def infer_freq(index, warn: bool = True) -> str | None:
"""
Infer the most likely frequency given the input index.
Parameters
----------
index : DatetimeIndex or TimedeltaIndex
If passed a Series will use the values of the series (NOT THE INDEX).
warn : bool, default True
.. deprecated:: 1.5.0
Returns
-------
str or None
None if no discernible frequency.
Raises
------
TypeError
If the index is not datetime-like.
ValueError
If there are fewer than three values.
Examples
--------
>>> idx = pd.date_range(start='2020/12/01', end='2020/12/30', periods=30)
>>> pd.infer_freq(idx)
'D'
"""
from pandas.core.api import (
DatetimeIndex,
Float64Index,
Index,
Int64Index,
)
if isinstance(index, ABCSeries):
values = index._values
if not (is_datetime64_dtype(values) or is_timedelta64_dtype(values)
or values.dtype == object):
raise TypeError("cannot infer freq from a non-convertible dtype "
f"on a Series of {index.dtype}")
index = values
inferer: _FrequencyInferer
if not hasattr(index, "dtype"):
pass
elif is_period_dtype(index.dtype):
raise TypeError("PeriodIndex given. Check the `freq` attribute "
"instead of using infer_freq.")
elif is_timedelta64_dtype(index.dtype):
# Allow TimedeltaIndex and TimedeltaArray
inferer = _TimedeltaFrequencyInferer(index, warn=warn)
return inferer.get_freq()
if isinstance(index, Index) and not isinstance(index, DatetimeIndex):
if isinstance(index, (Int64Index, Float64Index)):
raise TypeError(
f"cannot infer freq from a non-convertible index type {type(index)}"
)
index = index._values
if not isinstance(index, DatetimeIndex):
index = DatetimeIndex(index)
inferer = _FrequencyInferer(index, warn=warn)
return inferer.get_freq()
from datetime import datetime
import string
import numpy as np
from pandas.core.api import Series, DataFrame, DatetimeIndex
from pandas.stats.api import ols
N = 100
start = datetime(2009, 9, 2)
dateRange = DatetimeIndex(start, periods=N)
def makeDataFrame():
data = DataFrame(np.random.randn(N, 7),
columns=list(string.ascii_uppercase[:7]),
index=dateRange)
return data
def makeSeries():
return Series(np.random.randn(N), index=dateRange)
#-------------------------------------------------------------------------------
# Standard rolling linear regression
X = makeDataFrame()
Y = makeSeries()