Python中的 Matplotlib.dates.ConciseDateFormatter 类

Matplotlib是Python中用于数组二维图的惊人可视化库。 Matplotlib 是一个基于 NumPy 数组构建的多平台数据可视化库，旨在与更广泛的 SciPy 堆栈配合使用。

matplotlib.dates.ConciseDateFormatter

matplotlib.dates.ConciseDateFormatter类用于找出用于日期的最佳格式，并使其尽可能紧凑但完整。这更常与AutoDateLocator一起使用。

Syntax: class matplotlib.dates.ConciseDateFormatter(locator, tz=None, formats=None, offset_formats=None, zero_formats=None, show_offset=True)

Parameters:

locator: This parameter represents the locator that this axis uses.

tz: It is an optional parameter that accepts a string that is passed to dates.date2num.

formats: It is an optional list of 6 strings. It is used to format strings for 6 levels of tick labelling as years, months, days, hours, minutes and seconds. These strings has format codes same as that of strftime. Its defaults are as [ ‘%Y’, ‘%b’, ‘%d’, ‘%H:%M’, ‘%H:%M’, ‘%S.%f’]

zero_formats: It is an optional list of 6 strings. It is used for formatting strings for tick labels that are “zero” for any given tick level. For example, if most ticks are months, ticks around 1 February 2020 will get labeled “Jan” 2020 “March”.Its defaults are as [”, ‘%Y’, ‘%b’, ‘%b-%d’, ‘%H:%M’, ‘%H:%M’].

offset_formats: It is an optional list of 6 strings.It is used for formatting strings for 6 levels that is applied to the “offset” string on the right side of x-axis or top of y-axis. This should completely specify date when combined with the tick labels. The defaults are as [”, ‘%Y’, ‘%Y-%b’, ‘%Y-%b-%d’, ‘%Y-%b-%d’, ‘%Y-%b-%d %H:%M’].

show_offset: It accepts a boolean value and decides whether to show offset or not. By default it is set to True.

示例 1：

import numpy as np
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
  
   
# dummy date
dummy_date = np.arange("2020-04-10", 
                       "2020-05-14",
                       dtype ="datetime64")
  
random_x = np.random.rand(len(dummy_date))
   
figure, axes = plt.subplots()
  
axes.plot(dummy_date, random_x)
axes.xaxis.set(
    major_locator = mdates.AutoDateLocator(minticks = 1,
                                           maxticks = 5),
)
  
locator = mdates.AutoDateLocator(minticks = 15,
                                 maxticks = 20)
formatter = mdates.ConciseDateFormatter(locator)
  
axes.xaxis.set_major_locator(locator)
axes.xaxis.set_major_formatter(formatter)
   
plt.show()

输出：

示例 2：

import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
  
  
dummy_date = datetime.datetime(2020, 2, 1)
  
# random date generator
dates = np.array([dummy_date + datetime.timedelta(hours =(2 * i))
                  for i in range(732)])
date_length = len(dates)
  
np.random.seed(194567801)
y_axis = np.cumsum(np.random.randn(date_length))
  
lims = [(np.datetime64('2020-02'), 
         np.datetime64('2020-04')),
        (np.datetime64('2020-02-03'), 
         np.datetime64('2020-02-15')),
        (np.datetime64('2020-02-03 11:00'), 
         np.datetime64('2020-02-04 13:20'))]
  
figure, axes = plt.subplots(3, 1, 
                            constrained_layout = True, 
                            figsize =(6, 6))
  
for nn, ax in enumerate(axes):
      
    locator = mdates.AutoDateLocator(minticks = 3, maxticks = 7)
    formatter = mdates.ConciseDateFormatter(locator)
      
    ax.xaxis.set_major_locator(locator)
    ax.xaxis.set_major_formatter(formatter)
  
    ax.plot(dates, y_axis)
    ax.set_xlim(lims[nn])
      
axes[0].set_title('Concise Date Formatter')
  
plt.show()

输出：

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