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(",","")))

Easiest would be

df = df.astype(float)
df['YEAR'] = df['YEAR'].astype(int)

Also, your code fails because you have two columns with the same name BBPWN, so when you do df['BBPWN'], you will get a dataframe with those two columns. Then, df['BBPWN'].str will fail.

Your columns contain commas which are not parseable to floats. Just remove them from the string before converting them to floats.

df['price1'] = df['price1'].str.replace(',', '').astype(float)