σ šŠhXƒγσt•SSKJr SSKJrJrJr \(a SSKJr SSKJ r \"SSS9r "SS \\ 5r g ) ι)Ϊ annotations)Ϊ TYPE_CHECKINGΪGenericΪTypeVar)ΪExpr)ΪTimeUnitΪExprTr)Ϊboundcσ•\rSrSrSSjrSSjrSSjrSSjrSSjrSSjr SSjr SS jr SS jr SS jr SS jrSS jrSSjrSSjrSSjrSSjrSSjrSSjrSSjrSSjrS SjrS!S"SjjrS#SjrS$SjrSrg)%ΪExprDateTimeNamespaceι cσ•Xlg©N©Ϊ_expr)ΪselfΪexprs ΪcC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\narwhals/expr_dt.pyΪ__init__ΪExprDateTimeNamespace.__init__ s€Ψ σcσB^•TRRU4Sj5$)uπExtract the date from underlying DateTime representation. Returns: A new expression. Raises: NotImplementedError: If pandas default backend is being used. Examples: >>> from datetime import datetime >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... {"a": [datetime(2012, 1, 7, 10), datetime(2027, 12, 13)]} ... ) >>> df = nw.from_native(df_native) >>> df.select(nw.col("a").dt.date()).to_native() shape: (2, 1) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ a β”‚ β”‚ --- β”‚ β”‚ date β”‚ β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•‘ β”‚ 2012-01-07 β”‚ β”‚ 2027-12-13 β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσj>•TRRU5RR5$r)rΪ_to_compliant_exprΪdtΪdate©Ϊplxrs €rΪΪ,ExprDateTimeNamespace.date..-σ#ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧBΡBΤDr©rΪ_with_elementwise©rs`rrΪExprDateTimeNamespace.datesψ€π8z‰zΧ+Ρ+ά Dσ π rcσB^•TRRU4Sj5$)uΏExtract year from underlying DateTime representation. Returns the year number in the calendar date. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(1978, 6, 1), datetime(2065, 1, 1)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.year().alias("year")) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” |Narwhals DataFrame| |------------------| | a year| |0 1978-06-01 1978| |1 2065-01-01 2065| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσj>•TRRU5RR5$r)rrrΪyearrs €rrΪ,ExprDateTimeNamespace.year..Jr!rr"r$s`rr(ΪExprDateTimeNamespace.year0sψ€π2z‰zΧ+Ρ+ά Dσ π rcσB^•TRRU4Sj5$)amExtract month from underlying DateTime representation. Returns the month number starting from 1. The return value ranges from 1 to 12. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table({"a": [datetime(1978, 6, 1), datetime(2065, 1, 1)]}) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.month().alias("month")).to_native() pyarrow.Table a: timestamp[us] month: int64 ---- a: [[1978-06-01 00:00:00.000000,2065-01-01 00:00:00.000000]] month: [[6,1]] cσj>•TRRU5RR5$r)rrrΪmonthrs €rrΪ-ExprDateTimeNamespace.month..ds#ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧCΡCΤErr"r$s`rr-ΪExprDateTimeNamespace.monthMsψ€π,z‰zΧ+Ρ+ά Eσ π rcσB^•TRRU4Sj5$)aŽExtract day from underlying DateTime representation. Returns the day of month starting from 1. The return value ranges from 1 to 31. (The last day of month differs by months.) Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table({"a": [datetime(1978, 6, 1), datetime(2065, 1, 1)]}) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.day().alias("day")).to_native() pyarrow.Table a: timestamp[us] day: int64 ---- a: [[1978-06-01 00:00:00.000000,2065-01-01 00:00:00.000000]] day: [[1,1]] cσj>•TRRU5RR5$r)rrrΪdayrs €rrΪ+ExprDateTimeNamespace.day..~s#ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧAΡAΤCrr"r$s`rr2ΪExprDateTimeNamespace.daygsψ€π,z‰zΧ+Ρ+ά Cσ π rcσB^•TRRU4Sj5$)uσExtract hour from underlying DateTime representation. Returns the hour number from 0 to 23. Returns: A new expression. Examples: >>> from datetime import datetime >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... {"a": [datetime(1978, 1, 1, 1), datetime(2065, 1, 1, 10)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.hour().alias("hour")) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” | Narwhals DataFrame | |------------------------------| |shape: (2, 2) | |β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”| |β”‚ a ┆ hour β”‚| |β”‚ --- ┆ --- β”‚| |β”‚ datetime[ΞΌs] ┆ i8 β”‚| |β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•ͺ══════║| |β”‚ 1978-01-01 01:00:00 ┆ 1 β”‚| |β”‚ 2065-01-01 10:00:00 ┆ 10 β”‚| |β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”˜| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσj>•TRRU5RR5$r)rrrΪhourrs €rrΪ,ExprDateTimeNamespace.hour..‘r!rr"r$s`rr7ΪExprDateTimeNamespace.hoursψ€π>z‰zΧ+Ρ+ά Dσ π rcσB^•TRRU4Sj5$)aAExtract minutes from underlying DateTime representation. Returns the minute number from 0 to 59. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(1978, 1, 1, 1, 1), datetime(2065, 1, 1, 10, 20)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.minute().alias("minute")).to_native() a minute 0 1978-01-01 01:01:00 1 1 2065-01-01 10:20:00 20 cσj>•TRRU5RR5$r)rrrΪminuters €rrΪ.ExprDateTimeNamespace.minute..Ίσ#ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧDΡDΤFrr"r$s`rr<ΪExprDateTimeNamespace.minute€sψ€π*z‰zΧ+Ρ+ά Fσ π rcσB^•TRRU4Sj5$)a§Extract seconds from underlying DateTime representation. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... datetime(1978, 1, 1, 1, 1, 1), ... datetime(2065, 1, 1, 10, 20, 30), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.second().alias("second")).to_native() pyarrow.Table a: timestamp[us] second: int64 ---- a: [[1978-01-01 01:01:01.000000,2065-01-01 10:20:30.000000]] second: [[1,30]] cσj>•TRRU5RR5$r)rrrΪsecondrs €rrΪ.ExprDateTimeNamespace.second..Ωr>rr"r$s`rrBΪExprDateTimeNamespace.second½sψ€π6z‰zΧ+Ρ+ά Fσ π rcσB^•TRRU4Sj5$)aΰExtract milliseconds from underlying DateTime representation. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... datetime(1978, 1, 1, 1, 1, 1, 0), ... datetime(2065, 1, 1, 10, 20, 30, 67000), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... nw.col("a").dt.millisecond().alias("millisecond") ... ).to_native() pyarrow.Table a: timestamp[us] millisecond: int64 ---- a: [[1978-01-01 01:01:01.000000,2065-01-01 10:20:30.067000]] millisecond: [[0,67]] cσj>•TRRU5RR5$r)rrrΪ millisecondrs €rrΪ3ExprDateTimeNamespace.millisecond..ϊσ#ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧIΡIΤKrr"r$s`rrGΪ!ExprDateTimeNamespace.millisecondάσψ€π:z‰zΧ+Ρ+ά Kσ π rcσB^•TRRU4Sj5$)aγExtract microseconds from underlying DateTime representation. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... datetime(1978, 1, 1, 1, 1, 1, 0), ... datetime(2065, 1, 1, 10, 20, 30, 67000), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... nw.col("a").dt.microsecond().alias("microsecond") ... ).to_native() pyarrow.Table a: timestamp[us] microsecond: int64 ---- a: [[1978-01-01 01:01:01.000000,2065-01-01 10:20:30.067000]] microsecond: [[0,67000]] cσj>•TRRU5RR5$r)rrrΪ microsecondrs €rrΪ3ExprDateTimeNamespace.microsecond..rIrr"r$s`rrNΪ!ExprDateTimeNamespace.microsecondύrKrcσB^•TRRU4Sj5$)aαExtract Nanoseconds from underlying DateTime representation. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... datetime(1978, 1, 1, 1, 1, 1, 0), ... datetime(2065, 1, 1, 10, 20, 30, 67000), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... nw.col("a").dt.nanosecond().alias("nanosecond") ... ).to_native() pyarrow.Table a: timestamp[us] nanosecond: int64 ---- a: [[1978-01-01 01:01:01.000000,2065-01-01 10:20:30.067000]] nanosecond: [[0,67000000]] cσj>•TRRU5RR5$r)rrrΪ nanosecondrs €rrΪ2ExprDateTimeNamespace.nanosecond..<s#ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧHΡHΤJrr"r$s`rrSΪ ExprDateTimeNamespace.nanosecondsψ€π:z‰zΧ+Ρ+ά Jσ π rcσB^•TRRU4Sj5$)uΥGet ordinal day. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(a_ordinal_day=nw.col("a").dt.ordinal_day()) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” | Narwhals DataFrame | |---------------------------| | a a_ordinal_day| |0 2020-01-01 1| |1 2020-08-03 216| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσj>•TRRU5RR5$r)rrrΪ ordinal_dayrs €rrΪ3ExprDateTimeNamespace.ordinal_day..WrIrr"r$s`rrXΪ!ExprDateTimeNamespace.ordinal_day?sψ€π.z‰zΧ+Ρ+ά Kσ π rcσB^•TRRU4Sj5$)uExtract the week day from the underlying Date representation. Returns: Returns the ISO weekday number where monday = 1 and sunday = 7 Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(a_week_day=nw.col("a").dt.weekday()) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” | Narwhals DataFrame | |------------------------| | a a_week_day| |0 2020-01-01 3| |1 2020-08-03 1| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσj>•TRRU5RR5$r)rrrΪweekdayrs €rrΪ/ExprDateTimeNamespace.weekday..rs#ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧEΡEΤGrr"r$s`rr]ΪExprDateTimeNamespace.weekdayZsψ€π.z‰zΧ+Ρ+ά Gσ π rcσB^•TRRU4Sj5$)uΊGet total minutes. Returns: A new expression. Notes: The function outputs the total minutes in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... {"a": [timedelta(minutes=10), timedelta(minutes=20, seconds=40)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_total_minutes=nw.col("a").dt.total_minutes() ... ).to_native() shape: (2, 2) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ a ┆ a_total_minutes β”‚ β”‚ --- ┆ --- β”‚ β”‚ duration[ΞΌs] ┆ i64 β”‚ β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•ͺ═════════════════║ β”‚ 10m ┆ 10 β”‚ β”‚ 20m 40s ┆ 20 β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσj>•TRRU5RR5$r)rrrΪ total_minutesrs €rrΪ5ExprDateTimeNamespace.total_minutes..–σ#ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧKΡKΤMrr"r$s`rrbΪ#ExprDateTimeNamespace.total_minutesuσ ψ€π@z‰zΧ+Ρ+ά Mσ π rcσB^•TRRU4Sj5$)uΏGet total seconds. Returns: A new expression. Notes: The function outputs the total seconds in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... {"a": [timedelta(seconds=10), timedelta(seconds=20, milliseconds=40)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_total_seconds=nw.col("a").dt.total_seconds() ... ).to_native() shape: (2, 2) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ a ┆ a_total_seconds β”‚ β”‚ --- ┆ --- β”‚ β”‚ duration[ΞΌs] ┆ i64 β”‚ β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•ͺ═════════════════║ β”‚ 10s ┆ 10 β”‚ β”‚ 20s 40ms ┆ 20 β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσj>•TRRU5RR5$r)rrrΪ total_secondsrs €rrΪ5ExprDateTimeNamespace.total_seconds..Ίrdrr"r$s`rriΪ#ExprDateTimeNamespace.total_seconds™rfrcσB^•TRRU4Sj5$)u}Get total milliseconds. Returns: A new expression. Notes: The function outputs the total milliseconds in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... { ... "a": [ ... timedelta(milliseconds=10), ... timedelta(milliseconds=20, microseconds=40), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_total_milliseconds=nw.col("a").dt.total_milliseconds() ... ).to_native() shape: (2, 2) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ a ┆ a_total_milliseconds β”‚ β”‚ --- ┆ --- β”‚ β”‚ duration[ΞΌs] ┆ i64 β”‚ β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•ͺ══════════════════════║ β”‚ 10ms ┆ 10 β”‚ β”‚ 20040Β΅s ┆ 20 β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσj>•TRRU5RR5$r)rrrΪtotal_millisecondsrs €rrΪ:ExprDateTimeNamespace.total_milliseconds..γσ#ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧPΡPΤRrr"r$s`rrnΪ(ExprDateTimeNamespace.total_milliseconds½s ψ€πJz‰zΧ+Ρ+ά Rσ π rcσB^•TRRU4Sj5$)a†Get total microseconds. Returns: A new expression. Notes: The function outputs the total microseconds in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... timedelta(microseconds=10), ... timedelta(milliseconds=1, microseconds=200), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_total_microseconds=nw.col("a").dt.total_microseconds() ... ).to_native() pyarrow.Table a: duration[us] a_total_microseconds: int64 ---- a: [[10,1200]] a_total_microseconds: [[10,1200]] cσj>•TRRU5RR5$r)rrrΪtotal_microsecondsrs €rrΪ:ExprDateTimeNamespace.total_microseconds.. rprr"r$s`rrtΪ(ExprDateTimeNamespace.total_microsecondsζs ψ€πDz‰zΧ+Ρ+ά Rσ π rcσB^•TRRU4Sj5$)aώGet total nanoseconds. Returns: A new expression. Notes: The function outputs the total nanoseconds in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... { ... "a": pd.to_datetime( ... [ ... "2024-01-01 00:00:00.000000001", ... "2024-01-01 00:00:00.000000002", ... ] ... ) ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_diff_total_nanoseconds=nw.col("a").diff().dt.total_nanoseconds() ... ).to_native() a a_diff_total_nanoseconds 0 2024-01-01 00:00:00.000000001 NaN 1 2024-01-01 00:00:00.000000002 1.0 cσj>•TRRU5RR5$r)rrrΪtotal_nanosecondsrs €rrΪ9ExprDateTimeNamespace.total_nanoseconds...s#ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧOΡOΤQrr"r$s`rryΪ'ExprDateTimeNamespace.total_nanoseconds s ψ€πBz‰zΧ+Ρ+ά Qσ π rcσF^^•TRRUU4Sj5$)uW Convert a Date/Time/Datetime column into a String column with the given format. Arguments: format: Format to format temporal column with. Returns: A new expression. Notes: Unfortunately, different libraries interpret format directives a bit differently. - Chrono, the library used by Polars, uses `"%.f"` for fractional seconds, whereas pandas and Python stdlib use `".%f"`. - PyArrow interprets `"%S"` as "seconds, including fractional seconds" whereas most other tools interpret it as "just seconds, as 2 digits". --- Therefore, we make the following adjustments. - for pandas-like libraries, we replace `"%S.%f"` with `"%S%.f"`. - for PyArrow, we replace `"%S.%f"` with `"%S"`. --- Workarounds like these don't make us happy, and we try to avoid them as much as possible, but here we feel like it's the best compromise. If you just want to format a date/datetime Series as a local datetime string, and have it work as consistently as possible across libraries, we suggest using: - `"%Y-%m-%dT%H:%M:%S%.f"` for datetimes - `"%Y-%m-%d"` for dates --- Though note that, even then, different tools may return a different number of trailing zeros. Nonetheless, this is probably consistent enough for most applications. If you have an application where this is not enough, please open an issue and let us know. Examples: >>> from datetime import datetime >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... {"a": [datetime(2020, 3, 1), datetime(2020, 5, 1)]} ... ) >>> df = nw.from_native(df_native) >>> df.select(nw.col("a").dt.to_string("%Y/%m/%d %H:%M:%S")) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” | Narwhals DataFrame | |-----------------------| |shape: (2, 1) | |β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”| |β”‚ a β”‚| |β”‚ --- β”‚| |β”‚ str β”‚| |β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•‘| |β”‚ 2020/03/01 00:00:00 β”‚| |β”‚ 2020/05/01 00:00:00 β”‚| |β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσl>•TRRU5RRT5$r)rrrΪ to_string)rΪformatrs €€rrΪ1ExprDateTimeNamespace.to_string..qs%ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧGΡGΘΤOrr")rrs``rr~ΪExprDateTimeNamespace.to_string1s ω€π~z‰zΧ+Ρ+έ Oσ π rcσF^^•TRRUU4Sj5$)aReplace time zone. Arguments: time_zone: Target time zone. Returns: A new expression. Examples: >>> from datetime import datetime, timezone >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... { ... "a": [ ... datetime(2024, 1, 1, tzinfo=timezone.utc), ... datetime(2024, 1, 2, tzinfo=timezone.utc), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.select(nw.col("a").dt.replace_time_zone("Asia/Kathmandu")).to_native() a 0 2024-01-01 00:00:00+05:45 1 2024-01-02 00:00:00+05:45 cσl>•TRRU5RRT5$r)rrrΪreplace_time_zone©rrΪ time_zones €€rrΪ9ExprDateTimeNamespace.replace_time_zone..σ&ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧOΡOΠPYΤZrr")rr†s``rr„Ϊ'ExprDateTimeNamespace.replace_time_zonetsω€π6z‰zΧ+Ρ+έ Zσ π rcσf^^•Tc Sn[U5eTRRUU4Sj5$)aConvert to a new time zone. If converting from a time-zone-naive column, then conversion happens as if converting from UTC. Arguments: time_zone: Target time zone. Returns: A new expression. Examples: >>> from datetime import datetime, timezone >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... { ... "a": [ ... datetime(2024, 1, 1, tzinfo=timezone.utc), ... datetime(2024, 1, 2, tzinfo=timezone.utc), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.select(nw.col("a").dt.convert_time_zone("Asia/Kathmandu")).to_native() a 0 2024-01-01 05:45:00+05:45 1 2024-01-02 05:45:00+05:45 z…Target `time_zone` cannot be `None` in `convert_time_zone`. Please use `replace_time_zone(None)` if you want to remove the time zone.cσl>•TRRU5RRT5$r)rrrΪconvert_time_zoner…s €€rrΪ9ExprDateTimeNamespace.convert_time_zone..΅rˆr)Ϊ TypeErrorrr#)rr†Ϊmsgs`` rrŒΪ'ExprDateTimeNamespace.convert_time_zone“s7ω€π< Ρ πZˆCά˜C“.Р؏z‰zΧ+Ρ+έ Zσ π rcσv^^•TS;aST<S3n[U5eTRRUU4Sj5$)uReturn a timestamp in the given time unit. Arguments: time_unit: One of - 'ns': nanosecond. - 'us': microsecond. - 'ms': millisecond. Returns: A new expression. Examples: >>> from datetime import date >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"date": [date(2001, 1, 1), None]}) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("date").dt.timestamp("ms").alias("timestamp_ms")) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” | Narwhals DataFrame | |-----------------------------| |shape: (2, 2) | |β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”| |β”‚ date ┆ timestamp_ms β”‚| |β”‚ --- ┆ --- β”‚| |β”‚ date ┆ i64 β”‚| |β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•ͺ══════════════║| |β”‚ 2001-01-01 ┆ 978307200000 β”‚| |β”‚ null ┆ null β”‚| |β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ >ΪmsΪnsΪusz=invalid `time_unit` Expected one of {'ns', 'us', 'ms'}, got Ϊ.cσl>•TRRU5RRT5$r)rrrΪ timestamp)rrΪ time_units €€rrΪ1ExprDateTimeNamespace.timestamp..ΰs%ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧGΡGΘ ΤRr)Ϊ ValueErrorrr#)rr˜rs`` rr—ΪExprDateTimeNamespace.timestampΈsMω€πB Π.Σ .πAΨAJΑ ΘQπPπ τ˜S“/Π !؏z‰zΧ+Ρ+έ Rσ π rcσF^^•TRRUU4Sj5$)uDivide the date/datetime range into buckets. Arguments: every: Length of bucket. Must be of form ``, where `multiple` is a positive integer and `unit` is one of - 'ns': nanosecond. - 'us': microsecond. - 'ms': millisecond. - 's': second. - 'm': minute. - 'h': hour. - 'd': day. - 'mo': month. - 'q': quarter. - 'y': year. Returns: Expression of data type `Date` or `Datetime`. Examples: >>> from datetime import datetime >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"datetime": [datetime(2021, 3, 1, 12, 34)]}) >>> df = nw.from_native(df_native) >>> df.with_columns( ... nw.col("datetime").dt.truncate("1h").alias("datetime_trunc") ... ) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” | Narwhals DataFrame | |---------------------------------------------| |shape: (1, 2) | |β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”| |β”‚ datetime ┆ datetime_trunc β”‚| |β”‚ --- ┆ --- β”‚| |β”‚ datetime[ΞΌs] ┆ datetime[ΞΌs] β”‚| |β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•ͺ═════════════════════║| |β”‚ 2021-03-01 12:34:00 ┆ 2021-03-01 12:00:00 β”‚| |β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσl>•TRRU5RRT5$r)rrrΪtruncate)rΪeveryrs €€rrΪ0ExprDateTimeNamespace.truncate..s%ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧFΡFΐuΤMrr")rrŸs``rržΪExprDateTimeNamespace.truncateγs ω€πVz‰zΧ+Ρ+έ Mσ π rcσF^^•TRRUU4Sj5$)u·Offset this date by a relative time offset. Arguments: by: The offset. Must be of form ``, where `multiple` is a positive integer and `unit` is one of - 'ns': nanosecond. - 'us': microsecond. - 'ms': millisecond. - 's': second. - 'm': minute. - 'h': hour. - 'd': day. - 'mo': month. - 'q': quarter. - 'y': year. Returns: Expression of data type `Date` or `Datetime`. Examples: >>> from datetime import datetime >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"datetime": [datetime(2021, 3, 1, 12, 34)]}) >>> df = nw.from_native(df_native) >>> df.with_columns( ... nw.col("datetime").dt.offset_by("1h").alias("datetime_offset_by_1h") ... ) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” | Narwhals DataFrame | |-----------------------------------------------| |shape: (1, 2) | |β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”| |β”‚ datetime ┆ datetime_offset_by_1h β”‚| |β”‚ --- ┆ --- β”‚| |β”‚ datetime[ΞΌs] ┆ datetime[ΞΌs] β”‚| |β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•ͺ═══════════════════════║| |β”‚ 2021-03-01 12:34:00 ┆ 2021-03-01 13:34:00 β”‚| |β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ cσl>•TRRU5RRT5$r)rrrΪ offset_by)rΪbyrs €€rrΪ1ExprDateTimeNamespace.offset_by..>s%ψ€˜Ÿ ™ Χ5Ρ5°cΣ:Χ=Ρ=ΧGΡGΘΤKrr")rr₯s``rr€ΪExprDateTimeNamespace.offset_bys ω€πVz‰zΧ+Ρ+έ Kσ π rrN)rr ΪreturnΪNone)r¨r )rΪstrr¨r )r†z str | Noner¨r )r†rͺr¨r )r”)r˜rr¨r )rŸrͺr¨r )r₯rͺr¨r )Ϊ__name__Ϊ __module__Ϊ __qualname__Ϊ__firstlineno__rrr(r-r2r7r<rBrGrNrSrXr]rbrirnrtryr~r„rŒr—ržr€Ϊ__static_attributes__©rrr r s‰†ττ τ@ τ: τ4 τ4! τF τ2 τ> τB τB τB τ6 τ6" τH" τH' τR$ τL# τJA τF τ># φJ) τV- χ^- rr N) Ϊ __future__rΪtypingrrrΪ narwhals.exprrΪnarwhals.typingrr r r°rrΪr΅s5πέ"η2Ρ2ζέ"έ(α˜vΡ&€τs  ˜G E™Nυs  r