I have this dataframe:
number total_complaint_count first_complaint_on last_complaint_on
0 0000000000 77 2021-10-29 2021-12-05
1 00000000000 1 2021-11-12 2021-11-12
2 000000000000 1 2021-11-07 2021-11-07
3 00020056234 1 2021-11-23 2021-11-23
4 0002266648 1 2021-11-02 2021-11-02
5 0012684202 1 2021-11-15 2021-11-15
6 0033774983 1 2021-11-12 2021-11-12
7 00420056234 1 2021-11-23 2021-11-23
8 00620056234 1 2021-11-23 2021-11-23
9 0101276541 1 2021-11-16 2021-11-16
And this dataframe:
number total_complaint_count first_complaint_on last_complaint_on
0 0000000000 3 2021-10-29 2021-15-05
1 00000000000 1 2021-11-12 2021-16-12
2 000000000000 1 2021-11-07 2021-16-07
How can I merge them by the number so that the total_complaint_count getts added to the one in the first dataframe and last_complaint_on date is updated with the later date?
Like this:
number total_complaint_count first_complaint_on last_complaint_on
0 0000000000 80 2021-10-29 2021-15-05
1 00000000000 2 2021-11-12 2021-16-12
2 000000000000 2 2021-11-07 2021-16-07
3 00020056234 1 2021-11-23 2021-11-23
4 0002266648 1 2021-11-02 2021-11-02
5 0012684202 1 2021-11-15 2021-11-15
6 0033774983 1 2021-11-12 2021-11-12
7 00420056234 1 2021-11-23 2021-11-23
8 00620056234 1 2021-11-23 2021-11-23
9 0101276541 1 2021-11-16 2021-11-16
I tried this approach:
if df2["number"].isin(df1, "number"):
df.loc(df2["number"].isin(df1, "number"))
But don't know how to proceed further.
If the number is not present in the first dataframe, it's simply added to the rows.
Dict of the first table for reproduction:
{'number': {0: '0000000000', 1: '00000000000', 2: '000000000000', 3: '00020056234', 4: '0002266648', 5: '0012684202', 6: '0033774983', 7: '00420056234', 8: '00620056234', 9: '0101276541'}, 'total_complaint_count': {0: 77, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1}, 'first_complaint_on': {0: '2021-10-29', 1: '2021-11-12', 2: '2021-11-07', 3: '2021-11-23', 4: '2021-11-02', 5: '2021-11-15', 6: '2021-11-12', 7: '2021-11-23', 8: '2021-11-23', 9: '2021-11-16'}, 'last_complaint_on': {0: '2021-12-05', 1: '2021-11-12', 2: '2021-11-07', 3: '2021-11-23', 4: '2021-11-02', 5: '2021-11-15', 6: '2021-11-12', 7: '2021-11-23', 8: '2021-11-23', 9: '2021-11-16'}, 'subject_id': {0: [UUID('5321d243-d4a2-49e0-aa56-d269e613122e'), UUID('f4a91830-d1cd-423d-a1f6-175153a7b040'), UUID('4e9732b2-9b9a-4eaa-9bd6-5e72209b3274'), UUID('cc7027e7-ceb6-42aa-a588-c6b0dfa5bd5a'), UUID('8e340596-f1cf-4251-a61e-7b349b9979db'), UUID('6fb85e65-aa40-42fb-8f0a-2341ada3cc46'), UUID('3c858da6-5360-4fdd-8b5b-d246b1273b6e'), UUID('6618fa6a-0b82-4083-816a-e2e517a7af86'), UUID('e447569a-e070-4977-a6fd-c8c40046b1d6'), UUID('a70c2f71-94b3-4fd8-80b2-36b6e6229582'), UUID('d7a0214e-79ef-4997-8001-1b46f02af85f'), UUID('16687c59-c828-49a1-8a09-37466eab63aa'), UUID('87813a24-bdf3-4283-bb0b-236f152cd990'), UUID('ba6ca856-66b8-4d33-920d-a9e62c934866'), UUID('6eedd713-738b-4dce-8fa6-27667858a5c6'), UUID('92aae302-91b1-46c6-876c-5e580ffe249c'), UUID('c1949065-7f14-4b71-b2da-8c8e8328152f'), UUID('0c7d6553-0e19-41a5-b8c2-a1f69d27ee60'), UUID('f6f822a7-7e8c-423b-b7df-ffb3b73d6384'), UUID('ad058b09-c90c-4914-9fbd-f03f14135b20'), UUID('90a9d8df-fa66-4ea1-bc5b-e74f883e8474'), UUID('7b1d34aa-92b4-4bde-8939-213ab071910c'), UUID('51f313b0-05ee-4eb9-8e75-f9b7388a3f67'), UUID('d5a33682-4e34-40ee-8b57-2269a5e443f2'), UUID('f9fa8a64-c6e2-4aab-94c2-6558543d13f9'), UUID('9812c9cf-5c8d-4898-9253-00c9bb6cf1f4'), UUID('a252c20b-87db-44b7-994f-7dbea59f5ed5'), UUID('2d6972a9-dbc7-41b3-9063-0beecaf3fca4'), UUID('04e95d9d-8fba-4493-a503-921cf4709be0'), UUID('7f180dff-614c-422f-beb9-8c32148dceea'), UUID('61601ddf-7c74-4b58-8595-1aecdf897798'), UUID('eb3a1766-0f07-48ae-8857-a70988ee33cc'), UUID('e71d0b17-4783-4368-901c-f775dfb21959'), UUID('12b10576-253a-459a-a6a4-c9dfd07a76f9'), UUID('55343f62-f89e-4a5b-b7a4-ee3366c94ea0'), UUID('c28f9866-c5e5-4034-8237-abdd7cde2cda'), UUID('071314e7-e63b-4d0c-9c92-20bec9d5d1b5'), UUID('70932cb5-81e2-4ea2-ad2e-1b6a4f54312d'), UUID('c97fec02-5a1d-43e8-b715-6242eea64f5e'), UUID('9d1d89ba-741e-4797-b7c4-01aeb05a9eb0'), UUID('e32346bc-92a4-485b-8979-1400a68310c0'), UUID('2df2a49e-8a2c-4261-b7ea-624e8da43788'), UUID('b73d54dd-0ee5-4c9b-bf24-fcf3da0617f3'), UUID('25f57a95-b36d-49ab-b8b9-42a2ef915a80'), UUID('6f81c96f-8009-4192-8f00-d89c93848f50'), UUID('5b70e8ff-b80c-4ecc-ac63-d3d8a31c6244'), UUID('d1a4d512-ba1b-4361-9aa5-722a67a51f4d'), UUID('cf316e9c-1a7a-4337-b11e-4ba09cdde9a4'), UUID('7d1fb284-284d-4f8c-9e32-6bc5514b5bf3'), UUID('a041ba69-9571-480b-a68c-8c90d56d7666'), UUID('b1209765-f3ee-4823-a1a8-87999a8bd671'), UUID('21ac988d-0d84-49eb-81cf-db88277bd1a7'), UUID('dde67191-3e87-4d1b-a275-00e646ed9b13'), UUID('825f987d-e064-4343-a82d-b94b3e10cf58'), UUID('c076f72f-9835-4b3a-8e70-2659b9e07bc5'), UUID('f79b42aa-7818-4298-b9b4-8a120cdc11e3'), UUID('c430b06b-ddcd-4a6d-b691-1c6c8db272a6'), UUID('567e3013-a81c-4777-b101-280d55d8e54d'), UUID('93c0643b-777f-4a15-94a2-45cc8d9ec62f'), UUID('a807435f-96de-49d4-b1d8-bcac7a174d80'), UUID('565ea841-4f7b-43a4-a11d-863f260b081c'), UUID('b0f079a9-bfae-489a-a789-27216bafcda1'), UUID('afcbcda9-0439-482f-bac2-a61681998227'), UUID('ac62c8a3-5dfe-4fa6-a727-1ef05d227a91'), UUID('65a59510-37b5-4b80-b3fb-f487e925ebff'), UUID('7b4c7b6d-c7ef-492a-89c4-d9a8a2868428'), UUID('b9d27f11-bfc3-468a-86ba-7fb1bbf9d338'), UUID('53355721-a518-45dc-8717-e97cee6b6f6c'), UUID('d28c672a-8a6d-42b5-9849-19b45752ab39'), UUID('8056cd85-1044-4ec5-a8bf-02a88772b404'), UUID('63c511d0-1edb-4f84-bf53-eb75e5ceb9b8'), UUID('f48d2ac3-ff7a-4635-8e3c-c65623322b54'), UUID('b4e5213e-7dcb-4cff-8e30-34a84652c537'), UUID('3335d582-a068-421e-9085-a6cfbbaa7e5c'), UUID('5f440360-07dc-4ef1-ab63-bc452b07e9a8'), UUID('b0e4999c-f7a1-4293-93a9-c5323703f0a4'), UUID('9be20a6b-b185-412a-adad-387bc3f5ab48')], 1: [UUID('fc6421cf-b014-429c-a50e-1fcc25138844')], 2: [UUID('e97eb783-0821-4c97-a3a1-412d9976ace4')], 3: [UUID('220fdb90-61b3-479c-ba3a-3bdb74d3e71a')], 4: [UUID('92d0a2b4-669b-42dc-9d87-06bfb53e6ca6')], 5: [UUID('0c1b8c0c-8eaf-4918-ad87-78f2fa1bbc46')], 6: [UUID('fd3b92b2-80da-4fdc-acd5-9e171358673b')], 7: [UUID('dd0d1b79-6d21-40b3-bea4-77a162687978')], 8: [UUID('46b1ce56-f42a-4105-a1ac-ec50fc867be9')], 9: [UUID('10ebe98c-d0f6-45ca-aa6a-6a9a3bb260b6')]}}
Second table:
{'number': {787: '0000000000', 4391: '0000000000', 694: '0000000000', 1106: '0000000000', 4682: '0000000000', 11223: '0000000000', 773: '0000000000', 1555: '0000000000', 1377: '0000000000', 287: '0000000000'}, 'subject': {787: 'Other', 4391: 'Calls pretending to be government, businesses, or family and friends', 694: 'Warranties & protection plans', 1106: 'Other', 4682: 'Dropped call or no message', 11223: 'No Subject Provided', 773: 'Warranties & protection plans', 1555: 'Other', 1377: 'Medical & prescriptions', 287: 'Other'}, 'subject_id': {787: UUID('5321d243-d4a2-49e0-aa56-d269e613122e'), 4391: UUID('f4a91830-d1cd-423d-a1f6-175153a7b040'), 694: UUID('4e9732b2-9b9a-4eaa-9bd6-5e72209b3274'), 1106: UUID('cc7027e7-ceb6-42aa-a588-c6b0dfa5bd5a'), 4682: UUID('8e340596-f1cf-4251-a61e-7b349b9979db'), 11223: UUID('6fb85e65-aa40-42fb-8f0a-2341ada3cc46'), 773: UUID('3c858da6-5360-4fdd-8b5b-d246b1273b6e'), 1555: UUID('6618fa6a-0b82-4083-816a-e2e517a7af86'), 1377: UUID('e447569a-e070-4977-a6fd-c8c40046b1d6'), 287: UUID('a70c2f71-94b3-4fd8-80b2-36b6e6229582')}}
CodePudding user response：
Use concat with aggregate sum and GroupBy.last with GroupBy.agg:
df = (pd.concat([df1, df2])
.groupby('number', sort=False, as_index=False)
.agg({'total_complaint_count':'sum',
'first_complaint_on':'last',
'last_complaint_on':'last'}) )
print (df)
number total_complaint_count first_complaint_on last_complaint_on
0 0000000000 80 2021-10-29 2021-15-05
1 00000000000 2 2021-11-12 2021-16-12
2 000000000000 2 2021-11-07 2021-16-07
3 00020056234 1 2021-11-23 2021-11-23
4 0002266648 1 2021-11-02 2021-11-02
5 0012684202 1 2021-11-15 2021-11-15
6 0033774983 1 2021-11-12 2021-11-12
7 00420056234 1 2021-11-23 2021-11-23
8 00620056234 1 2021-11-23 2021-11-23
9 0101276541 1 2021-11-16 2021-11-16
CodePudding user response：
I would just set columns that i dont need to sum as index, as show here:
import pandas as pd
df1 = pd.DataFrame({"number": ['0000000000', '00000000000', '000000000000'],
"total_complaint_count":[3,1,1],
"first_complaint_on":['2021-10-29','2021-11-12','2021-11-07'],
"last_complaint_on": ['2021-15-05', '2021-16-12', '2021-16-07']}).set_index(["number","first_complaint_on","last_complaint_on"])
df2 = pd.DataFrame({"number": ['0000000000', '00000000000', '000000000000', '00020056234'],
"total_complaint_count":[77,1,1,1],
"first_complaint_on":['2021-10-29','2021-11-12','2021-11-07','2021-11-23'],
"last_complaint_on": ['2021-15-05', '2021-16-12', '2021-16-07','2021-11-23']}).set_index(["number","first_complaint_on","last_complaint_on"])
df_result = pd.concat([df1, df2]).groupby(["number", "first_complaint_on", "last_complaint_on"]).sum().reset_index()