Convert pandas to pyspark dataframe

Convert pandas to pyspark dataframe DEFAULT

Optimize conversion between PySpark and pandas DataFrames

Apache Arrow is an in-memory columnar data format used in Apache Spark to efficiently transfer data between JVM and Python processes. This is beneficial to Python developers that work with pandas and NumPy data. However, its usage is not automatic and requires some minor changes to configuration or code to take full advantage and ensure compatibility.

PyArrow versions

PyArrow is installed in Databricks Runtime. For information on the version of PyArrow available in each Databricks Runtime version, see the Databricks runtime release notes.

Supported SQL types

All Spark SQL data types are supported by Arrow-based conversion except , of , and nested . is represented as a instead of . is supported only when PyArrow is equal to or higher than 0.10.0.

Convert PySpark DataFrames to and from pandas DataFrames

Arrow is available as an optimization when converting a PySpark DataFrame to a pandas DataFrame with and when creating a PySpark DataFrame from a pandas DataFrame with . To use Arrow for these methods, set the Spark configuration to . This configuration is disabled by default.

In addition, optimizations enabled by could fall back to a non-Arrow implementation if an error occurs before the computation within Spark. You can control this behavior using the Spark configuration .

Example

importnumpyasnpimportpandasaspd# Enable Arrow-based columnar data transfersspark.conf.set("spark.sql.execution.arrow.enabled","true")# Generate a pandas DataFramepdf=pd.DataFrame(np.random.rand(100,3))# Create a Spark DataFrame from a pandas DataFrame using Arrowdf=spark.createDataFrame(pdf)# Convert the Spark DataFrame back to a pandas DataFrame using Arrowresult_pdf=df.select("*").toPandas()

Using the Arrow optimizations produces the same results as when Arrow is not enabled. Even with Arrow, 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.

In addition, not all Spark data types are supported and an error can be raised if a column has an unsupported type. If an error occurs during , Spark falls back to create the DataFrame without Arrow.


Sours: https://docs.databricks.com/spark/latest/spark-sql/spark-pandas.html

Optimize conversion between PySpark and pandas DataFrames

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Apache Arrow is an in-memory columnar data format used in Apache Spark to efficiently transfer data between JVM and Python processes. This is beneficial to Python developers that work with pandas and NumPy data. However, its usage is not automatic and requires some minor changes to configuration or code to take full advantage and ensure compatibility.

PyArrow versions

PyArrow is installed in Databricks Runtime. For information on the version of PyArrow available in each Databricks Runtime version, see the Databricks runtime release notes.

Supported SQL types

All Spark SQL data types are supported by Arrow-based conversion except , of , and nested . is represented as a instead of . is supported only when PyArrow is equal to or higher than 0.10.0.

Convert PySpark DataFrames to and from pandas DataFrames

Arrow is available as an optimization when converting a PySpark DataFrame to a pandas DataFrame with and when creating a PySpark DataFrame from a pandas DataFrame with . To use Arrow for these methods, set the Spark configuration to . This configuration is disabled by default.

In addition, optimizations enabled by could fall back to a non-Arrow implementation if an error occurs before the computation within Spark. You can control this behavior using the Spark configuration .

Example

Using the Arrow optimizations produces the same results as when Arrow is not enabled. Even with Arrow, 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.

In addition, not all Spark data types are supported and an error can be raised if a column has an unsupported type. If an error occurs during , Spark falls back to create the DataFrame without Arrow.

Sours: https://docs.microsoft.com/en-us/azure/databricks/spark/latest/spark-sql/spark-pandas
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While working with a huge dataset Python Pandas DataFrame are not good enough to perform complex transformation operations hence if you have a Spark cluster, it’s better to convert Pandas to PySpark DataFrame, apply the complex transformations on Spark cluster, and convert it back.

In this article, I will explain steps in converting Pandas to PySpark DataFrame and how to Optimize the Pandas to PySpark DataFrame Conversion by enabling Apache Arrow.

1. Create Pandas DataFrame

In order to convert Pandas to PySpark DataFrame first, let’s create Pandas DataFrame with some test data. In order to use pandas you have to import it first using

Operations on Pyspark run faster than Python 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 it’s a good option to consider PySpark. PySpark processes operations many times faster than pandas.

2. Convert Pandas to PySpark (Spark) DataFrame

Spark provides a method to convert Pandas to Spark DataFrame, Spark by default infers the schema based on the Pandas data types to PySpark data types.

If you want all data types to String use .

3. Change Column Names & DataTypes while Converting

If you wanted to change the schema (column name & data type) while converting Pandas to PySpark DataFrame, create a PySpark Schema using StructType and use it for the schema.

4. Use Apache Arrow to Convert Pandas to Spark DataFrame

Apache Spark uses Apache Arrow which is an in-memory columnar format to transfer the data between Python and JVM. You need to enable to use Arrow as this is disabled by default and have Apache Arrow (PyArrow) install on all Spark cluster nodes using or by directly downloading from Apache Arrow for Python.

You need to have Spark compatible Apache Arrow installed to use the above statement, In case if you have not installed Apache Arrow you get the below error.

When an error occurs, Spark automatically fallback to non-Arrow optimization implementation, this can be controlled by .

Note: Apache Arrow currently support all Spark SQL data types except ,  of , and nested .

5. Complete Example of Convert Pandas to Spark Dataframe

Belo is complete example to convert Pandas to PySpark DataFrame.

Conclusion

In this article, you have learned how easy to convert Pandas to Spark DataFrame and optimize the conversion using Apache Arrow (in-memory columnar format)

Happy Learning!!

Tags: Pandas

NNK

SparkByExamples.com is a Big Data and Spark examples community page, all examples are simple and easy to understand and well tested in our development environment Read more ..

How to Convert Pandas to PySpark DataFrame
Sours: https://sparkbyexamples.com/pyspark/convert-pandas-to-pyspark-dataframe/
Data Wrangling with PySpark for Data Scientists Who Know Pandas - Andrew Ray

PySpark Usage Guide for Pandas with Apache Arrow

Apache Arrow in PySpark

Apache Arrow is an in-memory columnar data format that is used in Spark to efficiently transfer data between JVM and Python processes. This currently is most beneficial to Python users that work with Pandas/NumPy data. Its usage is not automatic and might require some minor changes to configuration or code to take full advantage and ensure compatibility. This guide will give a high-level description of how to use Arrow in Spark and highlight any differences when working with Arrow-enabled data.

Ensure PyArrow Installed

To use Apache Arrow in PySpark, the recommended version of PyArrow should be installed. If you install PySpark using pip, then PyArrow can be brought in as an extra dependency of the SQL module with the command . Otherwise, you must ensure that PyArrow is installed and available on all cluster nodes. You can install using pip or conda from the conda-forge channel. See PyArrow installation for details.

Enabling for Conversion to/from Pandas

Arrow is available as an optimization when converting a Spark DataFrame to a Pandas DataFrame using the call and when creating a Spark DataFrame from a Pandas DataFrame with . To use Arrow when executing these calls, users need to first set the Spark configuration to . This is disabled by default.

In addition, optimizations enabled by could fallback automatically to non-Arrow optimization implementation if an error occurs before the actual computation within Spark. This can be controlled by .

Find full example code at "examples/src/main/python/sql/arrow.py" in the Spark repo.

Using the above optimizations with Arrow will produce the same results as when Arrow is not enabled. Note that even with Arrow, 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. Not all Spark data types are currently supported and an error can be raised if a column has an unsupported type, see Supported SQL Types. If an error occurs during , Spark will fall back to create the DataFrame without Arrow.

Pandas UDFs (a.k.a. Vectorized UDFs)

Pandas UDFs are user defined functions that are executed by Spark using Arrow to transfer data and Pandas to work with the data, which allows vectorized operations. A Pandas UDF is defined using the as a decorator or to wrap the function, and no additional configuration is required. A Pandas UDF behaves as a regular PySpark function API in general.

Before Spark 3.0, Pandas UDFs used to be defined with . From Spark 3.0 with Python 3.6+, you can also use Python type hints. Using Python type hints are preferred and using will be deprecated in the future release.

Note that the type hint should use in all cases but there is one variant that should be used for its input or output type hint instead when the input or output column is of . The following example shows a Pandas UDF which takes long column, string column and struct column, and outputs a struct column. It requires the function to specify the type hints of and as below:

Find full example code at "examples/src/main/python/sql/arrow.py" in the Spark repo.

In the following sections, it describes the combinations of the supported type hints. For simplicity, variant is omitted.

Series to Series

The type hint can be expressed as , … -> .

By using with the function having such type hints above, it creates a Pandas UDF where the given function takes one or more and outputs one . The output of the function should always be of the same length as the input. Internally, PySpark will execute a Pandas UDF by splitting columns into batches and calling the function for each batch as a subset of the data, then concatenating the results together.

The following example shows how to create this Pandas UDF that computes the product of 2 columns.

Find full example code at "examples/src/main/python/sql/arrow.py" in the Spark repo.

For detailed usage, please see

Iterator of Series to Iterator of Series

The type hint can be expressed as -> .

By using with the function having such type hints above, it creates a Pandas UDF where the given function takes an iterator of and outputs an iterator of . The length of the entire output from the function should be the same length of the entire input; therefore, it can prefetch the data from the input iterator as long as the lengths are the same. In this case, the created Pandas UDF requires one input column when the Pandas UDF is called. To use multiple input columns, a different type hint is required. See Iterator of Multiple Series to Iterator of Series.

It is also useful when the UDF execution requires initializing some states although internally it works identically as Series to Series case. The pseudocode below illustrates the example.

The following example shows how to create this Pandas UDF:

Find full example code at "examples/src/main/python/sql/arrow.py" in the Spark repo.

For detailed usage, please see

Iterator of Multiple Series to Iterator of Series

The type hint can be expressed as -> .

By using with the function having such type hints above, it creates a Pandas UDF where the given function takes an iterator of a tuple of multiple and outputs an iterator of . In this case, the created pandas UDF requires multiple input columns as many as the series in the tuple when the Pandas UDF is called. Otherwise, it has the same characteristics and restrictions as Iterator of Series to Iterator of Series case.

The following example shows how to create this Pandas UDF:

Find full example code at "examples/src/main/python/sql/arrow.py" in the Spark repo.

For detailed usage, please see

Series to Scalar

The type hint can be expressed as , … -> .

By using with the function having such type hints above, it creates a Pandas UDF similar to PySpark’s aggregate functions. The given function takes and returns a scalar value. The return type should be a primitive data type, and the returned scalar can be either a python primitive type, e.g., or or a numpy data type, e.g., or . should ideally be a specific scalar type accordingly.

This UDF can be also used with and . It defines an aggregation from one or more to a scalar value, where each represents a column within the group or window.

Note that this type of UDF does not support partial aggregation and all data for a group or window will be loaded into memory. Also, only unbounded window is supported with Grouped aggregate Pandas UDFs currently. The following example shows how to use this type of UDF to compute mean with a group-by and window operations:

Find full example code at "examples/src/main/python/sql/arrow.py" in the Spark repo.

For detailed usage, please see

Pandas Function APIs

Pandas Function APIs can directly apply a Python native function against the whole by using Pandas instances. Internally it works similarly with Pandas UDFs by using Arrow to transfer data and Pandas to work with the data, which allows vectorized operations. However, A Pandas Function API behaves as a regular API under PySpark instead of , and Python type hints in Pandas Functions APIs are optional and do not affect how it works internally at this moment although they might be required in the future.

From Spark 3.0, grouped map pandas UDF is now categorized as a separate Pandas Function API, . It is still possible to use it with and as it was; however, it is preferred to use directly. Using will be deprecated in the future.

Grouped Map

Grouped map operations with Pandas instances are supported by which requires a Python function that takes a and return another . It maps each group to each in the Python function.

This API implements the “split-apply-combine” pattern which consists of three steps:

  • Split the data into groups by using .
  • Apply a function on each group. The input and output of the function are both . The input data contains all the rows and columns for each group.
  • Combine the results into a new PySpark .

To use , the user needs to define the following:

  • A Python function that defines the computation for each group.
  • A object or a string that defines the schema of the output PySpark .

The column labels of the returned must either match the field names in the defined output schema if specified as strings, or match the field data types by position if not strings, e.g. integer indices. See pandas.DataFrame on how to label columns when constructing a .

Note that all data for a group will be loaded into memory before the function is applied. This can lead to out of memory exceptions, especially if the group sizes are skewed. The configuration for maxRecordsPerBatch is not applied on groups and it is up to the user to ensure that the grouped data will fit into the available memory.

The following example shows how to use to subtract the mean from each value in the group.

Find full example code at "examples/src/main/python/sql/arrow.py" in the Spark repo.

For detailed usage, please see .

Map

Map operations with Pandas instances are supported by which maps an iterator of s to another iterator of s that represents the current PySpark and returns the result as a PySpark . The functions takes and outputs an iterator of . It can return the output of arbitrary length in contrast to some Pandas UDFs although internally it works similarly with Series to Series Pandas UDF.

The following example shows how to use :

Find full example code at "examples/src/main/python/sql/arrow.py" in the Spark repo.

For detailed usage, please see .

Co-grouped Map

Co-grouped map operations with Pandas instances are supported by which allows two PySpark s to be cogrouped by a common key and then a Python function applied to each cogroup. It consists of the following steps:

  • Shuffle the data such that the groups of each dataframe which share a key are cogrouped together.
  • Apply a function to each cogroup. The input of the function is two (with an optional tuple representing the key). The output of the function is a .
  • Combine the s from all groups into a new PySpark .

To use , the user needs to define the following:

  • A Python function that defines the computation for each cogroup.
  • A object or a string that defines the schema of the output PySpark .

The column labels of the returned must either match the field names in the defined output schema if specified as strings, or match the field data types by position if not strings, e.g. integer indices. See pandas.DataFrame on how to label columns when constructing a .

Note that all data for a cogroup will be loaded into memory before the function is applied. This can lead to out of memory exceptions, especially if the group sizes are skewed. The configuration for maxRecordsPerBatch is not applied and it is up to the user to ensure that the cogrouped data will fit into the available memory.

The following example shows how to use to perform an asof join between two datasets.

Find full example code at "examples/src/main/python/sql/arrow.py" in the Spark repo.

For detailed usage, please see .

Usage Notes

Supported SQL Types

Currently, all Spark SQL data types are supported by Arrow-based conversion except , of , and nested .

Setting Arrow Batch Size

Data partitions in Spark are converted into Arrow record batches, which can temporarily lead to high memory usage in the JVM. To avoid possible out of memory exceptions, the size of the Arrow record batches can be adjusted by setting the conf “spark.sql.execution.arrow.maxRecordsPerBatch” to an integer that will determine the maximum number of rows for each batch. The default value is 10,000 records per batch. If the number of columns is large, the value should be adjusted accordingly. Using this limit, each data partition will be made into 1 or more record batches for processing.

Timestamp with Time Zone Semantics

Spark internally stores timestamps as UTC values, and timestamp data that is brought in without a specified time zone is converted as local time to UTC with microsecond resolution. When timestamp data is exported or displayed in Spark, the session time zone is used to localize the timestamp values. The session time zone is set with the configuration ‘spark.sql.session.timeZone’ and will default to the JVM system local time zone if not set. Pandas uses a type with nanosecond resolution, , with optional time zone on a per-column basis.

When timestamp data is transferred from Spark to Pandas it will be converted to nanoseconds and each column will be converted to the Spark session time zone then localized to that time zone, which removes the time zone and displays values as local time. This will occur when calling or with timestamp columns.

When timestamp data is transferred from Pandas to Spark, it will be converted to UTC microseconds. This occurs when calling with a Pandas DataFrame or when returning a timestamp from a . These conversions are done automatically to ensure Spark will have data in the expected format, so it is not necessary to do any of these conversions yourself. Any nanosecond values will be truncated.

Note that a standard UDF (non-Pandas) will load timestamp data as Python datetime objects, which is different than a Pandas timestamp. It is recommended to use Pandas time series functionality when working with timestamps in s to get the best performance, see here for details.

Recommended Pandas and PyArrow Versions

For usage with pyspark.sql, the supported versions of Pandas is 0.24.2 and PyArrow is 0.15.1. Higher versions may be used, however, compatibility and data correctness can not be guaranteed and should be verified by the user.

Compatibility Setting for PyArrow >= 0.15.0 and Spark 2.3.x, 2.4.x

Since Arrow 0.15.0, a change in the binary IPC format requires an environment variable to be compatible with previous versions of Arrow <= 0.14.1. This is only necessary to do for PySpark users with versions 2.3.x and 2.4.x that have manually upgraded PyArrow to 0.15.0. The following can be added to to use the legacy Arrow IPC format:

This will instruct PyArrow >= 0.15.0 to use the legacy IPC format with the older Arrow Java that is in Spark 2.3.x and 2.4.x. Not setting this environment variable will lead to a similar error as described in SPARK-29367 when running s or with Arrow enabled. More information about the Arrow IPC change can be read on the Arrow 0.15.0 release blog.

Sours: https://spark.apache.org/docs/3.0.1/sql-pyspark-pandas-with-arrow.html

To convert pyspark dataframe pandas

How to Convert Pandas to PySpark DataFrame ?

In this article, we will learn How to Convert Pandas to PySpark DataFrame. Sometimes we will get csv, xlsx, etc. format data, and we have to store it in PySpark DataFrame and that can be done by loading data in Pandas then converted PySpark DataFrame. For conversion, we pass the Pandas dataframe into the CreateDataFrame() method.

Syntax: spark.createDataframe(data, schema)

Parameter:

  • data – list of values on which dataframe is created.
  • schema – It’s the structure of dataset or list of column names.

where spark is the SparkSession object.

Example 1: Create a DataFrame and then Convert using spark.createDataFrame() method

Python3

 

 

 

 

 

 

Output:



Example 2: Create a DataFrame and then Convert using spark.createDataFrame() method

In this method, we are using Apache Arrow to convert Pandas to Pyspark DataFrame.

Python3

 

 

 

 

 

 

 

 

Output:

Example 3: Load a DataFrame from CSV and then Convert 

In this method, we can easily read the CSV file in Pandas Dataframe as well as in Pyspark Dataframe. The dataset used here is heart.csv.

Python3



 

 

Output:

Python3

 

Output:

We can also convert pyspark Dataframe to pandas Dataframe. For this, we will use DataFrame.toPandas() method.

Syntax: DataFrame.toPandas()

Returns the contents of this DataFrame as Pandas pandas.DataFrame.

Python3

Output:

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Sours: https://www.geeksforgeeks.org/how-to-convert-pandas-to-pyspark-dataframe/
How to convert spark df to pandas df and vice versa - Pyspark questions and answers

Bryan Cutler

If you are a Pandas or NumPy user and have ever tried to create a Spark DataFrame from local data, you might have noticed that it is an unbearably slow process. In fact, the time it takes to do so usually prohibits this from any data set that is at all interesting. Starting from Spark 2.3, the addition of SPARK-22216 enables creating a DataFrame from Pandas using Arrow to make this process much more efficient. You can now transfer large data sets to Spark from your local Pandas session almost instantly and also be sure that your data types are preserved. This post will demonstrate a simple example of how to do this and walk through the Spark internals of how it is accomplished.

A simple example to create a DataFrame from Pandas

For this example, we will generate a 2D array of random doubles from NumPy that is 1,000,000 x 10. We will then wrap this NumPy data with Pandas, applying a label for each column name, and use this as our input into Spark.

To input this data into Spark with Arrow, we first need to enable it with the below config. This could also be included in to be enabled for all sessions. Spark simply takes the Pandas DataFrame as input and converts it into a Spark DataFrame which is distributed across the cluster. Using Arrow, the schema is automatically transferred to Spark and data type information will be retained, but you can also manually specify the schema to override if desired.

Assuming an existing Spark session

That’s all there is to it! The Pandas DataFrame will be sliced up according to the number from which can be set by the conf “spark.default.parallelism” for the default scheduler. Depending on the size of the data you are importing to Spark, you might need to tweak this setting.

The above can be found as a notebook gist here to try out for yourself.

How it Works Behind the Scenes

The code path for this is pretty straight-forward and boils down to just a few key steps. All the work is done in from session.py, which is invoked from after the input is found to be a Pandas DataFrame and Arrow is enabled.

  1. Slice the Pandas DataFrame into chunks according to the number for default parallelism

  2. Convert each chunk of Pandas data into an Arrow

  3. Convert the schema from Arrow to Spark

  4. Send the es to the JVM which become a

  5. Wrap the JavaRDD with the Spark schema to create a

Let’s look at these steps in a bit more detail to examine performance. First, slicing the Pandas DataFrame is a cheap operation because it only uses references to the original data and does not make copies. Converting the slices to Arrow record batches will end up copying the data since it came from slices, but it is efficiently copied as chunks. Arrow can perform zero-copy conversions to/from Pandas data and will do so automatically when it is able to safely reference the data.

Step 3 will create a Spark schema from Arrow schema, which is a simple mapping. Arrow has detailed type definitions and supports all types available in Spark, however Spark only supports ya subset of Arrow types, so you might need to be careful what you are importing. For example a union type is supported in Arrow, but not Spark. At the time of writing this and are fully supported, see the Spark documentation for more info.

Step 4 is where the Arrow data is sent to the JVM. This is necessary in actualizing the DataFrame and will allow Spark to perform SQL operations completely within the JVM. Here the Arrow record batches are written to a temporary file in where they are read back in chunks by the JVM and then parallelized to an RDD. Writing to a temporary file was done to meld with existing code and is definitely much better than transferring the data over a call with Py4J. In practice, this works pretty well and doesn’t seem to be much of a bottleneck and I’m not sure if setting up a local socket to send the data would do better, but could be an area to check out in the future.

With all the above complete, the final step is done in which maps the partitions of the containing the Arrow record batches to an iterator and uses that along with the schema to construct the DataFrame.

Performance Comparison with Arrow Disabled

Here is a few benchmarks of comparing the wall-clock time of calling with and without Arrow enabled. The data used is random doubles similar to the example above, the column Size below is the total number of double values transferred. The runs were done on laptop in Spark local mode with default Spark settings, each timing is the best of 3 consecutive iterations.

SizeWith ArrowWithout Arrow
50,00014.2 ms334 ms
100,00015.6 ms643 ms
500,00021.9 ms3.13 s
1,000,00029.6 ms6.35 s
5,000,000107 ms31.5 s
10,000,000245 ms63 s

I won’t get into the details of the code path of when Arrow is disabled, but there are a few reasons that make it inefficient. First, Spark does not look at the Pandas DataFrame to get data type information, it tries to infer itself. It can not make use of NumPy data chunks and must iterate over each record and read each value as a Python object. When it prepares the data to send to the JVM, it must serialize each scalar value in the pickle format. Finally, once on the JVM, it goes through another set of conversions to apply the proper Scala type.

Download this notebook to try out the above examples or here for the gist

Written on January 6, 2018

Sours: https://bryancutler.github.io/createDataFrame/

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The student. The whole procedure took about 15 minutes. After that I was ordered to put on my pants, button up my jacket and go to the next room.



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