importmatplotlib.pyplotaspltimportnumpyasnpx=np.array([12,45,7,32,89,54,23,67,14,91])y=np.array([99,31,72,56,19,88,43,61,35,77])plt.scatter(x,y)plt.title("Basic Scatter Plot")plt.xlabel("X Values")plt.ylabel("Y Values")plt.show()

Output

Explanation: plt.scatter(x, y) creates a scatter plot on a 2D plane to visualize the relationship between two variables, with a title and axis labels added for clarity and context.

Syntax

matplotlib.pyplot.scatter(x, y, s=None, c=None, marker=None, cmap=None, alpha=None, edgecolors=None, label=None)

Parameters:

Returns: This function returns a PathCollection object representing the scatter plot points. This object can be used to further customize the plot or to update it dynamically.

Examples

Example 1: In this example, we compare the height and weight of two different groups using different colors for each group.

x1=np.array([160,165,170,175,180,185,190,195,200,205])y1=np.array([55,58,60,62,64,66,68,70,72,74])x2=np.array([150,155,160,165,170,175,180,195,200,205])y2=np.array([50,52,54,56,58,64,66,68,70,72])plt.scatter(x1,y1,color='blue',label='Group 1')plt.scatter(x2,y2,color='red',label='Group 2')plt.xlabel('Height (cm)')plt.ylabel('Weight (kg)')plt.title('Comparison of Height vs Weight between two groups')plt.legend()plt.show()

Output

Explanation: We define NumPy arrays x1,y1 andx2,y2for height and weight data of two groups. Using plt.scatter(), Group 1 is plotted in blue and Group 2 in red, each with labels. The x-axis and y-axis are labeled "Height (cm)" and "Weight (kg)" for clarity.

Example 2:This example demonstrates how to customize a scatter plot using different marker sizes and colors for each point. Transparency and edge colors are also adjusted.

x=np.array([3,12,9,20,5,18,22,11,27,16])y=np.array([95,55,63,77,89,50,41,70,58,83])a=[20,50,100,200,500,1000,60,90,150,300]# sizeb=['red','green','blue','purple','orange','black','pink','brown','yellow','cyan']# colorplt.scatter(x,y,s=a,c=b,alpha=0.6,edgecolors='w',linewidth=1)plt.title("Scatter Plot with Varying Colors and Sizes")plt.show()

Output

Explanation:NumPy arrays x andy set point coordinates,a defines marker sizes andbassigns colors.plt.scatter() plots the points with transparency, white edges and linewidth. A title is added before displaying the plot.

Example 3: This example shows how to create a bubble plot where the size of each point (bubble) represents a variable's magnitude. Edge color and alpha transparency are also used.

x=[1,2,3,4,5]y=[2,3,5,7,11]sizes=[30,80,150,200,300]# Bubble sizesplt.scatter(x,y,s=sizes,alpha=0.5,edgecolors='blue',linewidths=2)plt.title("Bubble Plot Example")plt.xlabel("X-axis")plt.ylabel("Y-axis")plt.show()

Output

Explanation: Listsx and y define point coordinates, while sizes sets the marker (bubble) sizes. The plt.scatter() plots the bubbles with 50% transparency (alpha=0.5), blue edges and edge width of 2. Axis labels and a title are added before displaying the plot.

Example 4:In this example, we map data values to colors using a colormap and add a colorbar. This helps in visualizing a third variable via color intensity.

x=np.random.randint(50,150,100)y=np.random.randint(50,150,100)colors=np.random.rand(100)# Random float values for color mappingsizes=20*np.random.randint(10,100,100)plt.scatter(x,y,c=colors,s=sizes,cmap='viridis',alpha=0.7)plt.colorbar(label='Color scale')plt.title("Scatter Plot with Colormap and Colorbar")plt.show()

Output

Explanation:Random arraysx andy set 100 points, with colors mapped using 'viridis' and varying sizes.plt.scatter() plots them with 0.7 transparency andplt.colorbar()adds a color legend.

Example 5:This final example illustrates how to change the marker style using the marker parameter. Here, triangle markers are used with magenta color.

plt.scatter(x,y,marker='^',color='magenta',s=100,alpha=0.7)plt.title("Scatter Plot with Triangle Markers")plt.show()

Output

Explanation: This code plots points with triangle markers ('^') in magenta color, size 100, and 0.7 transparency. A title is added before displaying the plot.