File size: 2,100 Bytes
69c22e0 9cb9eef 69c22e0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
#!/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
# In[2]:
DATA_DIR = "../data/"
PROCESSED_DIR = "../processed/"
FACET_DIR = "home_values_forecasts/"
FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
# In[3]:
data_frames = []
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))
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",
"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["RegionType"] == "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
# In[9]:
save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
|