I would like to use polars, but when I try to subtract a 1x3 numpy array from three columns of the DataFrame. The problem is that is does not matter in which order the subtraction is applied:
import numpy as np
import polars as pl
# create polars dataframe:
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
df = pl.DataFrame(data, schema=['x', 'y', 'z']).with_columns(
pl.all().cast(pl.Float64)
)
# subraction array:
arr = np.array([2, 5, 8], dtype=np.float64)
# subtract array from DataFrame
df.with_columns(
pl.col('x') - arr[0],
pl.col('y') - arr[1],
pl.col('z') - arr[2],
)
"""
This one is correct, top row should be negative and bottom row positive
| | x | y | z |
|---:|----:|----:|----:|
| 0 | -1 | -1 | -1 |
| 1 | 0 | 0 | 0 |
| 2 | 1 | 1 | 1 |
"""
df.with_columns(
arr[0] - pl.col('x'),
arr[1] - pl.col('y'),
arr[2] - pl.col('z'),
)
"""
This one is incorrect. The top row should be positive and the bottom row should
be negative.
| | x | y | z |
|---:|----:|----:|----:|
| 0 | -1 | -1 | -1 |
| 1 | 0 | 0 | 0 |
| 2 | 1 | 1 | 1 |
"""
Can't reproduce this, looks fine to me as of 0.16.5:
In [57]: df.with_columns((
...: pl.col('x') - arr[0],
...: pl.col('y') - arr[1],
...: pl.col('z') - arr[2],
...: ))
...:
Out[57]:
shape: (3, 3)
┌──────┬──────┬──────┐
│ x ┆ y ┆ z │
│ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 │
╞══════╪══════╪══════╡
│ -1.0 ┆ -1.0 ┆ -1.0 │
│ 0.0 ┆ 0.0 ┆ 0.0 │
│ 1.0 ┆ 1.0 ┆ 1.0 │
└──────┴──────┴──────┘
In [58]: df.with_columns((
...: arr[0] - pl.col('x'),
...: arr[1] - pl.col('y'),
...: arr[2] - pl.col('z'),
...: ))
Out[58]:
shape: (3, 3)
┌──────┬──────┬──────┐
│ x ┆ y ┆ z │
│ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 │
╞══════╪══════╪══════╡
│ 1.0 ┆ 1.0 ┆ 1.0 │
│ 0.0 ┆ 0.0 ┆ 0.0 │
│ -1.0 ┆ -1.0 ┆ -1.0 │
└──────┴──────┴──────┘