N.B. This answer is less efficient from the point of view that pandas is built on top of numpy. Please consider numpy if going for efficiency.

As for this answer, there is a significant amount of work done using pandas data frames, so adding additional conversion to numpy means writing extra code. So if one is performing an analysis in say jupyter notebook, then we can surely let the programming language do a bit of work under the hood.

Big thank you to @Chris for noticing this.

pandas version (theoretically less efficient than numpy)

Create a list with float values:

Copyy = [0.1234, 0.6789, 0.5678]

Convert the list of float values to pandas Series

Copys = pd.Series(data=y)

Round values to three decimal values

Copyprint(s.round(3))

returns

Copy0    0.123
1    0.679
2    0.568
dtype: float64

Convert to integer

Copyprint(s.astype(int))

returns

Copy0    0
1    0
2    0
dtype: int64

Pipe it all

Copypd.Series(data=y).round(3)

User pd.to_numeric with errors ='coerce' i.e

df.loc[(pd.to_numeric(df['highest_rank'],errors='coerce')) < 2]

Output:

  rank       team  rating  highest_rank  highest_rating
0     1    Germany    2097             1            2205
1     2     Brazil    2086             1            2161
2     3      Spain    2011             1            2147
4     5  Argentina    1967             1            2128

Here's a different way to solve your problem

In [3]: df
Out[3]: 
         time    magnitude
0  1379945444   156.627598
1  1379945447  1474.648726
2  1379945448  1477.448999
3  1379945449  1474.886202
4  1379945699  1371.454224

In [4]: df.dtypes
Out[4]: 
time           int64
magnitude    float64
dtype: object

Convert your epoch timestamps to seconds

In [7]: df['time'] = pd.to_datetime(df['time'],unit='s')

Set the index

In [8]: df.set_index('time',inplace=True)

In [9]: df
Out[9]: 
                       magnitude
time                            
2013-09-23 14:10:44   156.627598
2013-09-23 14:10:47  1474.648726
2013-09-23 14:10:48  1477.448999
2013-09-23 14:10:49  1474.886202
2013-09-23 14:14:59  1371.454224

Groupby 1min and mean the results (how= can be an arbitrary function as well)

In [10]: df.resample('1Min',how=np.mean)
Out[10]: 
                       magnitude
time                            
2013-09-23 14:10:00  1145.902881
2013-09-23 14:11:00          NaN
2013-09-23 14:12:00          NaN
2013-09-23 14:13:00          NaN
2013-09-23 14:14:00  1371.454224

The code in your loop:

obj = int(round(obj))

Only changes which object the name obj refers to. It does not modify the data stored in the series. If you want to do this you need to know where in the series the data is stored and update it there.

E.g.

for i, num in enumerate(df['a']):
    df['a'].iloc[i] = int(round(obj))

Use the pandas.DataFrame.astype(<type>) function to manipulate column dtypes.

Copy>>> df = pd.DataFrame(np.random.rand(3,4), columns=list("ABCD"))
>>> df
          A         B         C         D
0  0.542447  0.949988  0.669239  0.879887
1  0.068542  0.757775  0.891903  0.384542
2  0.021274  0.587504  0.180426  0.574300
>>> df[list("ABCD")] = df[list("ABCD")].astype(int)
>>> df
   A  B  C  D
0  0  0  0  0
1  0  0  0  0
2  0  0  0  0

EDIT:

To handle missing values:

Copy>>> df
          A         B     C         D
0  0.475103  0.355453  0.66  0.869336
1  0.260395  0.200287   NaN  0.617024
2  0.517692  0.735613  0.18  0.657106
>>> df[list("ABCD")] = df[list("ABCD")].fillna(0.0).astype(int)
>>> df
   A  B  C  D
0  0  0  0  0
1  0  0  0  0
2  0  0  0  0