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import pandas as pd |
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import os |
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from helpers import ( |
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get_data_path_for_config, |
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get_combined_df, |
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save_final_df_as_jsonl, |
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handle_slug_column_mappings, |
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set_home_type, |
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) |
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CONFIG_NAME = "sales" |
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data_frames = [] |
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exclude_columns = [ |
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"RegionID", |
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"SizeRank", |
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"RegionName", |
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"RegionType", |
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"StateName", |
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"Home Type", |
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] |
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slug_column_mappings = { |
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"_median_sale_to_list_": "Median Sale to List Ratio", |
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"_mean_sale_to_list_": "Mean Sale to List Ratio", |
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"_median_sale_price_": "Median Sale Price", |
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"_pct_sold_above_list_": "% Sold Above List", |
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"_pct_sold_below_list_": "% Sold Below List", |
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"_sales_count_now_": "Nowcast", |
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} |
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data_dir_path = get_data_path_for_config(CONFIG_NAME) |
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for filename in os.listdir(data_dir_path): |
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if filename.endswith(".csv"): |
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print("processing " + filename) |
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if "month" in filename: |
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continue |
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cur_df = pd.read_csv(os.path.join(data_dir_path, filename)) |
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cur_df = set_home_type(cur_df, filename) |
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data_frames = 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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combined_df = get_combined_df( |
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data_frames, |
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[ |
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"RegionID", |
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"SizeRank", |
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"RegionName", |
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"RegionType", |
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"StateName", |
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"Home Type", |
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"Date", |
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], |
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) |
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combined_df |
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final_df = combined_df.rename( |
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columns={ |
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"RegionID": "Region ID", |
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"SizeRank": "Size Rank", |
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"RegionName": "Region", |
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"RegionType": "Region Type", |
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"StateName": "State", |
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} |
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) |
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final_df["Date"] = pd.to_datetime(final_df["Date"]) |
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final_df.sort_values(by=["Region ID", "Home Type", "Date"]) |
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final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d") |
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final_df |
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save_final_df_as_jsonl(CONFIG_NAME, final_df) |
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