CODEWITHSUNDEEP
pythondataframepyspark
frm
frm

Reputation: 687

How can I create a dataframe using other dataframe (PySpark)?

I'm using PySpark v1.6.1 and I want to create a dataframe using another one:

Right now is using .map(func) creating an RDD using that function (which transforms from one row from the original type and returns a row with the new one). But this is creating an RDD and I don't wont that.

Is there a nicer way to do this?

Upvotes: 5

Views: 24422

Answers (1)

Prem
Prem

Reputation: 11955

from pyspark.sql.functions import unix_timestamp, col, to_date, struct

####
#sample data
####
df = sc.parallelize([[25, 'Prem', 'M', '12-21-2006 11:00:05','abc', '1'],
                      [20, 'Kate', 'F', '05-30-2007 10:05:00', 'asdf', '2'],
                      [40, 'Cheng', 'M', '12-30-2017 01:00:01', 'qwerty', '3']]).\
    toDF(["age","name","sex","datetime_in_strFormat","initial_col_name","col_in_strFormat"])

#create 'struct' type column by combining first 3 columns of sample data - (this is built to answer query #1)
df = df.withColumn("struct_col", struct('age', 'name', 'sex')).\
    drop('age', 'name', 'sex')
df.show()
df.printSchema()

####
#query 1
####
#Convert a field that has a struct of three values (i.e. 'struct_col') in different columns (i.e. 'name', 'age' & 'sex')
df = df.withColumn('name', col('struct_col.name')).\
    withColumn('age', col('struct_col.age')).\
    withColumn('sex', col('struct_col.sex')).\
    drop('struct_col')
df.show()
df.printSchema()

####
#query 2
####
#Convert the timestamp from string (i.e. 'datetime_in_strFormat') to datetime (i.e. 'datetime_in_tsFormat')
df = df.withColumn('datetime_in_tsFormat',
                   unix_timestamp(col('datetime_in_strFormat'), 'MM-dd-yyyy hh:mm:ss').cast("timestamp"))
df.show()
df.printSchema()

####
#query 3
####
#create more columns using above timestamp (e.g. fetch date value from timestamp column)
df = df.withColumn('datetime_in_dateFormat', to_date(col('datetime_in_tsFormat')))
df.show()

####
#query 4.a
####
#Change column name (e.g. 'initial_col_name' is renamed to 'new_col_name)
df = df.withColumnRenamed('initial_col_name', 'new_col_name')
df.show()

####
#query 4.b
####
#Change column type (e.g. string type in 'col_in_strFormat' is coverted to double type in 'col_in_doubleFormat')
df = df.withColumn("col_in_doubleFormat", col('col_in_strFormat').cast("double"))
df.show()
df.printSchema()

Sample data:

+---------------------+----------------+----------------+------------+
|datetime_in_strFormat|initial_col_name|col_in_strFormat|  struct_col|
+---------------------+----------------+----------------+------------+
|  12-21-2006 11:00:05|             abc|               1| [25,Prem,M]|
|  05-30-2007 10:05:00|            asdf|               2| [20,Kate,F]|
|  12-30-2017 01:00:01|          qwerty|               3|[40,Cheng,M]|
+---------------------+----------------+----------------+------------+
root
 |-- datetime_in_strFormat: string (nullable = true)
 |-- initial_col_name: string (nullable = true)
 |-- col_in_strFormat: string (nullable = true)
 |-- struct_col: struct (nullable = false)
 |    |-- age: long (nullable = true)
 |    |-- name: string (nullable = true)
 |    |-- sex: string (nullable = true)

Final output data:

+---------------------+------------+----------------+-----+---+---+--------------------+----------------------+-------------------+
|datetime_in_strFormat|new_col_name|col_in_strFormat| name|age|sex|datetime_in_tsFormat|datetime_in_dateFormat|col_in_doubleFormat|
+---------------------+------------+----------------+-----+---+---+--------------------+----------------------+-------------------+
|  12-21-2006 11:00:05|         abc|               1| Prem| 25|  M| 2006-12-21 11:00:05|            2006-12-21|                1.0|
|  05-30-2007 10:05:00|        asdf|               2| Kate| 20|  F| 2007-05-30 10:05:00|            2007-05-30|                2.0|
|  12-30-2017 01:00:01|      qwerty|               3|Cheng| 40|  M| 2017-12-30 01:00:01|            2017-12-30|                3.0|
+---------------------+------------+----------------+-----+---+---+--------------------+----------------------+-------------------+

root
 |-- datetime_in_strFormat: string (nullable = true)
 |-- new_col_name: string (nullable = true)
 |-- col_in_strFormat: string (nullable = true)
 |-- name: string (nullable = true)
 |-- age: long (nullable = true)
 |-- sex: string (nullable = true)
 |-- datetime_in_tsFormat: timestamp (nullable = true)
 |-- datetime_in_dateFormat: date (nullable = true)
 |-- col_in_doubleFormat: double (nullable = true)

Upvotes: 4

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