Adds support for converting from polars decimal type to nushell values.
This fix works by first converting a polars decimal series to an f64
series, then converting to Value::Float
Co-authored-by: Jack Wright <jack.wright@nike.com>
# Description
This PR fixes a problem where not equal in polars wasn't working with
strings.
## Before
```nushell
let a = ls | polars into-df
$a.type != "dir"
Error: nu:🐚:type_mismatch
× Type mismatch during operation.
╭─[entry #16:1:1]
1 │ $a.type != "dir"
· ─┬ ─┬ ──┬──
· │ │ ╰── string
· │ ╰── type mismatch for operator
· ╰── NuDataFrame
╰────
```
## After
```nushell
let a = ls | polars into-df
$a.type != "dir"
╭──#──┬─type──╮
│ 0 │ false │
│ 1 │ false │
│ 2 │ false │
...
```
/cc @ayax79 to make sure I did this right.
# User-Facing Changes
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# Tests + Formatting
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# Description
Turns out there are duplicate conversion functions: `as_i64` and
`as_f64`. In most cases, these can be replaced with `as_int` and
`as_float`, respectively.
Closes#13654
# User-Facing Changes
- Short flags are now fully type-checked,
including null and record signatures for literal arguments:
```nushell
def test [-v: record<l: int>] {};
test -v null # error
test -v {l: ""} # error
def test2 [-v: int] {};
let v = ""
test2 -v $v # error
```
- `polars unpivot` `--index`/`--on` and `into value --columns`
now accept `list` values
# Description
Provides the ability to decomes struct columns into seperate columns for
each field:
<img width="655" alt="Screenshot 2024-10-16 at 09 57 22"
src="https://github.com/user-attachments/assets/6706bd36-8d38-4365-b58d-ba82f2d5ba9a">
# User-Facing Changes
- provides a new command `polars unnest` for decomposing struct fields
into separate columns.
Closes#13654
# User-Facing Changes
- Short flags are now fully type-checked,
including null and record signatures for literal arguments:
```nushell
def test [-v: record<l: int>] {};
test -v null # error
test -v {l: ""} # error
def test2 [-v: int] {};
let v = ""
test2 -v $v # error
```
- `polars unpivot` `--index`/`--on` and `into value --columns`
now accept `list` values
# Description
Provides the ability to decomes struct columns into seperate columns for
each field:
<img width="655" alt="Screenshot 2024-10-16 at 09 57 22"
src="https://github.com/user-attachments/assets/6706bd36-8d38-4365-b58d-ba82f2d5ba9a">
# User-Facing Changes
- provides a new command `polars unnest` for decomposing struct fields
into separate columns.
# Description
This changes the names returned by CustomValue::name() of the various
custom value structs to just say the name of the thing they represent.
For instance "DataFrameCustomValue" is not just "DataFrame".
# User-Facing Changes
- Places such as or errors where NuDataFrameCustomValue would be seen,
now just shows as NuDataFrame.
# Description
This fixes an issue with converting to a dataframe when specifying a
struct in the schema. Things like the following now work correctly:
```nushell
[[foo bar]; [{a: "a_0", b:"b_0"} 1] [{a: "a_1", b: "b_1" } 2]] | polars into-df -s {foo: {a: str, b: str}, bar: u8}
```
# Description
Introduces a new flag `--truncate-ragged-lines` for `polars open` that
will truncate lines that are longer than the schema.
# User-Facing Changes
- Introduction of the flag `--truncate-ragged-lines` for `polars open`
# Description
This request exposes the prelude::polars::len expression. It is ended
for doing fast select count(*) like operations:
<img width="626" alt="Screenshot 2024-09-26 at 18 14 45"
src="https://github.com/user-attachments/assets/74285fc6-f99c-46e0-9226-9a7d41738d78">
# User-Facing Changes
- Introduction of the `polars len` command
# Description
This adds support for reading and writing binary types in the polars
commands.
The `BinaryOffset` type can be read into a Nushell native `Value` type
no problem, but unfortunately this is a lossy conversion, as there's
no Nushell-native semantic equivalent to the fixed size binary type
in Arrow.
# User-Facing Changes
`polars open` and `polars save` now work with binary types.
# Description
Fixes: #12726 and #13185
Previously converting columns that contained null caused polars to force
a dtype of object even when using a schema.
Now:
1. When using a schema, the type the schema defines for the column will
always be used.
2. When a schema is not used, the previous type is used when a value is
null.
# User-Facing Changes
- The type defined by the schema we be respected when passing in a null
value `[a]; [null] | polars into-df -s {a: str}` will create a df with
an str dtype column with one null value versus a column of type object.
- *BREAKING CHANGE* If you define a schema, all columns must be in the
schema.
# Description
This resurrects the work from #12866 and fixes#12732.
Polars panics for a plethora or reasons. While handling panics is
generally frowned upon, in cases like with `polars collect` a panic
cause a lot of work to be lost. Often you might have multiple dataframes
in memory and you are trying one operation and lose all state.
While it possible the panic can leave things a strange state, it is
pretty unlikely as part of a polars pipeline. Most of the time polars
objects are not manipulating dataframes in memory mutability, but rather
creating a new dataframe the operations being applied. This is always
the case with a lazy pipeline. After the collect call, the original
dataframes are intact still and I haven't observed any side effects.
# Description
In order to be more consistent with it's nu counterpart, `polars save`
now returns an empty pipeline instead of a message of the saved file.
# User-Facing Changes
- `polars save` no longer displays a save message, making it consistent
with `save` behavior.
# Description
This PR:
- Removes the lazy_command, expr_command macros and migrates the
commands that were utilizing them.
- Removes the custom logic in DataFrameValues::is_equals to use the
polars DataFrame version of PartialEq
- Adds examples to commands that previously did not have examples or had
inadequate ones.
NOTE: A lot of examples now have a `polars sort` at the end. This is
needed due to the comparison in the result. The new polars version of
equals cares about the ordering. I removed the custom equals logic as it
causes comparisons to lock up when comparing dataframes that contain a
row that contains a list. I discovered this issue when adding examples
to `polars implode`
# Description
Previously there were no examples or explanations that you can use '*'
to select all columns. Updated description and added a new example.
# Description
In order to be more consistent with the nushell terminology and with
polars expression terminology `polars concatenate` is now `polars
str-join`. `polars str-join` can also be used as expression.
<img width="857" alt="Screenshot 2024-09-04 at 12 41 25"
src="https://github.com/user-attachments/assets/8cc5a0c2-194c-49ec-9fe1-65ec4825414d">
# User-Facing Changes
- `polars concatenate` is now `polars str-join`
- `polars str-join` can be used as an expression
# Description
Allows `polars str-lengths` to be used as an expression:
<img width="826" alt="Screenshot 2024-09-04 at 13 57 45"
src="https://github.com/user-attachments/assets/b74139e0-e8ba-4910-84c2-cf4be4a084b6">
# User-Facing Changes
- `polars str-lengths` can be used as an expression.
- char length is now the default. Use the --bytes flag to get bytes
length.
# Description
Adds the ability for `polars replace` and `polars replace-all` to work
as expressions.
# User-Facing Changes
- `polars replace` can be used with polars expressions
- `polars replace-all` can be used with polars expressions
# Description
The meaning of the word usage is specific to describing how a command
function is *used* and not a synonym for general description. Usage can
be used to describe the SYNOPSIS or EXAMPLES sections of a man page
where the permitted argument combinations are shown or example *uses*
are given.
Let's not confuse people and call it what it is a description.
Our `help` command already creates its own *Usage* section based on the
available arguments and doesn't refer to the description with usage.
# User-Facing Changes
`help commands` and `scope commands` will now use `description` or
`extra_description`
`usage`-> `description`
`extra_usage` -> `extra_description`
Breaking change in the plugin protocol:
In the signature record communicated with the engine.
`usage`-> `description`
`extra_usage` -> `extra_description`
The same rename also takes place for the methods on
`SimplePluginCommand` and `PluginCommand`
# Tests + Formatting
- Updated plugin protocol specific changes
# After Submitting
- [ ] update plugin protocol doc
# Description
Fixes issue [12828](https://github.com/nushell/nushell/issues/12828).
When attempting a `polars collect` on an eager dataframe, we return
dataframe as is. However, before this fix I failed to increment the
internal cache reference count. This caused the value to be dropped from
the internal cache when the references were decremented again.
This fix adds a call to cache.get to increment the value before
returning.
# Description
This pull request merges `polars sink` and `polars to-*` into one
command `polars save`.
# User-Facing Changes
- `polars to-*` commands have all been replaced with `polars save`. When
saving a lazy frame to a type that supports a polars sink operation, a
sink operation will be performed. Sink operations are much more
performant, performing a collect while streaming to the file system.
# 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
# Description
Prior this pull request `polars first` and `polars last` would collect a
lazy frame into an eager frame before performing operations. Now `polars
first` will to a `LazyFrame::limit` and `polars last` will perform a
`LazyFrame::tail`. This is really useful in working with very large
datasets.
# Description
When opening a dataframe the default operation will be to create a lazy
frame if possible. This works much better with large datasets and
supports hive format.
# User-Facing Changes
- `--lazy` is nolonger a valid option. `--eager` must be used to
explicitly open an eager dataframe.
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Per discussion on
[Discord](https://discord.com/channels/601130461678272522/864228801851949077/1265718178927870045)
# Description
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To facilitate column-oriented dataframe construction, this PR added a
`--as-columns` flag to `polars into-df` command so that when specified,
and when input shape is record of lists, each list will be treated as a
column rather than a cell value, i.e. `{a: [1 3], b: [2 4]} | polars
into-df --as-columns` returns the same dataframe as `[[a b];[1 2] [3 4]]
| polars into-df`
# User-Facing Changes
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helps us keep track of breaking changes. -->
A new flag `--as-columns`, no change of semantics if this flag is
unspecified.
# Tests + Formatting
<!--
Don't forget to add tests that cover your changes.
Make sure you've run and fixed any issues with these commands:
- `cargo fmt --all -- --check` to check standard code formatting (`cargo
fmt --all` applies these changes)
- `cargo clippy --workspace -- -D warnings -D clippy::unwrap_used` to
check that you're using the standard code style
- `cargo test --workspace` to check that all tests pass (on Windows make
sure to [enable developer
mode](https://learn.microsoft.com/en-us/windows/apps/get-started/developer-mode-features-and-debugging))
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automatically
> toolkit check pr
> ```
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---------
Co-authored-by: Ben Yang <ben@ya.ng>
# Description
Makes `polars unpivot` use the same arguments as `polars pivot` and
makes it consistent with the polars' rust api. Additionally, support for
the polar's streaming engine has been exposed on eager dataframes.
Previously, it would only work with lazy dataframes.
# User-Facing Changes
* `polars unpivot` argument `--columns`|`-c` has been renamed to
`--index`|`-i`
* `polars unpivot` argument `--values`|`-v` has been renamed to
`--on`|`-o`
* `polars unpivot` short argument for `--streamable` is now `-t` to make
it consistent with `polars pivot`. It was made `-t` for `polars pivot`
because `-s` is short for `--short`
There was a bug where anytime the plugin cache remove was called, the
plugin gc was turned back on. This probably happened when I added the
reference counter logic.
# 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.
Addresses performance issues that @maxim-uvarov found with CSV and JSON
lines.
This ensures that the schema inference follows the polars defaults of
100 lines. Recent changes caused the default values to be override and
caused the entire file to be scanned when inferring the schema.
This allows performance debugging to be turned on by setting:
```nushell
$env.POLARS_PLUGIN_PERF = "true"
```
Furthermore, this improves the other plugin debugging by allowing the
env variable for debugging to be set at any time versus having to be
available when nushell is launched:
```nushell
$env.POLARS_PLUGIN_DEBUG = "true"
```
This plugin introduces a `perf` function that will output timing
results. This works very similar to the perf function available in
nu_utils::utils::perf. This version prints everything to std error to
not break the plugin stream and uses the engine interface to see if the
env variable is configured.
This pull requests uses this `perf` function when:
* opening csv files as dataframes
* opening json lines files as dataframes
This will hopefully help provide some more fine grained information on
how long it takes polars to open different dataframes. The `perf` can
also be utilized later for other dataframes use cases.
Per discussion on discord dataframes channel with @maxim-uvarov and pyz.
When converting a dataframe to an nushell value via `polars into-nu`,
the index column should not be added by default and should only be added
when specifying `--index`
As reported by @maxim-uvarov and pyz in the dataframes discord channel:
```nushell
[[a b]; [1 1] [1 2] [2 1] [2 2] [3 1] [3 2]] | polars into-df | polars with-column ((polars col a) / (polars col b)) --name c
× Type mismatch.
╭─[entry #45:1:102]
1 │ [[a b]; [1 1] [1 2] [2 1] [2 2] [3 1] [3 2]] | polars into-df | polars with-column ((polars col a) / (polars col b)) --name c
· ───────┬──────
· ╰── Right hand side not a dataframe expression
╰────
```
This pull request corrects the type casting on the right hand side and
allows more than just polars literal expressions.
