TabLab

Parquet viewer

Open and view Parquet files with our interactive viewer. Analyze, filter, and visualize your columnar data with ease.

Format
Click anywhere to select a fileor drag and drop a file here
Accepts Parquet file (.parquet)

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Parquet Viewer Features

Efficient browsing
Navigate large columnar datasets with ease
Lightning-fast performance
Open and analyze even large Parquet files instantly
Sort functionality
Arrange data by any column for quick analysis
Advanced filtering
Focus on specific data segments with powerful filters
Aggregation capabilities
Perform sum, average, count and other calculations on your data
AI-powered analysis
Ask questions about your data in plain English and get instant insights

How to view Parquet files online

  1. Upload your Parquet file using the upload button
  2. View your data in a structured table format
  3. Sort columns by clicking on column headers
  4. Filter data using the filter controls
  5. Ask questions about your data using the AI assistant
  6. Perform aggregations and advanced analysis
  7. Export or share your visualized data

How to view Parquet files in Python

Here are three effective ways to view Parquet files in Python using different libraries. Each approach has its own advantages depending on your specific needs and file sizes.

Viewing Parquet files with Pandas

Pandas provides a straightforward approach for viewing files and works well for most common data tasks:

First, we need to install pandas

pip install pandas

Then we can load and view the parquet file

import pandas as pd
df = pd.read_parquet('path/to/file.parquet')
print(df.head())

Viewing Parquet files with DuckDB

DuckDB is an in-process SQL OLAP database that's perfect for larger files and analytical workloads:

First, we need to install duckdb

pip install duckdb

Then we can query the parquet file directly

import duckdb
result = duckdb.sql(f"SELECT * FROM 'path/to/file.parquet' LIMIT 10").fetchdf()
print(result)

Viewing Parquet files with ClickHouse

ClickHouse is a high-performance column-oriented database system that's excellent for large-scale data processing:

First, we need to install clickhouse-connect

pip install clickhouse-connect

Then we can load and query the parquet file

import clickhouse_connect
client = clickhouse_connect.get_client(host='localhost', port=8123)
query = f"SELECT * FROM file('path/to/file.parquet') LIMIT 10"
result = client.query(query).result_set
print(result)