Added content to matplotlib_fundamentals

contrib/plotting-visualization/index.md

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 # List of sections
 
 - [Installing Matplotlib](matplotlib_installation.md)
+- [Matplotlib fundamentals](matplotlib_fundamentals.md)

contrib/plotting-visualization/matplotlib_fundamentals.md

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+# Plotting & Visualization with Matplotlib
+
+## Introduction to Matplotlib
+
+### Overview of Matplotlib
+Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is highly customizable and supports a wide range of plot types, making it a versatile tool for data visualization.
+
+### Installation and Setup
+Before you can use Matplotlib, you need to install it. You can install Matplotlib using pip, Python's package manager:
+
+```
+pip install matplotlib
+```
+
+### Basic Usage and Structure of a Matplotlib Plot
+Once installed, you can start using Matplotlib to create plots. A basic Matplotlib plot involves creating a figure and adding one or more axes (plots) to it.
+
+```python
+import matplotlib.pyplot as plt
+
+# Create a figure and an axes
+fig, ax = plt.subplots()
+
+# Plot some data on the axes
+ax.plot([1, 2, 3, 4], [1, 4, 2, 3])
+
+# Display the plot
+plt.show()
+```
+
+### Basic Plotting Functions
+Creating Line Charts
+Line charts are useful for visualizing trends over time or continuous data. Here's how to create a simple line chart:
+
+```python
+
+import matplotlib.pyplot as plt
+
+x = [1, 2, 3, 4]
+y = [1, 4, 2, 3]
+
+plt.plot(x, y)
+plt.title('Line Chart')
+plt.xlabel('X-axis Label')
+plt.ylabel('Y-axis Label')
+plt.show()
+```
+### Creating Scatter Plots
+Scatter plots are used to visualize the relationship between two variables. Each point represents an observation.
+
+```python
+
+import matplotlib.pyplot as plt
+
+x = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11]
+y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78]
+
+plt.scatter(x, y)
+plt.title('Scatter Plot')
+plt.xlabel('X-axis Label')
+plt.ylabel('Y-axis Label')
+plt.show()
+```
+### Creating Bar Charts
+Bar charts are useful for comparing different categories or groups. Each bar represents a category.
+```python
+
+import matplotlib.pyplot as plt
+
+x = ['A', 'B', 'C', 'D']
+y = [3, 7, 8, 5]
+
+plt.bar(x, y)
+plt.title('Bar Chart')
+plt.xlabel('Categories')
+plt.ylabel('Values')
+plt.show()
+```
+### Creating Histograms
+Histograms are used to visualize the distribution of a dataset. They show the frequency of different ranges of values.
+
+```python
+
+import matplotlib.pyplot as plt
+
+data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
+
+plt.hist(data, bins=4)
+plt.title('Histogram')
+plt.xlabel('Value')
+plt.ylabel('Frequency')
+plt.show()
+```
+
+## Customising Plots
+Customising Markers, Colours, and Line Styles
+You can customize the appearance of plots by changing markers, colors, and line styles.
+
+```python
+
+import matplotlib.pyplot as plt
+
+x = [1, 2, 3, 4]
+y = [1, 4, 2, 3]
+
+plt.plot(x, y, marker='o', color='r', linestyle='--')
+plt.title('Custom Line Chart')
+plt.xlabel('X-axis Label')
+plt.ylabel('Y-axis Label')
+plt.show()
+```
+## Adding Titles and Labels
+Titles and labels help to provide context and make the plot easier to understand.
+
+```python
+import matplotlib.pyplot as plt
+
+x = [1, 2, 3, 4]
+y = [1, 4, 2, 3]
+
+plt.plot(x, y)
+plt.title('Line Chart with Titles and Labels')
+plt.xlabel('X-axis Label')
+plt.ylabel('Y-axis Label')
+plt.show()
+```
+
+## Adding Legends
+Legends are useful when you have multiple plots on the same figure and need to differentiate between them.
+
+```python
+import matplotlib.pyplot as plt
+
+x = [1, 2, 3, 4]
+y1 = [1, 4, 2, 3]
+y2 = [2, 2, 3, 4]
+
+plt.plot(x, y1, label='Line 1')
+plt.plot(x, y2, label='Line 2')
+plt.title('Line Chart with Legends')
+plt.xlabel('X-axis Label')
+plt.ylabel('Y-axis Label')
+plt.legend()
+plt.show()
+```
+## Adding Grid Lines
+Grid lines can make your plot easier to read by providing reference points.
+
+```python
+import matplotlib.pyplot as plt
+
+x = [1, 2, 3, 4]
+y = [1, 4, 2, 3]
+
+plt.plot(x, y)
+plt.title('Line Chart with Grid Lines')
+plt.xlabel('X-axis Label')
+plt.ylabel('Y-axis Label')
+plt.grid(True)
+plt.show()
+```
+
+## Saving Plots
+You can save your plots to a file in various formats such as PNG, PDF, SVG, etc.
+
+```python
+import matplotlib.pyplot as plt
+
+x = [1, 2, 3, 4]
+y = [1, 4, 2, 3]
+
+plt.plot(x, y)
+plt.title('Line Chart')
+plt.xlabel('X-axis Label')
+plt.ylabel('Y-axis Label')
+plt.savefig('line_chart.png')
+plt.show()
+```
+## Conclusion
+
+In conclusion, this guide offers a comprehensive introduction to Matplotlib, covering installation, basic plot creation, and customization options. It serves as a starting point for further exploration of Matplotlib's advanced features and capabilities.