def unique(self):
values = self._values
if hasattr(values, 'unique'):
result = values.unique()
else:
from pandas.core.nanops import unique1d
result = unique1d(values)
return result
def unique(self):
values = self._values
if hasattr(values, 'unique'):
result = values.unique()
else:
from pandas.core.nanops import unique1d
result = unique1d(values)
return result
def unique(self):
"""
Return array of unique values in the object. Significantly faster than
numpy.unique. Includes NA values.
Returns
-------
uniques : ndarray
"""
from pandas.core.nanops import unique1d
return unique1d(self.values)
def unique(self):
"""
Return array of unique values in the object. Significantly faster than
numpy.unique. Includes NA values.
Returns
-------
uniques : ndarray
"""
from pandas.core.nanops import unique1d
return unique1d(self.values)
def pivot_annual(series, freq=None):
"""
Deprecated. Use ``pivot_table`` instead.
Group a series by years, taking leap years into account.
The output has as many rows as distinct years in the original series,
and as many columns as the length of a leap year in the units corresponding
to the original frequency (366 for daily frequency, 366*24 for hourly...).
The fist column of the output corresponds to Jan. 1st, 00:00:00,
while the last column corresponds to Dec, 31st, 23:59:59.
Entries corresponding to Feb. 29th are masked for non-leap years.
For example, if the initial series has a daily frequency, the 59th column
of the output always corresponds to Feb. 28th, the 61st column to Mar. 1st,
and the 60th column is masked for non-leap years.
With a hourly initial frequency, the (59*24)th column of the output always
correspond to Feb. 28th 23:00, the (61*24)th column to Mar. 1st, 00:00, and
the 24 columns between (59*24) and (61*24) are masked.
If the original frequency is less than daily, the output is equivalent to
``series.convert('A', func=None)``.
Parameters
----------
series : Series
freq : string or None, default None
Returns
-------
annual : DataFrame
"""
msg = "pivot_annual is deprecated. Use pivot_table instead"
warnings.warn(msg, FutureWarning)
index = series.index
year = index.year
years = nanops.unique1d(year)
if freq is not None:
freq = freq.upper()
else:
freq = series.index.freq
if freq == 'D':
width = 366
offset = index.dayofyear - 1
# adjust for leap year
offset[(~isleapyear(year)) & (offset >= 59)] += 1
columns = lrange(1, 367)
# todo: strings like 1/1, 1/25, etc.?
elif freq in ('M', 'BM'):
width = 12
offset = index.month - 1
columns = lrange(1, 13)
elif freq == 'H':
width = 8784
grouped = series.groupby(series.index.year)
defaulted = grouped.apply(lambda x: x.reset_index(drop=True))
defaulted.index = defaulted.index.droplevel(0)
offset = np.asarray(defaulted.index)
offset[~isleapyear(year) & (offset >= 1416)] += 24
columns = lrange(1, 8785)
else:
raise NotImplementedError(freq)
flat_index = (year - years.min()) * width + offset
flat_index = _ensure_platform_int(flat_index)
values = np.empty((len(years), width))
values.fill(np.nan)
values.put(flat_index, series.values)
return DataFrame(values, index=years, columns=columns)
def pivot_annual(series, freq=None):
"""
Group a series by years, taking leap years into account.
The output has as many rows as distinct years in the original series,
and as many columns as the length of a leap year in the units corresponding
to the original frequency (366 for daily frequency, 366*24 for hourly...).
The fist column of the output corresponds to Jan. 1st, 00:00:00,
while the last column corresponds to Dec, 31st, 23:59:59.
Entries corresponding to Feb. 29th are masked for non-leap years.
For example, if the initial series has a daily frequency, the 59th column
of the output always corresponds to Feb. 28th, the 61st column to Mar. 1st,
and the 60th column is masked for non-leap years.
With a hourly initial frequency, the (59*24)th column of the output always
correspond to Feb. 28th 23:00, the (61*24)th column to Mar. 1st, 00:00, and
the 24 columns between (59*24) and (61*24) are masked.
If the original frequency is less than daily, the output is equivalent to
``series.convert('A', func=None)``.
Parameters
----------
series : TimeSeries
freq : string or None, default None
Returns
-------
annual : DataFrame
"""
index = series.index
year = index.year
years = nanops.unique1d(year)
if freq is not None:
freq = freq.upper()
else:
freq = series.index.freq
if freq == 'D':
width = 366
offset = index.dayofyear - 1
# adjust for leap year
offset[(-isleapyear(year)) & (offset >= 59)] += 1
columns = range(1, 367)
# todo: strings like 1/1, 1/25, etc.?
elif freq in ('M', 'BM'):
width = 12
offset = index.month - 1
columns = range(1, 13)
elif freq == 'H':
width = 8784
grouped = series.groupby(series.index.year)
defaulted = grouped.apply(lambda x: x.reset_index(drop=True))
defaulted.index = defaulted.index.droplevel(0)
offset = np.asarray(defaulted.index)
offset[-isleapyear(year) & (offset >= 1416)] += 24
columns = range(1, 8785)
else:
raise NotImplementedError(freq)
flat_index = (year - years.min()) * width + offset
flat_index = com._ensure_platform_int(flat_index)
values = np.empty((len(years), width))
values.fill(np.nan)
values.put(flat_index, series.values)
return DataFrame(values, index=years, columns=columns)
def convert_to_annual(series, freq=None):
"""
Group a series by years, taking leap years into account.
The output has as many rows as distinct years in the original series,
and as many columns as the length of a leap year in the units corresponding
to the original frequency (366 for daily frequency, 366*24 for hourly...).
The fist column of the output corresponds to Jan. 1st, 00:00:00,
while the last column corresponds to Dec, 31st, 23:59:59.
Entries corresponding to Feb. 29th are masked for non-leap years.
For example, if the initial series has a daily frequency, the 59th column
of the output always corresponds to Feb. 28th, the 61st column to Mar. 1st,
and the 60th column is masked for non-leap years.
With a hourly initial frequency, the (59*24)th column of the output always
correspond to Feb. 28th 23:00, the (61*24)th column to Mar. 1st, 00:00, and
the 24 columns between (59*24) and (61*24) are masked.
If the original frequency is less than daily, the output is equivalent to
``series.convert('A', func=None)``.
Parameters
----------
series : TimeSeries
freq : string or None, default None
Returns
-------
annual : DataFrame
"""
index = series.index
year = index.year
years = nanops.unique1d(year)
if freq is not None:
freq = freq.upper()
if freq == 'D':
width = 366
offset = index.dayofyear - 1
# adjust for leap year
offset[(-isleapyear(year)) & (offset >= 59)] += 1
columns = range(1, 367)
# todo: strings like 1/1, 1/25, etc.?
elif freq in ('M', 'BM'):
width = 12
offset = index.month - 1
columns = range(1, 13)
else:
raise NotImplementedError(freq)
flat_index = (year - years.min()) * width + offset
values = np.empty((len(years), width), dtype=series.dtype)
if not np.issubdtype(series.dtype, np.integer):
values.fill(np.nan)
else:
raise Exception('need to upcast')
values.put(flat_index, series.values)
return DataFrame(values, index=years, columns=columns)
