Bumps [indexmap](https://github.com/indexmap-rs/indexmap) from 2.3.0 to
2.4.0.
<details>
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<blockquote>
<h2>2.4.0</h2>
<ul>
<li>Added methods <code>IndexMap::append</code> and
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<li><a
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Add <code>append</code> methods</li>
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# Description
This exposes the `LazyFrame::sink_*` functionality to allow a streaming
collect directly to the filesystem. This useful when working with data
that is too large to fit into memory.
# User-Facing Changes
- Introduction of the `polars sink` command
Bumps [indexmap](https://github.com/indexmap-rs/indexmap) from 2.2.6 to
2.3.0.
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<h2>2.3.0</h2>
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Bumps [uuid](https://github.com/uuid-rs/uuid) from 1.9.1 to 1.10.0.
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<h2>Deprecations</h2>
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<li><a href="https://github.com/Vrajs16"><code>@Vrajs16</code></a> made
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<p><strong>Full Changelog</strong>: <a
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prepare for 1.10.0 release</li>
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<li><a
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Use expr instead of ident</li>
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Use const identifer in uuid macro</li>
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# Description
Upgrading to Polars 0.41
# User-Facing Changes
* `polars melt` has been renamed to `polars unpivot` to match the change
in the polars API. Additionally, it now supports lazy dataframes.
Introduced a `--streamable` option to use the polars streaming engine
for lazy frames.
* The parameter `outer` has been replaced with `full` in `polars join`
to match polars change.
* `polars value-count` now supports the column (rename count column),
parallelize (multithread), sort, and normalize options.
The list of polars changes can be found
[here](https://github.com/pola-rs/polars/releases/tag/rs-0.41.2)
In this pull request, I converted the `perf` function within `nu_utils`
to a macro. This change facilitates easier usage within plugins by
allowing the use of `env_logger` and setting `RUST_LOG=nu_plugin_polars`
(or another plugin). Without this conversion, the `RUST_LOG` variable
would need to be set to `RUST_LOG=nu_utils::utils`, which is less
intuitive and impossible to narrow the perf results to one plugin.
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# Description
@maxim-uvarov did a ton of research and work with the dply-rs author and
ritchie from polars and found out that the allocator matters on macos
and it seems to be what was messing up the performance of polars plugin.
ritchie suggested to use jemalloc but i switched it to mimalloc to match
nushell and it seems to run better.
## Before (default allocator)
note - using 1..10 vs 1..100 since it takes so long. also notice how
high the `max` timings are compared to mimalloc below.
```nushell
❯ 1..10 | each {timeit {polars open Data7602DescendingYearOrder.csv | polars group-by year | polars agg (polars col geo_count | polars sum) | polars collect | null}} | | {mean: ($in | math avg), min: ($in | math min), max: ($in | math max), stddev: ($in | into int | into float | math stddev | into int | $'($in)ns' | into duration)}
╭────────┬─────────────────────────╮
│ mean │ 4sec 999ms 605µs 995ns │
│ min │ 983ms 627µs 42ns │
│ max │ 13sec 398ms 135µs 791ns │
│ stddev │ 3sec 476ms 479µs 939ns │
╰────────┴─────────────────────────╯
❯ use std bench
❯ bench { polars open Data7602DescendingYearOrder.csv | polars group-by year | polars agg (polars col geo_count | polars sum) | polars collect | null } -n 10
╭───────┬────────────────────────╮
│ mean │ 6sec 220ms 783µs 983ns │
│ min │ 1sec 184ms 997µs 708ns │
│ max │ 18sec 882ms 81µs 708ns │
│ std │ 5sec 350ms 375µs 697ns │
│ times │ [list 10 items] │
╰───────┴────────────────────────╯
```
## After (using mimalloc)
```nushell
❯ 1..100 | each {timeit {polars open Data7602DescendingYearOrder.csv | polars group-by year | polars agg (polars col geo_count | polars sum) | polars collect | null}} | | {mean: ($in | math avg), min: ($in | math min), max: ($in | math max), stddev: ($in | into int | into float | math stddev | into int | $'($in)ns' | into duration)}
╭────────┬───────────────────╮
│ mean │ 103ms 728µs 902ns │
│ min │ 97ms 107µs 42ns │
│ max │ 149ms 430µs 84ns │
│ stddev │ 5ms 690µs 664ns │
╰────────┴───────────────────╯
❯ use std bench
❯ bench { polars open Data7602DescendingYearOrder.csv | polars group-by year | polars agg (polars col geo_count | polars sum) | polars collect | null } -n 100
╭───────┬───────────────────╮
│ mean │ 103ms 620µs 195ns │
│ min │ 97ms 541µs 166ns │
│ max │ 130ms 262µs 166ns │
│ std │ 4ms 948µs 654ns │
│ times │ [list 100 items] │
╰───────┴───────────────────╯
```
## After (using jemalloc - just for comparison)
```nushell
❯ 1..100 | each {timeit {polars open Data7602DescendingYearOrder.csv | polars group-by year | polars agg (polars col geo_count | polars sum) | polars collect | null}} | | {mean: ($in | math avg), min: ($in | math min), max: ($in | math max), stddev: ($in | into int | into float | math stddev | into int | $'($in)ns' | into duration)}
╭────────┬───────────────────╮
│ mean │ 113ms 939µs 777ns │
│ min │ 108ms 337µs 333ns │
│ max │ 166ms 467µs 458ns │
│ stddev │ 6ms 175µs 618ns │
╰────────┴───────────────────╯
❯ use std bench
❯ bench { polars open Data7602DescendingYearOrder.csv | polars group-by year | polars agg (polars col geo_count | polars sum) | polars collect | null } -n 100
╭───────┬───────────────────╮
│ mean │ 114ms 363µs 530ns │
│ min │ 108ms 804µs 833ns │
│ max │ 143ms 521µs 459ns │
│ std │ 5ms 88µs 56ns │
│ times │ [list 100 items] │
╰───────┴───────────────────╯
```
## After (using parquet + mimalloc)
```nushell
❯ 1..100 | each {timeit {polars open data.parquet | polars group-by year | polars agg (polars col geo_count | polars sum) | polars collect | null}} | | {mean: ($in | math avg), min: ($in | math min), max: ($in | math max), stddev: ($in | into int | into float | math stddev | into int | $'($in)ns' | into duration)}
╭────────┬──────────────────╮
│ mean │ 34ms 255µs 492ns │
│ min │ 31ms 787µs 250ns │
│ max │ 76ms 408µs 416ns │
│ stddev │ 4ms 472µs 916ns │
╰────────┴──────────────────╯
❯ use std bench
❯ bench { polars open data.parquet | polars group-by year | polars agg (polars col geo_count | polars sum) | polars collect | null } -n 100
╭───────┬──────────────────╮
│ mean │ 34ms 897µs 562ns │
│ min │ 31ms 518µs 542ns │
│ max │ 65ms 943µs 625ns │
│ std │ 3ms 450µs 741ns │
│ times │ [list 100 items] │
╰───────┴──────────────────╯
```
# User-Facing Changes
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This reverts commit 68adc4657f.
# Description
Reverts the lazyframe refactor (#12669) for the next release, since
there are still a few lingering issues. This temporarily solves #12863
and #12828. After the release, the lazyframes can be added back and
cleaned up.
# Description
Removes the old `nu-cmd-dataframe` crate in favor of the polars plugin.
As such, this PR also removes the `dataframe` feature, related CI, and
full releases of nushell.
This moves to predominantly supporting only lazy dataframes for most
operations. It removes a lot of the type conversion between lazy and
eager dataframes based on what was inputted into the command.
For the most part the changes will mean:
* You will need to run `polars collect` after performing operations
* The into-lazy command has been removed as it is redundant.
* When opening files a lazy frame will be outputted by default if the
reader supports lazy frames
A list of individual command changes can be found
[here](https://hackmd.io/@nucore/Bk-3V-hW0)
---------
Co-authored-by: Ian Manske <ian.manske@pm.me>
# Description
All polars commands that output a file were not handling relative paths
correctly.
A command like
``` [[a b]; [6 2] [1 4] [4 1]] | polars into-df | polars to-parquet foo.json```
was outputting the foo.json to the directory of the plugin executable.
This pull request pulls in nu-path and using it for resolving the file paths.
Related discussion
https://discord.com/channels/601130461678272522/1227612017171501136/1227889870358183966
# User-Facing Changes
None
# Tests + Formatting
Done, added tests for each of the polars to-* commands.
---------
Co-authored-by: Jack Wright <jack.wright@disqo.com>
