Thematplotlib.pyplot.plot() is used to create 2D plots such as line graphs and scatter plots. Theplot()function allows us to plot data points, customize line styles, markers and colors making it useful for various types of visualizations. In this article, we'll see how to use this function to plot data in Python.

Syntax: matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)

Parameters:

• x, y: Represent horizontal and vertical coordinates for the data points.
• fmt: A format string that defines the line style, marker and colour.
• data:The optional parameter can be an object containing labelled data which makes it easier to handle datasets directly.

Returns:A list ofLine2D objects each representing a segment of the plotted data.

There are many ways of creating plot usingMatplotlib.pyplot.plot() function some of their examples are:

Example 1: Line Plots in Matplotlib

Here we useMatplotlib'splot() function to create a simple line plot with the data [1, 2, 3, 4, 5].

importmatplotlib.pyplotaspltplt.plot([1,2,3,4,5])plt.title('Basic Line Plot')plt.show()

Output:

Example 2: Multiple Lines Using Matplotlib

We will plot sine and cosine functions on the same graph.

• plt.plot(x, y1, label='Sin(x)', color='blue'):Plots sine curve with x values and y1 values labeling it as "Sin(x)" and using a blue color for the line.
• plt.plot(x, y2, label='Cos(x)', color='red', linestyle='--'): Plots cosine curve with x values and y2 values labeling it as "Cos(x)" using a red color and a dashed line style.

importmatplotlib.pyplotaspltimportnumpyasnpx=np.linspace(0,2*np.pi,100)y1=np.sin(x)y2=np.cos(x)plt.plot(x,y1,label='Sin(x)',color='blue')plt.plot(x,y2,label='Cos(x)',color='red',linestyle='--')plt.xlabel('X-axis')plt.ylabel('Y-axis')plt.title('Multiple Lines Plot')plt.legend()plt.show()

Output:

Example 3:Scatter Plot with Custom Markers

We will generate and customize scatter plot with 50 random data points featuring red circular markers.

• plt.plot(x, y, marker='o', linestyle='', color='red', label='Scatter Plot'): Plots a scatter plot with x and y values and using red circular markers (marker='o') with no connecting lines (linestyle='') and labeling it as "Scatter Plot".

importmatplotlib.pyplotaspltimportnumpyasnpnp.random.seed(42)x=np.random.rand(50)y=np.random.rand(50)plt.plot(x,y,marker='o',linestyle='',color='red',label='Scatter Plot')plt.xlabel('X-axis')plt.ylabel('Y-axis')plt.title('Scatter Plot Example')plt.legend()plt.show()

Output:

Example 4: Plotting Multiple Curves

We are creating a line plot with two curves: a blue curvey = x^2and an orange curvey = 1 - x^3 generating data randomly. The plot is limited to the range [0, 1] on both axes showcasing a visual representation of mathematical functions.

importmatplotlib.pyplotaspltimportnumpyasnpnp.random.seed(19680801)xdata=np.random.random([2,10])xdata1=xdata[0,:]xdata2=xdata[1,:]xdata1.sort()xdata2.sort()ydata1=xdata1**2ydata2=1-xdata2**3plt.plot(xdata1,ydata1,color='tab:blue')plt.plot(xdata2,ydata2,color='tab:orange')plt.xlim([0,1])plt.ylim([0,1])plt.title('matplotlib.pyplot.plot() example 2')plt.show()

Output:

With the flexibility to customize line styles, markers and colors Matplotlib's plot() function provides various possibilities for visualizing our data in Python.

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