def to_offset(value):
"""Converts a value to an offset and validates the offset.
Args:
value (int or str or offset) : Value of offset.
Returns:
offset : Valid offset.
"""
if isinstance(value, int):
assert value > 0, 'offset must be greater than zero'
offset = value
elif isinstance(value, str):
error = 'offset must be a valid string'
assert can_be_type(type=pd.tseries.frequencies.to_offset,
string=value), error
offset = pd.tseries.frequencies.to_offset(value)
assert offset.n > 0, 'offset must be greater than zero'
else:
assert is_offset(value), 'invalid offset'
assert value.n > 0, 'offset must be greater than zero'
offset = value
return offset
def cutoff_data(df, threshold):
"""Cuts off data before the threshold.
Args:
df (DataFrame) : Data frame to cutoff data.
threshold (int or str or Timestamp) : Threshold to apply on data.
If integer, the threshold will be the time at `n + 1` in the index.
If string, the threshold can be an offset or timestamp.
An offset will be applied relative to the first time in the index.
Returns:
DataFrame, Timestamp : Returns the data frame and the applied cutoff time.
"""
if isinstance(threshold, int):
assert threshold > 0, 'threshold must be greater than zero'
df = df.iloc[threshold:]
if df.empty:
return df, None
cutoff_time = df.index[0]
elif isinstance(threshold, str):
if can_be_type(type=pd.tseries.frequencies.to_offset,
string=threshold):
threshold = pd.tseries.frequencies.to_offset(threshold)
assert threshold.n > 0, 'threshold must be greater than zero'
cutoff_time = df.index[0] + threshold
elif can_be_type(type=pd.Timestamp, string=threshold):
cutoff_time = pd.Timestamp(threshold)
else:
raise ValueError('invalid threshold')
else:
is_timestamp = isinstance(threshold, pd.Timestamp)
assert is_timestamp, 'invalid threshold'
cutoff_time = threshold
if cutoff_time != df.index[0]:
df = df[df.index >= cutoff_time]
if df.empty:
return df, None
return df, cutoff_time
