σ LpˆjL}γσ„•SSKJr SSKJrJrJr SSKJrJr \(a SSK J r SSK J r \"SSS9r "S S \\ 5rg ) ι)Ϊ annotations)Ϊ TYPE_CHECKINGΪGenericΪTypeVar)ΪExprKindΪExprNode)ΪExpr)ΪTimeUnitΪExprTr )Ϊboundcσ•\rSrSrSSjrSSjrSSjrSSjrSSjrSSjr SSjr SS jr SS jr SS jr SS jrSS jrSSjrSSjrSSjrSSjrSSjrSSjrSSjrSSjrS SjrS!S"SjjrS#SjrS$SjrSrg)%ΪExprDateTimeNamespaceιcσ•Xlg)N©Ϊ_expr)ΪselfΪexprs ΪL/var/www/html/pdf-tiff/venv/lib/python3.13/site-packages/narwhals/expr_dt.pyΪ__init__ΪExprDateTimeNamespace.__init__s€Ψ σcσh•URR[[RS55$)uΠExtract the date from underlying DateTime representation. 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”˜ zdt.date©rΪ _append_noderrΪ ELEMENTWISE©rs rΪdateΪExprDateTimeNamespace.dates&€π2z‰zΧ&Ρ&€x΄Χ0DΡ0DΐiΣ'PΣQΠQrcσh•URR[[RS55$)uŸExtract year from underlying DateTime representation. Returns the year number in the calendar date. Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(1978, 6, 1), datetime(2065, 1, 1)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.year().alias("year")) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” |Narwhals DataFrame| |------------------| | a year| |0 1978-06-01 1978| |1 2065-01-01 2065| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ zdt.yearrrs rΪyearΪExprDateTimeNamespace.year-s&€π,z‰zΧ&Ρ&€x΄Χ0DΡ0DΐiΣ'PΣQΠQrcσh•URR[[RS55$)aMExtract month from underlying DateTime representation. Returns the month number starting from 1. The return value ranges from 1 to 12. 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]] zdt.monthrrs rΪmonthΪExprDateTimeNamespace.monthEs&€π&z‰zΧ&Ρ&€x΄Χ0DΡ0DΐjΣ'QΣRΠRrcσh•URR[[RS55$)anExtract day from underlying DateTime representation. Returns the day of month starting from 1. The return value ranges from 1 to 31. (The last day of month differs by months.) Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table({"a": [datetime(1978, 6, 1), datetime(2065, 1, 1)]}) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.day().alias("day")).to_native() pyarrow.Table a: timestamp[us] day: int64 ---- a: [[1978-06-01 00:00:00.000000,2065-01-01 00:00:00.000000]] day: [[1,1]] zdt.dayrrs rΪdayΪExprDateTimeNamespace.dayZs&€π&z‰zΧ&Ρ&€x΄Χ0DΡ0DΐhΣ'OΣPΠPrcσh•URR[[RS55$)uΣExtract hour from underlying DateTime representation. Returns the hour number from 0 to 23. 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”˜ zdt.hourrrs rΪhourΪExprDateTimeNamespace.houros&€π8z‰zΧ&Ρ&€x΄Χ0DΡ0DΐiΣ'PΣQΠQrcσh•URR[[RS55$)a!Extract minutes from underlying DateTime representation. Returns the minute number from 0 to 59. Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(1978, 1, 1, 1, 1), datetime(2065, 1, 1, 10, 20)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.minute().alias("minute")).to_native() a minute 0 1978-01-01 01:01:00 1 1 2065-01-01 10:20:00 20 z dt.minuterrs rΪminuteΪExprDateTimeNamespace.minutes&€π$z‰zΧ&Ρ&€x΄Χ0DΡ0DΐkΣ'RΣSΠSrcσh•URR[[RS55$)a‡Extract seconds from underlying DateTime representation. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... datetime(1978, 1, 1, 1, 1, 1), ... datetime(2065, 1, 1, 10, 20, 30), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("a").dt.second().alias("second")).to_native() pyarrow.Table a: timestamp[us] second: int64 ---- a: [[1978-01-01 01:01:01.000000,2065-01-01 10:20:30.000000]] second: [[1,30]] z dt.secondrrs rΪsecondΪExprDateTimeNamespace.second‘s&€π0z‰zΧ&Ρ&€x΄Χ0DΡ0DΐkΣ'RΣSΠSrcσh•URR[[RS55$)aΐExtract milliseconds from underlying DateTime representation. 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]] zdt.millisecondrrs rΪ millisecondΪ!ExprDateTimeNamespace.millisecond»σ'€π4z‰zΧ&Ρ&€x΄Χ0DΡ0DΠFVΣ'WΣXΠXrcσh•URR[[RS55$)aΓExtract microseconds from underlying DateTime representation. Examples: >>> from datetime import datetime >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table( ... { ... "a": [ ... datetime(1978, 1, 1, 1, 1, 1, 0), ... datetime(2065, 1, 1, 10, 20, 30, 67000), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... nw.col("a").dt.microsecond().alias("microsecond") ... ).to_native() pyarrow.Table a: timestamp[us] microsecond: int64 ---- a: [[1978-01-01 01:01:01.000000,2065-01-01 10:20:30.067000]] microsecond: [[0,67000]] zdt.microsecondrrs rΪ microsecondΪ!ExprDateTimeNamespace.microsecondΧr5rcσh•URR[[RS55$)aΑExtract Nanoseconds from underlying DateTime representation. 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]] z dt.nanosecondrrs rΪ nanosecondΪ ExprDateTimeNamespace.nanosecondσs&€π4z‰zΧ&Ρ&€x΄Χ0DΡ0DΐoΣ'VΣWΠWrcσh•URR[[RS55$)u΅Get ordinal day. Examples: >>> from datetime import datetime >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns(a_ordinal_day=nw.col("a").dt.ordinal_day()) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” | Narwhals DataFrame | |---------------------------| | a a_ordinal_day| |0 2020-01-01 1| |1 2020-08-03 216| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ zdt.ordinal_dayrrs rΪ ordinal_dayΪ!ExprDateTimeNamespace.ordinal_days'€π(z‰zΧ&Ρ&€x΄Χ0DΡ0DΠFVΣ'WΣXΠXrcσh•URR[[RS55$)uΰExtract the week day from the underlying Date representation. Note that 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”˜ z dt.weekdayrrs rΪweekdayΪExprDateTimeNamespace.weekday%s&€π,z‰zΧ&Ρ&€x΄Χ0DΡ0DΐlΣ'SΣTΠTrcσh•URR[[RS55$)ušGet total minutes. Notes: The function outputs the total minutes in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... {"a": [timedelta(minutes=10), timedelta(minutes=20, seconds=40)]} ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_total_minutes=nw.col("a").dt.total_minutes() ... ).to_native() shape: (2, 2) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ a ┆ a_total_minutes β”‚ β”‚ --- ┆ --- β”‚ β”‚ duration[ΞΌs] ┆ i64 β”‚ β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•ͺ═════════════════║ β”‚ 10m ┆ 10 β”‚ β”‚ 20m 40s ┆ 20 β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ zdt.total_minutesrrs rΪ total_minutesΪ#ExprDateTimeNamespace.total_minutes=σ'€π:z‰zΧ&Ρ&€x΄Χ0DΡ0DΠFXΣ'YΣZΠZrcσh•URR[[RS55$)uŸGet total seconds. 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”˜ zdt.total_secondsrrs rΪ total_secondsΪ#ExprDateTimeNamespace.total_seconds\rErcσh•URR[[RS55$)u]Get total milliseconds. Notes: The function outputs the total milliseconds in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame( ... { ... "a": [ ... timedelta(milliseconds=10), ... timedelta(milliseconds=20, microseconds=40), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_total_milliseconds=nw.col("a").dt.total_milliseconds() ... ).to_native() shape: (2, 2) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ a ┆ a_total_milliseconds β”‚ β”‚ --- ┆ --- β”‚ β”‚ duration[ΞΌs] ┆ i64 β”‚ β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•ͺ══════════════════════║ β”‚ 10ms ┆ 10 β”‚ β”‚ 20040Β΅s ┆ 20 β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ zdt.total_millisecondsrrs rΪtotal_millisecondsΪ(ExprDateTimeNamespace.total_milliseconds{s-€πDz‰zΧ&Ρ&ά ”XΧ)Ρ)Π+BΣ Cσ π rcσh•URR[[RS55$)afGet total microseconds. 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]] zdt.total_microsecondsrrs rΪtotal_microsecondsΪ(ExprDateTimeNamespace.total_microseconds‘s,€π>z‰zΧ&Ρ&ά ”XΧ)Ρ)Π+BΣ Cσ π rcσh•URR[[RS55$)aήGet total nanoseconds. Notes: The function outputs the total nanoseconds in the int dtype by default, however, pandas may change the dtype to float when there are missing values, consider using `fill_null()` and `cast` in this case. Examples: >>> from datetime import timedelta >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... { ... "a": pd.to_datetime( ... [ ... "2024-01-01 00:00:00.000000001", ... "2024-01-01 00:00:00.000000002", ... ] ... ) ... } ... ) >>> df = nw.from_native(df_native) >>> df.with_columns( ... a_diff_total_nanoseconds=nw.col("a").diff().dt.total_nanoseconds() ... ).to_native() a a_diff_total_nanoseconds 0 2024-01-01 00:00:00.000000001 NaN 1 2024-01-01 00:00:00.000000002 1.0 zdt.total_nanosecondsrrs rΪtotal_nanosecondsΪ'ExprDateTimeNamespace.total_nanosecondsΔs,€π<z‰zΧ&Ρ&ά ”XΧ)Ρ)Π+AΣ Bσ π rcσf•URR[[RSUS95$)u7 Convert a Date/Time/Datetime column into a String column with the given format. Arguments: format: Format to format temporal column with. 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”˜ z dt.to_string)Ϊformatr)rrSs rΪ to_stringΪExprDateTimeNamespace.to_stringζs.€πxz‰zΧ&Ρ&ά ”XΧ)Ρ)¨>ΐ&Ρ Iσ π rcσf•URR[[RSUS95$)a}Replace time zone. Arguments: time_zone: Target time zone. Examples: >>> from datetime import datetime, timezone >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame( ... { ... "a": [ ... datetime(2024, 1, 1, tzinfo=timezone.utc), ... datetime(2024, 1, 2, tzinfo=timezone.utc), ... ] ... } ... ) >>> df = nw.from_native(df_native) >>> df.select(nw.col("a").dt.replace_time_zone("Asia/Kathmandu")).to_native() a 0 2024-01-01 00:00:00+05:45 1 2024-01-02 00:00:00+05:45 zdt.replace_time_zone©Ϊ time_zoner)rrXs rΪreplace_time_zoneΪ'ExprDateTimeNamespace.replace_time_zone&s.€π0z‰zΧ&Ρ&ά ”XΧ)Ρ)Π+AΘYΡ Wσ π rcσ†•Uc Sn[U5eURR[[R SUS95$)aηConvert 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. 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.zdt.convert_time_zonerW)Ϊ TypeErrorrrrrr)rrXΪmsgs rΪconvert_time_zoneΪ'ExprDateTimeNamespace.convert_time_zoneBsF€π6 Ρ πZˆCά˜C“.Р؏z‰zΧ&Ρ&ά ”XΧ)Ρ)Π+AΘYΡ Wσ π rcσ–•US;aSU<S3n[U5eURR[[R SUS95$)uγReturn a timestamp in the given time unit. Arguments: time_unit: One of - 'ns': nanosecond. - 'us': microsecond. - 'ms': millisecond. Examples: >>> from datetime import date >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"date": [date(2001, 1, 1), None]}) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("date").dt.timestamp("ms").alias("timestamp_ms")) β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” | Narwhals DataFrame | |-----------------------------| |shape: (2, 2) | |β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”| |β”‚ date ┆ timestamp_ms β”‚| |β”‚ --- ┆ --- β”‚| |β”‚ date ┆ i64 β”‚| |β•žβ•β•β•β•β•β•β•β•β•β•β•β•β•ͺ══════════════║| |β”‚ 2001-01-01 ┆ 978307200000 β”‚| |β”‚ null ┆ null β”‚| |β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜| β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ >ΪmsΪnsΪusz=invalid `time_unit` Expected one of {'ns', 'us', 'ms'}, got Ϊ.z dt.timestamp)Ϊ time_unit)Ϊ ValueErrorrrrrr)rrer]s rΪ timestampΪExprDateTimeNamespace.timestampdsZ€π< Π.Σ .πAΨAJΑ ΘQπPπ τ˜S“/Π !؏z‰zΧ&Ρ&ά ”XΧ)Ρ)¨>ΐYΡ Oσ π rcσf•URR[[RSUS95$)uQDivide 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. 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”˜ z dt.truncate)Ϊeveryr)rrjs rΪtruncateΪExprDateTimeNamespace.truncateŒs.€πPz‰zΧ&Ρ&ά ”XΧ)Ρ)¨=ΐΡ Fσ π rcσf•URR[[RSUS95$)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. Notes: For the Ibis backend, offsetting timezone-aware data by a calendar unit ('d', 'mo', 'q' or 'y') raises: Ibis stores such values as UTC, so the result would diverge from other backends across DST transitions. 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”˜ z dt.offset_by)Ϊbyr)rrns rΪ offset_byΪExprDateTimeNamespace.offset_byΈs.€πZz‰zΧ&Ρ&ά ”XΧ)Ρ)¨>ΈbΡ Aσ π rrN)rr ΪreturnΪNone)rqr )rSΪstrrqr )rXz str | Nonerqr )rXrsrqr )rc)rer rqr )rjrsrqr )rnrsrqr )Ϊ__name__Ϊ __module__Ϊ __qualname__Ϊ__firstlineno__rrr!r$r'r*r-r0r3r7r:r=r@rCrGrJrMrPrTrYr^rgrkroΪ__static_attributes__©rrrrs†ττRτ6Rτ0Sτ*Qτ*Rτ<Tτ(Tτ4Yτ8Yτ8Xτ8Yτ,Uτ0[τ>[τ>$ τL! τF  τD> τ@ τ8  φD& τP* χX/ rrN)Ϊ __future__rΪtypingrrrΪnarwhals._expression_parsingrrΪ narwhals.exprr Ϊnarwhals.typingr r rryrrΪrs8πέ"η2Ρ2η;ζέ"έ(α˜vΡ&€τY  ˜G E™NυY  r