logx and loglog can now accept the value 'sym' for symlog scaling. (GH24867) • Added support for ISO week year format (‘%G-%V-%u’) when parsing datetimes using to_datetime() (GH16607) • Indexing of DataFrame • If {'foo': [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601- formatted dates. infer_datetime_format [boolean, default False] If True and parse_dates is enabled use to_datetime() after pd.read_csv. Note: read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you can arrange for your data

logx and loglog can now accept the value 'sym' for symlog scaling. (GH24867) • Added support for ISO week year format (‘%G-%V-%u’) when parsing datetimes using to_datetime() (GH16607) • Indexing of DataFrame • If {'foo': [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601- formatted dates. infer_datetime_format [boolean, default False] If True and parse_dates is enabled use to_datetime() after pd.read_csv. Note: read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you can arrange for your data

• If {'foo': [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601- formatted dates. infer_datetime_format [boolean, default False] If True and parse_dates is enabled data analysis toolkit, Release 1.3.3 Note: read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you can arrange for your data just the values array • date_format : string, type of date conversion, ‘epoch’ for timestamp, ‘iso’ for ISO8601. • double_precision : The number of decimal places to use when encoding floating point values

• If {'foo': [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601- formatted dates. infer_datetime_format [boolean, default False] If True and parse_dates is enabled data analysis toolkit, Release 1.3.4 Note: read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you can arrange for your data just the values array • date_format : string, type of date conversion, ‘epoch’ for timestamp, ‘iso’ for ISO8601. • double_precision : The number of decimal places to use when encoding floating point values

• If {'foo': [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601- formatted dates. infer_datetime_format [boolean, default False] If True and parse_dates is enabled use to_datetime() after pd.read_csv. Note: read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you can arrange for your data just the values array • date_format : string, type of date conversion, ‘epoch’ for timestamp, ‘iso’ for ISO8601. • double_precision : The number of decimal places to use when encoding floating point values

• If {'foo': [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601- formatted dates. infer_datetime_format [boolean, default False] If True and parse_dates is enabled use to_datetime() after pd.read_csv. Note: read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you can arrange for your data the JSON Table Schema • date_format : string, type of date conversion, ‘epoch’ for timestamp, ‘iso’ for ISO8601. • double_precision : The number of decimal places to use when encoding floating point values

content (GH9784) • Bug in DataFrame.to_json() where a datetime column label would not be written out in ISO format with orient="table" (GH28130) • Bug in DataFrame.to_parquet() where writing to GCS would fail • If {'foo': [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601- formatted dates. infer_datetime_format [boolean, default False] If True and parse_dates is enabled use to_datetime() after pd.read_csv. Note: read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you can arrange for your data

timestamps. See the Datetime data types for implications (GH9086). • to_timedelta() now supports iso-formated timedelta strings (GH21877) • Series and DataFrame now support Iterable objects in the constructor • If {'foo': [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601- formatted dates. infer_datetime_format [boolean, default False] If True and parse_dates is enabled use to_datetime() after pd.read_csv. Note: read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you can arrange for your data

• If {'foo': [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601- formatted dates. infer_datetime_format [boolean, default False] If True and parse_dates is enabled use to_datetime() after pd.read_csv. Note: read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you can arrange for your data the JSON Table Schema • date_format : string, type of date conversion, ‘epoch’ for timestamp, ‘iso’ for ISO8601. • double_precision : The number of decimal places to use when encoding floating point values

110152 86.5 B77 S Rothes [1 rows x 13 columns] (Interested in her story? See *`Wikipedia en.wikipedia.org/wiki/No%C3%ABl_Leslie,_Countess_of_Rothes>`__*!) The string method Series.str.contains() • If {'foo': [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’. A fast-path exists for iso8601- formatted dates. infer_datetime_format [boolean, default False] If True and parse_dates is enabled use to_datetime() after pd.read_csv. Note: read_csv has a fast_path for parsing datetime strings in iso8601 format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you can arrange for your data