From 9efc72c26af85f2719e96738047e6c2bb3e8bc8b Mon Sep 17 00:00:00 2001 From: Joris Van den Bossche Date: Sat, 5 Jul 2014 16:12:02 +0200 Subject: [PATCH] DOC: remove mention of TimeSeries in docs --- doc/source/dsintro.rst | 6 ++---- doc/source/faq.rst | 7 +++---- doc/source/overview.rst | 3 +-- doc/source/timeseries.rst | 10 +++++----- 4 files changed, 11 insertions(+), 15 deletions(-) diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index adcf2fca9b4c5..9221f2685d79b 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -577,10 +577,8 @@ row-wise. For example: df - df.iloc[0] -In the special case of working with time series data, if the Series is a -TimeSeries (which it will be automatically if the index contains datetime -objects), and the DataFrame index also contains dates, the broadcasting will be -column-wise: +In the special case of working with time series data, and the DataFrame index +also contains dates, the broadcasting will be column-wise: .. ipython:: python :okwarning: diff --git a/doc/source/faq.rst b/doc/source/faq.rst index 20762e3fc039f..1fc8488e92fde 100644 --- a/doc/source/faq.rst +++ b/doc/source/faq.rst @@ -207,9 +207,9 @@ properties. Here are the pandas equivalents: Frequency conversion ~~~~~~~~~~~~~~~~~~~~ -Frequency conversion is implemented using the ``resample`` method on TimeSeries -and DataFrame objects (multiple time series). ``resample`` also works on panels -(3D). Here is some code that resamples daily data to monthly: +Frequency conversion is implemented using the ``resample`` method on Series +and DataFrame objects with a DatetimeIndex or PeriodIndex. ``resample`` also +works on panels (3D). Here is some code that resamples daily data to montly: .. ipython:: python @@ -369,4 +369,3 @@ just a thin layer around the ``QTableView``. mw = MainWidget() mw.show() app.exec_() - diff --git a/doc/source/overview.rst b/doc/source/overview.rst index 49a788def2854..b1addddc2121d 100644 --- a/doc/source/overview.rst +++ b/doc/source/overview.rst @@ -9,7 +9,7 @@ Package overview :mod:`pandas` consists of the following things * A set of labeled array data structures, the primary of which are - Series/TimeSeries and DataFrame + Series and DataFrame * Index objects enabling both simple axis indexing and multi-level / hierarchical axis indexing * An integrated group by engine for aggregating and transforming data sets @@ -32,7 +32,6 @@ Data structures at a glance :widths: 15, 20, 50 1, Series, "1D labeled homogeneously-typed array" - 1, TimeSeries, "Series with index containing datetimes" 2, DataFrame, "General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed columns" 3, Panel, "General 3D labeled, also size-mutable array" diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index b69b523d9c908..ce1035e91391a 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -1008,7 +1008,7 @@ Time series-related instance methods Shifting / lagging ~~~~~~~~~~~~~~~~~~ -One may want to *shift* or *lag* the values in a TimeSeries back and forward in +One may want to *shift* or *lag* the values in a time series back and forward in time. The method for this is ``shift``, which is available on all of the pandas objects. @@ -1026,7 +1026,7 @@ The shift method accepts an ``freq`` argument which can accept a ts.shift(5, freq='BM') Rather than changing the alignment of the data and the index, ``DataFrame`` and -``TimeSeries`` objects also have a ``tshift`` convenience method that changes +``Series`` objects also have a ``tshift`` convenience method that changes all the dates in the index by a specified number of offsets: .. ipython:: python @@ -1569,7 +1569,7 @@ time zones using ``tz_convert``: rng_berlin[5] rng_eastern[5].tz_convert('Europe/Berlin') -Localization of Timestamps functions just like DatetimeIndex and TimeSeries: +Localization of Timestamps functions just like DatetimeIndex and Series: .. ipython:: python @@ -1577,8 +1577,8 @@ Localization of Timestamps functions just like DatetimeIndex and TimeSeries: rng[5].tz_localize('Asia/Shanghai') -Operations between TimeSeries in different time zones will yield UTC -TimeSeries, aligning the data on the UTC timestamps: +Operations between Series in different time zones will yield UTC +Series, aligning the data on the UTC timestamps: .. ipython:: python