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

Your solution is possible change with pass mask to loc function:

mask = df["Column3"]!="String1"
df.loc[mask,"Column3"] = df.loc[mask,"Column3"].astype(np.float64)

Another idea if convert all possible numeric, non numeric generate missing values, so replaced by original:

df['Column3'] = pd.to_numeric(df['Column3'], errors='coerce').fillna(df['Column3'])

For test is posible use checking type:

print (df['Column3'].apply(type))
0       <class 'str'>
1     <class 'float'>
2     <class 'float'>
3     <class 'float'>
4     <class 'float'>
5     <class 'float'>
6     <class 'float'>
7       <class 'str'>
8     <class 'float'>
9     <class 'float'>
10    <class 'float'>
11    <class 'float'>
12    <class 'float'>
13      <class 'str'>
14    <class 'float'>
Name: Column3, dtype: object

In my opinion still best should be only numeric column with missing values for non match values:

df['Column3'] = pd.to_numeric(df['Column3'], errors='coerce')

Solution for pandas 0.24+ for converting numeric with missing values:

df = pd.DataFrame({'column name':[7500000.0,7500000.0, np.nan]})
print (df['column name'])
0    7500000.0
1    7500000.0
2          NaN
Name: column name, dtype: float64

df['column name'] = df['column name'].astype(np.int64)

ValueError: Cannot convert non-finite values (NA or inf) to integer

#http://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
df['column name'] = df['column name'].astype('Int64')
print (df['column name'])
0    7500000
1    7500000
2        NaN
Name: column name, dtype: Int64

I think you need cast to numpy.int64:

df['column name'].astype(np.int64)

Sample:

df = pd.DataFrame({'column name':[7500000.0,7500000.0]})
print (df['column name'])
0    7500000.0
1    7500000.0
Name: column name, dtype: float64

df['column name'] = df['column name'].astype(np.int64)
#same as
#df['column name'] = df['column name'].astype(pd.np.int64)
print (df['column name'])
0    7500000
1    7500000
Name: column name, dtype: int64

If some NaNs in columns need replace them to some int (e.g. 0) by fillna, because type of NaN is float:

df = pd.DataFrame({'column name':[7500000.0,np.nan]})

df['column name'] = df['column name'].fillna(0).astype(np.int64)
print (df['column name'])
0    7500000
1          0
Name: column name, dtype: int64

Also check documentation - missing data casting rules

EDIT:

Convert values with NaNs is buggy:

df = pd.DataFrame({'column name':[7500000.0,np.nan]})

df['column name'] = df['column name'].values.astype(np.int64)
print (df['column name'])
0                7500000
1   -9223372036854775808
Name: column name, dtype: int64