diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst
index 9221f2685d79b..5a62e7dccea34 100644
--- a/doc/source/dsintro.rst
+++ b/doc/source/dsintro.rst
@@ -1,18 +1,23 @@
 .. currentmodule:: pandas
-.. _dsintro:
-
 
 .. ipython:: python
    :suppress:
 
    import numpy as np
-   from pandas import *
-   randn = np.random.randn
    np.set_printoptions(precision=4, suppress=True)
-   set_option('display.precision', 4, 'display.max_columns', 8)
-   options.display.max_rows=15
    import pandas as pd
+   pd.set_option('display.precision', 4, 'display.max_columns', 8)
+   pd.options.display.max_rows = 15
+
+   import matplotlib
+   try:
+      matplotlib.style.use('ggplot')
+   except AttributeError:
+      pd.options.display.mpl_style = 'default'
+   import matplotlib.pyplot as plt
+   plt.close('all')
 
+.. _dsintro:
 
 ************************
 Intro to Data Structures
@@ -26,9 +31,7 @@ objects. To get started, import numpy and load pandas into your namespace:
 .. ipython:: python
 
    import numpy as np
-   # will use a lot in examples
-   randn = np.random.randn
-   from pandas import *
+   import pandas as pd
 
 Here is a basic tenet to keep in mind: **data alignment is intrinsic**. The link
 between labels and data will not be broken unless done so explicitly by you.
@@ -36,13 +39,6 @@ between labels and data will not be broken unless done so explicitly by you.
 We'll give a brief intro to the data structures, then consider all of the broad
 categories of functionality and methods in separate sections.
 
-When using pandas, we recommend the following import convention:
-
-.. code-block:: python
-
-   import pandas as pd
-
-
 .. _basics.series:
 
 Series
@@ -60,7 +56,7 @@ labels are collectively referred to as the **index**. The basic method to create
 
 ::
 
-    >>> s = Series(data, index=index)
+    >>> s = pd.Series(data, index=index)
 
 Here, ``data`` can be many different things:
 
@@ -78,11 +74,11 @@ index is passed, one will be created having values ``[0, ..., len(data) - 1]``.
 
 .. ipython:: python
 
-   s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
+   s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
    s
    s.index
 
-   Series(randn(5))
+   pd.Series(np.random.randn(5))
 
 .. note::
 
@@ -101,8 +97,8 @@ constructed from the sorted keys of the dict, if possible.
 .. ipython:: python
 
    d = {'a' : 0., 'b' : 1., 'c' : 2.}
-   Series(d)
-   Series(d, index=['b', 'c', 'd', 'a'])
+   pd.Series(d)
+   pd.Series(d, index=['b', 'c', 'd', 'a'])
 
 .. note::
 
@@ -113,7 +109,7 @@ provided. The value will be repeated to match the length of **index**
 
 .. ipython:: python
 
-   Series(5., index=['a', 'b', 'c', 'd', 'e'])
+   pd.Series(5., index=['a', 'b', 'c', 'd', 'e'])
 
 Series is ndarray-like
 ~~~~~~~~~~~~~~~~~~~~~~
@@ -211,7 +207,7 @@ Series can also have a ``name`` attribute:
 
 .. ipython:: python
 
-   s = Series(np.random.randn(5), name='something')
+   s = pd.Series(np.random.randn(5), name='something')
    s
    s.name
 
@@ -254,13 +250,13 @@ keys.
 
 .. ipython:: python
 
-    d = {'one' : Series([1., 2., 3.], index=['a', 'b', 'c']),
-         'two' : Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
-    df = DataFrame(d)
+    d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
+         'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
+    df = pd.DataFrame(d)
     df
 
-    DataFrame(d, index=['d', 'b', 'a'])
-    DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])
+    pd.DataFrame(d, index=['d', 'b', 'a'])
+    pd.DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])
 
 The row and column labels can be accessed respectively by accessing the
 **index** and **columns** attributes:
@@ -286,8 +282,8 @@ result will be ``range(n)``, where ``n`` is the array length.
 
    d = {'one' : [1., 2., 3., 4.],
         'two' : [4., 3., 2., 1.]}
-   DataFrame(d)
-   DataFrame(d, index=['a', 'b', 'c', 'd'])
+   pd.DataFrame(d)
+   pd.DataFrame(d, index=['a', 'b', 'c', 'd'])
 
 From structured or record array
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -296,12 +292,12 @@ This case is handled identically to a dict of arrays.
 
 .. ipython:: python
 
-   data = np.zeros((2,),dtype=[('A', 'i4'),('B', 'f4'),('C', 'a10')])
-   data[:] = [(1,2.,'Hello'),(2,3.,"World")]
+   data = np.zeros((2,), dtype=[('A', 'i4'),('B', 'f4'),('C', 'a10')])
+   data[:] = [(1,2.,'Hello'), (2,3.,"World")]
 
-   DataFrame(data)
-   DataFrame(data, index=['first', 'second'])
-   DataFrame(data, columns=['C', 'A', 'B'])
+   pd.DataFrame(data)
+   pd.DataFrame(data, index=['first', 'second'])
+   pd.DataFrame(data, columns=['C', 'A', 'B'])
 
 .. note::
 
@@ -316,9 +312,9 @@ From a list of dicts
 .. ipython:: python
 
    data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]
-   DataFrame(data2)
-   DataFrame(data2, index=['first', 'second'])
-   DataFrame(data2, columns=['a', 'b'])
+   pd.DataFrame(data2)
+   pd.DataFrame(data2, index=['first', 'second'])
+   pd.DataFrame(data2, columns=['a', 'b'])
 
 .. _basics.dataframe.from_dict_of_tuples:
 
@@ -329,11 +325,11 @@ You can automatically create a multi-indexed frame by passing a tuples dictionar
 
 .. ipython:: python
 
-   DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2},
-              ('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4},
-              ('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6},
-              ('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8},
-              ('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}})
+   pd.DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2},
+                 ('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4},
+                 ('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6},
+                 ('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8},
+                 ('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}})
 
 .. _basics.dataframe.from_series:
 
@@ -376,7 +372,7 @@ For example:
 .. ipython:: python
 
    data
-   DataFrame.from_records(data, index='C')
+   pd.DataFrame.from_records(data, index='C')
 
 .. _basics.dataframe.from_items:
 
@@ -391,15 +387,15 @@ of columns:
 
 .. ipython:: python
 
-   DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])])
+   pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])])
 
 If you pass ``orient='index'``, the keys will be the row labels. But in this
 case you must also pass the desired column names:
 
 .. ipython:: python
 
-   DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])],
-                        orient='index', columns=['one', 'two', 'three'])
+   pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])],
+                           orient='index', columns=['one', 'two', 'three'])
 
 Column selection, addition, deletion
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -465,7 +461,7 @@ derived from existing columns.
 
 .. ipython:: python
 
-   iris = read_csv('data/iris.data')
+   iris = pd.read_csv('data/iris.data')
    iris.head()
 
    (iris.assign(sepal_ratio = iris['SepalWidth'] / iris['SepalLength'])
@@ -564,8 +560,8 @@ union of the column and row labels.
 
 .. ipython:: python
 
-    df = DataFrame(randn(10, 4), columns=['A', 'B', 'C', 'D'])
-    df2 = DataFrame(randn(7, 3), columns=['A', 'B', 'C'])
+    df = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D'])
+    df2 = pd.DataFrame(np.random.randn(7, 3), columns=['A', 'B', 'C'])
     df + df2
 
 When doing an operation between DataFrame and Series, the default behavior is
@@ -583,8 +579,8 @@ also contains dates, the broadcasting will be column-wise:
 .. ipython:: python
    :okwarning:
 
-   index = date_range('1/1/2000', periods=8)
-   df = DataFrame(randn(8, 3), index=index, columns=list('ABC'))
+   index = pd.date_range('1/1/2000', periods=8)
+   df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=list('ABC'))
    df
    type(df['A'])
    df - df['A']
@@ -619,8 +615,8 @@ Boolean operators work as well:
 
 .. ipython:: python
 
-   df1 = DataFrame({'a' : [1, 0, 1], 'b' : [0, 1, 1] }, dtype=bool)
-   df2 = DataFrame({'a' : [0, 1, 1], 'b' : [1, 1, 0] }, dtype=bool)
+   df1 = pd.DataFrame({'a' : [1, 0, 1], 'b' : [0, 1, 1] }, dtype=bool)
+   df2 = pd.DataFrame({'a' : [0, 1, 1], 'b' : [1, 1, 0] }, dtype=bool)
    df1 & df2
    df1 | df2
    df1 ^ df2
@@ -660,7 +656,7 @@ Similarly, the dot method on Series implements dot product:
 
 .. ipython:: python
 
-   s1 = Series(np.arange(5,10))
+   s1 = pd.Series(np.arange(5,10))
    s1.dot(s1)
 
 DataFrame is not intended to be a drop-in replacement for ndarray as its
@@ -682,7 +678,7 @@ R package):
 
 .. ipython:: python
 
-   baseball = read_csv('data/baseball.csv')
+   baseball = pd.read_csv('data/baseball.csv')
    print(baseball)
    baseball.info()
 
@@ -704,21 +700,21 @@ default:
 
 .. ipython:: python
 
-   DataFrame(randn(3, 12))
+   pd.DataFrame(np.random.randn(3, 12))
 
 You can change how much to print on a single row by setting the ``display.width``
 option:
 
 .. ipython:: python
 
-   set_option('display.width', 40) # default is 80
+   pd.set_option('display.width', 40) # default is 80
 
-   DataFrame(randn(3, 12))
+   pd.DataFrame(np.random.randn(3, 12))
 
 .. ipython:: python
    :suppress:
 
-   reset_option('display.width')
+   pd.reset_option('display.width')
 
 You can also disable this feature via the ``expand_frame_repr`` option.
 This will print the table in one block.
@@ -731,8 +727,8 @@ accessed like attributes:
 
 .. ipython:: python
 
-   df = DataFrame({'foo1' : np.random.randn(5),
-                   'foo2' : np.random.randn(5)})
+   df = pd.DataFrame({'foo1' : np.random.randn(5),
+                      'foo2' : np.random.randn(5)})
    df
    df.foo1
 
@@ -770,9 +766,9 @@ From 3D ndarray with optional axis labels
 
 .. ipython:: python
 
-   wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
-              major_axis=date_range('1/1/2000', periods=5),
-              minor_axis=['A', 'B', 'C', 'D'])
+   wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
+                 major_axis=pd.date_range('1/1/2000', periods=5),
+                 minor_axis=['A', 'B', 'C', 'D'])
    wp
 
 
@@ -781,9 +777,9 @@ From dict of DataFrame objects
 
 .. ipython:: python
 
-   data = {'Item1' : DataFrame(randn(4, 3)),
-           'Item2' : DataFrame(randn(4, 2))}
-   Panel(data)
+   data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)),
+           'Item2' : pd.DataFrame(np.random.randn(4, 2))}
+   pd.Panel(data)
 
 Note that the values in the dict need only be **convertible to
 DataFrame**. Thus, they can be any of the other valid inputs to DataFrame as
@@ -803,7 +799,7 @@ For example, compare to the construction above:
 
 .. ipython:: python
 
-   Panel.from_dict(data, orient='minor')
+   pd.Panel.from_dict(data, orient='minor')
 
 Orient is especially useful for mixed-type DataFrames. If you pass a dict of
 DataFrame objects with mixed-type columns, all of the data will get upcasted to
@@ -811,11 +807,11 @@ DataFrame objects with mixed-type columns, all of the data will get upcasted to
 
 .. ipython:: python
 
-   df = DataFrame({'a': ['foo', 'bar', 'baz'],
-                   'b': np.random.randn(3)})
+   df = pd.DataFrame({'a': ['foo', 'bar', 'baz'],
+                      'b': np.random.randn(3)})
    df
    data = {'item1': df, 'item2': df}
-   panel = Panel.from_dict(data, orient='minor')
+   panel = pd.Panel.from_dict(data, orient='minor')
    panel['a']
    panel['b']
    panel['b'].dtypes
@@ -838,8 +834,8 @@ a DataFrame with a two-level index to a Panel.
 
 .. ipython:: python
 
-   midx = MultiIndex(levels=[['one', 'two'], ['x','y']], labels=[[1,1,0,0],[1,0,1,0]])
-   df = DataFrame({'A' : [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)
+   midx = pd.MultiIndex(levels=[['one', 'two'], ['x','y']], labels=[[1,1,0,0],[1,0,1,0]])
+   df = pd.DataFrame({'A' : [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)
    df.to_panel()
 
 .. _dsintro.panel_item_selection:
@@ -897,7 +893,7 @@ Another way to change the dimensionality of an object is to ``squeeze`` a 1-len
 .. ipython:: python
 
    wp.reindex(items=['Item1']).squeeze()
-   wp.reindex(items=['Item1'],minor=['B']).squeeze()
+   wp.reindex(items=['Item1'], minor=['B']).squeeze()
 
 
 Conversion to DataFrame
@@ -910,9 +906,9 @@ method:
 
 .. ipython:: python
 
-   panel = Panel(np.random.randn(3, 5, 4), items=['one', 'two', 'three'],
-                 major_axis=date_range('1/1/2000', periods=5),
-                 minor_axis=['a', 'b', 'c', 'd'])
+   panel = pd.Panel(np.random.randn(3, 5, 4), items=['one', 'two', 'three'],
+                    major_axis=pd.date_range('1/1/2000', periods=5),
+                    minor_axis=['a', 'b', 'c', 'd'])
    panel.to_frame()
 
 
@@ -931,7 +927,6 @@ containers.
     DataFrames
   - **minor_axis**: axis 3, it is the **columns** of each of the DataFrames
 
-
 ``Panel4D`` is a sub-class of ``Panel``, so most methods that work on Panels are
 applicable to Panel4D. The following methods are disabled:
 
@@ -944,11 +939,11 @@ From 4D ndarray with optional axis labels
 
 .. ipython:: python
 
-   p4d = Panel4D(randn(2, 2, 5, 4),
-              labels=['Label1','Label2'],
-              items=['Item1', 'Item2'],
-              major_axis=date_range('1/1/2000', periods=5),
-              minor_axis=['A', 'B', 'C', 'D'])
+   p4d = pd.Panel4D(np.random.randn(2, 2, 5, 4),
+                    labels=['Label1','Label2'],
+                    items=['Item1', 'Item2'],
+                    major_axis=pd.date_range('1/1/2000', periods=5),
+                    minor_axis=['A', 'B', 'C', 'D'])
    p4d
 
 
@@ -957,9 +952,9 @@ From dict of Panel objects
 
 .. ipython:: python
 
-   data = { 'Label1' : Panel({ 'Item1' : DataFrame(randn(4, 3)) }),
-            'Label2' : Panel({ 'Item2' : DataFrame(randn(4, 2)) }) }
-   Panel4D(data)
+   data = { 'Label1' : pd.Panel({ 'Item1' : pd.DataFrame(np.random.randn(4, 3)) }),
+            'Label2' : pd.Panel({ 'Item2' : pd.DataFrame(np.random.randn(4, 2)) }) }
+   pd.Panel4D(data)
 
 Note that the values in the dict need only be **convertible to Panels**.
 Thus, they can be any of the other valid inputs to Panel as per above.
@@ -1022,7 +1017,7 @@ Here we slice to a Panel4D.
         orders  = [ 'cool', 'labels','items','major_axis','minor_axis'],
         slices  = { 'labels' : 'labels', 'items' : 'items',
 	                'major_axis' : 'major_axis', 'minor_axis' : 'minor_axis' },
-        slicer  = Panel4D,
+        slicer  = pd.Panel4D,
         aliases = { 'major' : 'major_axis', 'minor' : 'minor_axis' },
         stat_axis    = 2)
 
diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst
index 517c91c93d821..4ada4d4bbdfe5 100644
--- a/doc/source/enhancingperf.rst
+++ b/doc/source/enhancingperf.rst
@@ -5,17 +5,14 @@
 .. ipython:: python
    :suppress:
 
-   import os
-   import csv
-   from pandas import DataFrame, Series
-   import pandas as pd
-   pd.options.display.max_rows=15
-
    import numpy as np
    np.random.seed(123456)
-   randn = np.random.randn
-   randint = np.random.randint
    np.set_printoptions(precision=4, suppress=True)
+   import pandas as pd
+   pd.options.display.max_rows=15
+
+   import os
+   import csv
 
 
 *********************
@@ -49,7 +46,10 @@ We have a DataFrame to which we want to apply a function row-wise.
 
 .. ipython:: python
 
-   df = DataFrame({'a': randn(1000), 'b': randn(1000),'N': randint(100, 1000, (1000)), 'x': 'x'})
+   df = pd.DataFrame({'a': np.random.randn(1000),
+                      'b': np.random.randn(1000),
+                      'N': np.random.randint(100, 1000, (1000)),
+                      'x': 'x'})
    df
 
 Here's the function in pure python:
@@ -94,7 +94,8 @@ hence we'll concentrate our efforts cythonizing these two functions.
 Plain cython
 ~~~~~~~~~~~~
 
-First we're going to need to import the cython magic function to ipython:
+First we're going to need to import the cython magic function to ipython (for
+cython versions >=0.21 you can use ``%load_ext Cython``):
 
 .. ipython:: python
 
@@ -335,7 +336,7 @@ We simply take the plain python code from above and annotate with the ``@jit`` d
 
     def compute_numba(df):
        result = apply_integrate_f_numba(df['a'].values, df['b'].values, df['N'].values)
-       return Series(result, index=df.index, name='result')
+       return pd.Series(result, index=df.index, name='result')
 
 Similar to above, we directly pass ``numpy`` arrays directly to the numba function. Further
 we are wrapping the results to provide a nice interface by passing/returning pandas objects.
@@ -433,18 +434,13 @@ First let's create a few decent-sized arrays to play with:
 
 .. ipython:: python
 
-   import pandas as pd
-   from pandas import DataFrame, Series
-   from numpy.random import randn
-   import numpy as np
    nrows, ncols = 20000, 100
-   df1, df2, df3, df4 = [DataFrame(randn(nrows, ncols)) for _ in range(4)]
+   df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows, ncols)) for _ in range(4)]
 
 
 Now let's compare adding them together using plain ol' Python versus
 :func:`~pandas.eval`:
 
-
 .. ipython:: python
 
    %timeit df1 + df2 + df3 + df4
@@ -467,10 +463,9 @@ Now let's do the same thing but with comparisons:
 
 :func:`~pandas.eval` also works with unaligned pandas objects:
 
-
 .. ipython:: python
 
-   s = Series(randn(50))
+   s = pd.Series(np.random.randn(50))
    %timeit df1 + df2 + df3 + df4 + s
 
 .. ipython:: python
@@ -515,7 +510,7 @@ evaluate an expression in the "context" of a :class:`~pandas.DataFrame`.
 
 .. ipython:: python
 
-   df = DataFrame(randn(5, 2), columns=['a', 'b'])
+   df = pd.DataFrame(np.random.randn(5, 2), columns=['a', 'b'])
    df.eval('a + b')
 
 Any expression that is a valid :func:`pandas.eval` expression is also a valid
@@ -530,7 +525,7 @@ it must be a valid Python identifier.
 
 .. ipython:: python
 
-   df = DataFrame(dict(a=range(5), b=range(5, 10)))
+   df = pd.DataFrame(dict(a=range(5), b=range(5, 10)))
    df.eval('c = a + b')
    df.eval('d = a + b + c')
    df.eval('a = 1')
@@ -540,7 +535,7 @@ The equivalent in standard Python would be
 
 .. ipython:: python
 
-   df = DataFrame(dict(a=range(5), b=range(5, 10)))
+   df = pd.DataFrame(dict(a=range(5), b=range(5, 10)))
    df['c'] = df.a + df.b
    df['d'] = df.a + df.b + df.c
    df['a'] = 1
@@ -555,8 +550,8 @@ For example,
 
 .. code-block:: python
 
-   df = DataFrame(randn(5, 2), columns=['a', 'b'])
-   newcol = randn(len(df))
+   df = pd.DataFrame(np.random.randn(5, 2), columns=['a', 'b'])
+   newcol = np.random.randn(len(df))
    df.eval('b + newcol')
 
    UndefinedVariableError: name 'newcol' is not defined
@@ -567,8 +562,8 @@ expression by placing the ``@`` character in front of the name. For example,
 
 .. ipython:: python
 
-   df = DataFrame(randn(5, 2), columns=list('ab'))
-   newcol = randn(len(df))
+   df = pd.DataFrame(np.random.randn(5, 2), columns=list('ab'))
+   newcol = np.random.randn(len(df))
    df.eval('b + @newcol')
    df.query('b < @newcol')
 
@@ -582,7 +577,7 @@ name in an expression.
 
 .. ipython:: python
 
-   a = randn()
+   a = np.random.randn()
    df.query('@a < a')
    df.loc[a < df.a]  # same as the previous expression
 
@@ -710,8 +705,8 @@ you have an expression--for example
 
 .. ipython:: python
 
-   df = DataFrame({'strings': np.repeat(list('cba'), 3),
-                   'nums': np.repeat(range(3), 3)})
+   df = pd.DataFrame({'strings': np.repeat(list('cba'), 3),
+                      'nums': np.repeat(range(3), 3)})
    df
    df.query('strings == "a" and nums == 1')
 
diff --git a/doc/source/faq.rst b/doc/source/faq.rst
index 32290839ad71d..0ac33db8495c8 100644
--- a/doc/source/faq.rst
+++ b/doc/source/faq.rst
@@ -8,26 +8,18 @@ Frequently Asked Questions (FAQ)
 .. ipython:: python
    :suppress:
 
-   from datetime import datetime
    import numpy as np
    np.random.seed(123456)
-   from pandas import *
-   options.display.max_rows=15
-   randn = np.random.randn
-   randint = np.random.randint
    np.set_printoptions(precision=4, suppress=True)
-   from dateutil.relativedelta import relativedelta
-   from pandas.tseries.api import *
-   from pandas.tseries.offsets import *
-   import matplotlib.pyplot as plt
-   plt.close('all')
+   import pandas as pd
+   pd.options.display.max_rows = 15
    import matplotlib
    try:
       matplotlib.style.use('ggplot')
    except AttributeError:
-      options.display.mpl_style = 'default'
-   from pandas.compat import lrange
-
+      pd.options.display.mpl_style = 'default'
+   import matplotlib.pyplot as plt
+   plt.close('all')
 
 .. _df-memory-usage:
 
@@ -45,11 +37,11 @@ when calling ``df.info()``:
 .. ipython:: python
 
     dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
-               'complex128', 'object', 'bool']
+              'complex128', 'object', 'bool']
     n = 5000
     data = dict([ (t, np.random.randint(100, size=n).astype(t))
                     for t in dtypes])
-    df = DataFrame(data)
+    df = pd.DataFrame(data)
     df['categorical'] = df['object'].astype('category')
 
     df.info()
@@ -126,14 +118,14 @@ pandas ``Period`` and ``PeriodIndex``:
 
 .. ipython:: python
 
-   pnow('D')  # scikits.timeseries.now()
-   Period(year=2007, month=3, day=15, freq='D')
-   p = Period('1984Q3')
+   pd.pnow('D')  # scikits.timeseries.now()
+   pd.Period(year=2007, month=3, day=15, freq='D')
+   p = pd.Period('1984Q3')
    p
    p.asfreq('D', 'start')
    p.asfreq('D', 'end')
    (p + 3).asfreq('T') + 6 * 60 + 30
-   rng = period_range('1990', '2010', freq='A')
+   rng = pd.period_range('1990', '2010', freq='A')
    rng
    rng.asfreq('B', 'end') - 3
 
@@ -173,8 +165,8 @@ works on panels (3D). Here is some code that resamples daily data to montly:
 
 .. ipython:: python
 
-   rng = period_range('Jan-2000', periods=50, freq='M')
-   data = Series(np.random.randn(50), index=rng)
+   rng = pd.period_range('Jan-2000', periods=50, freq='M')
+   data = pd.Series(np.random.randn(50), index=rng)
    data
    data.resample('A', how=np.mean)
 
@@ -186,11 +178,11 @@ adopted to pandas's data structures. For example:
 
 .. ipython:: python
 
-   rng = period_range('1987Q2', periods=10, freq='Q-DEC')
-   data = Series(np.random.randn(10), index=rng)
+   rng = pd.period_range('1987Q2', periods=10, freq='Q-DEC')
+   data = pd.Series(np.random.randn(10), index=rng)
 
    @savefig skts_ts_plot.png
-   plt.figure(); data.plot()
+   data.plot()
 
 Converting to and from period format
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -215,8 +207,8 @@ with a DatetimeIndex to PeriodIndex:
 
 .. ipython:: python
 
-   rng = date_range('1/1/2000', periods=200, freq='D')
-   data = Series(np.random.randn(200), index=rng)
+   rng = pd.date_range('1/1/2000', periods=200, freq='D')
+   data = pd.Series(np.random.randn(200), index=rng)
    data[:10]
    data.index
    data.resample('M', kind='period')
@@ -226,8 +218,8 @@ interval (``'start'`` or ``'end'``) convention:
 
 .. ipython:: python
 
-   rng = period_range('Jan-2000', periods=50, freq='M')
-   data = Series(np.random.randn(50), index=rng)
+   rng = pd.period_range('Jan-2000', periods=50, freq='M')
+   data = pd.Series(np.random.randn(50), index=rng)
    resampled = data.resample('A', kind='timestamp', convention='end')
    resampled.index
 
@@ -244,7 +236,7 @@ using something similar to the following:
 
    x = np.array(list(range(10)), '>i4') # big endian
    newx = x.byteswap().newbyteorder() # force native byteorder
-   s = Series(newx)
+   s = pd.Series(newx)
 
 See `the NumPy documentation on byte order
 <http://docs.scipy.org/doc/numpy/user/basics.byteswapping.html>`__ for more
diff --git a/doc/source/gotchas.rst b/doc/source/gotchas.rst
index addeddcb0bdde..cf4a86d530180 100644
--- a/doc/source/gotchas.rst
+++ b/doc/source/gotchas.rst
@@ -4,13 +4,11 @@
 .. ipython:: python
    :suppress:
 
-   import os
    import numpy as np
-   from pandas import *
-   options.display.max_rows=15
-   randn = np.random.randn
    np.set_printoptions(precision=4, suppress=True)
-   from pandas.compat import lrange
+   import pandas as pd
+   pd.options.display.max_rows=15
+
 
 *******************
 Caveats and Gotchas
@@ -27,7 +25,7 @@ what the result of
 
 .. code-block:: python
 
-    >>> if Series([False, True, False]):
+    >>> if pd.Series([False, True, False]):
          ...
 
 should be. Should it be ``True`` because it's not zero-length? ``False`` because there are ``False`` values?
@@ -64,10 +62,10 @@ To evaluate single-element pandas objects in a boolean context, use the method `
 
 .. ipython:: python
 
-   Series([True]).bool()
-   Series([False]).bool()
-   DataFrame([[True]]).bool()
-   DataFrame([[False]]).bool()
+   pd.Series([True]).bool()
+   pd.Series([False]).bool()
+   pd.DataFrame([[True]]).bool()
+   pd.DataFrame([[False]]).bool()
 
 Bitwise boolean
 ~~~~~~~~~~~~~~~
@@ -147,7 +145,7 @@ arrays. For example:
 
 .. ipython:: python
 
-   s = Series([1, 2, 3, 4, 5], index=list('abcde'))
+   s = pd.Series([1, 2, 3, 4, 5], index=list('abcde'))
    s
    s.dtype
 
@@ -228,9 +226,9 @@ following code will generate exceptions:
 
 .. code-block:: python
 
-   s = Series(range(5))
+   s = pd.Series(range(5))
    s[-1]
-   df = DataFrame(np.random.randn(5, 4))
+   df = pd.DataFrame(np.random.randn(5, 4))
    df
    df.ix[-2:]
 
@@ -255,7 +253,7 @@ consider the following Series:
 
 .. ipython:: python
 
-   s = Series(randn(6), index=list('abcdef'))
+   s = pd.Series(np.random.randn(6), index=list('abcdef'))
    s
 
 Suppose we wished to slice from ``c`` to ``e``, using integers this would be
@@ -294,8 +292,8 @@ concise means of selecting data from a pandas object:
 
 .. ipython:: python
 
-   df = DataFrame(randn(6, 4), columns=['one', 'two', 'three', 'four'],
-                  index=list('abcdef'))
+   df = pd.DataFrame(np.random.randn(6, 4), columns=['one', 'two', 'three', 'four'],
+                     index=list('abcdef'))
    df
    df.ix[['b', 'c', 'e']]
 
@@ -326,7 +324,7 @@ cases where an index contains, say, both integers and strings:
 
 .. ipython:: python
 
-   s = Series([1, 2, 3], index=['a', 0, 1])
+   s = pd.Series([1, 2, 3], index=['a', 0, 1])
    s
    s.ix[[0, 1]]
    s.reindex([0, 1])
@@ -345,10 +343,10 @@ The use of ``reindex_like`` can potentially change the dtype of a ``Series``.
 
 .. ipython:: python
 
-   series = Series([1, 2, 3])
-   x = Series([True])
+   series = pd.Series([1, 2, 3])
+   x = pd.Series([True])
    x.dtype
-   x = Series([True]).reindex_like(series)
+   x = pd.Series([True]).reindex_like(series)
    x.dtype
 
 This is because ``reindex_like`` silently inserts ``NaNs`` and the ``dtype``
@@ -371,10 +369,10 @@ can be represented using a 64-bit integer is limited to approximately 584 years:
 
 .. ipython:: python
 
-   begin = Timestamp.min
+   begin = pd.Timestamp.min
    begin
 
-   end = Timestamp.max
+   end = pd.Timestamp.max
    end
 
 See :ref:`here <timeseries.oob>` for ways to represent data outside these bound.
@@ -404,10 +402,10 @@ of the new set of columns rather than the original ones:
    print(open('tmp.csv').read())
 
    date_spec = {'nominal': [1, 2], 'actual': [1, 3]}
-   df = read_csv('tmp.csv', header=None,
-                 parse_dates=date_spec,
-                 keep_date_col=True,
-                 index_col=0)
+   df = pd.read_csv('tmp.csv', header=None,
+                    parse_dates=date_spec,
+                    keep_date_col=True,
+                    index_col=0)
 
    # index_col=0 refers to the combined column "nominal" and not the original
    # first column of 'KORD' strings
@@ -417,6 +415,7 @@ of the new set of columns rather than the original ones:
 .. ipython:: python
    :suppress:
 
+   import os
    os.remove('tmp.csv')
 
 
@@ -569,7 +568,7 @@ using something similar to the following:
 
    x = np.array(list(range(10)), '>i4') # big endian
    newx = x.byteswap().newbyteorder() # force native byteorder
-   s = Series(newx)
+   s = pd.Series(newx)
 
 See `the NumPy documentation on byte order
 <http://docs.scipy.org/doc/numpy/user/basics.byteswapping.html>`__ for more