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import pandas as pd |
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import os |
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def get_data_path_for_config(config_name): |
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data_dir = "../data" |
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return os.path.join(data_dir, config_name) |
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def coalesce_columns( |
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df, |
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): |
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columns_to_coalesce = [col for col in df.columns if "_" not in col] |
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for index, row in df.iterrows(): |
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for col in df.columns: |
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for column_to_coalesce in columns_to_coalesce: |
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if column_to_coalesce in col and "_" in col: |
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if not pd.isna(row[col]): |
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df.at[index, column_to_coalesce] = row[col] |
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continue |
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combined_df = df[columns_to_coalesce] |
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return combined_df |
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def set_home_type(cur_df, filename): |
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if "_sfrcondo_" in filename: |
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cur_df["Home Type"] = "all homes" |
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if "_sfrcondomfr_" in filename: |
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cur_df["Home Type"] = "all homes plus multifamily" |
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elif "_sfr_" in filename: |
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cur_df["Home Type"] = "SFR" |
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elif "_condo_" in filename: |
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cur_df["Home Type"] = "condo/co-op" |
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elif "_mfr_" in filename: |
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cur_df["Home Type"] = "multifamily" |
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return cur_df |
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def get_combined_df(data_frames, on): |
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combined_df = None |
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if len(data_frames) > 1: |
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combined_df = data_frames[0] |
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for i in range(1, len(data_frames)): |
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cur_df = data_frames[i] |
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combined_df = pd.merge( |
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combined_df, |
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cur_df, |
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on=on, |
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how="outer", |
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suffixes=("", "_" + str(i)), |
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) |
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elif len(data_frames) == 1: |
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combined_df = data_frames[0] |
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combined_df = coalesce_columns(combined_df) |
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return combined_df |
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def get_melted_df( |
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df, |
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exclude_columns, |
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columns_to_pivot, |
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col_name, |
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filename, |
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): |
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smoothed = "_sm_" in filename |
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seasonally_adjusted = "_sa_" in filename |
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if smoothed: |
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col_name += " (Smoothed)" |
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if seasonally_adjusted: |
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col_name += " (Seasonally Adjusted)" |
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df = pd.melt( |
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df, |
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id_vars=exclude_columns, |
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value_vars=columns_to_pivot, |
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var_name="Date", |
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value_name=col_name, |
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) |
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return df |
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def save_final_df_as_jsonl(config_name, df): |
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processed_dir = "../processed/" |
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if not os.path.exists(processed_dir): |
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os.makedirs(processed_dir) |
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full_path = os.path.join(processed_dir, config_name + ".jsonl") |
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df.to_json(full_path, orient="records", lines=True) |
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def handle_slug_column_mappings( |
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data_frames, slug_column_mappings, exclude_columns, filename, cur_df |
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): |
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columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns] |
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for slug, col_name in slug_column_mappings.items(): |
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if slug in filename: |
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cur_df = get_melted_df( |
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cur_df, |
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exclude_columns, |
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columns_to_pivot, |
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col_name, |
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filename, |
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) |
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data_frames.append(cur_df) |
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break |
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return data_frames |
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