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Upgrading to polars 0.44 (#14478)

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Upgrading to polars 0.44
This commit is contained in:
Jack Wright 2024-11-29 17:39:07 -08:00 committed by GitHub
parent e1f74a6d57
commit 0172ad8461
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GPG Key ID: B5690EEEBB952194
12 changed files with 836 additions and 232 deletions
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883
Cargo.lock generated
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File diff suppressed because it is too large Load Diff

12
crates/nu_plugin_polars/Cargo.toml
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@ -31,11 +31,11 @@ mimalloc = { version = "0.1.42" }
num = {version = "0.4"}
serde = { version = "1.0", features = ["derive"] }
sqlparser = { version = "0.49"}
polars-io = { version = "0.43", features = ["avro"]}
polars-arrow = { version = "0.43"}
polars-ops = { version = "0.43", features = ["pivot"]}
polars-plan = { version = "0.43", features = ["regex"]}
polars-utils = { version = "0.43"}
polars-io = { version = "0.44", features = ["avro"]}
polars-arrow = { version = "0.44"}
polars-ops = { version = "0.44", features = ["pivot"]}
polars-plan = { version = "0.44", features = ["regex"]}
polars-utils = { version = "0.44"}
typetag = "0.2"
env_logger = "0.11.3"
log.workspace = true
@ -81,7 +81,7 @@ features = [
"to_dummies",
]
optional = false
version = "0.43"
version = "0.44"
[dev-dependencies]
nu-cmd-lang = { path = "../nu-cmd-lang", version = "0.100.1" }

6
crates/nu_plugin_polars/src/dataframe/command/aggregation/quantile.rs
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@ -10,7 +10,7 @@ use nu_protocol::{
Category, Example, LabeledError, PipelineData, ShellError, Signature, Span, SyntaxShape, Type,
Value,
};
use polars::prelude::{lit, QuantileInterpolOptions};
use polars::prelude::{lit, QuantileMethod};
#[derive(Clone)]
pub struct LazyQuantile;
@ -109,7 +109,7 @@ impl PluginCommand for LazyQuantile {
PolarsPluginObject::NuExpression(expr) => {
let expr: NuExpression = expr
.into_polars()
.quantile(lit(quantile), QuantileInterpolOptions::default())
.quantile(lit(quantile), QuantileMethod::default())
.into();
expr.to_pipeline_data(plugin, engine, call.head)
}
@ -136,7 +136,7 @@ fn command(
let lazy = NuLazyFrame::new(
lazy.from_eager,
lazy.to_polars()
.quantile(lit(quantile), QuantileInterpolOptions::default()),
.quantile(lit(quantile), QuantileMethod::default()),
);
lazy.to_pipeline_data(plugin, engine, call.head)

19
crates/nu_plugin_polars/src/dataframe/command/core/open.rs
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@ -28,9 +28,7 @@ use polars::{
},
};
use polars_io::{
avro::AvroReader, csv::read::CsvReadOptions, prelude::ParallelStrategy, HiveOptions,
};
use polars_io::{avro::AvroReader, csv::read::CsvReadOptions, HiveOptions};
const DEFAULT_INFER_SCHEMA: usize = 100;
@ -179,20 +177,7 @@ fn from_parquet(
) -> Result<Value, ShellError> {
if !call.has_flag("eager")? {
let file: String = call.req(0)?;
let args = ScanArgsParquet {
n_rows: None,
cache: true,
parallel: ParallelStrategy::Auto,
rechunk: false,
row_index: None,
low_memory: false,
cloud_options: None,
use_statistics: false,
hive_options: HiveOptions::default(),
glob: true,
include_file_paths: None,
};
let args = ScanArgsParquet::default();
let df: NuLazyFrame = LazyFrame::scan_parquet(file, args)
.map_err(|e| ShellError::GenericError {
error: "Parquet reader error".into(),

12
crates/nu_plugin_polars/src/dataframe/command/core/summary.rs
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@ -10,8 +10,8 @@ use nu_protocol::{
use polars::{
chunked_array::ChunkedArray,
prelude::{
AnyValue, DataFrame, DataType, Float64Type, IntoSeries, NewChunkedArray,
QuantileInterpolOptions, Series, StringType,
AnyValue, Column as PolarsColumn, DataFrame, DataType, Float64Type, IntoSeries,
NewChunkedArray, QuantileMethod, StringType,
},
};
@ -184,7 +184,6 @@ fn command(
let tail = df
.as_ref()
.get_columns()
.iter()
.filter(|col| !matches!(col.dtype(), &DataType::Object("object", _)))
.map(|col| {
@ -200,7 +199,7 @@ fn command(
.clone()
.into_iter()
.map(|q| {
col.quantile_reduce(q, QuantileInterpolOptions::default())
col.quantile_reduce(q, QuantileMethod::default())
.ok()
.map(|s| s.into_series("quantile".into()))
.and_then(|ca| ca.cast(&DataType::Float64).ok())
@ -221,7 +220,10 @@ fn command(
ChunkedArray::<Float64Type>::from_slice_options(name.into(), &descriptors).into_series()
});
let res = head.chain(tail).collect::<Vec<Series>>();
let res = head
.chain(tail)
.map(PolarsColumn::from)
.collect::<Vec<PolarsColumn>>();
let polars_df = DataFrame::new(res).map_err(|e| ShellError::GenericError {
error: "Dataframe Error".into(),

2
crates/nu_plugin_polars/src/dataframe/command/data/rename.rs
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@ -185,7 +185,7 @@ fn command_lazy(
}
let lazy = lazy.to_polars();
let lazy: NuLazyFrame = lazy.rename(&columns, &new_names).into();
let lazy: NuLazyFrame = lazy.rename(&columns, &new_names, true).into();
lazy.to_pipeline_data(plugin, engine, call.head)
}

5
crates/nu_plugin_polars/src/dataframe/command/string/str_slice.rs
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@ -160,13 +160,14 @@ fn command_df(
df: NuDataFrame,
) -> Result<PipelineData, ShellError> {
let start: i64 = call.req(0)?;
let start = Series::new("".into(), &[start]);
let start = Series::new("".into(), &[start]).into();
let length: Option<i64> = call.get_flag("length")?;
let length = match length {
Some(v) => Series::new("".into(), &[v as u64]),
None => Series::new_null("".into(), 1),
};
}
.into();
let series = df.as_series(call.head)?;

4
crates/nu_plugin_polars/src/dataframe/values/nu_dataframe/between_values.rs
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@ -5,8 +5,8 @@ use nu_protocol::{
};
use num::Zero;
use polars::prelude::{
BooleanType, ChunkCompare, ChunkedArray, DataType, Float64Type, Int64Type, IntoSeries,
NumOpsDispatchChecked, PolarsError, Series, StringNameSpaceImpl,
BooleanType, ChunkCompareEq, ChunkCompareIneq, ChunkedArray, DataType, Float64Type, Int64Type,
IntoSeries, NumOpsDispatchChecked, PolarsError, Series, StringNameSpaceImpl,
};
use std::ops::{Add, BitAnd, BitOr, Div, Mul, Sub};

87
crates/nu_plugin_polars/src/dataframe/values/nu_dataframe/conversion.rs
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@ -10,11 +10,11 @@ use polars::chunked_array::ChunkedArray;
use polars::datatypes::{AnyValue, PlSmallStr};
use polars::export::arrow::Either;
use polars::prelude::{
ChunkAnyValue, DataFrame, DataType, DatetimeChunked, Float32Type, Float64Type, Int16Type,
Int32Type, Int64Type, Int8Type, IntoSeries, ListBooleanChunkedBuilder, ListBuilderTrait,
ListPrimitiveChunkedBuilder, ListStringChunkedBuilder, ListType, NamedFrom, NewChunkedArray,
ObjectType, PolarsError, Schema, SchemaExt, Series, StructChunked, TemporalMethods, TimeUnit,
UInt16Type, UInt32Type, UInt64Type, UInt8Type,
ChunkAnyValue, Column as PolarsColumn, DataFrame, DataType, DatetimeChunked, Float32Type,
Float64Type, Int16Type, Int32Type, Int64Type, Int8Type, IntoSeries, ListBooleanChunkedBuilder,
ListBuilderTrait, ListPrimitiveChunkedBuilder, ListStringChunkedBuilder, ListType, NamedFrom,
NewChunkedArray, ObjectType, PolarsError, Schema, SchemaExt, Series, StructChunked,
TemporalMethods, TimeUnit, UInt16Type, UInt32Type, UInt64Type, UInt8Type,
};
use nu_protocol::{Record, ShellError, Span, Value};
@ -146,6 +146,16 @@ impl DerefMut for TypedColumn {
pub type ColumnMap = IndexMap<PlSmallStr, TypedColumn>;
pub fn create_column(
column: &PolarsColumn,
from_row: usize,
to_row: usize,
span: Span,
) -> Result<Column, ShellError> {
let series = column.as_materialized_series();
create_column_from_series(series, from_row, to_row, span)
}
pub fn create_column_from_series(
series: &Series,
from_row: usize,
to_row: usize,
@ -497,15 +507,19 @@ fn typed_column_to_series(name: PlSmallStr, column: TypedColumn) -> Result<Serie
insert_record(&mut column_values, record.clone(), &schema)?;
let df = from_parsed_columns(column_values)?;
for name in df.df.get_column_names() {
let series = df.df.column(name).map_err(|e| ShellError::GenericError {
error: format!(
"Error creating struct, could not get column name {name}: {e}"
),
msg: "".into(),
span: None,
help: None,
inner: vec![],
})?;
let series = df
.df
.column(name)
.map_err(|e| ShellError::GenericError {
error: format!(
"Error creating struct, could not get column name {name}: {e}"
),
msg: "".into(),
span: None,
help: None,
inner: vec![],
})?
.as_materialized_series();
if let Some(v) = structs.get_mut(name) {
let _ = v.append(series)
@ -524,15 +538,18 @@ fn typed_column_to_series(name: PlSmallStr, column: TypedColumn) -> Result<Serie
let structs: Vec<Series> = structs.into_values().collect();
let chunked =
StructChunked::from_series(column.name().to_owned(), structs.as_slice())
.map_err(|e| ShellError::GenericError {
error: format!("Error creating struct: {e}"),
msg: "".into(),
span: None,
help: None,
inner: vec![],
})?;
let chunked = StructChunked::from_series(
column.name().to_owned(),
structs.len(),
structs.iter(),
)
.map_err(|e| ShellError::GenericError {
error: format!("Error creating struct: {e}"),
msg: "".into(),
span: None,
help: None,
inner: vec![],
})?;
Ok(chunked.into_series())
}
_ => Err(ShellError::GenericError {
@ -558,13 +575,13 @@ fn typed_column_to_series(name: PlSmallStr, column: TypedColumn) -> Result<Serie
// This data can be used to create a Series object that can initialize
// the dataframe based on the type of data that is found
pub fn from_parsed_columns(column_values: ColumnMap) -> Result<NuDataFrame, ShellError> {
let mut df_series: Vec<Series> = Vec::new();
let mut df_columns: Vec<PolarsColumn> = Vec::new();
for (name, column) in column_values {
let series = typed_column_to_series(name, column)?;
df_series.push(series);
df_columns.push(series.into());
}
DataFrame::new(df_series)
DataFrame::new(df_columns)
.map(|df| NuDataFrame::new(false, df))
.map_err(|e| ShellError::GenericError {
error: "Error creating dataframe".into(),
@ -1245,7 +1262,8 @@ fn any_value_to_value(any_value: &AnyValue, span: Span) -> Result<Value, ShellEr
}
AnyValue::Datetime(a, time_unit, tz) => {
let nanos = nanos_from_timeunit(*a, *time_unit);
datetime_from_epoch_nanos(nanos, tz, span).map(|datetime| Value::date(datetime, span))
datetime_from_epoch_nanos(nanos, &tz.cloned(), span)
.map(|datetime| Value::date(datetime, span))
}
AnyValue::Duration(a, time_unit) => {
let nanos = match time_unit {
@ -1264,17 +1282,7 @@ fn any_value_to_value(any_value: &AnyValue, span: Span) -> Result<Value, ShellEr
}
AnyValue::Struct(_idx, _struct_array, _s_fields) => {
// This should convert to a StructOwned object.
let static_value =
any_value
.clone()
.into_static()
.map_err(|e| ShellError::GenericError {
error: "Cannot convert polars struct to static value".into(),
msg: e.to_string(),
span: Some(span),
help: None,
inner: Vec::new(),
})?;
let static_value = any_value.clone().into_static();
any_value_to_value(&static_value, span)
}
AnyValue::StructOwned(struct_tuple) => {
@ -1485,7 +1493,7 @@ mod tests {
let test_millis = 946_684_800_000;
assert_eq!(
any_value_to_value(
&AnyValue::Datetime(test_millis, TimeUnit::Milliseconds, &None),
&AnyValue::Datetime(test_millis, TimeUnit::Milliseconds, None),
span
)?,
Value::date(comparison_date, span)
@ -1575,6 +1583,7 @@ mod tests {
let test_bool_arr = BooleanArray::from([Some(true)]);
let test_struct_arr = StructArray::new(
DataType::Struct(fields.clone()).to_arrow(CompatLevel::newest()),
1,
vec![Box::new(test_int_arr), Box::new(test_bool_arr)],
None,
);

28
crates/nu_plugin_polars/src/dataframe/values/nu_dataframe/mod.rs
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@ -8,7 +8,9 @@ pub use operations::Axis;
use indexmap::map::IndexMap;
use nu_protocol::{did_you_mean, PipelineData, Record, ShellError, Span, Value};
use polars::prelude::{DataFrame, DataType, IntoLazy, PolarsObject, Series};
use polars::prelude::{
Column as PolarsColumn, DataFrame, DataType, IntoLazy, PolarsObject, Series,
};
use polars_plan::prelude::{lit, Expr, Null};
use polars_utils::total_ord::{TotalEq, TotalHash};
use std::{
@ -135,7 +137,7 @@ impl NuDataFrame {
}
pub fn try_from_series(series: Series, span: Span) -> Result<Self, ShellError> {
match DataFrame::new(vec![series]) {
match DataFrame::new(vec![series.into()]) {
Ok(dataframe) => Ok(NuDataFrame::new(false, dataframe)),
Err(e) => Err(ShellError::GenericError {
error: "Error creating dataframe".into(),
@ -191,13 +193,16 @@ impl NuDataFrame {
}
pub fn try_from_series_vec(columns: Vec<Series>, span: Span) -> Result<Self, ShellError> {
let dataframe = DataFrame::new(columns).map_err(|e| ShellError::GenericError {
error: "Error creating dataframe".into(),
msg: format!("Unable to create DataFrame: {e}"),
span: Some(span),
help: None,
inner: vec![],
})?;
let columns_converted: Vec<PolarsColumn> = columns.into_iter().map(Into::into).collect();
let dataframe =
DataFrame::new(columns_converted).map_err(|e| ShellError::GenericError {
error: "Error creating dataframe".into(),
msg: format!("Unable to create DataFrame: {e}"),
span: Some(span),
help: None,
inner: vec![],
})?;
Ok(Self::new(false, dataframe))
}
@ -295,14 +300,15 @@ impl NuDataFrame {
.df
.get_columns()
.first()
.expect("We have already checked that the width is 1");
.expect("We have already checked that the width is 1")
.as_materialized_series();
Ok(series.clone())
}
pub fn get_value(&self, row: usize, span: Span) -> Result<Value, ShellError> {
let series = self.as_series(span)?;
let column = conversion::create_column(&series, row, row + 1, span)?;
let column = conversion::create_column_from_series(&series, row, row + 1, span)?;
if column.len() == 0 {
Err(ShellError::AccessEmptyContent { span })

6
crates/nu_plugin_polars/src/dataframe/values/nu_dataframe/operations.rs
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@ -1,5 +1,5 @@
use nu_protocol::{ast::Operator, ShellError, Span, Spanned, Value};
use polars::prelude::{DataFrame, Series};
use polars::prelude::{Column as PolarsColumn, DataFrame};
use crate::values::CustomValueSupport;
use crate::PolarsPlugin;
@ -137,7 +137,7 @@ impl NuDataFrame {
series.rename(name.into());
series
})
.collect::<Vec<Series>>();
.collect::<Vec<PolarsColumn>>();
let df_new = DataFrame::new(new_cols).map_err(|e| ShellError::GenericError {
error: "Error creating dataframe".into(),
@ -195,7 +195,7 @@ impl NuDataFrame {
}),
}
})
.collect::<Result<Vec<Series>, ShellError>>()?;
.collect::<Result<Vec<PolarsColumn>, ShellError>>()?;
let df_new = DataFrame::new(new_cols).map_err(|e| ShellError::GenericError {
error: "Error appending dataframe".into(),

4
crates/nu_plugin_polars/src/dataframe/values/nu_expression/mod.rs
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@ -215,12 +215,12 @@ pub fn expr_to_value(expr: &Expr, span: Span) -> Result<Value, ShellError> {
AggExpr::Quantile {
expr,
quantile,
interpol,
method,
} => Ok(Value::record(
record! {
"expr" => expr_to_value(expr.as_ref(), span)?,
"quantile" => expr_to_value(quantile.as_ref(), span)?,
"interpol" => Value::string(format!("{interpol:?}"), span),
"method" => Value::string(format!("{method:?}"), span),
},
span,
)),