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()andplt.ylabel(): These functions label the x-axis and y-axis, respectively.plt.xticks()andplt.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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