table.mdx

title	icon	description
table	table	Display data in an interactive table format

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.

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.

Returns

• Dict containing the table component metadata and processed data.

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)

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

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)

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)