#!/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)