How to get the schema definition from a dataframe in PySpark?

Question

In PySpark it you can define a schema and read data sources with this pre-defined schema, e. g.:

Schema = StructType([ Str         


        

Answer 1

You could re-use schema for existing Dataframe

l = [('Ankita',25,'F'),('Jalfaizy',22,'M'),('saurabh',20,'M'),('Bala',26,None)]
people_rdd=spark.sparkContext.parallelize(l)
schemaPeople = people_rdd.toDF(['name','age','gender'])

schemaPeople.show()

+--------+---+------+
|    name|age|gender|
+--------+---+------+
|  Ankita| 25|     F|
|Jalfaizy| 22|     M|
| saurabh| 20|     M|
|    Bala| 26|  null|
+--------+---+------+

spark.createDataFrame(people_rdd,schemaPeople.schema).show()

+--------+---+------+
|    name|age|gender|
+--------+---+------+
|  Ankita| 25|     F|
|Jalfaizy| 22|     M|
| saurabh| 20|     M|
|    Bala| 26|  null|
+--------+---+------+

Just use df.schema to get the underlying schema of dataframe

schemaPeople.schema

StructType(List(StructField(name,StringType,true),StructField(age,LongType,true),StructField(gender,StringType,true)))