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Pulling Data into a Pandas DataFrame

This guide demonstrates how to extract data from CloudQuant Data Liberator datasets into Python pandas DataFrames. The examples use daily_bars, a dataset containing US Equity daily OHLCV (Open, High, Low, Close, Volume) data with additional fields.

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Example: Pulling Daily Bars for AAPL

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Multi-line approach

today = '2024-06-20'
oneMonthAgo = '2024-05-20'
dataset = 'daily_bars'
mySymbols = 'AAPL'
query = liberator.query(name=dataset, symbols=mySymbols, as_of=today, back_to=oneMonthAgo)
df = liberator.get_dataframe(query)

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Single-line approach

df = liberator.get_dataframe(liberator.query(name='daily_bars', symbols='AAPL', as_of='2024-06-20', back_to='2024-05-20'))

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Key Features

  • Flexible symbol input — accepts single symbols, lists, or None (all symbols)
  • Granular time selection — supports daily, minute, second, and nanosecond resolution
  • Simple syntax — collapsible into single-line queries for efficiency
The query returns a properly formatted pandas DataFrame containing the requested historical data.