Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps

In this blog post, we’ll walk through a simple data visualization example using Matplotlib in Python. We aim to create a scatter plot showing the relationship between the total bill and tip amounts from a dataset. This example will help you understand how to create meaningful visualizations step-by-step, adjust aesthetics like size and colors, and save the plot as an image file.

Step 1: Importing the Required Libraries

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

• Matplotlib: This is a powerful library for creating static, interactive, and animated visualizations in Python.
• Pandas: A widely-used data manipulation library for reading and handling datasets.
• NumPy: Provides efficient numerical operations, such as creating sequences of numbers (used here for tick values).
  

Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps

Step 2: Loading the Dataset

df = pd.read_csv('tips.csv')
print(df)

Here, we use pandas.read_csv() to load the tips dataset from a CSV file. This dataset contains information about meals, including total bill, tip amount, and other variables like day, time, and the number of diners.

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Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps

Step 3: Creating the Scatter Plot

plt.figure(figsize=(8,6))
plt.title('Total bill vs tip')
plt.scatter(df['total_bill'], df['tip'], s=100, color='purple', alpha=0.5)

• plt.figure(figsize=(8,6)): This creates a new figure with a width of 8 inches and a height of 6 inches.
• plt.title(): Sets the title of the plot to “Total bill vs tip”.
• plt.scatter(): This function generates a scatter plot:
• df['total_bill']: Values for the x-axis (total bill).
• df['tip']: Values for the y-axis (tip amount).
• s=100: Sets the size of each data point to 100 units.
• color='purple': Sets the color of the points to purple.
• alpha=0.5: Sets the transparency of the points to 50%, allowing for overlapping points to be visible.

Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps

Step 4: Adding Labels and Customizing Ticks

plt.xlabel('Total Bill')
plt.ylabel('Tip')

plt.xticks(np.arange(5, 60, 5))
plt.yticks(np.arange(1, 11))

• plt.xlabel() and plt.ylabel(): These functions label the x-axis and y-axis, respectively.
• plt.xticks() and plt.yticks(): Set the tick values for the x and y axes:

• np.arange(5, 60, 5): Creates a range of values from 5 to 55 with a step size of 5 for the x-axis.
• np.arange(1, 11): Creates tick values from 1 to 10 for the y-axis.

Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps

Step 5: Adding a Grid

plt.grid()

plt.grid(): Displays a grid on the plot, making it easier to read the data points.

Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps

Step 6: Saving the Plot as an Image

plt.savefig('total_bill_vs_tips.png', format='png', dpi=300)

• plt.savefig(): Saves the plot as an image file.

• 'total_bill_vs_tips.png': The filename for the image.
• format='png': Specifies that the format should be PNG.
• dpi=300: Sets the resolution of the image to 300 DPI (dots per inch), making it suitable for printing.

Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps

Step 7: Displaying the Plot

plt.show()

plt.show(): Renders the plot in the output. This ensures the scatter plot is displayed when running the code in interactive environments like Jupyter Notebooks.

Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps

Output

When the code is executed, you’ll see a scatter plot showing how the total bill correlates with the tip amount. Points with a higher total bill tend to show higher tips, illustrating the positive relationship between the two variables.

The Code:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df=pd.read_csv('tips.csv')
print(df)


plt.figure(figsize=(8,6))
plt.title('Total bill vs tip')
plt.scatter(df['total_bill'],df['tip'],s=100,color='purple',alpha=0.5)
plt.xlabel('Total Bill')
plt.ylabel('Tip')

plt.xticks(np.arange(5,60,5))
plt.yticks(np.arange(1,11))

plt.grid()

plt.savefig('total_bill_vs_tips.png',format='png',dpi=300)

plt.show()

Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps

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Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps