| Name | tfs-pandas JSON |
| Version |
3.9.0
JSON |
| download |
| home_page | None |
| Summary | Read and write tfs files. |
| upload_time | 2024-11-14 16:13:22 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT |
| keywords |
mad-x
tfs
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# TFS-Pandas
[](https://github.com/pylhc/tfs/actions?query=workflow%3A%22Cron+Testing%22)
[](https://codeclimate.com/github/pylhc/tfs)
[](https://codeclimate.com/github/pylhc/tfs)
<!-- [](https://github.com/pylhc/tfs/) -->
[](https://github.com/pylhc/tfs/)
[](https://pypi.org/project/tfs-pandas/)
[](https://anaconda.org/conda-forge/tfs-pandas)
[](https://doi.org/10.5281/zenodo.5070986)
This package provides reading and writing functionality for [**Table Format System (TFS)** files](http://mad.web.cern.ch/mad/madx.old/Introduction/tfs.html).
Files are read into a `TfsDataFrame`, a class built on top of the famous `pandas.DataFrame`, which in addition to the normal behavior attaches a dictionary of headers to the `DataFrame`.
See the [API documentation](https://pylhc.github.io/tfs/) for details.
## Installing
Installation is easily done via `pip`:
```bash
python -m pip install tfs-pandas
```
One can also install in a `conda`/`mamba` environment via the `conda-forge` channel with:
```bash
conda install -c conda-forge tfs-pandas
```
## Example Usage
The package is imported as `tfs`, and exports top-level functions for reading and writing:
```python
import tfs
# Loading a TFS file is simple
data_frame = tfs.read("path_to_input.tfs", index="index_column")
# You can access and modify the headers with the .headers attribute
useful_variable = data_frame.headers["SOME_KEY"]
data_frame.headers["NEW_KEY"] = some_variable
# Manipulate data as you do with pandas DataFrames
data_frame["NEWCOL"] = data_frame.COL_A * data_frame.COL_B
# You can check the validity of a TfsDataFrame, and choose the behavior in case of errors
tfs.frame.validate(data_frame, non_unique_behavior="raise") # or choose "warn"
# Writing out to disk is simple too
tfs.write("path_to_output.tfs", data_frame, save_index="index_column")
```
Reading and writing compressed files is also supported, and done automatically based on the provided file extension:
```python
import tfs
# Reading a compressed file is simple, compression format is inferred
df = tfs.read("path_to_input.tfs.gz")
# When writing choose the compression format by providing the appropriate file extension
tfs.write("path_to_output.tfs.bz2", df)
tfs.write("path_to_output.tfs.zip", df)
```
## License
This project is licensed under the `MIT License` - see the [LICENSE](LICENSE) file for details.
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"description": "# TFS-Pandas\n\n[](https://github.com/pylhc/tfs/actions?query=workflow%3A%22Cron+Testing%22)\n[](https://codeclimate.com/github/pylhc/tfs)\n[](https://codeclimate.com/github/pylhc/tfs)\n<!-- [](https://github.com/pylhc/tfs/) -->\n[](https://github.com/pylhc/tfs/)\n[](https://pypi.org/project/tfs-pandas/)\n[](https://anaconda.org/conda-forge/tfs-pandas)\n[](https://doi.org/10.5281/zenodo.5070986)\n\nThis package provides reading and writing functionality for [**Table Format System (TFS)** files](http://mad.web.cern.ch/mad/madx.old/Introduction/tfs.html).\nFiles are read into a `TfsDataFrame`, a class built on top of the famous `pandas.DataFrame`, which in addition to the normal behavior attaches a dictionary of headers to the `DataFrame`.\n\nSee the [API documentation](https://pylhc.github.io/tfs/) for details.\n\n## Installing\n\nInstallation is easily done via `pip`:\n\n```bash\npython -m pip install tfs-pandas\n```\n\nOne can also install in a `conda`/`mamba` environment via the `conda-forge` channel with:\n\n```bash\nconda install -c conda-forge tfs-pandas\n```\n\n## Example Usage\n\nThe package is imported as `tfs`, and exports top-level functions for reading and writing:\n\n```python\nimport tfs\n\n# Loading a TFS file is simple\ndata_frame = tfs.read(\"path_to_input.tfs\", index=\"index_column\")\n\n# You can access and modify the headers with the .headers attribute\nuseful_variable = data_frame.headers[\"SOME_KEY\"]\ndata_frame.headers[\"NEW_KEY\"] = some_variable\n\n# Manipulate data as you do with pandas DataFrames\ndata_frame[\"NEWCOL\"] = data_frame.COL_A * data_frame.COL_B\n\n# You can check the validity of a TfsDataFrame, and choose the behavior in case of errors\ntfs.frame.validate(data_frame, non_unique_behavior=\"raise\") # or choose \"warn\"\n\n# Writing out to disk is simple too\ntfs.write(\"path_to_output.tfs\", data_frame, save_index=\"index_column\")\n```\n\nReading and writing compressed files is also supported, and done automatically based on the provided file extension:\n\n```python\nimport tfs\n\n# Reading a compressed file is simple, compression format is inferred\ndf = tfs.read(\"path_to_input.tfs.gz\")\n\n# When writing choose the compression format by providing the appropriate file extension\ntfs.write(\"path_to_output.tfs.bz2\", df)\ntfs.write(\"path_to_output.tfs.zip\", df)\n```\n\n## License\n\nThis project is licensed under the `MIT License` - see the [LICENSE](LICENSE) file for details.\n",
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