Combining Multiple Plots

Introduction

Data visualization helps us understand data patterns, trends, and relationships easily.
A dashboard combines different types of charts in one place to provide a complete view of data. In this notebook, we create three different plots:

Line Plot - Used to show trends over time.
Bar Plot - Used to compare values between categories.
Heatmap - Used to display relationships between variables.

Libraries Used

Pandas: Used for data handling and analysis.
NumPy: Used for generating numerical data.
Matplotlib: Used for creating basic visualizations.
Seaborn: Used for advanced statistical plots.

1. Line Plot - Monthly Sales Trend

A line plot represents data points connected by lines.
It is mainly used to understand changes and trends over time.

Objective:

To visualize monthly sales performance from January to June.

Code:

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]

sales = [120, 150, 170, 160, 190, 220]

plt.figure(figsize=(8, 4))
plt.plot(months, sales, marker="o", linewidth=2)

plt.title("Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales")

plt.show()

Observation:

Sales show an overall increasing trend.
A small decrease can be seen in April.
The highest sales are recorded in June.

Uses of Line Plot:

Tracking sales trends.
Monitoring growth over time.
Analyzing time-series data.

2. Bar Plot - Revenue by Category

A bar plot represents data using rectangular bars.
It is useful for comparing values among different categories.

Objective:

To compare revenue generated by different product categories.

Code:

categories = ["Electronics", "Furniture", "Clothing", "Books"]

revenue = [320, 210, 270, 140]

plt.figure(figsize=(8, 4))
plt.bar(categories, revenue)

plt.title("Revenue by Category")
plt.ylabel("Revenue")

plt.show()

Observation:

Electronics generates the highest revenue.
Furniture and Clothing have moderate revenue.
Books generate the lowest revenue.

Uses of Bar Plot:

Comparing categories.
Analyzing product performance.
Displaying survey or business data.

3. Heatmap - Correlation Analysis

A heatmap displays data values using different colors.
It helps identify relationships between numerical variables.

Objective:

To find correlations between Sales, Profit, Customers, and Returns.

Creating Dataset:

np.random.seed(42)

df = pd.DataFrame(
    {
        "Sales": np.random.randint(100, 300, 50),
        "Profit": np.random.randint(20, 100, 50),
        "Customers": np.random.randint(10, 50, 50),
        "Returns": np.random.randint(0, 15, 50),
    }
)

Code:

plt.figure(figsize=(6, 5))

sns.heatmap(
    df.corr(),
    annot=True,
    cmap="viridis"
)

plt.title("Correlation Heatmap")

plt.show()

Observation:

Heatmap shows the relationship between different variables.
Values close to 1 indicate positive correlation.
Values close to -1 indicate negative correlation.
Helps identify important patterns in data.

Uses of Heatmap:

Correlation analysis.
Finding relationships between variables.
Understanding complex datasets.

Combining Plots into a Dashboard

A dashboard combines multiple visualizations to provide meaningful insights. The three plots created in this notebook provide different views:

Visualization	Purpose
Line Plot	Shows monthly sales trends
Bar Plot	Compares revenue categories
Heatmap	Shows relationships between variables

Conclusion

Combining multiple plots in a single notebook helps analyze data from different perspectives.

Line plots help understand trends.
Bar plots help compare categories.
Heatmaps help analyze relationships.

Together, these visualizations create a simple dashboard that improves data understanding and supports better decision-making.

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Multiplot dashboard Practice

Combining Multiple Plots

Introduction

Libraries Used

1. Line Plot - Monthly Sales Trend

Objective:

Code:

Observation:

Uses of Line Plot:

2. Bar Plot - Revenue by Category

Objective:

Code:

Observation:

Uses of Bar Plot:

3. Heatmap - Correlation Analysis

Objective:

Creating Dataset:

Code:

Observation:

Uses of Heatmap:

Combining Plots into a Dashboard

Conclusion

​Combining Multiple Plots

​Introduction

​Libraries Used

​1. Line Plot - Monthly Sales Trend

​Objective:

​Code:

​Observation:

​Uses of Line Plot:

​2. Bar Plot - Revenue by Category

​Objective:

​Code:

​Observation:

​Uses of Bar Plot:

​3. Heatmap - Correlation Analysis

​Objective:

​Creating Dataset:

​Code:

​Observation:

​Uses of Heatmap:

​Combining Plots into a Dashboard

​Conclusion

Combining Multiple Plots

Introduction

Libraries Used

1. Line Plot - Monthly Sales Trend

Objective:

Code:

Observation:

Uses of Line Plot:

2. Bar Plot - Revenue by Category

Objective:

Code:

Observation:

Uses of Bar Plot:

3. Heatmap - Correlation Analysis

Objective:

Creating Dataset:

Code:

Observation:

Uses of Heatmap:

Combining Plots into a Dashboard

Conclusion