import csv from pathlib import Path import numpy as np import pandas as pd from collections import defaultdict FULL_DATA_SET_STRING = "../full_years_remove_flawed_rows.csv" FULL_DATA_SET_PATH = Path(FULL_DATA_SET_STRING) OUT_DATASET_STRING = "../trimmed_full_years_for_db.parquet" OUT_DATASET_PATH = Path(OUT_DATASET_STRING) OUT_FULL_DATASET_STRING = "../full_years_remove_flawed.parquet" OUT_FULL_DATASET_PATH = Path(OUT_FULL_DATASET_STRING) NUMERIC_FIELDS = ["WSPD","GST","WVHT","DPD","APD","PRES","ATMP","WTMP"] def load_data(data_path): print("Loading data") with open(data_path, newline='') as csv_file: loaded_np_data = pd.read_csv(csv_file) print("Writing out the full Parquet file") loaded_np_data.to_parquet(OUT_FULL_DATASET_PATH) print("Applying Sin() to the two degrees columns") loaded_np_data["WDIR"] = np.sin(np.deg2rad(loaded_np_data["WDIR"])) loaded_np_data["MWD"] = np.sin(np.deg2rad(loaded_np_data["MWD"])) print("calculating z-scores") for var in NUMERIC_FIELDS: var_mean = np.mean(loaded_np_data[var]) var_std = np.std(loaded_np_data[var]) var_zscore = (loaded_np_data[var] - var_mean)/var_std loaded_np_data[var] = var_zscore print("finding outlier rows") # calculate the rows to keep # for each column, is the z-score larger than 2 = loaded_np_data[NUMERIC_FIELDS].le(2) # are there less 2 columns meeting the condition above = keep the row output_np_data = loaded_np_data[loaded_np_data[NUMERIC_FIELDS].gt(2).sum(axis=1).lt(2)] print("exporting to parquet") output_np_data.set_index("TSTMP") output_np_data.to_parquet(OUT_DATASET_PATH) if __name__ == '__main__': print("Start") # Load data all_data = load_data(FULL_DATA_SET_PATH) # Calculate mean and std dev for each non-date column # Going to need to sin(X) for any circular numbers (WDIR & MWD) # Write out data removing rows # Probably want to write out the sin(X) for any circular numbers print("finished")