Modifying a column depending on multiple conditions from another column, Pandas

Question

I want to do something like:

if table['STATUS']=='A'or 'P':
    table['END_DATE']=end_date 

In order to replace the value of END_DATE for those rows that have a S or P in STATUS Which yields the

ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().

I get that it is due to the array adressing... This seems to be a petty common issue but I can't seem to find a suitable answer what is the Right constuct to use here? I can't figure it out from the docs...which say to use .any()

Answer 1

The if statement is ambiguous because it evaluates to a Series that maybe be empty, maybe have have some True values or False values. Hence its impossible to evaluate. See here: http://pandas.pydata.org/pandas-docs/dev/gotchas.html#using-if-truth-statements-with-pandas , http://pandas.pydata.org/pandas-docs/dev/indexing.html#boolean-indexing , http://pandas.pydata.org/pandas-docs/dev/basics.html#flexible-comparisons

This works in 0.12

In [2]: df = DataFrame(np.arange(20).reshape(10,2),columns=['A','B'])

In [3]: df['status'] = ['A']*4 + ['invalid'] * 2 + ['P'] * 4

This step won't be needed in 0.13

In [4]: df['end_date'] = np.nan

Edit from @DSM

In [5]: df.loc[(df['status'].isin(['A','P'])),'end_date'] = Timestamp('20130101')

In [6]: df
Out[6]: 
    A   B   status             end_date
0   0   1        A  2013-01-01 00:00:00
1   2   3        A  2013-01-01 00:00:00
2   4   5        A  2013-01-01 00:00:00
3   6   7        A  2013-01-01 00:00:00
4   8   9  invalid                  NaN
5  10  11  invalid                  NaN
6  12  13        P  2013-01-01 00:00:00
7  14  15        P  2013-01-01 00:00:00
8  16  17        P  2013-01-01 00:00:00
9  18  19        P  2013-01-01 00:00:00