Matplotlib provides sophisticated date plotting capabilities, standing on the shoulders of python datetime
and the addon module dateutil
.
Matplotlib represents dates using floating point numbers specifying the number of days since 00010101 UTC, plus 1. For example, 00010101, 06:00 is 1.25, not 0.25. Values < 1, i.e. dates before 00010101 UTC are not supported.
There are a number of helper functions to convert between datetime
objects and Matplotlib dates:
date2num  Convert datetime objects to Matplotlib dates. 
num2date  Convert Matplotlib dates to datetime objects. 
num2timedelta  Convert number of days to a timedelta object. 
epoch2num  Convert an epoch or sequence of epochs to the new date format, that is days since 0001. 
num2epoch  Convert days since 0001 to epoch. 
mx2num  Convert mx datetime instance (or sequence of mx instances) to the new date format. 
drange  Return a sequence of equally spaced Matplotlib dates. 
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 [1]: date(2006, 4, 1).toordinal()  date(1, 1, 1).toordinal() Out[1]: 732401
All the Matplotlib date converters, tickers and formatters are timezone aware. If no explicit timezone is provided, the rcParam timezone
is assumend. If you want to use a custom time zone, pass a datetime.tzinfo
instance with the tz keyword argument to num2date()
, plot_date()
, and any custom date tickers or locators you create.
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.
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:
MicrosecondLocator
: locate microsecondsSecondLocator
: locate secondsMinuteLocator
: 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 DateLocator
(e.g., RRuleLocator
) to set the view limits and the tick locations. If called with interval_multiples=True
it will make ticks line up with sensible multiples of the tick intervals. E.g. if the interval is 4 hours, it will pick hours 0, 4, 8, etc as ticks. This behaviour is not guaranteed by default.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)
[source]
Convert datetime objects to Matplotlib dates.
Parameters: 


Returns: 

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)
[source]
Convert Matplotlib dates to datetime
objects.
Parameters: 


Returns: 

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)
[source]
Convert number of days to a timedelta
object.
If x is a sequence, a sequence of timedelta
objects will be returned.
Parameters: 


Returns: 

matplotlib.dates.drange(dstart, dend, delta)
[source]
Return a sequence of equally spaced Matplotlib dates.
The dates start at dstart and reach up to, but not including dend. They are spaced by delta.
Parameters: 


Returns: 

matplotlib.dates.epoch2num(e)
[source]
Convert an epoch or sequence of epochs to the new date format, that is days since 0001.
matplotlib.dates.num2epoch(d)
[source]
Convert days since 0001 to epoch. d can be a number or sequence.
matplotlib.dates.mx2num(mxdates)
[source]
Convert mx datetime
instance (or sequence of mx instances) to the new date format.
class matplotlib.dates.DateFormatter(fmt, tz=None)
[source]
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)
[source]
strftime(dt, fmt=None)
[source]
Deprecated since version 3.0: The strftime function was deprecated in Matplotlib 3.0 and will be removed in 3.2.
Refer to documentation for datetime.datetime.strftime()
fmt is a datetime.datetime.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)
[source]
Deprecated since version 3.0: The strftime_pre_1900 function was deprecated in Matplotlib 3.0 and will be removed in 3.2.
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)
[source]
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')
[source]
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)
[source]
Bases: matplotlib.ticker.Locator
Determines the tick locations when plotting dates.
This class is subclassed by other Locators and is not meant to be used on its own.
tz is a tzinfo
instance.
datalim_to_dt()
[source]
Convert axis data interval to datetime objects.
hms0d = {'byhour': 0, 'byminute': 0, 'bysecond': 0}
nonsingular(vmin, vmax)
[source]
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)
[source]
Set time zone info.
viewlim_to_dt()
[source]
Converts the view interval to datetime objects.
class matplotlib.dates.RRuleLocator(o, tz=None)
[source]
Bases: matplotlib.dates.DateLocator
autoscale()
[source]
Set the view limits to include the data range.
static get_unit_generic(freq)
[source]
tick_values(vmin, vmax)
[source]
Return the values of the located ticks given vmin and vmax.
Note
To get tick locations with the vmin and vmax values defined automatically for the associated axis
simply call the Locator instance:
>>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]
class matplotlib.dates.AutoDateLocator(tz=None, minticks=5, maxticks=None, interval_multiples=True)
[source]
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()
[source]
Try to choose the view limits intelligently.
get_locator(dmin, dmax)
[source]
Pick the best locator based on a distance.
nonsingular(vmin, vmax)
[source]
Given the proposed upper and lower extent, adjust the range if it is too close to being singular (i.e. a range of ~0).
refresh()
[source]
Refresh internal information based on current limits.
set_axis(axis)
[source]
tick_values(vmin, vmax)
[source]
Return the values of the located ticks given vmin and vmax.
Note
To get tick locations with the vmin and vmax values defined automatically for the associated axis
simply call the Locator instance:
>>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]
class matplotlib.dates.YearLocator(base=1, month=1, day=1, tz=None)
[source]
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()
[source]
Set the view limits to include the data range.
tick_values(vmin, vmax)
[source]
Return the values of the located ticks given vmin and vmax.
Note
To get tick locations with the vmin and vmax values defined automatically for the associated axis
simply call the Locator instance:
>>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]
class matplotlib.dates.MonthLocator(bymonth=None, bymonthday=1, interval=1, tz=None)
[source]
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurrences of each 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 occurrence.
class matplotlib.dates.WeekdayLocator(byweekday=1, interval=1, tz=None)
[source]
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurrences 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)
[source]
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurrences 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)
[source]
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurrences 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)
[source]
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurrences 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)
[source]
Bases: matplotlib.dates.RRuleLocator
Make ticks on occurrences 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)
[source]
Bases: matplotlib.dates.DateLocator
Make ticks on regular intervals of one or more microsecond(s).
Note
Due to the floating point representation of time in days since 00010101 UTC (plus 1), plotting data with microsecond time resolution does not work well with current dates.
If you want microsecond resolution time plots, it is strongly recommended to use floating point seconds, not datetimelike time representation.
If you really must use datetime.datetime() or similar and still need microsecond precision, your only chance is to use very early years; using year 0001 is recommended.
interval is the interval between each iteration. For example, if interval=2
, mark every second microsecond.
set_axis(axis)
[source]
set_data_interval(vmin, vmax)
[source]
set_view_interval(vmin, vmax)
[source]
tick_values(vmin, vmax)
[source]
Return the values of the located ticks given vmin and vmax.
Note
To get tick locations with the vmin and vmax values defined automatically for the associated axis
simply call the Locator instance:
>>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]
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)
[source]
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)
[source]
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)
[source]
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 arithmetic 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. Notice that if the calculated date is already Monday, for example, using MO(1) or MO(1) won't change the day. 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.
There are relative and absolute forms of the keyword arguments. The plural is relative, and the singular is absolute. For each argument in the order below, the absolute form is applied first (by setting each attribute to that value) and then the relative form (by adding the value to the attribute).
The order of attributes considered when this relativedelta is added to a datetime is:
Finally, weekday is applied, using the rule described above.
For example
>>> dt = datetime(2018, 4, 9, 13, 37, 0) >>> delta = relativedelta(hours=25, day=1, weekday=MO(1)) datetime(2018, 4, 2, 14, 37, 0)
First, the day is set to 1 (the first of the month), then 25 hours are added, to get to the 2nd day and 14th hour, finally the weekday is applied, but since the 2nd is already a Monday there is no effect.
normalized()
[source]
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)
[source]
Return seconds as days.
matplotlib.dates.minutes(m)
[source]
Return minutes as days.
matplotlib.dates.hours(h)
[source]
Return hours as days.
matplotlib.dates.weeks(w)
[source]
Return weeks as days.
© 2012–2018 Matplotlib Development Team. All rights reserved.
Licensed under the Matplotlib License Agreement.
https://matplotlib.org/3.0.0/api/dates_api.html