Matplotlib provides sophisticated date plotting capabilities, standing on the shoulders of python datetime
and the add-on module dateutil
.
By default, Matplotlib uses the units machinery described in units
to convert datetime.datetime
, and numpy.datetime64
objects when plotted on an x- or y-axis. The user does not need to do anything for dates to be formatted, but dates often have strict formatting needs, so this module provides many axis locators and formatters. A basic example using numpy.datetime64
is:
import numpy as np times = np.arange(np.datetime64('2001-01-02'), np.datetime64('2002-02-03'), np.timedelta64(75, 'm')) y = np.random.randn(len(times)) fig, ax = plt.subplots() ax.plot(times, y)
Matplotlib represents dates using floating point numbers specifying the number of days since a default epoch of 1970-01-01 UTC; for example, 1970-01-01, 06:00 is the floating point number 0.25. The formatters and locators require the use of datetime.datetime
objects, so only dates between year 0001 and 9999 can be represented. Microsecond precision is achievable for (approximately) 70 years on either side of the epoch, and 20 microseconds for the rest of the allowable range of dates (year 0001 to 9999). The epoch can be changed at import time via dates.set_epoch
or rcParams["dates.epoch"]
to other dates if necessary; see Date Precision and Epochs for a discussion.
Note
Before Matplotlib 3.3, the epoch was 0000-12-31 which lost modern microsecond precision and also made the default axis limit of 0 an invalid datetime. In 3.3 the epoch was changed as above. To convert old ordinal floats to the new epoch, users can do:
new_ordinal = old_ordinal + mdates.date2num(np.datetime64('0000-12-31'))
There are a number of helper functions to convert between datetime
objects and Matplotlib dates:
Convert a date string to a datenum using | |
Convert datetime objects to Matplotlib dates. | |
Convert Matplotlib dates to | |
Convert number of days to a | |
Return a sequence of equally spaced Matplotlib dates. | |
Set the epoch (origin for dates) for datetime calculations. | |
Get the epoch used by |
Note
Like Python's datetime.datetime
, Matplotlib 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 Matplotlib give as the number of days since 0001-01-01 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 0001-01-01 and 2006-04-01 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, rcParams["timezone"]
(default: 'UTC'
) is assumed. If you want to use a custom time zone, pass a datetime.tzinfo
instance with the tz keyword argument to num2date
, Axis.axis_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)
The available date tickers are:
MicrosecondLocator
: Locate microseconds.SecondLocator
: Locate seconds.MinuteLocator
: Locate minutes.HourLocator
: Locate hours.DayLocator
: Locate specified days of the month.WeekdayLocator
: Locate days of the week, e.g., MO, TU.MonthLocator
: Locate months, e.g., 7 for July.YearLocator
: Locate years that are multiples of base.RRuleLocator
: Locate using a matplotlib.dates.rrulewrapper
. rrulewrapper
is a simple wrapper around dateutil's dateutil.rrule
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.The available date formatters are:
AutoDateFormatter
: attempts to figure out the best format to use. This is most useful when used with the AutoDateLocator
.ConciseDateFormatter
: also attempts to figure out the best format to use, and to make the format as compact as possible while still having complete date information. This is most useful when used with the AutoDateLocator
.DateFormatter
: use strftime
format strings.Bases: matplotlib.ticker.Formatter
A Formatter
which attempts to figure out the best format to use. This is most useful when used with the AutoDateLocator
.
AutoDateFormatter
has a .scale
dictionary that maps tick scales (the interval in days between one major tick) to format strings; this dictionary defaults to
self.scaled = { DAYS_PER_YEAR: rcParams['date.autoformat.year'], DAYS_PER_MONTH: rcParams['date.autoformat.month'], 1: 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 formatter uses the format string corresponding to the lowest key in the dictionary that is greater or equal to the current scale. Dictionary entries can be customized:
locator = AutoDateLocator() formatter = AutoDateFormatter(locator) formatter.scaled[1/(24*60)] = '%M:%S' # only show min and sec
Custom callables can also be used instead of format strings. 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 formatter.scaled[1/(24*60)] = my_format_function
Autoformat the date labels.
ticker.Locator
Locator that this axis is using.
Passed to dates.date2num
.
The default format to use if none of the values in self.scaled
are greater than the unit returned by locator._get_unit()
.
rcParams["text.usetex"]
(default: False
)
To enable/disable the use of TeX's math mode for rendering the results of the formatter. If any entries in self.scaled
are set as functions, then it is up to the customized function to enable or disable TeX's math mode itself.
Bases: matplotlib.dates.DateLocator
On autoscale, this class picks the best DateLocator
to set the view limits and the tick locations.
Mapping of tick frequencies to multiples allowed for that ticking. The default is
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, 21], 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], }
where the keys are defined in dateutil.rrule
.
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.
When customizing, you should only modify the values for the existing keys. You should not add or delete entries.
Example for forcing ticks every 3 hours:
locator = AutoDateLocator() locator.intervald[HOURLY] = [3] # only show every 3 hours
datetime.tzinfo
Ticks timezone.
The minimum number of ticks desired; controls whether ticks occur yearly, monthly, etc.
The maximum number of ticks desired; controls the interval between ticks (ticking every other, every 3, etc.). For fine-grained 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.
Whether ticks should be chosen to be multiple of the interval, locking them 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.
Pick the best locator based on a distance.
Given the proposed upper and lower extent, adjust the range if it is too close to being singular (i.e. a range of ~0).
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]
Bases: matplotlib.ticker.Formatter
A Formatter
which attempts to figure out the best format to use for the date, and to make it as compact as possible, but still be complete. This is most useful when used with the AutoDateLocator
:
>>> locator = AutoDateLocator() >>> formatter = ConciseDateFormatter(locator)
ticker.Locator
Locator that this axis is using.
Passed to dates.date2num
.
Format strings for 6 levels of tick labelling: mostly years, months, days, hours, minutes, and seconds. Strings use the same format codes as strftime
. Default is ['%Y', '%b', '%d', '%H:%M', '%H:%M', '%S.%f']
Format strings for tick labels that are "zeros" for a given tick level. For instance, if most ticks are months, ticks around 1 Jan 2005 will be labeled "Dec", "2005", "Feb". The default is ['', '%Y', '%b', '%b-%d', '%H:%M', '%H:%M']
Format strings for the 6 levels that is applied to the "offset" string found on the right side of an x-axis, or top of a y-axis. Combined with the tick labels this should completely specify the date. The default is:
['', '%Y', '%Y-%b', '%Y-%b-%d', '%Y-%b-%d', '%Y-%b-%d %H:%M']
Whether to show the offset or not.
rcParams["text.usetex"]
(default: False
)
To enable/disable the use of TeX's math mode for rendering the results of the formatter.
See Formatting date ticks using ConciseDateFormatter
(Source code, png, pdf)
Autoformat the date labels. The default format is used to form an initial string, and then redundant elements are removed.
Return a short string version of the tick value.
Defaults to the position-independent long value.
Return the tick labels for all the ticks at once.
Bases: matplotlib.units.ConversionInterface
Converter for datetime.date
and datetime.datetime
data, or for date/time data represented as it would be converted by date2num
.
The 'unit' tag for such data is None or a tzinfo instance.
Return the AxisInfo
for unit.
unit is a tzinfo instance or None. The axis argument is required but not used.
If value is not already a number or sequence of numbers, convert it with date2num
.
The unit and axis arguments are not used.
Return the tzinfo instance of x or of its first element, or None
Bases: matplotlib.ticker.Formatter
Format a tick (in days since the epoch) with a strftime
format string.
strftime
format string
datetime.tzinfo
, default: rcParams["timezone"]
(default: 'UTC'
)
Ticks timezone.
rcParams["text.usetex"]
(default: False
)
To enable/disable the use of TeX's math mode for rendering the results of the formatter.
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.
Convert axis data interval to datetime objects.
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 time zone info.
Convert the view interval to datetime objects.
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)
.
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.
Bases: matplotlib.dates.DateLocator
Make ticks on regular intervals of one or more microsecond(s).
Note
By default, Matplotlib uses a floating point representation of time in days since the epoch, so plotting data with microsecond time resolution does not work well for dates that are far (about 70 years) from the epoch (check with get_epoch
).
If you want sub-microsecond resolution time plots, it is strongly recommended to use floating point seconds, not datetime-like time representation.
If you really must use datetime.datetime() or similar and still need microsecond precision, change the time origin via dates.set_epoch
to something closer to the dates being plotted. See Date Precision and Epochs.
interval is the interval between each iteration. For example, if interval=2
, mark every second microsecond.
[Deprecated]
Deprecated since version 3.5:
[Deprecated]
Deprecated since version 3.5:
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]
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.
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.
Bases: matplotlib.dates.DateLocator
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]
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.
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.
Bases: matplotlib.dates.RRuleLocator
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).
Convert datetime objects to Matplotlib dates.
datetime.datetime
or numpy.datetime64
or sequences of these
Number of days since the epoch. See get_epoch
for the epoch, which can be changed by rcParams["date.epoch"]
(default: '1970-01-01T00:00:00'
) or set_epoch
. If the epoch is "1970-01-01T00:00:00" (default) then noon Jan 1 1970 ("1970-01-01T12:00:00") returns 0.5.
The Gregorian calendar is assumed; this is not universal practice. For details see the module docstring.
Convert a date string to a datenum using dateutil.parser.parse
.
The dates to convert.
The default date to use when fields are missing in d.
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.
datetime
The date limits.
datetime.timedelta
Spacing of the dates.
numpy.array
A list floats representing Matplotlib dates.
[Deprecated] Convert UNIX time to days since Matplotlib epoch.
Time in seconds since 1970-01-01.
numpy.array
Time in days since Matplotlib epoch (see get_epoch()
).
Deprecated since version 3.5.
Get the epoch used by dates
.
String for the epoch (parsable by numpy.datetime64
).
Convert Matplotlib dates to datetime
objects.
Number of days (fraction part represents hours, minutes, seconds) since the epoch. See get_epoch
for the epoch, which can be changed by rcParams["date.epoch"]
(default: '1970-01-01T00:00:00'
) or set_epoch
.
rcParams["timezone"]
(default: 'UTC'
)
Timezone of x.
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.
[Deprecated] Convert days since Matplotlib epoch to UNIX time.
Time in days since Matplotlib epoch (see get_epoch()
).
numpy.array
Time in seconds since 1970-01-01.
Deprecated since version 3.5.
Convert number of days to a timedelta
object.
If x is a sequence, a sequence of timedelta
objects will be returned.
Number of days. The fraction part represents hours, minutes, seconds.
datetime.timedelta
or list[datetime.timedelta
]Bases: object
The relativedelta type is designed to be applied to an existing datetime and can replace specific components of that datetime, or represents an interval of time.
It 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 arithmetic operation on the original datetime value with the information in the relativedelta. weekday: One of the weekday instances (MO, TU, etc) available in the relativedelta module. 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. This argument is always relative e.g. if the calculated date is already Monday, using MO(1) or MO(-1) won't change the day. To effectively make it absolute, use it in combination with the day argument (e.g. day=1, MO(1) for first Monday of the month). 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 non-leap 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
>>> from datetime import datetime >>> from dateutil.relativedelta import relativedelta, MO >>> dt = datetime(2018, 4, 9, 13, 37, 0) >>> delta = relativedelta(hours=25, day=1, weekday=MO(1)) >>> dt + delta datetime.datetime(2018, 4, 2, 14, 37)
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.
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 a dateutil.relativedelta.relativedelta
object.
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:
count --
If given, this determines how many occurrences will be generated.
Note
As of version 2.5.0, the use of the keyword until
in conjunction with count
is deprecated, to make sure dateutil
is fully compliant with RFC-5545 Sec. 3.3.10. Therefore, until
and count
must not occur in the same call to rrule
.
until --
If given, this must be a datetime instance specifying the upper-bound limit of the recurrence. The last recurrence in the rule is the greatest datetime that is less than or equal to the value specified in the until
parameter.
Note
As of version 2.5.0, the use of the keyword until
in conjunction with count
is deprecated, to make sure dateutil
is fully compliant with RFC-5545 Sec. 3.3.10. Therefore, until
and count
must not occur in the same call to rrule
.
Return new rrule with same attributes except for those attributes given new values by whichever keyword arguments are specified.
Set the epoch (origin for dates) for datetime calculations.
The default epoch is rcParams["dates.epoch"]
(by default 1970-01-01T00:00).
If microsecond accuracy is desired, the date being plotted needs to be within approximately 70 years of the epoch. Matplotlib internally represents dates as days since the epoch, so floating point dynamic range needs to be within a factor of 2^52.
set_epoch
must be called before any dates are converted (i.e. near the import section) or a RuntimeError will be raised.
See also Date Precision and Epochs.
valid UTC date parsable by numpy.datetime64
(do not include timezone).
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Licensed under the Matplotlib License Agreement.
https://matplotlib.org/3.5.1/api/dates_api.html