Here is how my data looks like.
Complaint Type Created Date
0 Noise - Street/Sidewalk 10/31/2013 02:08:41 AM
1 Illegal Parking 10/31/2013 02:01:04 AM
2 Noise - Commercial 10/31/2013 02:00:24 AM
3 Noise - Vehicle 10/31/2013 01:56:23 AM
4 Rodent 10/31/2013 01:53:44 AM
... ... ...
111064 Maintenance or Facility 10/04/2013 12:01:13 AM
111065 Illegal Parking 10/04/2013 12:01:05 AM
111066 Noise - Street/Sidewalk 10/04/2013 12:00:45 AM
111067 Noise - Commercial 10/04/2013 12:00:28 AM
111068 Blocked Driveway 10/04/2013 12:00:10 AM
What I want to do here to remove all the complaints except these:
complaint_list=['Blocked Driveway','DOF Literature Request','GENRAL CONSTRUCTION','HEATING','Illegal Parking','NONCONST','PAINT-PLASTER','PLUMBING','Street Condition','Street Light Condition']
here is some portion of data in dictionary format.
{'Complaint Type': {0: 'Noise - Street/Sidewalk',
1: 'Illegal Parking',
2: 'Noise - Commercial',
3: 'Noise - Vehicle',
4: 'Rodent',
5: 'Noise - Commercial',
6: 'Blocked Driveway',
7: 'Noise - Commercial',
8: 'Noise - Commercial',
9: 'Noise - Commercial',
10: 'Noise - House of Worship',
11: 'Noise - Commercial',
12: 'Illegal Parking',
13: 'Noise - Vehicle',
14: 'Rodent',
15: 'Noise - House of Worship',
16: 'Noise - Street/Sidewalk',
17: 'Illegal Parking',
18: 'Street Light Condition',
19: 'Noise - Commercial',
20: 'Noise - House of Worship',
21: 'Noise - Commercial',
22: 'Noise - Vehicle',
23: 'Noise - Commercial',
24: 'Blocked Driveway',
25: 'Noise - Street/Sidewalk',
26: 'Street Light Condition',
27: 'Harboring Bees/Wasps',
28: 'Noise - Street/Sidewalk',
29: 'Street Light Condition',
30: 'Blocked Driveway',
31: 'Noise - Street/Sidewalk',
32: 'Taxi Complaint',
33: 'Noise - House of Worship',
34: 'Homeless Encampment',
35: 'Blocked Driveway',
36: 'Traffic Signal Condition',
37: 'Noise - Commercial',
38: 'Traffic Signal Condition',
39: 'Blocked Driveway',
40: 'Noise - Commercial',
41: 'Food Establishment',
42: 'Noise - Commercial',
43: 'Noise - Commercial',
44: 'Noise - Park',
45: 'Noise - Street/Sidewalk',
46: 'Noise - Commercial',
47: 'Taxi Complaint',
48: 'Noise - Commercial',
49: 'Noise - Street/Sidewalk',
50: 'Noise - Commercial',
51: 'Broken Muni Meter',
52: 'Blocked Driveway',
53: 'Noise - Commercial',
54: 'Benefit Card Replacement',
55: 'Noise - Commercial',
56: 'Sanitation Condition',
57: 'ELECTRIC',
58: 'PLUMBING',
59: 'HEATING',
60: 'ELECTRIC',
61: 'HEATING',
62: 'HEATING',
63: 'GENERAL CONSTRUCTION',
64: 'HEATING',
65: 'ELECTRIC',
66: 'GENERAL CONSTRUCTION',
67: 'Street Condition',
68: 'Consumer Complaint',
69: 'Blocked Driveway',
70: 'Derelict Vehicles',
71: 'Noise - Commercial',
72: 'Derelict Vehicles',
73: 'Noise',
74: 'Noise',
75: 'Blocked Driveway',
76: 'Noise - Commercial',
77: 'Drinking',
78: 'Indoor Air Quality',
79: 'Noise'},
'Created Date': {0: '10/31/2013 02:08:41 AM',
1: '10/31/2013 02:01:04 AM',
2: '10/31/2013 02:00:24 AM',
3: '10/31/2013 01:56:23 AM',
4: '10/31/2013 01:53:44 AM',
5: '10/31/2013 01:46:52 AM',
6: '10/31/2013 01:46:40 AM',
7: '10/31/2013 01:44:19 AM',
8: '10/31/2013 01:44:14 AM',
9: '10/31/2013 01:34:41 AM',
10: '10/31/2013 01:25:12 AM',
11: '10/31/2013 01:24:14 AM',
12: '10/31/2013 01:20:57 AM',
13: '10/31/2013 01:20:13 AM',
14: '10/31/2013 01:19:54 AM',
15: '10/31/2013 01:14:02 AM',
16: '10/31/2013 12:54:03 AM',
17: '10/31/2013 12:52:46 AM',
18: '10/31/2013 12:51:00 AM',
19: '10/31/2013 12:46:27 AM',
20: '10/31/2013 12:43:47 AM',
21: '10/31/2013 12:41:17 AM',
22: '10/31/2013 12:39:55 AM',
23: '10/31/2013 12:38:00 AM',
24: '10/31/2013 12:37:16 AM',
25: '10/31/2013 12:35:18 AM',
26: '10/31/2013 12:33:00 AM',
27: '10/31/2013 12:32:44 AM',
28: '10/31/2013 12:32:08 AM',
29: '10/31/2013 12:32:00 AM',
30: '10/31/2013 12:31:17 AM',
31: '10/31/2013 12:30:36 AM',
32: '10/31/2013 12:30:31 AM',
33: '10/31/2013 12:29:47 AM',
34: '10/31/2013 12:28:30 AM',
35: '10/31/2013 12:23:24 AM',
36: '10/31/2013 12:23:00 AM',
37: '10/31/2013 12:20:44 AM',
38: '10/31/2013 12:20:00 AM',
39: '10/31/2013 12:19:48 AM',
40: '10/31/2013 12:18:05 AM',
41: '10/31/2013 12:16:25 AM',
42: '10/31/2013 12:15:06 AM',
43: '10/31/2013 12:14:42 AM',
44: '10/31/2013 12:12:08 AM',
45: '10/31/2013 12:11:58 AM',
46: '10/31/2013 12:09:07 AM',
47: '10/31/2013 12:08:47 AM',
48: '10/31/2013 12:07:45 AM',
49: '10/31/2013 12:05:10 AM',
50: '10/31/2013 12:04:50 AM',
51: '10/31/2013 12:03:27 AM',
52: '10/31/2013 12:02:01 AM',
53: '10/31/2013 12:01:47 AM',
54: '10/31/2013 12:01:45 AM',
55: '10/31/2013 12:01:34 AM',
56: '10/31/2013 12:01:00 AM',
57: '10/31/2013 12:00:00 AM',
58: '10/31/2013 12:00:00 AM',
59: '10/31/2013 12:00:00 AM',
60: '10/31/2013 12:00:00 AM',
61: '10/31/2013 12:00:00 AM',
62: '10/31/2013 12:00:00 AM',
63: '10/31/2013 12:00:00 AM',
64: '10/31/2013 12:00:00 AM',
65: '10/31/2013 12:00:00 AM',
66: '10/31/2013 12:00:00 AM',
67: '10/30/2013 11:58:43 PM',
68: '10/30/2013 11:57:57 PM',
69: '10/30/2013 11:57:00 PM',
70: '10/30/2013 11:57:00 PM',
71: '10/30/2013 11:55:03 PM',
72: '10/30/2013 11:55:00 PM',
73: '10/30/2013 11:53:00 PM',
74: '10/30/2013 11:53:00 PM',
75: '10/30/2013 11:50:51 PM',
76: '10/30/2013 11:50:30 PM',
77: '10/30/2013 11:49:10 PM',
78: '10/30/2013 11:48:22 PM',
79: '10/30/2013 11:47:00 PM'}}
What I have done so far is that I have selected unique values from the complaint type and then added them to list and created a list with the complains given above in complaint_list.
all_complaint_list=list(task3['Complaint Type'].unique())
complaint_list=['Blocked Driveway','DOF Literature Request','GENRAL CONSTRUCTION','HEATING','Illegal Parking','NONCONST','PAINT-PLASTER','PLUMBING','Street Condition','Street Light Condition']
for i in complaint_list:
if i not in all_complaint_list:
continue
else:
all_complaint_list.remove(i)
I have used pandas.drop method but it doesn’t work.
>Solution :
First make that dictionary a Pandas Dataframe:
df = pd.DataFrame.from_dict(dict)
Then you can use isin:
df[df["Complaint Type"].isin(complaint_list)]
This should give you all the rows that match the items in complaint_list.