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

问题

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

Schema = StructType([ StructField("temperature", DoubleType(), True),
                      StructField("temperature_unit", StringType(), True),
                      StructField("humidity", DoubleType(), True),
                      StructField("humidity_unit", StringType(), True),
                      StructField("pressure", DoubleType(), True),
                      StructField("pressure_unit", StringType(), True)
                    ])

For some datasources it is possible to infer the schema from the data-source and get a dataframe with this schema definition.

Is it possible to get the schema definition (in the form described above) from a dataframe, where the data has been inferred before?

df.printSchema() prints the schema as a tree, but I need to reuse the schema, having it defined as above,so I can read a data-source with this schema that has been inferred before from another data-source.

回答1:

Yes it is possible. Use DataFrame.schema property

schema

Returns the schema of this DataFrame as a pyspark.sql.types.StructType.

>>> df.schema
StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true)))

New in version 1.3.

Schema can be also exported to JSON and imported back if needed.

回答2:

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)))

回答3:

The code below will give you a well formatted tabular schema definition of the known dataframe. Quite useful when you have very huge number of columns & where editing is cumbersome. You can then now apply it to your new dataframe & hand-edit any columns you may want to accordingly.

from pyspark.sql.types import StructType

schema = [i for i in df.schema] 

And then from here, you have your new schema:

NewSchema = StructType(schema)

来源：https://stackoverflow.com/questions/54503014/how-to-get-the-schema-definition-from-a-dataframe-in-pyspark