§ q-Phō ćóŗ—ddlmZddlZddlmZddlmZmZmZm Z ddl m Z ddl mZddlmZmZddlmZmZmZmZddlmZmZddlmZmZdd lmZm Z dd l!m"Z"m#Z#dd l$m%Z%m&Z&m'Z'm(Z(dd l)m*Z*m+Z+ej,e-¦«5ddl.m/Z0ddd¦«n #1swxYwYerNddl1Z1dd lm2Z2m3Z3m4Z4ddlm5Z5ddl/m6Z6m7Z7m8Z8m9Z9ddl:m;Z;mZ>m?Z?m@Z@ddl)mAZAe1jBdkrddlCmZnddlDmZdĘd„ZEdĒd„ZFdČd„ZGddœdÉd!„ZHdČd"„ZIdŹdĖd'„ZJdŹdĖd(„ZKdČd)„ZLdČd*„ZMdČd+„ZNdČd,„ZOdČd-„ZPdČd.„ZQdĢd1„ZRdĶdĪd4„ZSdĶdĪd5„ZTe d6d6d6d6d7œdĻdA„¦«ZUe d6d6d6dBœdŠdE„¦«ZUdFdddd7œdŃdH„ZUe d6d6dIœdŅdJ„¦«ZVe d6dKœdÓdL„¦«ZVd#ddIœdŌdM„ZV dÕdÖdT„ZW dÕddUœd×dY„ZXdddZœdŲd^„ZYdŁd_„ZZdd`œdŚdb„Z[dŁdc„Z\dŪdg„Z]edh¦«dŪdi„¦«Z^dÜdm„Z_dŻdn„Z` dŽdßdt„Zaddduddvœdąd~„Zbe¦«e+¦«dududududdududududue*dd€œ dįd’„¦«¦«Zce de*d“œdād–„¦«Zde dde*d—œdćd™„¦«Zde¦«e¦«dde*d—œdäd›„¦«¦«Zde¦«e*dœœdåd„¦«Zee d6džœdęd”„¦«Zfe dēd£„¦«Zfdudžœdčd„„Zfe d6džœdéd§„¦«Zge dźd؄¦«Zgddžœdėd©„Zge d6džœdģdŖ„¦«Zhe dķd«„¦«Zhe dīd¬„¦«Zhddžœdīd­„Zhe dļdšd°„¦«Zie dļdńd²„¦«Zi dņdódµ„Zied¶d·dø¬¹¦«dd#dŗœdōd¼„¦«Zjed¶d·dø¬¹¦«dd#dŗœdōd½„¦«Zke dõd愦«Zle dödĀ„¦«Zld÷dńZldS)ųé)Ś annotationsN)ŚSequence)Ś TYPE_CHECKINGŚAnyŚCallableŚoverload)Ś_AioDataFrameResultŚ_GeventDataFrameResult)Śdeprecate_renamed_parameterŚdeprecate_streaming_parameterŚ deprecatedŚissue_deprecation_warning)Śparse_into_expressionŚparse_into_list_of_expressions)Śissue_unstable_warningŚunstable)Ś extend_boolŚqualified_type_name)Śwrap_dfŚ wrap_expr)ŚDTYPE_TEMPORAL_UNITSŚDateŚDatetimeŚInt64)ŚDEFAULT_QUERY_OPT_FLAGSŚforward_old_opt_flags)Ś AwaitableŚ CollectionŚIterable)ŚLiteral)Ś DataFrameŚExprŚ LazyFrameŚSeries)ŚCorrelationMethodŚ EngineTypeŚ EpochTimeUnitŚIntoExprŚPolarsDataTypeŚQuantileMethod)Ś QueryOptFlags)éé )r Śnameśstr | list[str]Śreturnr"cót—t|t¦«r|g}ttj|¦«¦«S)zu Select a field in the current `struct.with_fields` scope. name Name of the field(s) to select. )Ś isinstanceŚstrrŚplrŚfield)r.s śU/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/polars/functions/lazy.pyr5r58s3€õ$ŃŌšŲˆvˆŻ •S”Y˜t‘_”_Ń %Ō %Š%ócó*—tjd¦«S)u© Alias for an element being evaluated in an `eval` or `filter` expression. Examples -------- A horizontal rank computation by taking the elements of a list >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... } ... ) >>> df.with_columns( ... pl.concat_list(["a", "b"]).list.eval(pl.element().rank()).alias("rank") ... ) shape: (3, 3) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”† rank ā”‚ ā”‚ --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† list[f64] ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 4 ā”† [1.0, 2.0] ā”‚ ā”‚ 8 ā”† 5 ā”† [2.0, 1.0] ā”‚ ā”‚ 3 ā”† 2 ā”† [2.0, 1.0] ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ A mathematical operation on array elements >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... } ... ) >>> df.with_columns( ... pl.concat_list(["a", "b"]).list.eval(pl.element() * 2).alias("a_b_doubled") ... ) shape: (3, 3) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”† a_b_doubled ā”‚ ā”‚ --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† list[i64] ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 4 ā”† [2, 8] ā”‚ ā”‚ 8 ā”† 5 ā”† [16, 10] ā”‚ ā”‚ 3 ā”† 2 ā”† [6, 4] ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ A filter operation on list elements >>> import polars as pl >>> df = pl.DataFrame({"a": [1, 8, 3], "b": [4, 5, 2]}) >>> df.with_columns( ... evens=pl.concat_list("a", "b").list.filter(pl.element() % 2 == 0) ... ) shape: (3, 3) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”† evens ā”‚ ā”‚ --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† list[i64] ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 4 ā”† [4] ā”‚ ā”‚ 8 ā”† 5 ā”† [8] ā”‚ ā”‚ 3 ā”† 2 ā”† [2] ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ Ś)ŚFŚcol©r7r6Śelementr=Ds€õH Œ5‰9Œ9Šr7Ścolumnsr3có“—|s7tdd¬¦«tj¦« d¦«Stj|Ž ¦«S)u\ Return the number of non-null values in the column. This function is syntactic sugar for `col(columns).count()`. Calling this function without any arguments returns the number of rows in the context. **This way of using the function is deprecated.** Please use :func:`len` instead. Parameters ---------- *columns One or more column names. Returns ------- Expr Expression of data type :class:`UInt32`. See Also -------- Expr.count Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 2, None], ... "b": [3, None, None], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.count("a")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ u32 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ Return the number of non-null values in multiple columns. >>> df.select(pl.count("b", "c")) shape: (1, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ b ā”† c ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ u32 ā”† u32 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 3 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ Return the number of rows in a context. **This way of using the function is deprecated.** Please use :func:`len` instead. >>> df.select(pl.count()) # doctest: +SKIP shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ count ā”‚ ā”‚ --- ā”‚ ā”‚ u32 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 3 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ z:`pl.count()` is deprecated. Please use `pl.len()` instead.z0.20.5©ŚversionŚcount)rr:ŚlenŚaliasr;rB©r>s r6rBrB‹s\€šH š&Ż!Ų HŲš ń ō š õŒu‰wŒw}Š}˜WŃ%Ō%Š%Ż Œ5'ˆ?× Ņ Ń "Ō "Š"r7F©ŚreverserGŚboolcóF—tj|Ž |¬¦«S)uT Return the cumulative count of the non-null values in the column. This function is syntactic sugar for `col(columns).cum_count()`. Parameters ---------- *columns Name(s) of the columns to use. reverse Reverse the operation. Examples -------- >>> df = pl.DataFrame({"a": [1, 2, None], "b": [3, None, None]}) >>> df.select(pl.cum_count("a")) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ u32 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”‚ ā”‚ 2 ā”‚ ā”‚ 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ rF)r:r;Ś cum_count)rGr>s r6rJrJŲs!€õ8 Œ5'ˆ?× $Ņ $ØWŠ $Ń 5Ō 5Š5r7cóB—tj|Ž ¦«S)uō Aggregate all column values into a list. This function is syntactic sugar for `pl.col(name).implode()`. Parameters ---------- *columns One or more column names. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 2, 3], ... "b": [9, 8, 7], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.implode("a")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ list[i64] ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ [1, 2, 3] ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.select(pl.implode("b", "c")) shape: (1, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ b ā”† c ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ list[i64] ā”† list[str] ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ [9, 8, 7] ā”† ["foo", "bar", "foo"] ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ )r:r;ŚimploderEs r6rLrL÷s€õP Œ5'ˆ?× "Ņ "Ń $Ō $Š$r7éŚcolumnŚddofŚintcóP—tj|¦« |¦«S)uQ Get the standard deviation. This function is syntactic sugar for `pl.col(column).std(ddof)`. Parameters ---------- column Column name. ddof ā€œDelta Degrees of Freedomā€: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.std("a")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 3.605551 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df["a"].std() 3.605551275463989 )r:r;Śstd©rNrOs r6rRrR"ó!€õH Œ5‰=Œ=× Ņ ˜TŃ "Ō "Š"r7cóP—tj|¦« |¦«S)u Get the variance. This function is syntactic sugar for `pl.col(column).var(ddof)`. Parameters ---------- column Column name. ddof ā€œDelta Degrees of Freedomā€: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... }, ... ) >>> df.select(pl.var("a")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•” ā”‚ 13.0 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df["a"].var() 13.0 )r:r;ŚvarrSs r6rVrVIrTr7cóB—tj|Ž ¦«S)uĆ Get the mean value. This function is syntactic sugar for `pl.col(columns).mean()`. Parameters ---------- *columns One or more column names. See Also -------- mean_horizontal Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.mean("a")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 4.0 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.select(pl.mean("a", "b")) shape: (1, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ f64 ā”† f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 4.0 ā”† 3.666667 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ )r:r;ŚmeanrEs r6rXrXps€õX Œ5'ˆ?× Ņ Ń !Ō !Š!r7cóB—tj|Ž ¦«S)uZ Get the median value. This function is syntactic sugar for `pl.col(columns).median()`. Parameters ---------- columns One or more column names. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.median("a")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 3.0 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.select(pl.median("a", "b")) shape: (1, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ f64 ā”† f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 3.0 ā”† 4.0 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ )r:r;ŚmedianrEs r6rZrZŸs€õP Œ5'ˆ?× !Ņ !Ń #Ō #Š#r7cóB—tj|Ž ¦«S)u_ Count unique values. This function is syntactic sugar for `pl.col(columns).n_unique()`. Parameters ---------- columns One or more column names. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 1], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.n_unique("a")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ u32 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.select(pl.n_unique("b", "c")) shape: (1, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ b ā”† c ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ u32 ā”† u32 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 3 ā”† 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ )r:r;Śn_uniquerEs r6r\r\Źs€õP Œ5'ˆ?× #Ņ #Ń %Ō %Š%r7cóB—tj|Ž ¦«S)uČ Approximate count of unique values. This function is syntactic sugar for `pl.col(columns).approx_n_unique()`, and uses the HyperLogLog++ algorithm for cardinality estimation. Parameters ---------- columns One or more column names. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 1], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.approx_n_unique("a")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ u32 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.select(pl.approx_n_unique("b", "c")) shape: (1, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ b ā”† c ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ u32 ā”† u32 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 3 ā”† 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ )r:r;Śapprox_n_uniquerEs r6r^r^õs€õR Œ5'ˆ?× *Ņ *Ń ,Ō ,Š,r7có†—|s ttj¦«¦«Stj|Ž ¦«S)uH Get the first column or value. This function has different behavior depending on the presence of `columns` values. If none given (the default), returns an expression that takes the first column of the context; otherwise, takes the first value of the given column(s). Parameters ---------- *columns One or more column names. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "baz"], ... } ... ) Return the first column: >>> df.select(pl.first()) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”‚ ā”‚ 8 ā”‚ ā”‚ 3 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ Return the first value for the given column(s): >>> df.select(pl.first("b")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ b ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 4 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.select(pl.first("a", "c")) shape: (1, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† c ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ i64 ā”† str ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† foo ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ )rr4Śfirstr:r;rEs r6r`r`!s9€šv š&Żœ™œŃ%Ō%Š%å Œ5'ˆ?× Ņ Ń "Ō "Š"r7có†—|s ttj¦«¦«Stj|Ž ¦«S)u@ Get the last column or value. This function has different behavior depending on the presence of `columns` values. If none given (the default), returns an expression that takes the last column of the context; otherwise, takes the last value of the given column(s). Parameters ---------- *columns One or more column names. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "baz"], ... } ... ) Return the last column: >>> df.select(pl.last()) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ c ā”‚ ā”‚ --- ā”‚ ā”‚ str ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ foo ā”‚ ā”‚ bar ā”‚ ā”‚ baz ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ Return the last value for the given column(s): >>> df.select(pl.last("a")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 3 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.select(pl.last("b", "c")) shape: (1, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ b ā”† c ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ i64 ā”† str ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 2 ā”† baz ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ )rr4Ślastr:r;rEs r6rbrbbs9€šv š%Żœ™œŃ$Ō$Š$å Œ5'ˆ?× Ņ Ń !Ō !Š!r7Śindicesśint | Sequence[int]có°—t|¦«dkr#t|dt¦«r|d}tt j|¦«¦«S)u‡ Get the nth column(s) of the context. Parameters ---------- indices One or more indices representing the columns to retrieve. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "baz"], ... } ... ) >>> df.select(pl.nth(1)) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ b ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 4 ā”‚ ā”‚ 5 ā”‚ ā”‚ 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.select(pl.nth(2, 0)) shape: (3, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ c ā”† a ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ str ā”† i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ foo ā”† 1 ā”‚ ā”‚ bar ā”† 8 ā”‚ ā”‚ baz ā”† 3 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ rMr)rCr2rrr4Ś index_cols)rcs r6Śnthrg£sJ€õR ˆ7|„|qŅŠZØ°¬ µHŃ=Ō=ŠŲ˜!”*ˆå •S”^ GŃ,Ō,Ń -Ō -Š-r7é ŚncóP—tj|¦« |¦«S)uG Get the first `n` rows. This function is syntactic sugar for `pl.col(column).head(n)`. Parameters ---------- column Column name. n Number of rows to return. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.head("a")) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”‚ ā”‚ 8 ā”‚ ā”‚ 3 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.select(pl.head("a", 2)) shape: (2, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”‚ ā”‚ 8 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ )r:r;Śhead©rNris r6rkrkŅó!€õX Œ5‰=Œ=× Ņ ˜aŃ Ō Š r7cóP—tj|¦« |¦«S)uF Get the last `n` rows. This function is syntactic sugar for `pl.col(column).tail(n)`. Parameters ---------- column Column name. n Number of rows to return. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.tail("a")) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”‚ ā”‚ 8 ā”‚ ā”‚ 3 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.select(pl.tail("a", 2)) shape: (2, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 8 ā”‚ ā”‚ 3 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ )r:r;Śtailrls r6rorormr7.)ŚmethodrOŚpropagate_nansŚeagerŚar(Śbrpr%ś int | NonerqrrśLiteral[False]có—dS©Nr<©rsrtrprOrqrrs r6Ścorrrz0s €š ˆ3r7)rprOrqś Literal[True]r$có—dSrxr<rys r6rzrz<s €šˆSr7Śpearsonś Expr | Seriesc ó~—|tdd¬¦«|r¦t|tj¦«s+t|tj¦«sd}t |¦«‚tjd„||fD¦«¦«}d„||fD¦«}| t|d||d œŽ¦« ¦«St|¦«}t|¦«}|d kr"ttj ||¦«¦«S|d kr#ttj |||¦«¦«Sd |›}t |¦«‚) u" Compute the Pearson's or Spearman rank correlation between two columns. Parameters ---------- a Column name or Expression. b Column name or Expression. ddof Has no effect, do not use. .. deprecated:: 1.17.0 method : {'pearson', 'spearman'} Correlation method. propagate_nans If `True` any `NaN` encountered will lead to `NaN` in the output. Defaults to `False` where `NaN` are regarded as larger than any finite number and thus lead to the highest rank. eager Evaluate immediately and return a `Series`; this requires that at least one of the given arguments is a `Series`. If set to `False` (default), return an expression instead. Examples -------- Pearson's correlation: >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.corr("a", "b")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 0.544705 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ Spearman rank correlation: >>> df.select(pl.corr("a", "b", method="spearman")) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 0.5 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ Eager evaluation: >>> s1 = pl.Series("a", [1, 8, 3]) >>> s2 = pl.Series("b", [4, 5, 2]) >>> pl.corr(s1, s2, eager=True) shape: (1,) Series: 'a' [f64] [ 0.544705 ] >>> pl.corr(s1, s2, method="spearman", eager=True) shape: (1,) Series: 'a' [f64] [ 0.5 ] Nz2the `ddof` parameter has no effect. Do not use it.z1.17.0r@z=expected at least one Series in 'corr' inputs if 'eager=True'cóF—g|]}t|tj¦«Æ|‘ŒSr<©r2Śplr$©Ś.0Śes r6ś zcorr..§ó)€ŠLŠLŠL Aµ:øaÅÄŃ3KŌ3KŠL˜aŠLŠLŠLr7c3ó\K—|]'}t|tj¦«r|jn|V—Œ(dSrx©r2r‚r$r.rƒs r6ś zcorr..Øó9č耩MŠMĄJ q­"¬)Ń4Ō4Š;!”&&ø!ŠMŠMŠMŠMŠMŠMr7F)rrrprqr}Śspearmanz3method must be one of {'pearson', 'spearman'}, got )rr2r‚r$Ś ValueErrorr!ŚselectrzŚ to_seriesrrr4Ś pearson_corrŚspearman_rank_corr) rsrtrprOrqrrŚmsgŚframeŚexprss r6rzrzHsY€šh ŠŻ!Ų @Ųš ń ō š š  š"Ż˜1bœiŃ(Ō(š "­J°q½"¼)Ń,DŌ,Dš "ŲQˆCŻ˜S‘/”/Š !å” ŠLŠLØ!ØQØŠLŃLŌLŃMŌMˆŲMŠMĄqČ!ĄfŠMŃMŌMˆŲ|Š|Ż %˜uØVĄNŠ SŠ SŠ Sń ō ē Š)‰+Œ+š õ " !Ń $Ō $ˆŻ ! !Ń $Ō $ˆą YŅ Š ŻSŌ-Øa°Ń3Ō3Ń4Ō4Š 4Ų zŅ !Š !ŻSŌ3°A°qø.ŃIŌIŃJŌJŠ JąTČ&ŠTŠTˆCŻ˜S‘/”/Š !r7)rOrrcó—dSrxr<©rsrtrOrrs r6Ścovr—¹s €š ˆ3r7)rOcó—dSrxr<r–s r6r—r—Ćs €šˆSr7cóŅ—|r„t|tj¦«s+t|tj¦«sd}t|¦«‚tjd„||fD¦«¦«}d„||fD¦«}| t |d|dœŽ¦« ¦«St|¦«}t|¦«}ttj|||¦«¦«S)u Compute the covariance between two columns/ expressions. Parameters ---------- a Column name or Expression. b Column name or Expression. ddof "Delta Degrees of Freedom": the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1. eager Evaluate immediately and return a `Series`; this requires that at least one of the given arguments is a `Series`. If set to `False` (default), return an expression instead. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... }, ... ) >>> df.select( ... x=pl.cov("a", "b"), ... y=pl.cov("a", "b", ddof=2), ... ) shape: (1, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ x ā”† y ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ f64 ā”† f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 3.0 ā”† 6.0 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ Eager evaluation: >>> s1 = pl.Series("a", [1, 8, 3]) >>> s2 = pl.Series("b", [4, 5, 2]) >>> pl.cov(s1, s2, eager=True) shape: (1,) Series: 'a' [f64] [ 3.0 ] z. r‡r7c3ó\K—|]'}t|tj¦«r|jn|V—Œ(dSrxr‰rƒs r6rŠzcov..r‹r7F)rrrO) r2r‚r$rr!rŽr—rrrr4)rsrtrOrrr’r“r”s r6r—r—Ķsį€šv š .Ż˜1bœiŃ(Ō(š "­J°q½"¼)Ń,DŌ,Dš "ŲPˆCŻ˜S‘/”/Š !å” ŠLŠLØ!ØQØŠLŃLŌLŃMŌMˆŲMŠMĄqČ!ĄfŠMŃMŌMˆŲ|Š|C Øeø$Š?Š?Š?Ń@Ō@×JŅJŃLŌLŠLå ! !Ń $Ō $ˆŻ ! !Ń $Ō $ˆŻœ  A tŃ,Ō,Ń-Ō-Š-r7r”śSequence[str] | Sequence[Expr]Śfunctionś$Callable[[Sequence[Series]], Series]Ś return_dtypeśPolarsDataType | Nonec ól—t|¦«}ttj|||dd¬¦«¦«S)u> Map a custom function over multiple columns/expressions. Produces a single Series result. Parameters ---------- exprs Expression(s) representing the input Series to the function. function Function to apply over the input. return_dtype dtype of the output Series. Returns ------- Expr Expression with the data type given by `return_dtype`. Examples -------- >>> def test_func(a, b, c): ... return a + b + c >>> df = pl.DataFrame( ... { ... "a": [1, 2, 3, 4], ... "b": [4, 5, 6, 7], ... } ... ) >>> >>> df.with_columns( ... ( ... pl.struct(["a", "b"]).map_batches( ... lambda x: test_func(x.struct.field("a"), x.struct.field("b"), 1) ... ) ... ).alias("a+b+c") ... ) shape: (4, 3) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”† a+b+c ā”‚ ā”‚ --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 4 ā”† 6 ā”‚ ā”‚ 2 ā”† 5 ā”† 8 ā”‚ ā”‚ 3 ā”† 6 ā”† 10 ā”‚ ā”‚ 4 ā”† 7 ā”† 12 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ F©Ś map_groupsŚreturns_scalar©rrr4Śmap_mul)r”rrŸs r6Ś map_batchesr§sC€õl +Ø5Ń 1Ō 1€EŻ Ż Œ Ų 8˜\°eČEš ń ō ń ō šr7)r¤śSequence[str | Expr]ś*Callable[[Sequence[Series]], Series | Any]r¤c ól—t|¦«}ttj|||d|¬¦«¦«S)uÅ Apply a custom/user-defined function (UDF) in a GroupBy context. .. warning:: This method is much slower than the native expressions API. Only use it if you cannot implement your logic otherwise. Parameters ---------- exprs Expression(s) representing the input Series to the function. function Function to apply over the input; should be of type Callable[[Series], Series]. return_dtype dtype of the output Series. returns_scalar If the function returns a single scalar as output. Returns ------- Expr Expression with the data type given by `return_dtype`. Examples -------- >>> df = pl.DataFrame( ... { ... "group": [1, 1, 2], ... "a": [1, 3, 3], ... "b": [5, 6, 7], ... } ... ) >>> df shape: (3, 3) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ group ā”† a ā”† b ā”‚ ā”‚ --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 1 ā”† 5 ā”‚ ā”‚ 1 ā”† 3 ā”† 6 ā”‚ ā”‚ 2 ā”† 3 ā”† 7 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ >>> ( ... df.group_by("group").agg( ... pl.map_groups( ... exprs=["a", "b"], ... function=lambda list_of_series: list_of_series[0] ... / list_of_series[0].sum() ... + list_of_series[1], ... return_dtype=pl.Float64, ... ).alias("my_custom_aggregation") ... ) ... ).sort("group") shape: (2, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ group ā”† my_custom_aggregation ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ i64 ā”† list[f64] ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† [5.25, 6.75] ā”‚ ā”‚ 2 ā”† [8.0] ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ The output for group `1` can be understood as follows: - group `1` contains Series `'a': [1, 3]` and `'b': [5, 6]` - applying the function to those lists of Series, one gets the output `[1 / 4 + 5, 3 / 4 + 6]`, i.e. `[5.25, 6.75]` Tr¢r„)r”rrŸr¤s r6r£r£TsG€õZ +Ø5Ń 1Ō 1€EŻ Ż Œ Ų Ų Ų ŲŲ)š  ń ō ń ō šr7©r¤rŸŚaccś"Callable[[Series, Series], Series]śSequence[Expr | str] | Exprc óȗt|d¬¦«}t|tj¦«r|g}t |¦«}t t j|||||¬¦«¦«S)uč Accumulate over multiple columns horizontally/ row wise with a left fold. Parameters ---------- acc Accumulator Expression. This is the value that will be initialized when the fold starts. For a sum this could for instance be lit(0). function Function to apply over the accumulator and the value. Fn(acc, value) -> new_value exprs Expressions to aggregate over. May also be a wildcard expression. returns_scalar Whether or not `function` applied returns a scalar. This must be set correctly by the user. return_dtype Output datatype. If not set, the dtype will be inferred based on the dtype of the accumulator. Notes ----- If you simply want the first encountered expression as accumulator, consider using `reduce`. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 2, 3], ... "b": [3, 4, 5], ... "c": [5, 6, 7], ... } ... ) >>> df shape: (3, 3) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”† c ā”‚ ā”‚ --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 3 ā”† 5 ā”‚ ā”‚ 2 ā”† 4 ā”† 6 ā”‚ ā”‚ 3 ā”† 5 ā”† 7 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ Horizontally sum over all columns and add 1. >>> df.select( ... pl.fold( ... acc=pl.lit(1), function=lambda acc, x: acc + x, exprs=pl.col("*") ... ).alias("sum"), ... ) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ sum ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 10 ā”‚ ā”‚ 13 ā”‚ ā”‚ 16 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ You can also apply a condition/predicate on all columns: >>> df = pl.DataFrame( ... { ... "a": [1, 2, 3], ... "b": [0, 1, 2], ... } ... ) >>> df shape: (3, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 0 ā”‚ ā”‚ 2 ā”† 1 ā”‚ ā”‚ 3 ā”† 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.filter( ... pl.fold( ... acc=pl.lit(True), ... function=lambda acc, x: acc & x, ... exprs=pl.col("*") > 1, ... ) ... ) shape: (1, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 3 ā”† 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ T©Ś str_as_litr«)rr2r‚r"rrr4Śfold)r¬rr”r¤rŸs r6r²r²­st€õ\  °Š 5Ń 5Ō 5€CŻ%œŃ!Ō!šŲˆå *Ø5Ń 1Ō 1€EŻ Ż ŒŲ Ų Ų Ų)Ų%š  ń ō ń ō šr7cóž—t|tj¦«r|g}t|¦«}t t j||¦«¦«S)u± Accumulate over multiple columns horizontally/ row wise with a left fold. Parameters ---------- function Function to apply over the accumulator and the value. Fn(acc, value) -> new_value exprs Expressions to aggregate over. May also be a wildcard expression. Notes ----- See `fold` for the version with an explicit accumulator. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 2, 3], ... "b": [0, 1, 2], ... } ... ) >>> df shape: (3, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 0 ā”‚ ā”‚ 2 ā”† 1 ā”‚ ā”‚ 3 ā”† 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ Horizontally sum over all columns. >>> df.select( ... pl.reduce(function=lambda acc, x: acc + x, exprs=pl.col("*")).alias("sum") ... ) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ sum ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”‚ ā”‚ 3 ā”‚ ā”‚ 5 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ )r2r‚r"rrr4Średuce©rr”s r6r“r“+sG€õn%œŃ!Ō!šŲˆå *Ø5Ń 1Ō 1€EŻ •S”Z Ø%Ń0Ō0Ń 1Ō 1Š1r7)Ś include_initr¶cóź—t|d¬¦«}t|tj¦«r|g}t |¦«}t t j||||¦« d¦«¦«S)un Cumulatively fold horizontally across columns with a left fold. Every cumulative result is added as a separate field in a Struct column. Parameters ---------- acc Accumulator expression. This is the value that will be initialized when the fold starts. For a sum this could for instance be lit(0). function Function to apply over the accumulator and the value. Fn(acc, value) -> new_value exprs Expressions to aggregate over. May also be a wildcard expression. include_init Include the initial accumulator state as struct field. Notes ----- If you simply want the first encountered expression as accumulator, consider using :func:`cum_reduce`. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 2, 3], ... "b": [3, 4, 5], ... "c": [5, 6, 7], ... } ... ) >>> df.with_columns( ... pl.cum_fold(acc=pl.lit(1), function=lambda acc, x: acc + x, exprs=pl.all()) ... ) shape: (3, 4) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”† c ā”† cum_fold ā”‚ ā”‚ --- ā”† --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† i64 ā”† struct[3] ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 3 ā”† 5 ā”† {2,5,10} ā”‚ ā”‚ 2 ā”† 4 ā”† 6 ā”† {3,7,13} ā”‚ ā”‚ 3 ā”† 5 ā”† 7 ā”† {4,9,16} ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ Tr°Ścum_fold) rr2r‚r"rrr4rørD)r¬rr”r¶s r6rørøisk€õl  °Š 5Ń 5Ō 5€CŻ%œŃ!Ō!šŲˆå *Ø5Ń 1Ō 1€EŻ •S”\ # x°ø ŃEŌE×KŅKČJŃWŌWŃ XŌ XŠXr7cóėt|tj¦«r|g}t|¦«}t t j||¦« d¦«¦«S)uź Cumulatively reduce horizontally across columns with a left fold. Every cumulative result is added as a separate field in a Struct column. Parameters ---------- function Function to apply over the accumulator and the value. Fn(acc, value) -> new_value exprs Expressions to aggregate over. May also be a wildcard expression. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 2, 3], ... "b": [3, 4, 5], ... "c": [5, 6, 7], ... } ... ) >>> df.with_columns(pl.cum_reduce(function=lambda acc, x: acc + x, exprs=pl.all())) shape: (3, 4) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”† c ā”† cum_reduce ā”‚ ā”‚ --- ā”† --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† i64 ā”† struct[3] ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 3 ā”† 5 ā”† {1,4,9} ā”‚ ā”‚ 2 ā”† 4 ā”† 6 ā”† {2,6,12} ā”‚ ā”‚ 3 ā”† 5 ā”† 7 ā”† {3,8,15} ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ Ś cum_reduce)r2r‚r"rrr4rŗrDrµs r6rŗrŗ§sU€õN%œŃ!Ō!šŲˆå *Ø5Ń 1Ō 1€EŻ •S”^ HØeŃ4Ō4×:Ņ:ø<ŃHŌHŃ IŌ IŠIr7Śyś str | ExprŚxcóĘ—t|t¦«rtj|¦«}t|t¦«rtj|¦«}t |d¦«s"dt |¦«›d}t |¦«‚t |d¦«s"dt |¦«›d}t |¦«‚ttj |j |j ¦«¦«S)uį Compute two argument arctan in radians. Returns the angle (in radians) in the plane between the positive x-axis and the ray from the origin to (x,y). Parameters ---------- y Column name or Expression. x Column name or Expression. Examples -------- >>> c = (2**0.5) / 2 >>> df = pl.DataFrame( ... { ... "y": [c, -c, c, -c], ... "x": [c, c, -c, -c], ... } ... ) >>> df.with_columns(pl.arctan2("y", "x").alias("atan2")) shape: (4, 3) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ y ā”† x ā”† atan2 ā”‚ ā”‚ --- ā”† --- ā”† --- ā”‚ ā”‚ f64 ā”† f64 ā”† f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 0.707107 ā”† 0.707107 ā”† 0.785398 ā”‚ ā”‚ -0.707107 ā”† 0.707107 ā”† -0.785398 ā”‚ ā”‚ 0.707107 ā”† -0.707107 ā”† 2.356194 ā”‚ ā”‚ -0.707107 ā”† -0.707107 ā”† -2.356194 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ Ś_pyexprz,`arctan2` expected a `str` or `Expr` got a `ś`) r2r3r:r;ŚhasattrrŚ TypeErrorrr4Śarctan2ræ)r»r½r’s r6rĆrĆÕsрõH!•SŃŌšŻ ŒE!‰HŒHˆŻ!•SŃŌšŻ ŒE!‰HŒHˆŻ 1iŃ Ō šŲVÕ=PŠQRŃ=SŌ=SŠVŠVŠVˆŻ˜‰nŒnŠŻ 1iŃ Ō šŲVÕ=PŠQRŃ=SŌ=SŠVŠVŠVˆŻ˜‰nŒnŠå •S”[ ¤ØA¬IŃ6Ō6Ń 7Ō 7Š7r7zI`arctan2d` is deprecated; use `arctan2` followed by `.degrees()` instead.cóF—t||¦« ¦«S)u” Compute two argument arctan in degrees. .. deprecated:: 1.0.0 Use `arctan2` followed by :meth:`Expr.degrees` instead. Returns the angle (in degrees) in the plane between the positive x-axis and the ray from the origin to (x,y). Parameters ---------- y Column name or Expression. x Column name or Expression. Examples -------- >>> c = (2**0.5) / 2 >>> df = pl.DataFrame( ... { ... "y": [c, -c, c, -c], ... "x": [c, c, -c, -c], ... } ... ) >>> df.select( # doctest: +SKIP ... pl.arctan2d("y", "x").alias("atan2d"), ... pl.arctan2("y", "x").alias("atan2"), ... ) shape: (4, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ atan2d ā”† atan2 ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ f64 ā”† f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 45.0 ā”† 0.785398 ā”‚ ā”‚ -45.0 ā”† -0.785398 ā”‚ ā”‚ 135.0 ā”† 2.356194 ā”‚ ā”‚ -135.0 ā”† -2.356194 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ )rĆŚdegrees)r»r½s r6Śarctan2drĘs€õV 1a‰=Œ=× Ņ Ń "Ō "Š"r7śCstr | PolarsDataType | Collection[str] | Collection[PolarsDataType]Ś more_columnsśstr | PolarsDataTypecóB—tjd¦«j|g|¢RŽS)u  Represent all columns except for the given columns. Syntactic sugar for `pl.all().exclude(columns)`. Parameters ---------- columns The name or datatype of the column(s) to exclude. Accepts regular expression input. Regular expressions should start with `^` and end with `$`. *more_columns Additional names or datatypes of columns to exclude, specified as positional arguments. Examples -------- Exclude by column name(s): >>> df = pl.DataFrame( ... { ... "aa": [1, 2, 3], ... "ba": ["a", "b", None], ... "cc": [None, 2.5, 1.5], ... } ... ) >>> df.select(pl.exclude("ba")) shape: (3, 2) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ aa ā”† cc ā”‚ ā”‚ --- ā”† --- ā”‚ ā”‚ i64 ā”† f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† null ā”‚ ā”‚ 2 ā”† 2.5 ā”‚ ā”‚ 3 ā”† 1.5 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ Exclude by regex, e.g. removing all columns whose names end with the letter "a": >>> df.select(pl.exclude("^.*a$")) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ cc ā”‚ ā”‚ --- ā”‚ ā”‚ f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•” ā”‚ null ā”‚ ā”‚ 2.5 ā”‚ ā”‚ 1.5 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ Exclude by dtype(s), e.g. removing all columns of type Int64 or Float64: >>> df.select(pl.exclude([pl.Int64, pl.Float64])) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ ba ā”‚ ā”‚ --- ā”‚ ā”‚ str ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•” ā”‚ a ā”‚ ā”‚ b ā”‚ ā”‚ null ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ Ś*)r:r;Śexclude)r>rČs r6rĢrĢ5s(€šL 1Œ5‰:Œ:Ō ˜gŠ 5Ø Š 5Š 5Š 5Š5r7cóN—tj|¦« ¦«S)z1Syntactic sugar for `pl.col("foo").agg_groups()`.)r:r;Ś agg_groups)rNs r6ŚgroupsrĻ~s€å Œ5‰=Œ=× #Ņ #Ń %Ō %Š%r7ŚnearestŚquantileś float | ExprŚ interpolationr*cóR—tj|¦« ||¦«S)a$ Syntactic sugar for `pl.col("foo").quantile(..)`. Parameters ---------- column Column name. quantile Quantile between 0.0 and 1.0. interpolation : {'nearest', 'higher', 'lower', 'midpoint', 'linear', 'equiprobable'} Interpolation method. )r:r;rŃ)rNrŃrÓs r6rŃrуs"€õ" Œ5‰=Œ=× !Ņ ! (ØMŃ :Ō :Š:r7T)Ś descendingŚ nulls_lastŚ multithreadedŚmaintain_orderśIntoExpr | Iterable[IntoExpr]Ś more_exprsrÕśbool | Sequence[bool]rÖr×rŲc óā—t|g|¢RŽ}t|t|¦«dd¦«}t|t|¦«dd¦«}tt j|||||¦«¦«S)uŲ Return the row indices that would sort the column(s). Parameters ---------- exprs Column(s) to arg sort by. Accepts expression input. Strings are parsed as column names. *more_exprs Additional columns to arg sort by, specified as positional arguments. descending Sort in descending order. When sorting by multiple columns, can be specified per column by passing a sequence of booleans. nulls_last Place null values last. multithreaded Sort using multiple threads. maintain_order Whether the order should be maintained if elements are equal. See Also -------- Expr.gather: Take values by index. Expr.rank : Get the rank of each row. Examples -------- Pass a single column name to compute the arg sort by that column. >>> df = pl.DataFrame( ... { ... "a": [0, 1, 1, 0], ... "b": [3, 2, 3, 2], ... "c": [1, 2, 3, 4], ... } ... ) >>> df.select(pl.arg_sort_by("a")) shape: (4, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ u32 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 0 ā”‚ ā”‚ 3 ā”‚ ā”‚ 1 ā”‚ ā”‚ 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ Compute the arg sort by multiple columns by either passing a list of columns, or by specifying each column as a positional argument. >>> df.select(pl.arg_sort_by(["a", "b"], descending=True)) shape: (4, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ u32 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 2 ā”‚ ā”‚ 1 ā”‚ ā”‚ 0 ā”‚ ā”‚ 3 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ Use gather to apply the arg sort to other columns. >>> df.select(pl.col("c").gather(pl.arg_sort_by("a"))) shape: (4, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ c ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”‚ ā”‚ 4 ā”‚ ā”‚ 2 ā”‚ ā”‚ 3 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ rÕr”rÖ)rrrCrr4Ś arg_sort_by)r”rÕrÖr×rŲrŚs r6rŻrŻ—ss€õp +Ø5Š >°:Š >Š >Š >€EŻ˜Z­ØU©¬°\Ą7ŃKŌK€JŻ˜Z­ØU©¬°\Ą7ŃKŌK€JŻ Ż Œ˜˜zØ:°}ĄnŃUŌUń ō šr7Śauto) Ś type_coercionŚpredicate_pushdownŚprojection_pushdownŚsimplify_expressionŚno_optimizationŚslice_pushdownŚcomm_subplan_elimŚcomm_subexpr_elimŚcluster_with_columnsŚcollapse_joinsŚ optimizationsŚengineŚ lazy_framesśIterable[LazyFrame]rßrąrįrārćräråręrērčrér+rźr&ślist[DataFrame]c ó’—| dvrtd¦«d„|D¦«} tj| | | j¦«}d„|D¦«}|S)a Collect multiple LazyFrames at the same time. This can run all the computation graphs in parallel or combined. Common Subplan Elimination is applied on the combined plan, meaning that diverging queries will run only once. Parameters ---------- lazy_frames A list of LazyFrames to collect. type_coercion Do type coercion optimization. .. deprecated:: 1.30.0 Use the `optimizations` parameters. predicate_pushdown Do predicate pushdown optimization. .. deprecated:: 1.30.0 Use the `optimizations` parameters. projection_pushdown Do projection pushdown optimization. .. deprecated:: 1.30.0 Use the `optimizations` parameters. simplify_expression Run simplify expressions optimization. .. deprecated:: 1.30.0 Use the `optimizations` parameters. no_optimization Turn off optimizations. .. deprecated:: 1.30.0 Use the `optimizations` parameters. slice_pushdown Slice pushdown optimization. .. deprecated:: 1.30.0 Use the `optimizations` parameters. comm_subplan_elim Will try to cache branching subplans that occur on self-joins or unions. .. deprecated:: 1.30.0 Use the `optimizations` parameters. comm_subexpr_elim Common subexpressions will be cached and reused. .. deprecated:: 1.30.0 Use the `optimizations` parameters. cluster_with_columns Combine sequential independent calls to with_columns .. deprecated:: 1.30.0 Use the `optimizations` parameters. collapse_joins Collapse a join and filters into a faster join .. deprecated:: 1.30.0 Use the `optimizations` parameters. optimizations The optimization passes done during query optimization. .. warning:: This functionality is considered **unstable**. It may be changed at any point without it being considered a breaking change. engine Select the engine used to process the query, optional. At the moment, if set to `"auto"` (default), the query is run using the polars in-memory engine. Polars will also attempt to use the engine set by the `POLARS_ENGINE_AFFINITY` environment variable. If it cannot run the query using the selected engine, the query is run using the polars in-memory engine. .. note:: The GPU engine does not support async, or running in the background. If either are enabled, then GPU execution is switched off. Returns ------- list of DataFrames The collected DataFrames, returned in the same order as the input LazyFrames. ©Ś streamingz old-streamingś&streaming mode is considered unstable.có—g|] }|j‘Œ Sr<©Ś_ldf©r„Ślfs r6r†zcollect_all..có€Š )Š )Š )rˆ2Œ7Š )Š )Š )r7có,—g|]}t|¦«‘ŒSr<)r)r„Śpydfs r6r†zcollect_all..gs€Š ,Š ,Š , gd‰mŒmŠ ,Š ,Š ,r7)rr4Ś collect_allŚ _pyoptflags)rėrßrąrįrārćräråręrērčrérźŚlfsŚoutŚresults r6rśrś÷sc€šRŠ/Š/Š/ŻŠGŃHŌHŠHą )Š )˜[Š )Ń )Ō )€CŻ Œ/˜#˜v }Ō'@Ń AŌ A€Cš-Š ,ØŠ ,Ń ,Ō ,€Fą €Mr7)rźréŚgeventś'_GeventDataFrameResult[list[DataFrame]]có—dSrxr<©rėr’rźrés r6Ścollect_all_asyncrls €š/2Øcr7)r’rźréśAwaitable[list[DataFrame]]có—dSrxr<rs r6rrvs €š"% r7śDAwaitable[list[DataFrame]] | _GeventDataFrameResult[list[DataFrame]]cóĀ—|dvrtd¦«|rt¦«n t¦«}d„|D¦«}tj|||j|j¦«|S)a; Collect multiple LazyFrames at the same time asynchronously in thread pool. .. warning:: This functionality is considered **unstable**. It may be changed at any point without it being considered a breaking change. Collects into a list of DataFrame (like :func:`polars.collect_all`), but instead of returning them directly, they are scheduled to be collected inside thread pool, while this method returns almost instantly. May be useful if you use gevent or asyncio and want to release control to other greenlets/tasks while LazyFrames are being collected. Parameters ---------- lazy_frames A list of LazyFrames to collect. gevent Return wrapper to `gevent.event.AsyncResult` instead of Awaitable optimizations The optimization passes done during query optimization. .. warning:: This functionality is considered **unstable**. It may be changed at any point without it being considered a breaking change. engine Select the engine used to process the query, optional. At the moment, if set to `"auto"` (default), the query is run using the polars in-memory engine. Polars will also attempt to use the engine set by the `POLARS_ENGINE_AFFINITY` environment variable. If it cannot run the query using the selected engine, the query is run using the polars in-memory engine. .. note:: The GPU engine does not support async, or running in the background. If either are enabled, then GPU execution is switched off. See Also -------- polars.collect_all : Collect multiple LazyFrames at the same time. LazyFrame.collect_async : To collect single frame. Notes ----- In case of error `set_exception` is used on `asyncio.Future`/`gevent.event.AsyncResult` and will be reraised by them. Returns ------- If `gevent=False` (default) then returns awaitable. If `gevent=True` then returns wrapper that has `.get(block=True, timeout=None)` method. rļrńcó—g|] }|j‘Œ Sr<rórõs r6r†z%collect_all_async..Ēr÷r7)rr r r4Ścollect_all_with_callbackrūŚ _callback_all)rėr’rźréržrüs r6rr€s‚€šBŠ/Š/Š/ŻŠGŃHŌHŠHš%+ŠEÕŃ Ō Š Õ0CŃ0EŌ0Eš š *Š )˜[Š )Ń )Ō )€CŻŌ!Ų ˆV]Ō.°Ō0Dńōšš €Mr7)récóN—d„|D¦«}tj||j¦«S)a> Explain multiple LazyFrames as if passed to `collect_all`. Common Subplan Elimination is applied on the combined plan, meaning that diverging queries will run only once. Parameters ---------- lazy_frames A list of LazyFrames to collect. optimizations The optimization passes done during query optimization. .. warning:: This functionality is considered **unstable**. It may be changed at any point without it being considered a breaking change. Returns ------- Explained plan. có—g|] }|j‘Œ Sr<rórõs r6r†zexplain_all..ér÷r7)r4Ś explain_allrū)rėrérüs r6r r Īs,€š6 *Š )˜[Š )Ń )Ō )€CŻ Œ?˜3  Ō 9Ń :Ō :Š:r7©rrŚ named_exprsr!có—dSrxr<©rrr”rs r6rŽrŽķó €š r7r#có—dSrxr<rs r6rŽrŽõrr7śDataFrame | LazyFramecól—|rtj¦«ntj¦«}|j|i|¤ŽS)uÄ Run polars expressions without a context. This is syntactic sugar for running `df.select` on an empty DataFrame (or LazyFrame if eager=False). Parameters ---------- *exprs Column(s) to select, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals. eager Evaluate immediately and return a `DataFrame` (default); if set to `False`, return a `LazyFrame` instead. **named_exprs Additional columns to select, specified as keyword arguments. The columns will be renamed to the keyword used. Returns ------- DataFrame or LazyFrame Examples -------- >>> foo = pl.Series("foo", [1, 2, 3]) >>> bar = pl.Series("bar", [3, 2, 1]) >>> pl.select(min=pl.min_horizontal(foo, bar)) shape: (3, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ min ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”‚ ā”‚ 2 ā”‚ ā”‚ 1 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ >>> pl.select(pl.int_range(0, 100_000, 2).alias("n"), eager=False).filter( ... pl.col("n") % 22_500 == 0 ... ).collect() shape: (5, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ n ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 0 ā”‚ ā”‚ 22500 ā”‚ ā”‚ 45000 ā”‚ ā”‚ 67500 ā”‚ ā”‚ 90000 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ )r‚r!r#rŽ)rrr”rŚ empty_frames r6rŽrŽżs7€št%*Š=•"”,‘.”..­r¬|©~¬~€KŲ ˆ;Ō ˜uŠ 4Ø Š 4Š 4Š4r7Ś conditioncó—dSrxr<©rrrs r6Ś arg_wherer;s€ŲQTŠQTr7có—dSrxr<rs r6rr?ó€ŲLOČCr7có —|rt|tj¦«s&dt|¦«j›}t |¦«‚| ¦« ttj |j ¦«¦«¦«  ¦«St|¦«}ttj|¦«¦«S)a· Return indices where `condition` evaluates `True`. Parameters ---------- condition Boolean expression to evaluate eager Evaluate immediately and return a `Series`; this requires that the given condition is itself a `Series`. If set to `False` (default), return an expression instead. See Also -------- Series.arg_true : Return indices where Series is True Examples -------- >>> df = pl.DataFrame({"a": [1, 2, 3, 4, 5]}) >>> df.select( ... [ ... pl.arg_where(pl.col("a") % 2 == 0), ... ] ... ).to_series() shape: (2,) Series: 'a' [u32] [ 1 3 ] z4expected Series in 'arg_where' if 'eager=True', got )r2r‚r$ŚtypeŚ__name__rŚto_framerŽrr:r;r.rrrr4)rrrr’s r6rrCs“€š@ š 3Ż˜)„R¤YŃ/Ō/š "š1Ż˜‘O”OŌ,š1š1š õ˜S‘/”/Š !Ų×!Ņ!Ń#Ō#×*Ņ*­9µQ“Uø9¼>Ń5JŌ5JŃ+KŌ+KŃLŌL×VŅVŃXŌXŠXå)Ø)Ń4Ō4ˆ Żœ yŃ1Ō1Ń2Ō2Š2r7có—dSrxr<©r”rrrŚs r6Ścoalescer#ps €š ˆ3r7có—dSrxr<r"s r6r#r#xs €š ˆSr7có—dSrxr<r"s r6r#r#€s €š Cr7cóR—|rx|g|¢}d„|D¦«x}sd}t|¦«‚d„|D¦«}tj|¦« t |d¬¦«¦« ¦«St |g|¢RŽ}ttj|¦«¦«S)u Folds the columns from left to right, keeping the first non-null value. Parameters ---------- exprs Columns to coalesce. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals. *more_exprs Additional columns to coalesce, specified as positional arguments. eager Evaluate immediately and return a `Series`; this requires that at least one of the given arguments is a `Series`. If set to `False` (default), return an expression instead. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, None, None, None], ... "b": [1, 2, None, None], ... "c": [5, None, 3, None], ... } ... ) >>> df.with_columns(pl.coalesce("a", "b", "c", 10).alias("d")) shape: (4, 4) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”† c ā”† d ā”‚ ā”‚ --- ā”† --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† i64 ā”† i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 1 ā”† 5 ā”† 1 ā”‚ ā”‚ null ā”† 2 ā”† null ā”† 2 ā”‚ ā”‚ null ā”† null ā”† 3 ā”† 3 ā”‚ ā”‚ null ā”† null ā”† null ā”† 10 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”˜ >>> df.with_columns(pl.coalesce(pl.col(["a", "b", "c"]), 10.0).alias("d")) shape: (4, 4) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† b ā”† c ā”† d ā”‚ ā”‚ --- ā”† --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† i64 ā”† f64 ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•” ā”‚ 1 ā”† 1 ā”† 5 ā”† 1.0 ā”‚ ā”‚ null ā”† 2 ā”† null ā”† 2.0 ā”‚ ā”‚ null ā”† null ā”† 3 ā”† 3.0 ā”‚ ā”‚ null ā”† null ā”† null ā”† 10.0 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ >>> s1 = pl.Series("a", [None, 2, None]) >>> s2 = pl.Series("b", [1, None, 3]) >>> pl.coalesce(s1, s2, eager=True) shape: (3,) Series: 'a' [i64] [ 1 2 3 ] cóF—g|]}t|tj¦«Æ|‘ŒSr<rrƒs r6r†zcoalesce..Ķs)€ŠHŠHŠH ­zø!½R¼YŃ/GŌ/GŠH˜1ŠHŠHŠHr7z:expected at least one Series in 'coalesce' if 'eager=True'cóT—g|]%}t|tj¦«r|jn|‘Œ&Sr<r‰rƒs r6r†zcoalesce..Ńs/€ŠLŠLŠLĄJ q­"¬)Ń4Ō4Š;!”&&ø!ŠLŠLŠLr7Fr) rr‚r!rŽr#rrrr4)r”rrrŚŚseriesr’s r6r#r#ˆs½€šF š .ŲŠ$˜Š$ˆŲHŠH eŠHŃHŌHŠHš "ŲNˆCŻ˜S‘/”/Š !ąLŠLĄeŠLŃLŌLˆŻŒ|˜FŃ#Ō#×*Ņ*­8°EĄŠ+GŃ+GŌ+GŃHŌH×RŅRŃTŌTŠTå.ØuŠB°zŠBŠBŠBˆŻœ eŃ,Ō,Ń-Ō-Š-r7Ś time_unitr'có—dSrxr<©rNr*s r6Ś from_epochr-Ųrr7śSeries | Sequence[int]có—dSrxr<r,s r6r-r-Üs €šˆSr7Śsś#str | Expr | Series | Sequence[int]có—t|t¦«rtj|¦«}n:t|tjtjf¦«st j|¦«}|dkr| t¦«S|dkr=| t¦«dz td¦«¦«S|tvr"| t|¦«¦«Sd|›}t|¦«‚)u¢ Utility function that parses an epoch timestamp (or Unix time) to Polars Date(time). Depending on the `time_unit` provided, this function will return a different dtype: - time_unit="d" returns pl.Date - time_unit="s" returns pl.Datetime["us"] (pl.Datetime's default) - time_unit="ms" returns pl.Datetime["ms"] - time_unit="us" returns pl.Datetime["us"] - time_unit="ns" returns pl.Datetime["ns"] Parameters ---------- column Series or expression to parse integers to pl.Datetime. time_unit The unit of time of the timesteps since epoch time. Examples -------- >>> df = pl.DataFrame({"timestamp": [1666683077, 1666683099]}).lazy() >>> df.select(pl.from_epoch(pl.col("timestamp"), time_unit="s")).collect() shape: (2, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ timestamp ā”‚ ā”‚ --- ā”‚ ā”‚ datetime[Ī¼s] ā”‚ ā•žā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 2022-10-25 07:31:17 ā”‚ ā”‚ 2022-10-25 07:31:39 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ The function can also be used in an eager context by passing a Series. >>> s = pl.Series([12345, 12346]) >>> pl.from_epoch(s, time_unit="d") shape: (2,) Series: '' [date] [ 2003-10-20 2003-10-21 ] Śdr0i@BŚusz=`time_unit` must be one of {'ns', 'us', 'ms', 's', 'd'}, got ) r2r3r:r;r‚r$r"Ścastrrrrr)rNr*r’s r6r-r-āsć€õ\&#ŃŌš#Ż”v‘”ˆˆŻ ˜„¤­B¬GŠ 4Ń 5Ō 5š#Ż”˜6Ń"Ō"ˆąCŅŠŲ{Š{4Ń Ō Š Ų cŅ Š Ų— ’ EŃ"Ō" YŃ.×4Ņ4µXød±^“^ŃDŌDŠDŲ Õ*Š *Š *Ų{Š{8 IŃ.Ō.Ń/Ō/Š/ą]ŠPYŠ]Š]ˆŻ˜‰oŒoŠr7Ś min_periodsŚ min_samplesz1.21.0r@)r7rOŚ window_sizec ó —|€|}t|t¦«rtj|¦«}t|t¦«rtj|¦«}t t j|j|j|||¦«¦«S)aé Compute the rolling covariance between two columns/ expressions. The window at a given row includes the row itself and the `window_size - 1` elements before it. .. versionchanged:: 1.21.0 The `min_periods` parameter was renamed `min_samples`. Parameters ---------- a Column name or Expression. b Column name or Expression. window_size The length of the window. min_samples The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size. ddof Delta degrees of freedom. The divisor used in calculations is `N - ddof`, where `N` represents the number of elements. )r2r3r:r;rr4Ś rolling_covræ©rsrtr8r7rOs r6r:r: sy€šBŠŲ!ˆ Ż!•SŃŌšŻ ŒE!‰HŒHˆŻ!•SŃŌšŻ ŒE!‰HŒHˆŻ Ż Œ˜œ  1¤9Økø;ČŃMŌMń ō šr7c ó —|€|}t|t¦«rtj|¦«}t|t¦«rtj|¦«}t t j|j|j|||¦«¦«S)aź Compute the rolling correlation between two columns/ expressions. The window at a given row includes the row itself and the `window_size - 1` elements before it. .. versionchanged:: 1.21.0 The `min_periods` parameter was renamed `min_samples`. Parameters ---------- a Column name or Expression. b Column name or Expression. window_size The length of the window. min_samples The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size. ddof Delta degrees of freedom. The divisor used in calculations is `N - ddof`, where `N` represents the number of elements. )r2r3r:r;rr4Ś rolling_corrrær;s r6r=r=L sz€šBŠŲ!ˆ Ż!•SŃŌšŻ ŒE!‰HŒHˆŻ!•SŃŌšŻ ŒE!‰HŒHˆŻ Ż Ō˜œ A¤IØ{øKČŃNŌNń ō šr7Śsqlcó—dSrxr<©r>s r6Śsql_exprrAx s€ą€Cr7ś Sequence[str]ś list[Expr]có—dSrxr<r@s r6rArA} s€Ų03°r7śstr | Sequence[str]śExpr | list[Expr]có†—t|t¦«r!ttj|¦«¦«Sd„|D¦«S)uó Parse one or more SQL expressions to Polars expression(s). Parameters ---------- sql One or more SQL expressions. Examples -------- Parse a single SQL expression: >>> df = pl.DataFrame({"a": [2, 1]}) >>> expr = pl.sql_expr("MAX(a)") >>> df.select(expr) shape: (1, 1) ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”‚ ā”‚ --- ā”‚ ā”‚ i64 ā”‚ ā•žā•ā•ā•ā•ā•ā•” ā”‚ 2 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ Parse multiple SQL expressions: >>> df.with_columns( ... *pl.sql_expr(["POWER(a,a) AS a_a", "CAST(a AS TEXT) AS a_txt"]), ... ) shape: (2, 3) ā”Œā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚ a ā”† a_a ā”† a_txt ā”‚ ā”‚ --- ā”† --- ā”† --- ā”‚ ā”‚ i64 ā”† i64 ā”† str ā”‚ ā•žā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•Ŗā•ā•ā•ā•ā•ā•ā•ā•” ā”‚ 2 ā”† 4 ā”† 2 ā”‚ ā”‚ 1 ā”† 1 ā”† 1 ā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ cóP—g|]#}ttj|¦«¦«‘Œ$Sr<)rr4rA)r„Śqs r6r†zsql_expr..¬ s(€Š8Š8Š8Øq• #œ, q™/œ/Ń*Ō*Š8Š8Š8r7)r2r3rr4rAr@s r6rArA sC€õP#•sŃŌš9Żœ cŃ*Ō*Ń+Ō+Š+ą8Š8°CŠ8Ń8Ō8Š8r7)r.r/r0r")r0r")r>r3r0r")r>r3rGrHr0r")rM)rNr3rOrPr0r")rcrdr0r")rh)rNr3rirPr0r")rsr(rtr(rpr%rOrurqrHrrrvr0r")rsr(rtr(rpr%rOrurqrHrrr{r0r$)rsr(rtr(rpr%rOrurqrHrrrHr0r~) rsr(rtr(rOrPrrrvr0r") rsr(rtr(rOrPrrr{r0r$) rsr(rtr(rOrPrrrHr0r~rx)r”rœrržrŸr r0r") r”rØrr©rŸr r¤rHr0r") r¬r(rr­r”r®r¤rHrŸr r0r")rr­r”r®r0r") r¬r(rr­r”r®r¶rHr0r")r»r¼r½r¼r0r")r>rĒrČrÉr0r")rNr3r0r")rŠ)rNr3rŃrŅrÓr*r0r")r”rŁrŚr(rÕrŪrÖrŪr×rHrŲrHr0r")rėrģrßrHrąrHrįrHrārHrćrHrärHrårHręrHrērHrčrHrér+rźr&r0rķ) rėrģr’r{rźr&rér+r0r) rėrģr’rvrźr&rér+r0r) rėrģr’rHrźr&rér+r0r)rėrģrér+r0r3)r”rŁrrr{rr(r0r!)r”rŁrrrvrr(r0r#)r”rŁrrrHrr(r0r)rr~rrrvr0r")rr~rrr{r0r$)rr~rrrHr0r~)r”rŁrŚr(rrrvr0r")r”rŁrŚr(rrr{r0r$)r”rŁrŚr(rrrHr0r~).)rNr¼r*r'r0r")rNr.r*r'r0r$)r0)rNr1r*r'r0r~) rsr¼rtr¼r8rPr7rurOrPr0r")r>r3r0r")r>rBr0rC)r>rEr0rF)mŚ __future__rŚ contextlibŚcollections.abcrŚtypingrrrrŚpolars._reexportŚ _reexportr‚Śpolars.functionsŚ functionsr:Śpolars._utils.async_r r Śpolars._utils.deprecationr r r rŚpolars._utils.parserrŚpolars._utils.unstablerrŚpolars._utils.variousrrŚpolars._utils.wraprrŚpolars.datatypesrrrrŚpolars.lazyframe.opt_flagsrrŚsuppressŚ ImportErrorŚ polars.polarsŚpolarsr4Śsysrrrr r!r"r#r$Śpolars._typingr%r&r'r(r)r*r+Ś version_infoŚwarningsŚtyping_extensionsr5r=rBrJrLrRrVrXrZr\r^r`rbrgrkrorzr—r§r£r²r“rørŗrĆrĘrĢrĻrŃrŻrśrr rŽrr#r-r:r=rAr<r7r6śrcsq šŲ"Š"Š"Š"Š"Š"ąŠŠŠŲ$Š$Š$Š$Š$Š$Ų9Š9Š9Š9Š9Š9Š9Š9Š9Š9Š9Š9ąŠŠŠŠŠŲŠŠŠŠŠŲLŠLŠLŠLŠLŠLŠLŠLššššššššššššš ššššššššDŠCŠCŠCŠCŠCŠCŠCŲBŠBŠBŠBŠBŠBŠBŠBŲ1Š1Š1Š1Š1Š1Š1Š1ŲHŠHŠHŠHŠHŠHŠHŠHŠHŠHŠHŠHššššššššš €ZŌ˜Ń%Ō%š š ŲŠŠŠŠŠš š š ń ō š š š š š š ųųųš š š š šš1Ų€J€J€JŲ?Š?Š?Š?Š?Š?Š?Š?Š?Š?ŲŠŠŠŠŠą9Š9Š9Š9Š9Š9Š9Š9Š9Š9Š9Š9ššššššššššššššššššššššš Ō˜7Ņ"Š"Ų'Š'Š'Š'Š'Š'Š'ą0Š0Š0Š0Š0Š0š &š &š &š &šDšDšDšDšNJ#šJ#šJ#šJ#šZ.3š6š6š6š6š6š6š>(%š(%š(%š(%šV$#š$#š$#š$#š$#šN$#š$#š$#š$#š$#šN,"š,"š,"š,"š^($š($š($š($šV(&š(&š(&š(&šV)-š)-š)-š)-šX>#š>#š>#š>#šB>"š>"š>"š>"šB,.š,.š,.š,.š^,!š,!š,!š,!š,!š^,!š,!š,!š,!š,!š^ š !$ŲŲŲšššššń „šš š !$ŲŲš ššššń „šš!*ŲŲ Ųšn"šn"šn"šn"šn"šn"šb š Ųš ššššń „šš š š ššššń „ššŲš F.šF.šF.šF.šF.šF.šX+/š;š;š;š;š;šB+/šVš !š VšVšVšVšVšVš|!Ų*.š {š{š{š{š{š{š|;2š;2š;2š;2šFš ;Yš;Yš;Yš;Yš;Yš;Yš|+Jš+Jš+Jš+Jš\/8š/8š/8š/8šd €Š WŃXŌXš*#š*#š*#ńYŌXš*#šZF6šF6šF6šF6šR&š&š&š&š%.š;š;š;š;š;š.).Ų(-ŲŲ š ]š]š]š]š]š]š@ŠŃ Ō ŲŠŃŌšŲ#Ų $Ų $Ų!ŲŲ"Ų"Ų!%ŲŲ#:ŲšpšpšpšpšpńŌń!Ō špšf š  Ų#:š 2š2š2š2š2ń „š2š š#ŲŲ#:š %š%š%š%š%ń „š%š €„ŲŠŃ Ō šŲŲ#:š IšIšIšIšIń!Ō ń „šIšX €„š$;š;š;š;š;š;ń „š;š< ššššššń „šš šššń „šš:>š;5š;5š;5š;5š;5š;5š| ŲCFŠTŠTŠTŠTŠTń „ŲTš ŲOŠOŠOń „ŲOš:?š*3š*3š*3š*3š*3š*3šZ š šššššń „šš šššń „šš šššń „šššM.šM.šM.šM.šM.šM.š` ŲOŠOŠOŠOń „ŲOš ą?Bššššń „šš MPš;š;š;š;š;š|Š˜]ØMĄ8ŠLŃLŌLš #Ųš (š(š(š(š(ńMŌLš(šVŠ˜]ØMĄ8ŠLŃLŌLš #Ųš (š(š(š(š(ńMŌLš(šV šššń „šš Ų3Š3Š3ń „Ų3š+9š+9š+9š+9š+9š+9sĀBĀBĀB