Hi, God Yuan
Absolutely, there are already many file formats supported, including CSV,
Feather, Parquet, Markdown, and more.
You could take a try for these code:
```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
})
df.to_parquet('data.parquet')
new_df = pd.read_parquet('data.parquet')
print(new_df)
```
Best
--------------
Xinyu Tan
On 2026/01/09 04:59:56 Yuan Tian wrote:
> Hi Xinyu,
>
> Totally agree with you, but just want to ask if pandas already have some
> interfaces for parquet, like:
> ```
> import pandas as pd
> df = pd.read_parquet('your_file.tsfile')
> df.to_parquet('your_file.tsfile')
> ```
>
>
> On Wed, Jan 7, 2026 at 10:36 PM Xinyu Tan <[email protected]> wrote:
>
> > Hi,
> >
> > Seeing such a heated discussion, I’d love to jump in and share some of my
> > recent takeaways. After spending the last seven months diving deep into the
> > Python ecosystem, I’ve developed a strong intuition about the future of
> > TsFile: Blending into the existing ecosystem is far more critical than just
> > polishing technical specs.
> >
> > Python is now the de facto standard for AI and data analysis. People often
> > complain about Python being "slow," but that’s never the real deal-breaker.
> > As long as a tool creates genuine value, the community will always find a
> > way to optimize the "hot" paths—whether that’s a rewrite in C++/Rust or
> > leveraging SIMD/CUDA and distributed computing. Performance can be
> > engineered later, but user adoption can’t be forced.
> >
> > The truth is, most python developers are "lazy". They don’t want to dig
> > into proprietary concepts like TsFileReader or ColumnSchema in our doc[1];
> > they don't even want to import a new library named tsfile if they don't
> > have to. The dream experience for a new user is to start using TsFile
> > without actually having to "learn" it.
> >
> > Ideally, they should be able to interact with TsFile just like they do
> > with Pandas:
> > ```
> > import pandas as pd
> > df = pd.read_tsfile('your_file.tsfile')
> > df.to_tsfile('your_file.tsfile')
> > ```
> >
> > I believe making TsFile a "first-class citizen" in the Pandas ecosystem is
> > our highest-leverage move.
> >
> > If we can bridge these interfaces and run "blind" performance benchmarks
> > against Parquet using mainstream datasets—and keep optimizing until we show
> > a clear edge—we won't need to push TsFile. The Python community will follow
> > the scent of a better tool and come to us naturally.
> >
> > [1]
> > https://tsfile.apache.org/zh/UserGuide/latest/QuickStart/QuickStart-PYTHON.html#%E8%AF%BB%E5%8F%96%E7%A4%BA%E4%BE%8B
> >
> > Best
> > ------------
> > Xinyu Tan
> >
> > On 2025/12/30 12:37:20 Jialin Qiao wrote:
> > > Hi all,
> > >
> > > With the release of TsFile 2.2.0, the project now offers
> > > multi-language SDKs (Python, Java, C++, C), enabling seamless data
> > > storage for terminal devices, real-time edge-side processing, and
> > > cloud-based data analysis. Its support for table models further
> > > simplifies data analysis and model training in Python.
> > >
> > > As AI continues to gain momentum, TsFile can serve as a foundational
> > > format for building industrial time-series datasets in the AI era.
> > >
> > > Here are some potential work we could do
> > > 1. Deeper alignment with the Python ecosystem, such as Pandas &
> > DataFrame.
> > > 2. Integration with HuggingFace Datasets.
> > > 3. Viewer of a TsFile.
> > > 4. Converter between other formats(such as Parquet, CSV, HDF5) and
> > TsFile.
> > >
> > > Welcome further ideas to advance the TsFile community :-)
> > >
> > > Thanks,
> > > Jialin Qiao
> > >
> >
>
