> ## Documentation Index > Fetch the complete documentation index at: https://python4ai.codewithsiva.dev/llms.txt > Use this file to discover all available pages before exploring further. # Data Visualization Guide > A comprehensive guide to Univariate, Bivariate, and Multivariate analysis using Matplotlib and Seaborn on student performance data Visualizing your data is one of the most critical steps in Data Science and Machine Learning. It helps you understand distributions, detect anomalies, identify correlations, and extract actionable insights. This guide walks through the three main types of data analysis using **Matplotlib** and **Seaborn**, using the student performance dataset (`student_dataset.csv`): 1. **Univariate Analysis:** Analyzing one variable at a time. 2. **Bivariate Analysis:** Analyzing relationships between two variables. 3. **Multivariate Analysis:** Analyzing interactions between three or more variables. *** ## 1. Setup and Loading Data First, import the required libraries and load the student performance dataset: ```python theme={null} import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # Set global Seaborn style sns.set_theme(style="whitegrid", palette="muted") # Load the dataset df = pd.read_csv('public/data/student_dataset.csv') ``` *** ## 2. Univariate Analysis (Analyzing Single Variables) Univariate analysis inspects the frequency, distribution, and spread of a **single column** without considering its relationship to other columns. ### A. Numerical Columns (Histograms & KDE) To visualize the spread of continuous values like `study_hours` or `exam_score`, use a histogram with a density curve: ```python theme={null} # Distribution of Study Hours plt.figure(figsize=(7, 4)) sns.histplot(data=df, x="study_hours", kde=True, color="teal") plt.title("Univariate Analysis: Distribution of Weekly Study Hours") plt.xlabel("Study Hours per Week") plt.ylabel("Number of Students") plt.show() ``` ### B. Categorical Columns (Count Plots) To visualize the frequency of discrete labels like `placement_status`, use a count plot: ```python theme={null} # Count of Placed vs. Not Placed Students plt.figure(figsize=(5, 4)) sns.countplot(data=df, x="placement_status", palette="pastel") plt.title("Univariate Analysis: Student Placement Counts") plt.xlabel("Placement Status") plt.ylabel("Student Count") plt.show() ``` *** ## 3. Bivariate Analysis (Analyzing Two Variables) Bivariate analysis studies how **two variables** interact with each other. ### A. Numerical vs. Numerical (Scatter Plots) To find correlations or associations between two continuous variables (e.g., `study_hours` vs. `exam_score`), use a scatter plot: ```python theme={null} # Relationship between study hours and exam scores plt.figure(figsize=(7, 4)) sns.scatterplot(data=df, x="study_hours", y="exam_score", alpha=0.6, color="purple") plt.title("Bivariate Analysis: Study Hours vs. Exam Scores") plt.xlabel("Weekly Study Hours") plt.ylabel("Exam Score (out of 100)") plt.show() ``` ### B. Numerical vs. Categorical (Box Plots / Violin Plots) To compare distributions of a continuous variable (like `exam_score`) across different categories (like `placement_status`), use a box plot or a violin plot: ```python theme={null} # Comparing exam scores across placement categories fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # Box Plot (shows quartiles and outliers) sns.boxplot(data=df, x="placement_status", y="exam_score", ax=axes[0], palette="Set2") axes[0].set_title("Exam Scores by Placement (Box Plot)") axes[0].set_xlabel("Placement Status") axes[0].set_ylabel("Exam Score") # Violin Plot (shows density shape of distributions) sns.violinplot(data=df, x="placement_status", y="exam_score", ax=axes[1], palette="Set2") axes[1].set_title("Exam Scores by Placement (Violin Plot)") axes[1].set_xlabel("Placement Status") axes[1].set_ylabel("Exam Score") plt.tight_layout() plt.show() ``` ### C. Categorical vs. Categorical (Grouped Count Plots) To study the overlap between two categories (e.g., how sleep habits affect placement), group a category using the `hue` parameter: ```python theme={null} # First, create a categorical 'sleep_category' column df['sleep_category'] = np.where(df['sleep_hours'] >= 7, 'Healthy', 'Sleep Deprived') # Plot grouped counts plt.figure(figsize=(7, 4)) sns.countplot(data=df, x="sleep_category", hue="placement_status", palette="coolwarm") plt.title("Bivariate Analysis: Sleep Quality vs. Placement Status") plt.xlabel("Sleep Category") plt.ylabel("Number of Students") plt.legend(title="Placement") plt.show() ``` *** ## 4. Multivariate Analysis (Analyzing Multiple Variables) Multivariate analysis looks at how **three or more variables** interact. It is highly useful for spotting complex patterns, trends, and identifying correlations across the entire dataset. ### A. Scatter Plots with Color & Size Mapping You can map a third variable to the **color (`hue`)** and a fourth variable to the **size (`size`)** of dots in a scatter plot: ```python theme={null} # Analyzing Study Hours vs. Exam Score, grouped by Placement, sized by Attendance plt.figure(figsize=(9, 5)) sns.scatterplot( data=df.sample(n=1000, random_state=42), # Sample 1000 rows for cleaner visual display x="study_hours", y="exam_score", hue="placement_status", size="attendance", sizes=(20, 200), alpha=0.7, palette="RdYlGn" ) plt.title("Multivariate Analysis: Study Hours vs. Exam Scores (Sized by Attendance)") plt.xlabel("Weekly Study Hours") plt.ylabel("Exam Score") plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left') # Place legend outside the chart plt.tight_layout() plt.show() ``` ### B. Correlation Heatmaps To evaluate all linear relationships across the entire dataset simultaneously, generate a correlation heatmap: ```python theme={null} # Select only numerical features numeric_df = df.select_dtypes(include=["number"]) # Calculate Pearson correlation matrix corr_matrix = numeric_df.corr() # Plot Heatmap plt.figure(figsize=(8, 6)) sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", fmt=".2f", vmin=-1, vmax=1, linewidths=0.5) plt.title("Multivariate Analysis: Correlation Heatmap of Student Metrics") plt.show() ``` *** ## Practice and Next Steps Before moving to the next section, make sure to practice your overall Data Visualization skills using the interactive notebook: Practice your skills using the interactive notebook. 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