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Proofreading/editing Getting Started for readability. Attempt 2, this time without extra files. #58919

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38 changes: 19 additions & 19 deletions doc/source/getting_started/index.rst
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Expand Up @@ -134,8 +134,8 @@ to explore, clean, and process your data. In pandas, a data table is called a :c
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pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). Importing data from each of these
data sources is provided by function with the prefix ``read_*``. Similarly, the ``to_*`` methods are used to store data.
pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). The ability to import data from each of these
data sources is provided by functions with the prefix, ``read_*``. Similarly, the ``to_*`` methods are used to store data.

.. image:: ../_static/schemas/02_io_readwrite.svg
:align: center
Expand Down Expand Up @@ -181,7 +181,7 @@ data sources is provided by function with the prefix ``read_*``. Similarly, the
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Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the
Selecting or filtering specific rows and/or columns? Filtering the data on a particular condition? Methods for slicing, selecting, and extracting the
data you need are available in pandas.

.. image:: ../_static/schemas/03_subset_columns_rows.svg
Expand Down Expand Up @@ -228,7 +228,7 @@ data you need are available in pandas.
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pandas provides plotting your data out of the box, using the power of Matplotlib. You can pick the plot type (scatter, bar, boxplot,...)
pandas provides plotting for your data right out of the box with the power of Matplotlib. Simply pick the plot type (scatter, bar, boxplot,...)
corresponding to your data.

.. image:: ../_static/schemas/04_plot_overview.svg
Expand Down Expand Up @@ -275,7 +275,7 @@ corresponding to your data.
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There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise.
There's no need to loop over all rows of your data table to do calculations. Column data manipulations work elementwise in pandas.
Adding a column to a :class:`DataFrame` based on existing data in other columns is straightforward.

.. image:: ../_static/schemas/05_newcolumn_2.svg
Expand Down Expand Up @@ -322,7 +322,7 @@ Adding a column to a :class:`DataFrame` based on existing data in other columns
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Basic statistics (mean, median, min, max, counts...) are easily calculable. These or custom aggregations can be applied on the entire
Basic statistics (mean, median, min, max, counts...) are easily calculable across data frames. These, or even custom aggregations, can be applied on the entire
data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine approach.

.. image:: ../_static/schemas/06_groupby.svg
Expand Down Expand Up @@ -369,8 +369,8 @@ data set, a sliding window of the data, or grouped by categories. The latter is
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Change the structure of your data table in multiple ways. You can :func:`~pandas.melt` your data table from wide to long/tidy form or :func:`~pandas.pivot`
from long to wide format. With aggregations built-in, a pivot table is created with a single command.
Change the structure of your data table in a variety of ways. You can use :func:`~pandas.melt` to reshape your data from a wide format to a long and tidy one. Use :func:`~pandas.pivot`
to go from long to wide format. With aggregations built-in, a pivot table can be created with a single command.

.. image:: ../_static/schemas/07_melt.svg
:align: center
Expand Down Expand Up @@ -416,7 +416,7 @@ from long to wide format. With aggregations built-in, a pivot table is created w
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Multiple tables can be concatenated both column wise and row wise as database-like join/merge operations are provided to combine multiple tables of data.
Multiple tables can be concatenated column wise or row wise with pandas' database-like join and merge operations.

.. image:: ../_static/schemas/08_concat_row.svg
:align: center
Expand Down Expand Up @@ -505,7 +505,7 @@ pandas has great support for time series and has an extensive set of tools for w
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Data sets do not only contain numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it.
Data sets often contain more than just numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it.

.. raw:: html

Expand Down Expand Up @@ -551,9 +551,9 @@ the pandas-equivalent operations compared to software you already know:
:class-card: comparison-card
:shadow: md

The `R programming language <https://www.r-project.org/>`__ provides the
``data.frame`` data structure and multiple packages, such as
`tidyverse <https://www.tidyverse.org>`__ use and extend ``data.frame``
The `R programming language <https://www.r-project.org/>`__ provides a
``data.frame`` data structure as well as packages like
`tidyverse <https://www.tidyverse.org>`__ which use and extend ``data.frame``
for convenient data handling functionalities similar to pandas.

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Expand All @@ -572,8 +572,8 @@ the pandas-equivalent operations compared to software you already know:
:class-card: comparison-card
:shadow: md

Already familiar to ``SELECT``, ``GROUP BY``, ``JOIN``, etc.?
Most of these SQL manipulations do have equivalents in pandas.
Already familiar with ``SELECT``, ``GROUP BY``, ``JOIN``, etc.?
Many SQL manipulations have equivalents in pandas.

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Expand Down Expand Up @@ -631,10 +631,10 @@ the pandas-equivalent operations compared to software you already know:
:class-card: comparison-card
:shadow: md

The `SAS <https://en.wikipedia.org/wiki/SAS_(software)>`__ statistical software suite
also provides the ``data set`` corresponding to the pandas ``DataFrame``.
Also SAS vectorized operations, filtering, string processing operations,
and more have similar functions in pandas.
`SAS <https://en.wikipedia.org/wiki/SAS_(software)>`__, the statistical software suite,
uses the ``data set`` structure, which closely corresponds pandas' ``DataFrame``.
Also SAS vectorized operations such as filtering or string processing operations
have similar functions in pandas.

+++

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