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

In [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:

In [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

You need to convert the floating point date to a format that can be converted by pd.to_datetime. Based on the description you have provided, you can extract (assuming fdate represents the floating point date value):

year = int(fdate)
month = int((fdate - year) * 12) + 1

and then convert that to a string in the form mmyyyy using an f-string:

f'{month:02d}{year:04d}'

That can then be converted to a datetime using format %m%Y. Wrapping it in a function:

def convert_date(fdate):
    year = int(fdate)
    month = int((fdate - year) * 12) + 1
    return f'{month:02d}{year:04d}'

you would then use it as:

pd.to_datetime(xr.apply_ufunc(convert_date, dset.time, vectorize=True), format='%m%Y')

If a column contains string or is treated as string, it will have a dtype of object (but not necessarily true backward -- more below). Here is a simple example:

import pandas as pd
df = pd.DataFrame({'SpT': ['string1', 'string2', 'string3'],
                   'num': ['0.1', '0.2', '0.3'],
                   'strange': ['0.1', '0.2', 0.3]})
print df.dtypes
#SpT        object
#num        object
#strange    object
#dtype: object

If a column contains only strings, we can apply len on it like what you did should work fine:

print df['num'].apply(lambda x: len(x))
#0    3
#1    3
#2    3

However, a dtype of object does not means it only contains strings. For example, the column strange contains objects with mixed types -- and some str and a float. Applying the function len will raise an error similar to what you have seen:

print df['strange'].apply(lambda x: len(x))
# TypeError: object of type 'float' has no len()

Thus, the problem could be that you have not properly converted the column to string, and the column still contains mixed object types.

Continuing the above example, let us convert strange to strings and check if apply works:

df['strange'] = df['strange'].astype(str)
print df['strange'].apply(lambda x: len(x))
#0    3
#1    3
#2    3

(There is a suspicious discrepancy between df_cleaned and df_clean there in your question, is it a typo or a mistake in the code that causes the problem?)

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.