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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import os
from helpers import (
get_data_path_for_config,
get_combined_df,
save_final_df_as_jsonl,
handle_slug_column_mappings,
set_home_type,
)
# In[2]:
CONFIG_NAME = "for_sale_listings"
# In[3]:
data_frames = []
exclude_columns = [
"RegionID",
"SizeRank",
"RegionName",
"RegionType",
"StateName",
"Home Type",
]
slug_column_mappings = {
"_mlp_": "Median Listing Price",
"_new_listings_": "New Listings",
"new_pending": "New Pending",
}
data_dir_path = get_data_path_for_config(CONFIG_NAME)
for filename in os.listdir(data_dir_path):
if filename.endswith(".csv"):
print("processing " + filename)
cur_df = pd.read_csv(os.path.join(data_dir_path, filename))
# ignore monthly data for now since it is redundant
if "month" in filename:
continue
cur_df = set_home_type(cur_df, filename)
data_frames = handle_slug_column_mappings(
data_frames, slug_column_mappings, exclude_columns, filename, cur_df
)
combined_df = get_combined_df(
data_frames,
[
"RegionID",
"SizeRank",
"RegionName",
"RegionType",
"StateName",
"Home Type",
"Date",
],
)
combined_df
# In[4]:
# Adjust column names
final_df = combined_df.rename(
columns={
"RegionID": "Region ID",
"SizeRank": "Size Rank",
"RegionName": "Region",
"RegionType": "Region Type",
"StateName": "State",
}
)
final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
final_df
# In[5]:
save_final_df_as_jsonl(CONFIG_NAME, final_df)
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