Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library.
The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.
Example:
import datetimeimport matplotlib.pyplot as pltfrom matplotlib.dates import DayLocator, HourLocator, DateFormatter, drangeimport numpy as np date1 = datetime.datetime(2000, 3, 2)date2 = datetime.datetime(2000, 3, 6)delta = datetime.timedelta(hours = 6)dates = drange(date1, date2, delta) y = np.arange(len(dates)) fig, ax = plt.subplots()ax.plot_date(dates, y ** 2) ax.set_xlim(dates[0], dates[-1]) ax.xaxis.set_major_locator(DayLocator())ax.xaxis.set_minor_locator(HourLocator(range(0, 25, 6)))ax.xaxis.set_major_formatter(DateFormatter('% Y-% m-% d')) ax.fmt_xdata = DateFormatter('% Y-% m-% d % H:% M:% S')fig.autofmt_xdate() plt.title("Matplotlib Axes Class Example")plt.show() |
Output:
matplotlib.axes.Axes.plot() Function
The Axes.plot() function in axes module of matplotlib library is used to plot y versus x as lines and/or markers.
Syntax: Axes.plot(self, *args, scalex=True, scaley=True, data=None, **kwargs)
Parameters: This method accept the following parameters that are described below:
- x, y: These parameter are the horizontal and vertical coordinates of the data points. x values are optional.
- fmt: This parameter is an optional parameter and it contains the string value.
- data: This parameter is an optional parameter and it is an object with labelled data.
Returns: This returns the following:
- lines:This returns the list of Line2D objects representing the plotted data.
Below examples illustrate the matplotlib.axes.Axes.plot() function in matplotlib.axes:
Example #1:
# Implementation of matplotlib function import matplotlib.pyplot as pltimport numpy as np # make an agg figurefig, ax = plt.subplots()ax.plot([1, 2, 3])ax.set_title('matplotlib.axes.Axes.plot() example 1')fig.canvas.draw()plt.show() |
Output:
Example #2:
# Implementation of matplotlib function import matplotlib.pyplot as pltimport numpy as np # Fixing random state for reproducibilitynp.random.seed(19680801) # create random dataxdata = np.random.random([2, 10]) # split the data into two partsxdata1 = xdata[0, :]xdata2 = xdata[1, :] # sort the data so it makes clean curvesxdata1.sort()xdata2.sort() # create some y data pointsydata1 = xdata1 ** 2ydata2 = 1 - xdata2 ** 3 # plot the datafig = plt.figure()ax = fig.add_subplot(1, 1, 1)ax.plot(xdata1, ydata1, color ='tab:blue')ax.plot(xdata2, ydata2, color ='tab:orange') # set the limitsax.set_xlim([0, 1])ax.set_ylim([0, 1]) ax.set_title('matplotlib.axes.Axes.plot() example 2') # display the plotplt.show() |
Output:
