DOC: improve readibility and conformation to PEP-8 standards

Author: saurav

doc/source/io.rst

@@ -145,7 +145,10 @@ usecols : list-like or callable, default ``None``
 
   .. ipython:: python
 
-     data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3'
+     data = ('col1,col2,col3\n'
+             'a,b,1\n'
+             'a,b,2\n'
+             'c,d,3')
      pd.read_csv(StringIO(data))
      pd.read_csv(StringIO(data), usecols=lambda x: x.upper() in ['COL1', 'COL3'])
 
@@ -191,7 +194,10 @@ skiprows : list-like or integer, default ``None``
 
   .. ipython:: python
 
-     data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3'
+     data = ('col1,col2,col3\n'
+             'a,b,1\n'
+             'a,b,2\n'
+             'c,d,3')
      pd.read_csv(StringIO(data))
      pd.read_csv(StringIO(data), skiprows=lambda x: x % 2 != 0)
 
@@ -366,7 +372,10 @@ columns:
 
 .. ipython:: python
 
-    data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'
+    data = ('a,b,c\n'
+            '1,2,3\n'
+            '4,5,6\n'
+            '7,8,9')
     print(data)
 
     df = pd.read_csv(StringIO(data), dtype=object)
@@ -387,7 +396,11 @@ of :func:`~pandas.read_csv`:
 
 .. ipython:: python
 
-    data = "col_1\n1\n2\n'A'\n4.22"
+    data = ("col_1\n"
+            "1\n"
+            "2\n"
+            "'A'\n"
+            "4.22")
     df = pd.read_csv(StringIO(data), converters={'col_1': str})
     df
     df['col_1'].apply(type).value_counts()
@@ -455,7 +468,10 @@ Specifying Categorical dtype
 
 .. ipython:: python
 
-   data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3'
+   data = ('col1,col2,col3\n'
+           'a,b,1\n'
+           'a,b,2\n'
+           'c,d,3')
 
    pd.read_csv(StringIO(data))
    pd.read_csv(StringIO(data)).dtypes
@@ -524,7 +540,10 @@ used as the column names:
 
 .. ipython:: python
 
-    data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'
+    data = ('a,b,c\n'
+            '1,2,3\n'
+            '4,5,6\n'
+            '7,8,9')
     print(data)
     pd.read_csv(StringIO(data))
 
@@ -543,7 +562,11 @@ If the header is in a row other than the first, pass the row number to
 
 .. ipython:: python
 
-    data = 'skip this skip it\na,b,c\n1,2,3\n4,5,6\n7,8,9'
+    data = ('skip this skip it\n'
+            'a,b,c\n'
+            '1,2,3\n'
+            '4,5,6\n'
+            '7,8,9')
     pd.read_csv(StringIO(data), header=1)
 
 .. note::
@@ -564,7 +587,9 @@ distinguish between them so as to prevent overwriting data:
 
 .. ipython :: python
 
-   data = 'a,b,a\n0,1,2\n3,4,5'
+   data = ('a,b,a\n'
+           '0,1,2\n'
+           '3,4,5')
    pd.read_csv(StringIO(data))
 
 There is no more duplicate data because ``mangle_dupe_cols=True`` by default,
@@ -632,15 +657,26 @@ be ignored. By default, completely blank lines will be ignored as well.
 
 .. ipython:: python
 
-   data = '\na,b,c\n  \n# commented line\n1,2,3\n\n4,5,6'
+   data = ('\n'
+           'a,b,c\n'
+           '  \n'
+           '# commented line\n'
+           '1,2,3\n'
+           '\n'
+           '4,5,6')
    print(data)
    pd.read_csv(StringIO(data), comment='#')
 
 If ``skip_blank_lines=False``, then ``read_csv`` will not ignore blank lines:
 
 .. ipython:: python
 
-   data = 'a,b,c\n\n1,2,3\n\n\n4,5,6'
+   data = ('a,b,c\n'
+           '\n'
+           '1,2,3\n'
+           '\n'
+           '\n'
+           '4,5,6')
    pd.read_csv(StringIO(data), skip_blank_lines=False)
 
 .. warning::
@@ -651,7 +687,10 @@ If ``skip_blank_lines=False``, then ``read_csv`` will not ignore blank lines:
 
    .. ipython:: python
 
-      data = '#comment\na,b,c\nA,B,C\n1,2,3'
+      data = ('#comment\n'
+              'a,b,c\n'
+              'A,B,C\n'
+              '1,2,3')
       pd.read_csv(StringIO(data), comment='#', header=1)
       data = 'A,B,C\n#comment\na,b,c\n1,2,3'
       pd.read_csv(StringIO(data), comment='#', skiprows=2)
@@ -661,15 +700,14 @@ If ``skip_blank_lines=False``, then ``read_csv`` will not ignore blank lines:
 
 .. ipython:: python
 
-   data = '\n'.join(['# empty',
-                     '# second empty line',
-                     '# third empty',
-                     'line',
-                     'X,Y,Z',
-                     '1,2,3',
-                     'A,B,C',
-                     '1,2.,4.',
-                     '5.,NaN,10.0'])
+   data = ('# empty\n'
+           '# second empty line\n'
+           '# third emptyline\n'
+           'X,Y,Z\n'
+           '1,2,3\n'
+           'A,B,C\n'
+           '1,2.,4.\n'
+           '5.,NaN,10.0\n')
    print(data)
    pd.read_csv(StringIO(data), comment='#', skiprows=4, header=1)
 
@@ -724,7 +762,9 @@ result in byte strings being decoded to unicode in the result:
 
 .. ipython:: python
 
-   data = b'word,length\nTr\xc3\xa4umen,7\nGr\xc3\xbc\xc3\x9fe,5'
+   data = (b'word,length\n'
+           b'Tr\xc3\xa4umen,7\n'
+           b'Gr\xc3\xbc\xc3\x9fe,5')
    data = data.decode('utf8').encode('latin-1')
    df = pd.read_csv(BytesIO(data), encoding='latin-1')
    df
@@ -745,12 +785,16 @@ first column will be used as the ``DataFrame``'s row names:
 
 .. ipython:: python
 
-    data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'
+    data = ('a,b,c\n'
+            '4,apple,bat,5.7\n'
+            '8,orange,cow,10')
     pd.read_csv(StringIO(data))
 
 .. ipython:: python
 
-    data = 'index,a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'
+    data = ('index,a,b,c\n'
+            '4,apple,bat,5.7\n'
+            '8,orange,cow,10')
     pd.read_csv(StringIO(data), index_col=0)
 
 Ordinarily, you can achieve this behavior using the ``index_col`` option.
@@ -761,7 +805,9 @@ index column inference and discard the last column, pass ``index_col=False``:
 
 .. ipython:: python
 
-    data = 'a,b,c\n4,apple,bat,\n8,orange,cow,'
+    data = ('a,b,c\n'
+            '4,apple,bat,\n'
+            '8,orange,cow,')
     print(data)
     pd.read_csv(StringIO(data))
     pd.read_csv(StringIO(data), index_col=False)
@@ -771,7 +817,9 @@ If a subset of data is being parsed using the ``usecols`` option, the
 
 .. ipython:: python
 
-    data = 'a,b,c\n4,apple,bat,\n8,orange,cow,'
+    data = ('a,b,c\n'
+            '4,apple,bat,\n'
+            '8,orange,cow,')
     print(data)
     pd.read_csv(StringIO(data), usecols=['b', 'c'])
     pd.read_csv(StringIO(data), usecols=['b', 'c'], index_col=0)
@@ -5451,6 +5499,7 @@ And here's the code:
    sz = 1000000
    df = pd.DataFrame({'A': randn(sz), 'B': [1] * sz})
 
+
    def test_sql_write(df):
        if os.path.exists('test.sql'):
            os.remove('test.sql')