如何在 Matplotlib、Seaborn 和 Pandas 中為分類等級建立不同顏色的散佈圖？

Scatter Plot with Different Colors for Categorical Levels

Using Matplotlib

To create a scatter plot where different categorical levels are represented by different colors using Matplotlib, follow these steps:

1. Import Matplotlib and the data frame you want to plot.
2. Define a dictionary that maps the categorical levels to plotting colors.
3. Use plt.scatter, passing in the x and y values and the c argument to specify the colors.

<code class="python">import matplotlib.pyplot as plt
import pandas as pd

colors = {'D':'tab:blue', 'E':'tab:orange', 'F':'tab:green', 'G':'tab:red', 'H':'tab:purple', 'I':'tab:brown', 'J':'tab:pink'}

df.scatter(df['carat'], df['price'], c=df['color'].map(colors))

plt.show()</code>

Using Seaborn

Seaborn is a wrapper around Matplotlib that provides a more user-friendly interface. To create a scatter plot with different colors for categorical levels using Seaborn, follow these steps:

1. Import Seaborn and the data frame you want to plot.
2. Use seaborn.scatterplot, passing in the x and y values and the hue parameter to specify the categorical level.

<code class="python">import seaborn as sns

sns.scatterplot(x='carat', y='price', data=df, hue='color')

plt.show()</code>

Using pandas.groupby & pandas.DataFrame.plot

You can also use pandas.groupby and pandas.DataFrame.plot to create a scatter plot with different colors for categorical levels. This method requires more manual work, but it gives you more control over the plot's appearance.

1. Import pandas and the data frame you want to plot.
2. Group the data frame by the categorical level.
3. Iterate over the groups and plot each one with a different color.

<code class="python">import pandas as pd

fig, ax = plt.subplots(figsize=(6, 6))

grouped = df.groupby('color')
for key, group in grouped:
    group.plot(ax=ax, kind='scatter', x='carat', y='price', label=key, color=colors[key])

plt.show()</code>

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