Comments (2)
I added "bug" since it shouldn't raise PanicException.
Interesting that if you use pyarrow to write it that polars can then read back the array containing parquet.
from polars.
import polars as pl
import numpy as np
import pyarrow.parquet as pq
df = pl.DataFrame(
{
"arr": np.random.rand(100_000, 512),
"arr2": np.random.rand(100_000, 512),
},
schema={
"arr": pl.Array(inner=pl.Float32, width=512),
"arr2": pl.Array(inner=pl.Float32, width=512),
},
)
pt = df.to_arrow()
pq.write_table(pt, 'example_table.parquet')
Simple way using parquet as a workaround
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Related Issues (20)
- Obfuscate/censor AccessKey in lazy serialize by default (have parameter to leave in the clear).
- Regression 0.20.15->0.20.16: ComputeError: conversion from `null` to `struct[100]` failed in column 'literal' for 0 out of 1 values HOT 1
- Casting a column to pl.Categorical is way slower than pandas (10-20x) HOT 2
- Minimal memory usage tests for read_ipc read_ipc_stream
- Forward_fill() and backward_fill() is about 25% slower in polars compared to pandas' counterparts HOT 6
- Rust code examples missing on page /user-guide/io/cloud-storage/#scanning-from-cloud-storage-with-query-optimisation
- Offset_by is about 4 times slower in polars compared to pandas' counterpart HOT 2
- `filter` + `arg_max` + `over` producing non-deterministic junk values HOT 1
- pyo3_runtime.PanicException: python function failed: PyErr { type: <class 'TypeError'>, value: TypeError("'list' object is not callable"), traceback: None } HOT 1
- support expressions in `Frame.unique()` HOT 2
- Read Options for Calamine HOT 1
- Support for pl.List('*') HOT 4
- See the polars df in Pycharm HOT 5
- schema_overrides failing HOT 4
- .over() performs quite slow in given sample HOT 4
- `.backward_fill()` does not consider `np.nan` to be invalid HOT 2
- from_arrow.consuming large memory HOT 2
- Inconsistent behavior with dataframe level arithmetic when using Python's `sum` HOT 1
- `read_csv` ignores `skip_rows_after_header` when `use_pyarrow=True` HOT 1
- group_by sum agg returns nulls for decimal columns in dataframes with 1000+ rows HOT 2
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