From 7312458f7e2901d0619500b0e9f9f4c51698ec00 Mon Sep 17 00:00:00 2001 From: jschendel Date: Thu, 14 Sep 2017 22:01:04 -0600 Subject: [PATCH] CLN: Fix Spelling Errors --- doc/source/advanced.rst | 10 +++++----- doc/source/api.rst | 2 +- doc/source/basics.rst | 2 +- doc/source/computation.rst | 2 +- doc/source/groupby.rst | 4 ++-- doc/source/indexing.rst | 2 +- doc/source/io.rst | 2 +- doc/source/merging.rst | 6 +++--- doc/source/missing_data.rst | 2 +- doc/source/options.rst | 4 ++-- doc/source/reshaping.rst | 2 +- doc/source/sparse.rst | 2 +- doc/source/style.ipynb | 2 +- doc/source/timeseries.rst | 18 +++++++++--------- doc/source/visualization.rst | 2 +- pandas/core/algorithms.py | 2 +- pandas/core/indexes/interval.py | 2 +- pandas/core/reshape/concat.py | 2 +- pandas/core/reshape/merge.py | 6 +++--- pandas/core/reshape/tile.py | 2 +- pandas/io/formats/excel.py | 4 ++-- pandas/io/pytables.py | 12 ++++++------ pandas/io/stata.py | 4 ++-- pandas/plotting/_misc.py | 2 +- pandas/plotting/_tools.py | 2 +- pandas/tests/frame/test_convert_to.py | 4 ++-- pandas/tests/groupby/test_transform.py | 2 +- pandas/tests/indexes/datetimes/test_tools.py | 2 +- pandas/tests/io/json/test_json_table_schema.py | 2 +- pandas/tests/io/parser/test_read_fwf.py | 2 +- pandas/tests/io/test_pytables.py | 8 ++++---- pandas/tests/plotting/test_datetimelike.py | 2 +- pandas/tests/series/test_dtypes.py | 2 +- pandas/tests/test_categorical.py | 2 +- pandas/tests/test_sorting.py | 2 +- pandas/tseries/util.py | 2 +- 36 files changed, 65 insertions(+), 65 deletions(-) diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index 3f145cf955664..3bda8c7eacb61 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -625,7 +625,7 @@ Index Types We have discussed ``MultiIndex`` in the previous sections pretty extensively. ``DatetimeIndex`` and ``PeriodIndex`` are shown :ref:`here `. ``TimedeltaIndex`` are :ref:`here `. -In the following sub-sections we will highlite some other index types. +In the following sub-sections we will highlight some other index types. .. _indexing.categoricalindex: @@ -645,7 +645,7 @@ and allows efficient indexing and storage of an index with a large number of dup df.dtypes df.B.cat.categories -Setting the index, will create create a ``CategoricalIndex`` +Setting the index, will create a ``CategoricalIndex`` .. ipython:: python @@ -681,7 +681,7 @@ Groupby operations on the index will preserve the index nature as well Reindexing operations, will return a resulting index based on the type of the passed indexer, meaning that passing a list will return a plain-old-``Index``; indexing with a ``Categorical`` will return a ``CategoricalIndex``, indexed according to the categories -of the PASSED ``Categorical`` dtype. This allows one to arbitrarly index these even with +of the PASSED ``Categorical`` dtype. This allows one to arbitrarily index these even with values NOT in the categories, similarly to how you can reindex ANY pandas index. .. ipython :: python @@ -722,7 +722,7 @@ Int64Index and RangeIndex Prior to 0.18.0, the ``Int64Index`` would provide the default index for all ``NDFrame`` objects. ``RangeIndex`` is a sub-class of ``Int64Index`` added in version 0.18.0, now providing the default index for all ``NDFrame`` objects. -``RangeIndex`` is an optimized version of ``Int64Index`` that can represent a monotonic ordered set. These are analagous to python `range types `__. +``RangeIndex`` is an optimized version of ``Int64Index`` that can represent a monotonic ordered set. These are analogous to python `range types `__. .. _indexing.float64index: @@ -963,7 +963,7 @@ index can be somewhat complicated. For example, the following does not work: s.loc['c':'e'+1] A very common use case is to limit a time series to start and end at two -specific dates. To enable this, we made the design design to make label-based +specific dates. To enable this, we made the design to make label-based slicing include both endpoints: .. ipython:: python diff --git a/doc/source/api.rst b/doc/source/api.rst index 1541bbccefe21..4e02f7b11f466 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -1291,7 +1291,7 @@ Index ----- **Many of these methods or variants thereof are available on the objects -that contain an index (Series/Dataframe) and those should most likely be +that contain an index (Series/DataFrame) and those should most likely be used before calling these methods directly.** .. autosummary:: diff --git a/doc/source/basics.rst b/doc/source/basics.rst index 42c28df3a6030..0990d2bd15ee6 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -923,7 +923,7 @@ Passing a named function will yield that name for the row: Aggregating with a dict +++++++++++++++++++++++ -Passing a dictionary of column names to a scalar or a list of scalars, to ``DataFame.agg`` +Passing a dictionary of column names to a scalar or a list of scalars, to ``DataFrame.agg`` allows you to customize which functions are applied to which columns. Note that the results are not in any particular order, you can use an ``OrderedDict`` instead to guarantee ordering. diff --git a/doc/source/computation.rst b/doc/source/computation.rst index 23699393958cf..14cfdbc364837 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -654,7 +654,7 @@ aggregation with, outputting a DataFrame: r['A'].agg([np.sum, np.mean, np.std]) -On a widowed DataFrame, you can pass a list of functions to apply to each +On a windowed DataFrame, you can pass a list of functions to apply to each column, which produces an aggregated result with a hierarchical index: .. ipython:: python diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index e1231b9a4a200..e9a7d8dd0a46e 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -561,7 +561,7 @@ must be either implemented on GroupBy or available via :ref:`dispatching .. note:: - If you pass a dict to ``aggregate``, the ordering of the output colums is + If you pass a dict to ``aggregate``, the ordering of the output columns is non-deterministic. If you want to be sure the output columns will be in a specific order, you can use an ``OrderedDict``. Compare the output of the following two commands: @@ -1211,7 +1211,7 @@ Groupby by Indexer to 'resample' data Resampling produces new hypothetical samples (resamples) from already existing observed data or from a model that generates data. These new samples are similar to the pre-existing samples. -In order to resample to work on indices that are non-datetimelike , the following procedure can be utilized. +In order to resample to work on indices that are non-datetimelike, the following procedure can be utilized. In the following examples, **df.index // 5** returns a binary array which is used to determine what gets selected for the groupby operation. diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 8474116c38082..edbc4e6d7fd22 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -714,7 +714,7 @@ Finally, one can also set a seed for ``sample``'s random number generator using Setting With Enlargement ------------------------ -The ``.loc/[]`` operations can perform enlargement when setting a non-existant key for that axis. +The ``.loc/[]`` operations can perform enlargement when setting a non-existent key for that axis. In the ``Series`` case this is effectively an appending operation diff --git a/doc/source/io.rst b/doc/source/io.rst index 8fbb23769492e..fcf7f6029197b 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -3077,7 +3077,7 @@ Compressed pickle files .. versionadded:: 0.20.0 -:func:`read_pickle`, :meth:`DataFame.to_pickle` and :meth:`Series.to_pickle` can read +:func:`read_pickle`, :meth:`DataFrame.to_pickle` and :meth:`Series.to_pickle` can read and write compressed pickle files. The compression types of ``gzip``, ``bz2``, ``xz`` are supported for reading and writing. `zip`` file supports read only and must contain only one data file to be read in. diff --git a/doc/source/merging.rst b/doc/source/merging.rst index a5ee1b1a9384c..72787ea97a782 100644 --- a/doc/source/merging.rst +++ b/doc/source/merging.rst @@ -1329,7 +1329,7 @@ By default we are taking the asof of the quotes. on='time', by='ticker') -We only asof within ``2ms`` betwen the quote time and the trade time. +We only asof within ``2ms`` between the quote time and the trade time. .. ipython:: python @@ -1338,8 +1338,8 @@ We only asof within ``2ms`` betwen the quote time and the trade time. by='ticker', tolerance=pd.Timedelta('2ms')) -We only asof within ``10ms`` betwen the quote time and the trade time and we exclude exact matches on time. -Note that though we exclude the exact matches (of the quotes), prior quotes DO propogate to that point +We only asof within ``10ms`` between the quote time and the trade time and we exclude exact matches on time. +Note that though we exclude the exact matches (of the quotes), prior quotes DO propagate to that point in time. .. ipython:: python diff --git a/doc/source/missing_data.rst b/doc/source/missing_data.rst index 65b411ccd4af2..b33b5c304853a 100644 --- a/doc/source/missing_data.rst +++ b/doc/source/missing_data.rst @@ -320,7 +320,7 @@ Interpolation The ``limit_direction`` keyword argument was added. -Both Series and Dataframe objects have an ``interpolate`` method that, by default, +Both Series and DataFrame objects have an ``interpolate`` method that, by default, performs linear interpolation at missing datapoints. .. ipython:: python diff --git a/doc/source/options.rst b/doc/source/options.rst index 1592caf90546c..f042e4d3f5120 100644 --- a/doc/source/options.rst +++ b/doc/source/options.rst @@ -313,9 +313,9 @@ display.large_repr truncate For DataFrames exceeding max_ro display.latex.repr False Whether to produce a latex DataFrame representation for jupyter frontends that support it. -display.latex.escape True Escapes special caracters in Dataframes, when +display.latex.escape True Escapes special characters in DataFrames, when using the to_latex method. -display.latex.longtable False Specifies if the to_latex method of a Dataframe +display.latex.longtable False Specifies if the to_latex method of a DataFrame uses the longtable format. display.latex.multicolumn True Combines columns when using a MultiIndex display.latex.multicolumn_format 'l' Alignment of multicolumn labels diff --git a/doc/source/reshaping.rst b/doc/source/reshaping.rst index fab83222b313f..1209c4a8d6be8 100644 --- a/doc/source/reshaping.rst +++ b/doc/source/reshaping.rst @@ -156,7 +156,7 @@ the level numbers: stacked.unstack('second') Notice that the ``stack`` and ``unstack`` methods implicitly sort the index -levels involved. Hence a call to ``stack`` and then ``unstack``, or viceversa, +levels involved. Hence a call to ``stack`` and then ``unstack``, or vice versa, will result in a **sorted** copy of the original DataFrame or Series: .. ipython:: python diff --git a/doc/source/sparse.rst b/doc/source/sparse.rst index cf16cee501a3e..89efa7b4be3ee 100644 --- a/doc/source/sparse.rst +++ b/doc/source/sparse.rst @@ -132,7 +132,7 @@ dtype, ``fill_value`` default changes: s.to_sparse() You can change the dtype using ``.astype()``, the result is also sparse. Note that -``.astype()`` also affects to the ``fill_value`` to keep its dense represantation. +``.astype()`` also affects to the ``fill_value`` to keep its dense representation. .. ipython:: python diff --git a/doc/source/style.ipynb b/doc/source/style.ipynb index c250787785e14..1d6ce163cf977 100644 --- a/doc/source/style.ipynb +++ b/doc/source/style.ipynb @@ -169,7 +169,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice the similarity with the standard `df.applymap`, which operates on DataFrames elementwise. We want you to be able to resuse your existing knowledge of how to interact with DataFrames.\n", + "Notice the similarity with the standard `df.applymap`, which operates on DataFrames elementwise. We want you to be able to reuse your existing knowledge of how to interact with DataFrames.\n", "\n", "Notice also that our function returned a string containing the CSS attribute and value, separated by a colon just like in a `