Data Visualization with Seaborn: 7 Steps Guide to Create Scatter Plot

In today’s world, data is more than just numbers—it’s a story waiting to be told. With tools like Python and Seaborn, you can transform raw data into visually appealing and insightful plots that help you make data-driven decisions. This blog walks you through a hands-on example of creating a professional scatter plot using Pandas, Seaborn, and Matplotlib. By the end, you’ll understand how to bring your data to life with a visually stunning and insightful plot.

We will use a dataset called tips.csv, which contains information about restaurant bills, tips, smoking habits, gender, and meal times. The goal is to visualize the relationship between the total bill amount and the tip received, while differentiating the data by customer gender.

Let’s understand the code step by step:

Step 1: Importing the Required Libraries (pandas, matplotlib, and seaborn)

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

1. pandas: Helps us read, manipulate, and analyze structured data.
2. seaborn: A high-level library for creating beautiful and informative visualizations.
3. matplotlib.pyplot: The foundation for plotting in Python, which Seaborn builds upon.

These libraries work together to give you both the power and flexibility to manipulate and visualize data.

Step 2: Loading the Dataset

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

Here, we load the dataset into a Pandas DataFrame called tips_data using the pd.read_csv() function. The tips.csv file contains restaurant data with columns like:

• total_bill: The total amount of the bill.
• tip: The tip amount given by the customer.
• gender: The gender of the customer (e.g., Male/Female).
• Other possible columns like day of the week, time of day, etc.

The print() function outputs the first few rows of the dataset, helping us understand its structure.

Step 3: Setting Up the Plotting Environment

plt.figure(figsize=(10, 6))
sns.set_theme(style='darkgrid')
sns.set_palette('RdBu')

• plt.figure(figsize=(10, 6)): Defines the size of the plot. A figure size of 10 inches wide and 6 inches tall ensures the plot is neither too small nor too cluttered.
• sns.set_theme(style='darkgrid'): Sets the overall style of the plot. The darkgrid style adds a subtle grid on a dark background, improving readability.
• sns.set_palette('RdBu'): Changes the color palette to Red-Blue (RdBu), providing a visually distinct color scheme for gender-based categorization.

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Step 4: Adding a Title

plt.title('Relation b/w total bill and tip', fontsize=20, color='green')

A well-chosen title sets the context for your audience. Here, we define:

• Title Text: “Relation b/w total bill and tip.”
• Font Size: 20, making it prominent.
• Color: Green, which stands out against the dark background.

Step 5: Creating the Scatter Plot

sns.scatterplot(data=tips_data, x='total_bill', y='tip',hue='gender', s=200,alpha=0.7,markers=['*','s'], style='gender')

This is where the graph is created! Let’s break down the arguments:

• data=tips_data: Specifies the dataset to use.
• x='total_bill' and y='tip': Define the x-axis (total bill amount) and y-axis (tip amount), respectively.
• hue='gender': Groups the data by gender, assigning different colors to each group.
• s=200: Sets the size of the scatter points, making them larger and easier to see.
• alpha=0.7: Adds transparency to the points, helping overlapping points stand out.
• markers=['*', 's'] and style='gender': Customizes markers for gender categories:
  • Asterisks (*) for one gender.
  • Squares (s) for the other gender.

Step 6: Customizing Axis Labels

plt.xlabel('Total Bill Amount', fontsize=15, color='red')
plt.ylabel('Tips Received', fontsize=15, color='red')

Descriptive axis labels ensure that viewers can quickly interpret the plot. Here, we define:

• X-axis Label: “Total Bill Amount.”
• Y-axis Label: “Tips Received.”
• Font Size: 15 for better readability.
• Color: Red, which contrasts well with the grid.

Step 7: Displaying the Plot

plt.show()

Finally, we use the plt.show() function to render the plot. This step brings everything together and displays the beautifully crafted scatter plot.

Interpreting the Plot

The resulting scatter plot reveals:

• The relationship between the total bill and tips: As the bill increases, tips generally increase too.
• Gender-based trends:
  • Each gender is represented with distinct markers and colors, making it easy to compare tipping behavior.
  • For example, one gender may consistently tip more for higher bills.

This simple yet effective visualization provides actionable insights for restaurants or data analysts studying customer behavior.

Why This Visualization Matters

1. Clarity: The scatter plot clearly shows trends and outliers.
2. Customization: Using Seaborn and Matplotlib allows you to customize every detail, from colors to markers.
3. Insights: Adding layers like hue and style enables deeper insights into categorical data.

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The Complete code

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

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


plt.figure(figsize=(10,6))
sns.set_theme(style='darkgrid')     # dark,white,whitegrid
sns.set_palette('RdBu')
plt.title('Relation b/w total bill and tip',fontsize=20, color='green')
sns.scatterplot(data=tips_data, x='total_bill',y='tip',hue='gender',s=200,alpha=0.7,markers=['*','s'],style='gender')

plt.xlabel('total bill amount',fontsize=15,color='red')
plt.ylabel('tips received',fontsize=15,color='red')

plt.show()