neulab/docprompting-conala huggingface dataset:
pandas.Period.end_time   Period.end_time
 
Get the Timestamp for the end of the period.  Returns 
 Timestamp
    See also  Period.start_time

Return the start Timestamp.  Period.dayofyear

Return the day of year.  Period.daysinmonth

Return the days in that month.  Period.dayofweek

Return the day of the week.
pandas.Period.freqstr   Period.freqstr
 
Return a string representation of the frequency.
pandas.Period.hour   Period.hour
 
Get the hour of the day component of the Period.  Returns 
 int

The hour as an integer, between 0 and 23.      See also  Period.second

Get the second component of the Period.  Period.minute

Get the minute component of the Period.    Examples 
>>> p = pd.Period("2018-03-11 13:03:12.050000")
>>> p.hour
13
  Period longer than a day 
>>> p = pd.Period("2018-03-11", freq="M")
>>> p.hour
0
pandas.Period.is_leap_year   Period.is_leap_year
 
Return True if the period’s year is in a leap year.
pandas.Period.minute   Period.minute
 
Get minute of the hour component of the Period.  Returns 
 int

The minute as an integer, between 0 and 59.      See also  Period.hour

Get the hour component of the Period.  Period.second

Get the second component of the Period.    Examples 
>>> p = pd.Period("2018-03-11 13:03:12.050000")
>>> p.minute
3
pandas.Period.now   Period.now()
 
Return the period of now’s date.
pandas.Period.qyear   Period.qyear
 
Fiscal year the Period lies in according to its starting-quarter. The year and the qyear of the period will be the same if the fiscal and calendar years are the same. When they are not, the fiscal year can be different from the calendar year of the period.  Returns 
 int

The fiscal year of the period.      See also  Period.year

Return the calendar year of the period.    Examples If the natural and fiscal year are the same, qyear and year will be the same. 
>>> per = pd.Period('2018Q1', freq='Q')
>>> per.qyear
2018
>>> per.year
2018
  If the fiscal year starts in April (Q-MAR), the first quarter of 2018 will start in April 2017. year will then be 2018, but qyear will be the fiscal year, 2018. 
>>> per = pd.Period('2018Q1', freq='Q-MAR')
>>> per.start_time
Timestamp('2017-04-01 00:00:00')
>>> per.qyear
2018
>>> per.year
2017
pandas.Period.second   Period.second
 
Get the second component of the Period.  Returns 
 int

The second of the Period (ranges from 0 to 59).      See also  Period.hour

Get the hour component of the Period.  Period.minute

Get the minute component of the Period.    Examples 
>>> p = pd.Period("2018-03-11 13:03:12.050000")
>>> p.second
12
pandas.Period.start_time   Period.start_time
 
Get the Timestamp for the start of the period.  Returns 
 Timestamp
    See also  Period.end_time

Return the end Timestamp.  Period.dayofyear

Return the day of year.  Period.daysinmonth

Return the days in that month.  Period.dayofweek

Return the day of the week.    Examples 
>>> period = pd.Period('2012-1-1', freq='D')
>>> period
Period('2012-01-01', 'D')
  
>>> period.start_time
Timestamp('2012-01-01 00:00:00')
  
>>> period.end_time
Timestamp('2012-01-01 23:59:59.999999999')
pandas.Period.strftime   Period.strftime()
 
Returns the string representation of the Period, depending on the selected fmt. fmt must be a string containing one or several directives. The method recognizes the same directives as the time.strftime() function of the standard Python distribution, as well as the specific additional directives %f, %F, %q. (formatting & docs originally from scikits.timeries).        
Directive Meaning Notes    
%a Locale’s abbreviated weekday name.   
%A Locale’s full weekday name.   
%b Locale’s abbreviated month name.   
%B Locale’s full month name.   
%c Locale’s appropriate date and time representation.   
%d Day of the month as a decimal number [01,31].   
%f ‘Fiscal’ year without a century as a decimal number [00,99] (1)  
%F ‘Fiscal’ year with a century as a decimal number (2)  
%H Hour (24-hour clock) as a decimal number [00,23].   
%I Hour (12-hour clock) as a decimal number [01,12].   
%j Day of the year as a decimal number [001,366].   
%m Month as a decimal number [01,12].   
%M Minute as a decimal number [00,59].   
%p Locale’s equivalent of either AM or PM. (3)  
%q Quarter as a decimal number [01,04]   
%S Second as a decimal number [00,61]. (4)  
%U Week number of the year (Sunday as the first day of the week) as a decimal number [00,53]. All days in a new year preceding the first Sunday are considered to be in week 0. (5)  
%w Weekday as a decimal number [0(Sunday),6].   
%W Week number of the year (Monday as the first day of the week) as a decimal number [00,53]. All days in a new year preceding the first Monday are considered to be in week 0. (5)  
%x Locale’s appropriate date representation.   
%X Locale’s appropriate time representation.   
%y Year without century as a decimal number [00,99].   
%Y Year with century as a decimal number.   
%Z Time zone name (no characters if no time zone exists).   
%% A literal '%' character.     Notes  The %f directive is the same as %y if the frequency is not quarterly. Otherwise, it corresponds to the ‘fiscal’ year, as defined by the qyear attribute. The %F directive is the same as %Y if the frequency is not quarterly. Otherwise, it corresponds to the ‘fiscal’ year, as defined by the qyear attribute. The %p directive only affects the output hour field if the %I directive is used to parse the hour. The range really is 0 to 61; this accounts for leap seconds and the (very rare) double leap seconds. The %U and %W directives are only used in calculations when the day of the week and the year are specified.  Examples 
>>> a = Period(freq='Q-JUL', year=2006, quarter=1)
>>> a.strftime('%F-Q%q')
'2006-Q1'
>>> # Output the last month in the quarter of this date
>>> a.strftime('%b-%Y')
'Oct-2005'
>>>
>>> a = Period(freq='D', year=2001, month=1, day=1)
>>> a.strftime('%d-%b-%Y')
'01-Jan-2001'
>>> a.strftime('%b. %d, %Y was a %A')
'Jan. 01, 2001 was a Monday'
pandas.Period.to_timestamp   Period.to_timestamp()
 
Return the Timestamp representation of the Period. Uses the target frequency specified at the part of the period specified by how, which is either Start or Finish.  Parameters 
 
freq:str or DateOffset


Target frequency. Default is ‘D’ if self.freq is week or longer and ‘S’ otherwise.  
how:str, default ‘S’ (start)


One of ‘S’, ‘E’. Can be aliased as case insensitive ‘Start’, ‘Finish’, ‘Begin’, ‘End’.    Returns 
 Timestamp
pandas.Period.week   Period.week
 
Get the week of the year on the given Period.  Returns 
 int
    See also  Period.dayofweek

Get the day component of the Period.  Period.weekday

Get the day component of the Period.    Examples 
>>> p = pd.Period("2018-03-11", "H")
>>> p.week
10
  
>>> p = pd.Period("2018-02-01", "D")
>>> p.week
5
  
>>> p = pd.Period("2018-01-06", "D")
>>> p.week
1
pandas.Period.weekday   Period.weekday
 
Day of the week the period lies in, with Monday=0 and Sunday=6. If the period frequency is lower than daily (e.g. hourly), and the period spans over multiple days, the day at the start of the period is used. If the frequency is higher than daily (e.g. monthly), the last day of the period is used.  Returns 
 int

Day of the week.      See also  Period.dayofweek

Day of the week the period lies in.  Period.weekday

Alias of Period.dayofweek.  Period.day

Day of the month.  Period.dayofyear

Day of the year.    Examples 
>>> per = pd.Period('2017-12-31 22:00', 'H')
>>> per.dayofweek
6
  For periods that span over multiple days, the day at the beginning of the period is returned. 
>>> per = pd.Period('2017-12-31 22:00', '4H')
>>> per.dayofweek
6
>>> per.start_time.dayofweek
6
  For periods with a frequency higher than days, the last day of the period is returned. 
>>> per = pd.Period('2018-01', 'M')
>>> per.dayofweek
2
>>> per.end_time.dayofweek
2
pandas.Period.weekofyear   Period.weekofyear
 
Get the week of the year on the given Period.  Returns 
 int
    See also  Period.dayofweek

Get the day component of the Period.  Period.weekday

Get the day component of the Period.    Examples 
>>> p = pd.Period("2018-03-11", "H")
>>> p.weekofyear
10
  
>>> p = pd.Period("2018-02-01", "D")
>>> p.weekofyear
5
  
>>> p = pd.Period("2018-01-06", "D")
>>> p.weekofyear
1
pandas.period_range   pandas.period_range(start=None, end=None, periods=None, freq=None, name=None)[source]
 
Return a fixed frequency PeriodIndex. The day (calendar) is the default frequency.  Parameters 
 
start:str or period-like, default None


Left bound for generating periods.  
end:str or period-like, default None


Right bound for generating periods.  
periods:int, default None


Number of periods to generate.  
freq:str or DateOffset, optional


Frequency alias. By default the freq is taken from start or end if those are Period objects. Otherwise, the default is "D" for daily frequency.  
name:str, default None


Name of the resulting PeriodIndex.    Returns 
 PeriodIndex
   Notes Of the three parameters: start, end, and periods, exactly two must be specified. To learn more about the frequency strings, please see this link. Examples 
>>> pd.period_range(start='2017-01-01', end='2018-01-01', freq='M')
PeriodIndex(['2017-01', '2017-02', '2017-03', '2017-04', '2017-05', '2017-06',
         '2017-07', '2017-08', '2017-09', '2017-10', '2017-11', '2017-12',
         '2018-01'],
        dtype='period[M]')
  If start or end are Period objects, they will be used as anchor endpoints for a PeriodIndex with frequency matching that of the period_range constructor. 
>>> pd.period_range(start=pd.Period('2017Q1', freq='Q'),
...                 end=pd.Period('2017Q2', freq='Q'), freq='M')
PeriodIndex(['2017-03', '2017-04', '2017-05', '2017-06'],
            dtype='period[M]')
pandas.PeriodDtype   classpandas.PeriodDtype(freq=None)[source]
 
An ExtensionDtype for Period data. This is not an actual numpy dtype, but a duck type.  Parameters 
 
freq:str or DateOffset


The frequency of this PeriodDtype.     Examples 
>>> pd.PeriodDtype(freq='D')
period[D]
  
>>> pd.PeriodDtype(freq=pd.offsets.MonthEnd())
period[M]
  Attributes       
freq The frequency object of this PeriodDtype.    Methods       
None
pandas.PeriodDtype.freq   propertyPeriodDtype.freq
 
The frequency object of this PeriodDtype.
pandas.PeriodIndex   classpandas.PeriodIndex(data=None, ordinal=None, freq=None, dtype=None, copy=False, name=None, **fields)[source]
 
Immutable ndarray holding ordinal values indicating regular periods in time. Index keys are boxed to Period objects which carries the metadata (eg, frequency information).  Parameters 
 
data:array-like (1d int np.ndarray or PeriodArray), optional


Optional period-like data to construct index with.  
copy:bool


Make a copy of input ndarray.  
freq:str or period object, optional


One of pandas period strings or corresponding objects.  
year:int, array, or Series, default None


month:int, array, or Series, default None


quarter:int, array, or Series, default None


day:int, array, or Series, default None


hour:int, array, or Series, default None


minute:int, array, or Series, default None


second:int, array, or Series, default None


dtype:str or PeriodDtype, default None

    See also  Index

The base pandas Index type.  Period

Represents a period of time.  DatetimeIndex

Index with datetime64 data.  TimedeltaIndex

Index of timedelta64 data.  period_range

Create a fixed-frequency PeriodIndex.    Examples 
>>> idx = pd.PeriodIndex(year=[2000, 2002], quarter=[1, 3])
>>> idx
PeriodIndex(['2000Q1', '2002Q3'], dtype='period[Q-DEC]')
  Attributes       
day The days of the period.  
dayofweek The day of the week with Monday=0, Sunday=6.  
day_of_week The day of the week with Monday=0, Sunday=6.  
dayofyear The ordinal day of the year.  
day_of_year The ordinal day of the year.  
days_in_month The number of days in the month.  
daysinmonth The number of days in the month.  
freq Return the frequency object if it is set, otherwise None.  
freqstr Return the frequency object as a string if its set, otherwise None.  
hour The hour of the period.  
is_leap_year Logical indicating if the date belongs to a leap year.  
minute The minute of the period.  
month The month as January=1, December=12.  
quarter The quarter of the date.  
second The second of the period.  
week The week ordinal of the year.  
weekday The day of the week with Monday=0, Sunday=6.  
weekofyear The week ordinal of the year.  
year The year of the period.          
end_time   
qyear   
start_time     Methods       
asfreq([freq, how]) Convert the PeriodArray to the specified frequency freq.  
strftime(*args, **kwargs) Convert to Index using specified date_format.  
to_timestamp([freq, how]) Cast to DatetimeArray/Index.
pandas.PeriodIndex.asfreq   PeriodIndex.asfreq(freq=None, how='E')[source]
 
Convert the PeriodArray to the specified frequency freq. Equivalent to applying pandas.Period.asfreq() with the given arguments to each Period in this PeriodArray.  Parameters 
 
freq:str


A frequency.  
how:str {‘E’, ‘S’}, default ‘E’


Whether the elements should be aligned to the end or start within pa period.  ‘E’, ‘END’, or ‘FINISH’ for end, ‘S’, ‘START’, or ‘BEGIN’ for start.  January 31st (‘END’) vs. January 1st (‘START’) for example.    Returns 
 PeriodArray

The transformed PeriodArray with the new frequency.      See also  pandas.arrays.PeriodArray.asfreq

Convert each Period in a PeriodArray to the given frequency.  Period.asfreq

Convert a Period object to the given frequency.    Examples 
>>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
>>> pidx
PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
dtype='period[A-DEC]')
  
>>> pidx.asfreq('M')
PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
'2015-12'], dtype='period[M]')
  
>>> pidx.asfreq('M', how='S')
PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01',
'2015-01'], dtype='period[M]')
pandas.PeriodIndex.day   propertyPeriodIndex.day
 
The days of the period.
pandas.PeriodIndex.day_of_week   propertyPeriodIndex.day_of_week
 
The day of the week with Monday=0, Sunday=6.
pandas.PeriodIndex.day_of_year   propertyPeriodIndex.day_of_year
 
The ordinal day of the year.
pandas.PeriodIndex.dayofweek   propertyPeriodIndex.dayofweek
 
The day of the week with Monday=0, Sunday=6.
pandas.PeriodIndex.dayofyear   propertyPeriodIndex.dayofyear
 
The ordinal day of the year.
pandas.PeriodIndex.days_in_month   propertyPeriodIndex.days_in_month
 
The number of days in the month.
pandas.PeriodIndex.daysinmonth   propertyPeriodIndex.daysinmonth
 
The number of days in the month.
pandas.PeriodIndex.end_time   propertyPeriodIndex.end_time
pandas.PeriodIndex.freq   propertyPeriodIndex.freq
 
Return the frequency object if it is set, otherwise None.
pandas.PeriodIndex.freqstr   propertyPeriodIndex.freqstr
 
Return the frequency object as a string if its set, otherwise None.
pandas.PeriodIndex.hour   propertyPeriodIndex.hour
 
The hour of the period.
pandas.PeriodIndex.is_leap_year   propertyPeriodIndex.is_leap_year
 
Logical indicating if the date belongs to a leap year.
pandas.PeriodIndex.minute   propertyPeriodIndex.minute
 
The minute of the period.
pandas.PeriodIndex.month   propertyPeriodIndex.month
 
The month as January=1, December=12.
pandas.PeriodIndex.quarter   propertyPeriodIndex.quarter
 
The quarter of the date.
pandas.PeriodIndex.qyear   propertyPeriodIndex.qyear
pandas.PeriodIndex.second   propertyPeriodIndex.second
 
The second of the period.
pandas.PeriodIndex.start_time   propertyPeriodIndex.start_time
pandas.PeriodIndex.strftime   PeriodIndex.strftime(*args, **kwargs)[source]
 
Convert to Index using specified date_format. Return an Index of formatted strings specified by date_format, which supports the same string format as the python standard library. Details of the string format can be found in python string format doc.  Parameters 
 
date_format:str


Date format string (e.g. “%Y-%m-%d”).    Returns 
 ndarray[object]

NumPy ndarray of formatted strings.      See also  to_datetime

Convert the given argument to datetime.  DatetimeIndex.normalize

Return DatetimeIndex with times to midnight.  DatetimeIndex.round

Round the DatetimeIndex to the specified freq.  DatetimeIndex.floor

Floor the DatetimeIndex to the specified freq.    Examples 
>>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"),
...                     periods=3, freq='s')
>>> rng.strftime('%B %d, %Y, %r')
Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',
       'March 10, 2018, 09:00:02 AM'],
      dtype='object')
pandas.PeriodIndex.to_timestamp   PeriodIndex.to_timestamp(freq=None, how='start')[source]
 
Cast to DatetimeArray/Index.  Parameters 
 
freq:str or DateOffset, optional


Target frequency. The default is ‘D’ for week or longer, ‘S’ otherwise.  
how:{‘s’, ‘e’, ‘start’, ‘end’}


Whether to use the start or end of the time period being converted.    Returns 
 DatetimeArray/Index
pandas.PeriodIndex.week   propertyPeriodIndex.week
 
The week ordinal of the year.
pandas.PeriodIndex.weekday   propertyPeriodIndex.weekday
 
The day of the week with Monday=0, Sunday=6.
pandas.PeriodIndex.weekofyear   propertyPeriodIndex.weekofyear
 
The week ordinal of the year.
pandas.PeriodIndex.year   propertyPeriodIndex.year
 
The year of the period.
pandas.plotting.andrews_curves   pandas.plotting.andrews_curves(frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwargs)[source]
 
Generate a matplotlib plot of Andrews curves, for visualising clusters of multivariate data. Andrews curves have the functional form:  f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) +

x_4 sin(2t) + x_5 cos(2t) + …   Where x coefficients correspond to the values of each dimension and t is linearly spaced between -pi and +pi. Each row of frame then corresponds to a single curve.  Parameters 
 
frame:DataFrame


Data to be plotted, preferably normalized to (0.0, 1.0).  
class_column:Name of the column containing class names


ax:matplotlib axes object, default None


samples:Number of points to plot in each curve


color:list or tuple, optional


Colors to use for the different classes.  
colormap:str or matplotlib colormap object, default None


Colormap to select colors from. If string, load colormap with that name from matplotlib.  **kwargs

Options to pass to matplotlib plotting method.    Returns 
 class:matplotlip.axis.Axes

   Examples 
>>> df = pd.read_csv(
...     'https://raw.github.com/pandas-dev/'
...     'pandas/main/pandas/tests/io/data/csv/iris.csv'
... )
>>> pd.plotting.andrews_curves(df, 'Name')
<AxesSubplot:title={'center':'width'}>
pandas.plotting.autocorrelation_plot   pandas.plotting.autocorrelation_plot(series, ax=None, **kwargs)[source]
 
Autocorrelation plot for time series.  Parameters 
 
series:Time series


ax:Matplotlib axis object, optional

**kwargs

Options to pass to matplotlib plotting method.    Returns 
 class:matplotlib.axis.Axes

   Examples The horizontal lines in the plot correspond to 95% and 99% confidence bands. The dashed line is 99% confidence band. 
>>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
>>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
>>> pd.plotting.autocorrelation_plot(s)
<AxesSubplot:title={'center':'width'}, xlabel='Lag', ylabel='Autocorrelation'>
pandas.plotting.bootstrap_plot   pandas.plotting.bootstrap_plot(series, fig=None, size=50, samples=500, **kwds)[source]
 
Bootstrap plot on mean, median and mid-range statistics. The bootstrap plot is used to estimate the uncertainty of a statistic by relaying on random sampling with replacement [1]. This function will generate bootstrapping plots for mean, median and mid-range statistics for the given number of samples of the given size.  1 
“Bootstrapping (statistics)” in https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29    Parameters 
 
series:pandas.Series


Series from where to get the samplings for the bootstrapping.  
fig:matplotlib.figure.Figure, default None


If given, it will use the fig reference for plotting instead of creating a new one with default parameters.  
size:int, default 50


Number of data points to consider during each sampling. It must be less than or equal to the length of the series.  
samples:int, default 500


Number of times the bootstrap procedure is performed.  **kwds

Options to pass to matplotlib plotting method.    Returns 
 matplotlib.figure.Figure

Matplotlib figure.      See also  DataFrame.plot

Basic plotting for DataFrame objects.  Series.plot

Basic plotting for Series objects.    Examples This example draws a basic bootstrap plot for a Series. 
>>> s = pd.Series(np.random.uniform(size=100))
>>> pd.plotting.bootstrap_plot(s)
<Figure size 640x480 with 6 Axes>
pandas.plotting.deregister_matplotlib_converters   pandas.plotting.deregister_matplotlib_converters()[source]
 
Remove pandas formatters and converters. Removes the custom converters added by register(). This attempts to set the state of the registry back to the state before pandas registered its own units. Converters for pandas’ own types like Timestamp and Period are removed completely. Converters for types pandas overwrites, like datetime.datetime, are restored to their original value.  See also  register_matplotlib_converters

Register pandas formatters and converters with matplotlib.
pandas.plotting.lag_plot   pandas.plotting.lag_plot(series, lag=1, ax=None, **kwds)[source]
 
Lag plot for time series.  Parameters 
 
series:Time series


lag:lag of the scatter plot, default 1


ax:Matplotlib axis object, optional

**kwds

Matplotlib scatter method keyword arguments.    Returns 
 class:matplotlib.axis.Axes

   Examples Lag plots are most commonly used to look for patterns in time series data. Given the following time series 
>>> np.random.seed(5)
>>> x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))
>>> s = pd.Series(x)
>>> s.plot()
<AxesSubplot:xlabel='Midrange'>
     A lag plot with lag=1 returns 
>>> pd.plotting.lag_plot(s, lag=1)
<AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>
pandas.plotting.parallel_coordinates   pandas.plotting.parallel_coordinates(frame, class_column, cols=None, ax=None, color=None, use_columns=False, xticks=None, colormap=None, axvlines=True, axvlines_kwds=None, sort_labels=False, **kwargs)[source]
 
Parallel coordinates plotting.  Parameters 
 
frame:DataFrame


class_column:str


Column name containing class names.  
cols:list, optional


A list of column names to use.  
ax:matplotlib.axis, optional


Matplotlib axis object.  
color:list or tuple, optional


Colors to use for the different classes.  
use_columns:bool, optional


If true, columns will be used as xticks.  
xticks:list or tuple, optional


A list of values to use for xticks.  
colormap:str or matplotlib colormap, default None


Colormap to use for line colors.  
axvlines:bool, optional


If true, vertical lines will be added at each xtick.  
axvlines_kwds:keywords, optional


Options to be passed to axvline method for vertical lines.  
sort_labels:bool, default False


Sort class_column labels, useful when assigning colors.  **kwargs

Options to pass to matplotlib plotting method.    Returns 
 class:matplotlib.axis.Axes

   Examples 
>>> df = pd.read_csv(
...     'https://raw.github.com/pandas-dev/'
...     'pandas/main/pandas/tests/io/data/csv/iris.csv'
... )
>>> pd.plotting.parallel_coordinates(
...     df, 'Name', color=('#556270', '#4ECDC4', '#C7F464')
... )
<AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>
pandas.plotting.plot_params   pandas.plotting.plot_params={'xaxis.compat': False}
 
Stores pandas plotting options. Allows for parameter aliasing so you can just use parameter names that are the same as the plot function parameters, but is stored in a canonical format that makes it easy to breakdown into groups later.