5 Best Ways to Create Scatter Plots and Color Mapping in Python

Rate this post

πŸ’‘ Problem Formulation: Scatter plots are crucial for visualizing the relationship between two numerical variables in data analysis. A common need is to color-map these points to represent an additional dimension, such as a category or a range of values. This article focuses on providing python-based solutions for generating scatter plots with color mapping, taking inputs as x and y coordinate arrays, and an additional array or parameter for color-coding each point, with the output being a colorful visualization of the data points.

Method 1: Using Matplotlib

The matplotlib library is a fundamental plotting library in Python. Using matplotlib.pyplot.scatter(), large datasets can be easily visualized as scatter plots where each point’s color can be controlled via the c parameter, which accepts an array of colors or variables to be mapped to colors through a colormap.

Here’s an example:

import matplotlib.pyplot as plt

x = [5, 7, 8, 5, 6, 7, 9]
y = [7, 4, 3, 5, 6, 1, 2]
sizes = [210, 410, 312, 214, 415, 312, 213]
colors = [0, 1, 2, 3, 4, 5, 6]

plt.scatter(x, y, s=sizes, c=colors, cmap='viridis')
plt.colorbar()
plt.show()

The scatter plot displays points with coordinates given by x and y arrays, sizes according to the sizes array, and colored based on the colors array using the ‘viridis’ colormap. A color bar is also added to relate the colors to the values.

Method 2: Using Seaborn

Seaborn simplifies the creation of visually appealing and informative statistical graphics in Python. It wraps Matplotlib functions and provides a high-level interface for drawing attractive scatter plots using the sns.scatterplot() function. Unique to Seaborn is its effortless integration with pandas DataFrames and automatic color mapping based on various categorical or numeric features.

Here’s an example:

import seaborn as sns
import pandas as pd

df = pd.DataFrame({
    'x': [5, 7, 8, 7, 2, 17, 2, 9],
    'y': [99, 86, 87, 88, 100, 86, 103, 87],
    'group': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'C']
})

sns.scatterplot(x='x', y='y', hue='group', data=df, palette='bright')
plt.show()

This Seaborn scatter plot uses a pandas DataFrame as input. Colors are automatically assigned to different groups, as indicated by the ‘hue’ parameter. The ‘palette’ parameter can be customized to change the color theme.

Method 3: Using Plotly

Plotly’s Python graphing library makes interactive, publication-quality graphs online. The plotly.graph_objects.Scatter() function allows highly interactive scatter plots, which can be color-mapped using color scales and are ideal for web-based datasets due to its dynamic nature, such as having zoomable plots and hover-over tips for data points.

Here’s an example:

import plotly.graph_objects as go

fig = go.Figure(data=go.Scatter(
    x=[1, 2, 3, 4],
    y=[10, 11, 12, 13],
    mode='markers',
    marker=dict(size=[40, 60, 80, 100],
                color=[0, 1, 2, 3],
                showscale=True)
))

fig.show()

The Plotly scatter plot visualizes points with interactive hover-over tips, dynamic scaling, and a color bar that represents the color values. It is exemplary for creating a user-friendly data exploration experience online.

Method 4: Using Pandas Plot

Pandas is primarily used for data manipulation, but it also supports basic plotting capabilities. Using the DataFrame.plot.scatter() method, a scatter plot can be quickly produced directly from a DataFrame. When the c parameter is supplied with a column name, it automatically maps the colors of the points according to the values in that column, much like Seaborn.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
    'x': range(1, 6),
    'y': range(2, 11, 2),
    'color': range(1, 101, 20)
})

df.plot.scatter(x='x', y='y', c='color', colormap='viridis')
plt.show()

Using Pandas, this scatter plot takes in the ‘x’ and ‘y’ series directly from the DataFrame and uses the ‘color’ series for color-coding, applied through the ‘viridis’ colormap.

Bonus One-Liner Method 5: Using Matplotlib Pyplot Inline

With the inline methodology in Matplotlib, one can quickly generate a colored scatter plot using a one-liner. This method condenses the process into a single line of code, which is beneficial for simple graphs when exploring data.

Here’s an example:

plt.scatter('x', 'y', c='color', data=df, cmap='viridis')

This one-liner command uses Matplotlib’s Pyplot interface to generate a scatter plot with minimal code, yet includes the power of color mapping through its ‘cmap’ parameter.

Summary/Discussion

  • Method 1: Matplotlib. Highly customizable. Suitable for technical scientific papers. Can be verbose for complex plots.
  • Method 2: Seaborn. Provides aesthetic defaults. Great for statistical analysis. Less flexible than Matplotlib for highly customized graphs.
  • Method 3: Plotly. Creates interactive plots perfect for the web. Can be overkill for static or simple exploratory data analysis.
  • Method 4: Pandas Plot. Convenient for quick plotting within the pandas workflow. Not as powerful as Matplotlib or Seaborn for customization.
  • Bonus One-Liner Method 5: Matplotlib Inline. Great for rapid, concise code. Not suitable for detailed, customized visuals.