Struct polars_core::datatypes::Logical

pub struct Logical<Logical: PolarsDataType, Physical: PolarsDataType>(pub ChunkedArray<Physical>, _, pub Option<DataType>);

Maps a logical type to a a chunked array implementation of the physical type. This saves a lot of compiler bloat and allows us to reuse functionality.

Tuple Fields

0: ChunkedArray<Physical>

2: Option<DataType>

Implementations

impl<K: PolarsDataType, T: PolarsDataType> Logical<K, T>

pub fn new_logical<J: PolarsDataType>(ca: ChunkedArray<T>) -> Logical<J, T>

impl<K: PolarsDataType, T: PolarsDataType> Logical<K, T> where
    Self: LogicalType, 

pub fn field(&self) -> Field

Methods from Deref<Target = ChunkedArray<T>>

pub fn abs(&self) -> Self

Convert all values to their absolute/positive value.

pub fn append(&mut self, other: &Self)

Append in place. This is done by adding the chunks of other to this ChunkedArray.

See also extend for appends to the underlying memory

pub fn cast_and_apply_in_place<F, S>(&self, f: F) -> ChunkedArray<S> where
    F: Fn(S::Native) -> S::Native + Copy,
    S: PolarsNumericType, 

Cast a numeric array to another numeric data type and apply a function in place. This saves an allocation.

pub fn extend(&mut self, other: &Self)

Extend the memory backed by this array with the values from other.

Different from ChunkedArray::append which adds chunks to this ChunkedArray extent appends the data from other to the underlying PrimitiveArray and thus may cause a reallocation.

However if this does not cause a reallocation, the resulting data structure will not have any extra chunks and thus will yield faster queries.

Prefer extend over append when you want to do a query after a single append. For instance during online operations where you add n rows and rerun a query.

Prefer append over extend when you want to append many times before doing a query. For instance when you read in multiple files and when to store them in a single DataFrame. In the latter case finish the sequence of append operations with a rechunk.

pub fn rolling_mean(&self, options: RollingOptions) -> Result<Series>

Apply a rolling mean (moving mean) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their mean.

pub fn rolling_sum(&self, options: RollingOptions) -> Result<Series>

Apply a rolling sum (moving sum) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their sum.

pub fn rolling_min(&self, options: RollingOptions) -> Result<Series>

Apply a rolling min (moving min) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their min.

pub fn rolling_max(&self, options: RollingOptions) -> Result<Series>

Apply a rolling max (moving max) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their max.

pub fn rolling_median(&self, options: RollingOptions) -> Result<Series>

Apply a rolling median (moving median) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be weighted according to the weights vector.

pub fn rolling_quantile(
    &self,
    quantile: f64,
    interpolation: QuantileInterpolOptions,
    options: RollingOptions
) -> Result<Series>

Apply a rolling quantile (moving quantile) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be weighted according to the weights vector.

pub fn rolling_mean(&self, options: RollingOptions) -> Result<Series>

Apply a rolling mean (moving mean) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their mean.

pub fn rolling_sum(&self, options: RollingOptions) -> Result<Series>

Apply a rolling sum (moving sum) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their sum.

pub fn rolling_min(&self, options: RollingOptions) -> Result<Series>

Apply a rolling min (moving min) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their min.

pub fn rolling_max(&self, options: RollingOptions) -> Result<Series>

Apply a rolling max (moving max) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their max.

pub fn rolling_median(&self, options: RollingOptions) -> Result<Series>

Apply a rolling median (moving median) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be weighted according to the weights vector.

pub fn rolling_quantile(
    &self,
    quantile: f64,
    interpolation: QuantileInterpolOptions,
    options: RollingOptions
) -> Result<Series>

Apply a rolling quantile (moving quantile) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be weighted according to the weights vector.

pub fn rolling_sum(&self, options: RollingOptions) -> Result<Series>

Apply a rolling sum (moving sum) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their sum.

pub fn rolling_median(&self, options: RollingOptions) -> Result<Series>

Apply a rolling median (moving median) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be weighted according to the weights vector.

pub fn rolling_quantile(
    &self,
    quantile: f64,
    interpolation: QuantileInterpolOptions,
    options: RollingOptions
) -> Result<Series>

Apply a rolling quantile (moving quantile) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be weighted according to the weights vector.

pub fn rolling_min(&self, options: RollingOptions) -> Result<Series>

Apply a rolling min (moving min) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their min.

pub fn rolling_max(&self, options: RollingOptions) -> Result<Series>

Apply a rolling max (moving max) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their max.

pub fn rolling_apply_float<F>(&self, window_size: usize, f: F) -> Result<Self> where
    F: Fn(&ChunkedArray<T>) -> Option<T::Native>, 

Apply a rolling custom function. This is pretty slow because of dynamic dispatch.

pub fn rolling_var(&self, options: RollingOptions) -> Result<Series>

Apply a rolling var (moving var) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their var.

pub fn rolling_std(&self, options: RollingOptions) -> Result<Series>

Apply a rolling std (moving std) over the values in this array. A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weights vector. The resulting values will be aggregated to their std.

pub fn arg_true(&self) -> IdxCa

pub fn all(&self) -> bool

Check if all values are true

pub fn any(&self) -> bool

Check if any value is true

pub fn is_nan(&self) -> BooleanChunked

pub fn is_not_nan(&self) -> BooleanChunked

pub fn is_finite(&self) -> BooleanChunked

pub fn is_infinite(&self) -> BooleanChunked

pub fn none_to_nan(&self) -> Self

Convert missing values to NaN values.

pub fn par_iter(&self) -> impl ParallelIterator<Item = Option<Series>> + '_

pub fn par_iter_indexed(
    &self
) -> impl IndexedParallelIterator<Item = Option<&str>>

pub fn par_iter(&self) -> impl ParallelIterator<Item = Option<&str>> + '_

pub fn to_ndarray(&self) -> Result<ArrayView1<'_, T::Native>>

Available on crate feature ndarray only.

If data is aligned in a single chunk and has no Null values a zero copy view is returned as an ndarray

pub fn to_ndarray<N>(&self) -> Result<Array2<N::Native>> where
    N: PolarsNumericType, 

Available on crate feature ndarray only.

If all nested Series have the same length, a 2 dimensional ndarray::Array is returned.

pub fn amortized_iter(
    &self
) -> AmortizedListIter<'_, impl Iterator<Item = Option<ArrayBox>> + '_>

This is an iterator over a ListChunked that save allocations. A Series is: 1. Arc ChunkedArray is: 2. Vec< 3. ArrayRef>

The ArrayRef we indicated with 3. will be updated during iteration. The Series will be pinned in memory, saving an allocation for

1. Arc<..>
2. Vec<…>

Warning

Though memory safe in the sense that it will not read unowned memory, UB, or memory leaks this function still needs precautions. The returned should never be cloned or taken longer than a single iteration, as every call on next of the iterator will change the contents of that Series.

pub fn apply_amortized<'a, F>(&'a self, f: F) -> Self where
    F: FnMut(UnstableSeries<'a>) -> Series, 

Apply a closure F elementwise.

pub fn try_apply_amortized<'a, F>(&'a self, f: F) -> Result<Self> where
    F: FnMut(UnstableSeries<'a>) -> Result<Series>, 

pub fn lst_join(&self, separator: &str) -> Result<Utf8Chunked>

Available on crate feature list only.

In case the inner dtype DataType::Utf8, the individual items will be joined into a single string separated by separator.

pub fn lst_max(&self) -> Series

Available on crate feature list only.

pub fn lst_min(&self) -> Series

Available on crate feature list only.

pub fn lst_sum(&self) -> Series

Available on crate feature list only.

pub fn lst_mean(&self) -> Float64Chunked

Available on crate feature list only.

pub fn lst_sort(&self, reverse: bool) -> ListChunked

Available on crate feature list only.

pub fn lst_reverse(&self) -> ListChunked

Available on crate feature list only.

pub fn lst_unique(&self) -> Result<ListChunked>

Available on crate feature list only.

pub fn lst_arg_min(&self) -> IdxCa

Available on crate feature list only.

pub fn lst_arg_max(&self) -> IdxCa

Available on crate feature list only.

pub fn lst_diff(&self, n: usize, null_behavior: NullBehavior) -> ListChunked

Available on crate features list and diff only.

pub fn lst_shift(&self, periods: i64) -> ListChunked

Available on crate feature list only.

pub fn lst_slice(&self, offset: i64, length: usize) -> ListChunked

Available on crate feature list only.

pub fn lst_lengths(&self) -> UInt32Chunked

Available on crate feature list only.

pub fn lst_get(&self, idx: i64) -> Result<Series>

Available on crate feature list only.

Get the value by index in the sublists. So index 0 would return the first item of every sublist and index -1 would return the last item of every sublist if an index is out of bounds, it will return a None.

pub fn lst_concat(&self, other: &[Series]) -> Result<ListChunked>

Available on crate feature list only.

pub fn set_fast_explode(&mut self)

pub fn to_logical(&mut self, inner_dtype: DataType)

pub unsafe fn get_object_unchecked(
    &self,
    index: usize
) -> Option<&dyn PolarsObjectSafe>

Available on crate feature object only.

Get a hold to an object that can be formatted or downcasted via the Any trait.

Safety

No bounds checks

pub fn get_object(&self, index: usize) -> Option<&dyn PolarsObjectSafe>

Available on crate feature object only.

Get a hold to an object that can be formatted or downcasted via the Any trait.

pub fn sample_n(
    &self,
    n: usize,
    with_replacement: bool,
    seed: Option<u64>
) -> Result<Self>

Available on crate feature random only.

Sample n datapoints from this ChunkedArray.

pub fn sample_frac(
    &self,
    frac: f64,
    with_replacement: bool,
    seed: Option<u64>
) -> Result<Self>

Available on crate feature random only.

Sample a fraction between 0.0-1.0 of this ChunkedArray.

pub fn json_path_match(&self, json_path: &str) -> Result<Utf8Chunked>

Available on crate feature strings only.

Extract json path, first match Refer to https://goessner.net/articles/JsonPath/

pub fn hex_decode(&self, strict: Option<bool>) -> Result<Utf8Chunked>

Available on crate feature strings only.

pub fn hex_encode(&self) -> Utf8Chunked

Available on crate feature strings only.

pub fn base64_decode(&self, strict: Option<bool>) -> Result<Utf8Chunked>

Available on crate feature strings only.

pub fn base64_encode(&self) -> Utf8Chunked

Available on crate feature strings only.

pub fn str_lengths(&self) -> UInt32Chunked

Available on crate feature strings only.

Get the length of the string values.

pub fn contains(&self, pat: &str) -> Result<BooleanChunked>

Available on crate feature strings only.

Check if strings contain a regex pattern

pub fn replace(&self, pat: &str, val: &str) -> Result<Utf8Chunked>

Available on crate feature strings only.

Replace the leftmost (sub)string by a regex pattern

pub fn replace_all(&self, pat: &str, val: &str) -> Result<Utf8Chunked>

Available on crate feature strings only.

Replace all (sub)strings by a regex pattern

pub fn extract(&self, pat: &str, group_index: usize) -> Result<Utf8Chunked>

Available on crate feature strings only.

Extract the nth capture group from pattern

pub fn to_lowercase(&self) -> Utf8Chunked

Available on crate feature strings only.

Modify the strings to their lowercase equivalent

pub fn to_uppercase(&self) -> Utf8Chunked

Available on crate feature strings only.

Modify the strings to their uppercase equivalent

pub fn concat(&self, other: &Utf8Chunked) -> Self

Available on crate feature strings only.

Concat with the values from a second Utf8Chunked

pub fn str_slice(&self, start: i64, length: Option<u64>) -> Result<Self>

Available on crate feature strings only.

Slice the string values Determines a substring starting from start and with optional length length of each of the elements in array. start can be negative, in which case the start counts from the end of the string.

pub fn first_non_null(&self) -> Option<usize>

Get the index of the first non null value in this ChunkedArray.

pub fn iter_validities(&self) -> impl Iterator<Item = Option<&Bitmap>> + '_

Get the buffer of bits representing null values

pub fn has_validity(&self) -> bool

Return if any the chunks in this [ChunkedArray] have a validity bitmap. no bitmap means no null values.

pub fn shrink_to_fit(&mut self)

Shrink the capacity of this array to fit it’s length.

pub fn unpack_series_matching_type(
    &self,
    series: &Series
) -> Result<&ChunkedArray<T>>

Series to ChunkedArray

pub fn chunk_id(&self) -> ChunkIdIter<'_>

Unique id representing the number of chunks

pub fn chunks(&self) -> &Vec<ArrayRef>

A reference to the chunks

pub fn is_optimal_aligned(&self) -> bool

Returns true if contains a single chunk and has no null values

pub fn null_count(&self) -> usize

Count the null values.

pub fn append_array(&mut self, other: ArrayRef) -> Result<()>

Append arrow array in place.

let mut array = Int32Chunked::new("array", &[1, 2]);
let array_2 = Int32Chunked::new("2nd", &[3]);

array.append(&array_2);
assert_eq!(Vec::from(&array), [Some(1), Some(2), Some(3)])

pub fn is_null(&self) -> BooleanChunked

Get a mask of the null values.

pub fn is_not_null(&self) -> BooleanChunked

Get a mask of the valid values.

pub fn dtype(&self) -> &DataType

Get data type of ChunkedArray.

pub fn name(&self) -> &str

Name of the ChunkedArray.

pub fn ref_field(&self) -> &Field

Get a reference to the field.

pub fn rename(&mut self, name: &str)

Rename this ChunkedArray.

pub fn cont_slice(&self) -> Result<&[T::Native]>

Contiguous slice

pub fn data_views(
    &self
) -> impl Iterator<Item = &[T::Native]> + DoubleEndedIterator

Get slices of the underlying arrow data. NOTE: null values should be taken into account by the user of these slices as they are handled separately

pub fn into_no_null_iter(
    &self
) -> impl Iterator<Item = T::Native> + '_ + Send + Sync + ExactSizeIterator + DoubleEndedIterator + TrustedLen

pub fn inner_dtype(&self) -> DataType

Get the inner data type of the list.

pub fn lhs_sub<N: Num + NumCast>(&self, lhs: N) -> Self

Apply lhs - self

pub fn lhs_div<N: Num + NumCast>(&self, lhs: N) -> Self

Apply lhs / self

pub fn lhs_rem<N: Num + NumCast>(&self, lhs: N) -> Self

Apply lhs % self

Trait Implementations

impl<K: PolarsDataType, T: PolarsDataType> Clone for Logical<K, T>

fn clone(&self) -> Self

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<K: PolarsDataType, T: PolarsDataType> Deref for Logical<K, T>

type Target = ChunkedArray<T>

The resulting type after dereferencing.

fn deref(&self) -> &Self::Target

Dereferences the value.

impl<K: PolarsDataType, T: PolarsDataType> DerefMut for Logical<K, T>

fn deref_mut(&mut self) -> &mut Self::Target

Mutably dereferences the value.