ó à'·håbãóЕSSKJr SSKrSSKJrJrJr SSKJr SSK J r SSK J r J r SSKJr SSKJr SSKJr SS KJrJr SS KJrJrJrJr SS KJr \R@"\!5 SSK"J#r$ SSS5 \ (aSS KJ%r% SS KJ&r&J'r'J(r(J)r) SSKJ*r*J+r+J,r, SSSS.SSjjr-SSS.SSjjr.SSSS.SSjjr/g!,(df  No=f)é)Ú annotationsN)Ú GeneratorÚIteratorÚSequence)Úreduce)Úchain)Ú TYPE_CHECKINGÚget_args)Ú functions)Ú ConcatMethod)Úordered_uniqueÚqualified_type_name)Úwrap_dfÚ wrap_exprÚwrap_ldfÚwrap_s)ÚInvalidOperationError)ÚIterable)Ú DataFrameÚExprÚ LazyFrameÚSeries)Ú FrameTypeÚ JoinStrategyÚ PolarsTypeÚverticalFT)ÚhowÚrechunkÚparallelc ó ^^^•[U5nU(d Sn[U5e[U5S:XaF[US[R [R [R45(aUS$URS5(Ga†[US[R [R45(d U<S[US5<3n[U5e[[R"SU555n[[U55VVs0sHupxX‡_M snnm[T5n [[!S[SU555U4S jS 9mT(dU<S 3n[#U5eUS:XaS OUR%S 5m[!UU4SjUV s/sHoªR'5PM sn 5R)TSS9R*"U 6n [US[R 5n U (aU R-5$U $USn SSKJn [U [R 5(GapUS:Xa![3[4R6"U55nGOöUS:Xa[[9[4R:"UV s/sHoªR'5PM sn UUSS95R-UR=5S9nGO•US:Xa![3[4R>"U55nGOnUS:Xa[[9[4R@"UV s/sHoªR'5PM sn UUSS95R-UR=5S9nGO US:Xa![3[4RB"U55nGOæSRES[G[H555nSUSU<3n[U5e[U [R5(aÇUS;a/[9[4R:"UUUURKS5S95$US;a/[9[4R@"UUUURKS5S95$US:Xa[9[4RL"UUS 95$SRES![G[H555nS"USU<3n[U5e[U [R 5(a3US:Xa [O[4RP"U55nO‚S#n[U5e[U [RR5(a;[U[4RV"UVs/sHnURXPM snU55$S$[U 5<S%3n[U5eU(aUR[5$U$s snnfs sn fs sn fs sn fs snf)&uÕ Combine multiple DataFrames, LazyFrames, or Series into a single object. Parameters ---------- items DataFrames, LazyFrames, or Series to concatenate. how : {'vertical', 'vertical_relaxed', 'diagonal', 'diagonal_relaxed', 'horizontal', 'align', 'align_full', 'align_inner', 'align_left', 'align_right'} Note that `Series` only support the `vertical` strategy. * vertical: Applies multiple `vstack` operations. * vertical_relaxed: Same as `vertical`, but additionally coerces columns to their common supertype *if* they are mismatched (eg: Int32 → Int64). * diagonal: Finds a union between the column schemas and fills missing column values with `null`. * diagonal_relaxed: Same as `diagonal`, but additionally coerces columns to their common supertype *if* they are mismatched (eg: Int32 → Int64). * horizontal: Stacks Series from DataFrames horizontally and fills with `null` if the lengths don't match. * align, align_full, align_left, align_right: Combines frames horizontally, auto-determining the common key columns and aligning rows using the same logic as `align_frames` (note that "align" is an alias for "align_full"). The "align" strategy determines the type of join used to align the frames, equivalent to the "how" parameter on `align_frames`. Note that the common join columns are automatically coalesced, but other column collisions will raise an error (if you need more control over this you should use a suitable `join` method directly). rechunk Make sure that the result data is in contiguous memory. parallel Only relevant for LazyFrames. This determines if the concatenated lazy computations may be executed in parallel. Examples -------- >>> df1 = pl.DataFrame({"a": [1], "b": [3]}) >>> df2 = pl.DataFrame({"a": [2], "b": [4]}) >>> pl.concat([df1, df2]) # default is 'vertical' strategy shape: (2, 2) ┌─────┬─────┠│ a ┆ b │ │ --- ┆ --- │ │ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1 ┆ 3 │ │ 2 ┆ 4 │ └─────┴─────┘ >>> df1 = pl.DataFrame({"a": [1], "b": [3]}) >>> df2 = pl.DataFrame({"a": [2.5], "b": [4]}) >>> pl.concat([df1, df2], how="vertical_relaxed") # 'a' coerced into f64 shape: (2, 2) ┌─────┬─────┠│ a ┆ b │ │ --- ┆ --- │ │ f64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1.0 ┆ 3 │ │ 2.5 ┆ 4 │ └─────┴─────┘ >>> df_h1 = pl.DataFrame({"l1": [1, 2], "l2": [3, 4]}) >>> df_h2 = pl.DataFrame({"r1": [5, 6], "r2": [7, 8], "r3": [9, 10]}) >>> pl.concat([df_h1, df_h2], how="horizontal") shape: (2, 5) ┌─────┬─────┬─────┬─────┬─────┠│ l1 ┆ l2 ┆ r1 ┆ r2 ┆ r3 │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1 ┆ 3 ┆ 5 ┆ 7 ┆ 9 │ │ 2 ┆ 4 ┆ 6 ┆ 8 ┆ 10 │ └─────┴─────┴─────┴─────┴─────┘ The "diagonal" strategy allows for some frames to have missing columns, the values for which are filled with `null`: >>> df_d1 = pl.DataFrame({"a": [1], "b": [3]}) >>> df_d2 = pl.DataFrame({"a": [2], "c": [4]}) >>> pl.concat([df_d1, df_d2], how="diagonal") shape: (2, 3) ┌─────┬──────┬──────┠│ a ┆ b ┆ c │ │ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ │ 1 ┆ 3 ┆ null │ │ 2 ┆ null ┆ 4 │ └─────┴──────┴──────┘ The "align" strategies require at least one common column to align on: >>> df_a1 = pl.DataFrame({"id": [1, 2], "x": [3, 4]}) >>> df_a2 = pl.DataFrame({"id": [2, 3], "y": [5, 6]}) >>> df_a3 = pl.DataFrame({"id": [1, 3], "z": [7, 8]}) >>> pl.concat([df_a1, df_a2, df_a3], how="align") # equivalent to "align_full" shape: (3, 4) ┌─────┬──────┬──────┬──────┠│ id ┆ x ┆ y ┆ z │ │ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ │ 1 ┆ 3 ┆ null ┆ 7 │ │ 2 ┆ 4 ┆ 5 ┆ null │ │ 3 ┆ null ┆ 6 ┆ 8 │ └─────┴──────┴──────┴──────┘ >>> pl.concat([df_a1, df_a2, df_a3], how="align_left") shape: (2, 4) ┌─────┬─────┬──────┬──────┠│ id ┆ x ┆ y ┆ z │ │ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ │ 1 ┆ 3 ┆ null ┆ 7 │ │ 2 ┆ 4 ┆ 5 ┆ null │ └─────┴─────┴──────┴──────┘ >>> pl.concat([df_a1, df_a2, df_a3], how="align_right") shape: (2, 4) ┌─────┬──────┬──────┬─────┠│ id ┆ x ┆ y ┆ z │ │ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1 ┆ null ┆ null ┆ 7 │ │ 3 ┆ null ┆ 6 ┆ 8 │ └─────┴──────┴──────┴─────┘ >>> pl.concat([df_a1, df_a2, df_a3], how="align_inner") shape: (0, 4) ┌─────┬─────┬─────┬─────┠│ id ┆ x ┆ y ┆ z │ │ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ └─────┴─────┴─────┴─────┘ zcannot concat empty listérÚalignz strategy is not supported for c3ó@# •UHoR5v• M g7f©N©Úcollect_schema©Ú.0Úes ÚiC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\polars/functions/eager.pyÚ Úconcat..¹sé€Ð.QÊ5Àa×/?Ñ/?×/AÐ/AÊ5ùó‚có0•[U5[U5-$r$)Úset)ÚxÚys r*ÚÚconcat..¾s€œS ›V¤c¨!£fš_óc3ó@# •UHoR5v• M g7fr$r%r's r*r+r,¿sé€Ð8²%¨Q×&Ñ&×(Ð(²%ùr-có(>•TRUS5$)Nr)Úget)ÚkÚkeys €r*r2r3Ásø€˜#Ÿ'™' ! Qœ-r4)r9z- strategy requires at least one common columnÚfullÚalign_có*>•URUTTSSS9$)NÚ right_leftT)ÚonrÚmaintain_orderÚcoalesce©Újoin)r0r1Ú common_colsÚ join_methods €€r*r2r3Ïs#ø€Ø—F‘FØØ&Ø'Ø'3Ø!%ð ñr4T)Úbyr?©Ú QueryOptFlagsrÚvertical_relaxed)rrÚ to_supertypes©Ú optimizationsÚdiagonalÚdiagonal_relaxedÚ horizontalz, c3ó8# •UHn[U5v• M g7fr$©Úrepr©r(Úms r*r+r,óé€ÐHÒ1G¨A¤ Q§ Ò1Gùó‚z DataFrame `how` must be one of {z}, got )rrHÚrelaxed)rLrM)rc3ó8# •UHn[U5v• M g7fr$rPrRs r*r+r,rTrUz LazyFrame `how` must be one of {z/Series only supports 'vertical' concat strategyzdid not expect type: z in `concat`).ÚlistÚ ValueErrorÚlenÚ isinstanceÚplrrrÚ startswithrÚ TypeErrorrÚ from_iterableÚ enumerater ÚsortedrrÚ removeprefixÚlazyÚsortÚselectÚcollectÚpolars.lazyframe.opt_flagsrGrÚplrÚ concat_dfrÚ concat_lfÚ_eagerÚconcat_df_diagonalÚconcat_lf_diagonalÚconcat_df_horizontalrBr r ÚendswithÚconcat_lf_horizontalrÚ concat_seriesrrÚ concat_exprÚ_pyexprr)ÚitemsrrrÚelemsÚmsgÚ all_columnsr8ÚvÚoutput_column_orderÚdfÚlfÚeagerÚfirstrGÚoutÚallowedr)rCrDr9s @@@r*Úconcatr€säú€ô^ ‹K€Eæ Ø(ˆÜ˜‹oÐÜ ˆU‹q‹œZØ ˆa‰”2—<‘<¤§¡¬B¯L©LÐ9÷ñðQ‰xˆà ‡~~g×Òܘ% ™(¤R§\¡\´2·<±<Ð$@×AÑAØ‘GÐ:Ô;NÈuÐUVÉxÓ;XÑ:[Ð\ˆCܘC“.Ð ôœ5×.Ò.Ñ.QÉ5Ó.QÓQÓRˆ Ü )¬.¸Ó*EÔ FÔGÒ F™˜ˆqŠtÑ FÒGˆÜ" 3›iÐÜÜ Ù,ÜÑ8±%Ó8Ó8ó ô(ñ  ˆ öØ‘GÐHÐIˆCÜ'¨Ó,Ð ,ð ˜W“n‰F¨#×*:Ñ*:¸8Ó*Dð ô õñ&+Ó+¢U˜r—‘–¡UÑ+ó ÷‰T[°ˆTÐ 6ß ‰Vð ð)ð *ð ô ˜5 ™8¤R§\¡\Ó2ˆÞ$ˆrz‰z‹|Ð,¨"Ð,ð !‰H€Eå8ä%œŸ™×&Ò&Ø *Ó Üœ#Ÿ-š-¨Ó.Ó/ŠCØ Ð&Ó &ÜÜ— ’ Ù).Ó/ª 2—W‘W–Y©Ñ/Ø#Ø%Ø"&ñ ó÷‰g M×$8Ñ$8Ó$:ˆgÐ;ò ðJÓ Üœ#×0Ò0°Ó7Ó8ŠCØ Ð&Ó &ÜÜ×&Ò&Ù).Ó/ª 2—W‘W–Y©Ñ/Ø#Ø%Ø"&ñ ó÷‰g M×$8Ñ$8Ó$:ˆgÐ;ò ðLÓ Üœ#×2Ò2°5Ó9Ó:ŠCà—i‘iÑH´¼,Ô1GÓHÓHˆGØ5°g°Y¸hÀsÁgÐNˆCܘS“/Ð !ä Eœ2Ÿ<™<× (Ñ (Ø Ð2Ó 2ÜÜ— ’ ØØ#Ø%Ø"%§,¡,¨yÓ"9ñ óð ðÐ4Ó 4ÜÜ×&Ò&ØØ#Ø%Ø"%§,¡,¨yÓ"9ñ óð ðLÓ ÜÜ×(Ò(ØØ%ñóð ð—i‘iÑH´¼,Ô1GÓHÓHˆGØ5°g°Y¸hÀsÁgÐNˆCܘS“/Ð !ä Eœ2Ÿ9™9× %Ñ %Ø *Ó Üœ×*Ò*¨5Ó1Ó2‰CàCˆCܘS“/Ð !ä Eœ2Ÿ7™7× #Ñ #ÜœŸš¹UÓ)CºU¸¨!¯)¬)¹UÑ)CÀWÓMÓNÐNà%Ô&9¸%Ó&@Ñ%CÀ<ÐPˆÜ˜‹nÐæØ{‰{‹}ÐØ €JùóqHùò<,ùò&0ùò0ùòh*DsÄU6Æ$U< ÊV ÌV Ô V r:)rÚ descendingcóî^^•[U5S:¬nS UU4SjjnSSKJn [XS5SR TUSS9nU(a+UR UR 5S9R5nU$) zLCreate a single master frame with all rows aligned on the common key values.éúc óF>•Xsup#upEXCRUTTSU3SSSS94$)NÚ:Tr=)rr>ÚsuffixÚ nulls_equalr@r?rA)Úidx_xÚidx_yÚ_r0Úy_idxr1Úalign_onrs €€r*Ú join_funcÚ"_alignment_join..join_func@sIø€ð#Љˆ‘ Ø—f‘fØ ØØØug;ØØØ'ðð ð ð r4rrFr!T)rErr?rJ)rˆútuple[int, LazyFrame]r‰rÚreturnr)rZÚpolars.lazyframerGrrdrfÚnonerc)rŒrrÚ idx_framesÚpost_align_collectrrGÚjoineds`` r*Ú_alignment_joinr–5sù€ô˜Z›¨CÑ/Ðð  Ø$ð  à$ð  ð ÷  ð  õ/ä IÓ *¨1Ñ -× 2Ñ 2Ø   ¸4ð3ð€FöØ—‘¨m×.@Ñ.@Ó.BÐC×HÑHÓJˆØ €Mr4)rrercóî•U(d/$[U5S:Xa7[US[R[R45(dUSn[U[ [ 45(a [U5n[UVs1sHn[U5iM sn5S:wa Sn[U5e[US[R5n[U[5(d[U[5(dU/OUnUVs/sH>n[U[R5(aURR5OUPM@ n n[U5V V s/sHup«X«R!54PM n n n [#XÉXS.6n [%U R'55n/nU H¦un nSU 3nUR'5Vs/sHLnUU3U;a)[(R*"UU35R-U5O[(R*"U5PMN nnU R."U6nUbUR/U5nUR1U5 M¨ U(a[(R2"U5$U$s snfs snfs sn n fs snf)u"! Align a sequence of frames using common values from one or more columns as a key. Frames that do not contain the given key values have rows injected (with nulls filling the non-key columns), and each resulting frame is sorted by the key. The original column order of input frames is not changed unless `select` is specified (in which case the final column order is determined from that). In the case where duplicate key values exist, the alignment behaviour is determined by the given alignment strategy specified in the `how` parameter (by default this is a full outer join, but if your data is suitable you can get a large speedup by setting `how="left"` instead). Note that this function does not result in a joined frame - you receive the same number of frames back that you passed in, but each is now aligned by key and has the same number of rows. Parameters ---------- frames Sequence of DataFrames or LazyFrames. on One or more columns whose unique values will be used to align the frames. select Optional post-alignment column select to constrain and/or order the columns returned from the newly aligned frames. descending Sort the alignment column values in descending order; can be a single boolean or a list of booleans associated with each column in `on`. how By default the row alignment values are determined using a full outer join strategy across all frames; if you know that the first frame contains all required keys, you can set `how="left"` for a large performance increase. Examples -------- >>> from datetime import date >>> df1 = pl.DataFrame( ... { ... "dt": [date(2022, 9, 1), date(2022, 9, 2), date(2022, 9, 3)], ... "x": [3.5, 4.0, 1.0], ... "y": [10.0, 2.5, 1.5], ... } ... ) >>> df2 = pl.DataFrame( ... { ... "dt": [date(2022, 9, 2), date(2022, 9, 3), date(2022, 9, 1)], ... "x": [8.0, 1.0, 3.5], ... "y": [1.5, 12.0, 5.0], ... } ... ) >>> df3 = pl.DataFrame( ... { ... "dt": [date(2022, 9, 3), date(2022, 9, 2)], ... "x": [2.0, 5.0], ... "y": [2.5, 2.0], ... } ... ) # doctest: +IGNORE_RESULT >>> pl.Config.set_tbl_formatting("UTF8_FULL") # doctest: +IGNORE_RESULT # # df1 df2 df3 # shape: (3, 3) shape: (3, 3) shape: (2, 3) # ┌────────────┬─────┬──────┠┌────────────┬─────┬──────┠┌────────────┬─────┬─────┠# │ dt ┆ x ┆ y │ │ dt ┆ x ┆ y │ │ dt ┆ x ┆ y │ # │ --- ┆ --- ┆ --- │ │ --- ┆ --- ┆ --- │ │ --- ┆ --- ┆ --- │ # │ date ┆ f64 ┆ f64 │ │ date ┆ f64 ┆ f64 │ │ date ┆ f64 ┆ f64 │ # â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ # │ 2022-09-01 ┆ 3.5 ┆ 10.0 │\ ,->│ 2022-09-02 ┆ 8.0 ┆ 1.5 │\ ,->│ 2022-09-03 ┆ 2.0 ┆ 2.5 │ # ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┤ \/ ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┤ \/ ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤ # │ 2022-09-02 ┆ 4.0 ┆ 2.5 │_/\,->│ 2022-09-03 ┆ 1.0 ┆ 12.0 │_/`-->│ 2022-09-02 ┆ 5.0 ┆ 2.0 │ # ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┤ /\ ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┤ └────────────┴─────┴─────┘ # │ 2022-09-03 ┆ 1.0 ┆ 1.5 │_/ `>│ 2022-09-01 ┆ 3.5 ┆ 5.0 │-//- # └────────────┴─────┴──────┘ └────────────┴─────┴──────┘ ... Align frames by the "dt" column: >>> af1, af2, af3 = pl.align_frames( ... df1, df2, df3, on="dt" ... ) # doctest: +IGNORE_RESULT # # df1 df2 df3 # shape: (3, 3) shape: (3, 3) shape: (3, 3) # ┌────────────┬─────┬──────┠┌────────────┬─────┬──────┠┌────────────┬──────┬──────┠# │ dt ┆ x ┆ y │ │ dt ┆ x ┆ y │ │ dt ┆ x ┆ y │ # │ --- ┆ --- ┆ --- │ │ --- ┆ --- ┆ --- │ │ --- ┆ --- ┆ --- │ # │ date ┆ f64 ┆ f64 │ │ date ┆ f64 ┆ f64 │ │ date ┆ f64 ┆ f64 │ # â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ # │ 2022-09-01 ┆ 3.5 ┆ 10.0 │----->│ 2022-09-01 ┆ 3.5 ┆ 5.0 │----->│ 2022-09-01 ┆ null ┆ null │ # ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┤ ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┤ ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤ # │ 2022-09-02 ┆ 4.0 ┆ 2.5 │----->│ 2022-09-02 ┆ 8.0 ┆ 1.5 │----->│ 2022-09-02 ┆ 5.0 ┆ 2.0 │ # ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┤ ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┤ ├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤ # │ 2022-09-03 ┆ 1.0 ┆ 1.5 │----->│ 2022-09-03 ┆ 1.0 ┆ 12.0 │----->│ 2022-09-03 ┆ 2.0 ┆ 2.5 │ # └────────────┴─────┴──────┘ └────────────┴─────┴──────┘ └────────────┴──────┴──────┘ ... Align frames by "dt" using "left" alignment, but keep only cols "x" and "y": >>> af1, af2, af3 = pl.align_frames( ... df1, df2, df3, on="dt", select=["x", "y"], how="left" ... ) # doctest: +IGNORE_RESULT # # af1 af2 af3 # shape: (3, 3) shape: (3, 3) shape: (3, 3) # ┌─────┬──────┠┌─────┬──────┠┌──────┬──────┠# │ x ┆ y │ │ x ┆ y │ │ x ┆ y │ # │ --- ┆ --- │ │ --- ┆ --- │ │ --- ┆ --- │ # │ f64 ┆ f64 │ │ f64 ┆ f64 │ │ f64 ┆ f64 │ # â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ â•žâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ # │ 3.5 ┆ 10.0 │ │ 3.5 ┆ 5.0 │ │ null ┆ null │ # ├╌╌╌╌╌┼╌╌╌╌╌╌┤ ├╌╌╌╌╌┼╌╌╌╌╌╌┤ ├╌╌╌╌╌╌┼╌╌╌╌╌╌┤ # │ 4.0 ┆ 2.5 │ │ 8.0 ┆ 1.5 │ │ 5.0 ┆ 2.0 │ # ├╌╌╌╌╌┼╌╌╌╌╌╌┤ ├╌╌╌╌╌┼╌╌╌╌╌╌┤ ├╌╌╌╌╌╌┼╌╌╌╌╌╌┤ # │ 1.0 ┆ 1.5 │ │ 1.0 ┆ 12.0 │ │ 2.0 ┆ 2.5 │ # └─────┴──────┘ └─────┴──────┘ └──────┴──────┘ ... Now data is aligned, and you can easily calculate the row-wise dot product: >>> (af1 * af2 * af3).fill_null(0).select(pl.sum_horizontal("*").alias("dot")) shape: (3, 1) ┌───────┠│ dot │ │ --- │ │ f64 │ â•žâ•â•â•â•â•â•â•â•¡ │ 0.0 │ ├╌╌╌╌╌╌╌┤ │ 167.5 │ ├╌╌╌╌╌╌╌┤ │ 47.0 │ └───────┘ r!rzJinput frames must be of a consistent type (all LazyFrame or all DataFrame))rŒrrr…)rZr[r\rrrrÚtupleÚtyper^ÚstrrrÚmetaÚ output_namer`rcr–r/r&ÚFÚcolÚaliasreÚappendÚ collect_all)r>rrerÚframesÚfrvr|ÚcrŒÚidxÚframer“Úalignment_frameÚ aligned_colsÚaligned_framesr{ÚsfxÚdf_colss r*Ú align_framesr¬Ys,€öX ؈ ä ˆ6ƒ{aÓ¤ ¨6°!©9´r·|±|ÄRÇ\Á\Ð6R× SÑ Sؘ‘ˆÜ&œ9¤hÐ/×0Ñ0Üv“ˆä ™VÓ $šV˜ŒDŽG™VÑ $Ó%¨Ó*à Xð ô˜‹nÐä v˜a‘y¤"§,¡,Ó /€EܘR¤×%Ñ%¬Z¸¼H×-EÑ-Eˆ"‰ÈB€BÙQSÓTÒQSÈA¬°A´r·w±w×)?Ñ)?—‘×#Ñ#Ô%ÀQÒFÑQS€HÐTô9BÀ&Ô8IÔJÒ8I©*¨#3Ÿ ™ › Ó%Ñ8I€JÑJÜ%Ø ¨Cò€Oô ×5Ñ5Ó7Ó8€LØ€NÛ‰ˆˆRØ#ˆiˆð×&Ñ&Ô(ó â(ð/0¨S°°¨;¸,Ó+FŒAEŠEQC˜u+Ó × $Ñ $ QÔ 'ÌAÏEÊEÐRSËHÒ TÙ(ð ð ð × "Ò " GÐ ,ˆØ Ñ Ø—‘˜Ó ˆAØ×јaÖ ñö-2Œ1=Š=˜Ó (ÐE°~ÐEùò? %ùòUùóKùò sÁ?I"ÃzBstr | Expr | Sequence[str] | Sequence[Expr] | Sequence[str | Expr]rrrez(str | Expr | Sequence[str | Expr] | Nonerr®rzlist[FrameType])0Ú __future__rÚ contextlibÚcollections.abcrrrÚ functoolsrÚ itertoolsrÚtypingr r Úpolars._reexportÚ _reexportr\Úpolarsr rÚpolars._typingr Úpolars._utils.variousr rÚpolars._utils.wraprrrrÚpolars.exceptionsrÚsuppressÚ ImportErrorÚ polars._plrÚ_plrrhrrrrrrrrr€r–r¬©r4r*ÚrÁs<ðÝ"ãß9Ñ9ÝÝß*åÝ!Ý'ßEßCÓCÝ3à×Ò˜Õ%Ý÷&öÝ(ç9Ó9ßBÑBð #ØØñ XØ ðXð ðXðð Xð ð Xð õ Xð|Ø(-ñ !Ø&ð!àð!ð ð!ð&ð !ð õ !ðNØ7;Ø(-ñ sFØ ,ðsFàJðsFð ðsFð 5ð sFð &ð sFð ö sF÷S &Õ%ús Á'Cà C%