narwhals.Expr.dt
year()
Extract year from underlying DateTime representation.
Returns the year number in the calendar date.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import datetime
>>> import narwhals as nw
>>> data = {
... "datetime": [
... datetime(1978, 6, 1),
... datetime(2024, 12, 13),
... datetime(2065, 1, 1),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(nw.col("datetime").dt.year().alias("year"))
We can then pass either pandas or Polars to func:
>>> func(df_pd)
datetime year
0 1978-06-01 1978
1 2024-12-13 2024
2 2065-01-01 2065
>>> func(df_pl)
shape: (3, 2)
┌─────────────────────┬──────┐
│ datetime ┆ year │
│ --- ┆ --- │
│ datetime[μs] ┆ i32 │
╞═════════════════════╪══════╡
│ 1978-06-01 00:00:00 ┆ 1978 │
│ 2024-12-13 00:00:00 ┆ 2024 │
│ 2065-01-01 00:00:00 ┆ 2065 │
└─────────────────────┴──────┘
month()
Extract month from underlying DateTime representation.
Returns the month number starting from 1. The return value ranges from 1 to 12.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import datetime
>>> import narwhals as nw
>>> data = {
... "datetime": [
... datetime(1978, 6, 1),
... datetime(2024, 12, 13),
... datetime(2065, 1, 1),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(
... nw.col("datetime").dt.year().alias("year"),
... nw.col("datetime").dt.month().alias("month"),
... )
We can then pass either pandas or Polars to func:
>>> func(df_pd)
datetime year month
0 1978-06-01 1978 6
1 2024-12-13 2024 12
2 2065-01-01 2065 1
>>> func(df_pl)
shape: (3, 3)
┌─────────────────────┬──────┬───────┐
│ datetime ┆ year ┆ month │
│ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ i32 ┆ i8 │
╞═════════════════════╪══════╪═══════╡
│ 1978-06-01 00:00:00 ┆ 1978 ┆ 6 │
│ 2024-12-13 00:00:00 ┆ 2024 ┆ 12 │
│ 2065-01-01 00:00:00 ┆ 2065 ┆ 1 │
└─────────────────────┴──────┴───────┘
day()
Extract 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:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import datetime
>>> import narwhals as nw
>>> data = {
... "datetime": [
... datetime(1978, 6, 1),
... datetime(2024, 12, 13),
... datetime(2065, 1, 1),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(
... nw.col("datetime").dt.year().alias("year"),
... nw.col("datetime").dt.month().alias("month"),
... nw.col("datetime").dt.day().alias("day"),
... )
We can then pass either pandas or Polars to func:
>>> func(df_pd)
datetime year month day
0 1978-06-01 1978 6 1
1 2024-12-13 2024 12 13
2 2065-01-01 2065 1 1
>>> func(df_pl)
shape: (3, 4)
┌─────────────────────┬──────┬───────┬─────┐
│ datetime ┆ year ┆ month ┆ day │
│ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ i32 ┆ i8 ┆ i8 │
╞═════════════════════╪══════╪═══════╪═════╡
│ 1978-06-01 00:00:00 ┆ 1978 ┆ 6 ┆ 1 │
│ 2024-12-13 00:00:00 ┆ 2024 ┆ 12 ┆ 13 │
│ 2065-01-01 00:00:00 ┆ 2065 ┆ 1 ┆ 1 │
└─────────────────────┴──────┴───────┴─────┘
ordinal_day()
Get ordinal day.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import datetime
>>> import narwhals as nw
>>> data = {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(a_ordinal_day=nw.col("a").dt.ordinal_day())
We can then pass either pandas or Polars to func:
>>> func(df_pd)
a a_ordinal_day
0 2020-01-01 1
1 2020-08-03 216
>>> func(df_pl)
shape: (2, 2)
┌─────────────────────┬───────────────┐
│ a ┆ a_ordinal_day │
│ --- ┆ --- │
│ datetime[μs] ┆ i16 │
╞═════════════════════╪═══════════════╡
│ 2020-01-01 00:00:00 ┆ 1 │
│ 2020-08-03 00:00:00 ┆ 216 │
└─────────────────────┴───────────────┘
hour()
Extract hour from underlying DateTime representation.
Returns the hour number from 0 to 23.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import datetime
>>> import narwhals as nw
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1),
... datetime(2024, 10, 13, 5),
... datetime(2065, 1, 1, 10),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(nw.col("datetime").dt.hour().alias("hour"))
We can then pass either pandas or Polars to func:
>>> func(df_pd)
datetime hour
0 1978-01-01 01:00:00 1
1 2024-10-13 05:00:00 5
2 2065-01-01 10:00:00 10
>>> func(df_pl)
shape: (3, 2)
┌─────────────────────┬──────┐
│ datetime ┆ hour │
│ --- ┆ --- │
│ datetime[μs] ┆ i8 │
╞═════════════════════╪══════╡
│ 1978-01-01 01:00:00 ┆ 1 │
│ 2024-10-13 05:00:00 ┆ 5 │
│ 2065-01-01 10:00:00 ┆ 10 │
└─────────────────────┴──────┘
minute()
Extract minutes from underlying DateTime representation.
Returns the minute number from 0 to 59.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import datetime
>>> import narwhals as nw
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1, 1),
... datetime(2024, 10, 13, 5, 30),
... datetime(2065, 1, 1, 10, 20),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(
... nw.col("datetime").dt.hour().alias("hour"),
... nw.col("datetime").dt.minute().alias("minute"),
... )
We can then pass either pandas or Polars to func:
>>> func(df_pd)
datetime hour minute
0 1978-01-01 01:01:00 1 1
1 2024-10-13 05:30:00 5 30
2 2065-01-01 10:20:00 10 20
>>> func(df_pl)
shape: (3, 3)
┌─────────────────────┬──────┬────────┐
│ datetime ┆ hour ┆ minute │
│ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ i8 ┆ i8 │
╞═════════════════════╪══════╪════════╡
│ 1978-01-01 01:01:00 ┆ 1 ┆ 1 │
│ 2024-10-13 05:30:00 ┆ 5 ┆ 30 │
│ 2065-01-01 10:20:00 ┆ 10 ┆ 20 │
└─────────────────────┴──────┴────────┘
second()
Extract seconds from underlying DateTime representation.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import datetime
>>> import narwhals as nw
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1, 1, 1),
... datetime(2024, 10, 13, 5, 30, 14),
... datetime(2065, 1, 1, 10, 20, 30),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(
... nw.col("datetime").dt.hour().alias("hour"),
... nw.col("datetime").dt.minute().alias("minute"),
... nw.col("datetime").dt.second().alias("second"),
... )
We can then pass either pandas or Polars to func:
>>> func(df_pd)
datetime hour minute second
0 1978-01-01 01:01:01 1 1 1
1 2024-10-13 05:30:14 5 30 14
2 2065-01-01 10:20:30 10 20 30
>>> func(df_pl)
shape: (3, 4)
┌─────────────────────┬──────┬────────┬────────┐
│ datetime ┆ hour ┆ minute ┆ second │
│ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ i8 ┆ i8 ┆ i8 │
╞═════════════════════╪══════╪════════╪════════╡
│ 1978-01-01 01:01:01 ┆ 1 ┆ 1 ┆ 1 │
│ 2024-10-13 05:30:14 ┆ 5 ┆ 30 ┆ 14 │
│ 2065-01-01 10:20:30 ┆ 10 ┆ 20 ┆ 30 │
└─────────────────────┴──────┴────────┴────────┘
millisecond()
Extract milliseconds from underlying DateTime representation.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import datetime
>>> import narwhals as nw
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1, 1, 1, 0),
... datetime(2024, 10, 13, 5, 30, 14, 505000),
... datetime(2065, 1, 1, 10, 20, 30, 67000),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(
... nw.col("datetime").dt.hour().alias("hour"),
... nw.col("datetime").dt.minute().alias("minute"),
... nw.col("datetime").dt.second().alias("second"),
... nw.col("datetime").dt.millisecond().alias("millisecond"),
... )
We can then pass either pandas or Polars to func:
>>> func(df_pd)
datetime hour minute second millisecond
0 1978-01-01 01:01:01.000 1 1 1 0
1 2024-10-13 05:30:14.505 5 30 14 505
2 2065-01-01 10:20:30.067 10 20 30 67
>>> func(df_pl)
shape: (3, 5)
┌─────────────────────────┬──────┬────────┬────────┬─────────────┐
│ datetime ┆ hour ┆ minute ┆ second ┆ millisecond │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ i8 ┆ i8 ┆ i8 ┆ i32 │
╞═════════════════════════╪══════╪════════╪════════╪═════════════╡
│ 1978-01-01 01:01:01 ┆ 1 ┆ 1 ┆ 1 ┆ 0 │
│ 2024-10-13 05:30:14.505 ┆ 5 ┆ 30 ┆ 14 ┆ 505 │
│ 2065-01-01 10:20:30.067 ┆ 10 ┆ 20 ┆ 30 ┆ 67 │
└─────────────────────────┴──────┴────────┴────────┴─────────────┘
microsecond()
Extract microseconds from underlying DateTime representation.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import datetime
>>> import narwhals as nw
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1, 1, 1, 0),
... datetime(2024, 10, 13, 5, 30, 14, 505000),
... datetime(2065, 1, 1, 10, 20, 30, 67000),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(
... nw.col("datetime").dt.hour().alias("hour"),
... nw.col("datetime").dt.minute().alias("minute"),
... nw.col("datetime").dt.second().alias("second"),
... nw.col("datetime").dt.microsecond().alias("microsecond"),
... )
We can then pass either pandas or Polars to func:
>>> func(df_pd)
datetime hour minute second microsecond
0 1978-01-01 01:01:01.000 1 1 1 0
1 2024-10-13 05:30:14.505 5 30 14 505000
2 2065-01-01 10:20:30.067 10 20 30 67000
>>> func(df_pl)
shape: (3, 5)
┌─────────────────────────┬──────┬────────┬────────┬─────────────┐
│ datetime ┆ hour ┆ minute ┆ second ┆ microsecond │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ i8 ┆ i8 ┆ i8 ┆ i32 │
╞═════════════════════════╪══════╪════════╪════════╪═════════════╡
│ 1978-01-01 01:01:01 ┆ 1 ┆ 1 ┆ 1 ┆ 0 │
│ 2024-10-13 05:30:14.505 ┆ 5 ┆ 30 ┆ 14 ┆ 505000 │
│ 2065-01-01 10:20:30.067 ┆ 10 ┆ 20 ┆ 30 ┆ 67000 │
└─────────────────────────┴──────┴────────┴────────┴─────────────┘
nanosecond()
Extract Nanoseconds from underlying DateTime representation
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import datetime
>>> import narwhals as nw
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1, 1, 1, 0),
... datetime(2024, 10, 13, 5, 30, 14, 500000),
... datetime(2065, 1, 1, 10, 20, 30, 60000),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(
... nw.col("datetime").dt.hour().alias("hour"),
... nw.col("datetime").dt.minute().alias("minute"),
... nw.col("datetime").dt.second().alias("second"),
... nw.col("datetime").dt.nanosecond().alias("nanosecond"),
... )
We can then pass either pandas or Polars to func:
>>> func(df_pd)
datetime hour minute second nanosecond
0 1978-01-01 01:01:01.000 1 1 1 0
1 2024-10-13 05:30:14.500 5 30 14 500000000
2 2065-01-01 10:20:30.060 10 20 30 60000000
>>> func(df_pl)
shape: (3, 5)
┌─────────────────────────┬──────┬────────┬────────┬────────────┐
│ datetime ┆ hour ┆ minute ┆ second ┆ nanosecond │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ i8 ┆ i8 ┆ i8 ┆ i32 │
╞═════════════════════════╪══════╪════════╪════════╪════════════╡
│ 1978-01-01 01:01:01 ┆ 1 ┆ 1 ┆ 1 ┆ 0 │
│ 2024-10-13 05:30:14.500 ┆ 5 ┆ 30 ┆ 14 ┆ 500000000 │
│ 2065-01-01 10:20:30.060 ┆ 10 ┆ 20 ┆ 30 ┆ 60000000 │
└─────────────────────────┴──────┴────────┴────────┴────────────┘
total_minutes()
Get 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:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import timedelta
>>> import narwhals as nw
>>> data = {"a": [timedelta(minutes=10), timedelta(minutes=20, seconds=40)]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(a_total_minutes=nw.col("a").dt.total_minutes())
We can then pass either pandas or Polars to func:
>>> func(df_pd)
a a_total_minutes
0 0 days 00:10:00 10
1 0 days 00:20:40 20
>>> func(df_pl)
shape: (2, 2)
┌──────────────┬─────────────────┐
│ a ┆ a_total_minutes │
│ --- ┆ --- │
│ duration[μs] ┆ i64 │
╞══════════════╪═════════════════╡
│ 10m ┆ 10 │
│ 20m 40s ┆ 20 │
└──────────────┴─────────────────┘
total_seconds()
Get 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:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import timedelta
>>> import narwhals as nw
>>> data = {"a": [timedelta(seconds=10), timedelta(seconds=20, milliseconds=40)]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(a_total_seconds=nw.col("a").dt.total_seconds())
We can then pass either pandas or Polars to func:
>>> func(df_pd)
a a_total_seconds
0 0 days 00:00:10 10
1 0 days 00:00:20.040000 20
>>> func(df_pl)
shape: (2, 2)
┌──────────────┬─────────────────┐
│ a ┆ a_total_seconds │
│ --- ┆ --- │
│ duration[μs] ┆ i64 │
╞══════════════╪═════════════════╡
│ 10s ┆ 10 │
│ 20s 40ms ┆ 20 │
└──────────────┴─────────────────┘
total_milliseconds()
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:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import timedelta
>>> import narwhals as nw
>>> data = {
... "a": [
... timedelta(milliseconds=10),
... timedelta(milliseconds=20, microseconds=40),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(
... a_total_milliseconds=nw.col("a").dt.total_milliseconds()
... )
We can then pass either pandas or Polars to func:
>>> func(df_pd)
a a_total_milliseconds
0 0 days 00:00:00.010000 10
1 0 days 00:00:00.020040 20
>>> func(df_pl)
shape: (2, 2)
┌──────────────┬──────────────────────┐
│ a ┆ a_total_milliseconds │
│ --- ┆ --- │
│ duration[μs] ┆ i64 │
╞══════════════╪══════════════════════╡
│ 10ms ┆ 10 │
│ 20040µs ┆ 20 │
└──────────────┴──────────────────────┘
total_microseconds()
Get 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:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import timedelta
>>> import narwhals as nw
>>> data = {
... "a": [
... timedelta(microseconds=10),
... timedelta(milliseconds=1, microseconds=200),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(
... a_total_microseconds=nw.col("a").dt.total_microseconds()
... )
We can then pass either pandas or Polars to func:
>>> func(df_pd)
a a_total_microseconds
0 0 days 00:00:00.000010 10
1 0 days 00:00:00.001200 1200
>>> func(df_pl)
shape: (2, 2)
┌──────────────┬──────────────────────┐
│ a ┆ a_total_microseconds │
│ --- ┆ --- │
│ duration[μs] ┆ i64 │
╞══════════════╪══════════════════════╡
│ 10µs ┆ 10 │
│ 1200µs ┆ 1200 │
└──────────────┴──────────────────────┘
total_nanoseconds()
Get 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:
>>> import pandas as pd
>>> import polars as pl
>>> from datetime import timedelta
>>> import narwhals as nw
>>> data = ["2024-01-01 00:00:00.000000001", "2024-01-01 00:00:00.000000002"]
>>> df_pd = pd.DataFrame({"a": pd.to_datetime(data)})
>>> df_pl = pl.DataFrame({"a": data}).with_columns(
... pl.col("a").str.to_datetime(time_unit="ns")
... )
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.with_columns(
... a_diff_total_nanoseconds=nw.col("a").diff().dt.total_nanoseconds()
... )
We can then pass either pandas or Polars to func:
>>> func(df_pd)
a a_diff_total_nanoseconds
0 2024-01-01 00:00:00.000000001 NaN
1 2024-01-01 00:00:00.000000002 1.0
>>> func(df_pl)
shape: (2, 2)
┌───────────────────────────────┬──────────────────────────┐
│ a ┆ a_diff_total_nanoseconds │
│ --- ┆ --- │
│ datetime[ns] ┆ i64 │
╞═══════════════════════════════╪══════════════════════════╡
│ 2024-01-01 00:00:00.000000001 ┆ null │
│ 2024-01-01 00:00:00.000000002 ┆ 1 │
└───────────────────────────────┴──────────────────────────┘
to_string(format)
Convert a Date/Time/Datetime column into a String column with the given format.
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 pandas as pd
>>> import polars as pl
>>> import narwhals as nw
>>> data = [
... datetime(2020, 3, 1),
... datetime(2020, 4, 1),
... datetime(2020, 5, 1),
... ]
>>> df_pd = pd.DataFrame({"a": data})
>>> df_pl = pl.DataFrame({"a": data})
We define a dataframe-agnostic function:
>>> @nw.narwhalify
... def func(df):
... return df.select(nw.col("a").dt.to_string("%Y/%m/%d %H:%M:%S"))
We can then pass either pandas or Polars to func:
>>> func(df_pd)
a
0 2020/03/01 00:00:00
1 2020/04/01 00:00:00
2 2020/05/01 00:00:00
>>> func(df_pl)
shape: (3, 1)
┌─────────────────────┐
│ a │
│ --- │
│ str │
╞═════════════════════╡
│ 2020/03/01 00:00:00 │
│ 2020/04/01 00:00:00 │
│ 2020/05/01 00:00:00 │
└─────────────────────┘