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Merge pull request #7671 from jorisvandenbossche/doc-timeseries
DOC: remove mention of TimeSeries in docs
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doc/source/dsintro.rst

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@@ -577,10 +577,8 @@ row-wise. For example:
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df - df.iloc[0]
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In the special case of working with time series data, if the Series is a
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TimeSeries (which it will be automatically if the index contains datetime
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objects), and the DataFrame index also contains dates, the broadcasting will be
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column-wise:
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In the special case of working with time series data, and the DataFrame index
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also contains dates, the broadcasting will be column-wise:
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.. ipython:: python
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:okwarning:

doc/source/faq.rst

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@@ -207,9 +207,9 @@ properties. Here are the pandas equivalents:
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Frequency conversion
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~~~~~~~~~~~~~~~~~~~~
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Frequency conversion is implemented using the ``resample`` method on TimeSeries
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and DataFrame objects (multiple time series). ``resample`` also works on panels
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(3D). Here is some code that resamples daily data to monthly:
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Frequency conversion is implemented using the ``resample`` method on Series
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and DataFrame objects with a DatetimeIndex or PeriodIndex. ``resample`` also
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works on panels (3D). Here is some code that resamples daily data to montly:
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.. ipython:: python
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@@ -369,4 +369,3 @@ just a thin layer around the ``QTableView``.
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mw = MainWidget()
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mw.show()
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app.exec_()
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doc/source/overview.rst

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@@ -9,7 +9,7 @@ Package overview
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:mod:`pandas` consists of the following things
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* A set of labeled array data structures, the primary of which are
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Series/TimeSeries and DataFrame
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Series and DataFrame
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* Index objects enabling both simple axis indexing and multi-level /
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hierarchical axis indexing
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* An integrated group by engine for aggregating and transforming data sets
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:widths: 15, 20, 50
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1, Series, "1D labeled homogeneously-typed array"
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1, TimeSeries, "Series with index containing datetimes"
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2, DataFrame, "General 2D labeled, size-mutable tabular structure with
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potentially heterogeneously-typed columns"
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3, Panel, "General 3D labeled, also size-mutable array"

doc/source/timeseries.rst

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@@ -1008,7 +1008,7 @@ Time series-related instance methods
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Shifting / lagging
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~~~~~~~~~~~~~~~~~~
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One may want to *shift* or *lag* the values in a TimeSeries back and forward in
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One may want to *shift* or *lag* the values in a time series back and forward in
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time. The method for this is ``shift``, which is available on all of the pandas
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objects.
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ts.shift(5, freq='BM')
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Rather than changing the alignment of the data and the index, ``DataFrame`` and
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``TimeSeries`` objects also have a ``tshift`` convenience method that changes
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``Series`` objects also have a ``tshift`` convenience method that changes
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all the dates in the index by a specified number of offsets:
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.. ipython:: python
@@ -1569,16 +1569,16 @@ time zones using ``tz_convert``:
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rng_berlin[5]
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rng_eastern[5].tz_convert('Europe/Berlin')
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Localization of Timestamps functions just like DatetimeIndex and TimeSeries:
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Localization of Timestamps functions just like DatetimeIndex and Series:
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.. ipython:: python
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rng[5]
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rng[5].tz_localize('Asia/Shanghai')
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Operations between TimeSeries in different time zones will yield UTC
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TimeSeries, aligning the data on the UTC timestamps:
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Operations between Series in different time zones will yield UTC
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Series, aligning the data on the UTC timestamps:
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.. ipython:: python
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