Checks I have checked that this issue has not already been reported. I have confirmed this bug exists on the latest version of Polars. Reproducible example import numpy as np import polars as pl df = pl.DataFrame({"a": [[4, 5, 6], [7, 8,...
Reproducible example What's the correct way to usestrict=Falsehere? scaler1=MaxAbsScaler().fit(data_raw["y"].to_numpy().reshape(-1,1))scaled_values1=scaler1.fit_transform(data_raw["y"].to_numpy().reshape(-1,1)).flatten()scaler2=MaxAbsScaler()scaled_values2=scaler2.fit_transform(...
一、测试数据生成代码首先,我用python构建了一个1千万行的数据集(csv文件大概接近900M),具体代码为: import pandas as pdimport polars as plimport numpy as npdef create_dataframe(n_rows, library): if library == 'pandas': data = { 'name': np.random.choice(['Alice', 'Bob', 'Charlie', 'Da...
For example, if you want to convert Polars DataFrames to pandas DataFrames and NumPy arrays, then run the following command when installing Polars:Shell (venv) $ python -m pip install "polars[numpy, pandas]" This command installs the Polars core and the functionality that you need to ...
import gc import time import numpy as np import polars as pl df = ( # I have a dataframe like this from reading a csv. pl.Series( name="x", values=np.random.choice( ["ASPARAGUS", "BROCCOLI", ""], size=30_000_000 ), ) .to_frame() .with_columns( pl.when(pl.col("x") =...
one of these beingNumPy. While NumPy’s core is written in C, it is still hamstrung by inherent problems with the way Python handles certain types in memory, such as strings for categorical data, leading to poor performance when handling these types (seethis fantastic blog postfromWes McKinney...
Another advantage Polars has is that, since it is written in Rust, it can make use of concurrency much better than pandas.Python is traditionally single-threaded, and although pandas uses the NumPy backend to speed up some operations, it is still mainly written in Python and has certain limit...
leverage powerful features like chaining, and understand its caveats. This book also shows you how to integrate Polars with other Python libraries such as pandas, numpy, and PyArrow, and explore deployment strategies for both on-premises and cloud environments like AWS, BigQuery, GCS, Snowflake, ...
chapter we ensure that their transition to polars is as smooth as possible by highlighting similarities and, more importantly, important differences between these tools. similarities no index and multiindex numpy versus arrow arrays rows versus columns differences in syntax common pitfalls to avoid part...
[bool | int | float | complex | str | bytes], /) -> None tests/unit/interop/numpy/test_to_numpy_series.py:58: error: Non-overlapping equality check (left operand type: "str", right operand type: "list[Any]") [comparison-overlap] tests/unit/interop/numpy/test_to_numpy_series.py:...