diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst
index bb424e29cfc21..acddf1bb3fe30 100644
--- a/doc/source/groupby.rst
+++ b/doc/source/groupby.rst
@@ -6,18 +6,16 @@
 
    import numpy as np
    np.random.seed(123456)
-   from pandas import *
-   options.display.max_rows=15
-   randn = np.random.randn
    np.set_printoptions(precision=4, suppress=True)
-   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 zip
+      pd.options.display.mpl_style = 'default'
+   import matplotlib.pyplot as plt
+   plt.close('all')
 
 *****************************
 Group By: split-apply-combine
@@ -105,11 +103,12 @@ consider the following DataFrame:
 
 .. ipython:: python
 
-   df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
-                          'foo', 'bar', 'foo', 'foo'],
-                   'B' : ['one', 'one', 'two', 'three',
-                          'two', 'two', 'one', 'three'],
-                   'C' : randn(8), 'D' : randn(8)})
+   df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
+                             'foo', 'bar', 'foo', 'foo'],
+                      'B' : ['one', 'one', 'two', 'three',
+                             'two', 'two', 'one', 'three'],
+                      'C' : np.random.randn(8),
+                      'D' : np.random.randn(8)})
    df
 
 We could naturally group by either the ``A`` or ``B`` columns or both:
@@ -142,7 +141,7 @@ output of aggregation functions will only contain unique index values:
 
    lst = [1, 2, 3, 1, 2, 3]
 
-   s = Series([1, 2, 3, 10, 20, 30], lst)
+   s = pd.Series([1, 2, 3, 10, 20, 30], lst)
 
    grouped = s.groupby(level=0)
 
@@ -189,7 +188,7 @@ however pass ``sort=False`` for potential speedups:
 
 .. ipython:: python
 
-   df2 = DataFrame({'X' : ['B', 'B', 'A', 'A'], 'Y' : [1, 2, 3, 4]})
+   df2 = pd.DataFrame({'X' : ['B', 'B', 'A', 'A'], 'Y' : [1, 2, 3, 4]})
    df2.groupby(['X'], sort=True).sum()
    df2.groupby(['X'], sort=False).sum()
 
@@ -203,10 +202,10 @@ however pass ``sort=False`` for potential speedups:
    n = 10
    weight = np.random.normal(166, 20, size=n)
    height = np.random.normal(60, 10, size=n)
-   time = date_range('1/1/2000', periods=n)
+   time = pd.date_range('1/1/2000', periods=n)
    gender = tm.choice(['male', 'female'], size=n)
-   df = DataFrame({'height': height, 'weight': weight,
-                           'gender': gender}, index=time)
+   df = pd.DataFrame({'height': height, 'weight': weight,
+                      'gender': gender}, index=time)
 
 .. ipython:: python
 
@@ -226,11 +225,12 @@ however pass ``sort=False`` for potential speedups:
 .. ipython:: python
    :suppress:
 
-   df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
-                          'foo', 'bar', 'foo', 'foo'],
-                   'B' : ['one', 'one', 'two', 'three',
-                          'two', 'two', 'one', 'three'],
-                   'C' : randn(8), 'D' : randn(8)})
+   df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
+                             'foo', 'bar', 'foo', 'foo'],
+                      'B' : ['one', 'one', 'two', 'three',
+                             'two', 'two', 'one', 'three'],
+                      'C' : np.random.randn(8),
+                      'D' : np.random.randn(8)})
 
 .. _groupby.multiindex:
 
@@ -248,8 +248,8 @@ natural to group by one of the levels of the hierarchy.
              ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
    tuples = list(zip(*arrays))
    tuples
-   index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
-   s = Series(randn(8), index=index)
+   index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
+   s = pd.Series(np.random.randn(8), index=index)
 
 .. ipython:: python
 
@@ -281,13 +281,13 @@ Also as of v0.6, grouping with multiple levels is supported.
              ['doo', 'doo', 'bee', 'bee', 'bop', 'bop', 'bop', 'bop'],
              ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
    tuples = list(zip(*arrays))
-   index = MultiIndex.from_tuples(tuples, names=['first', 'second', 'third'])
-   s = Series(randn(8), index=index)
+   index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second', 'third'])
+   s = pd.Series(np.random.randn(8), index=index)
 
 .. ipython:: python
 
    s
-   s.groupby(level=['first','second']).sum()
+   s.groupby(level=['first', 'second']).sum()
 
 More on the ``sum`` function and aggregation later.
 
@@ -499,9 +499,9 @@ to standardize the data within each group:
 
 .. ipython:: python
 
-   index = date_range('10/1/1999', periods=1100)
-   ts = Series(np.random.normal(0.5, 2, 1100), index)
-   ts = rolling_mean(ts, 100, 100).dropna()
+   index = pd.date_range('10/1/1999', periods=1100)
+   ts = pd.Series(np.random.normal(0.5, 2, 1100), index)
+   ts = pd.rolling_mean(ts, 100, 100).dropna()
 
    ts.head()
    ts.tail()
@@ -528,7 +528,7 @@ We can also visually compare the original and transformed data sets.
 
 .. ipython:: python
 
-   compare = DataFrame({'Original': ts, 'Transformed': transformed})
+   compare = pd.DataFrame({'Original': ts, 'Transformed': transformed})
 
    @savefig groupby_transform_plot.png
    compare.plot()
@@ -539,11 +539,11 @@ Another common data transform is to replace missing data with the group mean.
    :suppress:
 
    cols = ['A', 'B', 'C']
-   values = randn(1000, 3)
+   values = np.random.randn(1000, 3)
    values[np.random.randint(0, 1000, 100), 0] = np.nan
    values[np.random.randint(0, 1000, 50), 1] = np.nan
    values[np.random.randint(0, 1000, 200), 2] = np.nan
-   data_df = DataFrame(values, columns=cols)
+   data_df = pd.DataFrame(values, columns=cols)
 
 .. ipython:: python
 
@@ -599,7 +599,7 @@ than 2.
 
 .. ipython:: python
 
-   sf = Series([1, 1, 2, 3, 3, 3])
+   sf = pd.Series([1, 1, 2, 3, 3, 3])
    sf.groupby(sf).filter(lambda x: x.sum() > 2)
 
 The argument of ``filter`` must be a function that, applied to the group as a
@@ -610,7 +610,7 @@ with only a couple members.
 
 .. ipython:: python
 
-   dff = DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')})
+   dff = pd.DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')})
    dff.groupby('B').filter(lambda x: len(x) > 2)
 
 Alternatively, instead of dropping the offending groups, we can return a
@@ -672,9 +672,9 @@ next). This enables some operations to be carried out rather succinctly:
 
 .. ipython:: python
 
-   tsdf = DataFrame(randn(1000, 3),
-                    index=date_range('1/1/2000', periods=1000),
-                    columns=['A', 'B', 'C'])
+   tsdf = pd.DataFrame(np.random.randn(1000, 3),
+                       index=pd.date_range('1/1/2000', periods=1000),
+                       columns=['A', 'B', 'C'])
    tsdf.ix[::2] = np.nan
    grouped = tsdf.groupby(lambda x: x.year)
    grouped.fillna(method='pad')
@@ -689,8 +689,8 @@ The ``nlargest`` and ``nsmallest`` methods work on ``Series`` style groupbys:
 
 .. ipython:: python
 
-   s = Series([9, 8, 7, 5, 19, 1, 4.2, 3.3])
-   g = Series(list('abababab'))
+   s = pd.Series([9, 8, 7, 5, 19, 1, 4.2, 3.3])
+   g = pd.Series(list('abababab'))
    gb = s.groupby(g)
    gb.nlargest(3)
    gb.nsmallest(3)
@@ -721,8 +721,8 @@ The dimension of the returned result can also change:
     In [8]: grouped = df.groupby('A')['C']
 
     In [10]: def f(group):
-       ....:     return DataFrame({'original' : group,
-       ....:                       'demeaned' : group - group.mean()})
+       ....:     return pd.DataFrame({'original' : group,
+       ....:                          'demeaned' : group - group.mean()})
        ....:
 
     In [11]: grouped.apply(f)
@@ -732,8 +732,8 @@ The dimension of the returned result can also change:
 .. ipython:: python
 
     def f(x):
-      return Series([ x, x**2 ], index = ['x', 'x^s'])
-    s = Series(np.random.rand(5))
+      return pd.Series([ x, x**2 ], index = ['x', 'x^s'])
+    s = pd.Series(np.random.rand(5))
     s
     s.apply(f)
 
@@ -754,7 +754,7 @@ The dimension of the returned result can also change:
 
     .. ipython:: python
 
-        d = DataFrame({"a":["x", "y"], "b":[1,2]})
+        d = pd.DataFrame({"a":["x", "y"], "b":[1,2]})
         def identity(df):
             print df
             return df
@@ -802,9 +802,9 @@ can be used as group keys. If so, the order of the levels will be preserved:
 
 .. ipython:: python
 
-   data = Series(np.random.randn(100))
+   data = pd.Series(np.random.randn(100))
 
-   factor = qcut(data, [0, .25, .5, .75, 1.])
+   factor = pd.qcut(data, [0, .25, .5, .75, 1.])
 
    data.groupby(factor).mean()
 
@@ -813,27 +813,28 @@ can be used as group keys. If so, the order of the levels will be preserved:
 Grouping with a Grouper specification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Your may need to specify a bit more data to properly group. You can
+You may need to specify a bit more data to properly group. You can
 use the ``pd.Grouper`` to provide this local control.
 
 .. ipython:: python
 
-   import datetime as DT
-
-   df = DataFrame({
-          'Branch' : 'A A A A A A A B'.split(),
-          'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(),
-          'Quantity': [1,3,5,1,8,1,9,3],
-          'Date' : [
-                DT.datetime(2013,1,1,13,0),
-                DT.datetime(2013,1,1,13,5),
-                DT.datetime(2013,10,1,20,0),
-                DT.datetime(2013,10,2,10,0),
-                DT.datetime(2013,10,1,20,0),
-                DT.datetime(2013,10,2,10,0),
-                DT.datetime(2013,12,2,12,0),
-                DT.datetime(2013,12,2,14,0),
-                ]})
+   import datetime
+
+   df = pd.DataFrame({
+            'Branch' : 'A A A A A A A B'.split(),
+            'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(),
+            'Quantity': [1,3,5,1,8,1,9,3],
+            'Date' : [
+                datetime.datetime(2013,1,1,13,0),
+                datetime.datetime(2013,1,1,13,5),
+                datetime.datetime(2013,10,1,20,0),
+                datetime.datetime(2013,10,2,10,0),
+                datetime.datetime(2013,10,1,20,0),
+                datetime.datetime(2013,10,2,10,0),
+                datetime.datetime(2013,12,2,12,0),
+                datetime.datetime(2013,12,2,14,0),
+                ]
+            })
 
    df
 
@@ -862,7 +863,7 @@ Just like for a DataFrame or Series you can call head and tail on a groupby:
 
 .. ipython:: python
 
-   df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
+   df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
    df
 
    g = df.groupby('A')
@@ -894,7 +895,7 @@ To select from a DataFrame or Series the nth item, use the nth method. This is a
 
 .. ipython:: python
 
-   df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
+   df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
    g = df.groupby('A')
 
    g.nth(0)
@@ -919,7 +920,7 @@ As with other methods, passing ``as_index=False``, will achieve a filtration, wh
 
 .. ipython:: python
 
-   df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
+   df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
    g = df.groupby('A',as_index=False)
 
    g.nth(0)
@@ -929,8 +930,8 @@ You can also select multiple rows from each group by specifying multiple nth val
 
 .. ipython:: python
 
-   business_dates = date_range(start='4/1/2014', end='6/30/2014', freq='B')
-   df = DataFrame(1, index=business_dates, columns=['a', 'b'])
+   business_dates = pd.date_range(start='4/1/2014', end='6/30/2014', freq='B')
+   df = pd.DataFrame(1, index=business_dates, columns=['a', 'b'])
    # get the first, 4th, and last date index for each month
    df.groupby((df.index.year, df.index.month)).nth([0, 3, -1])
 
@@ -961,7 +962,7 @@ the values in column 1 where the group is "B" are 3 higher on average.
 .. ipython:: python
 
    np.random.seed(1234)
-   df = DataFrame(np.random.randn(50, 2))
+   df = pd.DataFrame(np.random.randn(50, 2))
    df['g'] = np.random.choice(['A', 'B'], size=50)
    df.loc[df['g'] == 'B', 1] += 3
 
@@ -1010,11 +1011,11 @@ column index name will be used as the name of the inserted column:
 .. ipython:: python
 
    df = pd.DataFrame({
-        'a':  [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2],
-        'b':  [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1],
-        'c':  [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
-        'd':  [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
-        })
+            'a':  [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2],
+            'b':  [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1],
+            'c':  [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
+            'd':  [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
+            })
 
    def compute_metrics(x):
        result = {'b_sum': x['b'].sum(), 'c_mean': x['c'].mean()}
diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst
index a1912032bc3bf..618a2ae42c65f 100644
--- a/doc/source/indexing.rst
+++ b/doc/source/indexing.rst
@@ -6,15 +6,10 @@
    :suppress:
 
    import numpy as np
-   import random
    np.random.seed(123456)
-   from pandas import *
-   options.display.max_rows=15
-   import pandas as pd
-   randn = np.random.randn
-   randint = np.random.randint
    np.set_printoptions(precision=4, suppress=True)
-   from pandas.compat import range, zip
+   import pandas as pd
+   pd.options.display.max_rows=15
 
 ***************************
 Indexing and Selecting Data
@@ -162,10 +157,10 @@ indexing functionality:
 
 .. ipython:: python
 
-   dates = date_range('1/1/2000', periods=8)
-   df = DataFrame(randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
+   dates = pd.date_range('1/1/2000', periods=8)
+   df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
    df
-   panel = Panel({'one' : df, 'two' : df - df.mean()})
+   panel = pd.Panel({'one' : df, 'two' : df - df.mean()})
    panel
 
 .. note::
@@ -208,7 +203,7 @@ as an attribute:
 
 .. ipython:: python
 
-   sa = Series([1,2,3],index=list('abc'))
+   sa = pd.Series([1,2,3],index=list('abc'))
    dfa = df.copy()
 
 .. ipython:: python
@@ -307,7 +302,7 @@ Selection By Label
 
   .. ipython:: python
 
-     dfl = DataFrame(np.random.randn(5,4), columns=list('ABCD'), index=date_range('20130101',periods=5))
+     dfl = pd.DataFrame(np.random.randn(5,4), columns=list('ABCD'), index=pd.date_range('20130101',periods=5))
      dfl
 
   .. code-block:: python
@@ -333,7 +328,7 @@ The ``.loc`` attribute is the primary access method. The following are valid inp
 
 .. ipython:: python
 
-   s1 = Series(np.random.randn(6),index=list('abcdef'))
+   s1 = pd.Series(np.random.randn(6),index=list('abcdef'))
    s1
    s1.loc['c':]
    s1.loc['b']
@@ -349,9 +344,9 @@ With a DataFrame
 
 .. ipython:: python
 
-   df1 = DataFrame(np.random.randn(6,4),
-                   index=list('abcdef'),
-                   columns=list('ABCD'))
+   df1 = pd.DataFrame(np.random.randn(6,4),
+                      index=list('abcdef'),
+                      columns=list('ABCD'))
    df1
    df1.loc[['a','b','d'],:]
 
@@ -403,7 +398,7 @@ The ``.iloc`` attribute is the primary access method. The following are valid in
 
 .. ipython:: python
 
-   s1 = Series(np.random.randn(5),index=list(range(0,10,2)))
+   s1 = pd.Series(np.random.randn(5), index=list(range(0,10,2)))
    s1
    s1.iloc[:3]
    s1.iloc[3]
@@ -419,9 +414,9 @@ With a DataFrame
 
 .. ipython:: python
 
-   df1 = DataFrame(np.random.randn(6,4),
-                   index=list(range(0,12,2)),
-                   columns=list(range(0,8,2)))
+   df1 = pd.DataFrame(np.random.randn(6,4),
+                      index=list(range(0,12,2)),
+                      columns=list(range(0,8,2)))
    df1
 
 Select via integer slicing
@@ -472,7 +467,7 @@ Out of range slice indexes are handled gracefully just as in Python/Numpy.
     x
     x[4:10]
     x[8:10]
-    s = Series(x)
+    s = pd.Series(x)
     s
     s.iloc[4:10]
     s.iloc[8:10]
@@ -488,7 +483,7 @@ returned)
 
 .. ipython:: python
 
-   dfl = DataFrame(np.random.randn(5,2),columns=list('AB'))
+   dfl = pd.DataFrame(np.random.randn(5,2), columns=list('AB'))
    dfl
    dfl.iloc[:,2:3]
    dfl.iloc[:,1:3]
@@ -516,7 +511,7 @@ A random selection of rows or columns from a Series, DataFrame, or Panel with th
 
 .. ipython :: python
 
-    s = Series([0,1,2,3,4,5])
+    s = pd.Series([0,1,2,3,4,5])
 
     # When no arguments are passed, returns 1 row.
     s.sample()
@@ -532,7 +527,7 @@ using the ``replace`` option:
 
 .. ipython :: python
 
-   s = Series([0,1,2,3,4,5])
+   s = pd.Series([0,1,2,3,4,5])
 
     # Without replacement (default):
     s.sample(n=6, replace=False)
@@ -547,7 +542,7 @@ to have different probabilities, you can pass the ``sample`` function sampling w
 
 .. ipython :: python
 
-    s = Series([0,1,2,3,4,5])
+    s = pd.Series([0,1,2,3,4,5])
     example_weights = [0, 0, 0.2, 0.2, 0.2, 0.4]
     s.sample(n=3, weights=example_weights)
 
@@ -561,21 +556,21 @@ as a string.
 
 .. ipython :: python
 
-    df2 = DataFrame({'col1':[9,8,7,6], 'weight_column':[0.5, 0.4, 0.1, 0]})
+    df2 = pd.DataFrame({'col1':[9,8,7,6], 'weight_column':[0.5, 0.4, 0.1, 0]})
     df2.sample(n = 3, weights = 'weight_column')
 
 ``sample`` also allows users to sample columns instead of rows using the ``axis`` argument.
 
 .. 	ipython :: python
 
-    df3 = DataFrame({'col1':[1,2,3], 'col2':[2,3,4]})
+    df3 = pd.DataFrame({'col1':[1,2,3], 'col2':[2,3,4]})
     df3.sample(n=1, axis=1)
 
 Finally, one can also set a seed for ``sample``'s random number generator using the ``random_state`` argument, which will accept either an integer (as a seed) or a numpy RandomState object.
 
 .. 	ipython :: python
 
-    df4 = DataFrame({'col1':[1,2,3], 'col2':[2,3,4]})
+    df4 = pd.DataFrame({'col1':[1,2,3], 'col2':[2,3,4]})
 
     # With a given seed, the sample will always draw the same rows.
     df4.sample(n=2, random_state=2)
@@ -594,7 +589,7 @@ In the ``Series`` case this is effectively an appending operation
 
 .. ipython:: python
 
-   se = Series([1,2,3])
+   se = pd.Series([1,2,3])
    se
    se[5] = 5.
    se
@@ -603,7 +598,7 @@ A ``DataFrame`` can be enlarged on either axis via ``.loc``
 
 .. ipython:: python
 
-   dfi = DataFrame(np.arange(6).reshape(3,2),
+   dfi = pd.DataFrame(np.arange(6).reshape(3,2),
                    columns=['A','B'])
    dfi
    dfi.loc[:,'C'] = dfi.loc[:,'A']
@@ -661,7 +656,7 @@ Using a boolean vector to index a Series works exactly as in a numpy ndarray:
 
 .. ipython:: python
 
-   s = Series(range(-3, 4))
+   s = pd.Series(range(-3, 4))
    s
    s[s > 0]
    s[(s < -1) | (s > 0.5)]
@@ -680,9 +675,9 @@ more complex criteria:
 
 .. ipython:: python
 
-   df2 = DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
-                    'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
-                    'c' : randn(7)})
+   df2 = pd.DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
+                       'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
+                       'c' : np.random.randn(7)})
 
    # only want 'two' or 'three'
    criterion = df2['a'].map(lambda x: x.startswith('t'))
@@ -713,7 +708,7 @@ select rows where one or more columns have values you want:
 
 .. ipython:: python
 
-   s = Series(np.arange(5),index=np.arange(5)[::-1],dtype='int64')
+   s = pd.Series(np.arange(5), index=np.arange(5)[::-1], dtype='int64')
    s
    s.isin([2, 4, 6])
    s[s.isin([2, 4, 6])]
@@ -733,8 +728,8 @@ in the membership check:
 
 .. ipython:: python
 
-   s_mi = Series(np.arange(6),
-                 index=pd.MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]))
+   s_mi = pd.Series(np.arange(6),
+                    index=pd.MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]))
    s_mi
    s_mi.iloc[s_mi.index.isin([(1, 'a'), (2, 'b'), (0, 'c')])]
    s_mi.iloc[s_mi.index.isin(['a', 'c', 'e'], level=1)]
@@ -746,8 +741,8 @@ wherever the element is in the sequence of values.
 
 .. ipython:: python
 
-   df = DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],
-                   'ids2': ['a', 'n', 'c', 'n']})
+   df = pd.DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],
+                      'ids2': ['a', 'n', 'c', 'n']})
 
    values = ['a', 'b', 1, 3]
 
@@ -801,8 +796,8 @@ Equivalent is ``df.where(df < 0)``
 .. ipython:: python
    :suppress:
 
-   dates = date_range('1/1/2000', periods=8)
-   df = DataFrame(randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
+   dates = pd.date_range('1/1/2000', periods=8)
+   df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
 
 .. ipython:: python
 
@@ -889,16 +884,10 @@ method that allows selection using an expression.
 You can get the value of the frame where column ``b`` has values
 between the values of columns ``a`` and ``c``. For example:
 
-.. ipython:: python
-   :suppress:
-
-   from numpy.random import randint, rand
-   np.random.seed(1234)
-
 .. ipython:: python
 
    n = 10
-   df = DataFrame(rand(n, 3), columns=list('abc'))
+   df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))
    df
 
    # pure python
@@ -912,7 +901,7 @@ with the name ``a``.
 
 .. ipython:: python
 
-   df = DataFrame(randint(n / 2, size=(n, 2)), columns=list('bc'))
+   df = pd.DataFrame(np.random.randint(n / 2, size=(n, 2)), columns=list('bc'))
    df.index.name = 'a'
    df
    df.query('a < b and b < c')
@@ -928,7 +917,7 @@ If instead you don't want to or cannot name your index, you can use the name
 
 .. ipython:: python
 
-   df = DataFrame(randint(n, size=(n, 2)), columns=list('bc'))
+   df = pd.DataFrame(np.random.randint(n, size=(n, 2)), columns=list('bc'))
    df
    df.query('index < b < c')
 
@@ -946,7 +935,7 @@ If instead you don't want to or cannot name your index, you can use the name
 
    .. ipython:: python
 
-      df = DataFrame({'a': randint(5, size=5)})
+      df = pd.DataFrame({'a': np.random.randint(5, size=5)})
       df.index.name = 'a'
       df.query('a > 2') # uses the column 'a', not the index
 
@@ -970,23 +959,20 @@ You can also use the levels of a ``DataFrame`` with a
 
 .. ipython:: python
 
-   import pandas.util.testing as tm
-
    n = 10
-   colors = tm.choice(['red', 'green'], size=n)
-   foods = tm.choice(['eggs', 'ham'], size=n)
+   colors = np.random.choice(['red', 'green'], size=n)
+   foods = np.random.choice(['eggs', 'ham'], size=n)
    colors
    foods
 
-   index = MultiIndex.from_arrays([colors, foods], names=['color', 'food'])
-   df = DataFrame(randn(n, 2), index=index)
+   index = pd.MultiIndex.from_arrays([colors, foods], names=['color', 'food'])
+   df = pd.DataFrame(np.random.randn(n, 2), index=index)
    df
    df.query('color == "red"')
 
 If the levels of the ``MultiIndex`` are unnamed, you can refer to them using
 special names:
 
-
 .. ipython:: python
 
    df.index.names = [None, None]
@@ -1008,9 +994,9 @@ having to specify which frame you're interested in querying
 
 .. ipython:: python
 
-   df = DataFrame(rand(n, 3), columns=list('abc'))
+   df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))
    df
-   df2 = DataFrame(rand(n + 2, 3), columns=df.columns)
+   df2 = pd.DataFrame(np.random.rand(n + 2, 3), columns=df.columns)
    df2
    expr = '0.0 <= a <= c <= 0.5'
    map(lambda frame: frame.query(expr), [df, df2])
@@ -1022,7 +1008,7 @@ Full numpy-like syntax
 
 .. ipython:: python
 
-   df = DataFrame(randint(n, size=(n, 3)), columns=list('abc'))
+   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)]
@@ -1065,8 +1051,9 @@ The ``in`` and ``not in`` operators
 .. ipython:: python
 
    # get all rows where columns "a" and "b" have overlapping values
-   df = DataFrame({'a': list('aabbccddeeff'), 'b': list('aaaabbbbcccc'),
-                   'c': randint(5, size=12), 'd': randint(9, size=12)})
+   df = pd.DataFrame({'a': list('aabbccddeeff'), 'b': list('aaaabbbbcccc'),
+                      'c': np.random.randint(5, size=12),
+                      'd': np.random.randint(9, size=12)})
    df
    df.query('a in b')
 
@@ -1139,8 +1126,8 @@ You can negate boolean expressions with the word ``not`` or the ``~`` operator.
 
 .. ipython:: python
 
-   df = DataFrame(rand(n, 3), columns=list('abc'))
-   df['bools'] = rand(len(df)) > 0.5
+   df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))
+   df['bools'] = np.random.rand(len(df)) > 0.5
    df.query('~bools')
    df.query('not bools')
    df.query('not bools') == df[~df.bools]
@@ -1192,7 +1179,7 @@ floating point values generated using ``numpy.random.randn()``.
 .. ipython:: python
    :suppress:
 
-   df = DataFrame(randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
+   df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
    df2 = df.copy()
 
 
@@ -1214,9 +1201,9 @@ should be taken instead.
 
 .. ipython:: python
 
-   df2 = DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
-                    'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
-                    'c' : np.random.randn(7)})
+   df2 = pd.DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
+                       'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
+                       'c' : np.random.randn(7)})
    df2.duplicated(['a','b'])
    df2.drop_duplicates(['a','b'])
    df2.drop_duplicates(['a','b'], take_last=True)
@@ -1242,7 +1229,7 @@ default value.
 
 .. ipython:: python
 
-   s = Series([1,2,3], index=['a','b','c'])
+   s = pd.Series([1,2,3], index=['a','b','c'])
    s.get('a')               # equivalent to s['a']
    s.get('x', default=-1)
 
@@ -1267,7 +1254,7 @@ numpy array.  For instance,
 
 .. ipython:: python
 
-  dflookup = DataFrame(np.random.rand(20,4), columns = ['A','B','C','D'])
+  dflookup = pd.DataFrame(np.random.rand(20,4), columns = ['A','B','C','D'])
   dflookup.lookup(list(range(0,10,2)), ['B','C','A','B','D'])
 
 .. _indexing.class:
@@ -1287,7 +1274,7 @@ lookups, data alignment, and reindexing. The easiest way to create an
 
 .. ipython:: python
 
-   index = Index(['e', 'd', 'a', 'b'])
+   index = pd.Index(['e', 'd', 'a', 'b'])
    index
    'd' in index
 
@@ -1296,26 +1283,26 @@ You can also pass a ``name`` to be stored in the index:
 
 .. ipython:: python
 
-   index = Index(['e', 'd', 'a', 'b'], name='something')
+   index = pd.Index(['e', 'd', 'a', 'b'], name='something')
    index.name
 
 The name, if set, will be shown in the console display:
 
 .. ipython:: python
 
-   index = Index(list(range(5)), name='rows')
-   columns = Index(['A', 'B', 'C'], name='cols')
-   df = DataFrame(np.random.randn(5, 3), index=index, columns=columns)
+   index = pd.Index(list(range(5)), name='rows')
+   columns = pd.Index(['A', 'B', 'C'], name='cols')
+   df = pd.DataFrame(np.random.randn(5, 3), index=index, columns=columns)
    df
    df['A']
 
+.. _indexing.set_metadata:
+
 Setting metadata
 ~~~~~~~~~~~~~~~~
 
 .. versionadded:: 0.13.0
 
-.. _indexing.set_metadata:
-
 Indexes are "mostly immutable", but it is possible to set and change their
 metadata, like the index ``name`` (or, for ``MultiIndex``, ``levels`` and
 ``labels``).
@@ -1328,7 +1315,7 @@ See :ref:`Advanced Indexing <advanced>` for usage of MultiIndexes.
 
 .. ipython:: python
 
-  ind = Index([1, 2, 3])
+  ind = pd.Index([1, 2, 3])
   ind.rename("apple")
   ind
   ind.set_names(["apple"], inplace=True)
@@ -1342,8 +1329,7 @@ See :ref:`Advanced Indexing <advanced>` for usage of MultiIndexes.
 
 .. ipython:: python
 
-
-  index = MultiIndex.from_product([range(3), ['one', 'two']], names=['first', 'second'])
+  index = pd.MultiIndex.from_product([range(3), ['one', 'two']], names=['first', 'second'])
   index
   index.levels[1]
   index.set_levels(["a", "b"], level=1)
@@ -1364,8 +1350,8 @@ operators. Difference is provided via the ``.difference()`` method.
 
 .. ipython:: python
 
-   a = Index(['c', 'b', 'a'])
-   b = Index(['c', 'e', 'd'])
+   a = pd.Index(['c', 'b', 'a'])
+   b = pd.Index(['c', 'e', 'd'])
    a | b
    a & b
    a.difference(b)
@@ -1377,8 +1363,8 @@ with duplicates dropped.
 
 .. ipython:: python
 
-   idx1 = Index([1, 2, 3, 4])
-   idx2 = Index([2, 3, 4, 5])
+   idx1 = pd.Index([1, 2, 3, 4])
+   idx2 = pd.Index([2, 3, 4, 5])
    idx1.sym_diff(idx2)
    idx1 ^ idx2
 
@@ -1401,10 +1387,10 @@ indexed DataFrame:
 .. ipython:: python
    :suppress:
 
-   data = DataFrame({'a' : ['bar', 'bar', 'foo', 'foo'],
-                     'b' : ['one', 'two', 'one', 'two'],
-                     'c' : ['z', 'y', 'x', 'w'],
-                     'd' : [1., 2., 3, 4]})
+   data = pd.DataFrame({'a' : ['bar', 'bar', 'foo', 'foo'],
+                        'b' : ['one', 'two', 'one', 'two'],
+                        'c' : ['z', 'y', 'x', 'w'],
+                        'd' : [1., 2., 3, 4]})
 
 .. ipython:: python
 
@@ -1482,12 +1468,12 @@ When setting values in a pandas object, care must be taken to avoid what is call
 
 .. ipython:: python
 
-   dfmi = DataFrame([list('abcd'),
-                     list('efgh'),
-                     list('ijkl'),
-                     list('mnop')],
-                    columns=MultiIndex.from_product([['one','two'],
-                                                     ['first','second']]))
+   dfmi = pd.DataFrame([list('abcd'),
+                        list('efgh'),
+                        list('ijkl'),
+                        list('mnop')],
+                       columns=pd.MultiIndex.from_product([['one','two'],
+                                                           ['first','second']]))
    dfmi
 
 Compare these two access methods:
@@ -1543,9 +1529,9 @@ which can take the values ``['raise','warn',None]``, where showing a warning is
 .. ipython:: python
    :okwarning:
 
-   dfb = DataFrame({'a' : ['one', 'one', 'two',
-                           'three', 'two', 'one', 'six'],
-                    'c' : np.arange(7)})
+   dfb = pd.DataFrame({'a' : ['one', 'one', 'two',
+                              'three', 'two', 'one', 'six'],
+                       'c' : np.arange(7)})
 
    # This will show the SettingWithCopyWarning
    # but the frame values will be set
@@ -1573,7 +1559,7 @@ This is the correct access method
 
 .. ipython:: python
 
-   dfc = DataFrame({'A':['aaa','bbb','ccc'],'B':[1,2,3]})
+   dfc = pd.DataFrame({'A':['aaa','bbb','ccc'],'B':[1,2,3]})
    dfc.loc[0,'A'] = 11
    dfc
 
diff --git a/doc/source/internals.rst b/doc/source/internals.rst
index 8b4f7360fc235..5899c3089cdac 100644
--- a/doc/source/internals.rst
+++ b/doc/source/internals.rst
@@ -6,15 +6,10 @@
    :suppress:
 
    import numpy as np
-   import random
    np.random.seed(123456)
-   from pandas import *
-   options.display.max_rows=15
-   import pandas as pd
-   randn = np.random.randn
-   randint = np.random.randint
    np.set_printoptions(precision=4, suppress=True)
-   from pandas.compat import range, zip
+   import pandas as pd
+   pd.options.display.max_rows = 15
 
 *********
 Internals
@@ -81,7 +76,7 @@ integer **labels**, and the level **names**:
 
 .. ipython:: python
 
-   index = MultiIndex.from_product([range(3), ['one', 'two']], names=['first', 'second'])
+   index = pd.MultiIndex.from_product([range(3), ['one', 'two']], names=['first', 'second'])
    index
    index.levels
    index.labels
@@ -210,7 +205,7 @@ Below is an example to define 2 original properties, "internal_cache" as a tempo
    class SubclassedDataFrame2(DataFrame):
 
        # temporary properties
-       _internal_names = DataFrame._internal_names + ['internal_cache']
+       _internal_names = pd.DataFrame._internal_names + ['internal_cache']
        _internal_names_set = set(_internal_names)
 
        # normal properties
@@ -244,5 +239,3 @@ Below is an example to define 2 original properties, "internal_cache" as a tempo
    # properties defined in _metadata are retained
    >>> df[['A', 'B']].added_property
    property
-
-
diff --git a/doc/source/io.rst b/doc/source/io.rst
index 7a4318fb02cfc..4c829c3252533 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -2407,7 +2407,7 @@ for some advanced strategies
    As of version 0.15.0, pandas requires ``PyTables`` >= 3.0.0. Stores written with prior versions of pandas / ``PyTables`` >= 2.3 are fully compatible (this was the previous minimum ``PyTables`` required version).
 
 .. warning::
-   
+
    There is a ``PyTables`` indexing bug which may appear when querying stores using an index.  If you see a subset of results being returned, upgrade to ``PyTables`` >= 3.2.  Stores created previously will need to be rewritten using the updated version.
 
 .. ipython:: python
diff --git a/doc/source/missing_data.rst b/doc/source/missing_data.rst
index 5a350b4d9a1e7..6f1a272e3c40d 100644
--- a/doc/source/missing_data.rst
+++ b/doc/source/missing_data.rst
@@ -82,7 +82,7 @@ pandas provides the :func:`~pandas.core.common.isnull` and
 .. ipython:: python
 
    df2['one']
-   isnull(df2['one'])
+   pd.isnull(df2['one'])
    df2['four'].notnull()
 
 **Summary:** ``NaN`` and ``None`` (in object arrays) are considered
@@ -99,7 +99,7 @@ pandas objects provide intercompatibility between ``NaT`` and ``NaN``.
 .. ipython:: python
 
    df2 = df.copy()
-   df2['timestamp'] = Timestamp('20120101')
+   df2['timestamp'] = pd.Timestamp('20120101')
    df2
    df2.ix[['a','c','h'],['one','timestamp']] = np.nan
    df2
@@ -260,7 +260,7 @@ use case of this is to fill a DataFrame with the mean of that column.
 
 .. ipython:: python
 
-        dff = pd.DataFrame(np.random.randn(10,3),columns=list('ABC'))
+        dff = pd.DataFrame(np.random.randn(10,3), columns=list('ABC'))
         dff.iloc[3:5,0] = np.nan
         dff.iloc[4:6,1] = np.nan
         dff.iloc[5:8,2] = np.nan
@@ -276,7 +276,7 @@ a Series in this case.
 
 .. ipython:: python
 
-        dff.where(notnull(dff),dff.mean(),axis='columns')
+        dff.where(pd.notnull(dff), dff.mean(), axis='columns')
 
 
 .. _missing_data.dropna:
@@ -321,7 +321,7 @@ performs linear interpolation at missing datapoints.
    :suppress:
 
    np.random.seed(123456)
-   idx = date_range('1/1/2000', periods=100, freq='BM')
+   idx = pd.date_range('1/1/2000', periods=100, freq='BM')
    ts = pd.Series(np.random.randn(100), index=idx)
    ts[1:20] = np.nan
    ts[60:80] = np.nan
@@ -368,7 +368,7 @@ You can also interpolate with a DataFrame:
 .. ipython:: python
 
    df = pd.DataFrame({'A': [1, 2.1, np.nan, 4.7, 5.6, 6.8],
-                   'B': [.25, np.nan, np.nan, 4, 12.2, 14.4]})
+                      'B': [.25, np.nan, np.nan, 4, 12.2, 14.4]})
    df
    df.interpolate()
 
@@ -425,7 +425,7 @@ at the new values.
    ser = pd.Series(np.sort(np.random.uniform(size=100)))
 
    # interpolate at new_index
-   new_index = ser.index | Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
+   new_index = ser.index | pd.Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
    interp_s = ser.reindex(new_index).interpolate(method='pchip')
    interp_s[49:51]
 
diff --git a/doc/source/options.rst b/doc/source/options.rst
index 9ede87422b21c..7e140b1b2deaf 100644
--- a/doc/source/options.rst
+++ b/doc/source/options.rst
@@ -154,7 +154,7 @@ lines are replaced by an ellipsis.
 
 .. ipython:: python
 
-   df=pd.DataFrame(np.random.randn(7,2))
+   df = pd.DataFrame(np.random.randn(7,2))
    pd.set_option('max_rows', 7)
    df
    pd.set_option('max_rows', 5)
@@ -166,7 +166,7 @@ dataframes to stretch across pages, wrapped over the full column vs row-wise.
 
 .. ipython:: python
 
-   df=pd.DataFrame(np.random.randn(5,10))
+   df = pd.DataFrame(np.random.randn(5,10))
    pd.set_option('expand_frame_repr', True)
    df
    pd.set_option('expand_frame_repr', False)
@@ -178,7 +178,7 @@ dataframes to stretch across pages, wrapped over the full column vs row-wise.
 
 .. ipython:: python
 
-   df=pd.DataFrame(np.random.randn(10,10))
+   df = pd.DataFrame(np.random.randn(10,10))
    pd.set_option('max_rows', 5)
    pd.set_option('large_repr', 'truncate')
    df
@@ -192,8 +192,8 @@ of this length or longer will be truncated with an ellipsis.
 
 .. ipython:: python
 
-   df=pd.DataFrame(np.array([['foo', 'bar', 'bim', 'uncomfortably long string'],
-                        ['horse', 'cow', 'banana', 'apple']]))
+   df = pd.DataFrame(np.array([['foo', 'bar', 'bim', 'uncomfortably long string'],
+                               ['horse', 'cow', 'banana', 'apple']]))
    pd.set_option('max_colwidth',40)
    df
    pd.set_option('max_colwidth', 6)
@@ -205,7 +205,7 @@ will be given.
 
 .. ipython:: python
 
-   df=pd.DataFrame(np.random.randn(10,10))
+   df = pd.DataFrame(np.random.randn(10,10))
    pd.set_option('max_info_columns', 11)
    df.info()
    pd.set_option('max_info_columns', 5)
@@ -219,7 +219,7 @@ can specify the option ``df.info(null_counts=True)`` to override on showing a pa
 
 .. ipython:: python
 
-   df=pd.DataFrame(np.random.choice([0,1,np.nan],size=(10,10)))
+   df  =pd.DataFrame(np.random.choice([0,1,np.nan], size=(10,10)))
    df
    pd.set_option('max_info_rows', 11)
    df.info()
@@ -232,7 +232,7 @@ suggestion.
 
 .. ipython:: python
 
-   df=pd.DataFrame(np.random.randn(5,5))
+   df = pd.DataFrame(np.random.randn(5,5))
    pd.set_option('precision',7)
    df
    pd.set_option('precision',4)
@@ -244,7 +244,7 @@ precision at which the number is stored.
 
 .. ipython:: python
 
-   df=pd.DataFrame(np.random.randn(6,6))
+   df = pd.DataFrame(np.random.randn(6,6))
    pd.set_option('chop_threshold', 0)
    df
    pd.set_option('chop_threshold', .5)
@@ -256,7 +256,8 @@ Options are 'right', and 'left'.
 
 .. ipython:: python
 
-   df=pd.DataFrame(np.array([np.random.randn(6), np.random.randint(1,9,6)*.1, np.zeros(6)]).T, columns=['A', 'B', 'C'], dtype='float')
+   df = pd.DataFrame(np.array([np.random.randn(6), np.random.randint(1,9,6)*.1, np.zeros(6)]).T,
+                     columns=['A', 'B', 'C'], dtype='float')
    pd.set_option('colheader_justify', 'right')
    df
    pd.set_option('colheader_justify', 'left')
diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst
index da37c92c88ecf..74cdc5a526585 100644
--- a/doc/source/r_interface.rst
+++ b/doc/source/r_interface.rst
@@ -5,8 +5,8 @@
 .. ipython:: python
    :suppress:
 
-   from pandas import *
-   options.display.max_rows=15
+   import pandas as pd
+   pd.options.display.max_rows = 15
 
 
 ******************
@@ -136,10 +136,8 @@ DataFrames into the equivalent R object (that is, **data.frame**):
 
 .. ipython:: python
 
-   from pandas import DataFrame
-
-   df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C':[7,8,9]},
-                  index=["one", "two", "three"])
+   df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C':[7,8,9]},
+                     index=["one", "two", "three"])
    r_dataframe = com.convert_to_r_dataframe(df)
 
    print(type(r_dataframe))