def METHOD_NAME(path_to_file: str) -> pd.DataFrame:
    """
    Reads the data with indicators from the given file path and fetches the education data for each indicator.
    :param path_to_file: Path to the CSV file.
    :return: DataFrame with education data.
    """
    # Read csv file with indicators
    indicators = pd.read_csv(
        path_to_file,
        encoding="latin-1",
        low_memory=False,
    )
    # Store unique indicator codes related to Education
    unique_series_codes = indicators["Series Code"].dropna().unique()
    with ThreadPoolExecutor() as executor:
        # Using list comprehension to run fetch_education_data concurrently
        wb_education = list(
            tqdm(
                executor.map(fetch_education_data, unique_series_codes),
                total=len(unique_series_codes),
                desc="Processing indicator",
            )
        )
    wb_education_df = pd.DataFrame()  # Create an empty DataFrame to store the result
    # The dataframes are really big which causes the kernel to break - the loop is a workaround.
    for df in tqdm(wb_education, desc="Concatenating dataframes"):
        wb_education_df = pd.concat([wb_education_df, df], ignore_index=True)
    return wb_education_df