# 5 Best Ways to Plot a Single Point in Matplotlib Python

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π‘ Problem Formulation: You have coordinates of a single point and you want to visualize it using Python’s matplotlib library. For instance, if the input is (5, 10), the desired output is a visual representation of this single point on a 2D plot.

## Method 1: Using plot Method

Matplotlib’s plot method can be used to plot a series of points. To plot a single point, you can pass the x and y coordinates as lists, and optionally include a marker style like ‘o’ to differentiate the point. By default, this will plot a line, so it’s essential to specify the linestyle as ‘none’ if you wish only to display the single point.

Here’s an example:

import matplotlib.pyplot as plt

x, y = 5, 10
plt.plot(x, y, 'o', linestyle='none')
plt.show()

The output is a 2D plot with a single point marked at (5, 10).

This example demonstrates how to plot a point using matplotlib’s plot method. By setting the linestyle to ‘none’, we ensure that no line is drawn between points. The ‘o’ marker is a common choice for highlighting individual points.

## Method 2: Using scatter Method

The scatter method in matplotlib is specifically designed for creating scatter plots. It’s very suitable for plotting individual points because, unlike plot, it doesn’t assume you want to connect points with lines. You can directly pass the coordinates as arguments and customize the appearance with additional parameters.

Here’s an example:

import matplotlib.pyplot as plt

x, y = 5, 10
plt.scatter(x, y)
plt.show()

The output is a scatter plot with a single point at (5, 10).

The advantage of the scatter method is its flexibility and the fact that it is meant for plotting individual points. This makes it perfect for our use-case of plotting a single point on the graph.

## Method 3: Using Annotation

In cases where you want to highlight a point and add text to it, matplotlib’s annotate function can be employed. This method is particularly useful if you need to annotate the plotted point with a label.

Here’s an example:

import matplotlib.pyplot as plt

x, y = 5, 10
plt.annotate('Point 1', (x, y), textcoords="offset points", xytext=(-15,10), ha='center')
plt.xlim(0, 15)
plt.ylim(0, 15)
plt.plot(x, y, 'o')  # to draw the point as 'o'
plt.show()

The output is a plot with a single point at (5, 10), annotated with the text ‘Point 1’.

This method plots the point and provides context through a label, which can improve the readability and informativeness of the plot.

## Method 4: Utilizing Custom Marker Sizes

Sometimes differentiating a point with just color or marker style isn’t enough. Matplotlib allows customization of the marker size using the s parameter in the scatter method, which stands for the size of the marker.

Here’s an example:

import matplotlib.pyplot as plt

x, y = 5, 10
plt.scatter(x, y, s=100)  # Setting marker size to 100
plt.show()

The output is a scatter plot with a distinctly larger point at (5, 10).

By increasing the size of the marker, the single point becomes more visible and prominent in the plot, which can help when presenting data or focusing on specific details.

## Bonus One-Liner Method 5: Using a Custom Style

If you’re aiming for simplicity and compact code, plotting a single point with a custom style can be done in one line by passing a style string to the plot function.

Here’s an example:

import matplotlib.pyplot as plt

plt.plot(5, 10, 'ro')  # 'ro' signifies red color and 'o' marker
plt.show()

The output is a plot displaying a single red point at the coordinates (5, 10).

This one-liner is the shortest and perhaps the most straightforward way to plot a point when axis labels, titles, or other configurations are not needed.

## Summary/Discussion

• Method 1: Using plot Method. Flexible and customizable. Requires explicit instruction not to connect points.
• Method 2: Using scatter Method. Intuitive method for individual points. Offers additional point-specific customization options.
• Method 3: Using Annotation. Allows for labeling and contextualizing points. A bit more complex but very informative.
• Method 4: Utilizing Custom Marker Sizes. Handy for emphasizing specific points. A simple change can have a large impact on the plot’s appearance.
• Method 5: Using a Custom Style. Quickest and easiest method for plotting a point. Best when there is no need for additional plot embellishments.