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Implemented a command to expose polar's pivot functionality (#13282)
# Description Implementing pivot support The example below is a port of the [python API example](https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.pivot.html) <img width="1079" alt="Screenshot 2024-07-01 at 14 29 27" src="https://github.com/nushell/nushell/assets/56345/277eb7a2-233b-4070-9d24-c2183805c1b8"> # User-Facing Changes * Introduction of the `polars pivot` command
This commit is contained in:
Jack Wright
GitHub
parent
4cdceca1f7
commit
ff27d6a18e
3 changed files with 268 additions and 1 deletions
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@ -33,7 +33,7 @@ serde = { version = "1.0", features = ["derive"] }
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sqlparser = { version = "0.47"}
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polars-io = { version = "0.41", features = ["avro"]}
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polars-arrow = { version = "0.41"}
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polars-ops = { version = "0.41"}
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polars-ops = { version = "0.41", features = ["pivot"]}
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polars-plan = { version = "0.41", features = ["regex"]}
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polars-utils = { version = "0.41"}
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typetag = "0.2"
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@ -10,6 +10,7 @@ mod first;
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mod get;
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mod last;
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mod open;
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mod pivot;
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mod query_df;
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mod rename;
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mod sample;
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@ -76,6 +77,7 @@ pub(crate) fn eager_commands() -> Vec<Box<dyn PluginCommand<Plugin = PolarsPlugi
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Box::new(FilterWith),
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Box::new(GetDF),
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Box::new(OpenDataFrame),
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Box::new(pivot::PivotDF),
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Box::new(UnpivotDF),
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Box::new(Summary),
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Box::new(FirstDF),
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265
crates/nu_plugin_polars/src/dataframe/eager/pivot.rs
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265
crates/nu_plugin_polars/src/dataframe/eager/pivot.rs
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@ -0,0 +1,265 @@
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use nu_plugin::{EngineInterface, EvaluatedCall, PluginCommand};
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use nu_protocol::{
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Category, Example, LabeledError, PipelineData, ShellError, Signature, Span, SyntaxShape, Type,
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Value,
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};
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use polars_ops::pivot::{pivot, PivotAgg};
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use crate::{
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dataframe::values::utils::convert_columns_string,
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values::{Column, CustomValueSupport, PolarsPluginObject},
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PolarsPlugin,
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};
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use super::super::values::NuDataFrame;
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#[derive(Clone)]
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pub struct PivotDF;
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impl PluginCommand for PivotDF {
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type Plugin = PolarsPlugin;
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fn name(&self) -> &str {
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"polars pivot"
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}
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fn usage(&self) -> &str {
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"Pivot a DataFrame from wide to long format."
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}
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fn signature(&self) -> Signature {
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Signature::build(self.name())
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.required_named(
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"on",
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SyntaxShape::List(Box::new(SyntaxShape::String)),
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"column names for pivoting",
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Some('o'),
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)
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.required_named(
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"index",
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SyntaxShape::List(Box::new(SyntaxShape::String)),
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"column names for indexes",
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Some('i'),
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)
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.required_named(
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"values",
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SyntaxShape::List(Box::new(SyntaxShape::String)),
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"column names used as value columns",
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Some('v'),
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)
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.named(
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"aggregate",
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SyntaxShape::String,
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"Aggregation to apply when pivoting. The following are supported: first, sum, min, max, mean, median, count, last",
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Some('a'),
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)
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.switch(
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"sort",
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"Sort columns",
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Some('s'),
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)
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.switch(
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"streamable",
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"Whether or not to use the polars streaming engine. Only valid for lazy dataframes",
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Some('t'),
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)
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.input_output_type(
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Type::Custom("dataframe".into()),
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Type::Custom("dataframe".into()),
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)
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.category(Category::Custom("dataframe".into()))
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}
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fn examples(&self) -> Vec<Example> {
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vec![
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Example {
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example: "[[name subject test_1 test_2]; [Cady maths 98 100] [Cady physics 99 100] [Karen maths 61 60] [Karen physics 58 60]] | polars into-df | polars pivot --on [subject] --index [name] --values [test_1]",
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description: "Perform a pivot in order to show individuals test score by subject",
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result: Some(
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NuDataFrame::try_from_columns(
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vec![
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Column::new(
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"name".to_string(),
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vec![Value::string("Cady", Span::test_data()), Value::string("Karen", Span::test_data())],
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),
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Column::new(
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"maths".to_string(),
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vec![Value::int(98, Span::test_data()), Value::int(61, Span::test_data())],
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),
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Column::new(
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"physics".to_string(),
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vec![Value::int(99, Span::test_data()), Value::int(58, Span::test_data())],
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),
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],
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None,
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)
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.expect("simple df for test should not fail")
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.into_value(Span::unknown())
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)
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}
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]
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}
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fn run(
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&self,
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plugin: &Self::Plugin,
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engine: &EngineInterface,
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call: &EvaluatedCall,
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input: PipelineData,
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) -> Result<PipelineData, LabeledError> {
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match PolarsPluginObject::try_from_pipeline(plugin, input, call.head)? {
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PolarsPluginObject::NuDataFrame(df) => command_eager(plugin, engine, call, df),
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PolarsPluginObject::NuLazyFrame(lazy) => {
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command_eager(plugin, engine, call, lazy.collect(call.head)?)
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}
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_ => Err(ShellError::GenericError {
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error: "Must be a dataframe or lazy dataframe".into(),
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msg: "".into(),
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span: Some(call.head),
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help: None,
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inner: vec![],
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}),
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}
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.map_err(LabeledError::from)
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}
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}
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fn command_eager(
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plugin: &PolarsPlugin,
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engine: &EngineInterface,
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call: &EvaluatedCall,
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df: NuDataFrame,
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) -> Result<PipelineData, ShellError> {
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let on_col: Vec<Value> = call.get_flag("on")?.expect("required value");
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let index_col: Vec<Value> = call.get_flag("index")?.expect("required value");
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let val_col: Vec<Value> = call.get_flag("values")?.expect("required value");
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let (on_col_string, id_col_span) = convert_columns_string(on_col, call.head)?;
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let (index_col_string, index_col_span) = convert_columns_string(index_col, call.head)?;
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let (val_col_string, val_col_span) = convert_columns_string(val_col, call.head)?;
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check_column_datatypes(df.as_ref(), &on_col_string, id_col_span)?;
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check_column_datatypes(df.as_ref(), &index_col_string, index_col_span)?;
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check_column_datatypes(df.as_ref(), &val_col_string, val_col_span)?;
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let aggregate: Option<PivotAgg> = call
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.get_flag::<String>("aggregate")?
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.map(pivot_agg_for_str)
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.transpose()?;
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let sort = call.has_flag("sort")?;
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let polars_df = df.to_polars();
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// todo add other args
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let pivoted = pivot(
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&polars_df,
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&on_col_string,
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Some(&index_col_string),
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Some(&val_col_string),
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sort,
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aggregate,
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None,
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)
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.map_err(|e| ShellError::GenericError {
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error: format!("Pivot error: {e}"),
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msg: "".into(),
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span: Some(call.head),
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help: None,
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inner: vec![],
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})?;
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let res = NuDataFrame::new(false, pivoted);
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res.to_pipeline_data(plugin, engine, call.head)
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}
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fn check_column_datatypes<T: AsRef<str>>(
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df: &polars::prelude::DataFrame,
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cols: &[T],
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col_span: Span,
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) -> Result<(), ShellError> {
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if cols.is_empty() {
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return Err(ShellError::GenericError {
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error: "Merge error".into(),
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msg: "empty column list".into(),
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span: Some(col_span),
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help: None,
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inner: vec![],
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});
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}
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// Checking if they are same type
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if cols.len() > 1 {
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for w in cols.windows(2) {
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let l_series = df
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.column(w[0].as_ref())
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.map_err(|e| ShellError::GenericError {
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error: "Error selecting columns".into(),
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msg: e.to_string(),
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span: Some(col_span),
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help: None,
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inner: vec![],
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})?;
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let r_series = df
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.column(w[1].as_ref())
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.map_err(|e| ShellError::GenericError {
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error: "Error selecting columns".into(),
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msg: e.to_string(),
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span: Some(col_span),
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help: None,
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inner: vec![],
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})?;
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if l_series.dtype() != r_series.dtype() {
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return Err(ShellError::GenericError {
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error: "Merge error".into(),
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msg: "found different column types in list".into(),
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span: Some(col_span),
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help: Some(format!(
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"datatypes {} and {} are incompatible",
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l_series.dtype(),
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r_series.dtype()
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)),
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inner: vec![],
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});
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}
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}
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}
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Ok(())
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}
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fn pivot_agg_for_str(agg: impl AsRef<str>) -> Result<PivotAgg, ShellError> {
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match agg.as_ref() {
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"first" => Ok(PivotAgg::First),
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"sum" => Ok(PivotAgg::Sum),
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"min" => Ok(PivotAgg::Min),
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"max" => Ok(PivotAgg::Max),
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"mean" => Ok(PivotAgg::Mean),
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"median" => Ok(PivotAgg::Median),
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"count" => Ok(PivotAgg::Count),
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"last" => Ok(PivotAgg::Last),
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s => Err(ShellError::GenericError {
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error: format!("{s} is not a valid aggregation"),
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msg: "".into(),
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span: None,
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help: Some(
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"Use one of the following: first, sum, min, max, mean, median, count, last".into(),
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),
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inner: vec![],
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}),
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}
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}
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#[cfg(test)]
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mod test {
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use crate::test::test_polars_plugin_command;
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use super::*;
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#[test]
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fn test_examples() -> Result<(), ShellError> {
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test_polars_plugin_command(&PivotDF)
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}
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}
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