def _get_freq(ax, series):
# get frequency from data
freq = getattr(series.index, 'freq', None)
if freq is None:
freq = getattr(series.index, 'inferred_freq', None)
ax_freq = getattr(ax, 'freq', None)
if ax_freq is None:
if hasattr(ax, 'left_ax'):
ax_freq = getattr(ax.left_ax, 'freq', None)
elif hasattr(ax, 'right_ax'):
ax_freq = getattr(ax.right_ax, 'freq', None)
# use axes freq if no data freq
if freq is None:
freq = ax_freq
# get the period frequency
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = frequencies.get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
return freq, ax_freq
def _maybe_convert_index(ax, data):
# tsplot converts automatically, but don't want to convert index
# over and over for DataFrames
if isinstance(data.index, (ABCDatetimeIndex, ABCPeriodIndex)):
freq = getattr(data.index, "freq", None)
if freq is None:
freq = getattr(data.index, "inferred_freq", None)
if isinstance(freq, DateOffset):
freq = freq.rule_code
if freq is None:
freq = _get_ax_freq(ax)
if freq is None:
raise ValueError("Could not get frequency alias for plotting")
freq = get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
if isinstance(data.index, ABCDatetimeIndex):
data = data.to_period(freq=freq)
elif isinstance(data.index, ABCPeriodIndex):
data.index = data.index.asfreq(freq=freq)
return data
def to_period(self, freq=None):
"""
Cast to PeriodArray/Index at a particular frequency.
Converts DatetimeArray/Index to PeriodArray/Index.
Parameters
----------
freq : string or Offset, optional
One of pandas' :ref:`offset strings <timeseries.offset_aliases>`
or an Offset object. Will be inferred by default.
Returns
-------
PeriodArray/Index
Raises
------
ValueError
When converting a DatetimeArray/Index with non-regular values,
so that a frequency cannot be inferred.
Examples
--------
>>> df = pd.DataFrame({"y": [1,2,3]},
... index=pd.to_datetime(["2000-03-31 00:00:00",
... "2000-05-31 00:00:00",
... "2000-08-31 00:00:00"]))
>>> df.index.to_period("M")
PeriodIndex(['2000-03', '2000-05', '2000-08'],
dtype='period[M]', freq='M')
Infer the daily frequency
>>> idx = pd.date_range("2017-01-01", periods=2)
>>> idx.to_period()
PeriodIndex(['2017-01-01', '2017-01-02'],
dtype='period[D]', freq='D')
See also
--------
pandas.PeriodIndex: Immutable ndarray holding ordinal values
pandas.DatetimeIndex.to_pydatetime: Return DatetimeIndex as object
"""
from pandas.core.arrays import PeriodArrayMixin
if self.tz is not None:
warnings.warn("Converting to PeriodArray/Index representation "
"will drop timezone information.", UserWarning)
if freq is None:
freq = self.freqstr or self.inferred_freq
if freq is None:
raise ValueError("You must pass a freq argument as "
"current index has none.")
freq = get_period_alias(freq)
return PeriodArrayMixin(self.values, freq=freq)
def _maybe_convert_index(self, data):
# tsplot converts automatically, but don't want to convert index
# over and over for DataFrames
from pandas.core.frame import DataFrame
if (isinstance(data.index, DatetimeIndex) and
isinstance(data, DataFrame)):
freq = getattr(data.index, 'freq', None)
if freq is None:
freq = getattr(data.index, 'inferred_freq', None)
if isinstance(freq, DateOffset):
freq = freq.rule_code
freq = get_period_alias(freq)
if freq is None:
ax = self._get_ax(0)
freq = getattr(ax, 'freq', None)
if freq is None:
raise ValueError('Could not get frequency alias for plotting')
data = DataFrame(data.values,
index=data.index.to_period(freq=freq),
columns=data.columns)
return data
def _use_dynamic_x(ax, data):
freq = _get_index_freq(data)
ax_freq = _get_ax_freq(ax)
if freq is None: # convert irregular if axes has freq info
freq = ax_freq
else: # do not use tsplot if irregular was plotted first
if (ax_freq is None) and (len(ax.get_lines()) > 0):
return False
if freq is None:
return False
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = frequencies.get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
if freq is None:
return False
# hack this for 0.10.1, creating more technical debt...sigh
if isinstance(data.index, ABCDatetimeIndex):
base = frequencies.get_freq(freq)
x = data.index
if (base <= frequencies.FreqGroup.FR_DAY):
return x[:1].is_normalized
return Period(x[0], freq).to_timestamp(tz=x.tz) == x[0]
return True
def _is_dynamic_freq(self, freq):
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = get_base_alias(freq)
freq = get_period_alias(freq)
return freq is not None
def _get_freq(ax, series):
# get frequency from data
freq = getattr(series.index, 'freq', None)
if freq is None:
freq = getattr(series.index, 'inferred_freq', None)
ax_freq = getattr(ax, 'freq', None)
if ax_freq is None:
if hasattr(ax, 'left_ax'):
ax_freq = getattr(ax.left_ax, 'freq', None)
elif hasattr(ax, 'right_ax'):
ax_freq = getattr(ax.right_ax, 'freq', None)
# use axes freq if no data freq
if freq is None:
freq = ax_freq
# get the period frequency
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = frequencies.get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
return freq, ax_freq
def _use_dynamic_x(ax, data):
freq = _get_index_freq(data)
ax_freq = _get_ax_freq(ax)
if freq is None: # convert irregular if axes has freq info
freq = ax_freq
else: # do not use tsplot if irregular was plotted first
if (ax_freq is None) and (len(ax.get_lines()) > 0):
return False
if freq is None:
return False
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
if freq is None:
return False
# hack this for 0.10.1, creating more technical debt...sigh
if isinstance(data.index, ABCDatetimeIndex):
base = get_freq(freq)
x = data.index
if base <= FreqGroup.FR_DAY:
return x[:1].is_normalized
return Period(x[0], freq).to_timestamp(tz=x.tz) == x[0]
return True
def _maybe_convert_index(ax, data):
# tsplot converts automatically, but don't want to convert index
# over and over for DataFrames
if isinstance(data.index, (ABCDatetimeIndex, ABCPeriodIndex)):
freq = getattr(data.index, 'freq', None)
if freq is None:
freq = getattr(data.index, 'inferred_freq', None)
if isinstance(freq, DateOffset):
freq = freq.rule_code
if freq is None:
freq = _get_ax_freq(ax)
if freq is None:
raise ValueError('Could not get frequency alias for plotting')
freq = get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
if isinstance(data.index, ABCDatetimeIndex):
data = data.to_period(freq=freq)
elif isinstance(data.index, ABCPeriodIndex):
data.index = data.index.asfreq(freq=freq)
return data
def _get_period_alias(freq) -> Optional[str]:
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = base_and_stride(freq)[0]
freq = get_period_alias(freq)
return freq
def to_period(self, freq=None):
"""
Cast to PeriodIndex at a particular frequency
"""
from pandas.tseries.period import PeriodIndex
if self.freq is None and freq is None:
msg = "You must pass a freq argument as current index has none."
raise ValueError(msg)
if freq is None:
freq = get_period_alias(self.freqstr)
return PeriodIndex(self.values, freq=freq)
def to_period(self, freq=None):
"""
Cast to PeriodIndex at a particular frequency
"""
from pandas.tseries.period import PeriodIndex
if self.freq is None and freq is None:
msg = "You must pass a freq argument as current index has none."
raise ValueError(msg)
if freq is None:
freq = get_period_alias(self.freqstr)
return PeriodIndex(self.values, freq=freq)
def _maybe_convert_index(self, data):
# tsplot converts automatically, but don't want to convert index
# over and over for DataFrames
from pandas.core.frame import DataFrame
if (isinstance(data.index, DatetimeIndex) and
isinstance(data, DataFrame)):
freq = getattr(data.index, 'freqstr', None)
freq = get_period_alias(freq)
if freq is None and hasattr(data.index, 'inferred_freq'):
freq = data.index.inferred_freq
if isinstance(freq, DateOffset):
freq = freq.rule_code
data = DataFrame(data.values,
index=data.index.to_period(freq=freq),
columns=data.columns)
return data
def _get_freq(ax, series):
# get frequency from data
freq = getattr(series.index, "freq", None)
if freq is None:
freq = getattr(series.index, "inferred_freq", None)
ax_freq = _get_ax_freq(ax)
# use axes freq if no data freq
if freq is None:
freq = ax_freq
# get the period frequency
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
return freq, ax_freq
def _maybe_convert_index(ax, data):
# tsplot converts automatically, but don't want to convert index
# over and over for DataFrames
if isinstance(data.index, DatetimeIndex):
freq = getattr(data.index, 'freq', None)
if freq is None:
freq = getattr(data.index, 'inferred_freq', None)
if isinstance(freq, DateOffset):
freq = freq.rule_code
if freq is None:
freq = getattr(ax, 'freq', None)
if freq is None:
raise ValueError('Could not get frequency alias for plotting')
freq = frequencies.get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
data = data.to_period(freq=freq)
return data
def tsplot(series, plotf, **kwargs):
"""
Plots a Series on the given Matplotlib axes or the current axes
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same kwargs as Axes.plot
"""
# Used inferred freq is possible, need a test case for inferred
freq = getattr(series.index, 'freq', None)
if freq is None and hasattr(series.index, 'inferred_freq'):
freq = series.index.inferred_freq
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = frequencies.get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
# Convert DatetimeIndex to PeriodIndex
if isinstance(series.index, DatetimeIndex):
series = series.to_period(freq=freq)
if freq != series.index.freq:
series = series.asfreq(freq)
style = kwargs.pop('style', None)
if 'ax' in kwargs:
ax = kwargs.pop('ax')
else:
import matplotlib.pyplot as plt
ax = plt.gca()
# Specialized ts plotting attributes for Axes
ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
xaxis.converter = DateConverter
ax.legendlabels = [kwargs.get('label', None)]
ax.view_interval = None
ax.date_axis_info = None
# format args and lot
mask = isnull(series)
if mask.any():
masked_array = np.ma.array(series.values)
masked_array = np.ma.masked_where(mask, masked_array)
args = [series.index, masked_array]
else:
args = [series.index, series]
if style is not None:
args.append(style)
plotf(ax, *args, **kwargs)
format_dateaxis(ax, ax.freq)
left = series.index[0] #get_datevalue(series.index[0], freq)
right = series.index[-1] #get_datevalue(series.index[-1], freq)
ax.set_xlim(left, right)
return ax
def _get_period_alias(freq) -> Optional[str]:
freqstr = to_offset(freq).rule_code
freq = get_period_alias(freqstr)
return freq
def _get_period_alias(freq) -> str | None:
freqstr = to_offset(freq).rule_code
freq = get_period_alias(freqstr)
return freq
def to_period(self, freq=None):
"""
Cast to PeriodArray/Index at a particular frequency.
Converts DatetimeArray/Index to PeriodArray/Index.
Parameters
----------
freq : string or Offset, optional
One of pandas' :ref:`offset strings <timeseries.offset_aliases>`
or an Offset object. Will be inferred by default.
Returns
-------
PeriodArray/Index
Raises
------
ValueError
When converting a DatetimeArray/Index with non-regular values,
so that a frequency cannot be inferred.
Examples
--------
>>> df = pd.DataFrame({"y": [1,2,3]},
... index=pd.to_datetime(["2000-03-31 00:00:00",
... "2000-05-31 00:00:00",
... "2000-08-31 00:00:00"]))
>>> df.index.to_period("M")
PeriodIndex(['2000-03', '2000-05', '2000-08'],
dtype='period[M]', freq='M')
Infer the daily frequency
>>> idx = pd.date_range("2017-01-01", periods=2)
>>> idx.to_period()
PeriodIndex(['2017-01-01', '2017-01-02'],
dtype='period[D]', freq='D')
See also
--------
pandas.PeriodIndex: Immutable ndarray holding ordinal values
pandas.DatetimeIndex.to_pydatetime: Return DatetimeIndex as object
"""
from pandas.core.arrays import PeriodArrayMixin
if self.tz is not None:
warnings.warn(
"Converting to PeriodArray/Index representation "
"will drop timezone information.", UserWarning)
if freq is None:
freq = self.freqstr or self.inferred_freq
if freq is None:
raise ValueError("You must pass a freq argument as "
"current index has none.")
freq = get_period_alias(freq)
return PeriodArrayMixin(self.values, freq=freq)
def tsplot(series, plotf, *args, **kwargs):
"""
Plots a Series on the given Matplotlib axes object
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same args and kwargs as Axes.plot
"""
# Used inferred freq is possible, need a test case for inferred
freq = getattr(series.index, 'freq', None)
if freq is None and hasattr(series.index, 'inferred_freq'):
freq = series.index.inferred_freq
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = frequencies.get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
# Convert DatetimeIndex to PeriodIndex
if isinstance(series.index, DatetimeIndex):
series = series.to_period(freq=freq)
if not isinstance(series.index, PeriodIndex):
#try to get it to DatetimeIndex then to period
if series.index.inferred_type == 'datetime':
idx = DatetimeIndex(series.index).to_period(freq=freq)
series = Series(series.values, idx, name=series.name)
else:
raise TypeError('series argument to tsplot must have '
'DatetimeIndex or PeriodIndex')
if freq != series.index.freq:
series = series.asfreq(freq)
style = kwargs.pop('style', None)
if 'ax' in kwargs:
ax = kwargs.pop('ax')
else:
ax = plt.gca()
# Specialized ts plotting attributes for Axes
ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
xaxis.converter = DateConverter
ax.legendlabels = [kwargs.get('label', None)]
ax.view_interval = None
ax.date_axis_info = None
# format args and lot
mask = isnull(series)
if mask.any():
masked_array = np.ma.array(series.values)
masked_array = np.ma.masked_where(mask, masked_array)
args = _check_plot_params(masked_array, series.index, freq, style,
*args)
else:
args = _check_plot_params(series, series.index, freq, style, *args)
plotted = plotf(ax, *args, **kwargs)
format_dateaxis(ax, ax.freq)
# when adding a right axis (using add_yaxis), for some reason the
# x axis limits don't get properly set. This gets around the problem
xlim = ax.get_xlim()
if xlim[0] == 0.0 and xlim[1] == 1.0:
# if xlim still at default values, autoscale the axis
ax.autoscale_view()
left = series.index[0] #get_datevalue(series.index[0], freq)
right = series.index[-1] #get_datevalue(series.index[-1], freq)
ax.set_xlim(left, right)
return plotted
def _get_period_alias(freq: timedelta | BaseOffset | str) -> str | None:
freqstr = to_offset(freq).rule_code
return get_period_alias(freqstr)
