Get started with matplotlib & seaborn.txt

Get started with matplotlib

 sqlQuery = "SELECT CAST(YEAR(OrderDate) AS CHAR(4)) AS OrderYear, \
                 SUM((UnitPrice * Quantity) + Tax) AS GrossRevenue \
             FROM salesorders \
             GROUP BY CAST(YEAR(OrderDate) AS CHAR(4)) \
             ORDER BY OrderYear"
 df_spark = spark.sql(sqlQuery)
 df_spark.show()
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 from matplotlib import pyplot as plt

 # matplotlib requires a Pandas dataframe, not a Spark one
 df_sales = df_spark.toPandas()

 # Create a bar plot of revenue by year
 plt.bar(x=df_sales['OrderYear'], height=df_sales['GrossRevenue'])

 # Display the plot
 plt.show()
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 # Clear the plot area
 plt.clf()

 # Create a bar plot of revenue by year
 plt.bar(x=df_sales['OrderYear'], height=df_sales['GrossRevenue'], color='orange')

 # Customize the chart
 plt.title('Revenue by Year')
 plt.xlabel('Year')
 plt.ylabel('Revenue')
 plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7)
 plt.xticks(rotation=45)

 # Show the figure
 plt.show()

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 # Clear the plot area
 plt.clf()

 # Create a Figure
 fig = plt.figure(figsize=(8,3))

 # Create a bar plot of revenue by year
 plt.bar(x=df_sales['OrderYear'], height=df_sales['GrossRevenue'], color='orange')

 # Customize the chart
 plt.title('Revenue by Year')
 plt.xlabel('Year')
 plt.ylabel('Revenue')
 plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7)
 plt.xticks(rotation=45)

 # Show the figure
 plt.show()
-----------------------------------------
 # Clear the plot area
 plt.clf()

 # Create a figure for 2 subplots (1 row, 2 columns)
 fig, ax = plt.subplots(1, 2, figsize = (10,4))

 # Create a bar plot of revenue by year on the first axis
 ax[0].bar(x=df_sales['OrderYear'], height=df_sales['GrossRevenue'], color='orange')
 ax[0].set_title('Revenue by Year')

 # Create a pie chart of yearly order counts on the second axis
 yearly_counts = df_sales['OrderYear'].value_counts()
 ax[1].pie(yearly_counts)
 ax[1].set_title('Orders per Year')
 ax[1].legend(yearly_counts.keys().tolist())

 # Add a title to the Figure
 fig.suptitle('Sales Data')

 # Show the figure
 plt.show()
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Use the seaborn library----------->
-----------------------------------------------

 import seaborn as sns

 # Clear the plot area
 plt.clf()

 # Create a bar chart
 ax = sns.barplot(x="OrderYear", y="GrossRevenue", data=df_sales)
 plt.show()
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 # Clear the plot area
 plt.clf()

 # Set the visual theme for seaborn
 sns.set_theme(style="whitegrid")

 # Create a bar chart
 ax = sns.barplot(x="OrderYear", y="GrossRevenue", data=df_sales)
 plt.show()
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 # Clear the plot area
 plt.clf()

 # Create a bar chart
 ax = sns.lineplot(x="OrderYear", y="GrossRevenue", data=df_sales)
 plt.show()