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#Initializing SparkSession:
>>> from pyspark.sql import SparkSession 
>>> spark = SparkSession \
            .builder \
            .appName("Python Spark SQL basic example") \ 
            .config("spark.some.config.option", "some-value") \ 
            .getOrCreate()
        
##Creating DataFrames:
#For creating Data Frames, and inferring and specifying schemas, 
#you need to follow these code guidelines.
#import pyspark class Row from module sql
from pyspark.sql import *

Infer Schema:
            >>> sc = spark.sparkContext

            >>> A = sc.textFile(“Filename.txt”)

            >>> B = lines.map(lambda x: x.split(“,”))

            >>> C = parts.map(lambda a: Row(col1=a[0],col2=int(a[1])))

            >>> C_df = spark.createDataFrame(C)

Specify Schema:
            >>> C = parts.map(lambda a: Row(col1=a[0], col2=int(a[1].strip())))

            >>> schemaString = “MyTable”

            >>> D = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]

            >>> E = StructType(D)

            >>> spark.createDataFrame(C, E).show()

From Spark Data Sources:
JSON
             >>>df = spark.read.json(“table.json)

            >>>df.show()  

            >>> df2 = spark.read.load(“tablee2.json”, format=”json”)

Parquet files
            >>> df3 = spark.read.load(“newFile.parquet”)

Inspect Data:
You can inspect and perform operations on the entered data with these command sets.

>>> df.dtypes                          — Return df column names and data types

>>> df.show()                          — Display the content of df

>>> df.head()                           — Return first n rows

>>> df.first(n)                          — Return the first n rows

>>> df.schema                        — Return the schema of df

>>> df.describe().show()         — Compute summary statistics

>>> df.columns                       — Return the columns of df

>>> df.count()                         — Count the number of rows in df

>>> df.distinct().count()          — Count the number of distinct rows in df

>>> df.printSchema()              — Print the schema of df

>>> df.explain()                       — Print the (logical and physical) plans


Column Operations:
These are the basic command sets that you need for performing operations on columns.

Add
>>> df = df.withColumn(‘col1’,df.table.col1) \ .withColumn(‘col2’,df.table.col2) \ .withColumn(‘col3’,df.table.col3) \ .withColumn(‘col4′,df.table.col4) \.withColumn(col5’, explode(df.table.col5))

Update
>>> df = df.withColumnRenamed(‘col1’, ‘column1’)

Remove
>>> df = df.drop(“col3”, “col4”)

>>> df = df.drop(df.col3).drop(df.col4)

Actions
GroupBy:
>>> df.groupBy(“col1”)\ .count() \ .show()

Filter:
>>> df.filter(df[“col2”]>4).show()

Sort:
>>> peopledf.sort(peopledf.age.desc()).collect()

>>> df.sort(“col1”, ascending=False).collect()

>>> df.orderBy([“col1″,”col3”],ascending=[0,1])\ .collect()

Missing & Replacing Values:
>>> df.na.fill(20).show()

>>> df.na.drop().show()

>>> df.na \ .replace(10, 20) \ .show()

Repartitioning:
>>> df.repartition(10)\ df with 10 partitions .rdd \.getNumPartitions()

>>> df.coalesce(1).rdd.getNumPartitions()

SQL Queries:
>>> from pyspark.sql import functions as f

Select
>>> df.select(“col1”).show()

>>> df.select(“col2″,”col3”) \ .show()

When
>>> df.select(“col1”, f.when(df.col2> 30, 1) \ .otherwise(0)) \ .show()

>>> df[df.col1.isin(“A”,”B”)] .collect()

Running SQL Queries Programmatically
Registering Data Frames as Views:
>>> peopledf.createGlobalTempView(“column1”)

>>> df.createTempView(“column1”)

>>> df.createOrReplaceTempView(“column2”)

Query Views
>>> df_one = spark.sql(“SELECT * FROM customer”).show()

>>> df_new = spark.sql(“SELECT * FROM global_temp.people”)\ .show()

Output Operations:
DataStructures:
>>> rdd_1 = df.rdd

>>> df.toJSON().first()

>>> df.toPandas()

Write & Save to Files
>>> df.select(“Col1”, “Col2”)\ .write \ .save(“newFile.parquet”)

>>> df.select(“col3”, “col5”) \ .write \ .save(“table_new.json”,format=”json”)

Stopping SparkSession
>>> spark.stop()