Synapse Spark

rm -r dp203 -f

git clone https://github.com/MicrosoftLearning/Dp-203-azure-data-engineer dp203

cd dp203/Allfiles/labs/05

./setup.ps1

1. Select any of the files in the orders folder, and then in the New notebook list on the toolbar, select Load to DataFrame. A dataframe is a structure in Spark that represents a tabular dataset.

2. 

3. 

1. %%pyspark
   df = spark.read.load('abfss://files@datalakexxxxxxx.dfs.core.windows.net/sales/orders/2019.csv', format='csv'
   ## If header exists uncomment line below
   ##, header=True
   )
   display(df.limit(10))
   

   %%pyspark
   df = spark.read.load('abfss://files@datalakexxxxxxx.dfs.core.windows.net/sales/orders/*.csv', format='csv'
   )
   display(df.limit(100))
   

   

2. %%pyspark
   from pyspark.sql.types import *
   from pyspark.sql.functions import *
    
   orderSchema = StructType([
       StructField("SalesOrderNumber", StringType()),
       StructField("SalesOrderLineNumber", IntegerType()),
       StructField("OrderDate", DateType()),
       StructField("CustomerName", StringType()),
       StructField("Email", StringType()),
       StructField("Item", StringType()),
       StructField("Quantity", IntegerType()),
       StructField("UnitPrice", FloatType()),
       StructField("Tax", FloatType())
       ])
    
   df = spark.read.load('abfss://files@datalakexxxxxxx.dfs.core.windows.net/sales/orders/*.csv', format='csv', schema=orderSchema)
   display(df.limit(100))
   

   

3. df.printSchema()
   

   

4. customers = df['CustomerName', 'Email']
   print(customers.count())
   print(customers.distinct().count())
   display(customers.distinct())
   

   

5. Run the new code cell, and review the results. Observe the following details:

   • When you perform an operation on a dataframe, the result is a new dataframe (in this case, a new customers dataframe is created by selecting a specific subset of columns from the df dataframe)
   • Dataframes provide functions such as count and distinct that can be used to summarize and filter the data they contain.
   • The dataframe['Field1', 'Field2', ...] syntax is a shorthand way of defining a subset of column. You can also use select method, so the first line of the code above could be written as customers = df.select("CustomerName", "Email")

ustomers = df.select("CustomerName", "Email").where(df['Item']=='Road-250 Red, 52')
print(customers.count())
print(customers.distinct().count())
display(customers.distinct())

productSales = df.select("Item", "Quantity").groupBy("Item").sum()
display(productSales)

yearlySales = df.select(year("OrderDate").alias("Year")).groupBy("Year").count().orderBy("Year")
display(yearlySales)

df.createOrReplaceTempView("salesorders")
 
spark_df = spark.sql("SELECT * FROM salesorders")
display(spark_df)

%%sql
SELECT YEAR(OrderDate) AS OrderYear,
       SUM((UnitPrice * Quantity) + Tax) AS GrossRevenue
FROM salesorders
GROUP BY YEAR(OrderDate)
ORDER BY OrderYear;

%%sql
SELECT * FROM salesorders

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()

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()

# 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()

# 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()

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()

# 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()

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