gtfparse

Parsing tools for GTF (gene transfer format) files.

Reporting parsing progress

Pass progress_callback(stage, completed, total) to read_gtf to connect parsing to your own progress UI. The callback runs synchronously; exceptions from it stop parsing and propagate to the caller. Without a callback, gtfparse does not display a progress bar or add a progress-library dependency.

Stage	Reports
read	(0, None) before Polars loads/filters the file; (rows, rows) after it succeeds
attributes	(0, rows), then every 10,000 rows, then (rows, rows)
convert	(0, None) before output conversion/transforms; (rows, rows) after success

Counts refer to rows retained after features filtering, not bytes or overall percentages. None means the total is unknown: loading and conversion expose only stage boundaries, so use an indeterminate indicator during those steps. Attribute expansion is omitted when expand_attribute_column=False. A stage that fails does not emit completion. An empty filtered result is supported; its attributes stage emits a single (0, 0) event. Empty input files retain the existing Polars NoDataError behavior.

For example, with the optional tqdm package installed:

from gtfparse import read_gtf
from tqdm.auto import tqdm

with tqdm(unit="rows") as bar:
    def show_progress(stage, completed, total):
        if stage != bar.desc:
            bar.total = total
            bar.reset()
            bar.set_description_str(stage)
        bar.total = total
        bar.update(completed - bar.n)
        bar.refresh()

    df = read_gtf("gene_annotations.gtf", progress_callback=show_progress)

parse_gtf reports the read stage, parse_gtf_and_expand_attributes reports read and attributes, parse_gtf_pandas reports read and convert, and expand_attribute_strings reports only attributes using the same callback.

Quoted attribute values preserve spaces, semicolons, and apostrophes. Raw attributes (expand_attribute_column=False) retain their original quote marks.

Example usage

Parsing all rows of a GTF file into a Pandas DataFrame

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

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"
}