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# Drawing a scatterplot in Matplotlib

Matplotlib
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Graphs Cookbook
schedule Jul 1, 2022
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# 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(, , marker="*", s=300) # starplt.scatter(, , marker="o", s=300) # circleplt.scatter(, , marker="+", s=300) # plusplt.scatter(, , marker="^", s=300) # triangleplt.scatter(, , marker="D", s=300) # diamondplt.scatter(, , 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.