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Updated on 2025-04-04

Python method to process Parquet files using FastParquet library

introduction

In the era of big data, the efficiency of data storage and processing is crucial. As a columnar storage format, Parquet has become a popular choice in big data processing due to its efficient compression and coding scheme. FastParquet is a library designed for Python developers that provides read and write operations to Parquet files and is known for its high performance and ease of use. This article will explore the use of the FastParquet library in depth to help readers understand how to use this tool to efficiently process Parquet files.

1. Introduction to Parquet file format

1.1 The structure of the Parquet file

A Parquet file is a self-described binary format that contains the meta information of the data and the actual data. The file consists of multiple row groups (Row Groups), each row group contains multiple columns (Column Chunks). The data in the column blocks are stored in columns, which facilitates efficient compression and encoding.

1.2 Advantages of Parquet Files

  • Column storage: Easy to compress and coding, and improve query efficiency.
  • Efficient compression: Supports a variety of compression algorithms, such as Snappy, Gzip, etc.
  • Strong compatibility: Supports multiple data models and programming languages.

2. Overview of FastParquet Library

2.1 Features of FastParquet

  • high performance: FastParquet is written in Cython and provides near-native performance.
  • Ease of use: Provides a simple API for Python developers to use.
  • flexibility: Supports read and write operations of multiple data types.

2.2 Install FastParquet

FastParquet can be easily installed through the pip command:

pip install fastparquet

3. Use FastParquet to read and write Parquet files

3.1 Reading Parquet file

Reading Parquet files with FastParquet is very simple. Here is a reading example:

import fastparquet as fp

# Read Parquet fileparquet_file = ('')

# Load data into Pandas DataFramedf = parquet_file.to_pandas()

3.2 Write to Parquet file

Writing data to Parquet files is also convenient. Here is a write example:

import pandas as pd
import fastparquet as fp

# Create a Pandas DataFramedf = ({
    'column1': [1, 2, 3],
    'column2': ['a', 'b', 'c']
})

# Write to Parquet file('', df)

4. Advanced features of FastParquet

4.1 Data partitioning

FastParquet supports data partitioning, which can distribute data into different files based on the values ​​of certain columns, which is very useful for processing large data sets.

# Suppose we have a DataFrame with date and salesdf = ({
    'date': pd.date_range('20230101', periods=6),
    'sales': [100, 150, 200, 250, 300, 350]
})

# Write Parquet file by date partition('sales_partitioned.parquet', df, partition_on=['date'])

4.2 Data filtering

FastParquet allows filtering when reading data, so that only data of interest can be loaded and processing efficiency can be improved.

# Filter data while readingfiltered_df = parquet_file.to_pandas(filters=[('sales', '>', 200)])

4.3 Data type mapping

FastParquet supports mapping data types in Parquet files to corresponding types in Python to ensure data consistency and accuracy.

# Define data type maptype_mapping = {
    'column1': 'int32',
    'column2': 'string'
}

# Read data using type mapdf = parquet_file.to_pandas(columns=type_mapping)

5. Performance optimization skills

5.1 Using appropriate compression algorithms

Choosing the right compression algorithm can significantly reduce file size and improve I/O performance. FastParquet supports a variety of compression algorithms, such as Snappy, Gzip, etc.

# Write data using Snappy compression algorithm('', df, compression='SNAPPY')

5.2 Batch processing of data

For large-scale datasets, batch processing reduces memory consumption and increases processing speed.

# Read data in batchesbatch_size = 50000
for df in parquet_file.iter_row_groups(batch_size):
    process(df)  # Assume that process is a function that processes data

5.3 Parallel processing

FastParquet supports parallel reading and writing data, which can take full advantage of multi-core CPUs.

# Read data in paralleldf = parquet_file.to_pandas(nthreads=4)

VI. Case Analysis

6.1 Log data processing

Suppose we have a Parquet file containing server logs, and we need to analyze these logs to find out the error message.

# Read log datalog_file = ('server_logs.parquet')
logs_df = log_file.to_pandas()

# Filter out error logserror_logs = logs_df[logs_df['log_level'] == 'ERROR']

# Analyze error logserror_analysis = error_logs.groupby('service').size()

6.2 Sales Data Analysis

We have a Parquet file with sales records and we need to calculate the total sales for each product.

# Read sales datasales_file = ('sales_records.parquet')
sales_df = sales_file.to_pandas()

# Calculate the total sales of each producttotal_sales = sales_df.groupby('product_id')['sales'].sum()

7. Summary

The FastParquet library provides Python developers with an efficient and easy-to-use tool to handle Parquet files. Through this article, readers should be able to master the basic usage methods of FastParquet and be able to use its advanced features to optimize data processing flow. Whether it is log analysis, sales data processing, or other big data application scenarios, FastParquet can become a right-hand assistant for developers!

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