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redfin_scraper.py
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redfin_scraper.py
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#!/usr/bin/env python
# coding: utf-8
# In[2]:
import os
import urllib
from datetime import datetime
import requests
import time
import wget
# In[3]:
# params
max_price = 900000
date_time = datetime.now().strftime("%m%d%Y")
download_folder = "/Users/szhang/Downloads/"
# base url
base_url = 'https://www.redfin.com/stingray/api/gis-csv?al=1&isRentals=false&market=dc&num_homes=350&ord=redfin-recommended-asc&page_number=1®ion_id={region_id}®ion_type=7&sf=1,2,3,5,6,7&status=9&uipt=1,2,3,4,5,6,7,8&v=8'
# fake headers
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
# In[3]:
elementary_schools = {
"Mosby Woods Elementary": 55716,
"Navy Elementary School": 54146,
"Poplar Tree Elementary": 53030,
"Colvin Run Elementary": 51493,
"Spring Hill Elementary": 104262,
"Oakton Elementary": 98453,
"Mill Run Elementary": 123504,
"Madison's Trust Elementary": 231881,
"Westbriar Elementary": 56040,
"Wolftrap Elementary": 129050
}
middle_schools = {
"Cooper Middle School": 142292,
# "Frost Middle School": 122278,
"Longfellow Middle School": 55897,
"Carson Middle School": 90165,
"Eagle Ridge Middle School": 116345,
"Rocky Run Middle": 159468,
"Franklin Middle School": 53305
}
high_schools = {
"McLean High School": 116658,
"Langley High School": 141517,
"Oakton High School": 121474,
"Briar Woods High School": 122224,
"Chantilly High School": 139177
}
# In[4]:
def _create_folder(folder: str, file_name: str):
path = os.path.join(folder, file_name)
if not os.path.exists(path):
os.mkdir(path)
print(path)
# create today's folder
print("creating date folder..")
_create_folder(download_folder, date_time)
today_path = download_folder + date_time
# create elementary_school's folder
print("creating elementary_school folder..")
elementary_school = "elementary_schools"
_create_folder(today_path, elementary_school)
e_school_path = today_path + "/" + elementary_school
# create middle school's folder
print("creating middle_school folder..")
middle_school = "middle_schools"
_create_folder(today_path, middle_school)
m_school_path = today_path + "/" + middle_school
# TODO: create high school's folder
print("creating high_school folder..")
high_school = "high_schools"
_create_folder(today_path, high_school)
h_school_path = today_path + "/" + high_school
# In[5]:
def _generate_url(base_url: str, region_id: str):
updated_url = base_url.format(region_id=region_id)
updated_url += "&max_price=" + str(max_price)
return updated_url
def _download_csv(url: str, path: str, school_name: str):
file_name = school + '.csv'
if not os.path.exists(path + "/" + school + '.csv'):
response = requests.get(url, headers=headers)
with open(os.path.join(path, school + '.csv'), 'wb') as f:
f.write(response.content)
else:
print("File of school zone: " + school + "is exist.")
# download elementaryschools houses
for school in elementary_schools:
time.sleep(2)
url = _generate_url(base_url, elementary_schools[school])
print(url)
_download_csv(url, e_school_path, school)
# download middle schools houses
for school in middle_schools:
time.sleep(2)
url = _generate_url(base_url, middle_schools[school])
print(url)
_download_csv(url, m_school_path, school)
# TODO: download high schools houses
for school in high_schools:
time.sleep(2)
url = _generate_url(base_url, high_schools[school])
print(url)
_download_csv(url, h_school_path, school)
# In[6]:
import pandas as pd
import glob
# In[7]:
# elementary_schools DF
e_joined_files = os.path.join(e_school_path, "*.csv")
e_joined_list = glob.glob(e_joined_files)
# print(e_joined_list)
e_dataframes = []
for file in e_joined_list:
df = pd.read_csv(file)
schools_info = file.split("/")
df['Elementary School'] = file.split("/")[-1].replace(".csv", "")
e_dataframes.append(df)
e_df = pd.concat(e_dataframes)
e_df['MLS Number'] = e_df['MLS#']
e_df = e_df.set_index('MLS#')
# e_df
# In[8]:
# middle schools DF
m_joined_files = os.path.join(m_school_path, "*.csv")
m_joined_list = glob.glob(m_joined_files)
# print(m_joined_list)
m_dataframes = []
for file in m_joined_list:
df = pd.read_csv(file)
schools_info = file.split("/")
df['Middle School'] = file.split("/")[-1].replace(".csv", "")
m_dataframes.append(df)
m_df = pd.concat(m_dataframes)
m_df['MLS Number'] = m_df['MLS#']
m_df = m_df.set_index('MLS#')
# m_df
# In[9]:
# high schools DF
h_joined_files = os.path.join(h_school_path, "*.csv")
h_joined_list = glob.glob(h_joined_files)
# print(h_joined_list)
h_dataframes = []
for file in h_joined_list:
df = pd.read_csv(file)
schools_info = file.split("/")
df['High School'] = file.split("/")[-1].replace(".csv", "")
h_dataframes.append(df)
h_df = pd.concat(h_dataframes)
h_df['MLS Number'] = h_df['MLS#']
h_df = h_df.set_index('MLS#')
# h_df
# In[10]:
# merge dataframes
merged_df = e_df.merge(m_df, how='outer').merge(h_df, how='outer')
# clean up dataframes
result = merged_df.drop(columns=['SOLD DATE', 'STATE OR PROVINCE', 'LOCATION', 'NEXT OPEN HOUSE START TIME', 'NEXT OPEN HOUSE END TIME', 'SOURCE', 'FAVORITE', 'INTERESTED', 'LATITUDE', 'LONGITUDE'])
result
# In[11]:
# pre-setting:
# NaN in HOA & days on market to -1
result['HOA/MONTH'] = result['HOA/MONTH'].fillna(-1)
result['DAYS ON MARKET'] = result['DAYS ON MARKET'].fillna(-1)
# year build to 9999
result['YEAR BUILT'] = result['YEAR BUILT'].fillna(9999)
#### DO FILTERING
# 1. filter TH or SFH
# 2. filter on market > 60 days
# 3. HOA < 250
# 1. Vacant Land
# 2. Single Family Residential
# 3. Condo/Co-op
# 4. Townhouse
house_type_to_keep = ["Single Family Residential", "Townhouse"]
rslt_df = result.loc[result['PROPERTY TYPE'].isin(house_type_to_keep) &
(result['HOA/MONTH'] < 200) & # HOA < 200
(result['DAYS ON MARKET'] < 40) & # days on market < 40
(result['YEAR BUILT'] > 1980)] # year build > 2000
rslt_df
# In[12]:
# sort result by year and price ASC
final = rslt_df.sort_values(by=['PRICE','YEAR BUILT'], ascending=True)
# write into result csv
final.to_csv(today_path + "/home_suggestion.csv", index=False)