Scatter Plots

Scatter Plots

• A scatter plot is a visual representation of how two variables relate to each other. we can use scatter plots to explore the relationship between two variables, for example by looking for any correlation between them.

• Matplotlib also supports more advanced plots, such as scatter plots. In this case, the scatter function is used to display data values as a collection of x, y coordinates represented by standalone dots.

importmatplotlib.pyplot as plt

#X axis values:

x = [2,3,7,29,8,5,13,11,22,33]

# Y axis values:

y = [4,7,55,43,2,4,11,22,33,44]

# Create scatter plot:

plt.scatter(x, y)

plt.show()

• Comparing plt.scatter() and plt.plot(): We can also produce the scatter plot shown above using another function within matplotlib.pyplot. Matplotlib'splt.plot() is a general-purpose plotting function that will allow user to create various different line or marker plots.

• We can achieve the same scatter plot as the one obtained in the section above with the following call to plt.plot(), using the same data:

plt.plot(x, y, "o")

plt.show()

• In this case, we had to include the marker "o" as a third argument, as otherwise plt.plot() would plot a line graph. The plot created with this code is identical to the plot created earlier with plt.scatter().

. • Here's a rule of thumb that can use :

a) If we need a basic scatter plot, use plt.plot(), especially if we want to prioritize performance.

b) If we want to customize our scatter plot by using more advanced plotting features, use plt.scatter().

• Example: We can create a simple scatter plot in Python by passing x and y values to plt.scatter():

# scatter_plotting.py

importmatplotlib.pyplot as plt

plt.style.use('fivethirtyeight')

x = [2, 4, 6, 6, 9, 2, 7, 2, 6, 1, 8, 4, 5, 9, 1, 2, 3, 7, 5, 8, 1, 3]

y = [7, 8, 2, 4, 6, 4, 9, 5, 9, 3, 6, 7, 2, 4, 6, 7, 1, 9, 4, 3, 6, 9]

plt.scatter(x, y)

plt.show()

Output:

Creating Advanced Scatterplots

• Scatterplots are especially important for data science because they can show data patterns that aren't obvious when viewed in other ways.

import matplotlib.pyplot as plt

x_axis1 =[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

y_axis1 =[5, 16, 34, 56, 32, 56, 32, 12, 76, 89]

x_axis2 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

y_axis2 = [53, 6, 46, 36, 15, 64, 73, 25, 82, 9]

plt.title("Prices over 10 years")

plt.scatter(x_axis1, y_axis1, color = 'darkblue', marker='x', label="item 1")

plt.scatter(x_axis2, y_axis2, color='darkred', marker='x', label="item 2")

plt.xlabel("Time (years)")

plt.ylabel("Price (dollars)")

plt.grid(True)

plt.legend()

plt.show()

• The chart displays two data sets. We distinguish between them by the colour of the marker.