§ q-Ph£`ãóz—ddlmZddlZddlmZmZmZddlmZddl m Z ddl m Z m Z ddlmZddlmZddlmZdd lmZmZdd lmZmZmZmZdd lmZej e!¦«5ddl"mZ#ddd¦«n #1swxYwYe rdd lm$Z$dd lm%Z%m&Z&m'Z'm(Z(ddlm)Z)m*Z*m+Z+ddddœd1d„Z,dddœd2d'„Z-dddd(œd3d0„Z.dS)4é)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)ÚhowÚrechunkÚparallelÚitemsúIterable[PolarsType]rr rÚboolrÚreturnrc ó< ‡‡‡—t|¦«}|sd}t|¦«‚t|¦«dkr?t|dtjtjtjf¦«r|dS| d¦«r”t|dtjtjf¦«s)|›dt|d¦«›}t|¦«‚ttj d„|D¦«¦«¦«}d„tt|¦«¦«D¦«Št‰¦«}tt!d„td „|D¦«¦«¦«ˆfd „¬ ¦«Š‰s|›d }t#|¦«‚|dkrd n| d¦«Št!ˆˆfd„d„|D¦«¦« ‰¬¦«j|Ž}t|dtj¦«} | r| ¦«n|S|d} ddlm} t| tj¦«r}|dkr#t1t3j|¦«¦«} n|dkrWt7t3jd„|D¦«||d¬¦«¦« |  ¦«¬¦«} nµ|dkr#t1t3j|¦«¦«} nŒ|dkrWt7t3jd„|D¦«||d¬¦«¦« |  ¦«¬¦«} n/|dkr#t1t3j |¦«¦«} nd !d„tEtF¦«D¦«¦«} d| ›d |›}t|¦«‚t| tj¦«ré|d!vr8t7t3j|||| $d"¦«¬¦«¦«S|d#vr8t7t3j|||| $d"¦«¬¦«¦«S|dkr#t7t3j%||¬$¦«¦«Sd !d%„tEtF¦«D¦«¦«} d&| ›d |›}t|¦«‚t| tj¦«r9|dkr"tMt3j'|¦«¦«} nyd'}t|¦«‚t| tj(¦«r,tSt3j*d(„|D¦«|¦«¦«Sd)t| ¦«›d*}t|¦«‚|r|  +¦«S| S)+uÉ 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>K—|]}| ¦«V—ŒdS©N©Úcollect_schema©Ú.0Úes úV/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/polars/functions/eager.pyú zconcat..¹s.èè€Ð.QÐ.QÀa¨q×/?Ò/?Ñ/AÔ/AÐ.QÐ.QÐ.QÐ.QÐ.QÐ.Qócó—i|]\}}||“Œ S©r2)r,ÚkÚvs r.ú zconcat..ºs€ÐGÐGÐG™˜˜1ˆq!ÐGÐGÐGr0có@—t|¦«t|¦«zSr()Úset)ÚxÚys r.úzconcat..¾s€S ™VœV¥c¨!¡f¤f™_€r0c3ó>K—|]}| ¦«V—ŒdSr(r)r+s r.r/zconcat..¿s.èè€Ð8Ð8¨Qa×&Ò&Ñ(Ô(Ð8Ð8Ð8Ð8Ð8Ð8r0có0•—‰ |d¦«S)Nr)Úget)r3Úkeys €r.r:zconcat..Ásø€˜#Ÿ'š' ! Q™-œ-€r0)r>z- strategy requires at least one common columnÚfullÚalign_có8•—| |‰‰dd¬¦«S)NÚ right_leftT)ÚonrÚmaintain_orderÚcoalesce©Újoin)r8r9Ú common_colsÚ join_methods €€r.r:zconcat..Ïs-ø€Ø—F’FØØ&Ø'Ø'3Ø!%ð ñôðr0có6—g|]}| ¦«‘ŒSr2©Úlazy©r,Údfs r.ú zconcat..Øs €Ð+Ð+Ð+˜r—’‘”Ð+Ð+Ð+r0)Úby©Ú QueryOptFlagsrÚvertical_relaxedcó6—g|]}| ¦«‘ŒSr2rKrMs r.rOzconcat..ëó €Ð/Ð/Ð/ 2R—W’W‘Y”YÐ/Ð/Ð/r0T)rrÚ to_supertypes©Ú optimizationsÚdiagonalÚdiagonal_relaxedcó6—g|]}| ¦«‘ŒSr2rKrMs r.rOzconcat..÷rUr0Ú horizontalz, c3ó4K—|]}t|¦«V—ŒdSr(©Úrepr©r,Úms r.r/zconcat..ó(èè€ÐHÐH¨A¥ Q¡¤ÐHÐHÐHÐHÐHÐHr0z DataFrame `how` must be one of {z}, got )rrSÚrelaxed)rYrZ)rc3ó4K—|]}t|¦«V—ŒdSr(r^r`s r.r/zconcat..rbr0z LazyFrame `how` must be one of {z/Series only supports 'vertical' concat strategycó—g|] }|j‘Œ Sr2)Ú_pyexprr+s r.rOzconcat..+s€Ð)CÐ)CÐ)C¸¨!¬)Ð)CÐ)CÐ)Cr0zdid not expect type: z in `concat`),ÚlistÚ ValueErrorÚlenÚ isinstanceÚplrrrÚ startswithrÚ TypeErrorrÚ from_iterableÚ enumerater ÚsortedrrÚ removeprefixÚsortÚselectÚcollectÚpolars.lazyframe.opt_flagsrRrÚplrÚ concat_dfrÚ concat_lfÚ_eagerÚconcat_df_diagonalÚconcat_lf_diagonalÚconcat_df_horizontalrGr r ÚendswithÚconcat_lf_horizontalrÚ concat_seriesrrÚ concat_exprr)r rrrÚelemsÚmsgÚ all_columnsÚoutput_column_orderÚlfÚeagerÚfirstrRÚoutÚallowedrHrIr>s @@@r.ÚconcatrŠsøøø€õ^ ‰KŒK€Eà ðØ(ˆÝ˜‰oŒoÐÝ ˆU‰ŒqŠˆZØ ˆaŒ•2”<¥¤­B¬LÐ9ñôˆðQŒxˆà ‡~‚~gÑÔñ+-ݘ% œ(¥R¤\µ2´<Ð$@ÑAÔAð !ØÐ\Ð\Õ;NÈuÐUVÌxÑ;XÔ;XÐ\Ð\ˆCݘC‘.”.Ð õ5Ô.Ð.QÐ.QÈ5Ð.QÑ.QÔ.QÑQÔQÑRÔRˆ ØGÐG¥ ­.¸Ñ*EÔ*EÑ FÔ FÐGÑGÔGˆÝ" 3™iœiÐÝÝ Ø,Ð,ÝÐ8Ð8°%Ð8Ñ8Ô8Ñ8Ô8ñ ô ð(Ð'Ð'Ð'ð  ñ ô ˆ ðð -ØÐIÐIÐIˆCÝ'¨Ñ,Ô,Ð ,ð ˜W’nnˆFˆF¨#×*:Ò*:¸8Ñ*DÔ*Dð ð Fðððððð,Ð+ UÐ+Ñ+Ô+ñ ô ÷ŠT[ˆTÑ !Ô !Ü Ð(ð *ð õ ˜5 œ8¥R¤\Ñ2Ô2ˆØ$Ð,ˆrzŠz‰|Œ|ˆ|¨"Ð,ð !ŒH€Eà8Ð8Ð8Ð8Ð8Ð8å%œÑ&Ô&ñIØ *Ò Ð Ý#œ-¨Ñ.Ô.Ñ/Ô/ˆC‰CØ Ð&Ò &Ð &ÝÝ” Ø/Ð/¨Ð/Ñ/Ô/Ø#Ø%Ø"&ð ñôñô÷Šg M×$8Ò$8Ñ$:Ô$:ˆgÑ;Ô;ð ‰CðJÒ Ð Ý#Ô0°Ñ7Ô7Ñ8Ô8ˆC‰CØ Ð&Ò &Ð &ÝÝÔ&Ø/Ð/¨Ð/Ñ/Ô/Ø#Ø%Ø"&ð ñôñô÷Šg M×$8Ò$8Ñ$:Ô$:ˆgÑ;Ô;ð ‰CðLÒ Ð Ý#Ô2°5Ñ9Ô9Ñ:Ô:ˆC‰Cà—i’iÐHÐHµ½,Ñ1GÔ1GÐHÑHÔHÑHÔHˆGØN°gÐNÐNÀsÐNÐNˆCݘS‘/”/Ð !å E2œ<Ñ (Ô (ð*Ø Ð2Ð 2Ð 2ÝÝ” ØØ#Ø%Ø"%§,¢,¨yÑ"9Ô"9ð ñôñôð ðÐ4Ð 4Ð 4ÝÝÔ&ØØ#Ø%Ø"%§,¢,¨yÑ"9Ô"9ð ñôñôð ðLÒ Ð ÝÝÔ(ØØ%ðñôñôð ð—i’iÐHÐHµ½,Ñ1GÔ1GÐHÑHÔHÑHÔHˆGØN°gÐNÐNÀsÐNÐNˆCݘS‘/”/Ð !å E2œ9Ñ %Ô %ð Ø *Ò Ð ÝÔ*¨5Ñ1Ô1Ñ2Ô2ˆCˆCàCˆCݘS‘/”/Ð !å E2œ7Ñ #Ô #ðÝœÐ)CÐ)C¸UÐ)CÑ)CÔ)CÀWÑMÔMÑNÔNÐNàPÕ&9¸%Ñ&@Ô&@ÐPÐPÐPˆÝ˜‰nŒnÐàðØ{Š{‰}Œ}ÐØ €Jr0r?)rÚ descendingÚ idx_framesútuple[int, LazyFrame]Úalign_onú list[str]rr‹úbool | Sequence[bool]rc󇇗t|¦«dk}d ˆˆfd„ }ddlm}t||¦«d  ‰|¬ ¦«}|r:| | ¦«¬ ¦« ¦«}|S) zLCreate a single master frame with all rows aligned on the common key values.éúÚidx_xrÚidx_yr#c ó\•—||c\}}\}}|| |‰‰d|›ddd¬¦«fS)Nú:TrB)rrCÚsuffixÚ nulls_equalrErDrF)r“r”Ú_r8Úy_idxr9rŽrs €€r.Ú join_funcz"_alignment_join..join_func@sVø€ð# EЉˆˆA‘ ˜Øa—f’fØ ØØØu;;ØØØ'ðñ ô ð ð r0rrQr%)rPr‹rW)r“rr”rr#r)riÚpolars.lazyframerRrrrrtÚnonerL)rŽrr‹rŒÚpost_align_collectr›rRÚjoineds`` r.Ú_alignment_joinr 5s®øø€õ˜Z™œ¨CÒ/Ðð  ð  ð  ð  ð  ð  ð  ð/Ð.Ð.Ð.Ð.Ð.å I˜zÑ *Ô *¨1Ô -× 2Ò 2°hÈ:Ð 2Ñ VÔ V€FØðKØ—’¨m×.@Ò.@Ñ.BÔ.BÑCÔC×HÒHÑJÔJˆØ €Mr0)rrsr‹ÚframesúFrameType | Iterable[FrameType]rCúBstr | Expr | Sequence[str] | Sequence[Expr] | Sequence[str | Expr]rsú(str | Expr | Sequence[str | Expr] | Noneúlist[FrameType]c󇇗|sgSt|¦«dkr4t|dtjtjf¦«s|d}t|t t f¦«rt|¦«}td„|D¦«¦«dkrd}t|¦«‚t|dtj¦«}t|t¦«st|t¦«s|gn|}d„|D¦«}d„t|¦«D¦«}t||||dœŽ} t|  ¦«¦«Šg} |D]a\} } d| ›Šˆˆfd „|  ¦«D¦«} | j| Ž}|| |¦«}|  |¦«Œb|rt#j| ¦«n| S) 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%rcó,—h|]}t|¦«’ŒSr2)Útype)r,Úfs r.ú zalign_frames..ës€Ð $Ð $Ð $˜D‰GŒGÐ $Ð $Ð $r0zJinput frames must be of a consistent type (all LazyFrame or all DataFrame)cóx—g|]7}t|tj¦«r|j ¦«n|‘Œ8Sr2)rjrkrÚmetaÚ output_name)r,Úcs r.rOz align_frames..ós<€ÐTÐTÐTÈA­°Aµr´wÑ)?Ô)?ÐF”×#Ò#Ñ%Ô%Ð%ÀQÐTÐTÐTr0có@—g|]\}}|| ¦«f‘ŒSr2rK)r,ÚidxÚframes r.rOz align_frames..÷s)€ÐJÐJÐJ©*¨#¨u3˜Ÿ š ™ œ Ð%ÐJÐJÐJr0)rŽrr‹r–cóž•—g|]I}|›‰›‰vr+tj|›‰›¦« |¦«ntj|¦«‘ŒJSr2)ÚFÚcolÚalias)r,r®Ú aligned_colsÚsfxs €€r.rOz align_frames..siø€ð ð ð àð/0¨;°¨;¨;¸,Ð+FÐ+FAŒEQ+˜++Ñ Ô × $Ò $ QÑ 'Ô 'Ð 'ÍAÌEÐRSÉHÌHð ð ð r0)rirjrkrrrrÚtuplermÚstrrror r7r*rsÚappendr³Ú collect_all)rCrrsr‹r¡r‚r†rŽrŒÚalignment_frameÚaligned_framesr°r…Údf_colsr©r¶r·s @@r.Ú align_framesr¿Wsøø€ðX ð؈ å ˆ6{„{aÒÐ¥ ¨6°!¬9µr´|ÅRÄ\Ð6RÑ SÔ SÐؘ”ˆÝ&9¥hÐ/Ñ0Ô0ðÝv‘”ˆå Ð $Ð $˜VÐ $Ñ $Ô $Ñ%Ô%¨Ò*Ð*à Xð õ˜‰nŒnÐå v˜a”y¥"¤,Ñ /Ô /€EݘR¥Ñ%Ô%Ð N­Z¸½HÑ-EÔ-EÐ Nˆ"ˆˆÈB€BØTÐTÐQSÐTÑTÔT€HðKÐJ½ À&Ñ8IÔ8IÐJÑJÔJ€JÝ%Ø ˜h¨C¸Jððð€Oõ ×5Ò5Ñ7Ô7Ñ8Ô8€LØ€NØð !ð !‰ˆˆRØ#ˆiˆiˆð ð ð ð ð à×&Ò&Ñ(Ô(ð ñ ô ˆð #ˆOÔ " GÐ ,ˆØ Ð Ø—’˜Ñ Ô ˆAØ×Ò˜aÑ Ô Ð Ð à,1Ð E1Œ=˜Ñ (Ô (Ð (°~ÐEr0) r r!rr rr"rr"r#r) rŒrrŽrrrr‹rr#r) r¡r¢rCr£rrrsr¤r‹rr#r¥)/Ú __future__rÚ contextlibÚcollections.abcrrrÚ functoolsrÚ itertoolsrÚtypingr r Úpolars._reexportÚ _reexportrkÚpolarsr r³Úpolars._typingr Úpolars._utils.variousr rÚpolars._utils.wraprrrrÚpolars.exceptionsrÚsuppressÚ ImportErrorÚ polars.polarsrvrrrrrrrrrŠr r¿r2r0r.úrÐstðØ"Ð"Ð"Ð"Ð"Ð"àÐÐÐØ9Ð9Ð9Ð9Ð9Ð9Ð9Ð9Ð9Ð9ØÐÐÐÐÐØÐÐÐÐÐØ*Ð*Ð*Ð*Ð*Ð*Ð*Ð*àÐÐÐÐÐØ!Ð!Ð!Ð!Ð!Ð!Ø'Ð'Ð'Ð'Ð'Ð'ØEÐEÐEÐEÐEÐEÐEÐEØCÐCÐCÐCÐCÐCÐCÐCÐCÐCÐCÐCØ3Ð3Ð3Ð3Ð3Ð3à€ZÔ˜Ñ%Ô%ð ð ØÐÐÐÐÐð ð ð ñ ô ð ð ð ð ð ð øøøð ð ð ð ððCØ(Ð(Ð(Ð(Ð(Ð(à9Ð9Ð9Ð9Ð9Ð9Ð9Ð9Ð9Ð9Ð9Ð9ØBÐBÐBÐBÐBÐBÐBÐBÐBÐBð #ØØð XðXðXðXðXðXð|Ø(-ð ððððððJØ7;Ø(-ð sFðsFðsFðsFðsFðsFðsFðsFsÁ%A8Á8A<Á?A<