# pytd vs td-client-python vs pandas-td

Treasure Data offers three different Python clients. The following list summarizes each client’s characteristics.

## td-client-python

- Basic REST API wrapper.
- Similar functionalities to td-client-{[ruby](https://github.com/treasure-data/td-client-ruby), [java](https://github.com/treasure-data/td-client-java), [node](https://github.com/treasure-data/td-client-node), [go](https://github.com/treasure-data/td-client-go)}.
- The capability is limited by [what Treasure Data REST API can do](https://docs.treasuredata.com/display/PD/REST+APIs+in+Treasure+Data).


## pytd

- Efficient connection to Trino(Presto) based on [presto-python-client](https://github.com/prestodb/presto-python-client).
- Multiple data ingestion methods and a variety of utility functions.


## pandas-td (deprecated)

- Old tool optimized for [pandas](https://pandas.pydata.org/) and [Jupyter Notebook](https://jupyter.org/).
- **pytd** offers a compatible function set under `pytd.pandas_td`.


## Choosing a Client

The client you choose depends on your specific use case. Here are some common guidelines:

- Use td-client-python if you want to execute *basic CRUD operations* from Python applications.
- Use **pytd** for (1) *analytical purpose* relying on pandas and Jupyter Notebook, and (2) achieving *more efficient data access*.


Info
There is a known difference to the  `pandas_td.to_td` function for type conversion. Since `pytd.writer.BulkImportWriter` (default writer pytd) uses CSV as an intermediate file before uploading a table, the column type might change via `pandas.read_csv`. To respect the column type as much as possible, you need to pass a fmt=”msgpack” argument to `to_td` function.