使用熊猫将过滤器列表应用于来自列表的数据框

Question

I have a list of columns to apply filters in a dataframe which comes from a list. The filter values comes from another list.

Earlier when the lists were of fixed, I used the following statement to get the job done:

df_result= df[(df[filterfieldList[0]] == filterValuesList[0]) & (df[filterfieldList[1]] == filterValuesList[1]) & (df[filterfieldList[2]] == filterValuesList[2])]

But as the weeks progressed I get a new requirement that the filtering list would be dynamic and I couldn't figure out how to do that now. As in, sometimes, the filters list will have only 2 fields to filter, sometime 3 or 5. How to do the filtering in this situation?

Sample Data:

A             B            C                   D                 E
Project 1        Org_1     Directory        MSTR           Configuration    
Project 1        Org_1     Directory        MSTR          Unable to Login
Project 1       Org_1   Desktop Software    MSTR             Configuration
Project 1      Org_1    Desktop Software    MSTR           Configuration]
Project 1      Org_1    Directory           MSTR          Unable to Login

Answer 1

I think need list comprehension for create mask s and then np.logical_and.reduce for reduce and last filter by boolean indexing :

filterfieldList = ['A','B','E']
filterValuesList = ['Project 1', 'Org_1', 'Unable to Login']

tups = zip(filterfieldList, filterValuesList)
df_result = df[np.logical_and.reduce([(df[i] == j) for i, j in tups])]
print (df_result)
           A      B          C     D                E
1  Project 1  Org_1  Directory  MSTR  Unable to Login
4  Project 1  Org_1  Directory  MSTR  Unable to Login

EDIT:

If need combine multiple filters per rows:

filterfieldList = ['A','B','E', 'E']
filterValuesList = ['Project 1', 'Org_1', 'Unable to Login', 'Configuration']

f = pd.DataFrame({'field': filterfieldList, 'val':filterValuesList})
f = f.groupby('field')['val'].apply(list)
print (f)
field
A                         [Project 1]
B                             [Org_1]
E    [Unable to Login, Configuration]
Name: val, dtype: object

df_result = df[np.logical_and.reduce([(df[i].isin(j)) for i, j in f.items()])]
print (df_result)
           A      B                 C     D                E
0  Project 1  Org_1         Directory  MSTR    Configuration
1  Project 1  Org_1         Directory  MSTR  Unable to Login
2  Project 1  Org_1  Desktop Software  MSTR    Configuration
4  Project 1  Org_1         Directory  MSTR  Unable to Login