ó Îïh´rãó„•SSKJr SSKJr SSKJr SSKJr \(a SSKJr SSKJ r \"SSS 9r "S S \\ 5r g ) é)Ú annotations)Ú TYPE_CHECKING)ÚGeneric)ÚTypeVar)ÚExpr)ÚTimeUnitÚExprTr)Úboundcóô•\rSrSrSSjrSSjrSSjrSSjrSSjrSSjr SSjr SS jr SS jr SS jr SS jrSS jrSSjrSSjrSSjrSSjrSSjrSSjrSSjrSSjrSSjrSS SjjrSrg)!ÚExprDateTimeNamespaceécó•Xlg©N©Ú_expr)ÚselfÚexprs ÚB/var/www/html/env/lib/python3.13/site-packages/narwhals/expr_dt.pyÚ__init__ÚExprDateTimeNamespace.__init__s€Ø ócól^•TRRU4SjTRR5$)uðExtract the date from underlying DateTime representation. Returns: A new expression. Raises: NotImplementedError: If pandas default backend is being used. Examples: >>> from datetime import datetime >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... {"a": [datetime(2012, 1, 7, 10), datetime(2027, 12, 13)]} ... ) >>> df = nw.from_native(df_native) >>> df.select(nw.col("a").dt.date()).to_native() shape: (2, 1) ┌────────────┠│ a │ │ --- │ │ date │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ 2012-01-07 │ │ 2027-12-13 │ └────────────┘ cój>•TRRU5RR5$r)rÚ_to_compliant_exprÚdtÚdate©Úplxrs €rÚÚ,ExprDateTimeNamespace.date../ó#ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×BÑBÔDr©rÚ __class__Ú _metadata©rs`rrÚExprDateTimeNamespace.dates+ø€ð8z‰z×#Ñ#Ü DÀdÇjÁj×FZÑFZó ð rcól^•TRRU4SjTRR5$)u¿Extract year from underlying DateTime representation. Returns the year number in the calendar date. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(1978, 6, 1), datetime(2065, 1, 1)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.year().alias("year")) ┌──────────────────┠|Narwhals DataFrame| |------------------| | a year| |0 1978-06-01 1978| |1 2065-01-01 2065| └──────────────────┘ cój>•TRRU5RR5$r)rrrÚyearrs €rrÚ,ExprDateTimeNamespace.year..Lr!rr"r%s`rr)ÚExprDateTimeNamespace.year2s+ø€ð2z‰z×#Ñ#Ü DÀdÇjÁj×FZÑFZó ð rcól^•TRRU4SjTRR5$)amExtract month from underlying DateTime representation. Returns the month number starting from 1. The return value ranges from 1 to 12. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table({"a": [datetime(1978, 6, 1), datetime(2065, 1, 1)]}) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.month().alias("month")).to_native() pyarrow.Table a: timestamp[us] month: int64 ---- a: [[1978-06-01 00:00:00.000000,2065-01-01 00:00:00.000000]] month: [[6,1]] cój>•TRRU5RR5$r)rrrÚmonthrs €rrÚ-ExprDateTimeNamespace.month..fs#ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×CÑCÔErr"r%s`rr.ÚExprDateTimeNamespace.monthOs,ø€ð,z‰z×#Ñ#Ü EØ J‰J× Ñ ó ð rcól^•TRRU4SjTRR5$)aŽExtract day from underlying DateTime representation. Returns the day of month starting from 1. The return value ranges from 1 to 31. (The last day of month differs by months.) Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table({"a": [datetime(1978, 6, 1), datetime(2065, 1, 1)]}) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.day().alias("day")).to_native() pyarrow.Table a: timestamp[us] day: int64 ---- a: [[1978-06-01 00:00:00.000000,2065-01-01 00:00:00.000000]] day: [[1,1]] cój>•TRRU5RR5$r)rrrÚdayrs €rrÚ+ExprDateTimeNamespace.day..s#ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×AÑAÔCrr"r%s`rr3ÚExprDateTimeNamespace.dayjs+ø€ð,z‰z×#Ñ#Ü CÀTÇZÁZ×EYÑEYó ð rcól^•TRRU4SjTRR5$)uóExtract hour from underlying DateTime representation. Returns the hour number from 0 to 23. Returns: A new expression. Examples: >>> from datetime import datetime >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... {"a": [datetime(1978, 1, 1, 1), datetime(2065, 1, 1, 10)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.hour().alias("hour")) ┌──────────────────────────────┠| Narwhals DataFrame | |------------------------------| |shape: (2, 2) | |┌─────────────────────┬──────â”| |│ a ┆ hour │| |│ --- ┆ --- │| |│ datetime[μs] ┆ i8 │| |â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡| |│ 1978-01-01 01:00:00 ┆ 1 │| |│ 2065-01-01 10:00:00 ┆ 10 │| |└─────────────────────┴──────┘| └──────────────────────────────┘ cój>•TRRU5RR5$r)rrrÚhourrs €rrÚ,ExprDateTimeNamespace.hour..¤r!rr"r%s`rr8ÚExprDateTimeNamespace.hour„s+ø€ð>z‰z×#Ñ#Ü DÀdÇjÁj×FZÑFZó ð rcól^•TRRU4SjTRR5$)aAExtract minutes from underlying DateTime representation. Returns the minute number from 0 to 59. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(1978, 1, 1, 1, 1), datetime(2065, 1, 1, 10, 20)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.minute().alias("minute")).to_native() a minute 0 1978-01-01 01:01:00 1 1 2065-01-01 10:20:00 20 cój>•TRRU5RR5$r)rrrÚminuters €rrÚ.ExprDateTimeNamespace.minute..½ó#ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×DÑDÔFrr"r%s`rr=ÚExprDateTimeNamespace.minute§s,ø€ð*z‰z×#Ñ#Ü FØ J‰J× Ñ ó ð rcól^•TRRU4SjTRR5$)a§Extract seconds from underlying DateTime representation. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... datetime(1978, 1, 1, 1, 1, 1), ... datetime(2065, 1, 1, 10, 20, 30), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.second().alias("second")).to_native() pyarrow.Table a: timestamp[us] second: int64 ---- a: [[1978-01-01 01:01:01.000000,2065-01-01 10:20:30.000000]] second: [[1,30]] cój>•TRRU5RR5$r)rrrÚsecondrs €rrÚ.ExprDateTimeNamespace.second..Ýr?rr"r%s`rrCÚExprDateTimeNamespace.secondÁs,ø€ð6z‰z×#Ñ#Ü FØ J‰J× Ñ ó ð rcól^•TRRU4SjTRR5$)aàExtract milliseconds from underlying DateTime representation. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... datetime(1978, 1, 1, 1, 1, 1, 0), ... datetime(2065, 1, 1, 10, 20, 30, 67000), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... nw.col("a").dt.millisecond().alias("millisecond") ... ).to_native() pyarrow.Table a: timestamp[us] millisecond: int64 ---- a: [[1978-01-01 01:01:01.000000,2065-01-01 10:20:30.067000]] millisecond: [[0,67]] cój>•TRRU5RR5$r)rrrÚ millisecondrs €rrÚ3ExprDateTimeNamespace.millisecond..ÿó#ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×IÑIÔKrr"r%s`rrHÚ!ExprDateTimeNamespace.millisecondáó,ø€ð:z‰z×#Ñ#Ü KØ J‰J× Ñ ó ð rcól^•TRRU4SjTRR5$)aãExtract microseconds from underlying DateTime representation. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... datetime(1978, 1, 1, 1, 1, 1, 0), ... datetime(2065, 1, 1, 10, 20, 30, 67000), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... nw.col("a").dt.microsecond().alias("microsecond") ... ).to_native() pyarrow.Table a: timestamp[us] microsecond: int64 ---- a: [[1978-01-01 01:01:01.000000,2065-01-01 10:20:30.067000]] microsecond: [[0,67000]] cój>•TRRU5RR5$r)rrrÚ microsecondrs €rrÚ3ExprDateTimeNamespace.microsecond..!rJrr"r%s`rrOÚ!ExprDateTimeNamespace.microsecondrLrcól^•TRRU4SjTRR5$)aáExtract Nanoseconds from underlying DateTime representation. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... datetime(1978, 1, 1, 1, 1, 1, 0), ... datetime(2065, 1, 1, 10, 20, 30, 67000), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... nw.col("a").dt.nanosecond().alias("nanosecond") ... ).to_native() pyarrow.Table a: timestamp[us] nanosecond: int64 ---- a: [[1978-01-01 01:01:01.000000,2065-01-01 10:20:30.067000]] nanosecond: [[0,67000000]] cój>•TRRU5RR5$r)rrrÚ nanosecondrs €rrÚ2ExprDateTimeNamespace.nanosecond..Cs#ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×HÑHÔJrr"r%s`rrTÚ ExprDateTimeNamespace.nanosecond%s,ø€ð:z‰z×#Ñ#Ü JØ J‰J× Ñ ó ð rcól^•TRRU4SjTRR5$)uÕGet ordinal day. Returns: A new expression. Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(a_ordinal_day=nw.col("a").dt.ordinal_day()) ┌───────────────────────────┠| Narwhals DataFrame | |---------------------------| | a a_ordinal_day| |0 2020-01-01 1| |1 2020-08-03 216| └───────────────────────────┘ cój>•TRRU5RR5$r)rrrÚ ordinal_dayrs €rrÚ3ExprDateTimeNamespace.ordinal_day.._rJrr"r%s`rrYÚ!ExprDateTimeNamespace.ordinal_dayGs,ø€ð.z‰z×#Ñ#Ü KØ J‰J× Ñ ó ð rcól^•TRRU4SjTRR5$)uExtract the week day from the underlying Date representation. Returns: Returns the ISO weekday number where monday = 1 and sunday = 7 Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(a_week_day=nw.col("a").dt.weekday()) ┌────────────────────────┠| Narwhals DataFrame | |------------------------| | a a_week_day| |0 2020-01-01 3| |1 2020-08-03 1| └────────────────────────┘ cój>•TRRU5RR5$r)rrrÚweekdayrs €rrÚ/ExprDateTimeNamespace.weekday..{s#ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×EÑEÔGrr"r%s`rr^ÚExprDateTimeNamespace.weekdaycs,ø€ð.z‰z×#Ñ#Ü GØ J‰J× Ñ ó ð rcól^•TRRU4SjTRR5$)uºGet total minutes. Returns: A new expression. Notes: The function outputs the total minutes in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... {"a": [timedelta(minutes=10), timedelta(minutes=20, seconds=40)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_total_minutes=nw.col("a").dt.total_minutes() ... ).to_native() shape: (2, 2) ┌──────────────┬─────────────────┠│ a ┆ a_total_minutes │ │ --- ┆ --- │ │ duration[μs] ┆ i64 │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ 10m ┆ 10 │ │ 20m 40s ┆ 20 │ └──────────────┴─────────────────┘ cój>•TRRU5RR5$r)rrrÚ total_minutesrs €rrÚ5ExprDateTimeNamespace.total_minutes.. ó#ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×KÑKÔMrr"r%s`rrcÚ#ExprDateTimeNamespace.total_minutesó-ø€ð@z‰z×#Ñ#Ü MØ J‰J× Ñ ó ð rcól^•TRRU4SjTRR5$)u¿Get total seconds. Returns: A new expression. Notes: The function outputs the total seconds in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... {"a": [timedelta(seconds=10), timedelta(seconds=20, milliseconds=40)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_total_seconds=nw.col("a").dt.total_seconds() ... ).to_native() shape: (2, 2) ┌──────────────┬─────────────────┠│ a ┆ a_total_seconds │ │ --- ┆ --- │ │ duration[μs] ┆ i64 │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ 10s ┆ 10 │ │ 20s 40ms ┆ 20 │ └──────────────┴─────────────────┘ cój>•TRRU5RR5$r)rrrÚ total_secondsrs €rrÚ5ExprDateTimeNamespace.total_seconds..Årerr"r%s`rrjÚ#ExprDateTimeNamespace.total_seconds¤rgrcól^•TRRU4SjTRR5$)u}Get total milliseconds. Returns: A new expression. Notes: The function outputs the total milliseconds in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... { ... "a": [ ... timedelta(milliseconds=10), ... timedelta(milliseconds=20, microseconds=40), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_total_milliseconds=nw.col("a").dt.total_milliseconds() ... ).to_native() shape: (2, 2) ┌──────────────┬──────────────────────┠│ a ┆ a_total_milliseconds │ │ --- ┆ --- │ │ duration[μs] ┆ i64 │ â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ 10ms ┆ 10 │ │ 20040µs ┆ 20 │ └──────────────┴──────────────────────┘ cój>•TRRU5RR5$r)rrrÚtotal_millisecondsrs €rrÚ:ExprDateTimeNamespace.total_milliseconds..ïó#ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×PÑPÔRrr"r%s`rroÚ(ExprDateTimeNamespace.total_millisecondsÉs-ø€ðJz‰z×#Ñ#Ü RØ J‰J× Ñ ó ð rcól^•TRRU4SjTRR5$)a†Get total microseconds. Returns: A new expression. Notes: The function outputs the total microseconds in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... timedelta(microseconds=10), ... timedelta(milliseconds=1, microseconds=200), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_total_microseconds=nw.col("a").dt.total_microseconds() ... ).to_native() pyarrow.Table a: duration[us] a_total_microseconds: int64 ---- a: [[10,1200]] a_total_microseconds: [[10,1200]] cój>•TRRU5RR5$r)rrrÚtotal_microsecondsrs €rrÚ:ExprDateTimeNamespace.total_microseconds..rqrr"r%s`rruÚ(ExprDateTimeNamespace.total_microsecondsós-ø€ðDz‰z×#Ñ#Ü RØ J‰J× Ñ ó ð rcól^•TRRU4SjTRR5$)aþGet total nanoseconds. Returns: A new expression. Notes: The function outputs the total nanoseconds in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... { ... "a": pd.to_datetime( ... [ ... "2024-01-01 00:00:00.000000001", ... "2024-01-01 00:00:00.000000002", ... ] ... ) ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_diff_total_nanoseconds=nw.col("a").diff().dt.total_nanoseconds() ... ).to_native() a a_diff_total_nanoseconds 0 2024-01-01 00:00:00.000000001 NaN 1 2024-01-01 00:00:00.000000002 1.0 cój>•TRRU5RR5$r)rrrÚtotal_nanosecondsrs €rrÚ9ExprDateTimeNamespace.total_nanoseconds..<s#ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×OÑOÔQrr"r%s`rrzÚ'ExprDateTimeNamespace.total_nanosecondss-ø€ðBz‰z×#Ñ#Ü QØ J‰J× Ñ ó ð rcóp^^•TRRUU4SjTRR5$)u› Convert a Date/Time/Datetime column into a String column with the given format. Arguments: format: Format to format temporal column with. Returns: A new expression. Notes: Unfortunately, different libraries interpret format directives a bit differently. - Chrono, the library used by Polars, uses `"%.f"` for fractional seconds, whereas pandas and Python stdlib use `".%f"`. - PyArrow interprets `"%S"` as "seconds, including fractional seconds" whereas most other tools interpret it as "just seconds, as 2 digits". Therefore, we make the following adjustments: - for pandas-like libraries, we replace `"%S.%f"` with `"%S%.f"`. - for PyArrow, we replace `"%S.%f"` with `"%S"`. Workarounds like these don't make us happy, and we try to avoid them as much as possible, but here we feel like it's the best compromise. If you just want to format a date/datetime Series as a local datetime string, and have it work as consistently as possible across libraries, we suggest using: - `"%Y-%m-%dT%H:%M:%S%.f"` for datetimes - `"%Y-%m-%d"` for dates though note that, even then, different tools may return a different number of trailing zeros. Nonetheless, this is probably consistent enough for most applications. If you have an application where this is not enough, please open an issue and let us know. Examples: >>> from datetime import datetime >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... { ... "a": [ ... datetime(2020, 3, 1), ... datetime(2020, 5, 1), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.select(nw.col("a").dt.to_string("%Y/%m/%d %H:%M:%S")) ┌───────────────────────┠| Narwhals DataFrame | |-----------------------| |shape: (2, 1) | |┌─────────────────────â”| |│ a │| |│ --- │| |│ str │| |â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡| |│ 2020/03/01 00:00:00 │| |│ 2020/05/01 00:00:00 │| |└─────────────────────┘| └───────────────────────┘ cól>•TRRU5RRT5$r)rrrÚ to_string)rÚformatrs €€rrÚ1ExprDateTimeNamespace.to_string..…s%ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×GÑGÈÔOrr")rr€s``rrÚExprDateTimeNamespace.to_string@s-ù€ðHz‰z×#Ñ#Ý OØ J‰J× Ñ ó ð rcóp^^•TRRUU4SjTRR5$)aReplace time zone. Arguments: time_zone: Target time zone. Returns: A new expression. Examples: >>> from datetime import datetime, timezone >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... { ... "a": [ ... datetime(2024, 1, 1, tzinfo=timezone.utc), ... datetime(2024, 1, 2, tzinfo=timezone.utc), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.select(nw.col("a").dt.replace_time_zone("Asia/Kathmandu")).to_native() a 0 2024-01-01 00:00:00+05:45 1 2024-01-02 00:00:00+05:45 cól>•TRRU5RRT5$r)rrrÚreplace_time_zone©rrÚ time_zones €€rrÚ9ExprDateTimeNamespace.replace_time_zone..¥ó(ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×OÑOØôrr")rr‡s``rr…Ú'ExprDateTimeNamespace.replace_time_zone‰s0ù€ð6z‰z×#Ñ#õ ð J‰J× Ñ ó  ð rcó^^•Tc Sn[U5eTRRUU4SjTRR5$)aConvert to a new time zone. If converting from a time-zone-naive column, then conversion happens as if converting from UTC. Arguments: time_zone: Target time zone. Returns: A new expression. Examples: >>> from datetime import datetime, timezone >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... { ... "a": [ ... datetime(2024, 1, 1, tzinfo=timezone.utc), ... datetime(2024, 1, 2, tzinfo=timezone.utc), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.select(nw.col("a").dt.convert_time_zone("Asia/Kathmandu")).to_native() a 0 2024-01-01 05:45:00+05:45 1 2024-01-02 05:45:00+05:45 z…Target `time_zone` cannot be `None` in `convert_time_zone`. Please use `replace_time_zone(None)` if you want to remove the time zone.cól>•TRRU5RRT5$r)rrrÚconvert_time_zoner†s €€rrÚ9ExprDateTimeNamespace.convert_time_zone..Ír‰r)Ú TypeErrorrr#r$)rr‡Úmsgs`` rrÚ'ExprDateTimeNamespace.convert_time_zone«sHù€ð< Ñ ðZˆCܘC“.Ð Øz‰z×#Ñ#õ ð J‰J× Ñ ó  ð rcó ^^•TS;aST<S3n[U5eTRRUU4SjTRR5$)uÌReturn a timestamp in the given time unit. Arguments: time_unit: {'ns', 'us', 'ms'} Time unit. 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")) ┌─────────────────────────────┠| Narwhals DataFrame | |-----------------------------| |shape: (2, 2) | |┌────────────┬──────────────â”| |│ date ┆ timestamp_ms │| |│ --- ┆ --- │| |│ date ┆ i64 │| |â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡| |│ 2001-01-01 ┆ 978307200000 │| |│ null ┆ null │| |└────────────┴──────────────┘| └─────────────────────────────┘ >ÚmsÚnsÚusz=invalid `time_unit` Expected one of {'ns', 'us', 'ms'}, got Ú.cól>•TRRU5RRT5$r)rrrÚ timestamp)rrÚ time_units €€rrÚ1ExprDateTimeNamespace.timestamp..ùs%ø€˜Ÿ ™ ×5Ñ5°cÓ:×=Ñ=×GÑGÈ ÔRr)Ú ValueErrorrr#r$)rr™rs`` rr˜ÚExprDateTimeNamespace.timestampÓsYù€ð> Ð.Ó .ðAØAJÁ ÈQðPð ô˜S“/Ð !Øz‰z×#Ñ#Ý RØ J‰J× Ñ ó ð rrN)rr ÚreturnÚNone)rr )r€Ústrrr )r‡z str | Nonerr )r‡rŸrr )r•)r™rrr )Ú__name__Ú __module__Ú __qualname__Ú__firstlineno__rrr)r.r3r8r=rCrHrOrTrYr^rcrjrorurzrr…rr˜Ú__static_attributes__©rrr r s„†ôô ô@ ô: ô6 ô4! ôF ô4 ô@  ôD  ôD  ôD ô8 ô8# ôJ# ôJ( ôT% ôN$ ôLG ôR  ôD& ÷P( ñ( rr N) Ú __future__rÚtypingrrrÚ narwhals.exprrÚnarwhals.typingrr r r¥rrÚrªs8ðÝ"å ÝÝæÝ"Ý(á˜vÑ&€ôm  ˜G E™Nõm  r