corrected missing shellerror type (#439)

Cargo.lock

@@ -120,11 +120,10 @@ dependencies = [
 
 [[package]]
 name = "arrow2"
-version = "0.7.0"
+version = "0.8.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d873e2775c3d87a4e8d77aa544cbd43f34a0779d5164c59e7c6a1dd0678eb395"
+checksum = "d3452b2ae9727464a31a726c07ffec0c0da3b87831610d9ac99fc691c78b3a44"
 dependencies = [
- "ahash",
  "arrow-format",
  "base64",
  "chrono",
@@ -628,9 +627,9 @@ dependencies = [
 
 [[package]]
 name = "dirs"
-version = "3.0.2"
+version = "4.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "30baa043103c9d0c2a57cf537cc2f35623889dc0d405e6c3cccfadbc81c71309"
+checksum = "ca3aa72a6f96ea37bbc5aa912f6788242832f75369bdfdadcb0e38423f100059"
 dependencies = [
  "dirs-sys",
 ]
@@ -1823,9 +1822,9 @@ dependencies = [
 
 [[package]]
 name = "parquet2"
-version = "0.6.0"
+version = "0.8.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "db82df54cdd88931d29b850190915b9069bb93fba8e1aefc0d59d8ca81603d6d"
+checksum = "41051fae4c0fab9040e291b360c6c8037d09d482aa83e94e37f3d080a32a58c3"
 dependencies = [
  "async-stream",
  "bitpacking",
@@ -1916,9 +1915,9 @@ checksum = "8b870d8c151b6f2fb93e84a13146138f05d02ed11c7e7c54f8826aaaf7c9f184"
 
 [[package]]
 name = "polars"
-version = "0.17.0"
+version = "0.18.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7c94a25d46e93b64eac7848c028a545dc08fa01e148e4942c5442b3843c3a598"
+checksum = "3e9211d1bb8d2d81541e4ab80ce9148a8e2a987d6412c2a48017fbbe24231ea1"
 dependencies = [
  "polars-core",
  "polars-io",
@@ -1927,9 +1926,9 @@ dependencies = [
 
 [[package]]
 name = "polars-arrow"
-version = "0.17.0"
+version = "0.18.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1cc4488d2f2d6b901bb6e5728e58966013a272cae48861070b676215a79b4a99"
+checksum = "fa5ee9c385bf6643893f98efa80ff5a07169b50f65962c7843c0a13e12f0b0cf"
 dependencies = [
  "arrow2",
  "num 0.4.0",
@@ -1938,9 +1937,9 @@ dependencies = [
 
 [[package]]
 name = "polars-core"
-version = "0.17.0"
+version = "0.18.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6771524063d742a08163d96875ca5df71dff7113f27da58db5ec5fa164165bf6"
+checksum = "3cb1de44e479ce2764a7a3ad057e16f434efa334feb993284e1a48bb8888c6d1"
 dependencies = [
  "ahash",
  "anyhow",
@@ -1963,15 +1962,15 @@ dependencies = [
 
 [[package]]
 name = "polars-io"
-version = "0.17.0"
+version = "0.18.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "11a5f5f51525043ee7befd49e586e6919345237826a5f17b53956f8242100957"
+checksum = "8bcb74f52ee9ff84863ae01de6ba25db092a9880302db4bf8f351f65b3ff0d12"
 dependencies = [
  "ahash",
  "anyhow",
  "arrow2",
  "csv-core",
- "dirs 3.0.2",
+ "dirs 4.0.0",
  "lazy_static",
  "lexical",
  "memchr",
@@ -1987,9 +1986,9 @@ dependencies = [
 
 [[package]]
 name = "polars-lazy"
-version = "0.17.0"
+version = "0.18.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "da3ea647e2fa59d1bbbf90929c5d10ef6a9018aac256d1c6d0e8248211804b61"
+checksum = "43f91022ba6463df71ad6eb80ac2307884578d9959e85e1fe9dac18988291d46"
 dependencies = [
  "ahash",
  "itertools",

crates/nu-command/Cargo.toml

@@ -54,7 +54,7 @@ crossterm = "0.22.1"
 num = {version="0.4.0", optional=true}
 
 [dependencies.polars]
-version = "0.17.0"
+version = "0.18.0"
 optional = true
 features = ["default", "parquet", "json", "serde", "object", "checked_arithmetic", "strings"]
 

crates/nu-command/src/dataframe/README.md

@@ -1,3 +1,3 @@
 # nu-dataframe
 
-The nu-dataframe crate holds the definitions of the dataframe structure
+The nu-dataframe crate holds the definitions of the dataframe structures and commands

crates/nu-command/src/dataframe/mod.rs

@@ -1,3 +1,3 @@
 mod nu_dataframe;
 
-pub use nu_dataframe::commands::{DataTypes, DescribeDF, OpenDataFrame, ToDataFrame};
+pub use nu_dataframe::{DataTypes, DescribeDF, OpenDataFrame, ToDataFrame};

crates/nu-command/src/dataframe/nu_dataframe/commands/mod.rs

@@ -1,12 +0,0 @@
-mod describe;
-mod dtypes;
-mod open;
-mod to_df;
-
-pub use describe::DescribeDF;
-pub use dtypes::DataTypes;
-pub use open::OpenDataFrame;
-pub use to_df::ToDataFrame;
-
-#[cfg(test)]
-mod test_dataframe;

crates/nu-command/src/dataframe/nu_dataframe/describe.rs

@@ -1,6 +1,4 @@
-use crate::dataframe::nu_dataframe::Column;
+use super::nu_dataframe::{Column, NuDataFrame};
-
-use super::super::NuDataFrame;
 
 use nu_protocol::{
     ast::Call,
@@ -19,7 +17,7 @@ pub struct DescribeDF;
 
 impl Command for DescribeDF {
     fn name(&self) -> &str {
-        "describe"
+        "describe-df"
     }
 
     fn usage(&self) -> &str {
@@ -27,13 +25,13 @@ impl Command for DescribeDF {
     }
 
     fn signature(&self) -> Signature {
-        Signature::build(self.name().to_string()).category(Category::Custom("dataframe".into()))
+        Signature::build(self.name()).category(Category::Custom("dataframe".into()))
     }
 
     fn examples(&self) -> Vec<Example> {
         vec![Example {
             description: "dataframe description",
-            example: "[[a b]; [1 1] [1 1]] | to df | describe",
+            example: "[[a b]; [1 1] [1 1]] | to df | describe-df",
             result: Some(
                 NuDataFrame::try_from_columns(vec![
                     Column::new(
@@ -134,13 +132,14 @@ fn command(
         .map(|col| {
             let count = col.len() as f64;
 
-            let sum = match col.sum_as_series().cast(&DataType::Float64) {
+            let sum = col
-                Ok(ca) => match ca.get(0) {
+                .sum_as_series()
+                .cast(&DataType::Float64)
+                .ok()
+                .and_then(|ca| match ca.get(0) {
                     AnyValue::Float64(v) => Some(v),
                     _ => None,
-                },
+                });
-                Err(_) => None,
-            };
 
             let mean = match col.mean_as_series().get(0) {
                 AnyValue::Float64(v) => Some(v),
@@ -157,54 +156,50 @@ fn command(
                 _ => None,
             };
 
-            let min = match col.min_as_series().cast(&DataType::Float64) {
+            let min = col
-                Ok(ca) => match ca.get(0) {
+                .min_as_series()
+                .cast(&DataType::Float64)
+                .ok()
+                .and_then(|ca| match ca.get(0) {
                     AnyValue::Float64(v) => Some(v),
                     _ => None,
-                },
+                });
-                Err(_) => None,
-            };
 
-            let q_25 = match col.quantile_as_series(0.25) {
+            let q_25 = col
-                Ok(ca) => match ca.cast(&DataType::Float64) {
+                .quantile_as_series(0.25)
-                    Ok(ca) => match ca.get(0) {
+                .ok()
-                        AnyValue::Float64(v) => Some(v),
+                .and_then(|ca| ca.cast(&DataType::Float64).ok())
-                        _ => None,
+                .and_then(|ca| match ca.get(0) {
-                    },
-                    Err(_) => None,
-                },
-                Err(_) => None,
-            };
-
-            let q_50 = match col.quantile_as_series(0.50) {
-                Ok(ca) => match ca.cast(&DataType::Float64) {
-                    Ok(ca) => match ca.get(0) {
-                        AnyValue::Float64(v) => Some(v),
-                        _ => None,
-                    },
-                    Err(_) => None,
-                },
-                Err(_) => None,
-            };
-
-            let q_75 = match col.quantile_as_series(0.75) {
-                Ok(ca) => match ca.cast(&DataType::Float64) {
-                    Ok(ca) => match ca.get(0) {
-                        AnyValue::Float64(v) => Some(v),
-                        _ => None,
-                    },
-                    Err(_) => None,
-                },
-                Err(_) => None,
-            };
-
-            let max = match col.max_as_series().cast(&DataType::Float64) {
-                Ok(ca) => match ca.get(0) {
                     AnyValue::Float64(v) => Some(v),
                     _ => None,
-                },
+                });
-                Err(_) => None,
+
-            };
+            let q_50 = col
+                .quantile_as_series(0.50)
+                .ok()
+                .and_then(|ca| ca.cast(&DataType::Float64).ok())
+                .and_then(|ca| match ca.get(0) {
+                    AnyValue::Float64(v) => Some(v),
+                    _ => None,
+                });
+
+            let q_75 = col
+                .quantile_as_series(0.75)
+                .ok()
+                .and_then(|ca| ca.cast(&DataType::Float64).ok())
+                .and_then(|ca| match ca.get(0) {
+                    AnyValue::Float64(v) => Some(v),
+                    _ => None,
+                });
+
+            let max = col
+                .max_as_series()
+                .cast(&DataType::Float64)
+                .ok()
+                .and_then(|ca| match ca.get(0) {
+                    AnyValue::Float64(v) => Some(v),
+                    _ => None,
+                });
 
             let name = format!("{} ({})", col.name(), col.dtype());
             ChunkedArray::<Float64Type>::new_from_opt_slice(
@@ -226,12 +221,12 @@ fn command(
         });
 
     let res = head.chain(tail).collect::<Vec<Series>>();
-    let df = DataFrame::new(res).map_err(|e| {
+
-        ShellError::LabeledError("Dataframe Error".into(), e.to_string(), call.head)
+    DataFrame::new(res)
-    })?;
+        .map_err(|e| {
-    Ok(PipelineData::Value(NuDataFrame::dataframe_into_value(
+            ShellError::SpannedLabeledError("Dataframe Error".into(), e.to_string(), call.head)
-        df, call.head,
+        })
-    )))
+        .map(|df| PipelineData::Value(NuDataFrame::dataframe_into_value(df, call.head), None))
 }
 
 #[cfg(test)]

crates/nu-command/src/dataframe/nu_dataframe/dtypes.rs

@@ -1,4 +1,4 @@
-use super::super::{Column, NuDataFrame};
+use super::nu_dataframe::{Column, NuDataFrame};
 use nu_protocol::{
     ast::Call,
     engine::{Command, EngineState, Stack},
@@ -18,12 +18,12 @@ impl Command for DataTypes {
     }
 
     fn signature(&self) -> Signature {
-        Signature::build(self.name().to_string()).category(Category::Custom("dataframe".into()))
+        Signature::build(self.name()).category(Category::Custom("dataframe".into()))
     }
 
     fn examples(&self) -> Vec<Example> {
         vec![Example {
-            description: "drop column a",
+            description: "Dataframe dtypes",
             example: "[[a b]; [1 2] [3 4]] | to df | dtypes",
             result: Some(
                 NuDataFrame::try_from_columns(vec![
@@ -90,8 +90,8 @@ fn command(
     let names_col = Column::new("column".to_string(), names);
     let dtypes_col = Column::new("dtype".to_string(), dtypes);
 
-    let df = NuDataFrame::try_from_columns(vec![names_col, dtypes_col])?;
+    NuDataFrame::try_from_columns(vec![names_col, dtypes_col])
-    Ok(PipelineData::Value(df.into_value(call.head)))
+        .map(|df| PipelineData::Value(df.into_value(call.head), None))
 }
 
 #[cfg(test)]

crates/nu-command/src/dataframe/nu_dataframe/mod.rs

@@ -1,390 +1,14 @@
-pub mod commands;
+mod nu_dataframe;
 
-mod between_values;
+mod describe;
-mod conversion;
+mod dtypes;
-mod custom_value;
+mod open;
-mod operations;
+mod to_df;
 
-use conversion::{Column, ColumnMap};
+pub use describe::DescribeDF;
+pub use dtypes::DataTypes;
+pub use open::OpenDataFrame;
+pub use to_df::ToDataFrame;
 
-use indexmap::map::IndexMap;
+#[cfg(test)]
-use nu_protocol::{did_you_mean, PipelineData, ShellError, Span, Value};
+mod test_dataframe;
-use polars::prelude::{DataFrame, DataType, PolarsObject, Series};
-use serde::{Deserialize, Serialize};
-use std::{cmp::Ordering, fmt::Display, hash::Hasher};
-
-// DataFrameValue is an encapsulation of Nushell Value that can be used
-// to define the PolarsObject Trait. The polars object trait allows to
-// create dataframes with mixed datatypes
-#[derive(Clone, Debug)]
-pub struct DataFrameValue(Value);
-
-impl DataFrameValue {
-    fn new(value: Value) -> Self {
-        Self(value)
-    }
-
-    fn get_value(&self) -> Value {
-        self.0.clone()
-    }
-}
-
-impl Display for DataFrameValue {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "{}", self.0.get_type())
-    }
-}
-
-impl Default for DataFrameValue {
-    fn default() -> Self {
-        Self(Value::Nothing {
-            span: Span::unknown(),
-        })
-    }
-}
-
-impl PartialEq for DataFrameValue {
-    fn eq(&self, other: &Self) -> bool {
-        self.0.partial_cmp(&other.0).map_or(false, Ordering::is_eq)
-    }
-}
-impl Eq for DataFrameValue {}
-
-impl std::hash::Hash for DataFrameValue {
-    fn hash<H: Hasher>(&self, state: &mut H) {
-        match &self.0 {
-            Value::Nothing { .. } => 0.hash(state),
-            Value::Int { val, .. } => val.hash(state),
-            Value::String { val, .. } => val.hash(state),
-            // TODO. Define hash for the rest of types
-            _ => {}
-        }
-    }
-}
-
-impl PolarsObject for DataFrameValue {
-    fn type_name() -> &'static str {
-        "object"
-    }
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-pub struct NuDataFrame(DataFrame);
-
-impl AsRef<DataFrame> for NuDataFrame {
-    fn as_ref(&self) -> &polars::prelude::DataFrame {
-        &self.0
-    }
-}
-
-impl AsMut<DataFrame> for NuDataFrame {
-    fn as_mut(&mut self) -> &mut polars::prelude::DataFrame {
-        &mut self.0
-    }
-}
-
-impl NuDataFrame {
-    pub fn new(dataframe: DataFrame) -> Self {
-        Self(dataframe)
-    }
-
-    fn default_value(span: Span) -> Value {
-        let dataframe = DataFrame::default();
-        NuDataFrame::dataframe_into_value(dataframe, span)
-    }
-
-    pub fn dataframe_into_value(dataframe: DataFrame, span: Span) -> Value {
-        Value::CustomValue {
-            val: Box::new(Self::new(dataframe)),
-            span,
-        }
-    }
-
-    pub fn into_value(self, span: Span) -> Value {
-        Value::CustomValue {
-            val: Box::new(self),
-            span,
-        }
-    }
-
-    pub fn series_to_value(series: Series, span: Span) -> Result<Value, ShellError> {
-        match DataFrame::new(vec![series]) {
-            Ok(dataframe) => Ok(NuDataFrame::dataframe_into_value(dataframe, span)),
-            Err(e) => Err(ShellError::InternalError(e.to_string())),
-        }
-    }
-
-    pub fn try_from_iter<T>(iter: T) -> Result<Self, ShellError>
-    where
-        T: Iterator<Item = Value>,
-    {
-        // Dictionary to store the columnar data extracted from
-        // the input. During the iteration we check if the values
-        // have different type
-        let mut column_values: ColumnMap = IndexMap::new();
-
-        for value in iter {
-            match value {
-                Value::List { vals, .. } => {
-                    let cols = (0..vals.len())
-                        .map(|i| format!("{}", i))
-                        .collect::<Vec<String>>();
-
-                    conversion::insert_record(&mut column_values, &cols, &vals)?
-                }
-                Value::Record { cols, vals, .. } => {
-                    conversion::insert_record(&mut column_values, &cols, &vals)?
-                }
-                _ => {
-                    let key = "0".to_string();
-                    conversion::insert_value(value, key, &mut column_values)?
-                }
-            }
-        }
-
-        conversion::from_parsed_columns(column_values)
-    }
-
-    //pub fn try_from_series(columns: Vec<Series>) -> Result<Self, ShellError> {
-    //    let dataframe = DataFrame::new(columns)
-    //        .map_err(|e| ShellError::InternalError(format!("Unable to create DataFrame: {}", e)))?;
-
-    //    Ok(Self::new(dataframe))
-    //}
-
-    pub fn try_from_columns(columns: Vec<Column>) -> Result<Self, ShellError> {
-        let mut column_values: ColumnMap = IndexMap::new();
-
-        for column in columns {
-            let name = column.name().to_string();
-            for value in column {
-                conversion::insert_value(value, name.clone(), &mut column_values)?;
-            }
-        }
-
-        conversion::from_parsed_columns(column_values)
-    }
-
-    pub fn try_from_pipeline(input: PipelineData, span: Span) -> Result<Self, ShellError> {
-        match input.into_value(span) {
-            Value::CustomValue { val, span } => match val.as_any().downcast_ref::<NuDataFrame>() {
-                Some(df) => Ok(NuDataFrame(df.0.clone())),
-                None => Err(ShellError::CantConvert(
-                    "Dataframe not found".into(),
-                    "value is not a dataframe".into(),
-                    span,
-                )),
-            },
-            _ => Err(ShellError::CantConvert(
-                "Dataframe not found".into(),
-                "value is not a dataframe".into(),
-                span,
-            )),
-        }
-    }
-
-    pub fn column(&self, column: &str, span: Span) -> Result<Self, ShellError> {
-        let s = self.0.column(column).map_err(|_| {
-            let possibilities = self
-                .0
-                .get_column_names()
-                .iter()
-                .map(|name| name.to_string())
-                .collect::<Vec<String>>();
-
-            let option = did_you_mean(&possibilities, column).unwrap_or_else(|| column.to_string());
-            ShellError::DidYouMean(option, span)
-        })?;
-
-        let dataframe = DataFrame::new(vec![s.clone()])
-            .map_err(|e| ShellError::InternalError(e.to_string()))?;
-
-        Ok(Self(dataframe))
-    }
-
-    pub fn is_series(&self) -> bool {
-        self.0.width() == 1
-    }
-
-    pub fn as_series(&self, _span: Span) -> Result<Series, ShellError> {
-        if !self.is_series() {
-            return Err(ShellError::InternalError(
-                "DataFrame cannot be used as Series".into(),
-            ));
-        }
-
-        let series = self
-            .0
-            .get_columns()
-            .get(0)
-            .expect("We have already checked that the width is 1");
-
-        Ok(series.clone())
-    }
-
-    pub fn get_value(&self, row: usize, span: Span) -> Result<Value, ShellError> {
-        let series = self.as_series(Span::unknown())?;
-        let column = conversion::create_column(&series, row, row + 1)?;
-
-        if column.len() == 0 {
-            Err(ShellError::AccessBeyondEnd(series.len(), span))
-        } else {
-            let value = column
-                .into_iter()
-                .next()
-                .expect("already checked there is a value");
-            Ok(value)
-        }
-    }
-
-    // Print is made out a head and if the dataframe is too large, then a tail
-    pub fn print(&self) -> Result<Vec<Value>, ShellError> {
-        let df = &self.0;
-        let size: usize = 20;
-
-        if df.height() > size {
-            let sample_size = size / 2;
-            let mut values = self.head(Some(sample_size))?;
-            conversion::add_separator(&mut values, df);
-            let remaining = df.height() - sample_size;
-            let tail_size = remaining.min(sample_size);
-            let mut tail_values = self.tail(Some(tail_size))?;
-            values.append(&mut tail_values);
-
-            Ok(values)
-        } else {
-            Ok(self.head(Some(size))?)
-        }
-    }
-
-    pub fn head(&self, rows: Option<usize>) -> Result<Vec<Value>, ShellError> {
-        let to_row = rows.unwrap_or(5);
-        let values = self.to_rows(0, to_row)?;
-
-        Ok(values)
-    }
-
-    pub fn tail(&self, rows: Option<usize>) -> Result<Vec<Value>, ShellError> {
-        let df = &self.0;
-        let to_row = df.height();
-        let size = rows.unwrap_or(5);
-        let from_row = to_row.saturating_sub(size);
-
-        let values = self.to_rows(from_row, to_row)?;
-
-        Ok(values)
-    }
-
-    pub fn to_rows(&self, from_row: usize, to_row: usize) -> Result<Vec<Value>, ShellError> {
-        let df = &self.0;
-        let upper_row = to_row.min(df.height());
-
-        let mut size: usize = 0;
-        let columns = self
-            .0
-            .get_columns()
-            .iter()
-            .map(
-                |col| match conversion::create_column(col, from_row, upper_row) {
-                    Ok(col) => {
-                        size = col.len();
-                        Ok(col)
-                    }
-                    Err(e) => Err(e),
-                },
-            )
-            .collect::<Result<Vec<Column>, ShellError>>()?;
-
-        let mut iterators = columns
-            .into_iter()
-            .map(|col| (col.name().to_string(), col.into_iter()))
-            .collect::<Vec<(String, std::vec::IntoIter<Value>)>>();
-
-        let values = (0..size)
-            .into_iter()
-            .map(|_| {
-                let mut cols = vec![];
-                let mut vals = vec![];
-
-                for (name, col) in &mut iterators {
-                    cols.push(name.clone());
-
-                    match col.next() {
-                        Some(v) => vals.push(v),
-                        None => vals.push(Value::Nothing {
-                            span: Span::unknown(),
-                        }),
-                    };
-                }
-
-                Value::Record {
-                    cols,
-                    vals,
-                    span: Span::unknown(),
-                }
-            })
-            .collect::<Vec<Value>>();
-
-        Ok(values)
-    }
-
-    // Dataframes are considered equal if they have the same shape, column name and values
-    pub fn is_equal(&self, other: &Self) -> Option<Ordering> {
-        if self.as_ref().width() == 0 {
-            // checking for empty dataframe
-            return None;
-        }
-
-        if self.as_ref().get_column_names() != other.as_ref().get_column_names() {
-            // checking both dataframes share the same names
-            return None;
-        }
-
-        if self.as_ref().height() != other.as_ref().height() {
-            // checking both dataframes have the same row size
-            return None;
-        }
-
-        // sorting dataframe by the first column
-        let column_names = self.as_ref().get_column_names();
-        let first_col = column_names
-            .get(0)
-            .expect("already checked that dataframe is different than 0");
-
-        // if unable to sort, then unable to compare
-        let lhs = match self.as_ref().sort(*first_col, false) {
-            Ok(df) => df,
-            Err(_) => return None,
-        };
-
-        let rhs = match other.as_ref().sort(*first_col, false) {
-            Ok(df) => df,
-            Err(_) => return None,
-        };
-
-        for name in self.as_ref().get_column_names() {
-            let self_series = lhs.column(name).expect("name from dataframe names");
-
-            let other_series = rhs
-                .column(name)
-                .expect("already checked that name in other");
-
-            let self_series = match self_series.dtype() {
-                // Casting needed to compare other numeric types with nushell numeric type.
-                // In nushell we only have i64 integer numeric types and any array created
-                // with nushell untagged primitives will be of type i64
-                DataType::UInt32 => match self_series.cast(&DataType::Int64) {
-                    Ok(series) => series,
-                    Err(_) => return None,
-                },
-                _ => self_series.clone(),
-            };
-
-            if !self_series.series_equal(other_series) {
-                return None;
-            }
-        }
-
-        Some(Ordering::Equal)
-    }
-}

crates/nu-command/src/dataframe/nu_dataframe/nu_dataframe/between_values.rs

@@ -66,17 +66,21 @@ pub fn compute_between_series(
                     res.rename(&name);
                     NuDataFrame::series_to_value(res, operation_span)
                 }
-                Err(e) => Err(ShellError::InternalError(e.to_string())),
+                Err(e) => Err(ShellError::SpannedLabeledError(
+                    "Division error".into(),
+                    e.to_string(),
+                    right.span()?,
+                )),
             }
         }
         Operator::Equal => {
-            let mut res = Series::eq(lhs, rhs).into_series();
+            let mut res = Series::equal(lhs, rhs).into_series();
             let name = format!("eq_{}_{}", lhs.name(), rhs.name());
             res.rename(&name);
             NuDataFrame::series_to_value(res, operation_span)
         }
         Operator::NotEqual => {
-            let mut res = Series::neq(lhs, rhs).into_series();
+            let mut res = Series::not_equal(lhs, rhs).into_series();
             let name = format!("neq_{}_{}", lhs.name(), rhs.name());
             res.rename(&name);
             NuDataFrame::series_to_value(res, operation_span)
@@ -117,8 +121,10 @@ pub fn compute_between_series(
                         res.rename(&name);
                         NuDataFrame::series_to_value(res, operation_span)
                     }
-                    _ => Err(ShellError::InternalError(
+                    _ => Err(ShellError::SpannedLabeledError(
+                        "Incompatible types".into(),
                         "unable to cast to boolean".into(),
+                        right.span()?,
                     )),
                 }
             }
@@ -142,8 +148,10 @@ pub fn compute_between_series(
                         res.rename(&name);
                         NuDataFrame::series_to_value(res, operation_span)
                     }
-                    _ => Err(ShellError::InternalError(
+                    _ => Err(ShellError::SpannedLabeledError(
+                        "Incompatible types".into(),
                         "unable to cast to boolean".into(),
+                        right.span()?,
                     )),
                 }
             }
@@ -254,9 +262,9 @@ pub fn compute_series_single_value(
             }),
         },
         Operator::Equal => match &right {
-            Value::Int { val, .. } => compare_series_i64(&lhs, *val, ChunkedArray::eq, lhs_span),
+            Value::Int { val, .. } => compare_series_i64(&lhs, *val, ChunkedArray::equal, lhs_span),
             Value::Float { val, .. } => {
-                compare_series_decimal(&lhs, *val, ChunkedArray::eq, lhs_span)
+                compare_series_decimal(&lhs, *val, ChunkedArray::equal, lhs_span)
             }
             _ => Err(ShellError::OperatorMismatch {
                 op_span: operator.span,
@@ -267,9 +275,11 @@ pub fn compute_series_single_value(
             }),
         },
         Operator::NotEqual => match &right {
-            Value::Int { val, .. } => compare_series_i64(&lhs, *val, ChunkedArray::neq, lhs_span),
+            Value::Int { val, .. } => {
+                compare_series_i64(&lhs, *val, ChunkedArray::not_equal, lhs_span)
+            }
             Value::Float { val, .. } => {
-                compare_series_decimal(&lhs, *val, ChunkedArray::neq, lhs_span)
+                compare_series_decimal(&lhs, *val, ChunkedArray::not_equal, lhs_span)
             }
             _ => Err(ShellError::OperatorMismatch {
                 op_span: operator.span,
@@ -364,17 +374,25 @@ where
                     let casted = series.i64();
                     compute_casted_i64(casted, val, f, span)
                 }
-                Err(e) => Err(ShellError::InternalError(e.to_string())),
+                Err(e) => Err(ShellError::SpannedLabeledError(
+                    "Unable to cast to i64".into(),
+                    e.to_string(),
+                    span,
+                )),
             }
         }
         DataType::Int64 => {
             let casted = series.i64();
             compute_casted_i64(casted, val, f, span)
         }
-        _ => Err(ShellError::InternalError(format!(
+        _ => Err(ShellError::SpannedLabeledError(
-            "Series of type {} can not be used for operations with an i64 value",
+            "Incorrect type".into(),
-            series.dtype()
+            format!(
-        ))),
+                "Series of type {} can not be used for operations with an i64 value",
+                series.dtype()
+            ),
+            span,
+        )),
     }
 }
 
@@ -393,7 +411,11 @@ where
             let res = res.into_series();
             NuDataFrame::series_to_value(res, span)
         }
-        Err(e) => Err(ShellError::InternalError(e.to_string())),
+        Err(e) => Err(ShellError::SpannedLabeledError(
+            "Unable to cast to i64".into(),
+            e.to_string(),
+            span,
+        )),
     }
 }
 
@@ -415,17 +437,25 @@ where
                     let casted = series.f64();
                     compute_casted_f64(casted, val, f, span)
                 }
-                Err(e) => Err(ShellError::InternalError(e.to_string())),
+                Err(e) => Err(ShellError::SpannedLabeledError(
+                    "Unable to cast to f64".into(),
+                    e.to_string(),
+                    span,
+                )),
             }
         }
         DataType::Float64 => {
             let casted = series.f64();
             compute_casted_f64(casted, val, f, span)
         }
-        _ => Err(ShellError::InternalError(format!(
+        _ => Err(ShellError::SpannedLabeledError(
-            "Series of type {} can not be used for operations with a decimal value",
+            "Incorrect type".into(),
-            series.dtype()
+            format!(
-        ))),
+                "Series of type {} can not be used for operations with a decimal value",
+                series.dtype()
+            ),
+            span,
+        )),
     }
 }
 
@@ -444,7 +474,11 @@ where
             let res = res.into_series();
             NuDataFrame::series_to_value(res, span)
         }
-        Err(e) => Err(ShellError::InternalError(e.to_string())),
+        Err(e) => Err(ShellError::SpannedLabeledError(
+            "Unable to cast to f64".into(),
+            e.to_string(),
+            span,
+        )),
     }
 }
 
@@ -461,17 +495,25 @@ where
                     let casted = series.i64();
                     compare_casted_i64(casted, val, f, span)
                 }
-                Err(e) => Err(ShellError::InternalError(e.to_string())),
+                Err(e) => Err(ShellError::SpannedLabeledError(
+                    "Unable to cast to f64".into(),
+                    e.to_string(),
+                    span,
+                )),
             }
         }
         DataType::Int64 => {
             let casted = series.i64();
             compare_casted_i64(casted, val, f, span)
         }
-        _ => Err(ShellError::InternalError(format!(
+        _ => Err(ShellError::SpannedLabeledError(
-            "Series of type {} can not be used for operations with an i64 value",
+            "Incorrect type".into(),
-            series.dtype()
+            format!(
-        ))),
+                "Series of type {} can not be used for operations with an i64 value",
+                series.dtype()
+            ),
+            span,
+        )),
     }
 }
 
@@ -490,7 +532,11 @@ where
             let res = res.into_series();
             NuDataFrame::series_to_value(res, span)
         }
-        Err(e) => Err(ShellError::InternalError(e.to_string())),
+        Err(e) => Err(ShellError::SpannedLabeledError(
+            "Unable to cast to i64".into(),
+            e.to_string(),
+            span,
+        )),
     }
 }
 
@@ -512,17 +558,25 @@ where
                     let casted = series.f64();
                     compare_casted_f64(casted, val, f, span)
                 }
-                Err(e) => Err(ShellError::InternalError(e.to_string())),
+                Err(e) => Err(ShellError::SpannedLabeledError(
+                    "Unable to cast to i64".into(),
+                    e.to_string(),
+                    span,
+                )),
             }
         }
         DataType::Float64 => {
             let casted = series.f64();
             compare_casted_f64(casted, val, f, span)
         }
-        _ => Err(ShellError::InternalError(format!(
+        _ => Err(ShellError::SpannedLabeledError(
-            "Series of type {} can not be used for operations with a decimal value",
+            "Incorrect type".into(),
-            series.dtype()
+            format!(
-        ))),
+                "Series of type {} can not be used for operations with a decimal value",
+                series.dtype()
+            ),
+            span,
+        )),
     }
 }
 
@@ -541,7 +595,11 @@ where
             let res = res.into_series();
             NuDataFrame::series_to_value(res, span)
         }
-        Err(e) => Err(ShellError::InternalError(e.to_string())),
+        Err(e) => Err(ShellError::SpannedLabeledError(
+            "Unable to cast to f64".into(),
+            e.to_string(),
+            span,
+        )),
     }
 }
 
@@ -556,9 +614,17 @@ fn contains_series_pat(series: &Series, pat: &str, span: Span) -> Result<Value,
                     let res = res.into_series();
                     NuDataFrame::series_to_value(res, span)
                 }
-                Err(e) => Err(ShellError::InternalError(e.to_string())),
+                Err(e) => Err(ShellError::SpannedLabeledError(
+                    "Error using contains".into(),
+                    e.to_string(),
+                    span,
+                )),
             }
         }
-        Err(e) => Err(ShellError::InternalError(e.to_string())),
+        Err(e) => Err(ShellError::SpannedLabeledError(
+            "Unable to cast to string".into(),
+            e.to_string(),
+            span,
+        )),
     }
 }

crates/nu-command/src/dataframe/nu_dataframe/nu_dataframe/conversion.rs

@@ -122,69 +122,69 @@ pub fn create_column(
             Ok(Column::new(series.name().into(), values))
         }
         DataType::UInt8 => {
-            let casted = series
+            let casted = series.u8().map_err(|e| {
-                .u8()
+                ShellError::LabeledError("Error casting column to u8".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
             Ok(column_from_casted(casted, from_row, size))
         }
         DataType::UInt16 => {
-            let casted = series
+            let casted = series.u16().map_err(|e| {
-                .u16()
+                ShellError::LabeledError("Error casting column to u16".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
             Ok(column_from_casted(casted, from_row, size))
         }
         DataType::UInt32 => {
-            let casted = series
+            let casted = series.u32().map_err(|e| {
-                .u32()
+                ShellError::LabeledError("Error casting column to u32".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
             Ok(column_from_casted(casted, from_row, size))
         }
         DataType::UInt64 => {
-            let casted = series
+            let casted = series.u64().map_err(|e| {
-                .u64()
+                ShellError::LabeledError("Error casting column to u64".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
             Ok(column_from_casted(casted, from_row, size))
         }
         DataType::Int8 => {
-            let casted = series
+            let casted = series.i8().map_err(|e| {
-                .i8()
+                ShellError::LabeledError("Error casting column to i8".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
             Ok(column_from_casted(casted, from_row, size))
         }
         DataType::Int16 => {
-            let casted = series
+            let casted = series.i16().map_err(|e| {
-                .i16()
+                ShellError::LabeledError("Error casting column to i16".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
             Ok(column_from_casted(casted, from_row, size))
         }
         DataType::Int32 => {
-            let casted = series
+            let casted = series.i32().map_err(|e| {
-                .i32()
+                ShellError::LabeledError("Error casting column to i32".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
             Ok(column_from_casted(casted, from_row, size))
         }
         DataType::Int64 => {
-            let casted = series
+            let casted = series.i64().map_err(|e| {
-                .i64()
+                ShellError::LabeledError("Error casting column to i64".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
             Ok(column_from_casted(casted, from_row, size))
         }
         DataType::Float32 => {
-            let casted = series
+            let casted = series.f32().map_err(|e| {
-                .f32()
+                ShellError::LabeledError("Error casting column to f32".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
             Ok(column_from_casted(casted, from_row, size))
         }
         DataType::Float64 => {
-            let casted = series
+            let casted = series.f64().map_err(|e| {
-                .f64()
+                ShellError::LabeledError("Error casting column to f64".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
             Ok(column_from_casted(casted, from_row, size))
         }
         DataType::Boolean => {
-            let casted = series
+            let casted = series.bool().map_err(|e| {
-                .bool()
+                ShellError::LabeledError("Error casting column to bool".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
 
             let values = casted
                 .into_iter()
@@ -204,9 +204,9 @@ pub fn create_column(
             Ok(Column::new(casted.name().into(), values))
         }
         DataType::Utf8 => {
-            let casted = series
+            let casted = series.utf8().map_err(|e| {
-                .utf8()
+                ShellError::LabeledError("Error casting column to string".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
 
             let values = casted
                 .into_iter()
@@ -231,10 +231,10 @@ pub fn create_column(
                 .downcast_ref::<ChunkedArray<ObjectType<DataFrameValue>>>();
 
             match casted {
-                None => Err(ShellError::InternalError(format!(
+                None => Err(ShellError::LabeledError(
-                    "Object not supported for conversion: {}",
+                    "Error casting object from series".into(),
-                    x
+                    format!("Object not supported for conversion: {}", x),
-                ))),
+                )),
                 Some(ca) => {
                     let values = ca
                         .into_iter()
@@ -253,9 +253,9 @@ pub fn create_column(
             }
         }
         DataType::Date => {
-            let casted = series
+            let casted = series.date().map_err(|e| {
-                .date()
+                ShellError::LabeledError("Error casting column to date".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
 
             let values = casted
                 .into_iter()
@@ -285,9 +285,9 @@ pub fn create_column(
             Ok(Column::new(casted.name().into(), values))
         }
         DataType::Datetime => {
-            let casted = series
+            let casted = series.datetime().map_err(|e| {
-                .datetime()
+                ShellError::LabeledError("Error casting column to datetime".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
 
             let values = casted
                 .into_iter()
@@ -317,9 +317,9 @@ pub fn create_column(
             Ok(Column::new(casted.name().into(), values))
         }
         DataType::Time => {
-            let casted = series
+            let casted = series.time().map_err(|e| {
-                .time()
+                ShellError::LabeledError("Error casting column to time".into(), e.to_string())
-                .map_err(|e| ShellError::InternalError(e.to_string()))?;
+            })?;
 
             let values = casted
                 .into_iter()
@@ -338,10 +338,10 @@ pub fn create_column(
 
             Ok(Column::new(casted.name().into(), values))
         }
-        e => Err(ShellError::InternalError(format!(
+        e => Err(ShellError::LabeledError(
-            "Value not supported in nushell: {}",
+            "Error creating Dataframe".into(),
-            e
+            format!("Value not supported in nushell: {}", e),
-        ))),
+        )),
     }
 }
 
@@ -530,8 +530,7 @@ pub fn from_parsed_columns(column_values: ColumnMap) -> Result<NuDataFrame, Shel
         }
     }
 
-    match DataFrame::new(df_series) {
+    DataFrame::new(df_series)
-        Ok(df) => Ok(NuDataFrame::new(df)),
+        .map(|df| NuDataFrame::new(df))
-        Err(e) => Err(ShellError::InternalError(e.to_string())),
+        .map_err(|e| ShellError::LabeledError("Error creating dataframe".into(), e.to_string()))
-    }
 }

crates/nu-command/src/dataframe/nu_dataframe/nu_dataframe/mod.rs

@@ -0,0 +1,395 @@
+mod between_values;
+mod conversion;
+mod custom_value;
+mod operations;
+
+pub(super) use conversion::{Column, ColumnMap};
+
+use indexmap::map::IndexMap;
+use nu_protocol::{did_you_mean, PipelineData, ShellError, Span, Value};
+use polars::prelude::{DataFrame, DataType, PolarsObject, Series};
+use serde::{Deserialize, Serialize};
+use std::{cmp::Ordering, fmt::Display, hash::Hasher};
+
+// DataFrameValue is an encapsulation of Nushell Value that can be used
+// to define the PolarsObject Trait. The polars object trait allows to
+// create dataframes with mixed datatypes
+#[derive(Clone, Debug)]
+pub struct DataFrameValue(Value);
+
+impl DataFrameValue {
+    fn new(value: Value) -> Self {
+        Self(value)
+    }
+
+    fn get_value(&self) -> Value {
+        self.0.clone()
+    }
+}
+
+impl Display for DataFrameValue {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "{}", self.0.get_type())
+    }
+}
+
+impl Default for DataFrameValue {
+    fn default() -> Self {
+        Self(Value::Nothing {
+            span: Span::unknown(),
+        })
+    }
+}
+
+impl PartialEq for DataFrameValue {
+    fn eq(&self, other: &Self) -> bool {
+        self.0.partial_cmp(&other.0).map_or(false, Ordering::is_eq)
+    }
+}
+impl Eq for DataFrameValue {}
+
+impl std::hash::Hash for DataFrameValue {
+    fn hash<H: Hasher>(&self, state: &mut H) {
+        match &self.0 {
+            Value::Nothing { .. } => 0.hash(state),
+            Value::Int { val, .. } => val.hash(state),
+            Value::String { val, .. } => val.hash(state),
+            // TODO. Define hash for the rest of types
+            _ => {}
+        }
+    }
+}
+
+impl PolarsObject for DataFrameValue {
+    fn type_name() -> &'static str {
+        "object"
+    }
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+pub struct NuDataFrame(DataFrame);
+
+impl AsRef<DataFrame> for NuDataFrame {
+    fn as_ref(&self) -> &polars::prelude::DataFrame {
+        &self.0
+    }
+}
+
+impl AsMut<DataFrame> for NuDataFrame {
+    fn as_mut(&mut self) -> &mut polars::prelude::DataFrame {
+        &mut self.0
+    }
+}
+
+impl NuDataFrame {
+    pub fn new(dataframe: DataFrame) -> Self {
+        Self(dataframe)
+    }
+
+    fn default_value(span: Span) -> Value {
+        let dataframe = DataFrame::default();
+        NuDataFrame::dataframe_into_value(dataframe, span)
+    }
+
+    pub fn dataframe_into_value(dataframe: DataFrame, span: Span) -> Value {
+        Value::CustomValue {
+            val: Box::new(Self::new(dataframe)),
+            span,
+        }
+    }
+
+    pub fn into_value(self, span: Span) -> Value {
+        Value::CustomValue {
+            val: Box::new(self),
+            span,
+        }
+    }
+
+    pub fn series_to_value(series: Series, span: Span) -> Result<Value, ShellError> {
+        match DataFrame::new(vec![series]) {
+            Ok(dataframe) => Ok(NuDataFrame::dataframe_into_value(dataframe, span)),
+            Err(e) => Err(ShellError::SpannedLabeledError(
+                "Error creating dataframe".into(),
+                e.to_string(),
+                span,
+            )),
+        }
+    }
+
+    pub fn try_from_iter<T>(iter: T) -> Result<Self, ShellError>
+    where
+        T: Iterator<Item = Value>,
+    {
+        // Dictionary to store the columnar data extracted from
+        // the input. During the iteration we check if the values
+        // have different type
+        let mut column_values: ColumnMap = IndexMap::new();
+
+        for value in iter {
+            match value {
+                Value::List { vals, .. } => {
+                    let cols = (0..vals.len())
+                        .map(|i| format!("{}", i))
+                        .collect::<Vec<String>>();
+
+                    conversion::insert_record(&mut column_values, &cols, &vals)?
+                }
+                Value::Record { cols, vals, .. } => {
+                    conversion::insert_record(&mut column_values, &cols, &vals)?
+                }
+                _ => {
+                    let key = "0".to_string();
+                    conversion::insert_value(value, key, &mut column_values)?
+                }
+            }
+        }
+
+        conversion::from_parsed_columns(column_values)
+    }
+
+    //pub fn try_from_series(columns: Vec<Series>) -> Result<Self, ShellError> {
+    //    let dataframe = DataFrame::new(columns)
+    //        .map_err(|e| ShellError::InternalError(format!("Unable to create DataFrame: {}", e)))?;
+
+    //    Ok(Self::new(dataframe))
+    //}
+
+    pub fn try_from_columns(columns: Vec<Column>) -> Result<Self, ShellError> {
+        let mut column_values: ColumnMap = IndexMap::new();
+
+        for column in columns {
+            let name = column.name().to_string();
+            for value in column {
+                conversion::insert_value(value, name.clone(), &mut column_values)?;
+            }
+        }
+
+        conversion::from_parsed_columns(column_values)
+    }
+
+    pub fn try_from_pipeline(input: PipelineData, span: Span) -> Result<Self, ShellError> {
+        match input.into_value(span) {
+            Value::CustomValue { val, span } => match val.as_any().downcast_ref::<NuDataFrame>() {
+                Some(df) => Ok(NuDataFrame(df.0.clone())),
+                None => Err(ShellError::CantConvert(
+                    "Dataframe not found".into(),
+                    "value is not a dataframe".into(),
+                    span,
+                )),
+            },
+            _ => Err(ShellError::CantConvert(
+                "Dataframe not found".into(),
+                "value is not a dataframe".into(),
+                span,
+            )),
+        }
+    }
+
+    pub fn column(&self, column: &str, span: Span) -> Result<Self, ShellError> {
+        let s = self.0.column(column).map_err(|_| {
+            let possibilities = self
+                .0
+                .get_column_names()
+                .iter()
+                .map(|name| name.to_string())
+                .collect::<Vec<String>>();
+
+            let option = did_you_mean(&possibilities, column).unwrap_or_else(|| column.to_string());
+            ShellError::DidYouMean(option, span)
+        })?;
+
+        let dataframe = DataFrame::new(vec![s.clone()]).map_err(|e| {
+            ShellError::SpannedLabeledError("Error creating dataframe".into(), e.to_string(), span)
+        })?;
+
+        Ok(Self(dataframe))
+    }
+
+    pub fn is_series(&self) -> bool {
+        self.0.width() == 1
+    }
+
+    pub fn as_series(&self, span: Span) -> Result<Series, ShellError> {
+        if !self.is_series() {
+            return Err(ShellError::SpannedLabeledError(
+                "Error using as series".into(),
+                "dataframe has more than one column".into(),
+                span,
+            ));
+        }
+
+        let series = self
+            .0
+            .get_columns()
+            .get(0)
+            .expect("We have already checked that the width is 1");
+
+        Ok(series.clone())
+    }
+
+    pub fn get_value(&self, row: usize, span: Span) -> Result<Value, ShellError> {
+        let series = self.as_series(Span::unknown())?;
+        let column = conversion::create_column(&series, row, row + 1)?;
+
+        if column.len() == 0 {
+            Err(ShellError::AccessBeyondEnd(series.len(), span))
+        } else {
+            let value = column
+                .into_iter()
+                .next()
+                .expect("already checked there is a value");
+            Ok(value)
+        }
+    }
+
+    // Print is made out a head and if the dataframe is too large, then a tail
+    pub fn print(&self) -> Result<Vec<Value>, ShellError> {
+        let df = &self.0;
+        let size: usize = 20;
+
+        if df.height() > size {
+            let sample_size = size / 2;
+            let mut values = self.head(Some(sample_size))?;
+            conversion::add_separator(&mut values, df);
+            let remaining = df.height() - sample_size;
+            let tail_size = remaining.min(sample_size);
+            let mut tail_values = self.tail(Some(tail_size))?;
+            values.append(&mut tail_values);
+
+            Ok(values)
+        } else {
+            Ok(self.head(Some(size))?)
+        }
+    }
+
+    pub fn head(&self, rows: Option<usize>) -> Result<Vec<Value>, ShellError> {
+        let to_row = rows.unwrap_or(5);
+        let values = self.to_rows(0, to_row)?;
+
+        Ok(values)
+    }
+
+    pub fn tail(&self, rows: Option<usize>) -> Result<Vec<Value>, ShellError> {
+        let df = &self.0;
+        let to_row = df.height();
+        let size = rows.unwrap_or(5);
+        let from_row = to_row.saturating_sub(size);
+
+        let values = self.to_rows(from_row, to_row)?;
+
+        Ok(values)
+    }
+
+    pub fn to_rows(&self, from_row: usize, to_row: usize) -> Result<Vec<Value>, ShellError> {
+        let df = &self.0;
+        let upper_row = to_row.min(df.height());
+
+        let mut size: usize = 0;
+        let columns = self
+            .0
+            .get_columns()
+            .iter()
+            .map(
+                |col| match conversion::create_column(col, from_row, upper_row) {
+                    Ok(col) => {
+                        size = col.len();
+                        Ok(col)
+                    }
+                    Err(e) => Err(e),
+                },
+            )
+            .collect::<Result<Vec<Column>, ShellError>>()?;
+
+        let mut iterators = columns
+            .into_iter()
+            .map(|col| (col.name().to_string(), col.into_iter()))
+            .collect::<Vec<(String, std::vec::IntoIter<Value>)>>();
+
+        let values = (0..size)
+            .into_iter()
+            .map(|_| {
+                let mut cols = vec![];
+                let mut vals = vec![];
+
+                for (name, col) in &mut iterators {
+                    cols.push(name.clone());
+
+                    match col.next() {
+                        Some(v) => vals.push(v),
+                        None => vals.push(Value::Nothing {
+                            span: Span::unknown(),
+                        }),
+                    };
+                }
+
+                Value::Record {
+                    cols,
+                    vals,
+                    span: Span::unknown(),
+                }
+            })
+            .collect::<Vec<Value>>();
+
+        Ok(values)
+    }
+
+    // Dataframes are considered equal if they have the same shape, column name and values
+    pub fn is_equal(&self, other: &Self) -> Option<Ordering> {
+        if self.as_ref().width() == 0 {
+            // checking for empty dataframe
+            return None;
+        }
+
+        if self.as_ref().get_column_names() != other.as_ref().get_column_names() {
+            // checking both dataframes share the same names
+            return None;
+        }
+
+        if self.as_ref().height() != other.as_ref().height() {
+            // checking both dataframes have the same row size
+            return None;
+        }
+
+        // sorting dataframe by the first column
+        let column_names = self.as_ref().get_column_names();
+        let first_col = column_names
+            .get(0)
+            .expect("already checked that dataframe is different than 0");
+
+        // if unable to sort, then unable to compare
+        let lhs = match self.as_ref().sort(*first_col, false) {
+            Ok(df) => df,
+            Err(_) => return None,
+        };
+
+        let rhs = match other.as_ref().sort(*first_col, false) {
+            Ok(df) => df,
+            Err(_) => return None,
+        };
+
+        for name in self.as_ref().get_column_names() {
+            let self_series = lhs.column(name).expect("name from dataframe names");
+
+            let other_series = rhs
+                .column(name)
+                .expect("already checked that name in other");
+
+            let self_series = match self_series.dtype() {
+                // Casting needed to compare other numeric types with nushell numeric type.
+                // In nushell we only have i64 integer numeric types and any array created
+                // with nushell untagged primitives will be of type i64
+                DataType::UInt32 => match self_series.cast(&DataType::Int64) {
+                    Ok(series) => series,
+                    Err(_) => return None,
+                },
+                _ => self_series.clone(),
+            };
+
+            if !self_series.series_equal(other_series) {
+                return None;
+            }
+        }
+
+        Some(Ordering::Equal)
+    }
+}

crates/nu-command/src/dataframe/nu_dataframe/nu_dataframe/operations.rs

@@ -122,7 +122,7 @@ impl NuDataFrame {
         &self,
         other: &NuDataFrame,
         axis: Axis,
-        _span: Span,
+        span: Span,
     ) -> Result<Self, ShellError> {
         match axis {
             Axis::Row => {
@@ -147,8 +147,13 @@ impl NuDataFrame {
                     })
                     .collect::<Vec<Series>>();
 
-                let df_new = DataFrame::new(new_cols)
+                let df_new = DataFrame::new(new_cols).map_err(|e| {
-                    .map_err(|e| ShellError::InternalError(e.to_string()))?;
+                    ShellError::SpannedLabeledError(
+                        "Error creating dataframe".into(),
+                        e.to_string(),
+                        span,
+                    )
+                })?;
 
                 Ok(NuDataFrame::new(df_new))
             } //Axis::Column => {

crates/nu-command/src/dataframe/nu_dataframe/open.rs

@@ -1,4 +1,4 @@
-use super::super::NuDataFrame;
+use super::nu_dataframe::NuDataFrame;
 use nu_engine::CallExt;
 use nu_protocol::{
     ast::Call,
@@ -22,7 +22,7 @@ impl Command for OpenDataFrame {
     }
 
     fn signature(&self) -> Signature {
-        Signature::build(self.name().to_string())
+        Signature::build(self.name())
             .required(
                 "file",
                 SyntaxShape::Filepath,
@@ -54,7 +54,7 @@ impl Command for OpenDataFrame {
             .named(
                 "columns",
                 SyntaxShape::List(Box::new(SyntaxShape::String)),
-                "Columns to be selected from csv file. CSV file",
+                "Columns to be selected from csv file. CSV and Parquet file",
                 None,
             )
             .category(Category::Custom("dataframe".into()))
@@ -87,7 +87,7 @@ fn command(
     let span = call.head;
     let file: Spanned<PathBuf> = call.req(engine_state, stack, 0)?;
 
-    let df = match file.item.extension() {
+    match file.item.extension() {
         Some(e) => match e.to_str() {
             Some("csv") => from_csv(engine_state, stack, call),
             Some("parquet") => from_parquet(engine_state, stack, call),
@@ -101,11 +101,8 @@ fn command(
             "File without extension".into(),
             file.span,
         )),
-    }?;
+    }
-
+    .map(|df| PipelineData::Value(NuDataFrame::dataframe_into_value(df, span), None))
-    Ok(PipelineData::Value(NuDataFrame::dataframe_into_value(
-        df, span,
-    )))
 }
 
 fn from_parquet(
@@ -114,12 +111,25 @@ fn from_parquet(
     call: &Call,
 ) -> Result<polars::prelude::DataFrame, ShellError> {
     let file: Spanned<PathBuf> = call.req(engine_state, stack, 0)?;
-    let r = File::open(&file.item).map_err(|e| ShellError::InternalError(e.to_string()))?;
+    let columns: Option<Vec<String>> = call.get_flag(engine_state, stack, "columns")?;
+
+    let r = File::open(&file.item).map_err(|e| {
+        ShellError::SpannedLabeledError("Error opening file".into(), e.to_string(), file.span)
+    })?;
     let reader = ParquetReader::new(r);
 
-    reader
+    let reader = match columns {
-        .finish()
+        None => reader,
-        .map_err(|e| ShellError::InternalError(format!("{:?}", e)))
+        Some(columns) => reader.with_columns(Some(columns)),
+    };
+
+    reader.finish().map_err(|e| {
+        ShellError::SpannedLabeledError(
+            "Parquet reader error".into(),
+            format!("{:?}", e),
+            call.head,
+        )
+    })
 }
 
 fn from_json(
@@ -129,13 +139,15 @@ fn from_json(
 ) -> Result<polars::prelude::DataFrame, ShellError> {
     let file: Spanned<PathBuf> = call.req(engine_state, stack, 0)?;
 
-    let r = File::open(&file.item).map_err(|e| ShellError::InternalError(e.to_string()))?;
+    let r = File::open(&file.item).map_err(|e| {
+        ShellError::SpannedLabeledError("Error opening file".into(), e.to_string(), file.span)
+    })?;
 
     let reader = JsonReader::new(r);
 
-    reader
+    reader.finish().map_err(|e| {
-        .finish()
+        ShellError::SpannedLabeledError("Json reader error".into(), format!("{:?}", e), call.head)
-        .map_err(|e| ShellError::InternalError(e.to_string()))
+    })
 }
 
 fn from_csv(
@@ -151,15 +163,23 @@ fn from_csv(
     let columns: Option<Vec<String>> = call.get_flag(engine_state, stack, "columns")?;
 
     let csv_reader = CsvReader::from_path(&file.item)
-        .map_err(|e| ShellError::InternalError(e.to_string()))?
+        .map_err(|e| {
+            ShellError::SpannedLabeledError(
+                "Error creating CSV reader".into(),
+                e.to_string(),
+                file.span,
+            )
+        })?
         .with_encoding(CsvEncoding::LossyUtf8);
 
     let csv_reader = match delimiter {
         None => csv_reader,
         Some(d) => {
             if d.item.len() != 1 {
-                return Err(ShellError::InternalError(
+                return Err(ShellError::SpannedLabeledError(
-                    "Delimiter has to be one char".into(),
+                    "Incorrect delimiter".into(),
+                    "Delimiter has to be one character".into(),
+                    d.span,
                 ));
             } else {
                 let delimiter = match d.item.chars().next() {
@@ -188,7 +208,11 @@ fn from_csv(
         Some(columns) => csv_reader.with_columns(Some(columns)),
     };
 
-    csv_reader
+    csv_reader.finish().map_err(|e| {
-        .finish()
+        ShellError::SpannedLabeledError(
-        .map_err(|e| ShellError::InternalError(e.to_string()))
+            "Parquet reader error".into(),
+            format!("{:?}", e),
+            call.head,
+        )
+    })
 }

crates/nu-command/src/dataframe/nu_dataframe/to_df.rs

@@ -1,4 +1,4 @@
-use super::super::{Column, NuDataFrame};
+use super::nu_dataframe::{Column, NuDataFrame};
 
 use nu_protocol::{
     ast::Call,
@@ -19,7 +19,7 @@ impl Command for ToDataFrame {
     }
 
     fn signature(&self) -> Signature {
-        Signature::build(self.name().to_string()).category(Category::Custom("dataframe".into()))
+        Signature::build(self.name()).category(Category::Custom("dataframe".into()))
     }
 
     fn examples(&self) -> Vec<Example> {
@@ -94,8 +94,8 @@ impl Command for ToDataFrame {
         call: &Call,
         input: PipelineData,
     ) -> Result<PipelineData, ShellError> {
-        let df = NuDataFrame::try_from_iter(input.into_iter())?;
+        NuDataFrame::try_from_iter(input.into_iter())
-        Ok(PipelineData::Value(NuDataFrame::into_value(df, call.head)))
+            .map(|df| PipelineData::Value(NuDataFrame::into_value(df, call.head), None))
     }
 }
 

crates/nu-plugin/src/serializers/plugin_call.rs

@@ -382,4 +382,25 @@ mod tests {
             PluginResponse::Value(_) => panic!("returned wrong call type"),
         }
     }
+
+    #[test]
+    fn response_round_trip_error_none() {
+        let error = LabeledError {
+            label: "label".into(),
+            msg: "msg".into(),
+            span: None,
+        };
+        let response = PluginResponse::Error(error.clone());
+
+        let mut buffer: Vec<u8> = Vec::new();
+        encode_response(&response, &mut buffer).expect("unable to serialize message");
+        let returned =
+            decode_response(&mut buffer.as_slice()).expect("unable to deserialize message");
+
+        match returned {
+            PluginResponse::Error(msg) => assert_eq!(error, msg),
+            PluginResponse::Signature(_) => panic!("returned wrong call type"),
+            PluginResponse::Value(_) => panic!("returned wrong call type"),
+        }
+    }
 }