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

In [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

Use astype(float) e.g.:

df['col'] = df['col'].astype(float)

or convert_objects:

df = df.convert_objects(convert_numeric=True)

Example:

In [379]:

df = pd.DataFrame({'a':['1.23', '0.123']})
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 1 columns):
a    2 non-null object
dtypes: object(1)
memory usage: 32.0+ bytes
In [380]:

df['a'].astype(float)
Out[380]:
0    1.230
1    0.123
Name: a, dtype: float64

In [382]:

df = df.convert_objects(convert_numeric=True)
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 1 columns):
a    2 non-null float64
dtypes: float64(1)
memory usage: 32.0 bytes

UPDATE

If you're running version 0.17.0 or later then convert_objects has been replaced with the methods: to_numeric, to_datetime, and to_timestamp so instead of:

df['col'] = df['col'].astype(float)

you can do:

df['col'] = pd.to_numeric(df['col'])

note that by default any non convertible values will raise an error, if you want these to be forced to NaN then do:

df['col'] = pd.to_numeric(df['col'], errors='coerce')

You can first check what values cannot be converted by:

print (df[pd.to_numeric(df['cpu_system'], errors='coerce').isnull()])

and then use to_numeric with parameter erors='coerce' for convert bad values to NaN:

df['cpu_system'] = pd.to_numeric(df['cpu_system'], errors='coerce')

And filter problematic values out if necessary by boolean indexing:

df = df[df['cpu_system'].notnull()]

To convert from string to float in pandas (assuming you want to convert Employees and you loaded the data frame with df), you can use:

df['Employees'].apply(lambda x:float(x))

You have not given enough information about your input and expected output. So let us assume that hospital name or anything for that matter which is the input for your model is nan. You would like to remove it from the dataset because extracting features from 'nan' wouldn't make sense. Apart from that, if they are just other peripheral features, then it might be alright. In that case, if you wish to convert them into blank, then use:

df.replace(np.nan,' ', regex=True)`

Else, if you wish to remove that frame, you can check for nan using this.