pyspark: use of an additional column with a large number of categories to draw on a bar chart – Matplotlib

I have a pyspark data frame with 3 columns: Violation_Location, Violation_Code and Ticket_Frequency. However, there are several categories (that is, more than 100 each) in the Violation_Code and Violation_Location columns.

I would like to get the top 10 of Violation_Location and Violation_Code according to the frequencies of the tickets.

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|

So far, I can only trace the 10 best violation codes based on the frequency of the ticket. Any form of help is appreciated, thanks!


# 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()

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