Datasets:
monash_tsf

	from datetime import datetime
	from distutils.util import strtobool

	import numpy as np
	import pandas as pd


	# Converts the contents in a .tsf file into a dataframe and returns
	# it along with other meta-data of the dataset:
	# frequency, horizon, whether the dataset contains missing values and whether the series have equal lengths
	#
	# Parameters
	# full_file_path_and_name - complete .tsf file path
	# replace_missing_vals_with - a term to indicate the missing values in series in the returning dataframe
	# value_column_name - Any name that is preferred to have as the name of the column containing series values in the returning dataframe
	def convert_tsf_to_dataframe(
	full_file_path_and_name,
	replace_missing_vals_with="NaN",
	value_column_name="series_value",
	):
	col_names = []
	col_types = []
	all_data = {}
	line_count = 0
	frequency = None
	forecast_horizon = None
	contain_missing_values = None
	contain_equal_length = None
	found_data_tag = False
	found_data_section = False
	started_reading_data_section = False

	with open(full_file_path_and_name, "r", encoding="cp1252") as file:
	for line in file:
	# Strip white space from start/end of line
	line = line.strip()

	if line:
	if line.startswith("@"): # Read meta-data
	if not line.startswith("@data"):
	line_content = line.split(" ")
	if line.startswith("@attribute"):
	if len(line_content) != 3: # Attributes have both name and type
	raise ValueError("Invalid meta-data specification.")

	col_names.append(line_content[1])
	col_types.append(line_content[2])
	else:
	if len(line_content) != 2: # Other meta-data have only values
	raise ValueError("Invalid meta-data specification.")

	if line.startswith("@frequency"):
	frequency = line_content[1]
	elif line.startswith("@horizon"):
	forecast_horizon = int(line_content[1])
	elif line.startswith("@missing"):
	contain_missing_values = bool(strtobool(line_content[1]))
	elif line.startswith("@equallength"):
	contain_equal_length = bool(strtobool(line_content[1]))

	else:
	if len(col_names) == 0:
	raise ValueError("Missing attribute section. Attribute section must come before data.")

	found_data_tag = True
	elif not line.startswith("#"):
	if len(col_names) == 0:
	raise ValueError("Missing attribute section. Attribute section must come before data.")
	elif not found_data_tag:
	raise ValueError("Missing @data tag.")
	else:
	if not started_reading_data_section:
	started_reading_data_section = True
	found_data_section = True
	all_series = []

	for col in col_names:
	all_data[col] = []

	full_info = line.split(":")

	if len(full_info) != (len(col_names) + 1):
	raise ValueError("Missing attributes/values in series.")

	series = full_info[len(full_info) - 1]
	series = series.split(",")

	if len(series) == 0:
	raise ValueError(
	"A given series should contains a set of comma separated numeric values. At least one numeric value should be there in a series. Missing values should be indicated with ? symbol"
	)

	numeric_series = []

	for val in series:
	if val == "?":
	numeric_series.append(replace_missing_vals_with)
	else:
	numeric_series.append(float(val))

	if numeric_series.count(replace_missing_vals_with) == len(numeric_series):
	raise ValueError(
	"All series values are missing. A given series should contains a set of comma separated numeric values. At least one numeric value should be there in a series."
	)

	all_series.append(np.array(numeric_series, dtype=np.float32))

	for i in range(len(col_names)):
	att_val = None
	if col_types[i] == "numeric":
	att_val = int(full_info[i])
	elif col_types[i] == "string":
	att_val = str(full_info[i])
	elif col_types[i] == "date":
	att_val = datetime.strptime(full_info[i], "%Y-%m-%d %H-%M-%S")
	else:
	raise ValueError(
	"Invalid attribute type."
	) # Currently, the code supports only numeric, string and date types. Extend this as required.

	if att_val is None:
	raise ValueError("Invalid attribute value.")
	else:
	all_data[col_names[i]].append(att_val)

	line_count = line_count + 1

	if line_count == 0:
	raise ValueError("Empty file.")
	if len(col_names) == 0:
	raise ValueError("Missing attribute section.")
	if not found_data_section:
	raise ValueError("Missing series information under data section.")

	all_data[value_column_name] = all_series
	loaded_data = pd.DataFrame(all_data)

	return (
	loaded_data,
	frequency,
	forecast_horizon,
	contain_missing_values,
	contain_equal_length,
	)


	def convert_multiple(text: str) -> str:
	if text.isnumeric():
	return text
	if text == "half":
	return "0.5"


	def frequency_converter(freq: str):
	parts = freq.split("_")
	if len(parts) == 1:
	return BASE_FREQ_TO_PANDAS_OFFSET[parts[0]]
	if len(parts) == 2:
	return convert_multiple(parts[0]) + BASE_FREQ_TO_PANDAS_OFFSET[parts[1]]
	raise ValueError(f"Invalid frequency string {freq}.")


	BASE_FREQ_TO_PANDAS_OFFSET = {
	"seconds": "S",
	"minutely": "T",
	"minutes": "T",
	"hourly": "H",
	"hours": "H",
	"daily": "D",
	"days": "D",
	"weekly": "W",
	"weeks": "W",
	"monthly": "M",
	"months": "M",
	"quarterly": "Q",
	"quarters": "Q",
	"yearly": "Y",
	"years": "Y",
	}

	# Example of usage
	# loaded_data, frequency, forecast_horizon, contain_missing_values, contain_equal_length = convert_tsf_to_dataframe("TSForecasting/tsf_data/sample.tsf")

	# print(loaded_data)
	# print(frequency)
	# print(forecast_horizon)
	# print(contain_missing_values)
	# print(contain_equal_length)