# Description There are times where explicitly specifying a schema for a dataframe is needed such as: - Opening CSV and JSON lines files and needing provide more information to polars to keep it from failing or in a desire to override default type conversion - When converting a nushell value to a dataframe and wanting to override the default conversion behaviors. This pull requests provides: - A flag to allow specifying a schema when using dfr into-df - A flag to allow specifying a schema when using dfr open that works for CSV and JSON types - A new command `dfr schema` which displays schema information and will allow display support schema dtypes Schema is specified creating a record that has the key value and the dtype. Examples usages: ``` {a:1, b:{a:2}} | dfr into-df -s {a: u8, b: {a: i32}} | dfr schema {a: 1, b: {a: [1 2 3]}, c: [a b c]} | dfr into-df -s {a: u8, b: {a: list<u64>}, c: list<str>} | dfr schema dfr open -s {pid: i32, ppid: i32, name: str, status: str, cpu: f64, mem: i64, virtual: i64} /tmp/ps.jsonl | dfr schema ``` Supported dtypes: null bool u8 u16 u32 u64 i8 i16 i32 i64 f32 f64 str binary date datetime[time_unit: (ms, us, ns) timezone (optional)] duration[time_unit: (ms, us, ns)] time object unknown list[dtype] structs are also supported but are specified via another record: {a: u8, b: {d: str}} Another feature with the dfr schema command is that it returns the data back in a format that can be passed to provide a valid schema that can be passed in as schema argument: <img width="638" alt="Screenshot 2024-01-29 at 10 23 58" src="https://github.com/nushell/nushell/assets/56345/b49c3bff-5cda-4c86-975a-dfd91d991373"> --------- Co-authored-by: Jack Wright <jack.wright@disqo.com>
178 lines
6.6 KiB
Rust
178 lines
6.6 KiB
Rust
use super::super::values::NuExpression;
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use crate::dataframe::values::{Column, NuDataFrame};
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use chrono::{DateTime, FixedOffset};
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use nu_engine::CallExt;
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use nu_protocol::{
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ast::Call,
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engine::{Command, EngineState, Stack},
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Category, Example, PipelineData, ShellError, Signature, Span, Spanned, SyntaxShape, Type,
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Value,
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};
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use polars::{
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datatypes::{DataType, TimeUnit},
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prelude::NamedFrom,
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series::Series,
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};
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#[derive(Clone)]
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pub struct ExprDatePart;
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impl Command for ExprDatePart {
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fn name(&self) -> &str {
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"dfr datepart"
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}
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fn usage(&self) -> &str {
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"Creates an expression for capturing the specified datepart in a column."
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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(
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"Datepart name",
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SyntaxShape::String,
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"Part of the date to capture. Possible values are year, quarter, month, week, weekday, day, hour, minute, second, millisecond, microsecond, nanosecond",
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)
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.input_output_type(
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Type::Custom("expression".into()),
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Type::Custom("expression".into()),
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)
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.category(Category::Custom("expression".into()))
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}
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fn examples(&self) -> Vec<Example> {
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let dt = DateTime::<FixedOffset>::parse_from_str(
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"2021-12-30T01:02:03.123456789 +0000",
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"%Y-%m-%dT%H:%M:%S.%9f %z",
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)
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.expect("date calculation should not fail in test");
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vec![
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Example {
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description: "Creates an expression to capture the year date part",
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example: r#"[["2021-12-30T01:02:03.123456789"]] | dfr into-df | dfr as-datetime "%Y-%m-%dT%H:%M:%S.%9f" | dfr with-column [(dfr col datetime | dfr datepart year | dfr as datetime_year )]"#,
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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("datetime".to_string(), vec![Value::test_date(dt)]),
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Column::new("datetime_year".to_string(), vec![Value::test_int(2021)]),
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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::test_data()),
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),
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},
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Example {
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description: "Creates an expression to capture multiple date parts",
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example: r#"[["2021-12-30T01:02:03.123456789"]] | dfr into-df | dfr as-datetime "%Y-%m-%dT%H:%M:%S.%9f" |
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dfr with-column [ (dfr col datetime | dfr datepart year | dfr as datetime_year ),
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(dfr col datetime | dfr datepart month | dfr as datetime_month ),
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(dfr col datetime | dfr datepart day | dfr as datetime_day ),
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(dfr col datetime | dfr datepart hour | dfr as datetime_hour ),
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(dfr col datetime | dfr datepart minute | dfr as datetime_minute ),
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(dfr col datetime | dfr datepart second | dfr as datetime_second ),
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(dfr col datetime | dfr datepart nanosecond | dfr as datetime_ns ) ]"#,
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result: Some(
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NuDataFrame::try_from_series(
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vec![
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Series::new("datetime", &[dt.timestamp_nanos_opt()])
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.cast(&DataType::Datetime(TimeUnit::Nanoseconds, None))
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.expect("Error casting to datetime type"),
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Series::new("datetime_year", &[2021_i64]), // i32 was coerced to i64
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Series::new("datetime_month", &[12_i8]),
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Series::new("datetime_day", &[30_i8]),
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Series::new("datetime_hour", &[1_i8]),
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Series::new("datetime_minute", &[2_i8]),
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Series::new("datetime_second", &[3_i8]),
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Series::new("datetime_ns", &[123456789_i64]), // i32 was coerced to i64
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],
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Span::test_data(),
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)
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.expect("simple df for test should not fail")
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.into_value(Span::test_data()),
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),
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},
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]
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}
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fn search_terms(&self) -> Vec<&str> {
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vec![
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"year",
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"month",
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"week",
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"weekday",
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"quarter",
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"day",
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"hour",
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"minute",
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"second",
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"millisecond",
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"microsecond",
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"nanosecond",
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]
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}
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fn run(
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&self,
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engine_state: &EngineState,
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stack: &mut Stack,
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call: &Call,
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input: PipelineData,
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) -> Result<PipelineData, ShellError> {
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let part: Spanned<String> = call.req(engine_state, stack, 0)?;
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let expr = NuExpression::try_from_pipeline(input, call.head)?;
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let expr_dt = expr.into_polars().dt();
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let expr = match part.item.as_str() {
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"year" => expr_dt.year(),
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"quarter" => expr_dt.quarter(),
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"month" => expr_dt.month(),
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"week" => expr_dt.week(),
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"day" => expr_dt.day(),
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"hour" => expr_dt.hour(),
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"minute" => expr_dt.minute(),
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"second" => expr_dt.second(),
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"millisecond" => expr_dt.millisecond(),
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"microsecond" => expr_dt.microsecond(),
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"nanosecond" => expr_dt.nanosecond(),
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_ => {
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return Err(ShellError::UnsupportedInput {
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msg: format!("{} is not a valid datepart, expected one of year, month, day, hour, minute, second, millisecond, microsecond, nanosecond", part.item),
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input: "value originates from here".to_string(),
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msg_span: call.head,
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input_span: part.span,
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});
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}
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}.into();
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Ok(PipelineData::Value(
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NuExpression::into_value(expr, call.head),
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None,
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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 super::super::super::test_dataframe::test_dataframe;
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use super::*;
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use crate::dataframe::eager::ToNu;
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use crate::dataframe::eager::WithColumn;
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use crate::dataframe::expressions::ExprAlias;
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use crate::dataframe::expressions::ExprCol;
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use crate::dataframe::series::AsDateTime;
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#[test]
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fn test_examples() {
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test_dataframe(vec![
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Box::new(ExprDatePart {}),
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Box::new(ExprCol {}),
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Box::new(ToNu {}),
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Box::new(AsDateTime {}),
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Box::new(WithColumn {}),
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Box::new(ExprAlias {}),
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])
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}
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}
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