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Arum Puri
Arum Puri

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Comprehensive Tutorial: Exploring Seaborn Lineplot

#seaborn #datascience #tutorial #beginners

Seaborn's lineplot is a powerful tool for visualizing trends and relationships in your data. In this tutorial, we’ll use lineplot to analyze how student attendance impacts exam scores, customizing our visualization with colors, markers, styles, and more.

Who is This Tutorial For?

This tutorial is designed for those who:

  • Have experience using Python and libraries like Pandas.

  • Familiarity with code editors such as Visual Studio Code, Jupyter Notebook, or similar tools is recommended. For this tutorial, we’ll be using Visual Studio Code.

  • Ensure that Matplotlib and Seaborn are installed on your system. If you encounter any issues during installation, refer to the Matplotlib documentation and Seaborn documentation for guidance.

If you're new to Pandas, check out this Pandas crash course to get started.

What You'll Learn

By the end of this tutorial, you’ll know how to:

  1. Load and prepare a dataset.
  2. Create basic and enhanced line plots.
  3. Customize plots using attributes like background styles, colors, error bars, markers, and more.

Step 1: Setting Up Your Project

Download the Dataset

  1. Download the Student Performance Factors dataset from Kaggle.
  2. Extract the ZIP file and locate StudentPerformanceFactors.csv.

Organize Your Files

  1. Create a folder named data_visualization.
  2. Move the dataset to this folder.
  3. Create a new Python script file named visualization.py.

Step 2: Loading the Dataset

Start loading the data into a Pandas DataFrame.

Import the libraries.

# Import libraries import pandas as pd import matplotlib.pyplot as plt import seaborn as sns
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Loading Data

# Path of the file to read filepath = "StudentPerformanceFactors.csv" # Fill in the line below to read the file into a variable data student_data= pd.read_csv(filepath) # View the first few rows of the dataset print(student_data.head())
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Note:
If your dataset is located in a different folder, update filepath to reflect the correct relative path.

Step 3: Creating a Basic Line Plot

We’ll start by plotting how attendance affects exam scores.

Basic line plot

# Basic line plot # This line is where you will change your code sns.lineplot(data=student_data, x="Attendance", y="Exam_Score") # Add title and labels plt.title("How Attendance Affects Exam Scores") plt.xlabel("Attendance (days)") plt.ylabel("Exam Score") plt.show()
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Execute the code by running python3 visualization.py in the command line each time you want to test your changes.

Result:
Image description

Step 4: Enhancing the Visualization

1. Adding Categories with Hue
Add hue attribute to add a gender category on your graph.

sns.lineplot(data=student_data, x="Attendance", y="Exam_Score", hue="Gender")
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Result:
Image description

2. Customizing Colors
Use either predefined palettes or define custom colors.

Use a Predefined Palette

# Use a predefined palette sns.lineplot(data=student_data, x="Attendance", y="Exam_Score", hue="Gender", palette="coolwarm")
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Result:
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Use a Custom Palette

# Define and apply a custom color palette custom_palette = sns.color_palette(["#FF5733", "#33FF57"]) # Hex colors sns.lineplot(data=student_data, x="Attendance", y="Exam_Score", hue="Gender", palette=custom_palette)
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Result:
Image description

Step 5: Adding Additional Attributes

1. Error Bars
Visualize variability or confidence intervals using the errorbar attribute.

# Add error bars (standard deviation) sns.lineplot(data=student_data, x="Attendance", y="Exam_Score", hue="Gender", errorbar="sd")
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Result:
Image description

2. Differentiating Line Styles
Use the style attribute to represent categories with different line patterns.

# Differentiate line styles by gender sns.lineplot(data=student_data, x="Attendance", y="Exam_Score", hue="Gender", style="Gender")
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Result:
Image description

3. Customize Line Dashes

# Apply custom dashes for different categories sns.lineplot(data=student_data, x="Attendance", y="Exam_Score", hue="Gender", style="Gender", dashes=[(2, 2), (4, 4)])
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Result:
Image description

4. Add Markers to Highlight Data Points

# Add markers to the plot sns.lineplot(data=student_data, x="Attendance", y="Exam_Score", hue="Gender", style="Gender", markers=True, dashes=False)
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Result:
Image description

Step 6: Combining All Features

Finally, all these features are combined into a comprehensive line plot.

# Comprehensive line plot sns.lineplot( data=student_data, x="Attendance", y="Exam_Score", hue="Gender", style="Gender", palette="coolwarm", markers=True, dashes=[(2, 2), (4, 4)], errorbar="sd" ) # Add title and axis labels plt.title("Comprehensive Line Plot: Attendance vs Exam Scores") plt.xlabel("Attendance (days)") plt.ylabel("Exam Score") # Show the plot plt.show()
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Result:
Image description

Step 7: Additional Customizations

Change Background Color

# Customize background color plt.gca().set_facecolor("#EAEAF2") # Light greyish-blue plt.show()
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Result:
Image description

Seaborn’s lineplot is a flexible and customizable tool for visualizing data trends. In this tutorial, you’ve learned to:

  1. Create basic and enhanced line plots.
  2. Use features like hue, palette, errorbar, style, and markers.

Want to learn more? Check out my Seaborn Cheatsheet or read the Plot Selection Guide for inspiration on choosing the right plot for your data.

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