Category: Analytics

• ModelData2=ModelData.toPandas()  #CONVERTS SPARK DF TO PANDAS DF
• table_model = spark.createDataFrame(ModelData2) # CREATES SPARK DF
• table_model.write.saveAsTable(‘LIBRARYPATH.model_data’) #SAVES AS TABLE

AND

new_df = transformed_chrn2[[‘Var1’, ‘Var2’, ‘Var3’, ‘Var4′,’Var5’]]

table_df = spark.createDataFrame(new_df)

table_df.write.saveAsTable(‘directory_name.table_name’)

SOURCE

https://stackoverflow.com/questions/30664008/how-to-save-dataframe-directly-to-hive

https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-connect-to-sql-database

https://docs.microsoft.com/en-us/azure/databricks/getting-started/spark/dataframes

https://stackoverflow.com/questions/53212396/pyspark-saveastable-how-to-insert-new-data-to-existing-table

In Seaborn a bar-chart can be created using the sns.countplot method and passing it the data

https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed

https://seaborn.pydata.org/generated/seaborn.barplot.html

It is quite easy to create buckets based on one column using query. Note you can also use qcut

 

bucket1 =df.query(‘Column_1 >=0 and Column_1 <0.25’)

bucket2 =df.query(‘Column_1 >=0.25 and Column_1 <0.5’)

bucket3 =df.query(‘Column_1 >=0.5 and Column_1 <0.75’)

bucket4 =df.query(‘Column_1 >=0.75 and Column_1 <=1’)

spark= SparkSession.builder \
.master(“local”) \
.appName(“Data Exploration”) \
.getOrCreate()

#load data as Spark DataFrame
data2=spark.read.format(“csv”) \
.option(“header”,”true”) \
.option(“mode”,”DROPMALFORMED”) \
.load(‘/home/Desktop//input/*.csv’)

from pyspark.sql.functions import col
for col_name in data7.columns:
data7 = data7.withColumn(col_name, col(col_name).cast(‘float’))

 

data7.printSchema()