@@ -1299,6 +1299,31 @@ frequencies. We will refer to these aliases as *offset aliases*.
12991299 given frequency it will roll to the next value for ``start_date ``
13001300 (respectively previous for the ``end_date ``)
13011301
1302+ .. _timeseries.period_aliases :
1303+
1304+ Period aliases
1305+ ~~~~~~~~~~~~~~
1306+
1307+ A number of string aliases are given to useful common time series
1308+ frequencies. We will refer to these aliases as *period aliases *.
1309+
1310+ .. csv-table ::
1311+ :header: "Alias", "Description"
1312+ :widths: 15, 100
1313+
1314+ "B", "business day frequency"
1315+ "D", "calendar day frequency"
1316+ "W", "weekly frequency"
1317+ "M", "monthly frequency"
1318+ "Q", "quarterly frequency"
1319+ "A, Y", "yearly frequency"
1320+ "H", "hourly frequency"
1321+ "T, min", "minutely frequency"
1322+ "S", "secondly frequency"
1323+ "L, ms", "milliseconds"
1324+ "U, us", "microseconds"
1325+ "N", "nanoseconds"
1326+
13021327
13031328Combining aliases
13041329~~~~~~~~~~~~~~~~~
@@ -2083,7 +2108,7 @@ Period dtypes
20832108dtype similar to the :ref: `timezone aware dtype <timeseries.timezone_series >` (``datetime64[ns, tz] ``).
20842109
20852110The ``period `` dtype holds the ``freq `` attribute and is represented with
2086- ``period[freq] `` like ``period[D] `` or ``period[M] ``, using :ref: `frequency strings <timeseries.offset_aliases >`.
2111+ ``period[freq] `` like ``period[D] `` or ``period[M] ``, using :ref: `frequency strings <timeseries.period_aliases >`.
20872112
20882113.. ipython :: python
20892114
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