How to create a scatter plot with markers grouped by category in Python?

Plotting a Scatter Plot Grouped by Category in Python

Problem:

In Pandas, how can you create a scatter plot with markers representing data sorted into categories defined by a third column?

Solution:

To efficiently create a scatter plot grouped by category, use the plot function instead of scatter. This is because plot interprets the values in the third column as categories rather than numerical values.

Here's a step-by-step solution using plot:

1. Generate the data: Create a DataFrame with columns representing data on the x and y axes, as well as a column for the categories.
2. Group the data: Use groupby() to create groups for each category.
3. Plot the data: For each group, plot the x and y values using plot. Specify marker='o' to represent the data with circles.
4. Add a legend: Label the markers with the respective categories using ax.legend().

For example:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
np.random.seed(1974)

# Generate Data
num = 20
x, y = np.random.random((2, num))
labels = np.random.choice(['a', 'b', 'c'], num)
df = pd.DataFrame(dict(x=x, y=y, label=labels))

groups = df.groupby('label')

# Plot
fig, ax = plt.subplots()
ax.margins(0.05)
for name, group in groups:
    ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, label=name)
ax.legend()

plt.show()

This will produce a scatter plot with markers categorized by the values in the 'label' column and a legend that identifies the categories.

Additionally, you can customize the appearance of the plot by adjusting the ax.margins() parameter, setting the marker size (ms), and specifying a color palette for the markers.

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