Metadata-Version: 2.4
Name: gtfparse
Version: 2.6.3
Summary: Parsing library for extracting data frames of genomic features from GTF files
Author-email: Alex Rubinsteyn <alex.rubinsteyn@unc.edu>
Project-URL: Homepage, https://github.com/openvax/gtfparse
Project-URL: Bug Tracker, https://github.com/openvax/gtfparse/issues
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: polars>=0.20.2
Requires-Dist: pyarrow>=18.0.0
Requires-Dist: pandas>=2.1.0
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Requires-Dist: coveralls; extra == "dev"
Dynamic: license-file

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gtfparse
========
Parsing tools for GTF (gene transfer format) files.

# Example usage

## Parsing all rows of a GTF file into a Pandas DataFrame

```python
from gtfparse import read_gtf

# returns GTF with essential columns such as "feature", "seqname", "start", "end"
# alongside the names of any optional keys which appeared in the attribute column
df = read_gtf("gene_annotations.gtf")

# filter DataFrame to gene entries on chrY
df_genes = df[df["feature"] == "gene"]
df_genes_chrY = df_genes[df_genes["seqname"] == "Y"]
```


## Getting gene FPKM values from a StringTie GTF file

```python
from gtfparse import read_gtf

df = read_gtf(
    "Transcripts.gtf",
    column_converters={"FPKM": float})

gene_fpkms = {
    gene_name: fpkm
    for (gene_name, fpkm, feature)
    in zip(df["seqname"], df["FPKM"], df["feature"])
    if feature == "gene"
}
```


