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# How to Show Labels on Matplotlib Plots?

To show labels on Matplotlib plots, you can incorporate the following steps:

Firstly, import the necessary libraries:

 ```1 2 ``` ```import matplotlib.pyplot as plt import numpy as np ```

Next, create a figure and an axis object:

 ```1 ``` ```fig, ax = plt.subplots() ```

Note: For simplicity, we will use a single plot here, but you can adjust these steps accordingly for multiple plots.

Now, create your data and plot it using the `plot()` function:

 ```1 2 3 ``` ```x = np.array([1, 2, 3, 4, 5]) y = np.array([1, 4, 9, 16, 25]) ax.plot(x, y) ```

To add labels to your plot, use the `set_xlabel()` and `set_ylabel()` functions:

 ```1 2 ``` ```ax.set_xlabel('X-axis label') ax.set_ylabel('Y-axis label') ```

To set a title for your plot, use the `set_title()` function:

 ```1 ``` ```ax.set_title('Plot Title') ```

You can also adjust the tick labels using the `set_xticks()` and `set_yticks()` functions:

 ```1 2 ``` ```ax.set_xticks([1, 2, 3, 4, 5]) # Set custom tick locations for the x-axis ax.set_yticks([0, 5, 10, 15, 20, 25]) # Set custom tick locations for the y-axis ```

To display a legend, you can add a label parameter to your `plot()` function and use the `legend()` function:

 ```1 2 ``` ```ax.plot(x, y, label='Line') # Add label parameter to plot() function ax.legend() # Display the legend ```

Finally, to display your plot, use the `show()` function:

 ```1 ``` ```plt.show() ```

By following these steps, you can create a Matplotlib plot with labels, title, custom tick labels, and a legend to enhance its visual representation.

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## How to set the limits of x-axis and y-axis in Matplotlib?

To set the limits of the x-axis and y-axis in Matplotlib, you can use the `xlim()` and `ylim()` functions, respectively.

Here's an example of how to set the limits of the x-axis from 0 to 10, and the y-axis from -5 to 5:

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ``` ```import matplotlib.pyplot as plt # Sample data x = [1, 2, 3, 4, 5] y = [2, -3, 1, -4, 3] # Plotting the data plt.plot(x, y) # Setting the limits of the x-axis and y-axis plt.xlim(0, 10) plt.ylim(-5, 5) # Display the plot plt.show() ```

In this example, `plt.xlim(0, 10)` sets the limits of the x-axis from 0 to 10, and `plt.ylim(-5, 5)` sets the limits of the y-axis from -5 to 5.

## What is Matplotlib and what is its purpose?

Matplotlib is a data visualization library in Python that is used to create static, animated, and interactive visualizations in a variety of formats, such as 2D and 3D plots, histograms, bar charts, scatter plots, etc. Its purpose is to provide a flexible and powerful tool for visualizing data in a clear and concise manner, allowing users to explore and communicate their data effectively. It is widely used in various domains, including scientific research, data analysis, machine learning, and more.

## How to plot a polar plot in Matplotlib?

To plot a polar plot in Matplotlib, you can follow these steps:

Step 1: Import the required libraries

 ```1 2 ``` ```import numpy as np import matplotlib.pyplot as plt ```

Step 2: Create an array of angles and an array of corresponding values

 ```1 2 ``` ```theta = np.linspace(0, 2*np.pi, 100) r = np.sin(3*theta) ```

Step 3: Create a polar plot using the `polar` function

 ```1 ``` ```plt.polar(theta, r) ```

Step 4: Customize the plot (optional)

 ```1 2 ``` ```plt.title("Polar Plot") plt.grid(True) ```

Step 5: Display the plot

 ```1 ``` ```plt.show() ```

Putting it all together, the complete code would be:

 ``` 1 2 3 4 5 6 7 8 9 10 ``` ```import numpy as np import matplotlib.pyplot as plt theta = np.linspace(0, 2*np.pi, 100) r = np.sin(3*theta) plt.polar(theta, r) plt.title("Polar Plot") plt.grid(True) plt.show() ```

This will create a polar plot with sinusoidal wave plotted in it. You can customize the plot further by adding a legend, changing the line color/style, adding labels, etc.

## What is the purpose of linewidth parameter in Matplotlib?

The linewidth parameter in Matplotlib is used to set the width (or thickness) of lines in plots. It is an optional parameter that can be specified in various plotting functions, such as plot(), scatter(), and hist(). By adjusting the linewidth, you can control the visual appearance of lines in your plots, making them thicker or thinner as desired. This can be useful for emphasizing certain parts of the plot or achieving a specific visual effect.

## How to change the font size of Matplotlib plot labels?

To change the font size of Matplotlib plot labels, you can use the `fontsize` parameter of the `set_xlabel()` and `set_ylabel()` functions.

Here's an example:

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ``` ```import matplotlib.pyplot as plt # Generate some data for the plot x = [1, 2, 3, 4, 5] y = [1, 4, 9, 16, 25] # Create a figure and axis object fig, ax = plt.subplots() # Plot the data ax.plot(x, y) # Set label and font size for x-axis ax.set_xlabel('x-axis label', fontsize=14) # Set label and font size for y-axis ax.set_ylabel('y-axis label', fontsize=14) # Show the plot plt.show() ```

In this example, we set the font size for both the x-axis and y-axis labels to 14 using the `fontsize` parameter. You can adjust the font size to your preference.

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