processors/rentals.py

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

# In[2]:


import pandas as pd
import os

from helpers import (
    get_combined_df,
    save_final_df_as_jsonl,
    handle_slug_column_mappings,
)


# In[3]:


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


# In[7]:


data_frames = []

slug_column_mappings = {"": "Rent"}

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",
            "Home Type",
        ]

        if "_sfrcondomfr_" in filename:
            cur_df["Home Type"] = "all homes plus multifamily"
            # change column type to string
            cur_df["RegionName"] = cur_df["RegionName"].astype(str)
            if "City" in filename:
                exclude_columns = [
                    "RegionID",
                    "SizeRank",
                    "RegionName",
                    "RegionType",
                    "StateName",
                    "Home Type",
                    # City Specific
                    "State",
                    "Metro",
                    "CountyName",
                ]
            elif "Zip" in filename:
                exclude_columns = [
                    "RegionID",
                    "SizeRank",
                    "RegionName",
                    "RegionType",
                    "StateName",
                    "Home Type",
                    # Zip Specific
                    "State",
                    "City",
                    "Metro",
                    "CountyName",
                ]
            elif "County" in filename:
                exclude_columns = [
                    "RegionID",
                    "SizeRank",
                    "RegionName",
                    "RegionType",
                    "StateName",
                    "Home Type",
                    # County Specific
                    "State",
                    "Metro",
                    "StateCodeFIPS",
                    "MunicipalCodeFIPS",
                ]

        elif "_sfr_" in filename:
            cur_df["Home Type"] = "SFR"
        elif "_mfr_" in filename:
            cur_df["Home Type"] = "multifamily"

        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[8]:


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"]

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

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


# In[6]:


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

final_df


# In[7]:


save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)