示例

polars 1.43.2: Port pandas DataFrame code to polars on Alpine, without the wheel surprise or the silently wrong answers

已验证示例 — pypi polars 1.43.2: Port pandas DataFrame code to polars on Alpine, without the wheel surprise or the silently wrong answers. contract 在 python 3.12…

sha256:a238e626062c628c548646e46144b6aac21a07492cf10e1617eb4286c853ac05

MIT-0

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构建过它的签名密钥: 2

声明的环境 python 3.12 linux · musl x64 python 3.12 python pip

验证运行环境

环境	契约	阶段	运行日期
python 3.12 · linux alpine/x64 · docker ed25519:a2ec939a4c60e243	PASS	compile:SKIPPED · contract:PASS · load:PASS · resolve:PASS CONTAINER_RUN · pypi@1	2026-08-14
python 3.12 · linux alpine/x64 · docker ed25519:d91480838ac982c9	PASS	compile:SKIPPED · contract:PASS · load:PASS · resolve:PASS CONTAINER_RUN · pypi@1	2026-08-18

案例

MIGRATION

目标: Port pandas DataFrame code to polars on Alpine, without the wheel surprise or the silently wrong answers
包: polars 1.43.2

polars-runtime-32 1.43.2
符号: polars.DataFrame
polars.LazyFrame
polars.col
polars.len
DataFrame.filter
DataFrame.select
DataFrame.with_columns
DataFrame.with_row_index
DataFrame.group_by
GroupBy.agg
LazyFrame.collect
LazyFrame.explain
LazyFrame.collect_schema
Expr.map_elements
Expr.alias
Series.is_null
Series.is_nan
Series.fill_null
Series.fill_nan
Series.eq_missing
polars.exceptions.ColumnNotFoundError
polars.exceptions.DuplicateError
polars.exceptions.PerformanceWarning
DataFrame.vstack
DataFrame.hstack
DataFrame.extend
DataFrame.insert_column
DataFrame.drop_in_place
环境: python 3.12
创建时间: 2026-08-14T12:56:07Z

契约

assert pip took the musllinux wheel for the engine, polars_runtime_32-1.43.2-cp310-abi3-musllinux_1_2_x86_64.whl, reconstructed from the recorded dist-info tag
assert polars itself is a pure-Python py3-none-any distribution carrying no compiled file, which depends on polars-runtime-32==1.43.2 for the engine
assert the engine's shared object is named .abi3.so rather than this interpreter's EXT_SUFFIX, so one cp310-abi3 build serves 3.12
assert the interpreter reports musl and polars.__version__ agrees with the distribution metadata, which is the check a half-installed polars fails
assert a DataFrame has none of index, loc, iloc, at, iat, values, reset_index, sort_values, assign, query or apply
assert filtering renumbers rows from zero, and with_row_index before the filter is what carries the original positions through, as UInt32 data
assert brackets take a column by name and a row by position, and an unknown name raises ColumnNotFoundError
assert a boolean mask in brackets selects columns rather than rows, raising ValueError when its length does not match the column count
assert the same mask on a frame with as many columns as rows returns the wrong columns with no error at all
assert filter with a pl.col boolean expression returns the intended rows and select takes strings, pl.col and an anchored regex
assert an Expr has no truth value, so `and` and `or` both raise TypeError, and dropping the parentheses around & raises that identical message
assert separate positional predicates mean AND, and the keyword form is equality against a literal
assert with_columns returns a new frame, leaves the input's columns and values untouched, and has no inplace parameter
assert item assignment on a DataFrame raises TypeError naming DataFrame.with_columns as the replacement
assert the new frame shares the untouched column's Arrow allocation with the old one, so returning a frame copies the column list rather than the data
assert polars does keep a mutating escape hatch: hstack and vstack take in_place= and hand the same object back, and extend, insert_column and drop_in_place mutate the receiver
assert an unaliased expression is named after the first column it mentions, so with_columns replaces that column instead of adding one
assert explain returns the plan as a string while the per-row Python hook has still not been called once
assert the optimizer moves the filter below the projection, the reverse of both the unoptimized plan and the order written
assert collect runs the hook on only the two surviving rows, where the identical eager pipeline runs it on all six
assert a LazyFrame naming a missing column is built without complaint and raises ColumnNotFoundError only when the plan is resolved, which explain also does
assert a LazyFrame has no shape, and asking it for columns emits a PerformanceWarning because that resolves the schema
assert is_null and is_nan disagree on every hole, and is_nan over a null is null rather than False
assert null_count reports one null while the mean is nan, count excludes the null and pl.len counts every row
assert a column holding a NaN and no null reports null_count zero with a nan mean, so a missing-data check written as null_count() == 0 passes on it
assert min and max ignore the NaN that sum and mean propagate
assert comparison against a null yields null while eq_missing yields False
assert polars orders NaN above every number and compares it equal to itself, the opposite of Python, so a > 0 filter keeps the NaN and drops the null
assert nulls sort first in both directions unless nulls_last is set
assert drop_nulls keeps NaN and drop_nans keeps null, neither fill touches the other's hole, and fill_nan(None) collapses the two into one
assert an integer column with a missing value stays Int64 and its aggregates skip the null on both sides of the division
assert group_by returns the group keys first, then one column per agg expression in the order given
assert maintain_order gives first-appearance order, which is not sorted order, and without it only the set of rows is defined
assert two aggregates over one column collide as DuplicateError, worded one way inside agg and another inside select

文件

csx.json
requirements.txt
src/__init__.py
src/frames.py
src/install.py
test/contract.py

下载源代码构件 (tar.gz)

原始种子者

anonymous