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Dataframe Operation Examples in PySpark


Dataframe Operation Examples in PySpark.

PySpark - Dataframe Operations: 

(More Examples Coming Soon)

 

Adding New Column:

 

Using withColumn:


from pyspark.sql.functions import lit

df = sqlContext.createDataFrame(
\[(1, "a", 4), (3, "B", 5)\], ("col1", "col2", "col3"))

df\_col4 = df.withColumn("col4", lit(0))

df\_col4.show()
 

Using UDF:


from pyspark.sql.functions import udf
from pyspark.sql.types import \*

df = sqlContext.createDataFrame(
\[(1, "a", 4), (3, "B", 5)\], ("col1", "col2", "col3")) 

def checkType(value):
    if value == xxxxxx: return 'visa'
    elif value == yyyyyy: return 'mastercard'
    else: return 'amex'

udfCheckType = udf(checkType, StringType())
df\_with\_cat = df.withColumn("category", udfCheckType("col1"))
df\_with\_cat.show()
 

Using alias:


\# Let's say column 'A' exists  
df.select('\*', (df.A + 10).alias('A\_10'))
df.show()
   

Modify or Change  Column Names:

Using withColumnRenamed:


df = df.withColumnRenamed("col1", "newCol1")\\
       .withColumnRenamed("col2", "newCol2")
df.show()
df.printSchema()
 


dictNames = {'old1': 'col1', 'old2': 'col2'}
for col in df.schema.names:
   df = df.withColumnRenamed(col, dictNames\[col\])
 

Using selectExpr:


df1 = df.selectExpr("Place as PlaceName", "Code as PlaceCode")
df1.show()
df1.printSchema()
 

Using built-in functions:


from pyspark.sql.functions import exp

df\_new = df\_old.withColumn("col3", exp("col2"))  <-- modify col2 using "exp" into col3
df\_new.show()
 


from pyspark.sql.functions import rand

df\_new = df\_old.withColumn("col3", rand() ) <-- modify col2 using another function e.g. rand() into col3
df\_new.show()
 

Using toDF method:


newColumnList= list(map(lambda x: x.replace(" ", "\_"), df.columns))

df\_new = df.toDF(\*newColumnList)

df\_new.schema()
 

Handle NULL values:

Filter Column having "None\Null" value:

 


df.where(col("col1").isNull())

df.where(col("Col1").isNotNull())
 

Drop Null Values:


df.na.drop(subset=\["Col1"\])
 

Find entries in a column which are not null:


df.filter("col1 is not NULL")
 

Find entries in a column which are null:


df.filter("col1 is NULL")
 

Count Non-Null :


df.filter(df.col1.isNotNull()).count()
 

Distinct Values:


df.select('colA').distinct().collect()
  If too many Distinct Values , use limit.


df.select('colA').distinct().limit(10).collect()

df.select('colA').distinct().show(10)
 

Duplicate Values:

Drop Duplicate Values from a Column:


df.drop\_duplicates(subset = \['col1'\]).show()
 

Drop All Duplicate Values:


df.drop\_duplicates().show()
 

Dataframe Conversions:

 

DataFrame Column to Python List


list1 = df.select('col1').collect()
list1\[0\]
list1 = df.select("col1").rdd.flatMap(lambda x: x).collect()

   

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