Automatically upgrade your Polars code so it's compatible with future versions.
Easy:
pip install -U polars-upgrade
Run
polars-upgrade my_project --target-version=0.20.4
from the command line. Replace 0.20.4 and my_project with your Polars version,
and the name of your directory.
NOTE: this tool will modify your code! You're advised to stage your files before running it.
- repo: https://github.com/MarcoGorelli/polars-upgrade
rev: 0.3.2 # polars-upgrade version goes here
hooks:
- id: polars-upgrade
args: [--target-version=0.20.0] # Polars version goes hereInstall nbqa and then run
nbqa polars_upgrade my_project --target-version=0.20.4
In a Python script:
from polars_upgrade import rewrite, Settings
src = """\
import polars as pl
df.select(pl.count())
"""
settings = Settings(target_version=(0, 20, 4))
output = rewrite(src, settings=settings)
print(output)Output:
import polars as pl
df.select(pl.len())
If your snippet does not include import polars or import as pl,
then you will also need to provide pl and/or polars to aliases, else polars-upgrade will
not perform the rewrite. Example:
from polars_upgrade import rewrite, Settings
src = """\
df.select(pl.count())
"""
settings = Settings(target_version=(0, 20, 4))
output = rewrite(src, settings=settings, aliases={'pl'})
print(output)Output:
df.select(pl.len())
- pl.avg
+ pl.mean- df.groupby_dynamic
+ df.group_by_dynamic
- df.groupby_rolling
+ df.rolling
- df.rolling('ts', period='3d').apply
+ df.rolling('ts', period='3d').map_groups
- pl.col('a').rolling_apply
+ pl.col('a').rolling_map
- pl.col('a').apply
+ pl.col('a').map_elements
- pl.col('a').map
+ pl.col('a').map_batches
- pl.map
+ pl.map_batches
- pl.apply
+ pl.map_groups
- pl.col('a').any(drop_nulls=True)
+ pl.col('a').any(ignore_nulls=True)
- pl.col('a').all(drop_nulls=True)
+ pl.col('a').all(ignore_nulls=True)
- pl.col('a').value_counts(multithreaded=True)
+ pl.col('a').value_counts(parallel=True)- pl.col('a').is_not
+ pl.col('a').not_- pl.enable_string_cache(True)
+ pl.enable_string_cache()
- pl.enable_string_cache(False)
+ pl.disable_string_cache()
- pl.col('a').list.count_match
+ pl.col('a').list.count_matches
- pl.col('a').is_last
+ pl.col('a').is_last_distinct
- pl.col('a').is_first
+ pl.col('a').is_first_distinct
- pl.col('a').str.strip
+ pl.col('a').str.strip_chars
- pl.col('a').str.lstrip
+ pl.col('a').str.strip_chars_start
- pl.col('a').str.rstrip
+ pl.col('a').str.strip_chars_end
- pl.col('a').str.count_match
+ pl.col('a').str.count_matches
- pl.col("dt").dt.offset_by("1mo_saturating")
+ pl.col("dt").dt.offset_by("1mo")- df.group_by_dynamic('ts', every='3d', truncate=True)
+ df.group_by_dynamic('ts', every='3d', label='left')
- df.group_by_dynamic('ts', every='3d', truncate=False)
+ df.group_by_dynamic('ts', every='3d', label='datapoint')- pl.col('a').list.lengths
+ pl.col('a').list.len
- pl.col('a').str.lengths
+ pl.col('a').str.len_bytes
- pl.col('a').str.n_chars
+ pl.col('a').str.len_chars- pl.col('a').shift(periods=4)
+ pl.col('a').shift(n=4)
- pl.col('a').shift_and_fill(periods=4)
+ pl.col('a').shift_and_fill(n=4)
- pl.col('a').list.shift(periods=4)
+ pl.col('a').list.shift(n=4)
- pl.col('a').map_dict(remapping={1: 2})
+ pl.col('a').map_dict(mapping={1: 2})- pl.col('a').keep_name
+ pl.col('a').name.keep
- pl.col('a').suffix
+ pl.col('a').name.suffix
- pl.col('a').prefix
+ pl.col('a').name.prefix
- pl.col('a').map_alias
+ pl.col('a').name.map
- pl.col('a').str.ljust
+ pl.col('a').str.pad_end
- pl.col('a').str.rjust
+ pl.col('a').str.pad_start
- pl.col('a').zfill(alignment=3)
+ pl.col('a').zfill(length=3)
- pl.col('a').ljust(width=3)
+ pl.col('a').ljust(length=3)
- pl.col('a').rjust(width=3)
+ pl.col('a').rjust(length=3)- pl.col('a').dt.milliseconds
+ pl.col('a').dt.total_milliseconds
- pl.col('a').dt.microseconds
+ pl.col('a').dt.total_microseconds
- pl.col('a').dt.nanoseconds
+ pl.col('a').dt.total_nanoseconds(and so on for other units)
- pl.col('a').list.take
+ pl.col('a').list.gather
- pl.col('a').cumcount
+ pl.col('a').cum_count
- pl.col('a').cummax
+ pl.col('a').cum_max
- pl.col('a').cummin
+ pl.col('a').cum_min
- pl.col('a').cumprod
+ pl.col('a').cum_prod
- pl.col('a').cumsum
+ pl.col('a').cum_sum
- pl.col('a').cumcount
+ pl.col('a').cum_count
- pl.col('a').take
+ pl.col('a').gather
- pl.col('a').take_every
+ pl.col('a').gather_every
- pl.cumsum
+ pl.cum_sum
- pl.cumfold
+ pl.cum_fold
- pl.cumreduce
+ pl.cum_reduce
- pl.cumsum_horizontal
+ pl.cum_sum_horizontal
- pl.col('a').list.take(index=[1, 2])
+ pl.col('a').list.take(indices=[1, 2])
- pl.col('a').str.parse_int(radix=1)
+ pl.col('a').str.parse_int(base=1)- pl.col('a').str.json_extract
+ pl.col('a').str.json_decode- pl.col('a').map_dict({'a': 'b'})
+ pl.col('a').replace({'a': 'b'}, default=None)
- pl.col('a').map_dict({'a': 'b'}, default='c')
+ pl.col('a').replace({'a': 'b'}, default='c')- df.write_database(table_name='foo', if_exists="append")
+ df.write_database(table_name='foo', if_table_exists="append")- pl.col('a').where
+ pl.col('a').filter
- pl.count()
+ pl.len()
- df.with_row_count('row_number')
+ df.with_row_index('row_number')
- pl.scan_ndjson(source, row_count_name='foo', row_count_offset=3)
+ pl.scan_ndjson(source, row_index_name='foo', row_index_offset=3)
[...and similarly for `read_csv`, `read_csv_batched`, `scan_csv`, `read_ipc`, `read_ipc_stream`, `scan_ipc`, `read_parquet`, `scan_parquet`]- df.pivot(index=index, values=values, columns=columns, aggregate_function='count')
+ df.pivot(index=index, values=values, columns=columns, aggregate_function='len')- pl.read_excel(source, xlsx2csv_options=options, read_csv_options=read_options)
+ pl.read_excel(source, engine_options=options, read_options=read_options)- pl.threadpool_size
+ pl.thread_pool_size- df.pivot(a, b, c)
+ df.pivot(values=a, index=b, columns=c)- pl.col('a').meta.write_json
+ pl.col('a').meta.serialize
- lf.approx_n_unique()
+ lf.select(pl.all().approx_n_unique())This work is derivative of pyupgrade - many parts have been lifted verbatim. As required, I've included pyupgrade's license.