table(data: pd.DataFrame, title: Optional[str] = None, limit: Optional[int] = None) -> Dict
The table function provides an easy way to display datasets in an interactive table format, making it simple to explore and understand your data.

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Parameters

  • data (Pandas DataFrame): The dataset you want to display. Must be a Pandas DataFrame or a list of dictionaries.
  • title (str, optional): An optional title to display above the table.
  • limit (int, optional): Maximum number of rows to display. If not specified, shows all rows.
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Returns

  • Dict containing the table component metadata and processed data.

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Usage Example

Here’s an example of how to use the table function: 
from preswald import table

# Example DataFrame
import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}

df = pd.DataFrame(data)

# Display the dataset with a title
table(df, title="Employee Data")

# Display just the data
table(df)

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Key Features

  1. Automatic Data Processing: Handles various data types including timestamps, numpy arrays, and nested structures
  2. Interactive Display: Renders data in a format optimized for exploration and analysis
  3. Error Handling: Gracefully handles edge cases and provides clear error messages
  4. Flexible Input: Accepts both Pandas DataFrames and lists of dictionaries

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Data Type Support

The table component automatically handles various data types:
  • Basic types (strings, numbers, booleans)
  • Timestamps and datetime objects
  • Numpy arrays and numeric types
  • Missing values (None, NaN)
  • Nested data structures (lists, arrays)

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Best Practices

  1. Large Datasets: For large datasets, consider limiting the rows before display: 
    table(df, title="First 100 Rows", limit=100)
  2. Column Selection: Select relevant columns to improve readability: 
    table(df[['Name', 'Age', 'City']])
  3. Data Preprocessing: Clean and format data before display: 
    # Round numeric columns
df_clean = df.round(2)
table(df_clean)