You can also try this :

df = DataFrame(series).transpose()

Using the transpose() function you can interchange the indices and the columns. The output looks like this :

    a   b   c
0   1   2   3

I don't have your dataframe, but here's an example with small data to show that pandas contructs the dataframe as expected (using pandas 0.15.1 and python 3.4). As expected, NaNs are introduced when the indices don't match.

The last row of your data is ('intercept', ''), while all the other rows are numbers. So ('intercept', '') goes to the index of each series, and this is probably causing the values in the index to be "promoted" to strings.

>> s1 = pd.Series([1,2,3], index=pd.MultiIndex.from_tuples([(1,1),(1,2),(1,3)], names=['a','b']))
>>> s1
a  b
1  1    1
   2    2
   3    3
dtype: int64
>>> s2 = pd.Series([100,200,300], index=pd.MultiIndex.from_tuples([(1,2),(1,3),(1,4)], names=['a','b']))
>>> 
>>> s2
a  b
1  2    100
   3    200
   4    300
dtype: int64
>>> df = pd.DataFrame({'s1':s1, 's2':s2})
>>> df
     s1   s2
a b         
1 1   1  NaN
  2   2  100
  3   3  200
  4 NaN  300
>>> df.index.values
array([(1, 1), (1, 2), (1, 3), (1, 4)], dtype=object)

Here is how to create a DataFrame where each series is a row.

For a single Series (resulting in a single-row DataFrame):

Copyseries = pd.Series([1,2], index=['a','b'])
df = pd.DataFrame([series])

For multiple series with identical indices:

Copycols = ['a','b']
list_of_series = [pd.Series([1,2],index=cols), pd.Series([3,4],index=cols)]
df = pd.DataFrame(list_of_series, columns=cols)

For multiple series with possibly different indices:

Copylist_of_series = [pd.Series([1,2],index=['a','b']), pd.Series([3,4],index=['a','c'])]
df = pd.concat(list_of_series, axis=1).transpose()

To create a DataFrame where each series is a column, see the answers by others. Alternatively, one can create a DataFrame where each series is a row, as above, and then use df.transpose(). However, the latter approach is inefficient if the columns have different data types.

Rather than create 2 temporary dfs you can just pass these as params within a dict using the DataFrame constructor:

pd.DataFrame({'email':sf.index, 'list':sf.values})

There are lots of ways to construct a df, see the docs

Pandas Series and DataFrames Indexes can be changed by assignment. Thus, if your two Series are called s1 and s2, then

s2.index = s1.index

assigns s1's index to s2. Note that the two Series must have the same length or else an ValueError will be raised.

Alternatively, to form a DataFrame using s1 and s2, you could use

df = pd.DataFrame({'s1': s1.values, 's2': s2.values}, index=s1.index)

Note that s1.values returns a NumPy array of s1 values without the index. To form this DataFrame it is important to use .values here so that that Pandas does not try to align the values from s1 and s2 based on their respective (and differing!) Indexes.

To illustrate difference, consider:

import pandas as pd
s1 = pd.Series(range(4))
s2 = pd.Series(range(4), index=list('ABCD'))
df = pd.DataFrame({'s1': s1.values, 's2': s2.values}, index=s1.index)
#    s1  s2
# 0   0   0
# 1   1   1
# 2   2   2
# 3   3   3

df2 = pd.DataFrame({'s1': s1, 's2': s2}, index=s1.index)
#    s1  s2
# 0   0 NaN
# 1   1 NaN
# 2   2 NaN
# 3   3 NaN

In the second example, pd.DataFrame aligns values from s1 and s2 based on their respective indexes. index=s1.index crops the rows so that only values with index labels from s1.index are included. Since s2 has no values with these index labels, the values are filled with NaNs.