DOC: Clarify parentheses vs. brackets usage (GH#62314) updated

Author: AnandMukherjee2004

doc/source/getting_started/intro_tutorials/11_brackets_vs_parenthesis.rst

@@ -3,149 +3,4 @@
 Parentheses vs. Square Brackets in Python and pandas
 ====================================================
 
-Python and pandas use both **parentheses** ``()`` and **square brackets** ``[]``, but these have separate and important roles. Understanding their differences is essential for writing correct Python and pandas code.
-
-Overview of Bracket Usage
--------------------------
-
-+------------------------------------+-------------------------------+------------------------------+
-| Context                            | Parentheses ``()``            | Square Brackets ``[]``       |
-+====================================+===============================+==============================+
-| Function and method calls          | Yes: ``df.mean()``            | No                           |
-+------------------------------------+-------------------------------+------------------------------+
-| Tuple creation                     | Yes: ``(a, b, c)``            | No                           |
-+------------------------------------+-------------------------------+------------------------------+
-| List creation and item access      | No                            | Yes: ``a[0]``, ``[1, 2]``    |
-+------------------------------------+-------------------------------+------------------------------+
-| Dictionary item access             | No                            | Yes: ``mydict['key']``       |
-+------------------------------------+-------------------------------+------------------------------+
-| pandas column selection            | No                            | Yes: ``df['A']``,            |
-|                                    |                               |     ``df[['A', 'B']]``       |
-+------------------------------------+-------------------------------+------------------------------+
-| pandas row selection and slicing   | No                            | Yes: ``df[0:5]``,            |
-|                                    |                               |     ``df.loc[2]``            |
-+------------------------------------+-------------------------------+------------------------------+
-| Boolean indexing                   | No                            | Yes: ``df[df['col'] > 10]``  |
-+------------------------------------+-------------------------------+------------------------------+
-| Grouping/logical expressions       | Yes: ``(x + y) * z``          | No                           |
-+------------------------------------+-------------------------------+------------------------------+
-
-Detailed Explanations and Examples
-----------------------------------
-
-**Parentheses ``()``**
-
-- Used to *call* functions and methods, enclosing arguments:
-  .. code-block:: python
-
-      df.mean()
-      print("Hello, world!")
-      sum([1, 2, 3])
-
-- Used to create *tuples*, which are immutable ordered collections:
-  .. code-block:: python
-
-      coordinates = (10, 20)
-      empty_tuple = ()
-
-- Used for *grouping expressions* in mathematics and logic:
-  .. code-block:: python
-
-      result = (1 + 2) * 3
-      is_valid = (score > 0) and (score < 100)
-
-- Used to spread Python statements over multiple lines:
-
-  .. code-block:: python
-
-      total = (
-          1 +
-          2 +
-          3
-      )
-
-**Square Brackets ``[]``**
-
-- Used to *define* and *access* elements of Python lists:
-  .. code-block:: python
-
-      numbers = [1, 2, 3, 4]
-      first = numbers[0]
-      sub = numbers[1:3]
-
-- Used to *access* values in dictionaries by key:
-  .. code-block:: python
-
-      prices = {'apple': 40, 'banana': 10}
-      apple_price = prices['apple']
-
-- Used for all kinds of *indexing* and *selection* in pandas DataFrames and Series:
-
-  *Single column selection* (returns Series):
-  .. code-block:: python
-
-      df['A']
-
-  *Multiple columns selection* (returns DataFrame):
-  .. code-block:: python
-
-      df[['A', 'B']]
-
-    Here, the inner brackets create a Python list of column labels, and the outer brackets are pandas selection syntax.
-
-  *Row selection and slicing*:
-  .. code-block:: python
-
-      df[0:2]           # selects rows 0 and 1 by integer position
-      df.loc[2]         # selects row with label/index 2
-      df.iloc[2]        # selects row at integer position 2
-
-  *Boolean indexing (row filtering)*:
-  .. code-block:: python
-
-      df[df['A'] > 5]   # returns only rows where column 'A' is greater than 5
-
-Key Points to Remember
-----------------------
-
-- **Parentheses** are for function/method calls, tuple creation, grouping, and continuation of statements.
-- **Square brackets** are for creating and accessing lists, dictionary values, slicing sequences, and—critically for pandas—selecting/subsetting columns and rows.
-- In pandas, *single* square brackets select a single column as a Series (``df['A']``), while *double* square brackets select multiple columns as a DataFrame (``df[['A', 'B']]``) because the *inner brackets create a Python list* of column labels.
-- Boolean indexing in pandas always uses square brackets enclosing a boolean Series: ``df[df['A'] > 5]``.
-
-Common Pitfalls
----------------
-
-- Attempting to use parentheses for list/tensor/column access or slicing will result in errors.
-- Using single brackets with a list inside (like ``df[['A']]``) still returns a DataFrame, not a Series—bracket count and the type of object inside matters.
-- Remember that method calls (like ``df.mean()`` or ``df.groupby('A')``) always require parentheses, even if there are no arguments.
-
-Example Summary
----------------
-
-.. code-block:: python
-
-    import pandas as pd
-    df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
-
-    # Function and method calls
-    df.mean()
-
-    # Tuple creation
-    t = (1, 2, 3)
-
-    # List creation/access
-    mylist = [10, 20, 30]
-    first_item = mylist[0]
-
-    # pandas column selection
-    df['A']
-    df[['A', 'B']]
-
-    # pandas boolean indexing
-    df[df['B'] > 4]
-
-    # Grouping/logical expressions
-    selected = (df['A'] > 1) & (df['B'] < 6)
-
-Getting comfortable with the distinction between parentheses and square brackets is a major milestone for every new pandas user. This understanding leads to correct code and enhanced productivity when working in both core Python and pandas.
+In Python and pandas, parentheses ``()`` and square brackets ``[]`` have distinct uses, which can sometimes be confusing for new users. Parentheses are used to call functions and methods (for example, ``df.mean()``), to group expressions in calculations (such as ``(a + b) * c``), and to create tuples. Square brackets, on the other hand, are used for defining lists and for indexing or selecting data—in both core Python and pandas. For example, ``df['A']`` selects column ``A`` from a DataFrame, and ``df[0:3]`` selects rows by position. In pandas, square brackets are always used when you want to select or filter data, while parentheses are used any time you are calling a method or function. Remember: use ``[]`` for selection or indexing, and ``()`` for function calls and grouping.