diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index b550f59b8c5e6..d976b0c8c21a5 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -525,7 +525,7 @@ they have a MultiIndex: df.T.sort_index(level=1, axis=1) -The ``MultiIndex`` object has code to **explicity check the sort depth**. Thus, +The ``MultiIndex`` object has code to **explicitly check the sort depth**. Thus, if you try to index at a depth at which the index is not sorted, it will raise an exception. Here is a concrete example to illustrate this: diff --git a/doc/source/api.rst b/doc/source/api.rst index c7f815914358b..12dc0b0cb50b9 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -552,7 +552,7 @@ These can be accessed like ``Series.dt.``. String handling ~~~~~~~~~~~~~~~ ``Series.str`` can be used to access the values of the series as -strings and apply several methods to it. These can be acccessed like +strings and apply several methods to it. These can be accessed like ``Series.str.``. .. autosummary:: diff --git a/doc/source/basics.rst b/doc/source/basics.rst index b80ac18a2ac51..8658c6d45f1b6 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -1097,7 +1097,7 @@ Note that the same result could have been achieved using ts2.reindex(ts.index).fillna(method='ffill') :meth:`~Series.reindex` will raise a ValueError if the index is not monotonic -increasing or descreasing. :meth:`~Series.fillna` and :meth:`~Series.interpolate` +increasing or decreasing. :meth:`~Series.fillna` and :meth:`~Series.interpolate` will not make any checks on the order of the index. .. _basics.limits_on_reindex_fill: diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst index 2dc506db7eeba..317641f1b3eea 100644 --- a/doc/source/categorical.rst +++ b/doc/source/categorical.rst @@ -178,7 +178,7 @@ It's also possible to pass in the categories in a specific order: .. note:: - New categorical data are NOT automatically ordered. You must explicity pass ``ordered=True`` to + New categorical data are NOT automatically ordered. You must explicitly pass ``ordered=True`` to indicate an ordered ``Categorical``. @@ -342,7 +342,7 @@ necessarily make the sort order the same as the categories order. Multi Column Sorting ~~~~~~~~~~~~~~~~~~~~ -A categorical dtyped column will partcipate in a multi-column sort in a similar manner to other columns. +A categorical dtyped column will participate in a multi-column sort in a similar manner to other columns. The ordering of the categorical is determined by the ``categories`` of that column. .. ipython:: python diff --git a/doc/source/comparison_with_sas.rst b/doc/source/comparison_with_sas.rst index f51603750d61b..85d432b546f21 100644 --- a/doc/source/comparison_with_sas.rst +++ b/doc/source/comparison_with_sas.rst @@ -271,7 +271,7 @@ date/datetime columns. date2 = mdy(2, 15, 2015); date1_year = year(date1); date2_month = month(date2); - * shift date to begninning of next interval; + * shift date to beginning of next interval; date1_next = intnx('MONTH', date1, 1); * count intervals between dates; months_between = intck('MONTH', date1, date2); @@ -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 offets) - +not available in Base SAS (such as resampling and and custom offsets) - see the :ref:`timeseries documentation` for more details. .. ipython:: python @@ -350,7 +350,7 @@ Sorting in SAS is accomplished via ``PROC SORT`` run; pandas objects have a :meth:`~DataFrame.sort_values` method, which -takes a list of columnns to sort by. +takes a list of columns to sort by. .. ipython:: python diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst index 779ebf87fdf64..92ed85071ecb8 100644 --- a/doc/source/cookbook.rst +++ b/doc/source/cookbook.rst @@ -333,7 +333,7 @@ The :ref:`multindexing ` docs. # As Labelled Index df = df.set_index('row');df - # With Heirarchical Columns + # With Hierarchical Columns df.columns = pd.MultiIndex.from_tuples([tuple(c.split('_')) for c in df.columns]);df # Now stack & Reset df = df.stack(0).reset_index(1);df diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index 5a62e7dccea34..11c743d6ef047 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -46,7 +46,7 @@ Series .. warning:: - In 0.13.0 ``Series`` has internaly been refactored to no longer sub-class ``ndarray`` + In 0.13.0 ``Series`` has internally been refactored to no longer sub-class ``ndarray`` but instead subclass ``NDFrame``, similarly to the rest of the pandas containers. This should be a transparent change with only very limited API implications (See the :ref:`Internal Refactoring`) diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst index 762656ba05bd6..683cb671bca9d 100644 --- a/doc/source/ecosystem.rst +++ b/doc/source/ecosystem.rst @@ -155,7 +155,7 @@ or multi-indexed DataFrames. fredapi is a Python interface to the `Federal Reserve Economic Data (FRED) `__ provided by the Federal Reserve Bank of St. Louis. It works with both the FRED database and ALFRED database that contains point-in-time data (i.e. historic data revisions). fredapi provides a wrapper in python to the FRED -HTTP API, and also provides several conveninent methods for parsing and analyzing point-in-time data from ALFRED. +HTTP API, and also provides several convenient methods for parsing and analyzing point-in-time data from ALFRED. fredapi makes use of pandas and returns data in a Series or DataFrame. This module requires a FRED API key that you can obtain for free on the FRED website. diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst index ead4c10341fe9..946256d585c49 100644 --- a/doc/source/enhancingperf.rst +++ b/doc/source/enhancingperf.rst @@ -745,7 +745,7 @@ Technical Minutia Regarding Expression Evaluation Expressions that would result in an object dtype or involve datetime operations (because of ``NaT``) must be evaluated in Python space. The main reason for -this behavior is to maintain backwards compatbility with versions of numpy < +this behavior is to maintain backwards compatibility with versions of numpy < 1.7. In those versions of ``numpy`` a call to ``ndarray.astype(str)`` will truncate any strings that are more than 60 characters in length. Second, we can't pass ``object`` arrays to ``numexpr`` thus string comparisons must be diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index e517e9ab9935d..4ae2ee1927d1a 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -649,7 +649,7 @@ For dataframes with multiple columns, filters should explicitly specify a column .. note:: Some functions when applied to a groupby object will act as a **filter** on the input, returning - a reduced shape of the original (and potentitally eliminating groups), but with the index unchanged. + a reduced shape of the original (and potentially eliminating groups), but with the index unchanged. Passing ``as_index=False`` will not affect these transformation methods. For example: ``head, tail``. diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index af5087689ca4d..72f1e5749a886 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -138,7 +138,7 @@ lower-dimensional slices. Thus, Series; ``series[label]``; scalar value DataFrame; ``frame[colname]``; ``Series`` corresponding to colname - Panel; ``panel[itemname]``; ``DataFrame`` corresponing to the itemname + Panel; ``panel[itemname]``; ``DataFrame`` corresponding to the itemname Here we construct a simple time series data set to use for illustrating the indexing functionality: diff --git a/doc/source/io.rst b/doc/source/io.rst index 8001cd3723601..807838edb6bc4 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -3065,7 +3065,7 @@ indexed dimension as the ``where``. i = store.root.df.table.cols.index.index i.optlevel, i.kind -Ofentimes when appending large amounts of data to a store, it is useful to turn off index creation for each append, then recreate at the end. +Oftentimes when appending large amounts of data to a store, it is useful to turn off index creation for each append, then recreate at the end. .. ipython:: python @@ -4415,7 +4415,7 @@ whether imported ``Categorical`` variables are ordered. *Stata* supports partially labeled series. These series have value labels for some but not all data values. Importing a partially labeled series will produce - a ``Categorial`` with string categories for the values that are labeled and + a ``Categorical`` with string categories for the values that are labeled and numeric categories for values with no label. .. _io.other: diff --git a/doc/source/merging.rst b/doc/source/merging.rst index eb75c2982131a..074b15bbbcb66 100644 --- a/doc/source/merging.rst +++ b/doc/source/merging.rst @@ -135,7 +135,7 @@ functionality below. .. note:: It is worth noting however, that ``concat`` (and therefore ``append``) makes a full copy of the data, and that constantly reusing this function can - create a signifcant performance hit. If you need to use the operation over + create a significant performance hit. If you need to use the operation over several datasets, use a list comprehension. :: diff --git a/doc/source/timedeltas.rst b/doc/source/timedeltas.rst index 39b73b307be4e..c9aa10478714a 100644 --- a/doc/source/timedeltas.rst +++ b/doc/source/timedeltas.rst @@ -334,7 +334,7 @@ Similarly to other of the datetime-like indices, ``DatetimeIndex`` and ``PeriodI index=timedelta_range('1 days', periods=100, freq='h')) s -Selections work similary, with coercion on string-likes and slices: +Selections work similarly, with coercion on string-likes and slices: .. ipython:: python diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 50f104f4529fc..b5be9cf395feb 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -672,7 +672,7 @@ used exactly like a ``Timedelta`` - see the Note that some offsets (such as ``BQuarterEnd``) do not have a vectorized implementation. They can still be used but may -calculate signficantly slower and will raise a ``PerformanceWarning`` +calculate significantly slower and will raise a ``PerformanceWarning`` .. ipython:: python :okwarning: @@ -885,7 +885,7 @@ frequencies. We will refer to these aliases as *offset aliases* "H", "hourly frequency" "T, min", "minutely frequency" "S", "secondly frequency" - "L, ms", "milliseonds" + "L, ms", "milliseconds" "U, us", "microseconds" "N", "nanoseconds" @@ -1374,7 +1374,7 @@ frequency. Arithmetic is not allowed between ``Period`` with different ``freq`` p == Period('2012-01', freq='3M') -If ``Period`` freq is daily or higher (``D``, ``H``, ``T``, ``S``, ``L``, ``U``, ``N``), ``offsets`` and ``timedelta``-like can be added if the result can have the same freq. Otherise, ``ValueError`` will be raised. +If ``Period`` freq is daily or higher (``D``, ``H``, ``T``, ``S``, ``L``, ``U``, ``N``), ``offsets`` and ``timedelta``-like can be added if the result can have the same freq. Otherwise, ``ValueError`` will be raised. .. ipython:: python diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst index 4b42ceff4b46e..12be3037def75 100644 --- a/doc/source/visualization.rst +++ b/doc/source/visualization.rst @@ -466,7 +466,7 @@ When ``subplots=False`` / ``by`` is ``None``: This is the default of ``boxplot`` in historical reason. Note that ``plot(kind='box')`` returns ``Axes`` as default as the same as other plots. * if ``return_type`` is ``'axes'``, a :class:`matplotlib Axes ` containing the boxplot is returned. -* if ``return_type`` is ``'both'`` a namedtuple containging the :class:`matplotlib Axes ` +* if ``return_type`` is ``'both'`` a namedtuple containing the :class:`matplotlib Axes ` and :class:`matplotlib Lines ` is returned When ``subplots=True`` / ``by`` is some column of the DataFrame: