You can convert most of the columns by just calling convert_objects:

CopyIn [36]:

df = df.convert_objects(convert_numeric=True)
df.dtypes
Out[36]:
Date         object
WD            int64
Manpower    float64
2nd          object
CTR          object
2ndU        float64
T1            int64
T2          int64
T3           int64
T4        float64
dtype: object

For column '2nd' and 'CTR' we can call the vectorised str methods to replace the thousands separator and remove the '%' sign and then astype to convert:

CopyIn [39]:

df['2nd'] = df['2nd'].str.replace(',','').astype(int)
df['CTR'] = df['CTR'].str.replace('%','').astype(np.float64)
df.dtypes
Out[39]:
Date         object
WD            int64
Manpower    float64
2nd           int32
CTR         float64
2ndU        float64
T1            int64
T2            int64
T3            int64
T4           object
dtype: object
In [40]:

df.head()
Out[40]:
        Date  WD  Manpower   2nd   CTR  2ndU   T1  　　T2   T3     T4
0   2013/4/6   6       NaN  2645  5.27  0.29  407   533  454    368
1   2013/4/7   7       NaN  2118  5.89  0.31  257   659  583    369
2  2013/4/13   6       NaN  2470  5.38  0.29  354   531  473  　　383
3  2013/4/14   7       NaN  2033  6.77  0.37  396   748  681    458
4  2013/4/20   6       NaN  2690  5.38  0.29  361   528  541    381

Or you can do the string handling operations above without the call to astype and then call convert_objects to convert everything in one go.

UPDATE

Since version 0.17.0 convert_objects is deprecated and there isn't a top-level function to do this so you need to do:

df.apply(lambda col:pd.to_numeric(col, errors='coerce'))

See the docs and this related question: pandas: to_numeric for multiple columns

\xa0 is a non-breaking space (used as thousand separator here), which is not removed by your replace.

A safer approach might be to remove all non digits/dot/minus characters:

df = pd.DataFrame({'Profit': ['-$1\xa0000.00', '$605.00', '140.00 ']})

df['Profit'].str.replace('[^\d\.-]', '', regex=True).astype(float)

for only $ and \xa0:

df['Profit'].str.replace('[$\xa0]', '', regex=True).astype(float)

output:

0   -1000.0
1     605.0
2     680.0
3    -140.0
Name: Profit, dtype: float64

1. Using pandas.Series.astype

• You can use pandas.Series.astype

• You can do something like this :

    weather["Temp"] = weather.Temp.astype(float)
  

2. Using pd.to_numeric

• You can also use pd.to_numeric that will convert the column from object to float

• For details on how to use it checkout this link :http://pandas.pydata.org/pandas-docs/version/0.20/generated/pandas.to_numeric.html

• Example :

    s = pd.Series(['apple', '1.0', '2', -3])
    print(pd.to_numeric(s, errors='ignore'))
    print("=========================")
    print(pd.to_numeric(s, errors='coerce'))
  

• Output:

    0    apple
    1      1.0
    2        2
    3       -3
    =========================
    dtype: object
    0    NaN
    1    1.0
    2    2.0
    3   -3.0
    dtype: float64
  

• In your case you can do something like this:

    weather["Temp"] = pd.to_numeric(weather.Temp, errors='coerce')
  

3. Using convert_objects

• Other option is to use convert_objects

• Example is as follows

    >> pd.Series([1,2,3,4,'.']).convert_objects(convert_numeric=True)
  
    0     1
    1     2
    2     3
    3     4
    4   NaN
    dtype: float64
  

• You can use this as follows:

    weather["Temp"] = weather.Temp.convert_objects(convert_numeric=True)
  

• I have showed you examples because if any of your column won't have a number then it will be converted to NaN... so be careful while using it.