Targowek",
"Bialoleka",
"Targowek",
"Bialoleka",
"Targowek",
"Targowek",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Bialoleka",
"Targowek",
"Bialoleka",
"Targowek",
"Targowek",
"Targowek",
"Bialoleka",
"Targowek",
"Bialoleka",
"Targowek",
"Targowek",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Wlochy",
"Ochota",
"Wlochy",
"Ochota",
"Wlochy",
"Wlochy",
"Ochota",
"Wlochy",
"Ochota",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Subburbs",
"Subburbs",
"Southern Praga",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Wlochy",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wola",
"Ochota",
"Ochota",
"Wlochy",
"Ochota",
"Wlochy",
"Ochota",
"Ochota",
"Wlochy",
"Ochota",
"Wlochy",
"Wlochy",
"Ochota",
"Wlochy",
"Wlochy",
"Ochota",
"Wlochy",
"Ochota",
"Ochota",
"Ochota",
"Wlochy",
"Ochota",
"Wlochy",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Subburbs",
"Subburbs",
"Subburbs",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Wilanow",
"Ochota",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Ochota",
"Ochota",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Ochota",
"Wola",
"Wola",
"Wola",
"Ochota",
"Ochota",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Praga",
"Targowek",
"Targowek",
"Praga",
"Praga",
"Praga",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Zoliborz",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Southern Praga",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Southern Praga",
"Southern Praga",
"Wawer",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wawer",
"Southern Praga",
"Southern Praga",
"Wawer",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wawer",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Bielany",
"Bielany",
"Bielany",
"Zoliborz",
"Bielany",
"Bielany",
"Bielany",
"Zoliborz",
"Bielany",
"Bielany",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Mokotow",
"Southern Praga",
"Southern Praga",
"Wawer",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wawer",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wawer",
"Wawer",
"Wawer",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Wola",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wawer",
"Wawer",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wawer",
"Southern Praga",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Ochota",
"Subburbs",
"Subburbs",
"Subburbs",
"Bialoleka",
"Subburbs",
"Subburbs",
"Subburbs",
"Bialoleka",
"Subburbs",
"Subburbs",
"Subburbs",
"Bialoleka",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Subburbs",
"Subburbs",
"Subburbs",
"Wawer",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Praga",
"Southern Praga",
"Praga",
"Southern Praga",
"Praga",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Praga",
"Praga",
"Praga",
"Targowek",
"Praga",
"Targowek",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Wawer",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Ochota",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Ochota",
"Wlochy",
"Wlochy",
"Ochota",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Bielany",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Southern Praga",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Targowek",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Targowek",
"Targowek",
"Bialoleka",
"Targowek",
"Bialoleka",
"Targowek",
"Targowek",
"Bialoleka",
"Bialoleka",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Ursynow",
"Mokotow",
"Mokotow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Ursynow",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Targowek",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Subburbs",
"Wola",
"Wola",
"Wola",
"Wola",
"Downtown",
"Downtown",
"Downtown",
"Wola",
"Wola",
"Downtown",
"Downtown",
"Wola",
"Downtown",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Bialoleka",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Wlochy",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
"Bemowo",
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12638,
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11616,
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10416,
9211,
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10312,
12972,
13714,
10404,
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11755,
10543,
11279,
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10517,
9523,
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13669,
13733,
8800,
10967,
17851,
15735,
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9397,
12051,
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9594,
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10636,
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11038,
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14743,
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10714,
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10937,
11582,
12416,
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11979,
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12051,
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13018,
10351,
11475,
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14182,
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12619,
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9340,
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12821,
12821,
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14525,
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31137,
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31506,
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17588,
10215,
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23000,
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13933,
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13748,
16229,
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16523,
12042,
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19879,
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21052,
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12033,
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21739,
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31081,
45353,
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20315,
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18032,
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13125,
27293,
15000,
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14629,
19907,
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22000,
13570,
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16056,
25000,
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12060,
13750,
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11701,
6586,
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13765,
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10795,
13058,
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8535,
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9811,
15500,
16666,
16904,
8278,
10162,
10973,
10973,
16088,
8543,
11208,
12524,
12524,
12750,
11998,
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12000,
10909,
9230,
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10446,
15350,
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10918,
12608,
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9680,
12395,
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10436,
9969,
11094,
13044,
8791,
10727,
9061,
10924,
13108,
10170,
17588,
10078,
11589,
9686,
11381,
25000,
21923,
11800,
21596,
27941,
25000,
13000,
12156,
13191,
12245,
9189,
10775,
11636,
11851,
9854,
8697,
11488,
10328,
15500,
13888,
11428,
12506,
14098,
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13773,
12459,
15553,
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11261,
12482,
14489,
10027,
12033,
16647,
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12459,
8587,
12982,
12982,
11984,
10260,
9576,
10934,
10773,
10818,
11224,
14130,
11428,
11206,
12264,
17571,
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13900,
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10307,
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11977,
12015,
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7196,
14998,
13714,
15156,
7800,
13188,
12671,
12431,
14000,
15671,
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12321,
10800,
10539,
14710,
9698,
13102,
6024,
6024,
19192,
11757,
12193,
9039,
13500,
13893,
12461,
11111,
12500,
14385,
12934,
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8548,
11447,
11298,
12893,
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14180,
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9998,
20000,
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16930,
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11435,
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11764,
11466,
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9634,
11000,
8983,
11229,
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9725,
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12023,
10000,
10408,
10833,
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13789,
11021,
14027,
12555,
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10960,
10000,
11632,
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8846,
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11632,
14837,
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8400,
11811,
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12260,
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10027,
10027,
11626,
14192,
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13000,
13536,
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15394,
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15000,
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14625,
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9795,
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18333,
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15000,
9392,
11091,
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11237,
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8548,
13125,
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11520,
10412,
10636,
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15769,
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8565,
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11439,
10540,
11000,
15821,
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10102,
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18053,
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11979,
12807,
10500,
11038,
12600,
14743,
13775,
10777,
10714,
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8989,
10937,
11582,
12416,
12666,
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11979,
13600,
12051,
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13018,
10351,
11475,
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9540,
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11250,
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13750,
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13519,
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14182,
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9340,
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12821,
12821,
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31137,
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31506,
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26510,
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12510,
39121,
37499,
39121,
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18055,
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26000,
21800,
44900,
19751,
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17588,
10215,
15500,
23000,
12812,
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13933,
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15000,
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13748,
13748,
16229,
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18849,
16523,
12042,
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14404,
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17391,
19879,
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14905,
14629,
14682,
11716,
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19725,
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22005,
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25428,
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25485,
17900,
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17291,
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11485,
11705,
14019,
10540,
21052,
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14534,
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11315,
10750,
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11340,
12033,
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15518,
13565,
21739,
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31081,
45353,
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20000,
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20500,
18800,
20315,
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19900,
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18032,
16285,
14413,
17746,
13125,
27293,
15000,
16295,
12553,
13329,
13156,
14629,
19907,
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22000,
13570,
14895,
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16633,
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23629,
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7711,
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25000,
7818,
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21770,
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13982,
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21625,
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16847,
9810,
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16000,
16056,
25000,
14538,
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23250,
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13817,
14457,
12060,
13750,
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11701,
6586,
11224,
13765,
14373,
10795,
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8535,
16383,
14500,
9811,
15500,
16666,
16904,
8278,
10162,
10973,
10973,
16088,
8543,
11208,
12524,
12524,
12750,
11998,
9089,
12000,
10909,
9230,
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10446,
15350,
10672,
13150,
10918,
12608,
10504,
11218,
9680,
12395,
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10436,
9969,
11094,
13044,
8791,
10727,
9061,
10924,
13108,
10170,
17588,
10078,
11589,
9686,
11381,
25000,
21923,
11800,
21596,
27941,
25000,
13000,
12156,
13191,
12245,
9189,
10775,
11636,
11851,
9854,
8697,
11488,
10328,
15500,
13888,
11428,
12506,
14098,
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13773,
12459,
15553,
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10027,
12033,
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8587,
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12982,
11984,
10260,
9576,
10934,
10773,
10818,
11224,
14130,
11428,
11206,
12264,
17571,
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13900,
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11050,
11977,
12015,
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13714,
15156,
7800,
13188,
12671,
12431,
14000,
15671,
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12321,
10800,
10539,
14710,
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13102,
6024,
6024,
19192,
11757,
12193,
9039,
13500,
13893,
12461,
11111,
12500,
14385,
12934,
12666,
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8548,
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11298,
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14180,
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9998,
20000,
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12968,
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16930,
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11435,
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10540,
13000,
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11764,
11466,
12900,
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14032,
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10741,
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9634,
11000,
8983,
11229,
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9725,
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12023,
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10408,
10833,
13000,
13789,
11021,
14027,
12555,
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10960,
10000,
11632,
10267,
8846,
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11632,
14837,
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8400,
11811,
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12028,
10963,
10639,
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13714,
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13027,
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15040,
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12075,
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11618,
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11437,
11764,
11510,
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11437,
9549,
9999,
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11437,
11741,
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11822,
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11439,
16568,
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16395,
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26923,
14552,
13511,
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12371,
13895,
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16436,
17293,
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12875,
12866,
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26967,
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12312,
12983,
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14900,
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12556,
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13634,
14864,
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13968,
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12057,
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13133,
10500,
13170,
15000,
16255,
16198,
14326,
15000,
14036,
15250,
9963,
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31137,
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12750,
11998,
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10924,
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17588,
10078,
11589,
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25000,
21923,
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21596,
27941,
25000,
13000,
12156,
13191,
12245,
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10775,
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12033,
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10260,
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7800,
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12431,
14000,
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6024,
6024,
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9039,
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11632,
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11632,
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13000,
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},
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" \n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------------------------------------------------------------------------------------------------\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"for feature in unique_features:\n",
" feature_list=[\"unit_price\"]\n",
" feature_list.append(feature)\n",
"\n",
" df_temp=df[feature_list]\n",
" \n",
" print(\"\\n Unit price distribution in split by {}\".format(feature))\n",
" \n",
" fig = fig = px.box(df_temp, y=\"unit_price\", x=feature, points=\"suspectedoutliers\");\n",
" fig.update_yaxes(range=[5000, 25000])\n",
"\n",
"\n",
" print(\"----------------------------------------------------------------------------------------------------------------------------\\n\\n\\n\")\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Transforming data for ML models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Change categorical data into OneHot vectors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to transform categorical data into onehot columns before feeding it to ml models. As I will use the same data for DNNs I will also drop one column for each categorical feature to avoid collinearity. "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import OneHotEncoder"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"#Extracting categorical features\n",
"df_cat=df[unique_features]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OneHotEncoder(categories='auto', drop=None, dtype=,\n",
" handle_unknown='error', sparse=True)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"enc=OneHotEncoder()\n",
"enc.fit(df_cat)\n",
"\n",
"one_hot_val=enc.fit_transform(df_cat).toarray().astype(int)\n",
"one_hot_columns=enc.get_feature_names(unique_features)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"df_one_hot=pd.DataFrame(one_hot_val, columns=one_hot_columns)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['district_Bemowo', 'district_Bialoleka', 'district_Bielany',\n",
" 'district_Downtown', 'district_Mokotow', 'district_Ochota',\n",
" 'district_Other', 'district_Praga', 'district_Southern Praga',\n",
" 'district_Subburbs', 'district_Targowek', 'district_Ursynow',\n",
" 'district_Wawer', 'district_Wilanow', 'district_Wlochy',\n",
" 'district_Wola', 'district_Zoliborz', 'market_primary',\n",
" 'market_secondary', 'Building_material_breezeblock',\n",
" 'Building_material_brick', 'Building_material_cellular_concrete',\n",
" 'Building_material_concrete', 'Building_material_concrete_plate',\n",
" 'Building_material_hydroton', 'Building_material_not_specified',\n",
" 'Building_material_other', 'Building_material_reinforced_concrete',\n",
" 'Building_material_silikat', 'Building_material_wood',\n",
" 'Building_ownership_co_operative_ownership',\n",
" 'Building_ownership_co_operative_ownership_with_a_land_and_mortgage_registe',\n",
" 'Building_ownership_full_ownership', 'Building_ownership_not_specified',\n",
" 'Building_ownership_share', 'Building_type_apartment',\n",
" 'Building_type_block', 'Building_type_detached', 'Building_type_house',\n",
" 'Building_type_infill', 'Building_type_loft',\n",
" 'Building_type_not_specified', 'Building_type_ribbon',\n",
" 'Building_type_tenement', 'Construction_status_not_specified',\n",
" 'Construction_status_ready_to_use', 'Construction_status_to_completion',\n",
" 'Construction_status_to_renovation', 'Heating_boiler_room',\n",
" 'Heating_electrical', 'Heating_gas', 'Heating_not_specified',\n",
" 'Heating_other', 'Heating_tiled_stove', 'Heating_urban',\n",
" 'Windows_type_aluminium', 'Windows_type_not_specified',\n",
" 'Windows_type_plastic', 'Windows_type_wooden'],\n",
" dtype='object')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_one_hot.columns"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"not_specified_idx=[]\n",
"for i in range(0,df_one_hot.columns.shape[0]):\n",
" if \"not_specified\" in df_one_hot.columns[i]:\n",
" not_specified_idx.append(i)\n",
"not_specified_idx=np.asarray(not_specified_idx)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's drop all \"not_specified\" columns and \"market_secondary\" and \"Mokotow\" district to aboid collinearity. "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"drop_collinear_cols=list(df_one_hot.columns[not_specified_idx])\n",
"drop_collinear_cols.append('district_Mokotow')\n",
"drop_collinear_cols.append('market_secondary')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"#Function substracting list2 elements from list1\n",
"def list_diff(list1, list2):\n",
" out = []\n",
" for ele in list1:\n",
" if not ele in list2:\n",
" out.append(ele)\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['district_Bemowo', 'district_Bialoleka', 'district_Bielany',\n",
" 'district_Downtown', 'district_Mokotow', 'district_Ochota',\n",
" 'district_Other', 'district_Praga', 'district_Southern Praga',\n",
" 'district_Subburbs', 'district_Targowek', 'district_Ursynow',\n",
" 'district_Wawer', 'district_Wilanow', 'district_Wlochy',\n",
" 'district_Wola', 'district_Zoliborz', 'market_primary',\n",
" 'market_secondary', 'Building_material_breezeblock',\n",
" 'Building_material_brick', 'Building_material_cellular_concrete',\n",
" 'Building_material_concrete', 'Building_material_concrete_plate',\n",
" 'Building_material_hydroton', 'Building_material_not_specified',\n",
" 'Building_material_other', 'Building_material_reinforced_concrete',\n",
" 'Building_material_silikat', 'Building_material_wood',\n",
" 'Building_ownership_co_operative_ownership',\n",
" 'Building_ownership_co_operative_ownership_with_a_land_and_mortgage_registe',\n",
" 'Building_ownership_full_ownership', 'Building_ownership_not_specified',\n",
" 'Building_ownership_share', 'Building_type_apartment',\n",
" 'Building_type_block', 'Building_type_detached', 'Building_type_house',\n",
" 'Building_type_infill', 'Building_type_loft',\n",
" 'Building_type_not_specified', 'Building_type_ribbon',\n",
" 'Building_type_tenement', 'Construction_status_not_specified',\n",
" 'Construction_status_ready_to_use', 'Construction_status_to_completion',\n",
" 'Construction_status_to_renovation', 'Heating_boiler_room',\n",
" 'Heating_electrical', 'Heating_gas', 'Heating_not_specified',\n",
" 'Heating_other', 'Heating_tiled_stove', 'Heating_urban',\n",
" 'Windows_type_aluminium', 'Windows_type_not_specified',\n",
" 'Windows_type_plastic', 'Windows_type_wooden'],\n",
" dtype='object')"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_one_hot.columns"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"#Substracting collinear columns from categorical columns. Modyfing categorical data DataFrame\n",
"df_cat_columns=list_diff(df_one_hot.columns,drop_collinear_cols)\n",
"df_cat=df_one_hot[df_cat_columns]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Handling non categorical data"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"columns_base=list_diff(df.columns, unique_features)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# Removing columns without any predictive value and columns not within initial dataset (added to datasource after initial iteration). \n",
"\n",
"drop_columns_base=[\n",
" 'Price','latitude','longitude','lon_mod','lat_mod','grid_price','sample_size','City','subdistrict',\n",
" 'offer_date',\"index\",\"geo_Id\",\"east_bank\", \"floor_no\"\n",
" ]\n",
"columns_base=list_diff(columns_base, drop_columns_base)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Id',\n",
" 'Area',\n",
" 'build_year',\n",
" 'building_floors_num',\n",
" 'rooms_num',\n",
" 'Equipment_types_dishwasher',\n",
" 'Equipment_types_fridge',\n",
" 'Equipment_types_furniture',\n",
" 'Equipment_types_oven',\n",
" 'Equipment_types_stove',\n",
" 'Equipment_types_tv',\n",
" 'Equipment_types_washing_machine',\n",
" 'Extras_types_air_conditioning',\n",
" 'Extras_types_attic',\n",
" 'Extras_types_balcony',\n",
" 'Extras_types_basement',\n",
" 'Extras_types_garage',\n",
" 'Extras_types_garden',\n",
" 'Extras_types_lift',\n",
" 'Extras_types_separate_kitchen',\n",
" 'Extras_types_terrace',\n",
" 'Extras_types_two_storey',\n",
" 'Extras_types_usable_room',\n",
" 'Media_types_cable-television',\n",
" 'Media_types_cable_television',\n",
" 'Media_types_electricity',\n",
" 'Media_types_internet',\n",
" 'Media_types_phone',\n",
" 'Media_types_sewage',\n",
" 'Media_types_water',\n",
" 'Security_types_alarm',\n",
" 'Security_types_anti_burglary_door',\n",
" 'Security_types_closed_area',\n",
" 'Security_types_entryphone',\n",
" 'Security_types_monitoring',\n",
" 'Security_types_roller_shutters',\n",
" 'floor_num',\n",
" 'unit_price']"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Removing GoogleMaps and Restaurant data from initial data set as they were added after initial error analysis\n",
"columns_google_maps=['distance_driving','distance_transit', 'time_driving', 'time_transit']\n",
"columns_restaurants=[ 'restaurant_price_level', 'restaurant_mean_rating','restaurant_mean_popularity', 'restaurant_count',\"restaurant_ratings_count\"]\n",
"\n",
"columns_base=list_diff(columns_base, columns_google_maps)\n",
"columns_base=list_diff(columns_base, columns_restaurants)\n",
"columns_base"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model based on property data only"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Selecting best features"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"# Combining base numerical data with categorical data\n",
"df_num=df[columns_base]\n",
"ml_data=pd.concat([df_num,df_cat],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Id', 'Area', 'build_year', 'building_floors_num', 'rooms_num',\n",
" 'Equipment_types_dishwasher', 'Equipment_types_fridge',\n",
" 'Equipment_types_furniture', 'Equipment_types_oven',\n",
" 'Equipment_types_stove', 'Equipment_types_tv',\n",
" 'Equipment_types_washing_machine', 'Extras_types_air_conditioning',\n",
" 'Extras_types_attic', 'Extras_types_balcony', 'Extras_types_basement',\n",
" 'Extras_types_garage', 'Extras_types_garden', 'Extras_types_lift',\n",
" 'Extras_types_separate_kitchen', 'Extras_types_terrace',\n",
" 'Extras_types_two_storey', 'Extras_types_usable_room',\n",
" 'Media_types_cable-television', 'Media_types_cable_television',\n",
" 'Media_types_electricity', 'Media_types_internet', 'Media_types_phone',\n",
" 'Media_types_sewage', 'Media_types_water', 'Security_types_alarm',\n",
" 'Security_types_anti_burglary_door', 'Security_types_closed_area',\n",
" 'Security_types_entryphone', 'Security_types_monitoring',\n",
" 'Security_types_roller_shutters', 'floor_num', 'unit_price',\n",
" 'district_Bemowo', 'district_Bialoleka', 'district_Bielany',\n",
" 'district_Downtown', 'district_Ochota', 'district_Other',\n",
" 'district_Praga', 'district_Southern Praga', 'district_Subburbs',\n",
" 'district_Targowek', 'district_Ursynow', 'district_Wawer',\n",
" 'district_Wilanow', 'district_Wlochy', 'district_Wola',\n",
" 'district_Zoliborz', 'market_primary', 'Building_material_breezeblock',\n",
" 'Building_material_brick', 'Building_material_cellular_concrete',\n",
" 'Building_material_concrete', 'Building_material_concrete_plate',\n",
" 'Building_material_hydroton', 'Building_material_other',\n",
" 'Building_material_reinforced_concrete', 'Building_material_silikat',\n",
" 'Building_material_wood', 'Building_ownership_co_operative_ownership',\n",
" 'Building_ownership_co_operative_ownership_with_a_land_and_mortgage_registe',\n",
" 'Building_ownership_full_ownership', 'Building_ownership_share',\n",
" 'Building_type_apartment', 'Building_type_block',\n",
" 'Building_type_detached', 'Building_type_house', 'Building_type_infill',\n",
" 'Building_type_loft', 'Building_type_ribbon', 'Building_type_tenement',\n",
" 'Construction_status_ready_to_use', 'Construction_status_to_completion',\n",
" 'Construction_status_to_renovation', 'Heating_boiler_room',\n",
" 'Heating_electrical', 'Heating_gas', 'Heating_other',\n",
" 'Heating_tiled_stove', 'Heating_urban', 'Windows_type_aluminium',\n",
" 'Windows_type_plastic', 'Windows_type_wooden'],\n",
" dtype='object')"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ml_data.columns[:]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# Splitting data into X and y, removing price outliers\n",
"X=ml_data.copy()\n",
"X=X.query(\"unit_price<=25000 and unit_price>5000\")\n",
"y=X.unit_price\n",
"X.drop(columns=[\"unit_price\",\"Id\"],inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_selection import SelectKBest, f_regression\n",
"bestfeatures = SelectKBest(score_func=f_regression, k=\"all\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
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"data": {
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"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Feature | \n",
" Score | \n",
"
\n",
" \n",
" \n",
" \n",
" 39 | \n",
" district_Downtown | \n",
" 7061.087788 | \n",
"
\n",
" \n",
" 37 | \n",
" district_Bialoleka | \n",
" 4019.670677 | \n",
"
\n",
" \n",
" 74 | \n",
" Building_type_tenement | \n",
" 1944.798855 | \n",
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\n",
" \n",
" 52 | \n",
" market_primary | \n",
" 1795.763210 | \n",
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\n",
" \n",
" 68 | \n",
" Building_type_block | \n",
" 1738.136823 | \n",
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\n",
" \n",
" 44 | \n",
" district_Subburbs | \n",
" 1285.386747 | \n",
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\n",
" \n",
" 67 | \n",
" Building_type_apartment | \n",
" 1260.089487 | \n",
"
\n",
" \n",
" 1 | \n",
" build_year | \n",
" 1248.918973 | \n",
"
\n",
" \n",
" 86 | \n",
" Windows_type_wooden | \n",
" 1072.417253 | \n",
"
\n",
" \n",
" 75 | \n",
" Construction_status_ready_to_use | \n",
" 764.632223 | \n",
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\n",
" \n",
" 76 | \n",
" Construction_status_to_completion | \n",
" 631.575191 | \n",
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\n",
" \n",
" 35 | \n",
" floor_num | \n",
" 582.200143 | \n",
"
\n",
" \n",
" 50 | \n",
" district_Wola | \n",
" 576.595866 | \n",
"
\n",
" \n",
" 2 | \n",
" building_floors_num | \n",
" 572.941746 | \n",
"
\n",
" \n",
" 13 | \n",
" Extras_types_balcony | \n",
" 570.238415 | \n",
"
\n",
" \n",
" 11 | \n",
" Extras_types_air_conditioning | \n",
" 536.278968 | \n",
"
\n",
" \n",
" 83 | \n",
" Heating_urban | \n",
" 438.529114 | \n",
"
\n",
" \n",
" 65 | \n",
" Building_ownership_full_ownership | \n",
" 402.054301 | \n",
"
\n",
" \n",
" 45 | \n",
" district_Targowek | \n",
" 323.404624 | \n",
"
\n",
" \n",
" 3 | \n",
" rooms_num | \n",
" 311.584572 | \n",
"
\n",
" \n",
"
\n",
"
"
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" Feature Score\n",
"39 district_Downtown 7061.087788\n",
"37 district_Bialoleka 4019.670677\n",
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"52 market_primary 1795.763210\n",
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"1 build_year 1248.918973\n",
"86 Windows_type_wooden 1072.417253\n",
"75 Construction_status_ready_to_use 764.632223\n",
"76 Construction_status_to_completion 631.575191\n",
"35 floor_num 582.200143\n",
"50 district_Wola 576.595866\n",
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]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Selecting X features carrying most information about y\n",
"fit = bestfeatures.fit(X,y)\n",
"dfscores = pd.DataFrame(fit.scores_)\n",
"dfcolumns = pd.DataFrame(X.columns)\n",
"# Formatting scores into single DataFrame\n",
"featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
"featureScores.columns = ['Feature','Score']\n",
"featureScores.nlargest(50,'Score').head(20)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(33, 2)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Investigating how many features made it past 200 f_regression score threshold\n",
"featureScores.query(\"Score>200\").shape"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['build_year', 'building_floors_num', 'rooms_num',\n",
" 'Equipment_types_dishwasher', 'Equipment_types_fridge',\n",
" 'Equipment_types_furniture', 'Equipment_types_tv',\n",
" 'Equipment_types_washing_machine', 'Extras_types_air_conditioning',\n",
" 'Extras_types_balcony', 'Extras_types_garden', 'Extras_types_lift',\n",
" 'floor_num', 'district_Bemowo', 'district_Bialoleka',\n",
" 'district_Downtown', 'district_Subburbs', 'district_Targowek',\n",
" 'district_Wawer', 'district_Wola', 'district_Zoliborz',\n",
" 'market_primary', 'Building_material_brick',\n",
" 'Building_ownership_full_ownership', 'Building_type_apartment',\n",
" 'Building_type_block', 'Building_type_tenement',\n",
" 'Construction_status_ready_to_use',\n",
" 'Construction_status_to_completion', 'Heating_urban',\n",
" 'Windows_type_aluminium', 'Windows_type_plastic',\n",
" 'Windows_type_wooden'], dtype=object)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
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"# Creating list of selected top features\n",
"top_features=featureScores.query(\"Score>200\").Feature.unique()\n",
"top_features"
]
},
{
"cell_type": "code",
"execution_count": 38,
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" \n",
" \n",
"
\n",
" \n",
"
"
]
},
"metadata": {},
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}
],
"source": [
"# Visualizing selected features\n",
"\n",
"selected_features=featureScores.query(\"Score>200\")\n",
"\n",
"selected_features.sort_values(by=\"Score\",inplace=True,ascending=True)\n",
"\n",
"import plotly.express as px\n",
"fig = px.bar(selected_features, y='Feature', x='Score',orientation='h')\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Spliting into train and test sets"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"X=X[top_features]\n",
"\n",
"random_state=15\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"# Extracting train and test idx for later merge with additional data or geography coordinates\n",
"\n",
"test_idx=np.asarray(X_test.index)\n",
"train_idx=np.asarray(X_train.index)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"X_test_coors=df[[ \"unit_price\",'latitude', 'longitude',\"lat_mod\",\"lon_mod\"]].iloc[test_idx]\n",
"X_test_coors.reset_index(inplace=True,drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4987, 33)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"y_train.reset_index(drop=True,inplace=True)\n",
"y_test.reset_index(drop=True,inplace=True)\n",
"\n",
"X_train.reset_index(drop=True,inplace=True)\n",
"X_test.reset_index(drop=True,inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training initial model"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestRegressor"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"# Selecting RF regressor with some regularization parameteres\n",
"rf_model=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
" max_depth=25, max_features=15, max_leaf_nodes=None,\n",
" max_samples=None, min_impurity_decrease=0.01,\n",
" min_impurity_split=None, min_samples_leaf=5,\n",
" \n",
" n_estimators=500, n_jobs=2, oob_score=False,\n",
" random_state=10, verbose=0, warm_start=False)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
" max_depth=25, max_features=15, max_leaf_nodes=None,\n",
" max_samples=None, min_impurity_decrease=0.01,\n",
" min_impurity_split=None, min_samples_leaf=5,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=500, n_jobs=2, oob_score=False,\n",
" random_state=10, verbose=0, warm_start=False)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf_model.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7757492984608529"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Investigating Train set performance\n",
"rf_model.score(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6655517982906214"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Investigating Test set performance\n",
"rf_model.score(X_test,y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We still seem to have some overfitting - this could probably be handdled with better hyperparameter tuning. As the overfitting is not too bad for a Random Forest model I will proceed with current setup. "
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
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"polar": {
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},
"scene": {
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},
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"caxis": {
"gridcolor": "white",
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},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
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"zerolinecolor": "white",
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"ticks": "",
"title": {
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},
"title": {
"text": "Initial model - Feature importance"
},
"xaxis": {
"title": {
"text": "Importance"
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"yaxis": {
"title": {
"text": "Feature"
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"\n",
" \n",
" \n",
"
\n",
" \n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Investigating model feature importance\n",
"\n",
"importance_df=pd.DataFrame(rf_model.feature_importances_,columns=[\"coefficients\"])\n",
"importance_df[\"features\"]=X.columns\n",
"importance_df.sort_values(by=\"coefficients\",inplace=True)\n",
"\n",
"trace0=go.Bar(\n",
" x=importance_df.tail(10).coefficients,\n",
" y=importance_df.tail(10).features,\n",
" orientation=\"h\",\n",
" width=0.5,\n",
" marker=dict(\n",
" color=\"blue\",\n",
" \n",
"\n",
" opacity=0.5\n",
" ),\n",
" \n",
" )\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"data=[trace0]\n",
"figure=go.Figure(\n",
" data=data,\n",
" layout=go.Layout( \n",
" title=\"Initial model - Feature importance\",\n",
" xaxis=dict(title=\"Importance\"),\n",
" yaxis=dict(title=\"Feature\")\n",
"\n",
" \n",
" ))\n",
"iplot(figure)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"# Function comparing key performance KPIs for any sklearn model\n",
"\n",
"def performance_summary(model, X_test, y_test ):\n",
" \n",
" y_hat=model.predict(X_test)\n",
" \n",
" df_summary=pd.DataFrame(y_hat, columns=[\"y_hat\"])\n",
" df_summary[\"y_true\"]=y_test\n",
" df_summary[\"abs_error\"]=np.abs(df_summary.y_true-df_summary.y_hat)\n",
" df_summary[\"error\"]=df_summary.y_hat-df_summary.y_true\n",
" df_summary[\"relative_error\"]= (df_summary[\"error\"]/df_summary.y_true)\n",
" df_summary[\"relative_abs_error\"]= (df_summary[\"abs_error\"]/df_summary.y_true)\n",
" \n",
" print(\"Share of forecasts within 25% absolute error {:.3f}\\n\".format(df_summary.query(\"relative_abs_error<0.25\").shape[0]/df_summary.shape[0]))\n",
" \n",
" print(\"Share of forecasts within 10% absolute error {:.3f}\\n\".format(df_summary.query(\"relative_abs_error<0.10\").shape[0]/df_summary.shape[0]))\n",
" \n",
" print(\"Share of forecasts within 5% absolute error {:.3f}\\n\".format(df_summary.query(\"relative_abs_error<0.05\").shape[0]/df_summary.shape[0]))\n",
" \n",
" return(df_summary)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"# Function combining test data with coordinates and creating aggregated grid data for error mapping\n",
"\n",
"def test_error_mapping(df_summary,X_test_coors):\n",
" df_summary_dnn_coors=pd.concat([df_summary,X_test_coors], axis=1)\n",
" df_summary_dnn_coors[[\"relative_error\",\"relative_abs_error\"]]=df_summary_dnn_coors[[\"relative_error\",\"relative_abs_error\"]]*100\n",
" \n",
" # lat_mod and lon_mod are coordinates rounded into grid, which allows us to create around 600 tiles in warsaw and analyse mean error in each\n",
" df_map=df_summary_dnn_coors[[\"lat_mod\",\"lon_mod\",\"relative_error\",\"relative_abs_error\",\"abs_error\",\"error\",\"y_hat\",\"y_true\"]]\\\n",
" .groupby([\"lat_mod\",\"lon_mod\"], as_index=False).mean()\n",
" return(df_map)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Share of forecasts within 25% absolute error 0.912\n",
"\n",
"Share of forecasts within 10% absolute error 0.634\n",
"\n",
"Share of forecasts within 5% absolute error 0.375\n",
"\n"
]
}
],
"source": [
"df_summary=performance_summary(rf_model, X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" y_hat | \n",
" y_true | \n",
" abs_error | \n",
" error | \n",
" relative_error | \n",
" relative_abs_error | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 11159.682329 | \n",
" 11128.766393 | \n",
" 1177.861976 | \n",
" 30.915936 | \n",
" 0.025094 | \n",
" 0.102908 | \n",
"
\n",
" \n",
" std | \n",
" 2403.227325 | \n",
" 3094.201353 | \n",
" 1346.995704 | \n",
" 1789.155814 | \n",
" 0.146392 | \n",
" 0.107090 | \n",
"
\n",
" \n",
" min | \n",
" 5832.991302 | \n",
" 5083.000000 | \n",
" 0.277364 | \n",
" -12708.867441 | \n",
" -0.557246 | \n",
" 0.000024 | \n",
"
\n",
" \n",
" 25% | \n",
" 9495.179696 | \n",
" 8951.000000 | \n",
" 312.572331 | \n",
" -584.721788 | \n",
" -0.051806 | \n",
" 0.030674 | \n",
"
\n",
" \n",
" 50% | \n",
" 10932.310587 | \n",
" 10577.000000 | \n",
" 752.587469 | \n",
" 135.429784 | \n",
" 0.013567 | \n",
" 0.071721 | \n",
"
\n",
" \n",
" 75% | \n",
" 12502.698582 | \n",
" 12546.000000 | \n",
" 1536.980399 | \n",
" 881.170739 | \n",
" 0.088319 | \n",
" 0.136689 | \n",
"
\n",
" \n",
" max | \n",
" 20986.553687 | \n",
" 25000.000000 | \n",
" 12708.867441 | \n",
" 8824.520863 | \n",
" 1.122775 | \n",
" 1.122775 | \n",
"
\n",
" \n",
"
\n",
"
"
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"text/plain": [
" y_hat y_true abs_error error relative_error \\\n",
"count 4987.000000 4987.000000 4987.000000 4987.000000 4987.000000 \n",
"mean 11159.682329 11128.766393 1177.861976 30.915936 0.025094 \n",
"std 2403.227325 3094.201353 1346.995704 1789.155814 0.146392 \n",
"min 5832.991302 5083.000000 0.277364 -12708.867441 -0.557246 \n",
"25% 9495.179696 8951.000000 312.572331 -584.721788 -0.051806 \n",
"50% 10932.310587 10577.000000 752.587469 135.429784 0.013567 \n",
"75% 12502.698582 12546.000000 1536.980399 881.170739 0.088319 \n",
"max 20986.553687 25000.000000 12708.867441 8824.520863 1.122775 \n",
"\n",
" relative_abs_error \n",
"count 4987.000000 \n",
"mean 0.102908 \n",
"std 0.107090 \n",
"min 0.000024 \n",
"25% 0.030674 \n",
"50% 0.071721 \n",
"75% 0.136689 \n",
"max 1.122775 "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_summary.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Error analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating GeoHeatmap Class"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"# Importing a map filter, which removes irrelevant areas from contourplot used for heatmap\n",
"map_filter=pd.read_csv(r\"https://raw.githubusercontent.com/Jan-Majewski/Project_Portfolio/master/03_Real_Estate_pricing_in_Warsaw/map_filter.csv\",delimiter=\",\",header=None)\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"def validate_json(json_dict, df_contour):\n",
" \n",
" '''\n",
" As contour plots do not display correctly if even one of the contour layers is not populated with values and its geometry \n",
" is empty this function removes such instances\n",
" '''\n",
" valid_idx=[]\n",
" valid_features=[]\n",
" \n",
" for i in range(0,df_contour.shape[0]):\n",
" if len(json_dict[\"features\"][i][\"geometry\"][\"coordinates\"])>2:\n",
" valid_idx.append(i)\n",
" valid_features.append(json_dict[\"features\"][i])\n",
" \n",
" json_dict[\"features\"]=valid_features\n",
" df_contour=df_contour.iloc[valid_idx,:]\n",
" \n",
"\n",
" \n",
" return(json_dict,df_contour)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"# Function to interpolate grid data into a finer 100x100 mesh used for heatmaps\n",
"\n",
"def create_grid_data(x,y,z,grid_size):\n",
" \n",
" xi = linspace(x.min(),x.max(),grid_size);\n",
" yi = linspace(y.min(),y.max(),grid_size);\n",
" zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method=\"linear\")\n",
" \n",
" return(xi,yi,zi)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"class GeoHeatmap:\n",
" \n",
" \n",
" '''\n",
" Class used to create geojson countour and corresponding data DataFramea, \n",
" which is later used by plotly to create choropleth heatmap\n",
" '''\n",
" \n",
" def __init__(self, x,y,z,zstep):\n",
" xi,yi,zi=create_grid_data(x,y,z,100)\n",
" # Filtering created grid with map_filter\n",
" zi=zi*map_filter\n",
" \n",
"\n",
"\n",
" cs = plt.contourf(xi,yi,zi,range(-50,int(np.nanmax(zi)),zstep),cmap=plt.cm.jet)\n",
"\n",
"# cs = plt.contourf(xi,yi,zi,range(int(np.nanmin(zi)),int(np.nanmax(zi)),zstep),cmap=plt.cm.jet)\n",
" plt.close()\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" geojson = geojsoncontour.contourf_to_geojson(\n",
" contourf=cs,\n",
" ndigits=3,\n",
"\n",
" )\n",
"\n",
" json_dict=eval(geojson)\n",
"\n",
" arr_temp=np.ones([len(json_dict[\"features\"]),2])\n",
"\n",
" for i in range(0, len(json_dict[\"features\"])):\n",
" json_dict[\"features\"][i][\"id\"]=i\n",
"\n",
"\n",
" arr_temp[i,0]=i\n",
" arr_temp[i,1]=float(json_dict[\"features\"][i][\"properties\"][\"title\"])\n",
"\n",
" df_contour=pd.DataFrame(arr_temp, columns=[\"Id\",\"value\"])\n",
"\n",
"\n",
" self.json_dict, self.df_contour=validate_json(json_dict, df_contour)\n",
" \n",
" def plot(self, title, zmin, zmax):\n",
" geojson=self.json_dict\n",
" df=self.df_contour\n",
" \n",
" trace = go.Choroplethmapbox(\n",
" geojson=geojson,\n",
" locations=df.Id,\n",
" z=df.value,\n",
" colorscale=\"jet\",\n",
" name=\"\",\n",
" hovertemplate= \"Relative error: %{z:,.0f}% \",\n",
" \n",
" \n",
" showscale=True,\n",
" zauto=False,\n",
" zmax=zmax,\n",
" zmin=zmin,\n",
"\n",
"\n",
" marker_line_width=0,\n",
"\n",
" marker=dict(opacity=0.5),\n",
"\n",
" )\n",
" layout = go.Layout(\n",
" title=title,\n",
"\n",
" legend=dict(\n",
" orientation=\"h\"),\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" height = 800,\n",
" # top, bottom, left and right margins\n",
" margin = dict(t = 80, b = 0, l = 0, r = 0),\n",
" font = dict(color = 'dark grey', size = 18),\n",
"\n",
" mapbox = dict(\n",
"\n",
"\n",
" center = dict(\n",
" lat = center_coors[0],\n",
" lon = center_coors[1]\n",
" ),\n",
" # default level of zoom\n",
" zoom = 10.3,\n",
" # default map style\n",
" style = \"carto-positron\"\n",
" )\n",
"\n",
" )\n",
"\n",
" data=[trace]\n",
"\n",
"\n",
"\n",
" figure=dict(\n",
" data=data,\n",
" layout=layout,\n",
"\n",
"\n",
" )\n",
"\n",
"\n",
" iplot(figure)\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizing prediction error"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"# Creating a coors grid with average error used as map input\n",
"df_map=test_error_mapping(df_summary,X_test_coors)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"# Initializing GeoHeatmap Class\n",
"error_GeoHeatmap=GeoHeatmap(df_map.lon_mod,df_map.lat_mod, df_map.relative_error,5)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"linkText": "Export to plot.ly",
"plotlyServerURL": "https://plot.ly",
"showLink": false
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\n",
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"source": [
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Adding East Bank flag"
]
},
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"execution_count": 61,
"metadata": {},
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"source": [
"# Adding east_bank feature to test and train set based on previously extracted idx\n",
"X_train[\"east_bank\"]=df[\"east_bank\"].iloc[train_idx].values\n",
"X_test[\"east_bank\"]=df[\"east_bank\"].iloc[test_idx].values"
]
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{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
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"RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
" max_depth=25, max_features=15, max_leaf_nodes=None,\n",
" max_samples=None, min_impurity_decrease=0.01,\n",
" min_impurity_split=None, min_samples_leaf=5,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=500, n_jobs=2, oob_score=False,\n",
" random_state=10, verbose=0, warm_start=False)"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
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"source": [
"# Training model on enriched datasource\n",
"rf_model.fit(X_train,y_train)"
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{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
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"data": {
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]
},
"execution_count": 63,
"metadata": {},
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"source": [
"# Analyzing model performance\n",
"rf_model.score(X_test,y_test)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Share of forecasts within 25% absolute error 0.916\n",
"\n",
"Share of forecasts within 10% absolute error 0.637\n",
"\n",
"Share of forecasts within 5% absolute error 0.385\n",
"\n"
]
}
],
"source": [
"df_summary=performance_summary(rf_model, X_test, y_test)"
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},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
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"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" y_hat | \n",
" y_true | \n",
" abs_error | \n",
" error | \n",
" relative_error | \n",
" relative_abs_error | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
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\n",
" \n",
" mean | \n",
" 11161.353513 | \n",
" 11128.766393 | \n",
" 1163.803175 | \n",
" 32.587120 | \n",
" 0.024825 | \n",
" 0.101492 | \n",
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\n",
" \n",
" std | \n",
" 2411.597380 | \n",
" 3094.201353 | \n",
" 1340.451781 | \n",
" 1774.953048 | \n",
" 0.144598 | \n",
" 0.105934 | \n",
"
\n",
" \n",
" min | \n",
" 5845.777954 | \n",
" 5083.000000 | \n",
" 0.101406 | \n",
" -12736.130278 | \n",
" -0.535934 | \n",
" 0.000014 | \n",
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\n",
" \n",
" 25% | \n",
" 9459.796185 | \n",
" 8951.000000 | \n",
" 310.949220 | \n",
" -558.505103 | \n",
" -0.050330 | \n",
" 0.029884 | \n",
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\n",
" \n",
" 50% | \n",
" 10975.166644 | \n",
" 10577.000000 | \n",
" 750.628936 | \n",
" 143.799449 | \n",
" 0.013885 | \n",
" 0.070732 | \n",
"
\n",
" \n",
" 75% | \n",
" 12511.045358 | \n",
" 12546.000000 | \n",
" 1505.049911 | \n",
" 882.976851 | \n",
" 0.088799 | \n",
" 0.135543 | \n",
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\n",
" \n",
" max | \n",
" 20841.224622 | \n",
" 25000.000000 | \n",
" 12736.130278 | \n",
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" 1.185477 | \n",
" 1.185477 | \n",
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" y_hat y_true abs_error error relative_error \\\n",
"count 4987.000000 4987.000000 4987.000000 4987.000000 4987.000000 \n",
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"source": [
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},
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"execution_count": 66,
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"# Analyzing eastbank flag effect on feature importance\n",
"importance_df=pd.DataFrame(rf_model.feature_importances_,columns=[\"coefficients\"])\n",
"importance_df[\"features\"]=X_train.columns\n",
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"trace0=go.Bar(\n",
" x=importance_df.tail(10).coefficients,\n",
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" orientation=\"h\",\n",
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"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"data=[trace0]\n",
"figure=go.Figure(\n",
" data=data,\n",
" layout=go.Layout( \n",
" title=\"East bank flag added - Feature importance\",\n",
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"source": [
"## East-bank flag ballancing error on both sides of Vistula"
]
},
{
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"execution_count": 67,
"metadata": {},
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"trace0=go.Bar(\n",
" x=featureScores.tail(10).Score,\n",
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" orientation=\"h\",\n",
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"execution_count": 71,
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"# Analyzing features passing f_regression score threshold\n",
"featureScores.query(\"Score>200\").shape\n",
"top_features=featureScores.query(\"Score>200\").Feature.unique()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"# Repeating process of splitting data - same random state ensures comparability\n",
"\n",
"X=X[top_features]\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)\n",
"y_train.reset_index(drop=True,inplace=True)\n",
"y_test.reset_index(drop=True,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
" max_depth=25, max_features=15, max_leaf_nodes=None,\n",
" max_samples=None, min_impurity_decrease=0.01,\n",
" min_impurity_split=None, min_samples_leaf=5,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=500, n_jobs=2, oob_score=False,\n",
" random_state=10, verbose=0, warm_start=False)"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
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"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
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"source": [
"# Training model on data source with googlemaps data\n",
"rf_model.fit(X_train,y_train)\n",
"rf_model.score(X_test,y_test)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Share of forecasts within 25% absolute error 0.937\n",
"\n",
"Share of forecasts within 10% absolute error 0.690\n",
"\n",
"Share of forecasts within 5% absolute error 0.439\n",
"\n"
]
}
],
"source": [
"# Performance analysis\n",
"df_summary=performance_summary(rf_model, X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
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"data": {
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"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" y_hat | \n",
" y_true | \n",
" abs_error | \n",
" error | \n",
" relative_error | \n",
" relative_abs_error | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 11161.112366 | \n",
" 11128.766393 | \n",
" 1020.064673 | \n",
" 32.345973 | \n",
" 0.021116 | \n",
" 0.088994 | \n",
"
\n",
" \n",
" std | \n",
" 2517.794293 | \n",
" 3094.201353 | \n",
" 1207.601444 | \n",
" 1580.504794 | \n",
" 0.129155 | \n",
" 0.095945 | \n",
"
\n",
" \n",
" min | \n",
" 5738.905381 | \n",
" 5083.000000 | \n",
" 0.185510 | \n",
" -12380.710792 | \n",
" -0.526839 | \n",
" 0.000020 | \n",
"
\n",
" \n",
" 25% | \n",
" 9326.003461 | \n",
" 8951.000000 | \n",
" 249.986888 | \n",
" -479.112622 | \n",
" -0.042638 | \n",
" 0.024725 | \n",
"
\n",
" \n",
" 50% | \n",
" 10946.202199 | \n",
" 10577.000000 | \n",
" 638.837969 | \n",
" 96.950829 | \n",
" 0.009605 | \n",
" 0.061140 | \n",
"
\n",
" \n",
" 75% | \n",
" 12547.364005 | \n",
" 12546.000000 | \n",
" 1327.224263 | \n",
" 755.154385 | \n",
" 0.076540 | \n",
" 0.119597 | \n",
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\n",
" \n",
" max | \n",
" 21818.906420 | \n",
" 25000.000000 | \n",
" 12380.710792 | \n",
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" 1.220820 | \n",
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\n",
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"source": [
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]
},
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"execution_count": 76,
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"source": [
"df_map=test_error_mapping(df_summary,X_test_coors)\n",
"error_GeoHeatmap=GeoHeatmap(df_map.lon_mod,df_map.lat_mod, df_map.relative_error,5)\n",
"error_GeoHeatmap.plot(\"Google maps data added - Relative error heatmap [%]\",-30,30)"
]
},
{
"cell_type": "markdown",
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"source": [
"# Adding Restaurant data to capture location popularity"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['restaurant_price_level',\n",
" 'restaurant_mean_rating',\n",
" 'restaurant_mean_popularity',\n",
" 'restaurant_count',\n",
" 'restaurant_ratings_count']"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Features added based on local restaurants (within 1km) data\n",
"columns_restaurants"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
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"\n",
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\n",
" \n",
" \n",
" | \n",
" Specs | \n",
" Score | \n",
"
\n",
" \n",
" \n",
" \n",
" 38 | \n",
" distance_transit | \n",
" 15110.951918 | \n",
"
\n",
" \n",
" 37 | \n",
" distance_driving | \n",
" 14331.765525 | \n",
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\n",
" \n",
" 40 | \n",
" time_transit | \n",
" 11729.905071 | \n",
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\n",
" \n",
" 39 | \n",
" time_driving | \n",
" 9771.050976 | \n",
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\n",
" \n",
" 44 | \n",
" restaurant_count | \n",
" 8054.954714 | \n",
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\n",
" \n",
" 43 | \n",
" restaurant_mean_popularity | \n",
" 7599.663197 | \n",
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" \n",
" 49 | \n",
" district_Downtown | \n",
" 7061.087788 | \n",
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\n",
" \n",
" 45 | \n",
" restaurant_ratings_count | \n",
" 5505.569986 | \n",
"
\n",
" \n",
" 41 | \n",
" restaurant_price_level | \n",
" 4783.021585 | \n",
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\n",
" \n",
" 42 | \n",
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" 4512.937869 | \n",
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\n",
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" 1795.763210 | \n",
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\n",
" \n",
" 78 | \n",
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" 1738.136823 | \n",
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\n",
" \n",
" 54 | \n",
" district_Subburbs | \n",
" 1285.386747 | \n",
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\n",
" \n",
" 77 | \n",
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" 1260.089487 | \n",
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\n",
" \n",
" 1 | \n",
" build_year | \n",
" 1248.918973 | \n",
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\n",
" \n",
" 96 | \n",
" Windows_type_wooden | \n",
" 1072.417253 | \n",
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\n",
" \n",
" 85 | \n",
" Construction_status_ready_to_use | \n",
" 764.632223 | \n",
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\n",
" \n",
"
\n",
"
"
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"text/plain": [
" Specs Score\n",
"38 distance_transit 15110.951918\n",
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"39 time_driving 9771.050976\n",
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},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Repeating feature selection process\n",
"\n",
"df_num=df[columns_base+[\"east_bank\"]+columns_google_maps+columns_restaurants]\n",
"ml_data=pd.concat([df_num,df_cat],axis=1)\n",
"\n",
"X=ml_data.copy()\n",
"X=X.query(\"unit_price<=25000 and unit_price>5000\")\n",
"y=X.unit_price\n",
"X.drop(columns=[\"unit_price\",\"Id\"],inplace=True)\n",
"\n",
"fit = bestfeatures.fit(X,y)\n",
"dfscores = pd.DataFrame(fit.scores_)\n",
"dfcolumns = pd.DataFrame(X.columns)\n",
"\n",
"featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
"featureScores.columns = ['Specs','Score']\n",
"featureScores.nlargest(50,'Score').head(20)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(43, 2)"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Investigating number of features passing importance threshold\n",
"featureScores.query(\"Score>200\").shape\n",
"top_features=featureScores.query(\"Score>200\").Specs.unique()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"X=X[top_features]\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)\n",
"y_train.reset_index(drop=True,inplace=True)\n",
"y_test.reset_index(drop=True,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
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"RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
" max_depth=25, max_features=15, max_leaf_nodes=None,\n",
" max_samples=None, min_impurity_decrease=0.01,\n",
" min_impurity_split=None, min_samples_leaf=5,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=500, n_jobs=2, oob_score=False,\n",
" random_state=10, verbose=0, warm_start=False)"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
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"data": {
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"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
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"source": [
"# Training and scoring the model\n",
"rf_model.fit(X_train,y_train)\n",
"rf_model.score(X_test,y_test)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"Share of forecasts within 25% absolute error 0.939\n",
"\n",
"Share of forecasts within 10% absolute error 0.693\n",
"\n",
"Share of forecasts within 5% absolute error 0.446\n",
"\n"
]
}
],
"source": [
"# Model performance analysis\n",
"df_summary=performance_summary(rf_model, X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
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"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" y_hat | \n",
" y_true | \n",
" abs_error | \n",
" error | \n",
" relative_error | \n",
" relative_abs_error | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
" 4987.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 11157.505698 | \n",
" 11128.766393 | \n",
" 1011.659073 | \n",
" 28.739305 | \n",
" 0.020655 | \n",
" 0.088274 | \n",
"
\n",
" \n",
" std | \n",
" 2520.718485 | \n",
" 3094.201353 | \n",
" 1201.356772 | \n",
" 1570.379357 | \n",
" 0.128461 | \n",
" 0.095578 | \n",
"
\n",
" \n",
" min | \n",
" 5753.684067 | \n",
" 5083.000000 | \n",
" 0.328317 | \n",
" -12689.720184 | \n",
" -0.539988 | \n",
" 0.000033 | \n",
"
\n",
" \n",
" 25% | \n",
" 9303.500195 | \n",
" 8951.000000 | \n",
" 253.678220 | \n",
" -471.524392 | \n",
" -0.042963 | \n",
" 0.024451 | \n",
"
\n",
" \n",
" 50% | \n",
" 10954.167837 | \n",
" 10577.000000 | \n",
" 616.500692 | \n",
" 89.566120 | \n",
" 0.009131 | \n",
" 0.059925 | \n",
"
\n",
" \n",
" 75% | \n",
" 12509.783497 | \n",
" 12546.000000 | \n",
" 1333.592176 | \n",
" 747.952363 | \n",
" 0.075358 | \n",
" 0.119922 | \n",
"
\n",
" \n",
" max | \n",
" 21777.652215 | \n",
" 25000.000000 | \n",
" 12689.720184 | \n",
" 7792.430903 | \n",
" 1.230854 | \n",
" 1.230854 | \n",
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\n",
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\n",
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" y_hat y_true abs_error error relative_error \\\n",
"count 4987.000000 4987.000000 4987.000000 4987.000000 4987.000000 \n",
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"min 5753.684067 5083.000000 0.328317 -12689.720184 -0.539988 \n",
"25% 9303.500195 8951.000000 253.678220 -471.524392 -0.042963 \n",
"50% 10954.167837 10577.000000 616.500692 89.566120 0.009131 \n",
"75% 12509.783497 12546.000000 1333.592176 747.952363 0.075358 \n",
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" relative_abs_error \n",
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"mean 0.088274 \n",
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"25% 0.024451 \n",
"50% 0.059925 \n",
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"source": [
"## Feature importance - 3 Restautant features make it to top 10"
]
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"cell_type": "code",
"execution_count": 85,
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" \n",
"
\n",
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"source": [
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"importance_df[\"features\"]=X.columns\n",
"importance_df.sort_values(by=\"coefficients\",inplace=True)\n",
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"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
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" \n",
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"iplot(figure)\n"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Error analysis - better balance across popular locations such as Saska Kepa"
]
},
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