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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
# 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)
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