To plot a scatter pie chart using matplotlib, first import the necessary libraries such as matplotlib.pyplot. Next, create the data points for the scatter plot and specify the labels for the pie chart. Then, use the plt.scatter() function to plot the scatter plot with the desired data points. Finally, use the plt.pie() function to plot the pie chart with the specified labels. Customize the chart by adding a title, axis labels, and legend if needed. Display the chart using plt.show().

## How to label a scatter plot in matplotlib?

To label a scatter plot in Matplotlib, you can use the `plt.text()`

function to add text annotations to specific data points. Here's an example of how to label a scatter plot:

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import matplotlib.pyplot as plt # Generate some random data x = [1, 2, 3, 4, 5] y = [5, 4, 3, 2, 1] labels = ['A', 'B', 'C', 'D', 'E'] # Create a scatter plot plt.scatter(x, y) # Add labels to the data points for i, label in enumerate(labels): plt.text(x[i], y[i], label, fontsize=12, ha='right') # Add axis labels and title plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.title('Scatter Plot with Labels') # Display the plot plt.show() |

In this example, we have generated some random data points and corresponding labels. We then plot the scatter plot using `plt.scatter()`

, add text annotations for each data point using `plt.text()`

, and finally display the plot with labels using `plt.show()`

. You can customize the size, font, and position of the labels by adjusting the argument values in `plt.text()`

.

## How to change the axis limits in a scatter plot in matplotlib?

To change the axis limits in a scatter plot in Matplotlib, you can use the `xlim()`

and `ylim()`

functions. Here's an example:

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import matplotlib.pyplot as plt import numpy as np # Generate some random data x = np.random.rand(100) y = np.random.rand(100) # Create scatter plot plt.scatter(x, y) plt.xlabel('X-axis') plt.ylabel('Y-axis') # Set custom axis limits plt.xlim(0, 1) # Set x-axis limits from 0 to 1 plt.ylim(0, 1) # Set y-axis limits from 0 to 1 plt.show() |

In this example, we first generate some random data for the scatter plot. We then create the scatter plot using `scatter()`

. Finally, we set the custom axis limits using `xlim()`

for the x-axis and `ylim()`

for the y-axis.

## How to create a pie chart in matplotlib?

To create a pie chart in matplotlib, you can use the `plt.pie()`

function. Here's a step-by-step guide:

- Import the necessary libraries:

```
1
``` |
```
import matplotlib.pyplot as plt
``` |

- Create your data for the pie chart. This can be a list of values or a dictionary with labels and corresponding values:

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sizes = [20, 30, 40, 10] # Example list of values labels = ['A', 'B', 'C', 'D'] # Example labels |

- Plot the pie chart using the plt.pie() function:

```
1
``` |
```
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
``` |

- Add a title and show the pie chart:

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plt.title('My Pie Chart') plt.show() |

You can customize the pie chart further by adding colors, exploding certain slices, adding a legend, etc. Check the matplotlib documentation for more customization options.

## What is the syntax for creating a scatter plot in matplotlib?

To create a scatter plot in matplotlib, you can use the `plt.scatter()`

function. The syntax is as follows:

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import matplotlib.pyplot as plt # Create data x = [1, 2, 3, 4, 5] y = [10, 20, 15, 25, 30] # Create scatter plot plt.scatter(x, y) # Add labels and title plt.xlabel('X-axis label') plt.ylabel('Y-axis label') plt.title('Scatter Plot') # Display plot plt.show() |

In this example, we first import the necessary module `matplotlib.pyplot`

as `plt`

. Then we define our data points `x`

and `y`

. Next, we use the `plt.scatter()`

function to create a scatter plot with our data. Finally, we add labels to the axes and a title to the plot, and then display the plot using `plt.show()`

.

## What is a pie chart?

A pie chart is a circular statistical graphic that is divided into slices to illustrate numerical proportions. Each slice represents a proportion of the whole, and the size of each slice is proportional to the quantity it represents. Pie charts are commonly used to show percentages, proportions, and distributions of data.