As you probably know, Parquet is a columnar storage format, so writing such files is differs a little bit from the usual way of writing data to a file.

Table of Contents

Python package
Defining a schema
Columns and batches
Tables
Writing Pandas data frames
Data partitioning

In this article, I am going to show you how to define a Parquet schema in Python, how to manually prepare a Parquet table and write it to a file, how to convert a Pandas data frame into a Parquet table, and finally how to partition the data by the values in columns of the Parquet table.

Python package

First, we must install and import the PyArrow package. If you are using Conda installation looks like this:

conda install -c conda-forge pyarrow

After that, we have to import PyArrow and its Parquet module. Additionally, I import Pandas and the datetime module because I am going to need them in my examples.

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

Defining a schema

Column types can be automatically inferred, but for the sake of completeness, I am going to define the schema.

Imagine that I want to store emails of newsletter subscribers in a Parquet file. I have the timestamp when the person has subscribed to the newsletter, some user id, and the email. The following schema describes a table which contains all of that information.

subscription_schema = pa.schema([
    ('timestamp', pa.timestamp('ms')),
    ('id', pa.int32()),
    ('email', pa.string())
])

Columns and batches

A batch is a collection of equal-length arrays. Every array contains data of a single column. Those columns are aggregated into a batch using the schema we have just defined.

In my example, I will store three values in every column. Here are the values. One more time, note that I don’t need to specify the type explicitly.

timestamps = pa.array([
    datetime(2019, 9, 3, 9, 0, 0),
    datetime(2019, 9, 3, 10, 0, 0),
    datetime(2019, 9, 3, 11, 0, 0)
], type = pa.timestamp('ms'))

ids = pa.array([1, 2, 3], type = pa.int32())

emails = pa.array(
    ['first@example.com', 'second@example.com', 'third@example.com'],
    type = pa.string()
)

batch = pa.RecordBatch.from_arrays(
    [timestamps, ids, emails],
    names = subscription_schema
)

Tables

We use a Table to define a single logical dataset. It can consist of multiple batches. A table is a structure that can be written to a file using the write_table function.

table = pa.Table.from_batches([batch])
pq.write_table(table, 'test/subscriptions.parquet')

When I call the write_table function, it will write a single parquet file called subscriptions.parquet into the “test” directory in the current working directory.

Writing Pandas data frames

We can define the same data as a Pandas data frame. It may be easier to do it that way because we can generate the data row by row, which is conceptually more natural for most programmers.

dataframe = pd.DataFrame([
    [datetime(2019, 9, 3, 9, 0, 0), 1, 'first@example.com'],
    [datetime(2019, 9, 3, 10, 0, 0), 1, 'second@example.com'],
    [datetime(2019, 9, 3, 11, 0, 0), 1, 'third@example.com'],
], columns = ['timestamp', 'id', 'email'])

When the data frame is ready, we can use the from_pandas function to convert the data frame into a table. Such a table can be written into a file in exactly the same way as in the previous example.

table_from_pandas = pa.Table.from_pandas(dataframe)
pq.write_table(table_from_pandas, 'test/subscriptions_pandas.parquet')

Data partitioning

To partition the data, we must first decide which column we want to use for partitioning. It is possible to partition by multiple columns at the same time.

I am going to partition the subscriptions by the user id, which makes no sense in real life, but that does not matter in an example code ;)

To write partitioned data, we must call the write_to_dataset function. It accepts three arguments. The table to be stored, the directory in which it will create the partitioned directory structure, and the columns containing the partitioning keys.

pq.write_to_dataset(
    table,
    root_path='test/subscriptions_partitioned.parquet',
    partition_cols=['id']
)

The code above creates the “subscriptions_partitioned.parquet” directory which contains three subdirectories. Every subdirectory contains partitioned parquet files.

$cd example_partitioned.parquet/
$ls

id=1	id=2	id=3

How to write to a Parquet file in Python

Python package

Defining a schema

Columns and batches

Tables

Writing Pandas data frames

Data partitioning

Numpy reshape explained

What is the difference between training, validation, and test sets in machine learning

How to write to a Parquet file in Python

Python package

Defining a schema

Columns and batches

Tables

Writing Pandas data frames

Data partitioning

Numpy reshape explained

What is the difference between training, validation, and test sets in machine learning

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