Read data with Float32 and Float64 have different outputs

[balakhaniyan]

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

pl.concat([a, b.rename({'data': 'data2'})], how='horizontal').sort('data').with_columns(pl.all().map_elements(str, return_dtype=pl.String))

Log output

No response

Issue description

difference_in_read_data.xlsx

I read this file, using

a = pl.read_excel('difference_in_read_data.xlsx', read_options={
    'schema_overrides': {'data': pl.Float64}
})

b = pl.read_excel('difference_in_read_data.xlsx', read_options={
    'schema_overrides': {'data': pl.Float32}
})

and ran the code snippet

Expected behavior

I assumed that both must have same output regardless the type (Float32 or Float64) because there are Integers and there are no differences for Float32 and Float64

Installed versions

--------Version info---------
Polars:               0.20.31
Index type:           UInt32
Platform:             Windows-11-10.0.22631-SP0
Python:               3.12.2 (tags/v3.12.2:6abddd9, Feb  6 2024, 21:26:36) [MSC v.1937 64 bit (AMD64)]

----Optional dependencies----
adbc_driver_manager:  <not installed>
cloudpickle:          <not installed>
connectorx:           <not installed>
deltalake:            <not installed>
fastexcel:            <not installed>
fsspec:               <not installed>
gevent:               <not installed>
hvplot:               <not installed>
matplotlib:           <not installed>
nest_asyncio:         1.6.0
numpy:                <not installed>
openpyxl:             <not installed>
pandas:               <not installed>
pyarrow:              <not installed>
pydantic:             <not installed>
pyiceberg:            <not installed>
pyxlsb:               <not installed>
sqlalchemy:           <not installed>
torch:                <not installed>
xlsx2csv:             0.8.2
xlsxwriter:           3.2.0

[ritchie46]

What is the bug here? Can you show it?

[balakhaniyan]

@ritchie46
when I ran

pl.concat([a, b.rename({'data': 'data2'})], how='horizontal').sort('data').with_columns(
    (pl.col('data') - pl.col('data2')).alias('diff')
)

output is:

shape: (2_352, 3)
┌─────────────┬─────────────┬─────────┐
│ data        ┆ data2       ┆ diff    │
│ ---         ┆ ---         ┆ ---     │
│ f64         ┆ f32         ┆ f64     │
╞═════════════╪═════════════╪═════════╡
│ 1.6856309e7 ┆ 1.6856308e7 ┆ 1.0     │
│ 1.6874691e7 ┆ 1.6874692e7 ┆ -1.0    │
│ 1.6874691e7 ┆ 1.6874692e7 ┆ -1.0    │
│ 1.6874691e7 ┆ 1.6874692e7 ┆ -1.0    │
│ 1.6913661e7 ┆ 1.691366e7  ┆ 1.0     │
│ …           ┆ …           ┆ …       │
│ 7.0912e10   ┆ 7.0912e10   ┆ 22.0    │
│ 8.3216e10   ┆ 8.3216e10   ┆ 2630.0  │
│ 9.3109e10   ┆ 9.3109e10   ┆ -1275.0 │
│ 1.0732e11   ┆ 1.0732e11   ┆ 2368.0  │
│ 1.2984e11   ┆ 1.2984e11   ┆ 1616.0  │
└─────────────┴─────────────┴─────────┘

show that there are differences, is it OK?

[stinodego]

Float32 has a lower precision than Float64, so you will see a difference when representing large integer values. I would say these results are expected. Please use Float64 for better accuracy if that is relevant to your applications.