semvec/pypi-packages huggingface dataset:
polars-io 0.5.0
Lazy and eager reading of Stata and SAS files into Polars
Requires Python: >=3.10

# polars_io

Lazily read Stata (`.dta`), SAS (`.sas7bdat`, `.xpt`), fixed-width (`.txt`,
`.dat`, etc.), and newline delimited (`.txt`) files in
[`polars`](https://pola.rs).

## Installation

```bash
pip install polars_io
# Or:
uv add polars_io
```

## Usage

```python
import polars as pl
import polars_io as pio

# Lazily load a sas file.
lf = pio.scan_sas7bdat("huge_SAS_file.sas7bdat")

# Get its schema.
lf.collect_schema()

# Take a look at the first few rows.
lf.head().collect()

# Projection and predicate pushdown work!
(
    lf
    .filter(pl.col("birth_year").is_between(2000, 2010))
    .select(pl.col("usage").mean())
    .collect()
)

# Load fixed-width files.
col_locations = {"year": (10, 14), "population": (14, 20)}
pio.scan_fwf("populations.txt", col_locations)

# Eager versions of all functions are also available.
pio.read_dta("mortality_rates.dta")
```

See [the documentation](https://alipatti.com/polars_io) for more info.

## Details

The Stata and SAS implementations make use of the
[`readstat`](https://github.com/WizardMac/ReadStat) C library via the Python
bindings provided by [`pyreadstat`](https://github.com/Roche/pyreadstat). For
numeric types, reading uses zero-copy conversions from
`numpy -> pyarrow -> polars` and should be faster and have lower memory overhead
than reading the data into `pandas` and then calling `pl.from_pandas`
(benchmarks welcome).

## Contributing

PRs adding support for reading other formats are very welcome! (E.g., `.Rdata`,
Stata `.dct`, SPSS files, etc.)

## Known Issues

This packages fails to some read files with non-utf8 metadata (e.g., column
labels, notes on `.dta` files). This is a known issue with upstream packages
that is being worked on (see Roche/pyreadstat#298 and WizardMac/ReadStat#344).
photo-culling 0.1.3
Fast and minimal photo culling tool
Requires Python: >=3.12

# Photoculling
Fast image viewer, which uses aggressive prefetching and caching to very quickly move between photos.
Minimal interface to select photos to keep and copy to output directory.

# Installation
Installation is easiest using the uv package manager.

[Install uv](https://docs.astral.sh/uv/getting-started/installation/), which exists for linux, macOS, windows, e.g. via
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

Install photo-culling
```bash
uv tool install photo-culling
```
This installs a compatible python environment, the photoculling package and all its dependencies (mainly Qt).

Start culling!
```bash
cull path/to/directory/with/jpgs
```

# Controls 

- Left/right arrow keys or A/D: move 1 image
- Shift + left/right or Shift + A/D: move 10 images
- W/S: Select/unselect current image
- T: Toggle between going through all images or only selected images
- F11: toggle fullscreen
- ESC: select target directory for selected images and quit

In the top left corner the current position within the photo stack is displayed and in parenthesis the number of selected images.
For photos which have been selected this textbox is green.
When quitting, all selected photos (and all other files which share the selected filenames but with different extensions, e.g. RAW files) are copied to the selected target directory.