使用具有大量类别的附加列在条形图中绘制

Question

我有一个pyspark数据帧，有3列：违规位置、违规代码和罚单频率。但是，在违规\u代码和违规\u位置列中都有几个类别（即每个类别超过100个）

我想得到前10名的违反位置和违反码的机票频率的基础上

Precint = spark.sql("SELECT Violation_Location, Violation_Code, Count(*) as Ticket_Frequency from table_view2 group by Violation_Location, Violation_Code order by Ticket_Frequency desc")
Precint.show()

Violation_Location|Violation_Code|Ticket_Frequency|
+------------------+--------------+----------------+
|              null|            36|         1098296|
|              null|             7|          471754|
|              null|             5|          248774|
|                18|            14|          132123|
|               114|            21|           84051|
|                14|            14|           83664|
|                19|            46|           82640|
|                14|            69|           69006|

到目前为止，我只能根据罚单的频率来划分出十大违规地点。感谢任何形式的帮助，谢谢

# plot violation based on the states the cars were registered to
precintplot = Precint.toPandas()
plt.figure(figsize=(100,200))

#remove missing rows from Violation_Location first
precintplotnomiss = precintplot.dropna(subset=['Violation_Location'])
precintplotnomiss.head(10).plot(x='Violation_Location', y='Ticket_Frequency', kind='bar')
plt.title("Violations by Precint (top 10)")
plt.xlabel('Precint')
plt.ylabel('Ticket Frequency')

plt.show()