You can try df.column_name = df.column_name.astype(float). As for the NaN values, you need to specify how they should be converted, but you can use the .fillna method to do it.

Example:

In [12]: df
Out[12]: 
     a    b
0  0.1  0.2
1  NaN  0.3
2  0.4  0.5

In [13]: df.a.values
Out[13]: array(['0.1', nan, '0.4'], dtype=object)

In [14]: df.a = df.a.astype(float).fillna(0.0)

In [15]: df
Out[15]: 
     a    b
0  0.1  0.2
1  0.0  0.3
2  0.4  0.5

In [16]: df.a.values
Out[16]: array([ 0.1,  0. ,  0.4])

Python2 method to check if a string is a float:

def is_float(value):
  if value is None:
      return False
  try:
      float(value)
      return True
  except:
      return False

For the Python3 version of is_float see: Checking if a string can be converted to float in Python

A longer and more accurate name for this function could be: is_convertible_to_float(value)

What is, and is not a float in Python may surprise you:

The below unit tests were done using python2. Check it that Python3 has different behavior for what strings are convertable to float. One confounding difference is that any number of interior underscores are now allowed: (float("1_3.4") == float(13.4)) is True

val                   is_float(val) Note
--------------------  ----------   --------------------------------
""                    False        Blank string
"127"                 True         Passed string
True                  True         Pure sweet Truth
"True"                False        Vile contemptible lie
False                 True         So false it becomes true
"123.456"             True         Decimal
"      -127    "      True         Spaces trimmed
"\t\n12\r\n"          True         whitespace ignored
"NaN"                 True         Not a number
"NaNanananaBATMAN"    False        I am Batman
"-iNF"                True         Negative infinity
"123.E4"              True         Exponential notation
".1"                  True         mantissa only
"1_2_3.4"             False        Underscores not allowed
"12 34"               False        Spaces not allowed on interior
"1,234"               False        Commas gtfo
u'\x30'               True         Unicode is fine.
"NULL"                False        Null is not special
0x3fade               True         Hexadecimal
"6e7777777777777"     True         Shrunk to infinity
"1.797693e+308"       True         This is max value
"infinity"            True         Same as inf
"infinityandBEYOND"   False        Extra characters wreck it
"12.34.56"            False        Only one dot allowed
u'四'                 False        Japanese '4' is not a float.
"#56"                 False        Pound sign
"56%"                 False        Percent of what?
"0E0"                 True         Exponential, move dot 0 places
0**0                  True         0___0  Exponentiation
"-5e-5"               True         Raise to a negative number
"+1e1"                True         Plus is OK with exponent
"+1e1^5"              False        Fancy exponent not interpreted
"+1e1.3"              False        No decimals in exponent
"-+1"                 False        Make up your mind
"(1)"                 False        Parenthesis is bad

You think you know what numbers are? You are not so good as you think! Not big surprise.

Don't use this code on life-critical software!

Catching broad exceptions this way, killing canaries and gobbling the exception creates a tiny chance that a valid float as string will return false. The float(...) line of code can failed for any of a thousand reasons that have nothing to do with the contents of the string. But if you're writing life-critical software in a duck-typing prototype language like Python, then you've got much larger problems.

Another option might be numpy.asarray:

import numpy as np
a = ["1.1", "2.2", "3.2"]
b = np.asarray(a, dtype=float)

print(a, type(a), type(a[0]))
print(b, type(b), type(b[0]))

resulting in:

['1.1', '2.2', '3.2'] <class 'list'> <class 'str'>
[1.1 2.2 3.2] <class 'numpy.ndarray'> <class 'numpy.float64'>

convert_objects is deprecated.

For pandas >= 0.17.0, use pd.to_numeric

df["2nd"] = pd.to_numeric(df["2nd"])