好吧，有几种方法可以完成这项工作。以下是我想到的一些：

1. 带噪声的散点图：
   通常，如果您尝试使用散点图来绘制两个分类特征，您只会得到几个点，每个点都包含大量数据实例。因此，为了了解每个点的实际数量，我们可以为每个实例添加一些随机噪声：

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# This is to encode the data into numbers that can be used in our scatterplot
from sklearn.preprocessing import OrdinalEncoder
ord_enc = OrdinalEncoder()
enc_df = pd.DataFrame(ord_enc.fit_transform(df), columns=list(df.columns))
categories = pd.DataFrame(np.array(ord_enc.categories_).transpose(), columns=list(df.columns))

# Generate the random noise
xnoise, ynoise = np.random.random(len(df))/2, np.random.random(len(df))/2 # The noise is in the range 0 to 0.5

# Plot the scatterplot
plt.scatter(enc_df["Playing_Role"]+xnoise, enc_df["Bought_By"]+ynoise, alpha=0.5)
# You can also set xticks and yticks to be your category names:
plt.xticks([0.25, 1.25, 2.25], categories["Playing_Role"]) # The reason the xticks start at 0.25
# and go up in increments of 1 is because the center of the noise will be around 0.25 and ordinal
# encoded labels go up in increments of 1.
plt.yticks([0.25, 1.25, 2.25], categories["Bought_By"]) # This has the same reason explained for xticks

# Extra unnecessary styling...
plt.grid()
sns.despine(left=True, bottom=True)

2.带有噪声和色调
的散点图：我们可以使用$x$轴是一个特征，而$y$轴是随机噪声。然后，为了合并另一个特性，我们可以基于另一个特性“着色”实例：

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# Explained in approach 1
from sklearn.preprocessing import OrdinalEncoder
ord_enc = OrdinalEncoder()
enc_df = pd.DataFrame(ord_enc.fit_transform(df), columns=list(df.columns))
categories = pd.DataFrame(np.array(ord_enc.categories_).transpose(), columns=list(df.columns))

xnoise, ynoise = np.random.random(len(df))/2, np.random.random(len(df))/2

sns.relplot(x=enc_df["Playing_Role"]+xnoise, y=ynoise, hue=df["Bought_By"]) # Notice how for hue
# we use the original dataframe with labels instead of numbers.
# We can also set the x axis to be our categories
plt.xticks([0.25, 1.25, 2.25], categories["Playing_Role"]) # Explained in approach 1

# Extra unnecessary styling...
plt.yticks([])
sns.despine(left=True)

3. 带色调的猫图：
   最后，我们可以使用猫图，并根据其他特征在其中的一部分中着色：

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

sns.histplot(binwidth=0.5, x="Playing_Role", hue="Bought_By", data=df, stat="count", multiple="stack")