Core Features

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Interactive Code Editor

Mako Code includes a powerful Monaco-based code editor with:

• Syntax highlighting and multiple language support
• Real-time code execution environment
• Smart code completion
• Keyboard shortcuts for productivity
• Multiple tabs for organizing your work

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Data Management System

Efficiently manage your datasets with:

• Import functionality for various data formats (primarily Parquet)
• Dataset preview and exploration tools
• Column type detection and metadata display
• Efficient data serialization with Apache Arrow
• Smart file management with multiple tabs

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Split-Pane Interface

Work efficiently with a beautiful split-pane interface:

• Adjustable views for code, data, and visualizations
• Left sidebar for functions and documentation
• Right sidebar for data management
• Customizable layout to fit your workflow

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Polars Integration

Leverage the power of Polars for data manipulation:

import polars as pl

# Load a dataset
df = pl.read_parquet("sales_data.parquet")

# Perform transformations
result = df.filter(pl.col("revenue") > 1000) \
           .group_by("region") \
           .agg([
               pl.sum("revenue").alias("total_revenue"),
               pl.count().alias("transaction_count"),
               pl.mean("customer_satisfaction").alias("avg_satisfaction")
           ]) \
           .sort("total_revenue", descending=True)

# Display the result
print(result)

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Visualization Capabilities

Create beautiful visualizations with Bokeh:

from bokeh.plotting import figure
from bokeh.models import ColumnDataSource

# Create a Bokeh figure
source = ColumnDataSource(result.to_pandas())
p = figure(title="Revenue by Region", x_range=result["region"].to_list())

# Add a bar chart
p.vbar(x="region", top="total_revenue", width=0.8, source=source)

# Customize the chart
p.xaxis.axis_label = "Region"
p.yaxis.axis_label = "Total Revenue"

# Display the chart
show(p)

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SQL Support

Write and execute SQL queries against your datasets:

-- Example SQL query in Mako Code
SELECT 
    region,
    SUM(revenue) as total_revenue,
    COUNT(*) as transaction_count,
    AVG(customer_satisfaction) as avg_satisfaction
FROM sales_data
WHERE revenue > 1000
GROUP BY region
ORDER BY total_revenue DESC

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Export and Sharing

Easily export your work and share results:

• Export code as Python scripts
• Save visualizations as HTML or images
• Export transformed datasets as Parquet files
• Share analysis workflows with team members

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Keyboard Shortcuts

Boost your productivity with comprehensive keyboard shortcuts:

• ⌘/Ctrl + Enter: Run current file
• ⌘/Ctrl + Shift + P: New Polars file
• ⌘/Ctrl + Shift + L: New SQL file
• ⌘/Ctrl + Shift + B: New Bokeh visualization
• ⌘/Ctrl + S: Save Context File
• ⌘/Ctrl + E: Export your current tab script
• ⌘/Ctrl + Shift + I: Import data
• ⌘/Ctrl + D: Toggle data management sidebar