§ ­-Phýyãó„—ddlmZddlmZmZmZer ddlmZddlm Z edd¬¦«Z Gd„d ee ¦«Z d S) é)Ú annotations)Ú TYPE_CHECKINGÚGenericÚTypeVar)ÚExpr)ÚTimeUnitÚExprTr)ÚboundcóÈ—eZdZd%d„Zd&d„Zd&d„Zd&d„Zd&d „Zd&d „Zd&d „Z d&d „Z d&d „Z d&d„Z d&d„Z d&d„Zd&d„Zd&d„Zd&d„Zd&d„Zd&d„Zd&d„Zd'd„Zd(d„Zd)d„Zd*d+d!„Zd,d#„Zd$S)-ÚExprDateTimeNamespaceÚexprr ÚreturnÚNonecó—||_dS©N)Ú_expr)Úselfr s úP/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/narwhals/expr_dt.pyÚ__init__zExprDateTimeNamespace.__init__ s €ØˆŒ ˆ ˆ ócó>‡—‰j ˆfd„¦«S)u¨Extract 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 │ └────────────┘ cóf•—‰j |¦«j ¦«Sr)rÚ_to_compliant_exprÚdtÚdate©Úplxrs €rúz,ExprDateTimeNamespace.date..-ó&ø€˜œ ×5Ò5°cÑ:Ô:Ô=×BÒBÑDÔD€r©rÚ_with_elementwise_op©rs`rrzExprDateTimeNamespace.dates+ø€ð8Œz×.Ò.Ø DÐ DÐ DÐ Dñ ô ð rcó>‡—‰j ˆfd„¦«S)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| └──────────────────┘ cóf•—‰j |¦«j ¦«Sr)rrrÚyearrs €rrz,ExprDateTimeNamespace.year..Jrrr r"s`rr%zExprDateTimeNamespace.year0s+ø€ð2Œz×.Ò.Ø DÐ DÐ DÐ Dñ ô ð rcó>‡—‰j ˆfd„¦«S)aõExtract 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]] cóf•—‰j |¦«j ¦«Sr)rrrÚmonthrs €rrz-ExprDateTimeNamespace.month..ds&ø€˜œ ×5Ò5°cÑ:Ô:Ô=×CÒCÑEÔE€rr r"s`rr(zExprDateTimeNamespace.monthMs+ø€ð,Œz×.Ò.Ø EÐ EÐ EÐ Eñ ô ð rcó>‡—‰j ˆfd„¦«S)aExtract 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]] cóf•—‰j |¦«j ¦«Sr)rrrÚdayrs €rrz+ExprDateTimeNamespace.day..~s&ø€˜œ ×5Ò5°cÑ:Ô:Ô=×AÒAÑCÔC€rr r"s`rr+zExprDateTimeNamespace.daygs+ø€ð,Œz×.Ò.Ø CÐ CÐ CÐ Cñ ô ð rcó>‡—‰j ˆfd„¦«S)uÃExtract 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 │| |└─────────────────────┴──────┘| └──────────────────────────────┘ cóf•—‰j |¦«j ¦«Sr)rrrÚhourrs €rrz,ExprDateTimeNamespace.hour..¡rrr r"s`rr.zExprDateTimeNamespace.hours+ø€ð>Œz×.Ò.Ø DÐ DÐ DÐ Dñ ô ð rcó>‡—‰j ˆfd„¦«S)aÁExtract 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 cóf•—‰j |¦«j ¦«Sr)rrrÚminuters €rrz.ExprDateTimeNamespace.minute..ºó&ø€˜œ ×5Ò5°cÑ:Ô:Ô=×DÒDÑFÔF€rr r"s`rr1zExprDateTimeNamespace.minute¤s+ø€ð*Œz×.Ò.Ø FÐ FÐ FÐ Fñ ô ð rcó>‡—‰j ˆfd„¦«S)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]] cóf•—‰j |¦«j ¦«Sr)rrrÚsecondrs €rrz.ExprDateTimeNamespace.second..Ùr2rr r"s`rr5zExprDateTimeNamespace.second½s+ø€ð6Œz×.Ò.Ø FÐ FÐ FÐ Fñ ô ð rcó>‡—‰j ˆfd„¦«S)a¨Extract 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]] cóf•—‰j |¦«j ¦«Sr)rrrÚ millisecondrs €rrz3ExprDateTimeNamespace.millisecond..úó&ø€˜œ ×5Ò5°cÑ:Ô:Ô=×IÒIÑKÔK€rr r"s`rr8z!ExprDateTimeNamespace.millisecondÜó+ø€ð:Œz×.Ò.Ø KÐ KÐ KÐ Kñ ô ð rcó>‡—‰j ˆfd„¦«S)a«Extract 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]] cóf•—‰j |¦«j ¦«Sr)rrrÚ microsecondrs €rrz3ExprDateTimeNamespace.microsecond..r9rr r"s`rr=z!ExprDateTimeNamespace.microsecondýr:rcó>‡—‰j ˆfd„¦«S)a©Extract 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]] cóf•—‰j |¦«j ¦«Sr)rrrÚ nanosecondrs €rrz2ExprDateTimeNamespace.nanosecond..<s&ø€˜œ ×5Ò5°cÑ:Ô:Ô=×HÒHÑJÔJ€rr r"s`rr@z ExprDateTimeNamespace.nanoseconds+ø€ð:Œz×.Ò.Ø JÐ JÐ JÐ Jñ ô ð rcó>‡—‰j ˆfd„¦«S)umGet 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| └───────────────────────────┘ cóf•—‰j |¦«j ¦«Sr)rrrÚ ordinal_dayrs €rrz3ExprDateTimeNamespace.ordinal_day..Wr9rr r"s`rrCz!ExprDateTimeNamespace.ordinal_day?s+ø€ð.Œz×.Ò.Ø KÐ KÐ KÐ Kñ ô ð rcó>‡—‰j ˆfd„¦«S)uŸExtract 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| └────────────────────────┘ cóf•—‰j |¦«j ¦«Sr)rrrÚweekdayrs €rrz/ExprDateTimeNamespace.weekday..rs&ø€˜œ ×5Ò5°cÑ:Ô:Ô=×EÒEÑGÔG€rr r"s`rrFzExprDateTimeNamespace.weekdayZs+ø€ð.Œz×.Ò.Ø GÐ GÐ GÐ Gñ ô ð rcó>‡—‰j ˆfd„¦«S)u’Get 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 │ └──────────────┴─────────────────┘ cóf•—‰j |¦«j ¦«Sr)rrrÚ total_minutesrs €rrz5ExprDateTimeNamespace.total_minutes..–ó&ø€˜œ ×5Ò5°cÑ:Ô:Ô=×KÒKÑMÔM€rr r"s`rrIz#ExprDateTimeNamespace.total_minutesuó,ø€ð@Œz×.Ò.Ø MÐ MÐ MÐ Mñ ô ð rcó>‡—‰j ˆfd„¦«S)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 │ └──────────────┴─────────────────┘ cóf•—‰j |¦«j ¦«Sr)rrrÚ total_secondsrs €rrz5ExprDateTimeNamespace.total_seconds..ºrJrr r"s`rrNz#ExprDateTimeNamespace.total_seconds™rKrcó>‡—‰j ˆfd„¦«S)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 │ └──────────────┴──────────────────────┘ cóf•—‰j |¦«j ¦«Sr)rrrÚtotal_millisecondsrs €rrz:ExprDateTimeNamespace.total_milliseconds..ãó&ø€˜œ ×5Ò5°cÑ:Ô:Ô=×PÒPÑRÔR€rr r"s`rrQz(ExprDateTimeNamespace.total_milliseconds½s,ø€ðJŒz×.Ò.Ø RÐ RÐ RÐ Rñ ô ð rcó>‡—‰j ˆfd„¦«S)anGet 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]] cóf•—‰j |¦«j ¦«Sr)rrrÚtotal_microsecondsrs €rrz:ExprDateTimeNamespace.total_microseconds.. rRrr r"s`rrUz(ExprDateTimeNamespace.total_microsecondsæs,ø€ðDŒz×.Ò.Ø RÐ RÐ RÐ Rñ ô ð rcó>‡—‰j ˆfd„¦«S)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 cóf•—‰j |¦«j ¦«Sr)rrrÚtotal_nanosecondsrs €rrz9ExprDateTimeNamespace.total_nanoseconds...s&ø€˜œ ×5Ò5°cÑ:Ô:Ô=×OÒOÑQÔQ€rr r"s`rrXz'ExprDateTimeNamespace.total_nanoseconds s,ø€ðBŒz×.Ò.Ø QÐ QÐ QÐ Qñ ô ð rÚformatÚstrcóB‡‡—‰j ˆˆfd„¦«S)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 │| |└─────────────────────┘| └───────────────────────┘ cóh•—‰j |¦«j ‰¦«Sr)rrrÚ to_string)rrYrs €€rrz1ExprDateTimeNamespace.to_string..qs(ø€˜œ ×5Ò5°cÑ:Ô:Ô=×GÒGÈÑOÔO€rr )rrYs``rr]zExprDateTimeNamespace.to_string1s0øø€ð~Œz×.Ò.Ø OÐ OÐ OÐ OÐ Oñ ô ð rÚ time_zoneú str | NonecóB‡‡—‰j ˆˆfd„¦«S)aMReplace 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 cóh•—‰j |¦«j ‰¦«Sr)rrrÚreplace_time_zone©rrr^s €€rrz9ExprDateTimeNamespace.replace_time_zone..ó)ø€˜œ ×5Ò5°cÑ:Ô:Ô=×OÒOÐPYÑZÔZ€rr )rr^s``rrbz'ExprDateTimeNamespace.replace_time_zonets/øø€ð6Œz×.Ò.Ø ZÐ ZÐ ZÐ ZÐ Zñ ô ð rcóh‡‡—‰€d}t|¦«‚‰j ˆˆfd„¦«S)aÇConvert 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 Nz…Target `time_zone` cannot be `None` in `convert_time_zone`. Please use `replace_time_zone(None)` if you want to remove the time zone.cóh•—‰j |¦«j ‰¦«Sr)rrrÚconvert_time_zonercs €€rrz9ExprDateTimeNamespace.convert_time_zone..µrdr)Ú TypeErrorrr!)rr^Úmsgs`` rrgz'ExprDateTimeNamespace.convert_time_zone“sIøø€ð< Ð ðZˆCݘC‘.”.Ð ØŒz×.Ò.Ø ZÐ ZÐ ZÐ ZÐ Zñ ô ð rÚusÚ time_unitrcót‡‡—‰dvrd‰›d}t|¦«‚‰j ˆˆfd„¦«S)uãReturn 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")) ┌─────────────────────────────┠| 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 │| |└────────────┴──────────────┘| └─────────────────────────────┘ >ÚmsÚnsrjz=invalid `time_unit` Expected one of {'ns', 'us', 'ms'}, got ú.cóh•—‰j |¦«j ‰¦«Sr)rrrÚ timestamp)rrrks €€rrz1ExprDateTimeNamespace.timestamp..às(ø€˜œ ×5Ò5°cÑ:Ô:Ô=×GÒGÈ ÑRÔR€r)Ú ValueErrorrr!)rrkris`` rrqzExprDateTimeNamespace.timestamp¸sjøø€ðB Ð.Ð .Ð .ðPØAJðPðPðPð õ˜S‘/”/Ð !ØŒz×.Ò.Ø RÐ RÐ RÐ RÐ Rñ ô ð rÚeverycóB‡‡—‰j ˆˆfd„¦«S)uµDivide 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") ... ) ┌─────────────────────────────────────────────┠| Narwhals DataFrame | |---------------------------------------------| |shape: (1, 2) | |┌─────────────────────┬─────────────────────â”| |│ datetime ┆ datetime_trunc │| |│ --- ┆ --- │| |│ datetime[μs] ┆ datetime[μs] │| |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•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡| |│ 2021-03-01 12:34:00 ┆ 2021-03-01 12:00:00 │| |└─────────────────────┴─────────────────────┘| └─────────────────────────────────────────────┘ cóh•—‰j |¦«j ‰¦«Sr)rrrÚtruncate)rrsrs €€rrz0ExprDateTimeNamespace.truncate..s(ø€˜œ ×5Ò5°cÑ:Ô:Ô=×FÒFÀuÑMÔM€rr )rrss``rrvzExprDateTimeNamespace.truncateãs0øø€ðVŒz×.Ò.Ø MÐ MÐ MÐ MÐ Mñ ô ð rN)r r rr)rr )rYrZrr )r^r_rr )r^rZrr )rj)rkrrr )rsrZrr )Ú__name__Ú __module__Ú __qualname__rrr%r(r+r.r1r5r8r=r@rCrFrIrNrQrUrXr]rbrgrqrv©rrr r sö€€€€€ððððð ð ð ð ð@ ð ð ð ð: ð ð ð ð4 ð ð ð ð4! ð! ð! ð! ðF ð ð ð ð2 ð ð ð ð> ð ð ð ðB ð ð ð ðB ð ð ð ðB ð ð ð ð6 ð ð ð ð6" ð" ð" ð" ðH" ð" ð" ð" ðH' ð' ð' ð' ðR$ ð$ ð$ ð$ ðL# ð# ð# ð# ðJA ðA ðA ðA ðF ð ð ð ð># ð# ð# ð# ðJ) ð) ð) ð) ð) ðV- ð- ð- ð- ð- ð- rr N) Ú __future__rÚtypingrrrÚ narwhals.exprrÚnarwhals.typingrr r rzrrúrs·ðØ"Ð"Ð"Ð"Ð"Ð"à2Ð2Ð2Ð2Ð2Ð2Ð2Ð2Ð2Ð2àð)Ø"Ð"Ð"Ð"Ð"Ð"Ø(Ð(Ð(Ð(Ð(Ð(àˆ˜vÐ&Ñ&Ô&€ðD  ðD  ðD  ðD  ðD  ˜G EœNñD  ôD  ðD  ðD  ðD  r