diff --git a/doc/source/development/code_style.rst b/doc/source/development/code_style.rst
index 387f65ea583a0..5aa1c1099d6e0 100644
--- a/doc/source/development/code_style.rst
+++ b/doc/source/development/code_style.rst
@@ -9,7 +9,7 @@ pandas code style guide
.. contents:: Table of contents:
:local:
-*pandas* follows the `PEP8 `_
+pandas follows the `PEP8 `_
standard and uses `Black `_
and `Flake8 `_ to ensure a
consistent code format throughout the project. For details see the
diff --git a/doc/source/development/contributing.rst b/doc/source/development/contributing.rst
index d6955c5d4b8d2..17eba825d1c29 100644
--- a/doc/source/development/contributing.rst
+++ b/doc/source/development/contributing.rst
@@ -155,7 +155,7 @@ Using a Docker container
Instead of manually setting up a development environment, you can use `Docker
`_ to automatically create the environment with just several
-commands. Pandas provides a ``DockerFile`` in the root directory to build a Docker image
+commands. pandas provides a ``DockerFile`` in the root directory to build a Docker image
with a full pandas development environment.
**Docker Commands**
@@ -190,7 +190,7 @@ Note that you might need to rebuild the C extensions if/when you merge with upst
Installing a C compiler
~~~~~~~~~~~~~~~~~~~~~~~
-Pandas uses C extensions (mostly written using Cython) to speed up certain
+pandas uses C extensions (mostly written using Cython) to speed up certain
operations. To install pandas from source, you need to compile these C
extensions, which means you need a C compiler. This process depends on which
platform you're using.
@@ -1219,7 +1219,7 @@ This test shows off several useful features of Hypothesis, as well as
demonstrating a good use-case: checking properties that should hold over
a large or complicated domain of inputs.
-To keep the Pandas test suite running quickly, parametrized tests are
+To keep the pandas test suite running quickly, parametrized tests are
preferred if the inputs or logic are simple, with Hypothesis tests reserved
for cases with complex logic or where there are too many combinations of
options or subtle interactions to test (or think of!) all of them.
diff --git a/doc/source/development/maintaining.rst b/doc/source/development/maintaining.rst
index cd084ab263477..2a21704c27005 100644
--- a/doc/source/development/maintaining.rst
+++ b/doc/source/development/maintaining.rst
@@ -207,7 +207,7 @@ Only core team members can merge pull requests. We have a few guidelines.
1. You should typically not self-merge your own pull requests. Exceptions include
things like small changes to fix CI (e.g. pinning a package version).
2. You should not merge pull requests that have an active discussion, or pull
- requests that has any ``-1`` votes from a core maintainer. Pandas operates
+ requests that has any ``-1`` votes from a core maintainer. pandas operates
by consensus.
3. For larger changes, it's good to have a +1 from at least two core team members.
diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst
index ed6ce7e9759b6..cef9dae74dfd2 100644
--- a/doc/source/ecosystem.rst
+++ b/doc/source/ecosystem.rst
@@ -98,7 +98,7 @@ With Altair, you can spend more time understanding your data and its
meaning. Altair's API is simple, friendly and consistent and built on
top of the powerful Vega-Lite JSON specification. This elegant
simplicity produces beautiful and effective visualizations with a
-minimal amount of code. Altair works with Pandas DataFrames.
+minimal amount of code. Altair works with pandas DataFrames.
`Bokeh `__
@@ -110,7 +110,7 @@ graphics in the style of Protovis/D3, while delivering high-performance interact
large data to thin clients.
`Pandas-Bokeh `__ provides a high level API
-for Bokeh that can be loaded as a native Pandas plotting backend via
+for Bokeh that can be loaded as a native pandas plotting backend via
.. code:: python
@@ -185,7 +185,7 @@ IDE
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
IPython is an interactive command shell and distributed computing
-environment. IPython tab completion works with Pandas methods and also
+environment. IPython tab completion works with pandas methods and also
attributes like DataFrame columns.
`Jupyter Notebook / Jupyter Lab `__
@@ -199,7 +199,7 @@ Jupyter notebooks can be converted to a number of open standard output formats
Python) through 'Download As' in the web interface and ``jupyter convert``
in a shell.
-Pandas DataFrames implement ``_repr_html_``and ``_repr_latex`` methods
+pandas DataFrames implement ``_repr_html_``and ``_repr_latex`` methods
which are utilized by Jupyter Notebook for displaying
(abbreviated) HTML or LaTeX tables. LaTeX output is properly escaped.
(Note: HTML tables may or may not be
@@ -227,7 +227,7 @@ Its `Variable Explorer `__
allows users to view, manipulate and edit pandas ``Index``, ``Series``,
and ``DataFrame`` objects like a "spreadsheet", including copying and modifying
values, sorting, displaying a "heatmap", converting data types and more.
-Pandas objects can also be renamed, duplicated, new columns added,
+pandas objects can also be renamed, duplicated, new columns added,
copyed/pasted to/from the clipboard (as TSV), and saved/loaded to/from a file.
Spyder can also import data from a variety of plain text and binary files
or the clipboard into a new pandas DataFrame via a sophisticated import wizard.
@@ -274,13 +274,13 @@ The following data feeds are available:
`Quandl/Python `__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Quandl API for Python wraps the Quandl REST API to return
-Pandas DataFrames with timeseries indexes.
+pandas DataFrames with timeseries indexes.
`Pydatastream `__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
PyDatastream is a Python interface to the
`Refinitiv Datastream (DWS) `__
-REST API to return indexed Pandas DataFrames with financial data.
+REST API to return indexed pandas DataFrames with financial data.
This package requires valid credentials for this API (non free).
`pandaSDMX `__
@@ -355,7 +355,7 @@ Out-of-core
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Blaze provides a standard API for doing computations with various
-in-memory and on-disk backends: NumPy, Pandas, SQLAlchemy, MongoDB, PyTables,
+in-memory and on-disk backends: NumPy, pandas, SQLAlchemy, MongoDB, PyTables,
PySpark.
`Dask `__
@@ -401,7 +401,7 @@ If also displays progress bars.
`Ray `__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Pandas on Ray is an early stage DataFrame library that wraps Pandas and transparently distributes the data and computation. The user does not need to know how many cores their system has, nor do they need to specify how to distribute the data. In fact, users can continue using their previous Pandas notebooks while experiencing a considerable speedup from Pandas on Ray, even on a single machine. Only a modification of the import statement is needed, as we demonstrate below. Once you’ve changed your import statement, you’re ready to use Pandas on Ray just like you would Pandas.
+pandas on Ray is an early stage DataFrame library that wraps pandas and transparently distributes the data and computation. The user does not need to know how many cores their system has, nor do they need to specify how to distribute the data. In fact, users can continue using their previous pandas notebooks while experiencing a considerable speedup from pandas on Ray, even on a single machine. Only a modification of the import statement is needed, as we demonstrate below. Once you’ve changed your import statement, you’re ready to use pandas on Ray just like you would pandas.
.. code:: python
@@ -412,7 +412,7 @@ Pandas on Ray is an early stage DataFrame library that wraps Pandas and transpar
`Vaex `__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. Vaex is a python library for Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (10\ :sup:`9`) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).
+Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. Vaex is a python library for Out-of-Core DataFrames (similar to pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (10\ :sup:`9`) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).
* vaex.from_pandas
* vaex.to_pandas_df
@@ -422,7 +422,7 @@ Increasingly, packages are being built on top of pandas to address specific need
Extension data types
--------------------
-Pandas provides an interface for defining
+pandas provides an interface for defining
:ref:`extension types ` to extend NumPy's type
system. The following libraries implement that interface to provide types not
found in NumPy or pandas, which work well with pandas' data containers.
diff --git a/doc/source/getting_started/comparison/comparison_with_r.rst b/doc/source/getting_started/comparison/comparison_with_r.rst
index 358bb6ad951f0..864081002086b 100644
--- a/doc/source/getting_started/comparison/comparison_with_r.rst
+++ b/doc/source/getting_started/comparison/comparison_with_r.rst
@@ -5,11 +5,11 @@
Comparison with R / R libraries
*******************************
-Since ``pandas`` aims to provide a lot of the data manipulation and analysis
+Since pandas aims to provide a lot of the data manipulation and analysis
functionality that people use `R `__ for, this page
was started to provide a more detailed look at the `R language
`__ and its many third
-party libraries as they relate to ``pandas``. In comparisons with R and CRAN
+party libraries as they relate to pandas. In comparisons with R and CRAN
libraries, we care about the following things:
* **Functionality / flexibility**: what can/cannot be done with each tool
@@ -21,7 +21,7 @@ libraries, we care about the following things:
This page is also here to offer a bit of a translation guide for users of these
R packages.
-For transfer of ``DataFrame`` objects from ``pandas`` to R, one option is to
+For transfer of ``DataFrame`` objects from pandas to R, one option is to
use HDF5 files, see :ref:`io.external_compatibility` for an
example.
@@ -118,7 +118,7 @@ or by integer location
df <- data.frame(matrix(rnorm(1000), ncol=100))
df[, c(1:10, 25:30, 40, 50:100)]
-Selecting multiple columns by name in ``pandas`` is straightforward
+Selecting multiple columns by name in pandas is straightforward
.. ipython:: python
@@ -235,7 +235,7 @@ since the subclass sizes are possibly irregular. Using a data.frame called
tapply(baseball$batting.average, baseball.example$team,
max)
-In ``pandas`` we may use :meth:`~pandas.pivot_table` method to handle this:
+In pandas we may use :meth:`~pandas.pivot_table` method to handle this:
.. ipython:: python
@@ -268,7 +268,7 @@ column's values are less than another column's values:
subset(df, a <= b)
df[df$a <= df$b,] # note the comma
-In ``pandas``, there are a few ways to perform subsetting. You can use
+In pandas, there are a few ways to perform subsetting. You can use
:meth:`~pandas.DataFrame.query` or pass an expression as if it were an
index/slice as well as standard boolean indexing:
@@ -295,7 +295,7 @@ An expression using a data.frame called ``df`` in R with the columns ``a`` and
with(df, a + b)
df$a + df$b # same as the previous expression
-In ``pandas`` the equivalent expression, using the
+In pandas the equivalent expression, using the
:meth:`~pandas.DataFrame.eval` method, would be:
.. ipython:: python
@@ -347,7 +347,7 @@ summarize ``x`` by ``month``:
mean = round(mean(x), 2),
sd = round(sd(x), 2))
-In ``pandas`` the equivalent expression, using the
+In pandas the equivalent expression, using the
:meth:`~pandas.DataFrame.groupby` method, would be:
.. ipython:: python
diff --git a/doc/source/getting_started/comparison/comparison_with_stata.rst b/doc/source/getting_started/comparison/comparison_with_stata.rst
index 7b8d9c6be61db..014506cc18327 100644
--- a/doc/source/getting_started/comparison/comparison_with_stata.rst
+++ b/doc/source/getting_started/comparison/comparison_with_stata.rst
@@ -146,7 +146,7 @@ the pandas command would be:
# alternatively, read_table is an alias to read_csv with tab delimiter
tips = pd.read_table("tips.csv", header=None)
-Pandas can also read Stata data sets in ``.dta`` format with the :func:`read_stata` function.
+pandas can also read Stata data sets in ``.dta`` format with the :func:`read_stata` function.
.. code-block:: python
@@ -172,7 +172,7 @@ Similarly in pandas, the opposite of ``read_csv`` is :meth:`DataFrame.to_csv`.
tips.to_csv("tips2.csv")
-Pandas can also export to Stata file format with the :meth:`DataFrame.to_stata` method.
+pandas can also export to Stata file format with the :meth:`DataFrame.to_stata` method.
.. code-block:: python
@@ -583,7 +583,7 @@ should be used for comparisons.
outer_join[pd.isna(outer_join["value_x"])]
outer_join[pd.notna(outer_join["value_x"])]
-Pandas also provides a variety of methods to work with missing data -- some of
+pandas also provides a variety of methods to work with missing data -- some of
which would be challenging to express in Stata. For example, there are methods to
drop all rows with any missing values, replacing missing values with a specified
value, like the mean, or forward filling from previous rows. See the
@@ -674,7 +674,7 @@ Other considerations
Disk vs memory
~~~~~~~~~~~~~~
-Pandas and Stata both operate exclusively in memory. This means that the size of
+pandas and Stata both operate exclusively in memory. This means that the size of
data able to be loaded in pandas is limited by your machine's memory.
If out of core processing is needed, one possibility is the
`dask.dataframe `_
diff --git a/doc/source/getting_started/install.rst b/doc/source/getting_started/install.rst
index a6341451b1b80..70d145c54e919 100644
--- a/doc/source/getting_started/install.rst
+++ b/doc/source/getting_started/install.rst
@@ -184,7 +184,7 @@ You can find simple installation instructions for pandas in this document: ``ins
Installing from source
~~~~~~~~~~~~~~~~~~~~~~
-See the :ref:`contributing guide ` for complete instructions on building from the git source tree. Further, see :ref:`creating a development environment ` if you wish to create a *pandas* development environment.
+See the :ref:`contributing guide ` for complete instructions on building from the git source tree. Further, see :ref:`creating a development environment ` if you wish to create a pandas development environment.
Running the test suite
----------------------
@@ -249,7 +249,7 @@ Recommended dependencies
Optional dependencies
~~~~~~~~~~~~~~~~~~~~~
-Pandas has many optional dependencies that are only used for specific methods.
+pandas has many optional dependencies that are only used for specific methods.
For example, :func:`pandas.read_hdf` requires the ``pytables`` package, while
:meth:`DataFrame.to_markdown` requires the ``tabulate`` package. If the
optional dependency is not installed, pandas will raise an ``ImportError`` when
diff --git a/doc/source/getting_started/overview.rst b/doc/source/getting_started/overview.rst
index 57d87d4ec8a91..3043cf25c5312 100644
--- a/doc/source/getting_started/overview.rst
+++ b/doc/source/getting_started/overview.rst
@@ -6,7 +6,7 @@
Package overview
****************
-**pandas** is a `Python `__ package providing fast,
+pandas is a `Python `__ package providing fast,
flexible, and expressive data structures designed to make working with
"relational" or "labeled" data both easy and intuitive. It aims to be the
fundamental high-level building block for doing practical, **real-world** data
diff --git a/doc/source/reference/arrays.rst b/doc/source/reference/arrays.rst
index 1725c415fa020..5c068d8404cd6 100644
--- a/doc/source/reference/arrays.rst
+++ b/doc/source/reference/arrays.rst
@@ -16,7 +16,7 @@ For some data types, pandas extends NumPy's type system. String aliases for thes
can be found at :ref:`basics.dtypes`.
=================== ========================= ================== =============================
-Kind of Data Pandas Data Type Scalar Array
+Kind of Data pandas Data Type Scalar Array
=================== ========================= ================== =============================
TZ-aware datetime :class:`DatetimeTZDtype` :class:`Timestamp` :ref:`api.arrays.datetime`
Timedeltas (none) :class:`Timedelta` :ref:`api.arrays.timedelta`
@@ -29,7 +29,7 @@ Strings :class:`StringDtype` :class:`str` :ref:`api.array
Boolean (with NA) :class:`BooleanDtype` :class:`bool` :ref:`api.arrays.bool`
=================== ========================= ================== =============================
-Pandas and third-party libraries can extend NumPy's type system (see :ref:`extending.extension-types`).
+pandas and third-party libraries can extend NumPy's type system (see :ref:`extending.extension-types`).
The top-level :meth:`array` method can be used to create a new array, which may be
stored in a :class:`Series`, :class:`Index`, or as a column in a :class:`DataFrame`.
@@ -43,7 +43,7 @@ stored in a :class:`Series`, :class:`Index`, or as a column in a :class:`DataFra
Datetime data
-------------
-NumPy cannot natively represent timezone-aware datetimes. Pandas supports this
+NumPy cannot natively represent timezone-aware datetimes. pandas supports this
with the :class:`arrays.DatetimeArray` extension array, which can hold timezone-naive
or timezone-aware values.
@@ -162,7 +162,7 @@ If the data are tz-aware, then every value in the array must have the same timez
Timedelta data
--------------
-NumPy can natively represent timedeltas. Pandas provides :class:`Timedelta`
+NumPy can natively represent timedeltas. pandas provides :class:`Timedelta`
for symmetry with :class:`Timestamp`.
.. autosummary::
@@ -217,7 +217,7 @@ A collection of timedeltas may be stored in a :class:`TimedeltaArray`.
Timespan data
-------------
-Pandas represents spans of times as :class:`Period` objects.
+pandas represents spans of times as :class:`Period` objects.
Period
------
@@ -352,7 +352,7 @@ Nullable integer
----------------
:class:`numpy.ndarray` cannot natively represent integer-data with missing values.
-Pandas provides this through :class:`arrays.IntegerArray`.
+pandas provides this through :class:`arrays.IntegerArray`.
.. autosummary::
:toctree: api/
@@ -378,7 +378,7 @@ Pandas provides this through :class:`arrays.IntegerArray`.
Categorical data
----------------
-Pandas defines a custom data type for representing data that can take only a
+pandas defines a custom data type for representing data that can take only a
limited, fixed set of values. The dtype of a ``Categorical`` can be described by
a :class:`pandas.api.types.CategoricalDtype`.
diff --git a/doc/source/reference/series.rst b/doc/source/reference/series.rst
index 5131d35334693..f1069e46b56cc 100644
--- a/doc/source/reference/series.rst
+++ b/doc/source/reference/series.rst
@@ -280,7 +280,7 @@ Time Series-related
Accessors
---------
-Pandas provides dtype-specific methods under various accessors.
+pandas provides dtype-specific methods under various accessors.
These are separate namespaces within :class:`Series` that only apply
to specific data types.
diff --git a/doc/source/user_guide/basics.rst b/doc/source/user_guide/basics.rst
index e348111fe7881..5fa214d2ed389 100644
--- a/doc/source/user_guide/basics.rst
+++ b/doc/source/user_guide/basics.rst
@@ -52,7 +52,7 @@ Note, **these attributes can be safely assigned to**!
df.columns = [x.lower() for x in df.columns]
df
-Pandas objects (:class:`Index`, :class:`Series`, :class:`DataFrame`) can be
+pandas objects (:class:`Index`, :class:`Series`, :class:`DataFrame`) can be
thought of as containers for arrays, which hold the actual data and do the
actual computation. For many types, the underlying array is a
:class:`numpy.ndarray`. However, pandas and 3rd party libraries may *extend*
@@ -410,7 +410,7 @@ data structure with a scalar value:
pd.Series(['foo', 'bar', 'baz']) == 'foo'
pd.Index(['foo', 'bar', 'baz']) == 'foo'
-Pandas also handles element-wise comparisons between different array-like
+pandas also handles element-wise comparisons between different array-like
objects of the same length:
.. ipython:: python
@@ -804,7 +804,7 @@ Is equivalent to:
(df_p.pipe(extract_city_name)
.pipe(add_country_name, country_name="US"))
-Pandas encourages the second style, which is known as method chaining.
+pandas encourages the second style, which is known as method chaining.
``pipe`` makes it easy to use your own or another library's functions
in method chains, alongside pandas' methods.
@@ -1498,7 +1498,7 @@ Thus, for example, iterating over a DataFrame gives you the column names:
print(col)
-Pandas objects also have the dict-like :meth:`~DataFrame.items` method to
+pandas objects also have the dict-like :meth:`~DataFrame.items` method to
iterate over the (key, value) pairs.
To iterate over the rows of a DataFrame, you can use the following methods:
@@ -1741,7 +1741,7 @@ always uses them).
.. note::
Prior to pandas 1.0, string methods were only available on ``object`` -dtype
- ``Series``. Pandas 1.0 added the :class:`StringDtype` which is dedicated
+ ``Series``. pandas 1.0 added the :class:`StringDtype` which is dedicated
to strings. See :ref:`text.types` for more.
Please see :ref:`Vectorized String Methods ` for a complete
@@ -1752,7 +1752,7 @@ description.
Sorting
-------
-Pandas supports three kinds of sorting: sorting by index labels,
+pandas supports three kinds of sorting: sorting by index labels,
sorting by column values, and sorting by a combination of both.
.. _basics.sort_index:
@@ -1995,7 +1995,7 @@ columns of a DataFrame. NumPy provides support for ``float``,
``int``, ``bool``, ``timedelta64[ns]`` and ``datetime64[ns]`` (note that NumPy
does not support timezone-aware datetimes).
-Pandas and third-party libraries *extend* NumPy's type system in a few places.
+pandas and third-party libraries *extend* NumPy's type system in a few places.
This section describes the extensions pandas has made internally.
See :ref:`extending.extension-types` for how to write your own extension that
works with pandas. See :ref:`ecosystem.extensions` for a list of third-party
@@ -2032,7 +2032,7 @@ documentation sections for more on each type.
| Boolean (with NA) | :class:`BooleanDtype` | :class:`bool` | :class:`arrays.BooleanArray` | ``'boolean'`` | :ref:`api.arrays.bool` |
+-------------------+---------------------------+--------------------+-------------------------------+-----------------------------------------+-------------------------------+
-Pandas has two ways to store strings.
+pandas has two ways to store strings.
1. ``object`` dtype, which can hold any Python object, including strings.
2. :class:`StringDtype`, which is dedicated to strings.
@@ -2424,5 +2424,5 @@ All NumPy dtypes are subclasses of ``numpy.generic``:
.. note::
- Pandas also defines the types ``category``, and ``datetime64[ns, tz]``, which are not integrated into the normal
+ pandas also defines the types ``category``, and ``datetime64[ns, tz]``, which are not integrated into the normal
NumPy hierarchy and won't show up with the above function.
diff --git a/doc/source/user_guide/boolean.rst b/doc/source/user_guide/boolean.rst
index d690c1093399a..76c922fcef638 100644
--- a/doc/source/user_guide/boolean.rst
+++ b/doc/source/user_guide/boolean.rst
@@ -82,7 +82,7 @@ the ``NA`` really is ``True`` or ``False``, since ``True & True`` is ``True``,
but ``True & False`` is ``False``, so we can't determine the output.
-This differs from how ``np.nan`` behaves in logical operations. Pandas treated
+This differs from how ``np.nan`` behaves in logical operations. pandas treated
``np.nan`` is *always false in the output*.
In ``or``
diff --git a/doc/source/user_guide/categorical.rst b/doc/source/user_guide/categorical.rst
index 6a8e1767ef7e8..67f11bbb45b02 100644
--- a/doc/source/user_guide/categorical.rst
+++ b/doc/source/user_guide/categorical.rst
@@ -1011,7 +1011,7 @@ The following differences to R's factor functions can be observed:
* In contrast to R's ``factor`` function, using categorical data as the sole input to create a
new categorical series will *not* remove unused categories but create a new categorical series
which is equal to the passed in one!
-* R allows for missing values to be included in its ``levels`` (pandas' ``categories``). Pandas
+* R allows for missing values to be included in its ``levels`` (pandas' ``categories``). pandas
does not allow ``NaN`` categories, but missing values can still be in the ``values``.
@@ -1107,7 +1107,7 @@ are not numeric data (even in the case that ``.categories`` is numeric).
dtype in apply
~~~~~~~~~~~~~~
-Pandas currently does not preserve the dtype in apply functions: If you apply along rows you get
+pandas currently does not preserve the dtype in apply functions: If you apply along rows you get
a ``Series`` of ``object`` ``dtype`` (same as getting a row -> getting one element will return a
basic type) and applying along columns will also convert to object. ``NaN`` values are unaffected.
You can use ``fillna`` to handle missing values before applying a function.
diff --git a/doc/source/user_guide/cookbook.rst b/doc/source/user_guide/cookbook.rst
index 0a30d865f3c23..214b8a680fa7e 100644
--- a/doc/source/user_guide/cookbook.rst
+++ b/doc/source/user_guide/cookbook.rst
@@ -15,7 +15,7 @@ Simplified, condensed, new-user friendly, in-line examples have been inserted wh
augment the Stack-Overflow and GitHub links. Many of the links contain expanded information,
above what the in-line examples offer.
-Pandas (pd) and Numpy (np) are the only two abbreviated imported modules. The rest are kept
+pandas (pd) and Numpy (np) are the only two abbreviated imported modules. The rest are kept
explicitly imported for newer users.
These examples are written for Python 3. Minor tweaks might be necessary for earlier python
diff --git a/doc/source/user_guide/dsintro.rst b/doc/source/user_guide/dsintro.rst
index d698b316d321e..905877cca61db 100644
--- a/doc/source/user_guide/dsintro.rst
+++ b/doc/source/user_guide/dsintro.rst
@@ -78,13 +78,13 @@ Series can be instantiated from dicts:
When the data is a dict, and an index is not passed, the ``Series`` index
will be ordered by the dict's insertion order, if you're using Python
- version >= 3.6 and Pandas version >= 0.23.
+ version >= 3.6 and pandas version >= 0.23.
- If you're using Python < 3.6 or Pandas < 0.23, and an index is not passed,
+ If you're using Python < 3.6 or pandas < 0.23, and an index is not passed,
the ``Series`` index will be the lexically ordered list of dict keys.
In the example above, if you were on a Python version lower than 3.6 or a
-Pandas version lower than 0.23, the ``Series`` would be ordered by the lexical
+pandas version lower than 0.23, the ``Series`` would be ordered by the lexical
order of the dict keys (i.e. ``['a', 'b', 'c']`` rather than ``['b', 'a', 'c']``).
If an index is passed, the values in data corresponding to the labels in the
@@ -151,7 +151,7 @@ index (to disable :ref:`automatic alignment `, for example).
:attr:`Series.array` will always be an :class:`~pandas.api.extensions.ExtensionArray`.
Briefly, an ExtensionArray is a thin wrapper around one or more *concrete* arrays like a
-:class:`numpy.ndarray`. Pandas knows how to take an ``ExtensionArray`` and
+:class:`numpy.ndarray`. pandas knows how to take an ``ExtensionArray`` and
store it in a ``Series`` or a column of a ``DataFrame``.
See :ref:`basics.dtypes` for more.
@@ -290,9 +290,9 @@ based on common sense rules.
When the data is a dict, and ``columns`` is not specified, the ``DataFrame``
columns will be ordered by the dict's insertion order, if you are using
- Python version >= 3.6 and Pandas >= 0.23.
+ Python version >= 3.6 and pandas >= 0.23.
- If you are using Python < 3.6 or Pandas < 0.23, and ``columns`` is not
+ If you are using Python < 3.6 or pandas < 0.23, and ``columns`` is not
specified, the ``DataFrame`` columns will be the lexically ordered list of dict
keys.
diff --git a/doc/source/user_guide/duplicates.rst b/doc/source/user_guide/duplicates.rst
index 2993ca7799510..7cda067fb24ad 100644
--- a/doc/source/user_guide/duplicates.rst
+++ b/doc/source/user_guide/duplicates.rst
@@ -79,7 +79,7 @@ unique with :attr:`Index.is_unique`:
.. note::
Checking whether an index is unique is somewhat expensive for large datasets.
- Pandas does cache this result, so re-checking on the same index is very fast.
+ pandas does cache this result, so re-checking on the same index is very fast.
:meth:`Index.duplicated` will return a boolean ndarray indicating whether a
label is repeated.
diff --git a/doc/source/user_guide/enhancingperf.rst b/doc/source/user_guide/enhancingperf.rst
index ce9db0a5279c3..baad00076dbe6 100644
--- a/doc/source/user_guide/enhancingperf.rst
+++ b/doc/source/user_guide/enhancingperf.rst
@@ -689,7 +689,7 @@ name in an expression.
df.loc[a < df['a']] # same as the previous expression
With :func:`pandas.eval` you cannot use the ``@`` prefix *at all*, because it
-isn't defined in that context. ``pandas`` will let you know this if you try to
+isn't defined in that context. pandas will let you know this if you try to
use ``@`` in a top-level call to :func:`pandas.eval`. For example,
.. ipython:: python
diff --git a/doc/source/user_guide/groupby.rst b/doc/source/user_guide/groupby.rst
index 9696f14f03b56..1dbabd12dbdc9 100644
--- a/doc/source/user_guide/groupby.rst
+++ b/doc/source/user_guide/groupby.rst
@@ -614,7 +614,7 @@ For a grouped ``DataFrame``, you can rename in a similar manner:
grouped["C"].agg(["sum", "sum"])
- Pandas *does* allow you to provide multiple lambdas. In this case, pandas
+ pandas *does* allow you to provide multiple lambdas. In this case, pandas
will mangle the name of the (nameless) lambda functions, appending ``_``
to each subsequent lambda.
@@ -636,7 +636,7 @@ accepts the special syntax in :meth:`GroupBy.agg`, known as "named aggregation",
- The keywords are the *output* column names
- The values are tuples whose first element is the column to select
- and the second element is the aggregation to apply to that column. Pandas
+ and the second element is the aggregation to apply to that column. pandas
provides the ``pandas.NamedAgg`` namedtuple with the fields ``['column', 'aggfunc']``
to make it clearer what the arguments are. As usual, the aggregation can
be a callable or a string alias.
diff --git a/doc/source/user_guide/indexing.rst b/doc/source/user_guide/indexing.rst
index b11baad1e3eb5..530fdfba7d12c 100644
--- a/doc/source/user_guide/indexing.rst
+++ b/doc/source/user_guide/indexing.rst
@@ -46,7 +46,7 @@ Different choices for indexing
------------------------------
Object selection has had a number of user-requested additions in order to
-support more explicit location based indexing. Pandas now supports three types
+support more explicit location based indexing. pandas now supports three types
of multi-axis indexing.
* ``.loc`` is primarily label based, but may also be used with a boolean array. ``.loc`` will raise ``KeyError`` when the items are not found. Allowed inputs are:
@@ -315,7 +315,7 @@ Selection by label
.. versionchanged:: 1.0.0
- Pandas will raise a ``KeyError`` if indexing with a list with missing labels. See :ref:`list-like Using loc with
+ pandas will raise a ``KeyError`` if indexing with a list with missing labels. See :ref:`list-like Using loc with
missing keys in a list is Deprecated `.
pandas provides a suite of methods in order to have **purely label based indexing**. This is a strict inclusion based protocol.
@@ -433,7 +433,7 @@ Selection by position
This is sometimes called ``chained assignment`` and should be avoided.
See :ref:`Returning a View versus Copy `.
-Pandas provides a suite of methods in order to get **purely integer based indexing**. The semantics follow closely Python and NumPy slicing. These are ``0-based`` indexing. When slicing, the start bound is *included*, while the upper bound is *excluded*. Trying to use a non-integer, even a **valid** label will raise an ``IndexError``.
+pandas provides a suite of methods in order to get **purely integer based indexing**. The semantics follow closely Python and NumPy slicing. These are ``0-based`` indexing. When slicing, the start bound is *included*, while the upper bound is *excluded*. Trying to use a non-integer, even a **valid** label will raise an ``IndexError``.
The ``.iloc`` attribute is the primary access method. The following are valid inputs:
@@ -1812,7 +1812,7 @@ about!
Sometimes a ``SettingWithCopy`` warning will arise at times when there's no
obvious chained indexing going on. **These** are the bugs that
-``SettingWithCopy`` is designed to catch! Pandas is probably trying to warn you
+``SettingWithCopy`` is designed to catch! pandas is probably trying to warn you
that you've done this:
.. code-block:: python
@@ -1835,7 +1835,7 @@ When you use chained indexing, the order and type of the indexing operation
partially determine whether the result is a slice into the original object, or
a copy of the slice.
-Pandas has the ``SettingWithCopyWarning`` because assigning to a copy of a
+pandas has the ``SettingWithCopyWarning`` because assigning to a copy of a
slice is frequently not intentional, but a mistake caused by chained indexing
returning a copy where a slice was expected.
diff --git a/doc/source/user_guide/integer_na.rst b/doc/source/user_guide/integer_na.rst
index acee1638570f7..be38736f493b5 100644
--- a/doc/source/user_guide/integer_na.rst
+++ b/doc/source/user_guide/integer_na.rst
@@ -30,7 +30,7 @@ numbers.
Construction
------------
-Pandas can represent integer data with possibly missing values using
+pandas can represent integer data with possibly missing values using
:class:`arrays.IntegerArray`. This is an :ref:`extension types `
implemented within pandas.
diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst
index 184894bbafe28..ae22ee836cd8c 100644
--- a/doc/source/user_guide/io.rst
+++ b/doc/source/user_guide/io.rst
@@ -894,7 +894,7 @@ take full advantage of the flexibility of the date parsing API:
)
df
-Pandas will try to call the ``date_parser`` function in three different ways. If
+pandas will try to call the ``date_parser`` function in three different ways. If
an exception is raised, the next one is tried:
1. ``date_parser`` is first called with one or more arrays as arguments,
@@ -926,7 +926,7 @@ Note that performance-wise, you should try these methods of parsing dates in ord
Parsing a CSV with mixed timezones
++++++++++++++++++++++++++++++++++
-Pandas cannot natively represent a column or index with mixed timezones. If your CSV
+pandas cannot natively represent a column or index with mixed timezones. If your CSV
file contains columns with a mixture of timezones, the default result will be
an object-dtype column with strings, even with ``parse_dates``.
@@ -1602,7 +1602,7 @@ python engine is selected explicitly using ``engine='python'``.
Reading/writing remote files
''''''''''''''''''''''''''''
-You can pass in a URL to read or write remote files to many of Pandas' IO
+You can pass in a URL to read or write remote files to many of pandas' IO
functions - the following example shows reading a CSV file:
.. code-block:: python
@@ -2265,7 +2265,7 @@ The full list of types supported are described in the Table Schema
spec. This table shows the mapping from pandas types:
=============== =================
-Pandas type Table Schema type
+pandas type Table Schema type
=============== =================
int64 integer
float64 number
@@ -2661,7 +2661,7 @@ that contain URLs.
url_df = pd.DataFrame(
{
- "name": ["Python", "Pandas"],
+ "name": ["Python", "pandas"],
"url": ["https://www.python.org/", "https://pandas.pydata.org"],
}
)
@@ -3143,7 +3143,7 @@ one can pass an :class:`~pandas.io.excel.ExcelWriter`.
Writing Excel files to memory
+++++++++++++++++++++++++++++
-Pandas supports writing Excel files to buffer-like objects such as ``StringIO`` or
+pandas supports writing Excel files to buffer-like objects such as ``StringIO`` or
``BytesIO`` using :class:`~pandas.io.excel.ExcelWriter`.
.. code-block:: python
@@ -3177,7 +3177,7 @@ Pandas supports writing Excel files to buffer-like objects such as ``StringIO``
Excel writer engines
''''''''''''''''''''
-Pandas chooses an Excel writer via two methods:
+pandas chooses an Excel writer via two methods:
1. the ``engine`` keyword argument
2. the filename extension (via the default specified in config options)
@@ -3474,7 +3474,7 @@ for some advanced strategies
.. warning::
- Pandas uses PyTables for reading and writing HDF5 files, which allows
+ pandas uses PyTables for reading and writing HDF5 files, which allows
serializing object-dtype data with pickle. Loading pickled data received from
untrusted sources can be unsafe.
@@ -4734,7 +4734,7 @@ Several caveats.
* Duplicate column names and non-string columns names are not supported.
* The ``pyarrow`` engine always writes the index to the output, but ``fastparquet`` only writes non-default
- indexes. This extra column can cause problems for non-Pandas consumers that are not expecting it. You can
+ indexes. This extra column can cause problems for non-pandas consumers that are not expecting it. You can
force including or omitting indexes with the ``index`` argument, regardless of the underlying engine.
* Index level names, if specified, must be strings.
* In the ``pyarrow`` engine, categorical dtypes for non-string types can be serialized to parquet, but will de-serialize as their primitive dtype.
@@ -4894,7 +4894,7 @@ ORC
.. versionadded:: 1.0.0
Similar to the :ref:`parquet ` format, the `ORC Format `__ is a binary columnar serialization
-for data frames. It is designed to make reading data frames efficient. Pandas provides *only* a reader for the
+for data frames. It is designed to make reading data frames efficient. pandas provides *only* a reader for the
ORC format, :func:`~pandas.read_orc`. This requires the `pyarrow `__ library.
.. _io.sql:
diff --git a/doc/source/user_guide/missing_data.rst b/doc/source/user_guide/missing_data.rst
index 3c97cc7da6edb..7eb377694910b 100644
--- a/doc/source/user_guide/missing_data.rst
+++ b/doc/source/user_guide/missing_data.rst
@@ -81,7 +81,7 @@ Integer dtypes and missing data
-------------------------------
Because ``NaN`` is a float, a column of integers with even one missing values
-is cast to floating-point dtype (see :ref:`gotchas.intna` for more). Pandas
+is cast to floating-point dtype (see :ref:`gotchas.intna` for more). pandas
provides a nullable integer array, which can be used by explicitly requesting
the dtype:
@@ -735,7 +735,7 @@ However, these can be filled in using :meth:`~DataFrame.fillna` and it will work
reindexed[crit.fillna(False)]
reindexed[crit.fillna(True)]
-Pandas provides a nullable integer dtype, but you must explicitly request it
+pandas provides a nullable integer dtype, but you must explicitly request it
when creating the series or column. Notice that we use a capital "I" in
the ``dtype="Int64"``.
diff --git a/doc/source/user_guide/scale.rst b/doc/source/user_guide/scale.rst
index f36f27269a996..7f2419bc7f19d 100644
--- a/doc/source/user_guide/scale.rst
+++ b/doc/source/user_guide/scale.rst
@@ -4,7 +4,7 @@
Scaling to large datasets
*************************
-Pandas provides data structures for in-memory analytics, which makes using pandas
+pandas provides data structures for in-memory analytics, which makes using pandas
to analyze datasets that are larger than memory datasets somewhat tricky. Even datasets
that are a sizable fraction of memory become unwieldy, as some pandas operations need
to make intermediate copies.
@@ -13,7 +13,7 @@ This document provides a few recommendations for scaling your analysis to larger
It's a complement to :ref:`enhancingperf`, which focuses on speeding up analysis
for datasets that fit in memory.
-But first, it's worth considering *not using pandas*. Pandas isn't the right
+But first, it's worth considering *not using pandas*. pandas isn't the right
tool for all situations. If you're working with very large datasets and a tool
like PostgreSQL fits your needs, then you should probably be using that.
Assuming you want or need the expressiveness and power of pandas, let's carry on.
@@ -230,7 +230,7 @@ different library that implements these out-of-core algorithms for you.
Use other libraries
-------------------
-Pandas is just one library offering a DataFrame API. Because of its popularity,
+pandas is just one library offering a DataFrame API. Because of its popularity,
pandas' API has become something of a standard that other libraries implement.
The pandas documentation maintains a list of libraries implementing a DataFrame API
in :ref:`our ecosystem page `.
@@ -259,7 +259,7 @@ Inspecting the ``ddf`` object, we see a few things
* There are new attributes like ``.npartitions`` and ``.divisions``
The partitions and divisions are how Dask parallelizes computation. A **Dask**
-DataFrame is made up of many **Pandas** DataFrames. A single method call on a
+DataFrame is made up of many pandas DataFrames. A single method call on a
Dask DataFrame ends up making many pandas method calls, and Dask knows how to
coordinate everything to get the result.
diff --git a/doc/source/user_guide/sparse.rst b/doc/source/user_guide/sparse.rst
index 62e35cb994faf..3156e3088d860 100644
--- a/doc/source/user_guide/sparse.rst
+++ b/doc/source/user_guide/sparse.rst
@@ -6,7 +6,7 @@
Sparse data structures
**********************
-Pandas provides data structures for efficiently storing sparse data.
+pandas provides data structures for efficiently storing sparse data.
These are not necessarily sparse in the typical "mostly 0". Rather, you can view these
objects as being "compressed" where any data matching a specific value (``NaN`` / missing value, though any value
can be chosen, including 0) is omitted. The compressed values are not actually stored in the array.
@@ -116,7 +116,7 @@ Sparse accessor
.. versionadded:: 0.24.0
-Pandas provides a ``.sparse`` accessor, similar to ``.str`` for string data, ``.cat``
+pandas provides a ``.sparse`` accessor, similar to ``.str`` for string data, ``.cat``
for categorical data, and ``.dt`` for datetime-like data. This namespace provides
attributes and methods that are specific to sparse data.
diff --git a/doc/source/user_guide/timedeltas.rst b/doc/source/user_guide/timedeltas.rst
index 971a415088220..cb265d34229dd 100644
--- a/doc/source/user_guide/timedeltas.rst
+++ b/doc/source/user_guide/timedeltas.rst
@@ -100,7 +100,7 @@ The ``unit`` keyword argument specifies the unit of the Timedelta:
Timedelta limitations
~~~~~~~~~~~~~~~~~~~~~
-Pandas represents ``Timedeltas`` in nanosecond resolution using
+pandas represents ``Timedeltas`` in nanosecond resolution using
64 bit integers. As such, the 64 bit integer limits determine
the ``Timedelta`` limits.
diff --git a/doc/source/user_guide/timeseries.rst b/doc/source/user_guide/timeseries.rst
index 11ec90085d9bf..be2c67521dc5d 100644
--- a/doc/source/user_guide/timeseries.rst
+++ b/doc/source/user_guide/timeseries.rst
@@ -1549,7 +1549,7 @@ Converting to Python datetimes
Resampling
----------
-Pandas has a simple, powerful, and efficient functionality for performing
+pandas has a simple, powerful, and efficient functionality for performing
resampling operations during frequency conversion (e.g., converting secondly
data into 5-minutely data). This is extremely common in, but not limited to,
financial applications.
diff --git a/doc/source/user_guide/visualization.rst b/doc/source/user_guide/visualization.rst
index 46ab29a52747a..a6c3d9814b03d 100644
--- a/doc/source/user_guide/visualization.rst
+++ b/doc/source/user_guide/visualization.rst
@@ -776,7 +776,7 @@ See the `matplotlib pie documentation `__ around the source of the ``RuntimeWarning`` to control how these conditions are handled.
@@ -1372,7 +1372,7 @@ Deprecations
- ``Timestamp.offset`` property (and named arg in the constructor), has been deprecated in favor of ``freq`` (:issue:`12160`)
- ``pd.tseries.util.pivot_annual`` is deprecated. Use ``pivot_table`` as alternative, an example is :ref:`here ` (:issue:`736`)
- ``pd.tseries.util.isleapyear`` has been deprecated and will be removed in a subsequent release. Datetime-likes now have a ``.is_leap_year`` property (:issue:`13727`)
-- ``Panel4D`` and ``PanelND`` constructors are deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the `xarray package `__. Pandas provides a :meth:`~Panel4D.to_xarray` method to automate this conversion (:issue:`13564`).
+- ``Panel4D`` and ``PanelND`` constructors are deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the `xarray package `__. pandas provides a :meth:`~Panel4D.to_xarray` method to automate this conversion (:issue:`13564`).
- ``pandas.tseries.frequencies.get_standard_freq`` is deprecated. Use ``pandas.tseries.frequencies.to_offset(freq).rule_code`` instead (:issue:`13874`)
- ``pandas.tseries.frequencies.to_offset``'s ``freqstr`` keyword is deprecated in favor of ``freq`` (:issue:`13874`)
- ``Categorical.from_array`` has been deprecated and will be removed in a future version (:issue:`13854`)
diff --git a/doc/source/whatsnew/v0.20.0.rst b/doc/source/whatsnew/v0.20.0.rst
index 3f7a89112958b..a9e57f0039735 100644
--- a/doc/source/whatsnew/v0.20.0.rst
+++ b/doc/source/whatsnew/v0.20.0.rst
@@ -26,7 +26,7 @@ Highlights include:
.. warning::
- Pandas has changed the internal structure and layout of the code base.
+ pandas has changed the internal structure and layout of the code base.
This can affect imports that are not from the top-level ``pandas.*`` namespace, please see the changes :ref:`here `.
Check the :ref:`API Changes ` and :ref:`deprecations ` before updating.
@@ -243,7 +243,7 @@ The default is to infer the compression type from the extension (``compression='
UInt64 support improved
^^^^^^^^^^^^^^^^^^^^^^^
-Pandas has significantly improved support for operations involving unsigned,
+pandas has significantly improved support for operations involving unsigned,
or purely non-negative, integers. Previously, handling these integers would
result in improper rounding or data-type casting, leading to incorrect results.
Notably, a new numerical index, ``UInt64Index``, has been created (:issue:`14937`)
@@ -333,7 +333,7 @@ You must enable this by setting the ``display.html.table_schema`` option to ``Tr
SciPy sparse matrix from/to SparseDataFrame
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Pandas now supports creating sparse dataframes directly from ``scipy.sparse.spmatrix`` instances.
+pandas now supports creating sparse dataframes directly from ``scipy.sparse.spmatrix`` instances.
See the :ref:`documentation ` for more information. (:issue:`4343`)
All sparse formats are supported, but matrices that are not in :mod:`COOrdinate ` format will be converted, copying data as needed.
@@ -1355,7 +1355,7 @@ Deprecate Panel
^^^^^^^^^^^^^^^
``Panel`` is deprecated and will be removed in a future version. The recommended way to represent 3-D data are
-with a ``MultiIndex`` on a ``DataFrame`` via the :meth:`~Panel.to_frame` or with the `xarray package `__. Pandas
+with a ``MultiIndex`` on a ``DataFrame`` via the :meth:`~Panel.to_frame` or with the `xarray package `__. pandas
provides a :meth:`~Panel.to_xarray` method to automate this conversion (:issue:`13563`).
.. code-block:: ipython
diff --git a/doc/source/whatsnew/v0.21.0.rst b/doc/source/whatsnew/v0.21.0.rst
index 926bcaa21ac3a..6035b89aa8643 100644
--- a/doc/source/whatsnew/v0.21.0.rst
+++ b/doc/source/whatsnew/v0.21.0.rst
@@ -900,13 +900,13 @@ New behavior:
No automatic Matplotlib converters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Pandas no longer registers our ``date``, ``time``, ``datetime``,
+pandas no longer registers our ``date``, ``time``, ``datetime``,
``datetime64``, and ``Period`` converters with matplotlib when pandas is
imported. Matplotlib plot methods (``plt.plot``, ``ax.plot``, ...), will not
nicely format the x-axis for ``DatetimeIndex`` or ``PeriodIndex`` values. You
must explicitly register these methods:
-Pandas built-in ``Series.plot`` and ``DataFrame.plot`` *will* register these
+pandas built-in ``Series.plot`` and ``DataFrame.plot`` *will* register these
converters on first-use (:issue:`17710`).
.. note::
diff --git a/doc/source/whatsnew/v0.21.1.rst b/doc/source/whatsnew/v0.21.1.rst
index f930dfac869cd..2d72f6470fc81 100644
--- a/doc/source/whatsnew/v0.21.1.rst
+++ b/doc/source/whatsnew/v0.21.1.rst
@@ -34,7 +34,7 @@ Highlights include:
Restore Matplotlib datetime converter registration
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Pandas implements some matplotlib converters for nicely formatting the axis
+pandas implements some matplotlib converters for nicely formatting the axis
labels on plots with ``datetime`` or ``Period`` values. Prior to pandas 0.21.0,
these were implicitly registered with matplotlib, as a side effect of ``import
pandas``.
diff --git a/doc/source/whatsnew/v0.22.0.rst b/doc/source/whatsnew/v0.22.0.rst
index 66d3ab3305565..92b514ce59660 100644
--- a/doc/source/whatsnew/v0.22.0.rst
+++ b/doc/source/whatsnew/v0.22.0.rst
@@ -20,7 +20,7 @@ release note (singular!).
Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Pandas 0.22.0 changes the handling of empty and all-*NA* sums and products. The
+pandas 0.22.0 changes the handling of empty and all-*NA* sums and products. The
summary is that
* The sum of an empty or all-*NA* ``Series`` is now ``0``
diff --git a/doc/source/whatsnew/v0.23.0.rst b/doc/source/whatsnew/v0.23.0.rst
index cb811fd83d90d..f4caea9d363eb 100644
--- a/doc/source/whatsnew/v0.23.0.rst
+++ b/doc/source/whatsnew/v0.23.0.rst
@@ -189,7 +189,7 @@ resetting indexes. See the :ref:`Sorting by Indexes and Values
Extending pandas with custom types (experimental)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Pandas now supports storing array-like objects that aren't necessarily 1-D NumPy
+pandas now supports storing array-like objects that aren't necessarily 1-D NumPy
arrays as columns in a DataFrame or values in a Series. This allows third-party
libraries to implement extensions to NumPy's types, similar to how pandas
implemented categoricals, datetimes with timezones, periods, and intervals.
@@ -553,7 +553,7 @@ Other enhancements
- :class:`~pandas.tseries.offsets.WeekOfMonth` constructor now supports ``n=0`` (:issue:`20517`).
- :class:`DataFrame` and :class:`Series` now support matrix multiplication (``@``) operator (:issue:`10259`) for Python>=3.5
- Updated :meth:`DataFrame.to_gbq` and :meth:`pandas.read_gbq` signature and documentation to reflect changes from
- the Pandas-GBQ library version 0.4.0. Adds intersphinx mapping to Pandas-GBQ
+ the pandas-gbq library version 0.4.0. Adds intersphinx mapping to pandas-gbq
library. (:issue:`20564`)
- Added new writer for exporting Stata dta files in version 117, ``StataWriter117``. This format supports exporting strings with lengths up to 2,000,000 characters (:issue:`16450`)
- :func:`to_hdf` and :func:`read_hdf` now accept an ``errors`` keyword argument to control encoding error handling (:issue:`20835`)
@@ -593,7 +593,7 @@ Instantiation from dicts preserves dict insertion order for Python 3.6+
Until Python 3.6, dicts in Python had no formally defined ordering. For Python
version 3.6 and later, dicts are ordered by insertion order, see
`PEP 468 `_.
-Pandas will use the dict's insertion order, when creating a ``Series`` or
+pandas will use the dict's insertion order, when creating a ``Series`` or
``DataFrame`` from a dict and you're using Python version 3.6 or
higher. (:issue:`19884`)
@@ -643,7 +643,7 @@ Deprecate Panel
^^^^^^^^^^^^^^^
``Panel`` was deprecated in the 0.20.x release, showing as a ``DeprecationWarning``. Using ``Panel`` will now show a ``FutureWarning``. The recommended way to represent 3-D data are
-with a ``MultiIndex`` on a ``DataFrame`` via the :meth:`~Panel.to_frame` or with the `xarray package `__. Pandas
+with a ``MultiIndex`` on a ``DataFrame`` via the :meth:`~Panel.to_frame` or with the `xarray package `__. pandas
provides a :meth:`~Panel.to_xarray` method to automate this conversion (:issue:`13563`, :issue:`18324`).
.. code-block:: ipython
@@ -884,7 +884,7 @@ Extraction of matching patterns from strings
By default, extracting matching patterns from strings with :func:`str.extract` used to return a
``Series`` if a single group was being extracted (a ``DataFrame`` if more than one group was
-extracted). As of Pandas 0.23.0 :func:`str.extract` always returns a ``DataFrame``, unless
+extracted). As of pandas 0.23.0 :func:`str.extract` always returns a ``DataFrame``, unless
``expand`` is set to ``False``. Finally, ``None`` was an accepted value for
the ``expand`` parameter (which was equivalent to ``False``), but now raises a ``ValueError``. (:issue:`11386`)
@@ -1175,7 +1175,7 @@ Performance improvements
Documentation changes
~~~~~~~~~~~~~~~~~~~~~
-Thanks to all of the contributors who participated in the Pandas Documentation
+Thanks to all of the contributors who participated in the pandas Documentation
Sprint, which took place on March 10th. We had about 500 participants from over
30 locations across the world. You should notice that many of the
:ref:`API docstrings ` have greatly improved.
diff --git a/doc/source/whatsnew/v0.23.2.rst b/doc/source/whatsnew/v0.23.2.rst
index 9f24092d1d4ae..99650e8291d3d 100644
--- a/doc/source/whatsnew/v0.23.2.rst
+++ b/doc/source/whatsnew/v0.23.2.rst
@@ -11,7 +11,7 @@ and bug fixes. We recommend that all users upgrade to this version.
.. note::
- Pandas 0.23.2 is first pandas release that's compatible with
+ pandas 0.23.2 is first pandas release that's compatible with
Python 3.7 (:issue:`20552`)
.. warning::
diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
index 9a2e96f717d9b..9ef50045d5b5e 100644
--- a/doc/source/whatsnew/v0.24.0.rst
+++ b/doc/source/whatsnew/v0.24.0.rst
@@ -38,7 +38,7 @@ Enhancements
Optional integer NA support
^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Pandas has gained the ability to hold integer dtypes with missing values. This long requested feature is enabled through the use of :ref:`extension types `.
+pandas has gained the ability to hold integer dtypes with missing values. This long requested feature is enabled through the use of :ref:`extension types `.
.. note::
@@ -384,7 +384,7 @@ Other enhancements
- :meth:`Series.droplevel` and :meth:`DataFrame.droplevel` are now implemented (:issue:`20342`)
- Added support for reading from/writing to Google Cloud Storage via the ``gcsfs`` library (:issue:`19454`, :issue:`23094`)
- :func:`DataFrame.to_gbq` and :func:`read_gbq` signature and documentation updated to
- reflect changes from the `Pandas-GBQ library version 0.8.0
+ reflect changes from the `pandas-gbq library version 0.8.0
`__.
Adds a ``credentials`` argument, which enables the use of any kind of
`google-auth credentials
@@ -432,7 +432,7 @@ Other enhancements
Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Pandas 0.24.0 includes a number of API breaking changes.
+pandas 0.24.0 includes a number of API breaking changes.
.. _whatsnew_0240.api_breaking.deps:
@@ -1217,7 +1217,7 @@ Extension type changes
**Equality and hashability**
-Pandas now requires that extension dtypes be hashable (i.e. the respective
+pandas now requires that extension dtypes be hashable (i.e. the respective
``ExtensionDtype`` objects; hashability is not a requirement for the values
of the corresponding ``ExtensionArray``). The base class implements
a default ``__eq__`` and ``__hash__``. If you have a parametrized dtype, you should
@@ -1925,7 +1925,7 @@ Build changes
Other
^^^^^
-- Bug where C variables were declared with external linkage causing import errors if certain other C libraries were imported before Pandas. (:issue:`24113`)
+- Bug where C variables were declared with external linkage causing import errors if certain other C libraries were imported before pandas. (:issue:`24113`)
.. _whatsnew_0.24.0.contributors:
diff --git a/doc/source/whatsnew/v0.25.0.rst b/doc/source/whatsnew/v0.25.0.rst
index 7b4440148677b..43b42c5cb5648 100644
--- a/doc/source/whatsnew/v0.25.0.rst
+++ b/doc/source/whatsnew/v0.25.0.rst
@@ -36,7 +36,7 @@ Enhancements
Groupby aggregation with relabeling
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Pandas has added special groupby behavior, known as "named aggregation", for naming the
+pandas has added special groupby behavior, known as "named aggregation", for naming the
output columns when applying multiple aggregation functions to specific columns (:issue:`18366`, :issue:`26512`).
.. ipython:: python
@@ -53,7 +53,7 @@ output columns when applying multiple aggregation functions to specific columns
Pass the desired columns names as the ``**kwargs`` to ``.agg``. The values of ``**kwargs``
should be tuples where the first element is the column selection, and the second element is the
-aggregation function to apply. Pandas provides the ``pandas.NamedAgg`` namedtuple to make it clearer
+aggregation function to apply. pandas provides the ``pandas.NamedAgg`` namedtuple to make it clearer
what the arguments to the function are, but plain tuples are accepted as well.
.. ipython:: python
@@ -425,7 +425,7 @@ of ``object`` dtype. :attr:`Series.str` will now infer the dtype data *within* t
Categorical dtypes are preserved during groupby
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Previously, columns that were categorical, but not the groupby key(s) would be converted to ``object`` dtype during groupby operations. Pandas now will preserve these dtypes. (:issue:`18502`)
+Previously, columns that were categorical, but not the groupby key(s) would be converted to ``object`` dtype during groupby operations. pandas now will preserve these dtypes. (:issue:`18502`)
.. ipython:: python
@@ -545,14 +545,14 @@ with :attr:`numpy.nan` in the case of an empty :class:`DataFrame` (:issue:`26397
``__str__`` methods now call ``__repr__`` rather than vice versa
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Pandas has until now mostly defined string representations in a Pandas objects's
+pandas has until now mostly defined string representations in a pandas objects'
``__str__``/``__unicode__``/``__bytes__`` methods, and called ``__str__`` from the ``__repr__``
method, if a specific ``__repr__`` method is not found. This is not needed for Python3.
-In Pandas 0.25, the string representations of Pandas objects are now generally
+In pandas 0.25, the string representations of pandas objects are now generally
defined in ``__repr__``, and calls to ``__str__`` in general now pass the call on to
the ``__repr__``, if a specific ``__str__`` method doesn't exist, as is standard for Python.
-This change is backward compatible for direct usage of Pandas, but if you subclass
-Pandas objects *and* give your subclasses specific ``__str__``/``__repr__`` methods,
+This change is backward compatible for direct usage of pandas, but if you subclass
+pandas objects *and* give your subclasses specific ``__str__``/``__repr__`` methods,
you may have to adjust your ``__str__``/``__repr__`` methods (:issue:`26495`).
.. _whatsnew_0250.api_breaking.interval_indexing:
@@ -881,7 +881,7 @@ Other API changes
- Bug in :meth:`DatetimeIndex.snap` which didn't preserving the ``name`` of the input :class:`Index` (:issue:`25575`)
- The ``arg`` argument in :meth:`pandas.core.groupby.DataFrameGroupBy.agg` has been renamed to ``func`` (:issue:`26089`)
- The ``arg`` argument in :meth:`pandas.core.window._Window.aggregate` has been renamed to ``func`` (:issue:`26372`)
-- Most Pandas classes had a ``__bytes__`` method, which was used for getting a python2-style bytestring representation of the object. This method has been removed as a part of dropping Python2 (:issue:`26447`)
+- Most pandas classes had a ``__bytes__`` method, which was used for getting a python2-style bytestring representation of the object. This method has been removed as a part of dropping Python2 (:issue:`26447`)
- The ``.str``-accessor has been disabled for 1-level :class:`MultiIndex`, use :meth:`MultiIndex.to_flat_index` if necessary (:issue:`23679`)
- Removed support of gtk package for clipboards (:issue:`26563`)
- Using an unsupported version of Beautiful Soup 4 will now raise an ``ImportError`` instead of a ``ValueError`` (:issue:`27063`)
diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 2a2b511356a69..8a16bab63f1bf 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -10,7 +10,7 @@ I/O and LZMA
~~~~~~~~~~~~
Some users may unknowingly have an incomplete Python installation lacking the ``lzma`` module from the standard library. In this case, ``import pandas`` failed due to an ``ImportError`` (:issue:`27575`).
-Pandas will now warn, rather than raising an ``ImportError`` if the ``lzma`` module is not present. Any subsequent attempt to use ``lzma`` methods will raise a ``RuntimeError``.
+pandas will now warn, rather than raising an ``ImportError`` if the ``lzma`` module is not present. Any subsequent attempt to use ``lzma`` methods will raise a ``RuntimeError``.
A possible fix for the lack of the ``lzma`` module is to ensure you have the necessary libraries and then re-install Python.
For example, on MacOS installing Python with ``pyenv`` may lead to an incomplete Python installation due to unmet system dependencies at compilation time (like ``xz``). Compilation will succeed, but Python might fail at run time. The issue can be solved by installing the necessary dependencies and then re-installing Python.
diff --git a/doc/source/whatsnew/v0.25.2.rst b/doc/source/whatsnew/v0.25.2.rst
index c0c68ce4b1f44..a5ea8933762ab 100644
--- a/doc/source/whatsnew/v0.25.2.rst
+++ b/doc/source/whatsnew/v0.25.2.rst
@@ -8,7 +8,7 @@ including other versions of pandas.
.. note::
- Pandas 0.25.2 adds compatibility for Python 3.8 (:issue:`28147`).
+ pandas 0.25.2 adds compatibility for Python 3.8 (:issue:`28147`).
.. _whatsnew_0252.bug_fixes:
diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index 32175d344c320..ddc40d6d40594 100755
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -18,7 +18,7 @@ including other versions of pandas.
New deprecation policy
~~~~~~~~~~~~~~~~~~~~~~
-Starting with Pandas 1.0.0, pandas will adopt a variant of `SemVer`_ to
+Starting with pandas 1.0.0, pandas will adopt a variant of `SemVer`_ to
version releases. Briefly,
* Deprecations will be introduced in minor releases (e.g. 1.1.0, 1.2.0, 2.1.0, ...)
@@ -676,7 +676,7 @@ depending on how the results are cast back to the original dtype.
Increased minimum version for Python
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Pandas 1.0.0 supports Python 3.6.1 and higher (:issue:`29212`).
+pandas 1.0.0 supports Python 3.6.1 and higher (:issue:`29212`).
.. _whatsnew_100.api_breaking.deps:
@@ -749,7 +749,7 @@ See :ref:`install.dependencies` and :ref:`install.optional_dependencies` for mor
Build changes
^^^^^^^^^^^^^
-Pandas has added a `pyproject.toml `_ file and will no longer include
+pandas has added a `pyproject.toml `_ file and will no longer include
cythonized files in the source distribution uploaded to PyPI (:issue:`28341`, :issue:`20775`). If you're installing
a built distribution (wheel) or via conda, this shouldn't have any effect on you. If you're building pandas from
source, you should no longer need to install Cython into your build environment before calling ``pip install pandas``.
@@ -763,7 +763,7 @@ Other API changes
- :class:`core.groupby.GroupBy.transform` now raises on invalid operation names (:issue:`27489`)
- :meth:`pandas.api.types.infer_dtype` will now return "integer-na" for integer and ``np.nan`` mix (:issue:`27283`)
- :meth:`MultiIndex.from_arrays` will no longer infer names from arrays if ``names=None`` is explicitly provided (:issue:`27292`)
-- In order to improve tab-completion, Pandas does not include most deprecated attributes when introspecting a pandas object using ``dir`` (e.g. ``dir(df)``).
+- In order to improve tab-completion, pandas does not include most deprecated attributes when introspecting a pandas object using ``dir`` (e.g. ``dir(df)``).
To see which attributes are excluded, see an object's ``_deprecations`` attribute, for example ``pd.DataFrame._deprecations`` (:issue:`28805`).
- The returned dtype of :func:`unique` now matches the input dtype. (:issue:`27874`)
- Changed the default configuration value for ``options.matplotlib.register_converters`` from ``True`` to ``"auto"`` (:issue:`18720`).
diff --git a/doc/source/whatsnew/v1.1.3.rst b/doc/source/whatsnew/v1.1.3.rst
index acf1dafc59885..af714b1bb2ab1 100644
--- a/doc/source/whatsnew/v1.1.3.rst
+++ b/doc/source/whatsnew/v1.1.3.rst
@@ -16,7 +16,7 @@ Enhancements
Added support for new Python version
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Pandas 1.1.3 now supports Python 3.9 (:issue:`36296`).
+pandas 1.1.3 now supports Python 3.9 (:issue:`36296`).
Development Changes
^^^^^^^^^^^^^^^^^^^
diff --git a/doc/source/whatsnew/v1.2.0.rst b/doc/source/whatsnew/v1.2.0.rst
index cb0858fd678f8..2b63f6f99ba12 100644
--- a/doc/source/whatsnew/v1.2.0.rst
+++ b/doc/source/whatsnew/v1.2.0.rst
@@ -33,7 +33,7 @@ By default, duplicates continue to be allowed
pd.Series([1, 2], index=['a', 'a']).set_flags(allows_duplicate_labels=False)
-Pandas will propagate the ``allows_duplicate_labels`` property through many operations.
+pandas will propagate the ``allows_duplicate_labels`` property through many operations.
.. ipython:: python
:okexcept:
@@ -175,7 +175,7 @@ Other enhancements
Increased minimum version for Python
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Pandas 1.2.0 supports Python 3.7.1 and higher (:issue:`35214`).
+pandas 1.2.0 supports Python 3.7.1 and higher (:issue:`35214`).
.. _whatsnew_120.api_breaking.deps: