processors/home_values.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas as pd
import os

from helpers import (
    get_combined_df,
    save_final_df_as_jsonl,
    handle_slug_column_mappings,
)


# In[2]:


DATA_DIR = "../data"
PROCESSED_DIR = "../processed/"
FACET_DIR = "home_values/"
FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)


# In[5]:


data_frames = []

slug_column_mappings = {
    "_tier_0.0_0.33_": "Bottom Tier ZHVI",
    "_tier_0.33_0.67_": "Mid Tier ZHVI",
    "_tier_0.67_1.0_": "Top Tier ZHVI",
    "": "ZHVI",
}

for filename in os.listdir(FULL_DATA_DIR_PATH):
    if filename.endswith(".csv"):
        print("processing " + filename)
        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
        exclude_columns = [
            "RegionID",
            "SizeRank",
            "RegionName",
            "RegionType",
            "StateName",
            "Bedroom Count",
            "Home Type",
        ]

        if "Zip" in filename:
            continue
        if "Neighborhood" in filename:
            continue
        if "City" in filename:
            continue
        if "Metro" in filename:
            continue
        if "County" in filename:
            continue

        if "City" in filename:
            exclude_columns = exclude_columns + ["State", "Metro", "CountyName"]
        elif "Zip" in filename:
            exclude_columns = exclude_columns + [
                "State",
                "City",
                "Metro",
                "CountyName",
            ]
        elif "County" in filename:
            exclude_columns = exclude_columns + [
                "State",
                "Metro",
                "StateCodeFIPS",
                "MunicipalCodeFIPS",
            ]
        elif "Neighborhood" in filename:
            exclude_columns = exclude_columns + [
                "State",
                "City",
                "Metro",
                "CountyName",
            ]

        if "_bdrmcnt_1_" in filename:
            cur_df["Bedroom Count"] = "1-Bedroom"
        elif "_bdrmcnt_2_" in filename:
            cur_df["Bedroom Count"] = "2-Bedrooms"
        elif "_bdrmcnt_3_" in filename:
            cur_df["Bedroom Count"] = "3-Bedrooms"
        elif "_bdrmcnt_4_" in filename:
            cur_df["Bedroom Count"] = "4-Bedrooms"
        elif "_bdrmcnt_5_" in filename:
            cur_df["Bedroom Count"] = "5+-Bedrooms"
        else:
            cur_df["Bedroom Count"] = "All Bedrooms"

        if "_uc_sfr_" in filename:
            cur_df["Home Type"] = "SFR"
        elif "_uc_sfrcondo_" in filename:
            cur_df["Home Type"] = "all homes (SFR/condo)"
        elif "_uc_condo_" in filename:
            cur_df["Home Type"] = "condo"

        cur_df["StateName"] = cur_df["StateName"].astype(str)
        cur_df["RegionName"] = cur_df["RegionName"].astype(str)

        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",
        "Bedroom Count",
        "Home Type",
        "Date",
    ],
)

combined_df


# In[11]:


final_df = combined_df

for index, row in final_df.iterrows():
    if row["RegionType"] == "city":
        final_df.at[index, "City"] = row["RegionName"]
    elif row["RegionType"] == "county":
        final_df.at[index, "County"] = row["RegionName"]
    if row["RegionType"] == "state":
        final_df.at[index, "StateName"] = row["RegionName"]

# coalesce State and StateName columns
# final_df["State"] = final_df["State"].combine_first(final_df["StateName"])
# final_df["County"] = final_df["County"].combine_first(final_df["CountyName"])

# final_df = final_df.drop(
#     columns=[
#         "StateName",
#         # "CountyName"
#     ]
# )
final_df


# In[12]:


final_df = final_df.rename(
    columns={
        "RegionID": "Region ID",
        "SizeRank": "Size Rank",
        "RegionName": "Region",
        "RegionType": "Region Type",
        "StateCodeFIPS": "State Code FIPS",
        "StateName": "State",
        "MunicipalCodeFIPS": "Municipal Code FIPS",
    }
)

final_df


# In[13]:


save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)