search
Search
Publish
Guest 0reps
Thanks for the thanks!
close
account_circle
Profile
exit_to_app
Sign out
search
keyboard_voice
close
Searching Tips
Search for a recipe: "Creating a table in MySQL"
Search for an API documentation: "@append"
Search for code: "!dataframe"
Apply a tag filter: "#python"
Useful Shortcuts
/ to open search panel
Esc to close search panel
to navigate between search results
d to clear all current filters
Enter to expand content preview
Doc Search
Code Search Beta
SORRY NOTHING FOUND!
mic
Start speaking...
Voice search is only supported in Safari and Chrome.
Shrink
Navigate to
A
A
share
thumb_up_alt
bookmark
arrow_backShare
thumb_up
0
thumb_down
0
chat_bubble_outline
0
auto_stories new
settings

# Drawing a scatterplot in Matplotlib

Programming
chevron_right
Python
chevron_right
Matplotlib
chevron_right
Cookbooks
chevron_right
Graphs Cookbook
schedule Jun 9, 2022
Last updated
local_offer PythonMatplotlib
Tags

# Using the scatter function

To draw a basic 2D scatter plot in Matplotlib, we could use the `scatter(~)` function like so:

``` fig, ax = plt.subplots()ax.scatter([5,2,3], [1,2,4])plt.show() ```

The first argument is an array of your `x`s, and the second is an array of your `y`s.

This produces the following:

# Using the plot function

To draw a basic 2D scatter plot in Matplotlib, we could also use the `plot(~)` function like so:

``` plt.plot([5,2,3], [1,2,4], "o")plt.show() ```

Here, the third argument `"o"` indicates that you don't want the data points to be connected. This is needed since the default behavior is to connect the dots.

This produces the following:

# Customizing our plots

Both the `scatter(~)` and `plot(~)` methods allow for flexible customization.

## Marker size

Pass in the `markersize` parameter for `plot(~)` and the `s` parameter for `scatter(~)`:

``` plt.scatter([3,4],[5,6], s=10)plt.plot([3,4],[5,6], markersize=10) ```

## Marker color

Pass in the `color` parameter for both `plot(~)` and `scatter(~)`:

``` plt.plot([3,4], [5,6], color="r")plt.plot([3,4], [5,6], color="red")plt.plot([3,4], [5,6], color="#FF0000")plt.plot([3,4], [5,6], color=(1,0,0)) # Warning: this isn't your typical (0-255) interval ```

## Marker style

Pass in the `marker` parameter for both `plot(~)` and `scatter(~)`:

``` plt.scatter([1], [1], marker="*", s=300) # starplt.scatter([1], [2], marker="o", s=300) # circleplt.scatter([1], [3], marker="+", s=300) # plusplt.scatter([2], [1], marker="^", s=300) # triangleplt.scatter([2], [2], marker="D", s=300) # diamondplt.scatter([2], [3], marker="s", s=300) # square ```

This produces the following:

The colors are automatically chosen by Matplotlib.

# Difference between functions scatter and plot

Both functions can be used to draw scatter plots, but the difference is that the `plot(~)` function is more efficient but less flexible than the `scatter(~) `function. The `plot(~)` function does not allow for customization of individual points (e.g. changing color and size), while the `scatter(~)` allows for this. The implication of such a difference is that the `plot(~)` function has far less jobs to handle, making it more efficient.

As a rule of thumb, if you do not need to customize individual points, then opt for the `plot(~)` function.

Edited by 0 others
thumb_up
thumb_down
Ask a question or leave a feedback...
thumb_up
0
thumb_down
0
chat_bubble_outline
0
settings
A modern learning experience for data science and analytics