Matplotlib provides sophisticated date plotting capabilities, standing on the shoulders of python datetime
, the addon modules pytz
and dateutil
. datetime
objects are converted to floating point numbers which represent time in days since 00010101 UTC, plus 1. For example, 00010101, 06:00 is 1.25, not 0.25. The helper functions date2num()
, num2date()
and drange()
are used to facilitate easy conversion to and from datetime
and numeric ranges.
Note
Like Python’s datetime, mpl uses the Gregorian calendar for all conversions between dates and floating point numbers. This practice is not universal, and calendar differences can cause confusing differences between what Python and mpl give as the number of days since 00010101 and what other software and databases yield. For example, the US Naval Observatory uses a calendar that switches from Julian to Gregorian in October, 1582. Hence, using their calculator, the number of days between 00010101 and 20060401 is 732403, whereas using the Gregorian calendar via the datetime module we find:
In [31]:date(2006,4,1).toordinal()  date(1,1,1).toordinal() Out[31]:732401
A wide range of specific and general purpose date tick locators and formatters are provided in this module. See matplotlib.ticker
for general information on tick locators and formatters. These are described below.
All the matplotlib date converters, tickers and formatters are timezone aware, and the default timezone is given by the timezone parameter in your matplotlibrc
file. If you leave out a tz
timezone instance, the default from your rc file will be assumed. If you want to use a custom time zone, pass a pytz.timezone
instance with the tz keyword argument to num2date()
, plot_date()
, and any custom date tickers or locators you create. See pytz for information on pytz
and timezone handling.
The dateutil module provides additional code to handle date ticking, making it easy to place ticks on any kinds of dates. See examples below.
Most of the date tickers can locate single or multiple values. For example:
# import constants for the days of the week from matplotlib.dates import MO, TU, WE, TH, FR, SA, SU # tick on mondays every week loc = WeekdayLocator(byweekday=MO, tz=tz) # tick on mondays and saturdays loc = WeekdayLocator(byweekday=(MO, SA))
In addition, most of the constructors take an interval argument:
# tick on mondays every second week loc = WeekdayLocator(byweekday=MO, interval=2)
The rrule locator allows completely general date ticking:
# tick every 5th easter rule = rrulewrapper(YEARLY, byeaster=1, interval=5) loc = RRuleLocator(rule)
Here are all the date tickers:
MinuteLocator
: locate minutesHourLocator
: locate hoursDayLocator
: locate specified days of the monthWeekdayLocator
: Locate days of the week, e.g., MO, TUMonthLocator
: locate months, e.g., 7 for julyYearLocator
: locate years that are multiples of baseRRuleLocator
: locate using a matplotlib.dates.rrulewrapper
. The rrulewrapper
is a simple wrapper around a dateutil.rrule
(dateutil) which allow almost arbitrary date tick specifications. See rrule example.AutoDateLocator
: On autoscale, this class picks the best MultipleDateLocator
to set the view limits and the tick locations.Here all all the date formatters:
AutoDateFormatter
: attempts to figure out the best format to use. This is most useful when used with the AutoDateLocator
.DateFormatter
: use strftime()
format stringsIndexDateFormatter
: date plots with implicit x indexing.matplotlib.dates.date2num(d)
Converts datetime objects to Matplotlib dates.
Parameters: 
d : 

Returns: 
float or sequence of floats Number of days (fraction part represents hours, minutes, seconds) since 00010101 00:00:00 UTC, plus one. 
The addition of one here is a historical artifact. Also, note that the Gregorian calendar is assumed; this is not universal practice. For details see the module docstring.
matplotlib.dates.num2date(x, tz=None)
Parameters: 
x : float or sequence of floats Number of days (fraction part represents hours, minutes, seconds) since 00010101 00:00:00 UTC, plus one. tz : string, optional Timezone of x (defaults to rcparams TZ value). 

Returns: 
Dates are returned in timezone tz If x is a sequence, a sequence of be returned. 
The addition of one here is a historical artifact. Also, note that the Gregorian calendar is assumed; this is not universal practice. For details, see the module docstring.
matplotlib.dates.num2timedelta(x)
Converts number of days to a timdelta
object. If x is a sequence, a sequence of timedelta
objects will be returned.
Parameters: 
x : float, sequence of floats Number of days (fraction part represents hours, minutes, seconds) 

Returns: 

matplotlib.dates.drange(dstart, dend, delta)
Return a date range as float Gregorian ordinals. dstart and dend are datetime
instances. delta is a datetime.timedelta
instance.
matplotlib.dates.epoch2num(e)
Convert an epoch or sequence of epochs to the new date format, that is days since 0001.
matplotlib.dates.num2epoch(d)
Convert days since 0001 to epoch. d can be a number or sequence.
matplotlib.dates.mx2num(mxdates)
Convert mx datetime
instance (or sequence of mx instances) to the new date format.
class matplotlib.dates.DateFormatter(fmt, tz=None)
Bases: matplotlib.ticker.Formatter
Tick location is seconds since the epoch. Use a strftime()
format string.
Python only supports datetime
strftime()
formatting for years greater than 1900. Thanks to Andrew Dalke, Dalke Scientific Software who contributed the strftime()
code below to include dates earlier than this year.
fmt is a strftime() format string; tz is the
tzinfo
instance.illegal_s = re.compile('((^[^%])(%%)*%s)')
set_tzinfo(tz)
strftime(dt, fmt=None)
Refer to documentation for datetime.strftime.
fmt is a strftime()
format string.
Warning: For years before 1900, depending upon the current locale it is possible that the year displayed with %x might be incorrect. For years before 100, %y and %Y will yield zeropadded strings.
strftime_pre_1900(dt, fmt=None)
Call time.strftime for years before 1900 by rolling forward a multiple of 28 years.
fmt is a strftime()
format string.
Dalke: I hope I did this math right. Every 28 years the calendar repeats, except through century leap years excepting the 400 year leap years. But only if you’re using the Gregorian calendar.
class matplotlib.dates.IndexDateFormatter(t, fmt, tz=None)
Bases: matplotlib.ticker.Formatter
Use with IndexLocator
to cycle format strings by index.
t is a sequence of dates (floating point days). fmt is a strftime()
format string.
class matplotlib.dates.AutoDateFormatter(locator, tz=None, defaultfmt='%Y%m%d')
Bases: matplotlib.ticker.Formatter
This class attempts to figure out the best format to use. This is most useful when used with the AutoDateLocator
.
The AutoDateFormatter has a scale dictionary that maps the scale of the tick (the distance in days between one major tick) and a format string. The default looks like this:
self.scaled = { DAYS_PER_YEAR: rcParams['date.autoformat.year'], DAYS_PER_MONTH: rcParams['date.autoformat.month'], 1.0: rcParams['date.autoformat.day'], 1. / HOURS_PER_DAY: rcParams['date.autoformat.hour'], 1. / (MINUTES_PER_DAY): rcParams['date.autoformat.minute'], 1. / (SEC_PER_DAY): rcParams['date.autoformat.second'], 1. / (MUSECONDS_PER_DAY): rcParams['date.autoformat.microsecond'], }
The algorithm picks the key in the dictionary that is >= the current scale and uses that format string. You can customize this dictionary by doing:
>>> locator = AutoDateLocator() >>> formatter = AutoDateFormatter(locator) >>> formatter.scaled[1/(24.*60.)] = '%M:%S' # only show min and sec
A custom FuncFormatter
can also be used. The following example shows how to use a custom format function to strip trailing zeros from decimal seconds and adds the date to the first ticklabel:
>>> def my_format_function(x, pos=None): ... x = matplotlib.dates.num2date(x) ... if pos == 0: ... fmt = '%D %H:%M:%S.%f' ... else: ... fmt = '%H:%M:%S.%f' ... label = x.strftime(fmt) ... label = label.rstrip("0") ... label = label.rstrip(".") ... return label >>> from matplotlib.ticker import FuncFormatter >>> formatter.scaled[1/(24.*60.)] = FuncFormatter(my_format_function)
Autoformat the date labels. The default format is the one to use if none of the values in self.scaled
are greater than the unit returned by locator._get_unit()
.
class matplotlib.dates.DateLocator(tz=None)
Bases: matplotlib.ticker.Locator
Determines the tick locations when plotting dates.
tz is a tzinfo
instance.
datalim_to_dt()
Convert axis data interval to datetime objects.
hms0d = {'byhour': 0, 'byminute': 0, 'bysecond': 0}
nonsingular(vmin, vmax)
Given the proposed upper and lower extent, adjust the range if it is too close to being singular (i.e. a range of ~0).
set_tzinfo(tz)
Set time zone info.
viewlim_to_dt()
Converts the view interval to datetime objects.
class matplotlib.dates.RRuleLocator(o, tz=None)
Bases: matplotlib.dates.DateLocator
autoscale()
Set the view limits to include the data range.
static get_unit_generic(freq)
tick_values(vmin, vmax)
class matplotlib.dates.AutoDateLocator(tz=None, minticks=5, maxticks=None, interval_multiples=False)
Bases: matplotlib.dates.DateLocator
On autoscale, this class picks the best DateLocator
to set the view limits and the tick locations.
minticks is the minimum number of ticks desired, which is used to select the type of ticking (yearly, monthly, etc.).
maxticks is the maximum number of ticks desired, which controls any interval between ticks (ticking every other, every 3, etc.). For really finegrained control, this can be a dictionary mapping individual rrule frequency constants (YEARLY, MONTHLY, etc.) to their own maximum number of ticks. This can be used to keep the number of ticks appropriate to the format chosen in AutoDateFormatter
. Any frequency not specified in this dictionary is given a default value.
tz is a tzinfo
instance.
interval_multiples is a boolean that indicates whether ticks should be chosen to be multiple of the interval. This will lock ticks to ‘nicer’ locations. For example, this will force the ticks to be at hours 0,6,12,18 when hourly ticking is done at 6 hour intervals.
The AutoDateLocator has an interval dictionary that maps the frequency of the tick (a constant from dateutil.rrule) and a multiple allowed for that ticking. The default looks like this:
self.intervald = { YEARLY : [1, 2, 4, 5, 10, 20, 40, 50, 100, 200, 400, 500, 1000, 2000, 4000, 5000, 10000], MONTHLY : [1, 2, 3, 4, 6], DAILY : [1, 2, 3, 7, 14], HOURLY : [1, 2, 3, 4, 6, 12], MINUTELY: [1, 5, 10, 15, 30], SECONDLY: [1, 5, 10, 15, 30], MICROSECONDLY: [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000, 200000, 500000, 1000000], }
The interval is used to specify multiples that are appropriate for the frequency of ticking. For instance, every 7 days is sensible for daily ticks, but for minutes/seconds, 15 or 30 make sense. You can customize this dictionary by doing:
locator = AutoDateLocator() locator.intervald[HOURLY] = [3] # only show every 3 hours
autoscale()
Try to choose the view limits intelligently.
get_locator(dmin, dmax)
Pick the best locator based on a distance.
nonsingular(vmin, vmax)
refresh()
Refresh internal information based on current limits.
set_axis(axis)
tick_values(vmin, vmax)
class matplotlib.dates.YearLocator(base=1, month=1, day=1, tz=None)
Bases: matplotlib.dates.DateLocator
Make ticks on a given day of each year that is a multiple of base.
Examples:
# Tick every year on Jan 1st locator = YearLocator() # Tick every 5 years on July 4th locator = YearLocator(5, month=7, day=4)
Mark years that are multiple of base on a given month and day (default jan 1).
autoscale()
Set the view limits to include the data range.
tick_values(vmin, vmax)
class matplotlib.dates.MonthLocator(bymonth=None, bymonthday=1, interval=1, tz=None)
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurances of each month month, e.g., 1, 3, 12.
Mark every month in bymonth; bymonth can be an int or sequence. Default is range(1,13)
, i.e. every month.
interval is the interval between each iteration. For example, if interval=2
, mark every second occurance.
class matplotlib.dates.WeekdayLocator(byweekday=1, interval=1, tz=None)
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurances of each weekday.
Mark every weekday in byweekday; byweekday can be a number or sequence.
Elements of byweekday must be one of MO, TU, WE, TH, FR, SA, SU, the constants from dateutil.rrule
, which have been imported into the matplotlib.dates
namespace.
interval specifies the number of weeks to skip. For example, interval=2
plots every second week.
class matplotlib.dates.DayLocator(bymonthday=None, interval=1, tz=None)
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurances of each day of the month. For example, 1, 15, 30.
Mark every day in bymonthday; bymonthday can be an int or sequence.
Default is to tick every day of the month: bymonthday=range(1,32)
class matplotlib.dates.HourLocator(byhour=None, interval=1, tz=None)
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurances of each hour.
Mark every hour in byhour; byhour can be an int or sequence. Default is to tick every hour: byhour=range(24)
interval is the interval between each iteration. For example, if interval=2
, mark every second occurrence.
class matplotlib.dates.MinuteLocator(byminute=None, interval=1, tz=None)
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurances of each minute.
Mark every minute in byminute; byminute can be an int or sequence. Default is to tick every minute: byminute=range(60)
interval is the interval between each iteration. For example, if interval=2
, mark every second occurrence.
class matplotlib.dates.SecondLocator(bysecond=None, interval=1, tz=None)
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurances of each second.
Mark every second in bysecond; bysecond can be an int or sequence. Default is to tick every second: bysecond = range(60)
interval is the interval between each iteration. For example, if interval=2
, mark every second occurrence.
class matplotlib.dates.MicrosecondLocator(interval=1, tz=None)
Bases: matplotlib.dates.DateLocator
Make ticks on occurances of each microsecond.
interval is the interval between each iteration. For example, if interval=2
, mark every second microsecond.
set_axis(axis)
set_data_interval(vmin, vmax)
set_view_interval(vmin, vmax)
tick_values(vmin, vmax)
class matplotlib.dates.rrule(freq, dtstart=None, interval=1, wkst=None, count=None, until=None, bysetpos=None, bymonth=None, bymonthday=None, byyearday=None, byeaster=None, byweekno=None, byweekday=None, byhour=None, byminute=None, bysecond=None, cache=False)
Bases: dateutil.rrule.rrulebase
That’s the base of the rrule operation. It accepts all the keywords defined in the RFC as its constructor parameters (except byday, which was renamed to byweekday) and more. The constructor prototype is:
rrule(freq)
Where freq must be one of YEARLY, MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY, or SECONDLY.
Note
Per RFC section 3.3.10, recurrence instances falling on invalid dates and times are ignored rather than coerced:
Recurrence rules may generate recurrence instances with an invalid date (e.g., February 30) or nonexistent local time (e.g., 1:30 AM on a day where the local time is moved forward by an hour at 1:00 AM). Such recurrence instances MUST be ignored and MUST NOT be counted as part of the recurrence set.This can lead to possibly surprising behavior when, for example, the start date occurs at the end of the month:
>>> from dateutil.rrule import rrule, MONTHLY >>> from datetime import datetime >>> start_date = datetime(2014, 12, 31) >>> list(rrule(freq=MONTHLY, count=4, dtstart=start_date)) ... [datetime.datetime(2014, 12, 31, 0, 0), datetime.datetime(2015, 1, 31, 0, 0), datetime.datetime(2015, 3, 31, 0, 0), datetime.datetime(2015, 5, 31, 0, 0)]
Additionally, it supports the following keyword arguments:
Parameters: 


replace(**kwargs)
Return new rrule with same attributes except for those attributes given new values by whichever keyword arguments are specified.
class matplotlib.dates.relativedelta(dt1=None, dt2=None, years=0, months=0, days=0, leapdays=0, weeks=0, hours=0, minutes=0, seconds=0, microseconds=0, year=None, month=None, day=None, weekday=None, yearday=None, nlyearday=None, hour=None, minute=None, second=None, microsecond=None)
Bases: object
The relativedelta type is based on the specification of the excellent work done by M.A. Lemburg in his mx.DateTime extension. However, notice that this type does NOT implement the same algorithm as his work. Do NOT expect it to behave like mx.DateTime’s counterpart.
There are two different ways to build a relativedelta instance. The first one is passing it two date/datetime classes:
relativedelta(datetime1, datetime2)
The second one is passing it any number of the following keyword arguments:
relativedelta(arg1=x,arg2=y,arg3=z...) year, month, day, hour, minute, second, microsecond: Absolute information (argument is singular); adding or subtracting a relativedelta with absolute information does not perform an aritmetic operation, but rather REPLACES the corresponding value in the original datetime with the value(s) in relativedelta. years, months, weeks, days, hours, minutes, seconds, microseconds: Relative information, may be negative (argument is plural); adding or subtracting a relativedelta with relative information performs the corresponding aritmetic operation on the original datetime value with the information in the relativedelta. weekday: One of the weekday instances (MO, TU, etc). These instances may receive a parameter N, specifying the Nth weekday, which could be positive or negative (like MO(+1) or MO(2). Not specifying it is the same as specifying +1. You can also use an integer, where 0=MO. leapdays: Will add given days to the date found, if year is a leap year, and the date found is post 28 of february. yearday, nlyearday: Set the yearday or the nonleap year day (jump leap days). These are converted to day/month/leapdays information.
Here is the behavior of operations with relativedelta:
normalized()
Return a version of this object represented entirely using integer values for the relative attributes.
>>> relativedelta(days=1.5, hours=2).normalized() relativedelta(days=1, hours=14)
Returns:  Returns a dateutil.relativedelta.relativedelta object. 

weeks
matplotlib.dates.seconds(s)
Return seconds as days.
matplotlib.dates.minutes(m)
Return minutes as days.
matplotlib.dates.hours(h)
Return hours as days.
matplotlib.dates.weeks(w)
Return weeks as days.
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Licensed under the Matplotlib License Agreement.
http://matplotlib.org/2.1.0/api/dates_api.html