Overview
Domain 7 accounts for 5% of the exam through two official objectives. The first asks you to explain the advantages of the Pandas API on Spark, the pyspark.pandas module that lets pandas users run familiar code on distributed data. The second asks you to create and invoke pandas UDFs, the vectorised user-defined functions declared with pandas_udf.
Expect about two questions. You should know how to import the module, how to convert between pandas-on-Spark, Spark and plain pandas DataFrames, what the default index costs, and when operations pull data to the driver. For pandas UDFs, know the type-hint forms such as Series to Series and Iterator of Series, why Arrow makes them faster than ordinary Python UDFs, and how to call them from DataFrame code and SQL.
How it's tested
Items in this section are short. Some ask why a team would choose pyspark.pandas or what a conversion method returns. Others show a function decorated with pandas_udf and ask which type hints, return type or invocation is correct. Watch for distractors that confuse plain pandas, pandas-on-Spark and Spark DataFrames.
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State the advantage
A team with pandas code and growing data is described, and you identify what the Pandas API on Spark offers.
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Pick the conversion
You choose between to_pandas, to_spark, pandas_api and ps.from_pandas to move data between representations.
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Complete the pandas UDF
A decorated function is shown with a gap in its type hints or return type, and you supply the valid form.
A data scientist must apply a scipy calculation to a column of 500 million rows. A plain Python UDF would call the function once per row with serialisation overhead. A pandas UDF declared with @pandas_udf('double') and hints pd.Series -> pd.Series receives batches as pandas Series through Arrow, so the vectorised calculation runs on whole batches. The pandas UDF is therefore the efficient choice.
Objectives 2
Official objectives from the Databricks outline, in outline order.
7.1 Explain the advantages of using Pandas API on Sparkpandas syntax on distributed data with pyspark.pandas.
- What it means
- This objective covers why the Pandas API on Spark exists. Imported as import pyspark.pandas as ps, it provides a pandas-compatible API whose DataFrames are backed by Spark, so pandas code can scale beyond one machine's memory. It grew out of the Koalas project and became part of Apache Spark in version 3.2.
- What to understand
- Advantages include reusing pandas skills and code, one codebase for small and large data, and easy movement between representations: psdf.to_spark(), sdf.pandas_api(), ps.from_pandas(pdf) and psdf.to_pandas(). Costs include the default sequence index, which needs global ordering, and to_pandas, which collects everything to the driver. Some pandas functions are unsupported or behave differently.
- How it shows up
- A scenario describes pandas code hitting memory limits, and you identify the Pandas API on Spark as the low-rewrite option.
- Common confusion
- Assuming pandas-on-Spark data is held in driver memory like pandas.
7.2 Create and invoke Pandas UDFVectorised UDFs with pandas_udf and type hints.
- What it means
- This objective covers writing pandas UDFs with the pandas_udf decorator from pyspark.sql.functions. Spark sends column data to Python in Apache Arrow batches, the function processes whole pandas Series or DataFrames, and results return in the same columnar form. You invoke them like built-in functions in select or withColumn, or register them with spark.udf.register for SQL.
- What to understand
- Python type hints define the UDF variety: pd.Series -> pd.Series for scalar transformations of equal length, Iterator[pd.Series] -> Iterator[pd.Series] for expensive one-time setup per partition, and pd.Series -> scalar for grouped aggregation used with groupBy().agg(). The return type is given in the decorator, such as @pandas_udf('double'). Related pandas function APIs include applyInPandas and mapInPandas.
- How it shows up
- A pandas UDF definition is shown, and you choose the correct type hints, return type or way to call it.
- Common confusion
- Writing hints for single values instead of Series in a scalar pandas UDF.
High-yield concepts
The terms, rules and formulas this domain turns into questions most often.
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pyspark.pandas
Module providing the pandas API on Spark-backed DataFrames.
- Remember
- Import as ps by convention.
- Tested as
- Advantage questions.
- Trap
- Importing pandas and expecting distribution.
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Koalas
Open-source project merged into Spark 3.2 as the Pandas API on Spark.
- Remember
- Same idea, now built in.
- Tested as
- Background questions.
- Trap
- Installing Koalas separately for new code.
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to_pandas()
Collects a pandas-on-Spark DataFrame to a local pandas DataFrame.
- Remember
- Everything moves to the driver.
- Tested as
- Conversion questions.
- Trap
- Using it on very large data.
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to_spark() and pandas_api()
Convert pandas-on-Spark to Spark DataFrames and back.
- Remember
- No data collected to the driver.
- Tested as
- Conversion questions.
- Trap
- Confusing them with to_pandas.
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Default index
pandas-on-Spark attaches a sequence index by default.
- Remember
- Sequence index can be costly on large data.
- Tested as
- Performance questions.
- Trap
- Assuming indexes are free.
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pandas_udf
Decorator that creates a vectorised UDF using Arrow.
- Remember
- Return type given in the decorator.
- Tested as
- UDF creation questions.
- Trap
- Omitting the return type.
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Series to Series
Scalar pandas UDF processing batches of equal length.
- Remember
- Output length must match input.
- Tested as
- Type hint questions.
- Trap
- Returning an aggregated value.
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Iterator of Series
Pandas UDF that receives an iterator of batches.
- Remember
- Good for costly setup like loading a model once.
- Tested as
- Type hint questions.
- Trap
- Reloading the model per batch.
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Series to scalar
Grouped aggregate pandas UDF.
- Remember
- Used with groupBy().agg() or windows.
- Tested as
- Aggregation questions.
- Trap
- Using it inside withColumn without grouping.
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Apache Arrow
Columnar format used to move data between JVM and Python.
- Remember
- Reason pandas UDFs beat Python UDFs.
- Tested as
- Performance questions.
- Trap
- Thinking Arrow is a storage format on disk here.
Side by side
Concepts candidates mix up, compared on the point that decides the answer.
pandas vs pandas-on-Spark vs Spark DataFrame
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| pandas DataFrame | In-memory on one machine | Limited by driver memory | Using it for very large data |
| pandas-on-Spark DataFrame | pandas API backed by Spark | Distributed with pandas syntax | Assuming every pandas feature behaves identically |
| Spark DataFrame | Native distributed DataFrame | Full Spark SQL API | Expecting pandas methods like .iloc |
Python UDF vs pandas UDF
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Python UDF | Processes one row per call | Pickle serialisation, slower | Using it for heavy numeric work |
| pandas UDF | Processes batches as pandas objects | Arrow transfer, vectorised | Wrong type hints |
Pandas UDF type-hint forms
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Series -> Series | Element-wise batch transform | Same length in and out | Returning one value |
| Iterator[Series] -> Iterator[Series] | Batch iterator per partition | One-time setup reused | Ignoring the iterator form for model loading |
| Series -> scalar | Grouped aggregation | One value per group | Using it without groupBy |
Exam traps
Each trap is an answer that looks right. Learn the clue that should change your mind.
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Pandas-on-Spark is local
- What candidates pick
- Say pyspark.pandas loads all data into the driver.
- Why it looks right
- The syntax looks exactly like pandas.
- Why it's wrong
- pandas-on-Spark DataFrames are distributed across executors; only to_pandas brings data to the driver.
Clue to watch for The question asks where the data lives.
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to_pandas to convert to Spark
- What candidates pick
- Use to_pandas() to get a Spark DataFrame.
- Why it looks right
- The method name mentions pandas conversion.
- Why it's wrong
- to_pandas returns a local pandas DataFrame; to_spark returns a Spark DataFrame without collecting.
Clue to watch for The target is a Spark DataFrame.
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Python UDF is as fast
- What candidates pick
- Pick a standard udf for a heavy numeric column transform.
- Why it looks right
- Both are user-defined functions.
- Why it's wrong
- Standard Python UDFs process rows individually; pandas UDFs use Arrow batches and vectorised code.
Clue to watch for Large data and numeric libraries are mentioned.
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Scalar hints for aggregation
- What candidates pick
- Use pd.Series -> pd.Series for a per-group mean.
- Why it looks right
- It is the most common form.
- Why it's wrong
- Grouped aggregation needs a Series -> scalar UDF, returning one value per group.
Clue to watch for The output is one value per group.
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Missing return type
- What candidates pick
- Declare @pandas_udf without a return type and expect inference.
- Why it looks right
- Python hints describe input and output.
- Why it's wrong
- The Spark return type must be given in the decorator, such as 'double' or a DDL schema.
Clue to watch for Options differ only in the decorator argument.
Answer framework
For Pandas API on Spark and pandas UDF questions, step through these checks.
- Identify which representation each variable holds, whether plain pandas, pandas-on-Spark or a Spark DataFrame, since methods and memory behaviour differ between them.
- For conversions, choose to_spark or pandas_api to stay distributed, and use to_pandas only when the result is small.
- For custom logic, prefer built-in functions, then pandas UDFs over standard Python UDFs when vectorised libraries can do the work.
- Match the pandas UDF type hints to the task: Series to Series, Iterator of Series, or Series to scalar for aggregation.
- Confirm the return type in the decorator and the invocation context, such as withColumn, groupBy().agg() or a SQL registration.
Practice question
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Study strategy
Spend one focused session in a notebook. Import pyspark.pandas as ps, read a file with ps.read_csv or convert a Spark DataFrame with pandas_api(), and run a few familiar pandas operations such as groupby, sort_values and describe. Convert back with to_spark(), then try to_pandas() on a small sample and note that it returns local data.
Then write three pandas UDFs: a Series to Series transform, an Iterator of Series function that loads a lookup dictionary once, and a Series to scalar aggregate used in groupBy().agg(). Register one with spark.udf.register and call it from spark.sql. Compare the run time of the Series transform with an equivalent standard Python UDF on a larger DataFrame.
Write the three type-hint forms and the four conversion methods on a single revision card. Since this section carries only 5% of the marks, review the card before practice tests and spend the remaining time on the larger Domains 1 and 3.
FAQ
How much of the exam is Using Pandas API on Spark?
Using Pandas API on Spark is 5% of the scored exam, around two questions, built on two official objectives.
What is the Pandas API on Spark?
The pyspark.pandas module, which offers pandas-style DataFrames backed by Spark so pandas code can run on distributed data. It came from the Koalas project.
Is pandas-on-Spark the same as pandas?
The syntax is largely the same, but data is distributed, some functions are unsupported, and operations that need global ordering can be expensive.
What makes pandas UDFs faster than Python UDFs?
They receive data in Apache Arrow batches as pandas objects and run vectorised code, avoiding per-row serialisation and function calls.
Which type hints define a scalar pandas UDF?
pd.Series as the input and pd.Series as the output, with the same length. The Spark return type is set in the pandas_udf decorator.
Can pandas UDFs be used in SQL?
Yes. Register the function with spark.udf.register and call it by name in spark.sql queries.
Other Data & Analytics certifications
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Test whether you can choose the right conversion method and write correctly typed pandas UDFs in timed exam-style questions.
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