You need to enclose multiple conditions in braces due to operator precedence and use the bitwise and (&) and or (|) operators:
foo = df[(df['column1']==value) | (df['columns2'] == 'b') | (df['column3'] == 'c')]
If you use and or or, then pandas is likely to moan that the comparison is ambiguous. In that case, it is unclear whether we are comparing every value in a series in the condition, and what does it mean if only 1 or all but 1 match the condition. That is why you should use the bitwise operators or the numpy np.all or np.any to specify the matching criteria.
There is also the query method: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.query.html
but there are some limitations mainly to do with issues where there could be ambiguity between column names and index values.