DOC: add SPSS comparison guide structure (#60738)

* DOC: add SPSS comparison guide structure

- Create SPSS comparison documentation
- Add header and introduction sections
- Terminology translation table
- Create template for common operations comparison

Part of #60727

* DOC: edit SPSS comparison guide to documentation

- Added file to doc/source/getting_started/comparison/index.rst toctree
- Fixed formatting and whitespace issues to meet documentation standards

* DOC: edit minor whitespaces in SPSS comparison guide

* DOC: standardize class references in SPSS guide

* DOC: Fix RST section underline lengths in SPSS comparison

---------

Co-authored-by: jl_win_a <jl@win-a>

doc/source/getting_started/comparison/comparison_with_spss.rst

@@ -0,0 +1,229 @@
+.. _compare_with_spss:
+
+{{ header }}
+
+Comparison with SPSS
+********************
+For potential users coming from `SPSS <https://www.ibm.com/spss>`__, this page is meant to demonstrate
+how various SPSS operations would be performed using pandas.
+
+.. include:: includes/introduction.rst
+
+Data structures
+---------------
+
+General terminology translation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. csv-table::
+    :header: "pandas", "SPSS"
+    :widths: 20, 20
+
+    :class:`DataFrame`, data file
+    column, variable
+    row, case
+    groupby, split file
+    :class:`NaN`, system-missing
+
+:class:`DataFrame`
+~~~~~~~~~~~~~~~~~~
+
+A :class:`DataFrame` in pandas is analogous to an SPSS data file - a two-dimensional
+data source with labeled columns that can be of different types. As will be shown in this
+document, almost any operation that can be performed in SPSS can also be accomplished in pandas.
+
+:class:`Series`
+~~~~~~~~~~~~~~~
+
+A :class:`Series` is the data structure that represents one column of a :class:`DataFrame`. SPSS doesn't have a
+separate data structure for a single variable, but in general, working with a :class:`Series` is analogous
+to working with a variable in SPSS.
+
+:class:`Index`
+~~~~~~~~~~~~~~
+
+Every :class:`DataFrame` and :class:`Series` has an :class:`Index` -- labels on the *rows* of the data. SPSS does not
+have an exact analogue, as cases are simply numbered sequentially from 1. In pandas, if no index is
+specified, a :class:`RangeIndex` is used by default (first row = 0, second row = 1, and so on).
+
+While using a labeled :class:`Index` or :class:`MultiIndex` can enable sophisticated analyses and is ultimately an
+important part of pandas to understand, for this comparison we will essentially ignore the :class:`Index` and
+just treat the :class:`DataFrame` as a collection of columns. Please see the :ref:`indexing documentation<indexing>`
+for much more on how to use an :class:`Index` effectively.
+
+
+Copies vs. in place operations
+------------------------------
+
+.. include:: includes/copies.rst
+
+
+Data input / output
+-------------------
+
+Reading external data
+~~~~~~~~~~~~~~~~~~~~~
+
+Like SPSS, pandas provides utilities for reading in data from many formats. The ``tips`` dataset, found within
+the pandas tests (`csv <https://raw.githubusercontent.com/pandas-dev/pandas/main/pandas/tests/io/data/csv/tips.csv>`_)
+will be used in many of the following examples.
+
+In SPSS, you would use File > Open > Data to import a CSV file:
+
+.. code-block:: text
+
+    FILE > OPEN > DATA
+    /TYPE=CSV
+    /FILE='tips.csv'
+    /DELIMITERS=","
+    /FIRSTCASE=2
+    /VARIABLES=col1 col2 col3.
+
+The pandas equivalent would use :func:`read_csv`:
+
+.. code-block:: python
+
+   url = (
+       "https://raw.githubusercontent.com/pandas-dev"
+       "/pandas/main/pandas/tests/io/data/csv/tips.csv"
+   )
+   tips = pd.read_csv(url)
+   tips
+
+Like SPSS's data import wizard, ``read_csv`` can take a number of parameters to specify how the data should be parsed.
+For example, if the data was instead tab delimited, and did not have column names, the pandas command would be:
+
+.. code-block:: python
+
+   tips = pd.read_csv("tips.csv", sep="\t", header=None)
+
+   # alternatively, read_table is an alias to read_csv with tab delimiter
+   tips = pd.read_table("tips.csv", header=None)
+
+
+Data operations
+---------------
+
+Filtering
+~~~~~~~~~
+
+In SPSS, filtering is done through Data > Select Cases:
+
+.. code-block:: text
+
+    SELECT IF (total_bill > 10).
+    EXECUTE.
+
+In pandas, boolean indexing can be used:
+
+.. code-block:: python
+
+    tips[tips["total_bill"] > 10]
+
+
+Sorting
+~~~~~~~
+
+In SPSS, sorting is done through Data > Sort Cases:
+
+.. code-block:: text
+
+    SORT CASES BY sex total_bill.
+    EXECUTE.
+
+In pandas, this would be written as:
+
+.. code-block:: python
+
+    tips.sort_values(["sex", "total_bill"])
+
+
+String processing
+-----------------
+
+Finding length of string
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+In SPSS:
+
+.. code-block:: text
+
+    COMPUTE length = LENGTH(time).
+    EXECUTE.
+
+.. include:: includes/length.rst
+
+
+Changing case
+~~~~~~~~~~~~~
+
+In SPSS:
+
+.. code-block:: text
+
+    COMPUTE upper = UPCASE(time).
+    COMPUTE lower = LOWER(time).
+    EXECUTE.
+
+.. include:: includes/case.rst
+
+
+Merging
+-------
+
+In SPSS, merging data files is done through Data > Merge Files.
+
+.. include:: includes/merge_setup.rst
+.. include:: includes/merge.rst
+
+
+GroupBy operations
+------------------
+
+Split-file processing
+~~~~~~~~~~~~~~~~~~~~~
+
+In SPSS, split-file analysis is done through Data > Split File:
+
+.. code-block:: text
+
+    SORT CASES BY sex.
+    SPLIT FILE BY sex.
+    DESCRIPTIVES VARIABLES=total_bill tip
+      /STATISTICS=MEAN STDDEV MIN MAX.
+
+The pandas equivalent would be:
+
+.. code-block:: python
+
+    tips.groupby("sex")[["total_bill", "tip"]].agg(["mean", "std", "min", "max"])
+
+
+Missing data
+------------
+
+SPSS uses the period (``.``) for numeric missing values and blank spaces for string missing values.
+pandas uses ``NaN`` (Not a Number) for numeric missing values and ``None`` or ``NaN`` for string
+missing values.
+
+.. include:: includes/missing.rst
+
+
+Other considerations
+--------------------
+
+Output management
+-----------------
+
+While pandas does not have a direct equivalent to SPSS's Output Management System (OMS), you can
+capture and export results in various ways:
+
+.. code-block:: python
+
+   # Save summary statistics to CSV
+   tips.groupby('sex')[['total_bill', 'tip']].mean().to_csv('summary.csv')
+
+   # Save multiple results to Excel sheets
+   with pd.ExcelWriter('results.xlsx') as writer:
+       tips.describe().to_excel(writer, sheet_name='Descriptives')
+       tips.groupby('sex').mean().to_excel(writer, sheet_name='Means by Gender')

doc/source/getting_started/comparison/index.rst

@@ -14,3 +14,4 @@ Comparison with other tools
     comparison_with_spreadsheets
     comparison_with_sas
     comparison_with_stata
+    comparison_with_spss