ó à'·h-‰ãó‚•SSKJr SSKJr SSKJrJr SSKJr SSK J r \(aSSK J r SSK JrJr "SS 5rg ) é)Ú annotations)ÚSequence)Ú TYPE_CHECKINGÚCallable)Úparse_into_expression)Ú wrap_expr)ÚExpr)ÚIntoExprÚIntoExprColumncó¾•\rSrSrSrSrS,SjrS-SjrS.SS.S/S jjjrS0SS.S1S jjjr S0SS.S1S jjjr S-S jr S-S jr S-Sjr S2S3SjjrS2S3SjjrS-SjrS-SjrSS.S4SjjrS-SjrS-SjrS-SjrS-SjrSSS.S5SjjrS-SjrS-SjrS-SjrSS.S6SjjrS-S jrS-S!jrS"S#.S7S$jjrS-S%jrS"S&.S8S'jjr S9S(jr!S.S:S)jjr"S2S;S*jjr#S+r$g)<ÚExprArrayNameSpaceéz(Namespace for array related expressions.Úarrcó&•URUlg©N©Ú_pyexpr)ÚselfÚexprs ÚdC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\polars/expr/array.pyÚ__init__ÚExprArrayNameSpace.__init__s €Ø—|‘|ˆ ócóH•[URR55$)ub Return the number of elements in each array. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.len()) shape: (2, 1) ┌─────┠│ a │ │ --- │ │ u32 │ â•žâ•â•â•â•â•â•¡ │ 2 │ │ 2 │ └─────┘ )rrÚarr_len©rs rÚlenÚExprArrayNameSpace.lenó€ô*˜Ÿ™×-Ñ-Ó/Ó0Ð0rNF©Úas_arraycó‚•[U5nUb [U5OSn[URRXEU55$)u© Slice every subarray. Parameters ---------- offset Start index. Negative indexing is supported. length Length of the slice. If set to `None` (default), the slice is taken to the end of the list. as_array Return result as a fixed-length `Array`, otherwise as a `List`. If true `length` and `offset` must be constant values. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.slice(0, 1)) shape: (2, 1) ┌───────────┠│ a │ │ --- │ │ list[i64] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•¡ │ [1] │ │ [4] │ └───────────┘ >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.slice(0, 1, as_array=True)) shape: (2, 1) ┌───────────────┠│ a │ │ --- │ │ array[i64, 1] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ [1] │ │ [4] │ └───────────────┘ N)rrrÚ arr_slice)rÚoffsetÚlengthr!Ú offset_pyexprÚ length_pyexprs rÚsliceÚExprArrayNameSpace.slice-s>€ôh.¨fÓ5ˆ Ø9?Ñ9KÔ-¨fÔ5ÐQUˆ ܘŸ™×/Ñ/° ÈhÓWÓXÐXrcó"•URSXS9$)u8 Get the first `n` elements of the sub-arrays. Parameters ---------- n Number of values to return for each sublist. as_array Return result as a fixed-length `Array`, otherwise as a `List`. If true `n` must be a constant value. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.head(1)) shape: (2, 1) ┌───────────┠│ a │ │ --- │ │ list[i64] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•¡ │ [1] │ │ [4] │ └───────────┘ >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.head(1, as_array=True)) shape: (2, 1) ┌───────────────┠│ a │ │ --- │ │ array[i64, 1] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ [1] │ │ [4] │ └───────────────┘ rr )r()rÚnr!s rÚheadÚExprArrayNameSpace.heades€ðVz‰z˜!˜QˆzÐ2Ð2rcó`•[U5n[URRX255$)u6 Slice the last `n` values of every sublist. Parameters ---------- n Number of values to return for each sublist. as_array Return result as a fixed-length `Array`, otherwise as a `List`. If true `n` must be a constant value. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.tail(1)) shape: (2, 1) ┌───────────┠│ a │ │ --- │ │ list[i64] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•¡ │ [2] │ │ [3] │ └───────────┘ >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.tail(1, as_array=True)) shape: (2, 1) ┌───────────────┠│ a │ │ --- │ │ array[i64, 1] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ [2] │ │ [3] │ └───────────────┘ )rrrÚarr_tail)rr+r!Ún_pyexprs rÚtailÚExprArrayNameSpace.tail’s)€ôV)¨Ó+ˆÜ˜Ÿ™×.Ñ.¨xÓBÓCÐCrcóH•[URR55$)u_ Compute the min values of the sub-arrays. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.min()) shape: (2, 1) ┌─────┠│ a │ │ --- │ │ i64 │ â•žâ•â•â•â•â•â•¡ │ 1 │ │ 3 │ └─────┘ )rrÚarr_minrs rÚminÚExprArrayNameSpace.minÀrrcóH•[URR55$)u_ Compute the max values of the sub-arrays. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.max()) shape: (2, 1) ┌─────┠│ a │ │ --- │ │ i64 │ â•žâ•â•â•â•â•â•¡ │ 2 │ │ 4 │ └─────┘ )rrÚarr_maxrs rÚmaxÚExprArrayNameSpace.max×rrcóH•[URR55$)u_ Compute the sum values of the sub-arrays. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.sum()) shape: (2, 1) ┌─────┠│ a │ │ --- │ │ i64 │ â•žâ•â•â•â•â•â•¡ │ 3 │ │ 7 │ └─────┘ )rrÚarr_sumrs rÚsumÚExprArrayNameSpace.sumîrrcóJ•[URRU55$)u¬ Compute the std of the values of the sub-arrays. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.std()) shape: (2, 1) ┌──────────┠│ a │ │ --- │ │ f64 │ â•žâ•â•â•â•â•â•â•â•â•â•â•¡ │ 0.707107 │ │ 0.707107 │ └──────────┘ )rrÚarr_std©rÚddofs rÚstdÚExprArrayNameSpace.stdó€ô*˜Ÿ™×-Ñ-¨dÓ3Ó4Ð4rcóJ•[URRU55$)uf Compute the var of the values of the sub-arrays. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.var()) shape: (2, 1) ┌─────┠│ a │ │ --- │ │ f64 │ â•žâ•â•â•â•â•â•¡ │ 0.5 │ │ 0.5 │ └─────┘ )rrÚarr_varrAs rÚvarÚExprArrayNameSpace.varrErcóH•[URR55$)uo Compute the mean of the values of the sub-arrays. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2, 3], [1, 1, 16]]}, ... schema={"a": pl.Array(pl.Int64, 3)}, ... ) >>> df.select(pl.col("a").arr.mean()) shape: (2, 1) ┌─────┠│ a │ │ --- │ │ f64 │ â•žâ•â•â•â•â•â•¡ │ 2.0 │ │ 6.0 │ └─────┘ )rrÚarr_meanrs rÚmeanÚExprArrayNameSpace.mean3s€ô*˜Ÿ™×.Ñ.Ó0Ó1Ð1rcóH•[URR55$)ul Compute the median of the values of the sub-arrays. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [4, 3]]}, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.select(pl.col("a").arr.median()) shape: (2, 1) ┌─────┠│ a │ │ --- │ │ f64 │ â•žâ•â•â•â•â•â•¡ │ 1.5 │ │ 3.5 │ └─────┘ )rrÚ arr_medianrs rÚmedianÚExprArrayNameSpace.medianJs€ô*˜Ÿ™×0Ñ0Ó2Ó3Ð3r)Úmaintain_ordercóJ•[URRU55$)u Get the unique/distinct values in the array. Parameters ---------- maintain_order Maintain order of data. This requires more work. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [[1, 1, 2]], ... }, ... schema={"a": pl.Array(pl.Int64, 3)}, ... ) >>> df.select(pl.col("a").arr.unique()) shape: (1, 1) ┌───────────┠│ a │ │ --- │ │ list[i64] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•¡ │ [1, 2] │ └───────────┘ )rrÚ arr_unique)rrRs rÚuniqueÚExprArrayNameSpace.uniqueas€ô6˜Ÿ™×0Ñ0°Ó@ÓAÐArcóH•[URR55$)uÈ Count the number of unique values in every sub-arrays. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [[1, 1, 2], [2, 3, 4]], ... }, ... schema={"a": pl.Array(pl.Int64, 3)}, ... ) >>> df.with_columns(n_unique=pl.col("a").arr.n_unique()) shape: (2, 2) ┌───────────────┬──────────┠│ a ┆ n_unique │ │ --- ┆ --- │ │ array[i64, 3] ┆ u32 │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•¡ │ [1, 1, 2] ┆ 2 │ │ [2, 3, 4] ┆ 3 │ └───────────────┴──────────┘ )rrÚ arr_n_uniquers rÚn_uniqueÚExprArrayNameSpace.n_unique~s€ô.˜Ÿ™×2Ñ2Ó4Ó5Ð5rcóH•[URR55$)u  Convert an Array column into a List column with the same inner data type. Returns ------- Expr Expression of data type :class:`List`. Examples -------- >>> df = pl.DataFrame( ... data={"a": [[1, 2], [3, 4]]}, ... schema={"a": pl.Array(pl.Int8, 2)}, ... ) >>> df.select(pl.col("a").arr.to_list()) shape: (2, 1) ┌──────────┠│ a │ │ --- │ │ list[i8] │ â•žâ•â•â•â•â•â•â•â•â•â•â•¡ │ [1, 2] │ │ [3, 4] │ └──────────┘ )rrÚ arr_to_listrs rÚto_listÚExprArrayNameSpace.to_list—s€ô4˜Ÿ™×1Ñ1Ó3Ó4Ð4rcóH•[URR55$)u Evaluate whether any boolean value is true for every subarray. Examples -------- >>> df = pl.DataFrame( ... data={ ... "a": [ ... [True, True], ... [False, True], ... [False, False], ... [None, None], ... None, ... ] ... }, ... schema={"a": pl.Array(pl.Boolean, 2)}, ... ) >>> df.with_columns(any=pl.col("a").arr.any()) shape: (5, 2) ┌────────────────┬───────┠│ a ┆ any │ │ --- ┆ --- │ │ array[bool, 2] ┆ bool │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•¡ │ [true, true] ┆ true │ │ [false, true] ┆ true │ │ [false, false] ┆ false │ │ [null, null] ┆ false │ │ null ┆ null │ └────────────────┴───────┘ )rrÚarr_anyrs rÚanyÚExprArrayNameSpace.any³ó€ô@˜Ÿ™×-Ñ-Ó/Ó0Ð0rcóH•[URR55$)uŸ Evaluate whether all boolean values are true for every subarray. Examples -------- >>> df = pl.DataFrame( ... data={ ... "a": [ ... [True, True], ... [False, True], ... [False, False], ... [None, None], ... None, ... ] ... }, ... schema={"a": pl.Array(pl.Boolean, 2)}, ... ) >>> df.with_columns(all=pl.col("a").arr.all()) shape: (5, 2) ┌────────────────┬───────┠│ a ┆ all │ │ --- ┆ --- │ │ array[bool, 2] ┆ bool │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•¡ │ [true, true] ┆ true │ │ [false, true] ┆ false │ │ [false, false] ┆ false │ │ [null, null] ┆ true │ │ null ┆ null │ └────────────────┴───────┘ )rrÚarr_allrs rÚallÚExprArrayNameSpace.allÕrcr)Ú descendingÚ nulls_lastcóJ•[URRX55$)u˜ Sort the arrays in this column. Parameters ---------- descending Sort in descending order. nulls_last Place null values last. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [[3, 2, 1], [9, 1, 2]], ... }, ... schema={"a": pl.Array(pl.Int64, 3)}, ... ) >>> df.with_columns(sort=pl.col("a").arr.sort()) shape: (2, 2) ┌───────────────┬───────────────┠│ a ┆ sort │ │ --- ┆ --- │ │ array[i64, 3] ┆ array[i64, 3] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ [3, 2, 1] ┆ [1, 2, 3] │ │ [9, 1, 2] ┆ [1, 2, 9] │ └───────────────┴───────────────┘ >>> df.with_columns(sort=pl.col("a").arr.sort(descending=True)) shape: (2, 2) ┌───────────────┬───────────────┠│ a ┆ sort │ │ --- ┆ --- │ │ array[i64, 3] ┆ array[i64, 3] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ [3, 2, 1] ┆ [3, 2, 1] │ │ [9, 1, 2] ┆ [9, 2, 1] │ └───────────────┴───────────────┘ )rrÚarr_sort)rrhris rÚsortÚExprArrayNameSpace.sort÷s€ôP˜Ÿ™×.Ñ.¨zÓFÓGÐGrcóH•[URR55$)uø Reverse the arrays in this column. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [[3, 2, 1], [9, 1, 2]], ... }, ... schema={"a": pl.Array(pl.Int64, 3)}, ... ) >>> df.with_columns(reverse=pl.col("a").arr.reverse()) shape: (2, 2) ┌───────────────┬───────────────┠│ a ┆ reverse │ │ --- ┆ --- │ │ array[i64, 3] ┆ array[i64, 3] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ [3, 2, 1] ┆ [1, 2, 3] │ │ [9, 1, 2] ┆ [2, 1, 9] │ └───────────────┴───────────────┘ )rrÚ arr_reversers rÚreverseÚExprArrayNameSpace.reverse!s€ô.˜Ÿ™×1Ñ1Ó3Ó4Ð4rcóH•[URR55$)u, Retrieve the index of the minimal value in every sub-array. Returns ------- Expr Expression of data type :class:`UInt32` or :class:`UInt64` (depending on compilation). Examples -------- >>> df = pl.DataFrame( ... { ... "a": [[1, 2], [2, 1]], ... }, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.with_columns(arg_min=pl.col("a").arr.arg_min()) shape: (2, 2) ┌───────────────┬─────────┠│ a ┆ arg_min │ │ --- ┆ --- │ │ array[i64, 2] ┆ u32 │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•¡ │ [1, 2] ┆ 0 │ │ [2, 1] ┆ 1 │ └───────────────┴─────────┘ )rrÚ arr_arg_minrs rÚarg_minÚExprArrayNameSpace.arg_min:ó€ô:˜Ÿ™×1Ñ1Ó3Ó4Ð4rcóH•[URR55$)u, Retrieve the index of the maximum value in every sub-array. Returns ------- Expr Expression of data type :class:`UInt32` or :class:`UInt64` (depending on compilation). Examples -------- >>> df = pl.DataFrame( ... { ... "a": [[1, 2], [2, 1]], ... }, ... schema={"a": pl.Array(pl.Int64, 2)}, ... ) >>> df.with_columns(arg_max=pl.col("a").arr.arg_max()) shape: (2, 2) ┌───────────────┬─────────┠│ a ┆ arg_max │ │ --- ┆ --- │ │ array[i64, 2] ┆ u32 │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•¡ │ [1, 2] ┆ 1 │ │ [2, 1] ┆ 0 │ └───────────────┴─────────┘ )rrÚ arr_arg_maxrs rÚarg_maxÚExprArrayNameSpace.arg_maxYrvr©Ú null_on_oobcó`•[U5n[URRX255$)u{ Get the value by index in the sub-arrays. 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`. Parameters ---------- index Index to return per sub-array null_on_oob Behavior if an index is out of bounds: True -> set as null False -> raise an error Examples -------- >>> df = pl.DataFrame( ... {"arr": [[1, 2, 3], [4, 5, 6], [7, 8, 9]], "idx": [1, -2, 0]}, ... schema={"arr": pl.Array(pl.Int32, 3), "idx": pl.Int32}, ... ) >>> df.with_columns(get=pl.col("arr").arr.get("idx", null_on_oob=True)) shape: (3, 3) ┌───────────────┬─────┬─────┠│ arr ┆ idx ┆ get │ │ --- ┆ --- ┆ --- │ │ array[i32, 3] ┆ i32 ┆ i32 │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ [1, 2, 3] ┆ 1 ┆ 2 │ │ [4, 5, 6] ┆ -2 ┆ 5 │ │ [7, 8, 9] ┆ 0 ┆ 7 │ └───────────────┴─────┴─────┘ )rrrÚarr_get)rÚindexr|Ú index_pyexprs rÚgetÚExprArrayNameSpace.getxs)€ôF-¨UÓ3ˆ ܘŸ™×-Ñ-¨lÓHÓIÐIrcó"•URSSS9$)uœ Get the first value of the sub-arrays. Examples -------- >>> df = pl.DataFrame( ... {"a": [[1, 2, 3], [4, 5, 6], [7, 8, 9]]}, ... schema={"a": pl.Array(pl.Int32, 3)}, ... ) >>> df.with_columns(first=pl.col("a").arr.first()) shape: (3, 2) ┌───────────────┬───────┠│ a ┆ first │ │ --- ┆ --- │ │ array[i32, 3] ┆ i32 │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•¡ │ [1, 2, 3] ┆ 1 │ │ [4, 5, 6] ┆ 4 │ │ [7, 8, 9] ┆ 7 │ └───────────────┴───────┘ rTr{©rrs rÚfirstÚExprArrayNameSpace.firstžs€ð,x‰x˜ tˆxÐ,Ð,rcó"•URSSS9$)uŠ Get the last value of the sub-arrays. Examples -------- >>> df = pl.DataFrame( ... {"a": [[1, 2, 3], [4, 5, 6], [7, 9, 8]]}, ... schema={"a": pl.Array(pl.Int32, 3)}, ... ) >>> df.with_columns(last=pl.col("a").arr.last()) shape: (3, 2) ┌───────────────┬──────┠│ a ┆ last │ │ --- ┆ --- │ │ array[i32, 3] ┆ i32 │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ │ [1, 2, 3] ┆ 3 │ │ [4, 5, 6] ┆ 6 │ │ [7, 9, 8] ┆ 8 │ └───────────────┴──────┘ éÿÿÿÿTr{r„rs rÚlastÚExprArrayNameSpace.last¶s€ð,x‰x˜¨ˆxÐ-Ð-rT)Ú ignore_nullscó^•[USS9n[URRX255$)u  Join all string items in a sub-array and place a separator between them. This errors if inner type of array `!= String`. Parameters ---------- separator string to separate the items with ignore_nulls Ignore null values (default). If set to ``False``, null values will be propagated. If the sub-list contains any null values, the output is ``None``. Returns ------- Expr Expression of data type :class:`String`. Examples -------- >>> df = pl.DataFrame( ... {"s": [["a", "b"], ["x", "y"]], "separator": ["*", "_"]}, ... schema={ ... "s": pl.Array(pl.String, 2), ... "separator": pl.String, ... }, ... ) >>> df.with_columns(join=pl.col("s").arr.join(pl.col("separator"))) shape: (2, 3) ┌───────────────┬───────────┬──────┠│ s ┆ separator ┆ join │ │ --- ┆ --- ┆ --- │ │ array[str, 2] ┆ str ┆ str │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ │ ["a", "b"] ┆ * ┆ a*b │ │ ["x", "y"] ┆ _ ┆ x_y │ └───────────────┴───────────┴──────┘ T©Ú str_as_lit)rrrÚarr_join)rÚ separatorr‹Úseparator_pyexprs rÚjoinÚExprArrayNameSpace.joinÎs-€ôR1°ÀtÑLÐܘŸ™×.Ñ.Ð/?ÓNÓOÐOrcóH•[URR55$)uî Returns a column with a separate row for every array element. Returns ------- Expr Expression with the data type of the array elements. Examples -------- >>> df = pl.DataFrame( ... {"a": [[1, 2, 3], [4, 5, 6]]}, schema={"a": pl.Array(pl.Int64, 3)} ... ) >>> df.select(pl.col("a").arr.explode()) shape: (6, 1) ┌─────┠│ a │ │ --- │ │ i64 │ â•žâ•â•â•â•â•â•¡ │ 1 │ │ 2 │ │ 3 │ │ 4 │ │ 5 │ │ 6 │ └─────┘ )rrÚ arr_exploders rÚexplodeÚExprArrayNameSpace.explodeúrvr)Ú nulls_equalcó^•[USS9n[URRX255$)uÖ Check if sub-arrays contain the given item. Parameters ---------- item Item that will be checked for membership nulls_equal : bool, default True If True, treat null as a distinct value. Null values will not propagate. Returns ------- Expr Expression of data type :class:`Boolean`. Examples -------- >>> df = pl.DataFrame( ... {"a": [["a", "b"], ["x", "y"], ["a", "c"]]}, ... schema={"a": pl.Array(pl.String, 2)}, ... ) >>> df.with_columns(contains=pl.col("a").arr.contains("a")) shape: (3, 2) ┌───────────────┬──────────┠│ a ┆ contains │ │ --- ┆ --- │ │ array[str, 2] ┆ bool │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•¡ │ ["a", "b"] ┆ true │ │ ["x", "y"] ┆ false │ │ ["a", "c"] ┆ true │ └───────────────┴──────────┘ Tr)rrrÚ arr_contains)rÚitemr˜Ú item_pyexprs rÚcontainsÚExprArrayNameSpace.containss+€ôD,¨D¸TÑBˆ ܘŸ™×2Ñ2°;ÓLÓMÐMrcó^•[USS9n[URRU55$)u‚ Count how often the value produced by `element` occurs. Parameters ---------- element An expression that produces a single value Examples -------- >>> df = pl.DataFrame( ... {"a": [[1, 2], [1, 1], [2, 2]]}, schema={"a": pl.Array(pl.Int64, 2)} ... ) >>> df.with_columns(number_of_twos=pl.col("a").arr.count_matches(2)) shape: (3, 2) ┌───────────────┬────────────────┠│ a ┆ number_of_twos │ │ --- ┆ --- │ │ array[i64, 2] ┆ u32 │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ [1, 2] ┆ 1 │ │ [1, 1] ┆ 0 │ │ [2, 2] ┆ 2 │ └───────────────┴────────────────┘ Tr)rrrÚarr_count_matches)rÚelementÚelement_pyexprs rÚ count_matchesÚ ExprArrayNameSpace.count_matches>s*€ô4/¨wÀ4ÑHˆÜ˜Ÿ™×7Ñ7¸ÓGÓHÐHrcó •[U[5(aJ[U5nURR S5n[ U5R RU5$URR U5n[ U5$)uŽ Convert the Series of type `Array` to a Series of type `Struct`. Parameters ---------- fields If the name and number of the desired fields is known in advance a list of field names can be given, which will be assigned by index. Otherwise, to dynamically assign field names, a custom function can be used; if neither are set, fields will be `field_0, field_1 .. field_n`. Examples -------- Convert array to struct with default field name assignment: >>> df = pl.DataFrame( ... {"n": [[0, 1, 2], [3, 4, 5]]}, schema={"n": pl.Array(pl.Int8, 3)} ... ) >>> df.with_columns(struct=pl.col("n").arr.to_struct()) shape: (2, 2) ┌──────────────┬───────────┠│ n ┆ struct │ │ --- ┆ --- │ │ array[i8, 3] ┆ struct[3] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•¡ │ [0, 1, 2] ┆ {0,1,2} │ │ [3, 4, 5] ┆ {3,4,5} │ └──────────────┴───────────┘ Convert array to struct with field name assignment by function/index: >>> df = pl.DataFrame( ... {"n": [[0, 1, 2], [3, 4, 5]]}, schema={"n": pl.Array(pl.Int8, 3)} ... ) >>> df.select(pl.col("n").arr.to_struct(fields=lambda idx: f"n{idx}")).rows( ... named=True ... ) [{'n': {'n0': 0, 'n1': 1, 'n2': 2}}, {'n': {'n0': 3, 'n1': 4, 'n2': 5}}] Convert array to struct with field name assignment by index from a list of names: >>> df.select(pl.col("n").arr.to_struct(fields=["c1", "c2", "c3"])).rows( ... named=True ... ) [{'n': {'c1': 0, 'c2': 1, 'c3': 2}}, {'n': {'c1': 3, 'c2': 4, 'c3': 5}}] N)Ú isinstancerÚlistrÚ arr_to_structrÚstructÚ rename_fields)rÚfieldsÚ field_namesÚpyexprs rÚ to_structÚExprArrayNameSpace.to_struct[sj€ôd fœh× 'Ñ 'ܘv›,ˆKØ—\‘\×/Ñ/°Ó5ˆFܘVÓ$×+Ñ+×9Ñ9¸+ÓFÐ Fà—\‘\×/Ñ/°Ó7ˆFܘVÓ$Ð $rcó`•[U5n[URRU55$)uï Shift array values by the given number of indices. Parameters ---------- n Number of indices to shift forward. If a negative value is passed, values are shifted in the opposite direction instead. Notes ----- This method is similar to the `LAG` operation in SQL when the value for `n` is positive. With a negative value for `n`, it is similar to `LEAD`. Examples -------- By default, array values are shifted forward by one index. >>> df = pl.DataFrame( ... {"a": [[1, 2, 3], [4, 5, 6]]}, schema={"a": pl.Array(pl.Int64, 3)} ... ) >>> df.with_columns(shift=pl.col("a").arr.shift()) shape: (2, 2) ┌───────────────┬───────────────┠│ a ┆ shift │ │ --- ┆ --- │ │ array[i64, 3] ┆ array[i64, 3] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ [1, 2, 3] ┆ [null, 1, 2] │ │ [4, 5, 6] ┆ [null, 4, 5] │ └───────────────┴───────────────┘ Pass a negative value to shift in the opposite direction instead. >>> df.with_columns(shift=pl.col("a").arr.shift(-2)) shape: (2, 2) ┌───────────────┬─────────────────┠│ a ┆ shift │ │ --- ┆ --- │ │ array[i64, 3] ┆ array[i64, 3] │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ [1, 2, 3] ┆ [3, null, null] │ │ [4, 5, 6] ┆ [6, null, null] │ └───────────────┴─────────────────┘ )rrrÚ arr_shift)rr+r0s rÚshiftÚExprArrayNameSpace.shift•s)€ô\)¨Ó+ˆÜ˜Ÿ™×/Ñ/°Ó9Ó:Ð:rr)rr ÚreturnÚNone)r´r r)r$úint | str | Exprr%zint | str | Expr | Noner!Úboolr´r )é)r+r¶r!r·r´r )é)rBÚintr´r )rRr·r´r )rhr·rir·r´r )rúint | IntoExprColumnr|r·r´r )rr r‹r·r´r )r›r r˜r·r´r )r¡r r´r )r«z+Sequence[str] | Callable[[int], str] | Noner´r )r+r»r´r )%Ú__name__Ú __module__Ú __qualname__Ú__firstlineno__Ú__doc__Ú _accessorrrr(r,r1r5r9r=rCrHrLrPrUrYr]rarfrlrprtryrr…r‰r’r–rr£r®r²Ú__static_attributes__©rrr r sB†Ù2à€Iô$ô1ð4+/ð6Yð ñ 6Yà ð6Yð(ð6Yð ð 6Yð ö 6Yðp+3À÷+3ð+3ðZ,DÀ÷,Dð,Dô\1ô.1ô.1ö.5ö.5ô.2ô.4ð.05÷Bô:6ô25ô8 1ôD 1ðD*/À5÷(HôT5ô25ô>5ð>GL÷$JôL-ô0.ð0GK÷*PôX5ð>?C÷#NôJIð<EIð8%ØAð8%à õ8%÷t/;ñ/;rr N)Ú __future__rÚcollections.abcrÚtypingrrÚpolars._utils.parserÚpolars._utils.wraprÚpolarsr Úpolars._typingr r r rÃrrÚrËs)ðÝ"å$ß*å5Ý(æÝß7÷v;òv;r