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.datestr2num()
The matplotlib.dates.datestr2num() function is used to convert a date string to a datenum by the uses of dateutil.parser.parser().
Syntax: matplotlib.dates.datestr2num(d, default=None)
Parameters:
- d: It is a string or a sequence of strings representing the dates.
- default: This is an optional parameter that is a datetime instance. This is used when fields are not present in d, as a default.
Example 1:
from datetime import datetime import matplotlib.pyplot as plt from matplotlib.dates import ( DateFormatter, AutoDateLocator, AutoDateFormatter, datestr2num ) days = [ '30/01/2019', '31/01/2019', '01/02/2019', '02/02/2019', '03/02/2019', '04/02/2019'] data1 = [2, 5, 13, 6, 11, 7] data2 = [6, 3, 10, 3, 6, 5] z = datestr2num([ datetime.strptime(day, '%d/%m/%Y').strftime('%m/%d/%Y') for day in days ]) r = 0.25 figure = plt.figure(figsize =(8, 4)) axes = figure.add_subplot(111) axes.bar(z - r, data1, width = 2 * r, color ='g', align ='center', tick_label = day) axes.bar(z + r, data2, width = 2 * r, color ='y', align ='center', tick_label = day) axes.xaxis_date() axes.xaxis.set_major_locator( AutoDateLocator(minticks = 3, interval_multiples = False)) axes.xaxis.set_major_formatter(DateFormatter("%d/%m/%y")) plt.show() |
Output:
Example 2:
import matplotlib import matplotlib.pyplot as plt import matplotlib.dates dates = ['1920-05-06', '1920-05-07', '1947-05-08', '1920-05-09'] converted_dates = matplotlib.dates.datestr2num(dates) x_axis = (converted_dates) y_axis = range(0, 4) plt.plot_date( x_axis, y_axis, '-' ) plt.show() |
Output:
