4 Ways to Write Data to Parquet With Python: A Comparison

Introduction

Parquet is another open-access file format suitable for Data Hadoop that includes schemes for data compressing and encoding with increased proficiency fit for large data sets. It has gained more popularity in the fields of data processing and analytics thanks to its column-based structure that helps navigate through data sets and reduce the number of I/O operations. This paper focuses on four distinct approaches on how to write data to Parquet files: this includes performance, features, and the ease of using the identified methods in Python.

The four methods include:

Pandas
PyArrow
Fastparquet
Dask

1. Pandas

Pandas is the fourth and probably the most important Python library that we will use for data manipulation and analysis. It supports functions for getting and putting data into files in different formats, such as Parquet. For the opening of parquet data pandas depends on pyarrow or fastparquet.

Installation

Before writing data using Pandas into parquet files, it is necessary to install either PyArrow or Fastparquet package. You can install them using pip:

Writing Data to Parquet With Pandas

Here's an example of how to write a DataFrame to a Parquet file using Pandas:

import pandas as pd
# Create a sample DataFrame
data = {
 'name': ['Alice','Bob','Charlie'],
 'age': [25,30,35],
 'city': ['New York','San Francisco','Los Angeles']
}

df = pd.DataFrame(data)

# Write DataFrame to Parquet file
df.to_parquet('output_pandas.parquet',engine='pyarrow')
print(df)

Output:

 name  age           city
0    Alice   25       New York
1      Bob   30  San Francisco
2  Charlie   35    Los Angeles

Explanation

Pandas library is imported for data handling and data preparation.
Declares that the dictionary data representation of the sample details includes aspects like 'name', 'age', and 'city'.
Creates a Pandas DataFrame 'df' for the dictionary.
Specifies 'df. to_parquet()' to write the DataFrame to Parquet file.
As for the engine parameter which can be set to either 'pyarrow' or 'fastparquet', it determines the backend used when writing to Parquet files.
It provides the `verification code` to read and display the contents of the Parquet file to valid the information from the data.

Features and Performance

Ease of Use: Parquet support in pandas makes it easy to write DataFrames using its to_parquet method.
Flexibility: The OOM data model and query execution framework are based on either PyArrow or Fastparquet.
Performance: While Pandas work well for small to moderately big data sizes, they are not the best fit for big data because of the memory unit of operation.

2. PyArrow

PyArrow is a python library that brings data to the Arrow computing language and lets you read data in Pandas data structures and write out the same schemas in Arrow vectors and tables. The Snappy compression format is widely used for streaming and processing Parquet files.

import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq

# Create a sample DataFrame with a different dataset
data = {
    'product_id':[101,102,103],
    'product_name':['Laptop','Smartphone','Tablet'],
    'price':[999.99,499.99,299.99],
    'in_stock':[True,False,True]
}

df = pd.DataFrame(data)

# Convert DataFrame to Apache Arrow Table
table = pa.Table.from_pandas(df)

# Write Arrow Table to Parquet file with additional parameters
pq.write_table(table, 'output_pyarrow_custom.parquet', compression='snappy', flavor='spark', version='2.0')
import pyarrow.parquet as pq

# Read the Parquet file using PyArrow
table = pq.read_table('output_pyarrow_custom.parquet')
df = table.to_pandas()
print(df)

Output:

product_id product_name   price  in_stock
0         101       Laptop  999.99      True
1         102   Smartphone  499.99     False
2         103        Tablet  299.99      True

Detailed Steps Explanation

1. Create DataFrame:

A DataFrame 'df' is created from dict data containing product details by passing the dict data and necessary column names.
The DataFrame has four columns. Hence, when using the database, we will be dealing with fields that include 'product_id', 'product_name', price, and 'in_stock'.

2. Convert to Arrow Table:

This is achieved by using the ''pa. Table. from_pandas()'' function and passing the DataFrame df into it to obtain the Apache Arrow Table.

3. Write to Parquet:

The Arrow Table table is written to a Parquet file 'output_pyarrow_custom. parquet' with some more parameters related to the compression option, data compatibility mode, and Parquet file format version.

4. Verification:

The verification code reads the Parquet file back into Spark DataFrame and prints it, which will display the content of the specific file.

Features and Performance

Low-Level Control: In contrast to Pandas, PyArrow offers the writer option, which includes more options regarding Parquet writing.
Performance: PyArrow is more efficient than Panda in terms of speed and can work on a wider range of data sets.
Compatibility: It can seamlessly be used in combination with other big data tools and operational environments, making it popular in data engineering.

3. Fastparquet

Fastparquet is a Python library that can also be used to read and write Parquet files, with efficiency as one of its strengths. As pointed out by Tailor, it is characterized by speed and efficiency.

Installation

You can install Fastparquet using pip:

Writing Data to Parquet with Fastparquet

Here is an example of writing a DataFrame to a Parquet file with the help of the Fastparquet library.

import pandas as pd
import fastparquet as fp

# Create a sample DataFrame with a different dataset
data = {
    'order_id':[1001,1002,1003],
    'customer':['John Doe','Jane Smith','Emily Jones'],
    'total_amount':[250.75,125.50,320.00],
    'order_date':['2023-06-01','2023-06-02','2023-06-03']
}

df=pd.DataFrame(data)

# Write DataFrame to Parquet file using Fastparquet
fp.write('output_fastparquet.parquet', df)

# Read the Parquet file using Fastparquet
df = fp.ParquetFile('output_fastparquet.parquet').to_pandas()
print(df)

Output:

order_id     customer  total_amount  order_date
0      1001     John Doe       250.75  2023-06-01
1      1002   Jane Smith       125.50  2023-06-02
2      1003  Emily Jones       320.00  2023-06-03

Detailed Steps Explanation

1. Create DataFrame:

A dictionary of order data is created, and a DataFrame is called 'df'.
The DataFrame has four columns: In Modeling Warehouse, the possible fields include: 'order_id', 'customer', 'total_amount', and 'order_date'.

2. Write to Parquet:

As a result, the DataFrame 'df' is saved to the file 'output_fastparquet. parquet' with the help of the codeword 'Fastparquet' importing 'fp.write()' function.

3. Verification:

The verification code uses Fastparquet, an API for reading Parquet, to read the Parquet file back into a DataFrame and print it so that we can see its contents.
This process helps ensure accurate data writing and reading to and from the Parquet file using Fastparquet.

Features and Performance

Speed: It needs to be stressed that an important feature of Fastparquet is its readiness for operations that require a high speed and its ability to work with big data.
Memory Usage: All right, it is designed for low memory usage overall, which will be perfect for an environment with limited memory.
Compatibility: Fastparquet stands out for its resemblance to and compatibility with Pandas and other data processing functions.

4. Dask

Dask is a Python library with scaling capabilities that supports standard and parallel constructs depending on the size of the dataset. It is advantageous in managing data that does not fit in memory at all, making it good for large data sets.

Installation

You can install Dask along with PyArrow or Fastparquet using pip:

Parquet is an open-source file format used to store data using Apache Commercial. io Parquet format Parquet is a columnar storage format and is particularly popular in big data environments; writing data using Dask to Parquet works in the same way as writing data to dataframes.

Writing data using Dask to Parquet

Here's an example of how to write a DataFrame to a Parquet file using Dask:

import dask.dataframe as dd
import pandas as pd

# Create a sample DataFrame with a different dataset
data = {
    'employee_id':[1,2,3],
    'employee_name':['Alice Johnson','Bob Brown','Charlie Davis'],
    'department':['HR','Engineering','Marketing'],
    'salary':[70000,80000,60000]
}

df=pd.DataFrame(data)

#Convert Pandas DataFrame to Dask DataFrame
ddf = dd.from_pandas(df, npartitions=1)

# Write Dask DataFrame to Parquet file
ddf.to_parquet('output_dask.parquet', engine='pyarrow')  # or engine='fastparquet'

# Read the Parquet file using Dask
ddf_read = dd.read_parquet('output_dask.parquet', engine='pyarrow')
print(ddf_read.compute())

Output:

employee_id  employee_name  department  salary
0            1  Alice Johnson          HR   70000
1            2      Bob Brown  Engineering   80000
2            3  Charlie Davis   Marketing   60000

Detailed Steps Explanation

1. Create DataFrame:

The DataFrame 'df' is constructed from the dictionary data and all of the detail of the employees are included in this Data frame.
The DataFrame has four columns: EMP field includes 'employee_id', 'employee_name', 'department' and 'salary'.

2. Convert to Dask DataFrame:

To convert them, the Pandas DataFrame 'df' is transformed into Dask DataFrame 'ddf' and is again divided into a specific number of 'partitions using from_pandas(df, npartitions=1)'.

3. Write to Parquet:

In the end, the Dask DataFrame ddf is stored at Parquet in a file named 'output_dask. parquet' using the PyArrow engine and to_parquet().

4. Verification:

The code verifies whether the 'dd correctly read the Parquet file. read_parquet()' function, and the DataFrame resumes calculation to display its contents.
This process helps avoid writing the data to the Parquet file in the wrong format while carrying out operations using Dask and PyArrow.

Features and Performance

Scalability: Dask is a flexible tool meant for large-scale data processing, and it does not matter if the data does not fit into memory.
Parallel Computing: It uses parallel computing to enhance everyday computation processes for data.
Integration: This makes it compatible with other data computation frameworks, including Pandas, PyArrow, and Fastparquet.

Comparison

Let's compare these four methods based on different criteria:

1. Ease of Use

Pandas: Parquet is the easiest-to-use file format, offering a basic API for writing out DataFrames in Parquet format.
PyArrow: Pandapy requires fewer steps than the rest but offers less control over several aspects.
Fastparquet: In terms of usage complexity, it is very similar to PyArrow, but this package focuses on speed.
Dask: It is slightly more complex because of the concurrency features it provides; however, it is highly efficient, especially for large data sets.

2. Performance

Pandas are appropriate for the following datasets: small, Small to Medium, Small to Medium-Large, Small, medium, and up to large.
PyArrow: It is more improved and can work on larger data sets compared to a regular computer.
Dask: Suitable for large datasets as it allows parallelism in the execution of processes.

3. Flexibility and Control

Pandas: Difficult or impossible to have fine grain control over the ongoing Parquet writing process.
PyArrow: Forces low-level control and offers a high amount of flexibility.
Fastparquet: It has a similar 'hold' to PyArrow, with a primary emphasis on speed.
Dask: Very elastic and able to be effectively implemented in large scale data analysis.

4. Integration

Pandas: Suits well with numerous data processing activities related to data frames within the Pandas programming language.
PyArrow: He is designed to be excellently compatible with big data tools and frameworks.
Fastparquet: Closely integrates with many analytical libraries such as Pandas and other data processing libraries.
Dask: We can use it alongside other data processing libraries, and scaling up does not require numerous resources.

Best Practices and Considerations

1. Choose the Right Library:

Pandas with PyArrow: PyArrow is efficient and compatible, making it perfect for datasets that estimate to be small to medium.
Pandas with Fastparquet: These are quick operation units suitable for small scale and easy to integrate into different systems.
Dask: Preferred for applications that involve data that cannot fit into memory, such as big data applications, and come with the options of parallel and distributed computing.

2. Compression:

Consider using a compression algorithm like Snappy, GZIP, or Brotli to shrink file sizes and achieve faster speeds for reading and writing operations.
Select Snappy for medium speed and file compression ratio.

3. Partitioning:

When building a table, it is advisable to partition based on the frequently retrieved item-based columns.
Continue to utilize Dask's partitioning to really optimize the size of data sets for better parallelism.

4. Schema Management:

Parquet files should be homogenous so that they can be used interchangeably in different systems and applications due to their coherency.
To accomplish this, if the schema needs to be changed, utilize the schema evolution features while keeping compatibility with the previous state.

5. Data Types:

This one is more of a convention, but it would help if you defined the data types of your DataFrame explicitly so that there would be no type hinting as much as possible and educate the DataFrame on data types.
Manage categorical data effectively by converting data types to keep space utilization at manageable levels.

6. Parallelism:

Remember to take advantage of Dask as it makes parallel computational processing of large datasets possible.
Set the number of these two parts in Dask to arrive at an optimal number that corresponds to the size of the data and memory capacity.

Conclusion

There are two methods for selecting the proper way in Python to write data to Parquet files, and the proper one will depend on several factors, including the nature and size of your current dataset.

Pandas: Common for small to moderately large data set scenarios when readability is important.
PyArrow: Suitable for big sets of files or when fine-grained manipulations and speed are of paramount importance.
Fastparquet is ideal for use with large data sets, especially where high raw speed and efficient use of memory space are desirable.
Dask: The most suitable for extremely big data that are required to be processed in parallel and perhaps scaled to meet the needs of a large organization.

It should be noted that each method has advantages and limitations. Using this knowledge, you will be able to make a decision that is optimal for handling data.

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