diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index 25f7c5a3ad948..ca903dadc6eb1 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -672,7 +672,7 @@ The ``CategoricalIndex`` is **preserved** after indexing: df2.loc['a'].index Sorting the index will sort by the order of the categories (Recall that we -created the index with with ``CategoricalDtype(list('cab'))``, so the sorted +created the index with ``CategoricalDtype(list('cab'))``, so the sorted order is ``cab``.). .. ipython:: python diff --git a/doc/source/comparison_with_sas.rst b/doc/source/comparison_with_sas.rst index e9e0d7716af3a..214667119f7e0 100644 --- a/doc/source/comparison_with_sas.rst +++ b/doc/source/comparison_with_sas.rst @@ -279,7 +279,7 @@ date/datetime columns. The equivalent pandas operations are shown below. In addition to these functions pandas supports other Time Series features -not available in Base SAS (such as resampling and and custom offsets) - +not available in Base SAS (such as resampling and custom offsets) - see the :ref:`timeseries documentation` for more details. .. ipython:: python @@ -584,7 +584,7 @@ For example, in SAS you could do this to filter missing values. if value_x ^= .; run; -Which doesn't work in in pandas. Instead, the ``pd.isna`` or ``pd.notna`` functions +Which doesn't work in pandas. Instead, the ``pd.isna`` or ``pd.notna`` functions should be used for comparisons. .. ipython:: python diff --git a/doc/source/computation.rst b/doc/source/computation.rst index a64542fa71705..4285767654e25 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -512,7 +512,7 @@ a same sized result as the input. When using ``.resample()`` with an offset. Construct a new index that is the frequency of the offset. For each frequency bin, aggregate points from the input within a backwards-in-time looking window that fall in that bin. The result of this -aggregation is the output for that frequency point. The windows are fixed size size in the frequency space. Your result +aggregation is the output for that frequency point. The windows are fixed size in the frequency space. Your result will have the shape of a regular frequency between the min and the max of the original input object. To summarize, ``.rolling()`` is a time-based window operation, while ``.resample()`` is a frequency-based window operation. diff --git a/doc/source/io.rst b/doc/source/io.rst index 60dc89f8fd495..1785de54b7dd6 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -4529,7 +4529,7 @@ Several caveats. on an attempt at serialization. You can specify an ``engine`` to direct the serialization. This can be one of ``pyarrow``, or ``fastparquet``, or ``auto``. -If the engine is NOT specified, then the ``pd.options.io.parquet.engine`` option is checked; if this is also ``auto``, then +If the engine is NOT specified, then the ``pd.options.io.parquet.engine`` option is checked; if this is also ``auto``, then ``pyarrow`` is tried, and falling back to ``fastparquet``. See the documentation for `pyarrow `__ and `fastparquet `__ diff --git a/doc/source/release.rst b/doc/source/release.rst index cd763de42d162..8e063116cbf07 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -406,7 +406,7 @@ of all enhancements and bugs that have been fixed in 0.20.1. .. note:: - This is a combined release for 0.20.0 and and 0.20.1. + This is a combined release for 0.20.0 and 0.20.1. Version 0.20.1 contains one additional change for backwards-compatibility with downstream projects using pandas' ``utils`` routines. (:issue:`16250`) Thanks @@ -2918,7 +2918,7 @@ Improvements to existing features - clipboard functions use pyperclip (no dependencies on Windows, alternative dependencies offered for Linux) (:issue:`3837`). - Plotting functions now raise a ``TypeError`` before trying to plot anything - if the associated objects have have a dtype of ``object`` (:issue:`1818`, + if the associated objects have a dtype of ``object`` (:issue:`1818`, :issue:`3572`, :issue:`3911`, :issue:`3912`), but they will try to convert object arrays to numeric arrays if possible so that you can still plot, for example, an object array with floats. This happens before any drawing takes place which @@ -4082,7 +4082,7 @@ Bug Fixes columns (:issue:`1943`) - Fix time zone localization bug causing improper fields (e.g. hours) in time zones that have not had a UTC transition in a long time (:issue:`1946`) -- Fix errors when parsing and working with with fixed offset timezones +- Fix errors when parsing and working with fixed offset timezones (:issue:`1922`, :issue:`1928`) - Fix text parser bug when handling UTC datetime objects generated by dateutil (:issue:`1693`) @@ -4383,7 +4383,7 @@ Bug Fixes error (:issue:`1090`) - Consistently set name on groupby pieces (:issue:`184`) - Treat dict return values as Series in GroupBy.apply (:issue:`823`) -- Respect column selection for DataFrame in in GroupBy.transform (:issue:`1365`) +- Respect column selection for DataFrame in GroupBy.transform (:issue:`1365`) - Fix MultiIndex partial indexing bug (:issue:`1352`) - Enable assignment of rows in mixed-type DataFrame via .ix (:issue:`1432`) - Reset index mapping when grouping Series in Cython (:issue:`1423`) @@ -5040,7 +5040,7 @@ New Features - Add `melt` function to `pandas.core.reshape` - Add `level` parameter to group by level in Series and DataFrame descriptive statistics (:issue:`313`) -- Add `head` and `tail` methods to Series, analogous to to DataFrame (PR +- Add `head` and `tail` methods to Series, analogous to DataFrame (PR :issue:`296`) - Add `Series.isin` function which checks if each value is contained in a passed sequence (:issue:`289`) diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst index 43ccd372d9d5b..710212bc237cd 100644 --- a/doc/source/tutorials.rst +++ b/doc/source/tutorials.rst @@ -19,7 +19,7 @@ pandas Cookbook The goal of this cookbook (by `Julia Evans `_) is to give you some concrete examples for getting started with pandas. These are examples with real-world data, and all the bugs and weirdness that -that entails. +entails. Here are links to the v0.1 release. For an up-to-date table of contents, see the `pandas-cookbook GitHub repository `_. To run the examples in this tutorial, you'll need to