Panchi-kun is a versatile platform designed for developers and data teams to streamline analytics and visualization workflows. It offers a structured way to explore datasets, generate insights, and share results across organizations.
Whether you are building dashboards, running ad hoc queries, or teaching data concepts, panchi-kun aims to reduce friction between raw data and clear outputs. The following sections outline its core capabilities, technical choices, and practical guidance.
| Name | Type | Default Value | Description |
|---|---|---|---|
| engine | string | duckdb | Query execution engine for in‑memory or file‑based processing |
| cache_ttl | integer | 300 | Result cache time‑to‑live in seconds |
| output_format | string | dataframe | Preferred display format such as dataframe, json, or table |
| timeout | integer | 60 | Maximum query runtime in seconds before cancellation |
| log_level | {"": "", "td": "", "th": "", "tr": ""}info | Verbosity for runtime logging, options include debug, info, warn, error |
Getting started with panchi-kun
Begin by installing the package in your Python environment and initializing a lightweight session. This setup step connects panchi-kun to your chosen data source and configures runtime defaults.
From there, you can run simple commands to verify connectivity and inspect available tables. Early validation helps avoid surprises when you scale to larger pipelines or share projects with teammates.
Query execution and engine configuration
At the core of panchi-kun is its ability to translate high level requests into efficient execution plans. You can select engines such as duckdb for fast analytics or switch to alternative backends for specialized workloads.
Configuration options like cache_ttl and timeout give you control over resource usage and response latency. Adjusting these values allows panchi-kun to align with budget limits, SLA expectations, and interactive use cases.
Visualization and output formats
panchi-kun supports multiple output formats, including dataframe, json, and table views. Choosing the right format depends on whether you are debugging scripts, building APIs, or rendering dashboards.
When combined with its plotting integrations, panchi-kun can generate charts directly from query results, reducing the need to switch between tools and contexts.
Performance tuning and scaling
To handle larger datasets, you can fine-tune memory limits, partitioning, and engine selection. Proper tuning keeps query times predictable and avoids interruptions in shared or production environments.
Monitoring log output and cache behavior gives insight into bottlenecks. With the right settings, panchi-kun scales from exploratory notebooks to scheduled batch jobs.
Key points and recommendations
- Start with the default engine and validate connectivity before scaling pipelines.
- Use cache_ttl and timeout settings to align performance with workload demands.
- Select output formats based on whether you need debugging, API responses, or dashboard rendering.
- Monitor logs to identify bottlenecks and plan capacity upgrades.
- Leverage built in plotting to reduce context switching during analysis.
FAQ
Reader questions
How do I change the default query engine in panchi-kun?
Adjust the engine parameter in your session configuration to specify duckdb, pandas, or another supported backend, then restart the session to apply the change.
What should I do if queries are timing out frequently?
Increase the timeout value in seconds and review your engine configuration, then test with a subset of data to confirm that longer runtimes are acceptable.
Can panchi-kun cache results for faster repeated access?
Yes, panchi-kun uses a result cache governed by cache_ttl; lowering or raising this value lets you balance freshness of data against query speed.
Is it possible to export charts directly from panchi-kun?
You can generate charts using integrated plotting options and export them as images or embed them in dashboards, provided your output format and dependencies are correctly configured.