You can now, use round on dataframe

Option 1

In [661]: df.round({'Y': 2, 'X': 2})
Out[661]:
       Y     X  id WP_NER
0  35.97 -2.73   1  WP_01
1  35.59 -2.90   2  WP_02
2  35.33 -3.39   3  WP_03
3  35.39 -3.93   4  WP_04
4  35.58 -3.94   5  WP_05
5  35.52 -3.41   6  WP_06
6  35.76 -3.08   7  WP_07

Option 2

In [662]: cols = ['Y', 'X']

In [663]: df[cols] = df[cols].round(2)

In [664]: df
Out[664]:
       Y     X  id WP_NER
0  35.97 -2.73   1  WP_01
1  35.59 -2.90   2  WP_02
2  35.33 -3.39   3  WP_03
3  35.39 -3.93   4  WP_04
4  35.58 -3.94   5  WP_05
5  35.52 -3.41   6  WP_06
6  35.76 -3.08   7  WP_07

You are very close. You applied the round to the series of values given by df.value1. The return type is thus a Series. You need to assign that series back to the dataframe (or another dataframe with the same Index).

Also, there is a pandas.Series.round method which is basically a short hand for pandas.Series.apply(np.round).

>>> df.value1 = df.value1.round()
>>> print df
  item  value1  value2
0    a       1     1.3
1    a       2     2.5
2    a       0     0.0
3    b       3    -1.0
4    b       5    -1.0

You can customize for each column using a dict like

In [658]: df1.round({'a': 2, 'c': 2})
Out[658]:
      a       b     c
0  0.11  0.3333  0.56
1  0.22  0.4444  0.67

Or, You could do

In [649]: cols = ['a', 'c']

In [650]: df1[cols] = df1[cols].round(2)

In [651]: df1
Out[651]:
      a       b     c
0  0.11  0.3333  0.56
1  0.22  0.4444  0.67

on recent pandas versions, you can just df.round(2)