diff --git a/doc/source/conf.py b/doc/source/conf.py index c4b1a584836f5..77dd5d03d311c 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -431,7 +431,7 @@ "index", "pandas.tex", "pandas: powerful Python data analysis toolkit", - "Wes McKinney and the Pandas Development Team", + "Wes McKinney and the pandas Development Team", "manual", ) ] diff --git a/doc/source/development/contributing_codebase.rst b/doc/source/development/contributing_codebase.rst index 7e0b9c3200d3b..62559c4232b51 100644 --- a/doc/source/development/contributing_codebase.rst +++ b/doc/source/development/contributing_codebase.rst @@ -253,7 +253,7 @@ Testing type hints in code using pandas .. warning:: - * Pandas is not yet a py.typed library (:pep:`561`)! + * pandas is not yet a py.typed library (:pep:`561`)! The primary purpose of locally declaring pandas as a py.typed library is to test and improve the pandas-builtin type annotations. diff --git a/doc/source/development/contributing_documentation.rst b/doc/source/development/contributing_documentation.rst index 964f82be4fa7b..443470e6c50f9 100644 --- a/doc/source/development/contributing_documentation.rst +++ b/doc/source/development/contributing_documentation.rst @@ -14,7 +14,7 @@ experts. If something in the docs doesn't make sense to you, updating the relevant section after you figure it out is a great way to ensure it will help the next person. Please visit the `issues page `__ for a full list of issues that are currently open regarding the -Pandas documentation. +pandas documentation. diff --git a/doc/source/development/debugging_extensions.rst b/doc/source/development/debugging_extensions.rst index d63ecb3157cff..f09d73fa13b9a 100644 --- a/doc/source/development/debugging_extensions.rst +++ b/doc/source/development/debugging_extensions.rst @@ -6,7 +6,7 @@ Debugging C extensions ====================== -Pandas uses Cython and C/C++ `extension modules `_ to optimize performance. Unfortunately, the standard Python debugger does not allow you to step into these extensions. Cython extensions can be debugged with the `Cython debugger `_ and C/C++ extensions can be debugged using the tools shipped with your platform's compiler. +pandas uses Cython and C/C++ `extension modules `_ to optimize performance. Unfortunately, the standard Python debugger does not allow you to step into these extensions. Cython extensions can be debugged with the `Cython debugger `_ and C/C++ extensions can be debugged using the tools shipped with your platform's compiler. For Python developers with limited or no C/C++ experience this can seem a daunting task. Core developer Will Ayd has written a 3 part blog series to help guide you from the standard Python debugger into these other tools: diff --git a/doc/source/development/maintaining.rst b/doc/source/development/maintaining.rst index c7803d8401e4e..f177684b8c98f 100644 --- a/doc/source/development/maintaining.rst +++ b/doc/source/development/maintaining.rst @@ -430,7 +430,7 @@ Release git checkout git pull --ff-only upstream git clean -xdf - git commit --allow-empty --author="Pandas Development Team " -m "RLS: " + git commit --allow-empty --author="pandas Development Team " -m "RLS: " git tag -a v -m "Version " # NOTE that the tag is v1.5.2 with "v" not 1.5.2 git push upstream --follow-tags @@ -460,7 +460,7 @@ which will be triggered when the tag is pushed. 4. Create a `new GitHub release `_: - Tag: ```` - - Title: ``Pandas `` + - Title: ``pandas `` - Description: Copy the description of the last release of the same kind (release candidate, major/minor or patch release) - Files: ``pandas-.tar.gz`` source distribution just generated - Set as a pre-release: Only check for a release candidate diff --git a/doc/source/user_guide/10min.rst b/doc/source/user_guide/10min.rst index 0c3307cdd7473..3cdcb81c14961 100644 --- a/doc/source/user_guide/10min.rst +++ b/doc/source/user_guide/10min.rst @@ -19,7 +19,7 @@ Customarily, we import as follows: Basic data structures in pandas ------------------------------- -Pandas provides two types of classes for handling data: +pandas provides two types of classes for handling data: 1. :class:`Series`: a one-dimensional labeled array holding data of any type such as integers, strings, Python objects etc. diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst index 060db772b9682..9c48e66daacf0 100644 --- a/doc/source/user_guide/io.rst +++ b/doc/source/user_guide/io.rst @@ -1044,7 +1044,7 @@ Writing CSVs to binary file objects ``df.to_csv(..., mode="wb")`` allows writing a CSV to a file object opened binary mode. In most cases, it is not necessary to specify -``mode`` as Pandas will auto-detect whether the file object is +``mode`` as pandas will auto-detect whether the file object is opened in text or binary mode. .. ipython:: python @@ -1604,7 +1604,7 @@ Specifying ``iterator=True`` will also return the ``TextFileReader`` object: Specifying the parser engine '''''''''''''''''''''''''''' -Pandas currently supports three engines, the C engine, the python engine, and an experimental +pandas currently supports three engines, the C engine, the python engine, and an experimental pyarrow engine (requires the ``pyarrow`` package). In general, the pyarrow engine is fastest on larger workloads and is equivalent in speed to the C engine on most other workloads. The python engine tends to be slower than the pyarrow and C engines on most workloads. However, @@ -3910,7 +3910,7 @@ The look and feel of Excel worksheets created from pandas can be modified using .. note:: - As of Pandas 3.0, by default spreadsheets created with the ``to_excel`` method + As of pandas 3.0, by default spreadsheets created with the ``to_excel`` method will not contain any styling. Users wishing to bold text, add bordered styles, etc in a worksheet output by ``to_excel`` can do so by using :meth:`Styler.to_excel` to create styled excel files. For documentation on styling spreadsheets, see diff --git a/doc/source/whatsnew/v0.11.0.rst b/doc/source/whatsnew/v0.11.0.rst index f05cbc7f07d7d..dcb0d3229aa5d 100644 --- a/doc/source/whatsnew/v0.11.0.rst +++ b/doc/source/whatsnew/v0.11.0.rst @@ -12,7 +12,7 @@ Data have had quite a number of additions, and Dtype support is now full-fledged There are also a number of important API changes that long-time pandas users should pay close attention to. -There is a new section in the documentation, :ref:`10 Minutes to Pandas <10min>`, +There is a new section in the documentation, :ref:`10 Minutes to pandas <10min>`, primarily geared to new users. There is a new section in the documentation, :ref:`Cookbook `, a collection diff --git a/doc/source/whatsnew/v1.3.1.rst b/doc/source/whatsnew/v1.3.1.rst index a57995eb0db9a..f3b21554e668c 100644 --- a/doc/source/whatsnew/v1.3.1.rst +++ b/doc/source/whatsnew/v1.3.1.rst @@ -14,7 +14,7 @@ including other versions of pandas. Fixed regressions ~~~~~~~~~~~~~~~~~ -- Pandas could not be built on PyPy (:issue:`42355`) +- pandas could not be built on PyPy (:issue:`42355`) - :class:`DataFrame` constructed with an older version of pandas could not be unpickled (:issue:`42345`) - Performance regression in constructing a :class:`DataFrame` from a dictionary of dictionaries (:issue:`42248`) - Fixed regression in :meth:`DataFrame.agg` dropping values when the DataFrame had an Extension Array dtype, a duplicate index, and ``axis=1`` (:issue:`42380`) diff --git a/doc/source/whatsnew/v1.4.0.rst b/doc/source/whatsnew/v1.4.0.rst index ec0b04f8b3b77..91953f693190c 100644 --- a/doc/source/whatsnew/v1.4.0.rst +++ b/doc/source/whatsnew/v1.4.0.rst @@ -607,7 +607,7 @@ Deprecated Int64Index, UInt64Index & Float64Index :class:`Int64Index`, :class:`UInt64Index` and :class:`Float64Index` have been deprecated in favor of the base :class:`Index` class and will be removed in -Pandas 2.0 (:issue:`43028`). +pandas 2.0 (:issue:`43028`). For constructing a numeric index, you can use the base :class:`Index` class instead specifying the data type (which will also work on older pandas diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst index f4cd57af105dd..43aa63c284f38 100644 --- a/doc/source/whatsnew/v1.5.0.rst +++ b/doc/source/whatsnew/v1.5.0.rst @@ -63,7 +63,7 @@ We recommend installing the latest version of PyArrow to access the most recentl DataFrame interchange protocol implementation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Pandas now implement the DataFrame interchange API spec. +pandas now implement the DataFrame interchange API spec. See the full details on the API at https://data-apis.org/dataframe-protocol/latest/index.html The protocol consists of two parts: diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst index 16f53719ff3a7..cacbf8452ba32 100644 --- a/doc/source/whatsnew/v2.0.0.rst +++ b/doc/source/whatsnew/v2.0.0.rst @@ -57,7 +57,7 @@ can it now take all numpy numeric dtypes, i.e. pd.Index([1, 2, 3], dtype=np.uint16) pd.Index([1, 2, 3], dtype=np.float32) -The ability for :class:`Index` to hold the numpy numeric dtypes has meant some changes in Pandas +The ability for :class:`Index` to hold the numpy numeric dtypes has meant some changes in pandas functionality. In particular, operations that previously were forced to create 64-bit indexes, can now create indexes with lower bit sizes, e.g. 32-bit indexes. diff --git a/doc/source/whatsnew/v2.1.0.rst b/doc/source/whatsnew/v2.1.0.rst index d4eb5742ef928..495c8244142f9 100644 --- a/doc/source/whatsnew/v2.1.0.rst +++ b/doc/source/whatsnew/v2.1.0.rst @@ -67,7 +67,7 @@ DataFrame reductions preserve extension dtypes In previous versions of pandas, the results of DataFrame reductions (:meth:`DataFrame.sum` :meth:`DataFrame.mean` etc.) had NumPy dtypes, even when the DataFrames -were of extension dtypes. Pandas can now keep the dtypes when doing reductions over DataFrame +were of extension dtypes. pandas can now keep the dtypes when doing reductions over DataFrame columns with a common dtype (:issue:`52788`). *Old Behavior*