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" | |
) |