Matplotlib.dates.AutoDateFormatter class in Python (original) (raw)

Last Updated : 25 Apr, 2025

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

matplotlib.dates.AutoDateFormatter

The matplotlib.dates.AutoDateFormatter class is used to figure out the best format to use for the date. This is best used with AutoDateLocator. It has a dictionary that maps scale of the tick and a format string. By default it looks as below:

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 key in that dictionary that is greater then or equal to the current scale is picked by this algorithm and is used to format strings. This dictionary can also be customized like below:

locator = AutoDateLocator() formatter = AutoDateFormatter(locator)

only show hour and minute

formatter.scaled[1/(24.*60.)] = '%H:%M'

The default format is used if no value in self.scaled is greater than the unit returned by locator._get_unit().

Syntax: class matplotlib.dates.AutoDateFormatter(locator, tz=None, defaultfmt=’%Y-%m-%d’)

Parameters:

1. locator: It determines the tick locations when plotting dates.
2. tz: It holds the time-zone information
3. defaultfmt: If no format matches=ches the requirement this format is used as a default in years-months-days format.

Example 1:

Python3

import datetime

import matplotlib.pyplot as plt

from matplotlib.dates import AutoDateLocator, AutoDateFormatter, date2num

date = '2020-02-23'

low = 10

date_datetime = datetime.datetime.strptime(date, '% Y-% m-% d' )

int_date = date2num( date_datetime)

figure, axes = plt.subplots()

axes.bar(int_date, low, label = " ", color =" green")

locator = AutoDateLocator()

axes.xaxis.set_major_locator(locator)

axes.xaxis.set_major_formatter( AutoDateFormatter(locator) )

min_date = date2num( datetime.datetime.strptime( '2020-02-16' , '% Y-% m-% d' ) )

max_date = date2num( datetime.datetime.strptime( '2020-02-28' , '% Y-% m-% d' ) )

axes.set_xlim([min_date, max_date])

figure.autofmt_xdate()

plt.show()

Output:

Example 2:

Python3

import random

import matplotlib.pyplot as plt

import matplotlib.dates as mdates

random_data = [ float (random.randint( 1487517521 ,

`` 14213254713 ))

`` for _ in range ( 1000 )]

mpl_data = mdates.epoch2num(random_data)

fig, axes = plt.subplots( 1 , 1 )

axes.hist(mpl_data, bins = 51 , color = 'green' )

locator = mdates.AutoDateLocator()

axes.xaxis.set_major_locator(locator)

axes.xaxis.set_major_formatter(mdates.AutoDateFormatter(locator))

plt.show()

Output: