NOTE: pd.convert_objects has now been deprecated. You should use pd.Series.astype(float) or pd.to_numeric as described in other answers.

This is available in 0.11. Forces conversion (or set's to nan) This will work even when astype will fail; its also series by series so it won't convert say a complete string column

CopyIn [10]: df = DataFrame(dict(A = Series(['1.0','1']), B = Series(['1.0','foo'])))

In [11]: df
Out[11]: 
     A    B
0  1.0  1.0
1    1  foo

In [12]: df.dtypes
Out[12]: 
A    object
B    object
dtype: object

In [13]: df.convert_objects(convert_numeric=True)
Out[13]: 
   A   B
0  1   1
1  1 NaN

In [14]: df.convert_objects(convert_numeric=True).dtypes
Out[14]: 
A    float64
B    float64
dtype: object

Starting in 0.11.1 (coming out this week), replace has a new option to replace with a regex, so this becomes possible

In [14]: df = DataFrame('10.0%',index=range(100),columns=range(10))

In [15]: df.replace('%','',regex=True).astype('float')/100
Out[15]: 
<class 'pandas.core.frame.DataFrame'>
Int64Index: 100 entries, 0 to 99
Data columns (total 10 columns):
0    100  non-null values
1    100  non-null values
2    100  non-null values
3    100  non-null values
4    100  non-null values
5    100  non-null values
6    100  non-null values
7    100  non-null values
8    100  non-null values
9    100  non-null values
dtypes: float64(10)

And a bit faster

In [16]: %timeit df.replace('%','',regex=True).astype('float')/100
1000 loops, best of 3: 1.16 ms per loop

 In [18]: %timeit df.applymap(lambda x: float(x[:-1]))/100
1000 loops, best of 3: 1.67 ms per loop

Assuming all values can be correctly converted to float, you can use DataFrame.astype() function to convert the type of complete dataframe to float. Example -

df = df.astype(float)

Demo -

In [5]: df = pd.DataFrame(np.array([['1', '2', '3'], ['4', '5', '6']]))

In [6]: df.astype(float)
Out[6]:
   0  1  2
0  1  2  3
1  4  5  6

In [7]: df = df.astype(float)

In [8]: df.dtypes
Out[8]:
0    float64
1    float64
2    float64
dtype: object

.astype() function also has a raise_on_error argument (which defaults to True) which you can set to False to make it ignore errors . In such cases, the original value is used in the DataFrame -

In [10]: df = pd.DataFrame([['1', '2', '3'], ['4', '5', '6'],['blah','bloh','bleh']])

In [11]: df.astype(float,raise_on_error=False)
Out[11]:
      0     1     2
0     1     2     3
1     4     5     6
2  blah  bloh  bleh

To convert just a series/column to float, again assuming all values can be converted, you can use [Series.astype()][2] . Example -

df['somecol'] = df['somecol'].astype(<type>)

I think you need parameter errors='ignore' in to_numeric:

df = df.apply(pd.to_numeric, errors='ignore')
print (df.dtypes)
A     object
B      int64
C    float64
dtype: object

It working nice if not mixed values - numeric with strings:

df_list = [["a", "t", "2"], ["b", "3", np.nan]]
df = pd.DataFrame(df_list, columns = list("ABC"))

df = df.apply(pd.to_numeric, errors='ignore')
print (df)
   A  B    C
0  a  t  2.0 <=added t to column B for mixed values
1  b  3  NaN

print (df.dtypes)
A     object
B     object
C    float64
dtype: object

EDIT:

You can downcast also int to floats:

df = df.apply(pd.to_numeric, errors='ignore', downcast='float')
print (df.dtypes)
A     object
B    float32
C    float32
dtype: object

It is same as:

df = df.apply(lambda x: pd.to_numeric(x, errors='ignore', downcast='float'))
print (df.dtypes)
A     object
B    float32
C    float32
dtype: object

I don't know if I totally understood your question but you can try

dataframe4['column2'] = dataframe4['column1'].apply(lambda x : float(x))

Edit : If there are some numbers with commas, you can try:

dataframe4['column2'] = dataframe4['column1'].apply(lambda x : float(x.replace(",","")))

UPDATE: you don't need to convert your values afterwards, you can do it on-the-fly when reading your CSV:

In [165]: df=pd.read_csv(url, index_col=0, na_values=['(NA)']).fillna(0)

In [166]: df.dtypes
Out[166]:
GeoName                    object
ComponentName              object
IndustryId                  int64
IndustryClassification     object
Description                object
2004                        int64
2005                        int64
2006                        int64
2007                        int64
2008                        int64
2009                        int64
2010                        int64
2011                        int64
2012                        int64
2013                        int64
2014                      float64
dtype: object

If you need to convert multiple columns to numeric dtypes - use the following technique:

Sample source DF:

In [271]: df
Out[271]:
     id    a  b  c  d  e    f
0  id_3  AAA  6  3  5  8    1
1  id_9    3  7  5  7  3  BBB
2  id_7    4  2  3  5  4    2
3  id_0    7  3  5  7  9    4
4  id_0    2  4  6  4  0    2

In [272]: df.dtypes
Out[272]:
id    object
a     object
b      int64
c      int64
d      int64
e      int64
f     object
dtype: object

Converting selected columns to numeric dtypes:

In [273]: cols = df.columns.drop('id')

In [274]: df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')

In [275]: df
Out[275]:
     id    a  b  c  d  e    f
0  id_3  NaN  6  3  5  8  1.0
1  id_9  3.0  7  5  7  3  NaN
2  id_7  4.0  2  3  5  4  2.0
3  id_0  7.0  3  5  7  9  4.0
4  id_0  2.0  4  6  4  0  2.0

In [276]: df.dtypes
Out[276]:
id     object
a     float64
b       int64
c       int64
d       int64
e       int64
f     float64
dtype: object

PS if you want to select all string (object) columns use the following simple trick:

cols = df.columns[df.dtypes.eq('object')]