Matplotlib.axes.Axes.plot() in Python

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:

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import datetime
import matplotlib.pyplot as plt
from matplotlib.dates import DayLocator, HourLocator, DateFormatter, drange
import 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()

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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:

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# Implementation of matplotlib function
    
import matplotlib.pyplot as plt
import numpy as np
  
# make an agg figure
fig, ax = plt.subplots()
ax.plot([1, 2, 3])
ax.set_title('matplotlib.axes.Axes.plot() example 1')
fig.canvas.draw()
plt.show()

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Output:

Example #2:

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# Implementation of matplotlib function
  
import matplotlib.pyplot as plt
import numpy as np
  
# Fixing random state for reproducibility
np.random.seed(19680801)
  
# create random data
xdata = np.random.random([2, 10])
  
# split the data into two parts
xdata1 = xdata[0, :]
xdata2 = xdata[1, :]
  
# sort the data so it makes clean curves
xdata1.sort()
xdata2.sort()
  
# create some y data points
ydata1 = xdata1 ** 2
ydata2 = 1 - xdata2 ** 3
  
# plot the data
fig = 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 limits
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])
  
ax.set_title('matplotlib.axes.Axes.plot() example 2')
  
# display the plot
plt.show()

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Output:




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