按字典键值对过滤 pandas dataframe

Question

Is there a more elegant way to filtering a dataframe by one column and then for each subset, further filtering by another column? And have the resulting data in one dataframe? The filtering information is in a dictionary. The first filter is on col1 using the dict key. The 2nd filter is on col3 using its corresponding value.

df = pd.DataFrame({'col1': [1,1,1,2,2], 'col2': [2,2,2,2,2], 'col3': [1,6,7,5,9]})

df looks like the following

    |col1|col2|col3|
    |1   |2   |1   |
    |1   |2   |6   |
    |1   |2   |7   |
    |2   |2   |5   |
    |2   |2   |9   |

filter_dict = {1:5, 2:7}

df_new = df.somefunction(filter_dict)

Where col1 is 1, filter where col3 value is greater than 5. Where col1 is 2, filter by col3 value is greater than 7. This would result:

df_new

    |col1|col2|col3|
    |1   |2   |6   |
    |1   |2   |7   |
    |2   |2   |9   |

Answer 1

List comprehension and boolean indexing with concat

df_new = pd.concat([df[(df['col1'] == k) & (df['col3'] > v)] for k,v in filter_dict.items()])

   col1  col2  col3
1     1     2     6
2     1     2     7
4     2     2     9