> ## Documentation Index
> Fetch the complete documentation index at: https://ai.tharung.in/llms.txt
> Use this file to discover all available pages before exploring further.
# Multiplot dashboard Practice
# 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**:
1. **Line Plot** - Used to show trends over time.
2. **Bar Plot** - Used to compare values between categories.
3. **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:
```python theme={null}
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:
```python theme={null}
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:
```python theme={null}
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:
```python theme={null}
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.