Update NC_Crime.py
Browse files- NC_Crime.py +1 -7
NC_Crime.py
CHANGED
@@ -13,7 +13,7 @@
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# limitations under the License.
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# TODO: Address all TODOs and remove all explanatory comments
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"""TODO: Add a description here."""
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import csv
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import json
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@@ -82,7 +82,6 @@ class NCCrimeDataset(datasets.GeneratorBasedBuilder):
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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# Use the raw GitHub link to download the CSV file
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downloaded_file_path = dl_manager.download_and_extract(
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# "https://raw.githubusercontent.com/zening-wang2023/NC-Crime-Dataset/main/DCCR.csv.zip"
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"https://raw.githubusercontent.com/zening-wang2023/NC-Crime-Dataset/main/NC_v1.csv.zip"
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)
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@@ -92,11 +91,6 @@ class NCCrimeDataset(datasets.GeneratorBasedBuilder):
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": unzipped_file_path})
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]
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def _generate_examples(self, filepath):
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# Read the CSV file
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df = pd.read_csv(filepath) ## just for test
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# limitations under the License.
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# TODO: Address all TODOs and remove all explanatory comments
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"""TODO: Add a description here."""
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# Given the disparate sizes and column naming conventions of each raw dataset, it was NOT FEASIBLE to streamline the entire cleaning process within a single Python (.py) file. Therefore, a Jupyter notebook has been made available for those interested in delving into the intricacies of how the unified dataset was crafted.
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import csv
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import json
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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# Use the raw GitHub link to download the CSV file
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downloaded_file_path = dl_manager.download_and_extract(
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"https://raw.githubusercontent.com/zening-wang2023/NC-Crime-Dataset/main/NC_v1.csv.zip"
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)
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": unzipped_file_path})
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]
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def _generate_examples(self, filepath):
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# Read the CSV file
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df = pd.read_csv(filepath) ## just for test
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