Filtering dataframes in pandas : use a list of conditions

To the best of my knowledge, there is no way in Pandas for you to do what you want. However, although the following solution may not me the most pretty, you can zip a set of parallel lists as follows:

cols = ['col1', 'col2']
conditions = ['foo', 'bar']

df[eval(" & ".join(["(df['{0}'] == '{1}')".format(col, cond) 
   for col, cond in zip(cols, conditions)]))]

The string join results in the following:

>>> " & ".join(["(df['{0}'] == '{1}')".format(col, cond) 
    for col, cond in zip(cols, conditions)])

"(df['col1'] == 'foo') & (df['col2'] == 'bar')"

Which you then use eval to evaluate, effectively:

df[eval("(df['col1'] == 'foo') & (df['col2'] == 'bar')")]

For example:

df = pd.DataFrame({'col1': ['foo', 'bar, 'baz'], 'col2': ['bar', 'spam', 'ham']})

>>> df
  col1  col2
0  foo   bar
1  bar  spam
2  baz   ham

>>> df[eval(" & ".join(["(df['{0}'] == {1})".format(col, repr(cond)) 
            for col, cond in zip(cols, conditions)]))]
  col1 col2
0  foo  bar

I would like to point out an alternative for the accepted answer as eval is not necessary for solving this problem.

df = pd.DataFrame({'col1': ['foo', 'bar', 'baz'], 'col2': ['bar', 'spam', 'ham']})
cols = ['col1', 'col2']
values = ['foo', 'bar']
conditions = zip(cols, values)

def apply_conditions(df, conditions):
    assert len(conditions) > 0
    comps = [df[c] == v for c, v in conditions]
    result = comps[0]
    for comp in comps[1:]:
        result &= comp
    return result

def apply_conditions(df, conditions):
    assert len(conditions) > 0
    comps = [df[c] == v for c, v in conditions]
    return reduce(lambda c1, c2: c1 & c2, comps[1:], comps[0])

df[apply_conditions(df, conditions)]