processors/helpers.py

import pandas as pd
import os


def get_combined_df(data_frames, on):
 combined_df = None
 if len(data_frames) > 1:
 # iterate over dataframes and merge or concat
 combined_df = data_frames[0]
 for i in range(1, len(data_frames)):
 cur_df = data_frames[i]
 combined_df = pd.merge(
 combined_df,
 cur_df,
 on=on,
 how="outer",
 suffixes=("", "_" + str(i)),
 )
 elif len(data_frames) == 1:
 combined_df = data_frames[0]

 return combined_df


def coalesce_columns(
 df,
):
 columns_to_coalesce = [col for col in df.columns if "_" not in col]
 for index, row in df.iterrows():
 for col in df.columns:
 for column_to_coalesce in columns_to_coalesce:
 if column_to_coalesce in col and "_" in col:
 if not pd.isna(row[col]):
 df.at[index, column_to_coalesce] = row[col]

 # remove columns with underscores
 combined_df = df[columns_to_coalesce]
 return combined_df


def get_df(
 df,
 exclude_columns,
 columns_to_pivot,
 col_name,
 filename,
):
 smoothed = "_sm_" in filename
 seasonally_adjusted = "_sa_" in filename

 if smoothed:
 col_name += " (Smoothed)"
 if seasonally_adjusted:
 col_name += " (Seasonally Adjusted)"

 df = pd.melt(
 df,
 id_vars=exclude_columns,
 value_vars=columns_to_pivot,
 var_name="Date",
 value_name=col_name,
 )
 return df


def save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df):
 if not os.path.exists(FULL_PROCESSED_DIR_PATH):
 os.makedirs(FULL_PROCESSED_DIR_PATH)

 final_df.to_json(
 FULL_PROCESSED_DIR_PATH + "final.jsonl", orient="records", lines=True
 )


def handle_slug_column_mappings(
 data_frames, slug_column_mappings, exclude_columns, filename, cur_df
):
 # Identify columns to pivot
 columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]

 for slug, col_name in slug_column_mappings.items():
 if slug in filename:
 cur_df = get_df(
 cur_df,
 exclude_columns,
 columns_to_pivot,
 col_name,
 filename,
 )

 data_frames.append(cur_df)
 break

 return data_frames