Doc fixes

doc/source/io.rst

@@ -1878,14 +1878,14 @@ to be parsed.
 
 .. code-block:: python
 
-   read_excel('path_to_file.xls', 'Sheet1', parse_cols=2, index_col=None, na_values=['NA'])
+   read_excel('path_to_file.xls', 'Sheet1', parse_cols=2)
 
 If `parse_cols` is a list of integers, then it is assumed to be the file column
 indices to be parsed.
 
 .. code-block:: python
 
-   read_excel('path_to_file.xls', 'Sheet1', parse_cols=[0, 2, 3], index_col=None, na_values=['NA'])
+   read_excel('path_to_file.xls', 'Sheet1', parse_cols=[0, 2, 3])
 
 To write a DataFrame object to a sheet of an Excel file, you can use the
 ``to_excel`` instance method.  The arguments are largely the same as ``to_csv``

doc/source/reshaping.rst

@@ -199,9 +199,9 @@ the right thing:
 Reshaping by Melt
 -----------------
 
-The ``melt`` function found in ``pandas.core.reshape`` is useful to massage a
+The :func:`~pandas.melt` function is useful to massage a
 DataFrame into a format where one or more columns are identifier variables,
-while all other columns, considered measured variables, are "pivoted" to the
+while all other columns, considered measured variables, are "unpivoted" to the
 row axis, leaving just two non-identifier columns, "variable" and "value". The
 names of those columns can be customized by supplying the ``var_name`` and
 ``value_name`` parameters.

doc/source/visualization.rst

@@ -261,7 +261,7 @@ bar plot:
 .. ipython:: python
    :suppress:
 
-   plt.figure();
+   plt.figure()
 
 .. ipython:: python
 
@@ -275,7 +275,7 @@ To produce a stacked bar plot, pass ``stacked=True``:
 .. ipython:: python
    :suppress:
 
-   plt.figure();
+   plt.figure()
 
 .. ipython:: python
 
@@ -287,7 +287,7 @@ To get horizontal bar plots, pass ``kind='barh'``:
 .. ipython:: python
    :suppress:
 
-   plt.figure();
+   plt.figure()
 
 .. ipython:: python
 
@@ -320,7 +320,7 @@ New since 0.10.0, the ``by`` keyword can be specified to plot grouped histograms
 .. ipython:: python
    :suppress:
 
-   plt.figure();
+   plt.figure()
 
 .. ipython:: python
 
@@ -434,12 +434,12 @@ Scatter plot matrix
 .. _visualization.kde:
 
 *New in 0.8.0* You can create density plots using the Series/DataFrame.plot and
-setting `kind='kde'`:
+setting ``kind='kde'``:
 
 .. ipython:: python
    :suppress:
 
-   plt.figure();
+   plt.figure()
 
 .. ipython:: python
 
@@ -460,7 +460,7 @@ too dense to plot each point individually.
 .. ipython:: python
    :suppress:
 
-   plt.figure();
+   plt.figure()
 
 .. ipython:: python
 
@@ -486,7 +486,7 @@ given by column ``z``. The bins are aggregated with numpy's ``max`` function.
 .. ipython:: python
    :suppress:
 
-   plt.figure();
+   plt.figure()
 
 .. ipython:: python
 

pandas/core/reshape.py

@@ -617,52 +617,88 @@ def melt(frame, id_vars=None, value_vars=None,
          var_name=None, value_name='value', col_level=None):
     """
     "Unpivots" a DataFrame from wide format to long format, optionally leaving
-    id variables set
+    identifier variables set.
+
+    This function is useful to massage a DataFrame into a format where one
+    or more columns are identifier variables (`id_vars`), while all other
+    columns, considered measured variables (`value_vars`), are "unpivoted" to 
+    the row axis, leaving just two non-identifier columns, 'variable' and
+    'value'.
 
     Parameters
     ----------
     frame : DataFrame
-    id_vars : tuple, list, or ndarray
-    value_vars : tuple, list, or ndarray
-    var_name : scalar, if None uses frame.column.name or 'variable'
+    id_vars : tuple, list, or ndarray, optional
+        Column(s) to use as identifier variables.
+    value_vars : tuple, list, or ndarray, optional
+        Column(s) to unpivot. If not specified, uses all columns that
+        are not set as `id_vars`.
+    var_name : scalar
+        Name to use for the 'variable' column. If None it uses
+        ``frame.columns.name`` or 'variable'.
     value_name : scalar, default 'value'
-    col_level : scalar, if columns are a MultiIndex then use this level to melt
+        Name to use for the 'value' column.
+    col_level : int or string, optional
+        If columns are a MultiIndex then use this level to melt.
+
+    See also
+    --------
+    pivot_table
+    DataFrame.pivot
 
     Examples
     --------
     >>> import pandas as pd
     >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
     ...                    'B': {0: 1, 1: 3, 2: 5},
     ...                    'C': {0: 2, 1: 4, 2: 6}})
-
     >>> df
        A  B  C
     0  a  1  2
     1  b  3  4
     2  c  5  6
 
-    >>> melt(df, id_vars=['A'], value_vars=['B'])
+    >>> pd.melt(df, id_vars=['A'], value_vars=['B'])
+       A variable  value
+    0  a        B      1
+    1  b        B      3
+    2  c        B      5
+
+    >>> pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])
        A variable  value
     0  a        B      1
     1  b        B      3
     2  c        B      5
+    3  a        C      2
+    4  b        C      4
+    5  c        C      6
+
+    The names of 'variable' and 'value' columns can be customized:
 
-    >>> melt(df, id_vars=['A'], value_vars=['B'],
-    ... var_name='myVarname', value_name='myValname')
+    >>> pd.melt(df, id_vars=['A'], value_vars=['B'],
+    ...         var_name='myVarname', value_name='myValname')
        A myVarname  myValname
     0  a         B          1
     1  b         B          3
     2  c         B          5
 
+    If you have multi-index columns:
+
     >>> df.columns = [list('ABC'), list('DEF')]
+    >>> df 
+       A  B  C
+       D  E  F
+    0  a  1  2
+    1  b  3  4
+    2  c  5  6
 
-    >>> melt(df, col_level=0, id_vars=['A'], value_vars=['B'])
+    >>> pd.melt(df, col_level=0, id_vars=['A'], value_vars=['B'])
        A variable  value
     0  a        B      1
     1  b        B      3
     2  c        B      5
 
-    >>> melt(df, id_vars=[('A', 'D')], value_vars=[('B', 'E')])
+    >>> pd.melt(df, id_vars=[('A', 'D')], value_vars=[('B', 'E')])
       (A, D) variable_0 variable_1  value
     0      a          B          E      1
     1      b          B          E      3