Drawing Scatter Plots with Different Colors for Categorical Levels in Python with Matplotlib
In Matplotlib, a Python library for creating static, animated, and interactive visualizations in Python, you can plot different scatter plots with different colors for each level of a categorical variable by leveraging the c argument of the plt.scatter function.
<code class="python">import matplotlib.pyplot as plt
df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6], 'color': ['red', 'blue', 'green']})
colors = {'red': 'tab:red', 'blue': 'tab:blue', 'green': 'tab:green'}
plt.scatter(df['x'], df['y'], c=df['color'].map(colors))
plt.show()</code>By passing the c argument, a dictionary mapping color names to RGB values can be used to specify the color of each point. The map method of Pandas then applies the color mapping to the df['color'] column, effectively assigning each point a unique color.
<code class="python">colors = {'D': 'tab:blue', 'E': 'tab:orange', 'F': 'tab:green', 'G': 'tab:red', 'H': 'tab:purple', 'I': 'tab:brown', 'J': 'tab:pink'}
ax.scatter(df['carat'], df['price'], c=df['color'].map(colors))</code>This approach allows for a more customized color scheme and greater control over the colors used in the plot. By using a color dictionary, users can easily modify the color scheme as needed.
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