Harmonize column selection to bracket notation

As suggested by https://medium.com/dunder-data/minimally-sufficient-pandas-a8e67f2a2428#46f9

Author: katrinleinweber

doc/source/user_guide/indexing.rst

@@ -236,7 +236,7 @@ new column. In 0.21.0 and later, this will raise a ``UserWarning``:
 .. code-block:: ipython
 
     In [1]: df = pd.DataFrame({'one': [1., 2., 3.]})
-    In [2]: df.two = [4, 5, 6]
+    In [2]: df['two'] = [4, 5, 6]
     UserWarning: Pandas doesn't allow Series to be assigned into nonexistent columns - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute_access
     In [3]: df
     Out[3]:
@@ -540,7 +540,7 @@ The ``callable`` must be a function with one argument (the calling Series or Dat
                       columns=list('ABCD'))
    df1
 
-   df1.loc[lambda df: df.A > 0, :]
+   df1.loc[lambda df: df['A'] > 0, :]
    df1.loc[:, lambda df: ['A', 'B']]
 
    df1.iloc[:, lambda df: [0, 1]]
@@ -561,7 +561,7 @@ without using a temporary variable.
 
    bb = pd.read_csv('data/baseball.csv', index_col='id')
    (bb.groupby(['year', 'team']).sum()
-      .loc[lambda df: df.r > 100])
+      .loc[lambda df: df['r'] > 100])
 
 .. _indexing.deprecate_ix:
 
@@ -871,9 +871,9 @@ Boolean indexing
 Another common operation is the use of boolean vectors to filter the data.
 The operators are: ``|`` for ``or``, ``&`` for ``and``, and ``~`` for ``not``.
 These **must** be grouped by using parentheses, since by default Python will
-evaluate an expression such as ``df.A > 2 & df.B < 3`` as
-``df.A > (2 & df.B) < 3``, while the desired evaluation order is
-``(df.A > 2) & (df.B < 3)``.
+evaluate an expression such as ``df['A'] > 2 & df['B'] < 3`` as
+``df['A'] > (2 & df['B']) < 3``, while the desired evaluation order is
+``(df['A > 2) & (df['B'] < 3)``.
 
 Using a boolean vector to index a Series works exactly as in a NumPy ndarray:
 
@@ -1134,7 +1134,7 @@ between the values of columns ``a`` and ``c``. For example:
    df
 
    # pure python
-   df[(df.a < df.b) & (df.b < df.c)]
+   df[(df['a'] < df['b']) & (df['b'] < df['c'])]
 
    # query
    df.query('(a < b) & (b < c)')
@@ -1241,7 +1241,7 @@ Full numpy-like syntax:
    df = pd.DataFrame(np.random.randint(n, size=(n, 3)), columns=list('abc'))
    df
    df.query('(a < b) & (b < c)')
-   df[(df.a < df.b) & (df.b < df.c)]
+   df[(df['a'] < df['b']) & (df['b'] < df['c'])]
 
 Slightly nicer by removing the parentheses (by binding making comparison
 operators bind tighter than ``&`` and ``|``).
@@ -1279,12 +1279,12 @@ The ``in`` and ``not in`` operators
    df.query('a in b')
 
    # How you'd do it in pure Python
-   df[df.a.isin(df.b)]
+   df[df['a'].isin(df['b'])]
 
    df.query('a not in b')
 
    # pure Python
-   df[~df.a.isin(df.b)]
+   df[~df['a'].isin(df['b'])]
 
 
 You can combine this with other expressions for very succinct queries:
@@ -1297,7 +1297,7 @@ You can combine this with other expressions for very succinct queries:
    df.query('a in b and c < d')
 
    # pure Python
-   df[df.b.isin(df.a) & (df.c < df.d)]
+   df[df['b'].isin(df['a']) & (df['c'] < df['d'])]
 
 
 .. note::
@@ -1326,7 +1326,7 @@ to ``in``/``not in``.
    df.query('b == ["a", "b", "c"]')
 
    # pure Python
-   df[df.b.isin(["a", "b", "c"])]
+   df[df['b'].isin(["a", "b", "c"])]
 
    df.query('c == [1, 2]')
 
@@ -1338,7 +1338,7 @@ to ``in``/``not in``.
    df.query('[1, 2] not in c')
 
    # pure Python
-   df[df.c.isin([1, 2])]
+   df[df['c'].isin([1, 2])]
 
 
 Boolean operators
@@ -1362,7 +1362,7 @@ Of course, expressions can be arbitrarily complex too:
    shorter = df.query('a < b < c and (not bools) or bools > 2')
 
    # equivalent in pure Python
-   longer = df[(df.a < df.b) & (df.b < df.c) & (~df.bools) | (df.bools > 2)]
+   longer = df[(df['a'] < df['b']) & (df['b'] < df['c']) & (~df.bools) | (df.bools > 2)]
 
    shorter
    longer