Python scatter plot matplotlib

matplotlib.pyplot.scatter#

matplotlib.pyplot. scatter ( x , y , s = None , c = None , marker = None , cmap = None , norm = None , vmin = None , vmax = None , alpha = None , linewidths = None , * , edgecolors = None , plotnonfinite = False , data = None , ** kwargs ) [source] #

A scatter plot of y vs. x with varying marker size and/or color.

Parameters : x, y float or array-like, shape (n, )

s float or array-like, shape (n, ), optional

The marker size in points**2 (typographic points are 1/72 in.). Default is rcParams[‘lines.markersize’] ** 2 .

c array-like or list of colors or color, optional

The marker colors. Possible values:

• A scalar or sequence of n numbers to be mapped to colors using cmap and norm.
• A 2D array in which the rows are RGB or RGBA.
• A sequence of colors of length n.
• A single color format string.

Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. If you want to specify the same RGB or RGBA value for all points, use a 2D array with a single row. Otherwise, value-matching will have precedence in case of a size matching with x and y.

If you wish to specify a single color for all points prefer the color keyword argument.

Defaults to None . In that case the marker color is determined by the value of color, facecolor or facecolors. In case those are not specified or None , the marker color is determined by the next color of the Axes ‘ current «shape and fill» color cycle. This cycle defaults to rcParams[«axes.prop_cycle»] (default: cycler(‘color’, [‘#1f77b4’, ‘#ff7f0e’, ‘#2ca02c’, ‘#d62728’, ‘#9467bd’, ‘#8c564b’, ‘#e377c2’, ‘#7f7f7f’, ‘#bcbd22’, ‘#17becf’]) ).

The marker style. marker can be either an instance of the class or the text shorthand for a particular marker. See matplotlib.markers for more information about marker styles.

cmap str or Colormap , default: rcParams[«image.cmap»] (default: ‘viridis’ )

The Colormap instance or registered colormap name used to map scalar data to colors.

This parameter is ignored if c is RGB(A).

norm str or Normalize , optional

The normalization method used to scale scalar data to the [0, 1] range before mapping to colors using cmap. By default, a linear scaling is used, mapping the lowest value to 0 and the highest to 1.

If given, this can be one of the following:

• An instance of Normalize or one of its subclasses (see Colormap Normalization ).
• A scale name, i.e. one of «linear», «log», «symlog», «logit», etc. For a list of available scales, call matplotlib.scale.get_scale_names() . In that case, a suitable Normalize subclass is dynamically generated and instantiated.

This parameter is ignored if c is RGB(A).

vmin, vmax float, optional

When using scalar data and no explicit norm, vmin and vmax define the data range that the colormap covers. By default, the colormap covers the complete value range of the supplied data. It is an error to use vmin/vmax when a norm instance is given (but using a str norm name together with vmin/vmax is acceptable).

This parameter is ignored if c is RGB(A).

alpha float, default: None

The alpha blending value, between 0 (transparent) and 1 (opaque).

linewidths float or array-like, default: rcParams[«lines.linewidth»] (default: 1.5 )

The linewidth of the marker edges. Note: The default edgecolors is ‘face’. You may want to change this as well.

edgecolors None> or color or sequence of color, default: rcParams[«scatter.edgecolors»] (default: ‘face’ )

The edge color of the marker. Possible values:

• ‘face’: The edge color will always be the same as the face color.
• ‘none’: No patch boundary will be drawn.
• A color or sequence of colors.

For non-filled markers, edgecolors is ignored. Instead, the color is determined like with ‘face’, i.e. from c, colors, or facecolors.

plotnonfinite bool, default: False

Whether to plot points with nonfinite c (i.e. inf , -inf or nan ). If True the points are drawn with the bad colormap color (see Colormap.set_bad ).

Returns : PathCollection Other Parameters : data indexable object, optional

If given, the following parameters also accept a string s , which is interpreted as data[s] (unless this raises an exception):

**kwargs Collection properties

To plot scatter plots when markers are identical in size and color.

• The plot function will be faster for scatterplots where markers don’t vary in size or color.
• Any or all of x, y, s, and c may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted.
• Fundamentally, scatter works with 1D arrays; x, y, s, and c may be input as N-D arrays, but within scatter they will be flattened. The exception is c, which will be flattened only if its size matches the size of x and y.

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Matplotlib Scatter Plot — Tutorial and Examples

Matplotlib is one of the most widely used data visualization libraries in Python. From simple to complex visualizations, it’s the go-to library for most.

In this guide, we’ll take a look at how to plot a Scatter Plot with Matplotlib.

Scatter Plots explore the relationship between two numerical variables (features) of a dataset.

Import Data

We’ll be using the Ames Housing dataset and visualizing correlations between features from it.

Let’s import Pandas and load in the dataset:

import pandas as pd df = pd.read_csv('AmesHousing.csv') 

Plot a Scatter Plot in Matplotlib

Now, with the dataset loaded, let’s import Matplotlib, decide on the features we want to visualize, and construct a scatter plot:

import matplotlib.pyplot as plt import pandas as pd df = pd.read_csv('AmesHousing.csv') fig, ax = plt.subplots(figsize=(10, 6)) ax.scatter(x = df['Gr Liv Area'], y = df['SalePrice']) plt.xlabel("Living Area Above Ground") plt.ylabel("House Price") plt.show() 

Here, we’ve created a plot, using the PyPlot instance, and set the figure size. Using the returned Axes object, which is returned from the subplots() function, we’ve called the scatter() function.

We need to supply the x and y arguments as the features we’d like to use to populate the plot. Running this code results in:

We’ve also set the x and y labels to indicate what the variables represent. There’s a clear positive correlation between these two variables. The more area there is above ground-level, the higher the price of the house was.

There are a few outliers, but the vast majority follows this hypothesis.

Plotting Multiple Scatter Plots in Matplotlib

If you’d like to compare more than one variable against another, such as — check the correlation between the overall quality of the house against the sale price, as well as the area above ground level — there’s no need to make a 3D plot for this.

While 2D plots that visualize correlations between more than two variables exist, some of them aren’t fully beginner friendly.

An easy way to do this is to plot two plots — in one, we’ll plot the area above ground level against the sale price, in the other, we’ll plot the overall quality against the sale price.

Let’s take a look at how to do that:

import matplotlib.pyplot as plt import pandas as pd df = pd.read_csv('AmesHousing.csv') fig, ax = plt.subplots(2, figsize=(10, 6)) ax[0].scatter(x = df['Gr Liv Area'], y = df['SalePrice']) ax[0].set_xlabel("Living Area Above Ground") ax[0].set_ylabel("House Price") ax[1].scatter(x = df['Overall Qual'], y = df['SalePrice']) ax[1].set_xlabel("Overall Quality") ax[1].set_ylabel("House Price") plt.show() 

Here, we’ve called plt.subplots() , passing 2 to indicate that we’d like to instantiate two subplots in the figure.

We can access these via the Axes instance — ax . ax[0] refers to the first subplot’s axes, while ax[1] refers to the second subplot’s axes.

Here, we’ve called the scatter() function on each of them, providing them with labels. Running this code results in:

Plotting a 3D Scatter Plot in Matplotlib

If you don’t want to visualize this in two separate subplots, you can plot the correlation between these variables in 3D. Matplotlib has built-in 3D plotting functionality, so doing this is a breeze.

First, we’ll need to import the Axes3D class from mpl_toolkits.mplot3d . This special type of Axes is needed for 3D visualizations. With it, we can pass in another argument — z , which is the third feature we’d like to visualize.

Let’s go ahead and import the Axes3D object and plot a scatter plot against the previous three features:

import matplotlib.pyplot as plt import pandas as pd from mpl_toolkits.mplot3d import Axes3D df = pd.read_csv('AmesHousing.csv') fig = plt.figure() ax = fig.add_subplot(111, projection = '3d') x = df['SalePrice'] y = df['Gr Liv Area'] z = df['Overall Qual'] ax.scatter(x, y, z) ax.set_xlabel("Sale price") ax.set_ylabel("Living area above ground level") ax.set_zlabel("Overall quality") plt.show() 

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Running this code results in an interactive 3D visualization that we can pan and inspect in three-dimensional space:

Customizing Scatter Plot in Matplotlib

You can change how the plot looks like by supplying the scatter() function with additional arguments, such as color , alpha , etc:

ax.scatter(x = df['Gr Liv Area'], y = df['SalePrice'], color = "blue", edgecolors = "white", linewidths = 0.1, alpha = 0.7) 

Running this code would result in:

Conclusion

In this tutorial, we’ve gone over several ways to plot a scatter plot using Matplotlib and Python.

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