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55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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# Licensed under the MIT license.
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import argparse
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import os
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from azureml.core import Run
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def get_dict(dict_str):
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pairs = dict_str.strip("{}").split("\;")
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new_dict = {}
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for pair in pairs:
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key, value = pair.strip().split(":")
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new_dict[key.strip().strip("'")] = value.strip().strip("'")
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return new_dict
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print("Cleans the input data")
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# Get the input green_taxi_data. To learn more about how to access dataset in your script, please
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# see https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-with-datasets.
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run = Run.get_context()
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raw_data = run.input_datasets["raw_data"]
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parser = argparse.ArgumentParser("cleanse")
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parser.add_argument("--output_cleanse", type=str, help="cleaned taxi data directory")
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parser.add_argument("--useful_columns", type=str, help="useful columns to keep")
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parser.add_argument("--columns", type=str, help="rename column pattern")
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args = parser.parse_args()
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print("Argument 1(columns to keep): %s" % str(args.useful_columns.strip("[]").split("\;")))
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print("Argument 2(columns renaming mapping): %s" % str(args.columns.strip("{}").split("\;")))
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print("Argument 3(output cleansed taxi data path): %s" % args.output_cleanse)
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# These functions ensure that null data is removed from the dataset,
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# which will help increase machine learning model accuracy.
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useful_columns = [s.strip().strip("'") for s in args.useful_columns.strip("[]").split("\;")]
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columns = get_dict(args.columns)
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new_df = (raw_data.to_pandas_dataframe()
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.dropna(how='all')
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.rename(columns=columns))[useful_columns]
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new_df.reset_index(inplace=True, drop=True)
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if not (args.output_cleanse is None):
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os.makedirs(args.output_cleanse, exist_ok=True)
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print("%s created" % args.output_cleanse)
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path = args.output_cleanse + "/processed.parquet"
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write_df = new_df.to_parquet(path)
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