processors/home_values_forecasts.py

#!/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


# In[2]:


CONFIG_NAME = "home_values_forecasts"


# In[3]:


data_frames = []

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))

        cols = ["Month Over Month %", "Quarter Over Quarter %", "Year Over Year %"]
        if filename.endswith("sm_sa_month.csv"):
            # print('Smoothed')
            cur_df.columns = list(cur_df.columns[:-3]) + [
                x + " (Smoothed) (Seasonally Adjusted)" for x in cols
            ]
        else:
            # print('Raw')
            cur_df.columns = list(cur_df.columns[:-3]) + cols

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

        data_frames.append(cur_df)


combined_df = get_combined_df(
    data_frames,
    [
        "RegionID",
        "RegionType",
        "SizeRank",
        "StateName",
        "BaseDate",
    ],
)

combined_df


# In[4]:


# Adjust columns
final_df = combined_df
final_df = combined_df.drop("StateName", axis=1)
final_df = final_df.rename(
    columns={
        "CountyName": "County",
        "BaseDate": "Date",
        "RegionName": "Region",
        "RegionType": "Region Type",
        "RegionID": "Region ID",
        "SizeRank": "Size Rank",
    }
)

# iterate over rows of final_df and populate State and City columns if the regionType is msa
for index, row in final_df.iterrows():
    if row["Region Type"] == "msa":
        regionName = row["Region"]
        # final_df.at[index, 'Metro'] = regionName

        city = regionName.split(", ")[0]
        final_df.at[index, "City"] = city

        state = regionName.split(", ")[1]
        final_df.at[index, "State"] = 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)