ok, that would be great to support read_tsfile and to_tsfile in pandas. Maybe we should try to contact with pandas community
On Fri, Jan 9, 2026 at 1:44 PM Xinyu Tan <[email protected]> wrote: > 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 > > > > > > > > > >
