Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
multi-class-classification
Size:
1K - 10K
| import os | |
| def generate_labels(df, column_names, output_dir): | |
| """ | |
| Generates a list of unique values for each column in the specified dataframe, | |
| and writes each list to a separate file with the specified filename. | |
| Args: | |
| df (pandas.DataFrame): The dataframe to generate code lists from. | |
| column_names (list): A list of column names to generate code lists for. | |
| output_dir (str): The directory to write the code list files to. | |
| """ | |
| # Create the output directory if it doesn't exist | |
| os.makedirs(output_dir, exist_ok=True) | |
| # Iterate over the specified columns and generate a list of unique values for each column | |
| for column_name in column_names: | |
| if column_name == "ESCO_CODE": | |
| values = sorted(set(str(code) for code in df[column_name].tolist())) | |
| elif column_name == "ISCO_CODES": | |
| values = sorted(set(item for sublist in df[column_name].tolist() for item in sublist)) | |
| elif column_name == "ESCO_LABELS": | |
| values = sorted(set(item for sublist in df[column_name].tolist() for item in sublist)) | |
| values = sorted(set([str(val).strip() for val in values])) | |
| else: | |
| values = sorted(set(df[column_name].astype(str).tolist())) | |
| filename = os.path.join(output_dir, f"{column_name.lower()}.txt") | |
| with open(filename, "w") as f: | |
| f.write("\n".join(values)) | |
| columns_list = [ | |
| "ISCO_CODE_1", | |
| "ISCO_CODE_2", | |
| "ISCO_CODE_3", | |
| "ISCO_CODE_4", | |
| "ISCO_LABEL_1", | |
| "ISCO_LABEL_2", | |
| "ISCO_LABEL_3", | |
| "ISCO_LABEL_4", | |
| "ISCO_CODES", | |
| "ESCO_CODE", | |
| "ESCO_LABELS", | |
| "ESCO_OCCUPATION", | |
| ] | |
| for column_name in columns_list: | |
| generate_labels( | |
| isco_structure_df, | |
| [column_name], | |
| "../isco_esco_occupations_taxonomy/labels" | |
| ) |