Matplotlib.axes.Axes.plot() in Python

Last Updated : 12 Apr, 2020

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 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() 

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 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() 

Output:

Example #2:

# 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() 

Output:

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matplotlib.axes.Axes.plot() Function