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.DateFormatter
The matplotlib.dates.DateFormatter
class is used to format a tick (in seconds since the epoch) with a string of strftime format. Its base class is matplotlib.ticker.Formatter
.
Syntax: class matplotlib.dates.DateFormatter(fmt, tz=None)
Parameters:
- fmt: It accepts a strftime format string for formatting and is a required argument.
- tz: It holds information regarding the timezone. If set to none it ignores the timezone information while formatting of the date string.
Example 1:
import numpy import matplotlib.pyplot as plt import matplotlib.dates as mdates import pandas total_bars = 25 numpy.random.seed(total_bars) dates = pandas.date_range( '3/4/2020' , periods = total_bars, freq = 'm' ) diff = pandas.DataFrame( data = numpy.random.randn(total_bars), index = dates, columns = [ 'A' ] ) figure, axes = plt.subplots(figsize = ( 10 , 6 )) axes.xaxis.set_major_formatter(mdates.DateFormatter( '%Y-%m' )) axes.bar(diff.index, diff[ 'A' ], width = 25 , align = 'center' ) |
Output:
Example 2:
import matplotlib import matplotlib.pyplot as plt from datetime import datetime origin = [ '2020-02-05 17:17:55' , '2020-02-05 17:17:51' , '2020-02-05 17:17:49' ] a = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S' ) for d in origin] b = [ '35.764299' , '20.3008' , '36.94704' ] x = matplotlib.dates.date2num(a) formatter = matplotlib.dates.DateFormatter( '%H:%M:%S' ) figure = plt.figure() axes = figure.add_subplot( 1 , 1 , 1 ) axes.xaxis.set_major_formatter(formatter) plt.setp(axes.get_xticklabels(), rotation = 15 ) axes.plot(x, b) plt.show() |
Output:
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