Site icon Spark By {Examples}

Convert PySpark DataFrame to Pandas

Naveen Nelamali
pyspark dataframe to pandas

(Spark with Python) PySpark DataFrame can be converted to Python pandas DataFrame using a function toPandas(), In this article, I will explain how to create Pandas DataFrame from PySpark (Spark) DataFrame with examples.

Before we start first understand the main differences between the Pandas & PySpark, operations on Pyspark run faster than Pandas due to its distributed nature and parallel execution on multiple cores and machines.

In other words, pandas run operations on a single node whereas PySpark runs on multiple machines. If you are working on a Machine Learning application where you are dealing with larger datasets, PySpark processes operations many times faster than pandas. Refer to pandas DataFrame Tutorial beginners guide with examples

After processing data in PySpark we would need to convert it back to Pandas DataFrame for a further procession with Machine Learning application or any Python applications.

Key Points –

Prepare PySpark DataFrame

In order to explain with an example first let’s create a PySpark DataFrame.


import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()

data = [("James","","Smith","36636","M",60000),
        ("Michael","Rose","","40288","M",70000),
        ("Robert","","Williams","42114","",400000),
        ("Maria","Anne","Jones","39192","F",500000),
        ("Jen","Mary","Brown","","F",0)]

columns = ["first_name","middle_name","last_name","dob","gender","salary"]
pysparkDF = spark.createDataFrame(data = data, schema = columns)
pysparkDF.printSchema()
pysparkDF.show(truncate=False)

This yields below schema and result of the DataFrame.


root
 |-- first_name: string (nullable = true)
 |-- middle_name: string (nullable = true)
 |-- last_name: string (nullable = true)
 |-- dob: string (nullable = true)
 |-- gender: string (nullable = true)
 |-- salary: long (nullable = true)

+----------+-----------+---------+-----+------+------+
|first_name|middle_name|last_name|dob  |gender|salary|
+----------+-----------+---------+-----+------+------+
|James     |           |Smith    |36636|M     |60000 |
|Michael   |Rose       |         |40288|M     |70000 |
|Robert    |           |Williams |42114|      |400000|
|Maria     |Anne       |Jones    |39192|F     |500000|
|Jen       |Mary       |Brown    |     |F     |0     |
+----------+-----------+---------+-----+------+------+

Convert PySpark Dataframe to Pandas DataFrame

PySpark DataFrame provides a method toPandas() to convert it to Python Pandas DataFrame.

toPandas() results in the collection of all records in the PySpark DataFrame to the driver program and should be done only on a small subset of the data. running on larger dataset’s results in memory error and crashes the application. To deal with a larger dataset, you can also try increasing memory on the driver.


pandasDF = pysparkDF.toPandas()
print(pandasDF)

This yields the below panda’s DataFrame. Note that pandas add a sequence number to the result as a row Index. You can rename pandas columns by using rename() function.


  first_name middle_name last_name    dob gender  salary
0      James                 Smith  36636      M   60000
1    Michael        Rose            40288      M   70000
2     Robert              Williams  42114         400000
3      Maria        Anne     Jones  39192      F  500000
4        Jen        Mary     Brown             F       0

I have dedicated Python pandas Tutorial with Examples where I explained pandas concepts in detail.

Convert Spark Nested Struct DataFrame to Pandas

Most of the time data in PySpark DataFrame will be in a structured format meaning one column contains other columns so let’s see how it convert to Pandas. Here is an example with nested struct where we have firstname, middlename and lastname are part of the name column.


# Nested structure elements
from pyspark.sql.types import StructType, StructField, StringType,IntegerType
dataStruct = [(("James","","Smith"),"36636","M","3000"), \
      (("Michael","Rose",""),"40288","M","4000"), \
      (("Robert","","Williams"),"42114","M","4000"), \
      (("Maria","Anne","Jones"),"39192","F","4000"), \
      (("Jen","Mary","Brown"),"","F","-1") \
]

schemaStruct = StructType([
        StructField('name', StructType([
             StructField('firstname', StringType(), True),
             StructField('middlename', StringType(), True),
             StructField('lastname', StringType(), True)
             ])),
          StructField('dob', StringType(), True),
         StructField('gender', StringType(), True),
         StructField('salary', StringType(), True)
         ])
df = spark.createDataFrame(data=dataStruct, schema = schemaStruct)
df.printSchema()

pandasDF2 = df.toPandas()
print(pandasDF2)

Converting structured DataFrame to Pandas DataFrame results below output.


                   name    dob gender salary
0      (James, , Smith)  36636      M   3000
1     (Michael, Rose, )  40288      M   4000
2  (Robert, , Williams)  42114      M   4000
3  (Maria, Anne, Jones)  39192      F   4000
4    (Jen, Mary, Brown)             F     -1

FAQ on Convert PySpark DataFrame to Pandas

Why would I want to convert a PySpark DataFrame to a pandas DataFrame?

Converting PySpark DataFrames to Pandas allows you to leverage the extensive functionality and ease of use offered by the Pandas library for data manipulation, analysis, and visualization.

Are there any limitations or considerations when converting PySpark DataFrames to Pandas?

It’s essential to consider memory constraints, especially when dealing with large datasets. Converting large PySpark DataFrames to DataFrames may lead to out-of-memory errors if the data cannot fit into memory.

Can I convert any PySpark DataFrame to a Pandas DataFrame?

You can convert any PySpark DataFrame to a DataFrame using the toPandas() method. However, keep in mind the potential performance implications and ensure compatibility between PySpark and Pandas data types and structures.

How can I optimize the conversion process from PySpark to Pandas for better performance?

Optimizations can include selecting relevant columns before conversion, filtering out unnecessary data, and using appropriate data types to minimize memory usage. Additionally, consider using partitioning and caching in PySpark to optimize performance before converting to Pandas.

Conclusion

In this simple article, you have learned to convert Spark DataFrame to pandas using toPandas() function of the Spark DataFrame. Also have seen a similar example with complex nested structure elements. toPandas() results in the collection of all records in the DataFrame to the driver program and should be done on a small subset of the data.

Happy Learning !!

References

Exit mobile version