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diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md
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@@ -7,6 +7,7 @@
 - [Line Charts in Matplotlib](matplotlib-line-plots.md)
 - [Scatter Plots in Matplotlib](matplotlib-scatter-plot.md)
 - [Violin Plots in Matplotlib](matplotlib-violin-plots.md)
+- [subplots in Matplotlib](matplotlib-sub-plot.md)
 - [Introduction to Seaborn and Installation](seaborn-intro.md)
 - [Seaborn Plotting Functions](seaborn-plotting.md)
 - [Getting started with Seaborn](seaborn-basics.md)
diff --git a/contrib/plotting-visualization/matplotlib-sub-plot.md b/contrib/plotting-visualization/matplotlib-sub-plot.md
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+### 1. Using `plt.subplots()`
+
+The `plt.subplots()` function is a versatile and easy way to create a grid of subplots. It returns a figure and an array of Axes objects.
+
+#### Code Explanation
+
+1. **Import Libraries**:
+   ```python
+   import matplotlib.pyplot as plt
+   import numpy as np
+   ```
+
+2. **Generate Sample Data**:
+   ```python
+   x = np.linspace(0, 10, 100)
+   y1 = np.sin(x)
+   y2 = np.cos(x)
+   y3 = np.tan(x)
+   ```
+
+3. **Create Subplots**:
+   ```python
+   fig, axs = plt.subplots(3, 1, figsize=(8, 12))
+   ```
+
+   - `3, 1` indicates a 3-row, 1-column grid.
+   - `figsize` specifies the overall size of the figure.
+
+4. **Plot Data**:
+   ```python
+   axs[0].plot(x, y1, 'r')
+   axs[0].set_title('Sine Function')
+   
+   axs[1].plot(x, y2, 'g')
+   axs[1].set_title('Cosine Function')
+   
+   axs[2].plot(x, y3, 'b')
+   axs[2].set_title('Tangent Function')
+   ```
+
+5. **Adjust Layout and Show Plot**:
+   ```python
+   plt.tight_layout()
+   plt.show()
+   ```
+
+#### Result
+
+The result will be a figure with three vertically stacked subplots.
+![subplot Chart](images/subplots.png)
+
+### 2. Using `plt.subplot()`
+
+The `plt.subplot()` function allows you to add a single subplot at a time to a figure.
+
+#### Code Explanation
+
+1. **Import Libraries and Generate Data** (same as above).
+
+2. **Create Figure and Subplots**:
+   ```python
+   plt.figure(figsize=(8, 12))
+   
+   plt.subplot(3, 1, 1)
+   plt.plot(x, y1, 'r')
+   plt.title('Sine Function')
+   
+   plt.subplot(3, 1, 2)
+   plt.plot(x, y2, 'g')
+   plt.title('Cosine Function')
+   
+   plt.subplot(3, 1, 3)
+   plt.plot(x, y3, 'b')
+   plt.title('Tangent Function')
+   ```
+
+3. **Adjust Layout and Show Plot** (same as above).
+
+#### Result
+
+The result will be similar to the first method but created using individual subplot commands.
+
+![subplot Chart](images/subplots.png)
+
+### 3. Using `GridSpec`
+
+`GridSpec` allows for more complex subplot layouts.
+
+#### Code Explanation
+
+1. **Import Libraries and Generate Data** (same as above).
+
+2. **Create Figure and GridSpec**:
+   ```python
+   from matplotlib.gridspec import GridSpec
+   
+   fig = plt.figure(figsize=(8, 12))
+   gs = GridSpec(3, 1, figure=fig)
+   ```
+
+3. **Create Subplots**:
+   ```python
+   ax1 = fig.add_subplot(gs[0, 0])
+   ax1.plot(x, y1, 'r')
+   ax1.set_title('Sine Function')
+   
+   ax2 = fig.add_subplot(gs[1, 0])
+   ax2.plot(x, y2, 'g')
+   ax2.set_title('Cosine Function')
+   
+   ax3 = fig.add_subplot(gs[2, 0])
+   ax3.plot(x, y3, 'b')
+   ax3.set_title('Tangent Function')
+   ```
+
+4. **Adjust Layout and Show Plot** (same as above).
+
+#### Result
+
+The result will again be three subplots in a vertical stack, created using the flexible `GridSpec`. 
+
+![subplot Chart](images/subplots.png)
+
+### Summary
+
+- **`plt.subplots()`**: Creates a grid of subplots with shared axes.
+- **`plt.subplot()`**: Adds individual subplots in a figure.
+- **`GridSpec`**: Allows for complex and custom subplot layouts.
+
+By mastering these techniques, you can create detailed and organized visualizations, enhancing the clarity and comprehension of your data presentations.
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