Saving Dataframe as a table

  • 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


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

table_df = spark.createDataFrame(new_df)



Creating Buckets in Pandas using Query

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’)

Load Multipe CSV files in PySpark

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

#load data as Spark DataFrame“csv”) \
.option(“header”,”true”) \
.option(“mode”,”DROPMALFORMED”) \

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