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Overview

WaveAssist provides native support for pandas DataFrames, making it easy to store and retrieve tabular data.

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Working with DataFrames

import pandas as pd
import waveassist

# Create a sample DataFrame
df = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'score': [95.5, 88.0, 92.5]
})

# Store the DataFrame
waveassist.store_data("user_data", df)

# Retrieve the DataFrame
retrieved_df = waveassist.fetch_data("user_data")

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Supported DataFrame Features

  • All basic pandas data types
  • Index types (numeric, datetime, string)
  • Column names and types
  • DataFrame metadata

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DataFrame Limitations

  • Maximum size: 100MB
  • Supported column types:
    • Numeric (int, float)
    • String
    • Boolean
    • Datetime
    • Category

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Best Practices

  1. Clean and preprocess data before storing
  2. Use appropriate data types for columns
  3. Consider memory usage and size limits
  4. Handle missing values appropriately

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Example Use Cases

# Time series data
time_df = pd.DataFrame({
    'timestamp': pd.date_range('2025-01-01', periods=100),
    'value': np.random.randn(100)
})
waveassist.store_data("time_series", time_df)

# Categorical data
category_df = pd.DataFrame({
    'category': ['A', 'B', 'A', 'C'],
    'value': [1, 2, 3, 4]
})
category_df['category'] = category_df['category'].astype('category')
waveassist.store_data("categorical_data", category_df)