Python:CSV的重复键和值列表

Question

我在python中有一个数据列表，如下所示，它重复键和数据：

['id', '0c9d534a-5af4-4fa5-8cd7-668432775317', 'name', 'test company UK Sandbox', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '0c9d534a-5af4-4fa5-8cd7-668432775317', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '26.22', 'trailing_week_ppc', '0', 'image_path', 'Blank', 'start_date', '2015-06-01', 'id', '3c47d67b-d2b5-494e-8c37-0552dfb1449a', 'name', 'XXMA0069 - TEST', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '3c47d67b-d2b5-494e-8c37-0552dfb1449a', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '65.79', 'trailing_week_ppc', '0', 'image_path', '\\/api\\/v1\\/projects\\/3c47d67b-d2b5-494e-8c37-0552dfb1449a\\/image', 'start_date', '2015-10-05', 'id', 'ae326656-0523-4c8f-894d-18d6f615c4d4', 'name', 'XXMA006', 'account', 'test company', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', 'ae326656-0523-4c8f-894d-18d6f615c4d4', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '45.41', 'trailing_week_ppc', '33.33', 'image_path', 'Blank', 'start_date', '2015-10-05', 'id', '944036a2-0aaf-42bf-851b-9b0ced44df45', 'name', 'XXMA0076 - TEST', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '944036a2-0aaf-42bf-851b-9b0ced44df45', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '72.04', 'trailing_week_ppc', '23.08', 'image_path', '\\/api\\/v1\\/projects\\/944036a2-0aaf-42bf-851b-9b0ced44df45\\/image', 'start_date', '2016-10-24', 'id', '11fa1215-0b4a-4437-9c5e-31dbe9599b52', 'name', 'XXMA0082 - QEHB PFP Project', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '11fa1215-0b4a-4437-9c5e-31dbe9599b52', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '66.67', 'trailing_week_ppc', '0.0', 'image_path', 'Blank', 'start_date', '2017-01-03', 'id', 'da199d7f-b145-4c5f-89ec-3f9dcacf98d7', 'name', 'XXXX0536 - Audley Coopers Hill', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', 'da199d7f-b145-4c5f-89ec-3f9dcacf98d7', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '81.8', 'trailing_week_ppc', '10.34', 'image_path', 'Blank', 'start_date', '2017-02-10', 'id', '13cdb397-a45e-4241-a17e-65a9f20235ef', 'name', 'Development - TEST TEST', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '13cdb397-a45e-4241-a17e-65a9f20235ef', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '0', 'trailing_week_ppc', '0', 'image_path', 'Blank', 'start_date', '2017-02-15', 'id', '2dbd8b6e-a096-4f31-96ca-82662b44dc34', 'name', 'sean0007 - Test', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '2dbd8b6e-a096-4f31-96ca-82662b44dc34', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '0', 'trailing_week_ppc', '0', 'image_path', '\\/api\\/v1\\/projects\\/2dbd8b6e-a096-4f31-96ca-82662b44dc34\\/image', 'start_date', '2018-02-02']

我的最终目标是把它放到一个CSV文件中，但我一辈子都搞不清楚这个问题。你知道吗

我第一次尝试把它输入dict，但是重复的键意味着我最后只输出最后一个条目。你知道吗

我的第二次尝试是使用下面的

keys = tester[0:][::2]
values = tester[1:][::2]

这给了我两个列表，但我仍然不知道如何把它变成一个CSV。你知道吗

完整代码：

from tokenize import generate_tokens
from io import StringIO
import re

output = '//<![CDATA[window.gon={};gon.current_user={"email":"test.user@testcompany.com","phone":null,"id":"c41418fb-faa4-4772-bd24-ffd2caaed908","first_name":"Test","last_name":"TEST","last_active":"2018-03-08T14:55:49.861Z","last_view_id":"4d8fe22e-67cf-4479-80be-1b3c12468b72","last_project":"da199d7f-b145-4c5f-89ec-3f9dcacf98d7","has_seen_fre":true,"user_roles":[{"id":23213,"role":"PROJECT_ADMIN","XXeated_at":"2017-03-21T12:24:05.699Z","updated_at":"2017-03-21T12:24:05.699Z","company_id":"b7a0d950-a6fe-4c84-9a76-79eb43cae49f","project_id":"3c47d67b-d2b5-494e-8c37-0552dfb1449a","user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","default_view_setting_id":null,"status":"active"},{"id":23215,"role":"PROJECT_ADMIN","XXeated_at":"2017-03-21T12:42:20.179Z","updated_at":"2017-03-21T12:42:20.179Z","company_id":"b7a0d950-a6fe-4c84-9a76-79eb43cae49f","project_id":"ae326656-0523-4c8f-894d-18d6f615c4d4","user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","default_view_setting_id":null,"status":"active"},{"id":23439,"role":"PROJECT_ADMIN","XXeated_at":"2017-03-27T11:16:54.046Z","updated_at":"2017-03-27T11:16:54.046Z","company_id":"4319a85d-7545-4734-a127-7db9efa4b329","project_id":"13cdb397-a45e-4241-a17e-65a9f20235ef","user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","default_view_setting_id":null,"status":"active"},{"id":22906,"role":"PROJECT_ADMIN","XXeated_at":"2017-03-13T10:32:32.469Z","updated_at":"2017-03-13T10:32:32.469Z","company_id":"b7a0d950-a6fe-4c84-9a76-79eb43cae49f","project_id":"11fa1215-0b4a-4437-9c5e-31dbe9599b52","user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","default_view_setting_id":null,"status":"active"},{"id":22907,"role":"PROJECT_ADMIN","XXeated_at":"2017-03-13T10:35:00.433Z","updated_at":"2017-03-13T11:19:37.328Z","company_id":"b7a0d950-a6fe-4c84-9a76-79eb43cae49f","project_id":"0c9d534a-5af4-4fa5-8cd7-668432775317","user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","default_view_setting_id":null,"status":"active"},{"id":32698,"role":"PROJECT_ADMIN","XXeated_at":"2018-02-14T10:13:22.121Z","updated_at":"2018-02-14T10:13:22.121Z","company_id":"4319a85d-7545-4734-a127-7db9efa4b329","project_id":"2dbd8b6e-a096-4f31-96ca-82662b44dc34","user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","default_view_setting_id":null,"status":"active"},{"id":32701,"role":"PROJECT_ADMIN","XXeated_at":"2018-02-14T10:31:26.557Z","updated_at":"2018-02-14T10:31:26.557Z","company_id":"4319a85d-7545-4734-a127-7db9efa4b329","project_id":"944036a2-0aaf-42bf-851b-9b0ced44df45","user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","default_view_setting_id":null,"status":"active"},{"id":33468,"role":"PROJECT_ADMIN","XXeated_at":"2018-03-07T15:51:17.005Z","updated_at":"2018-03-07T15:51:17.005Z","company_id":"4319a85d-7545-4734-a127-7db9efa4b329","project_id":"da199d7f-b145-4c5f-89ec-3f9dcacf98d7","user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","default_view_setting_id":null,"status":"active"}],"projects":[{"project_id":"3c47d67b-d2b5-494e-8c37-0552dfb1449a","role":"PROJECT_ADMIN","company_id":"b7a0d950-a6fe-4c84-9a76-79eb43cae49f","notification_setting":{"instant_email":false,"daily_email":false,"user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","project_id":"3c47d67b-d2b5-494e-8c37-0552dfb1449a"}},{"project_id":"ae326656-0523-4c8f-894d-18d6f615c4d4","role":"PROJECT_ADMIN","company_id":"b7a0d950-a6fe-4c84-9a76-79eb43cae49f","notification_setting":{"instant_email":false,"daily_email":false,"user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","project_id":"ae326656-0523-4c8f-894d-18d6f615c4d4"}},{"project_id":"13cdb397-a45e-4241-a17e-65a9f20235ef","role":"PROJECT_ADMIN","company_id":"4319a85d-7545-4734-a127-7db9efa4b329","notification_setting":{"instant_email":false,"daily_email":false,"user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","project_id":"13cdb397-a45e-4241-a17e-65a9f20235ef"}},{"project_id":"11fa1215-0b4a-4437-9c5e-31dbe9599b52","role":"PROJECT_ADMIN","company_id":"b7a0d950-a6fe-4c84-9a76-79eb43cae49f","notification_setting":{"instant_email":false,"daily_email":false,"user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","project_id":"11fa1215-0b4a-4437-9c5e-31dbe9599b52"}},{"project_id":"0c9d534a-5af4-4fa5-8cd7-668432775317","role":"PROJECT_ADMIN","company_id":"b7a0d950-a6fe-4c84-9a76-79eb43cae49f","notification_setting":{"instant_email":false,"daily_email":false,"user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","project_id":"0c9d534a-5af4-4fa5-8cd7-668432775317"}},{"project_id":"2dbd8b6e-a096-4f31-96ca-82662b44dc34","role":"PROJECT_ADMIN","company_id":"4319a85d-7545-4734-a127-7db9efa4b329","notification_setting":{"instant_email":false,"daily_email":false,"user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","project_id":"2dbd8b6e-a096-4f31-96ca-82662b44dc34"}},{"project_id":"944036a2-0aaf-42bf-851b-9b0ced44df45","role":"PROJECT_ADMIN","company_id":"4319a85d-7545-4734-a127-7db9efa4b329","notification_setting":{"instant_email":false,"daily_email":false,"user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","project_id":"944036a2-0aaf-42bf-851b-9b0ced44df45"}},{"project_id":"da199d7f-b145-4c5f-89ec-3f9dcacf98d7","role":"PROJECT_ADMIN","company_id":"4319a85d-7545-4734-a127-7db9efa4b329","notification_setting":{"instant_email":false,"daily_email":false,"user_id":"c41418fb-faa4-4772-bd24-ffd2caaed908","project_id":"da199d7f-b145-4c5f-89ec-3f9dcacf98d7"}}]};gon.projects=[{"id":"0c9d534a-5af4-4fa5-8cd7-668432775317","name":"test company UK Sandbox","account":"test company - Pilot","account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","ea_project_id":"0c9d534a-5af4-4fa5-8cd7-668432775317","ea_account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","project_ppc_to_date":26.22,"trailing_week_ppc":0,"image_path":"","start_date":"2015-06-01"},{"id":"3c47d67b-d2b5-494e-8c37-0552dfb1449a","name":"XXMA0069 - TEST","account":"test company - Pilot","account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","ea_project_id":"3c47d67b-d2b5-494e-8c37-0552dfb1449a","ea_account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","project_ppc_to_date":65.79,"trailing_week_ppc":0,"image_path":"\/api\/v1\/projects\/3c47d67b-d2b5-494e-8c37-0552dfb1449a\/image","start_date":"2015-10-05"},{"id":"ae326656-0523-4c8f-894d-18d6f615c4d4","name":"XXMA006","account":"test company","account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","ea_project_id":"ae326656-0523-4c8f-894d-18d6f615c4d4","ea_account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","project_ppc_to_date":45.41,"trailing_week_ppc":33.33,"image_path":"","start_date":"2015-10-05"},{"id":"944036a2-0aaf-42bf-851b-9b0ced44df45","name":"XXMA0076 - TEST","account":"test company - Pilot","account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","ea_project_id":"944036a2-0aaf-42bf-851b-9b0ced44df45","ea_account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","project_ppc_to_date":72.04,"trailing_week_ppc":23.08,"image_path":"\/api\/v1\/projects\/944036a2-0aaf-42bf-851b-9b0ced44df45\/image","start_date":"2016-10-24"},{"id":"11fa1215-0b4a-4437-9c5e-31dbe9599b52","name":"XXMA0082 - QEHB PFP Project","account":"test company - Pilot","account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","ea_project_id":"11fa1215-0b4a-4437-9c5e-31dbe9599b52","ea_account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","project_ppc_to_date":66.67,"trailing_week_ppc":0.0,"image_path":"","start_date":"2017-01-03"},{"id":"da199d7f-b145-4c5f-89ec-3f9dcacf98d7","name":"XXXX0536 - Audley Coopers Hill","account":"test company - Pilot","account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","ea_project_id":"da199d7f-b145-4c5f-89ec-3f9dcacf98d7","ea_account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","project_ppc_to_date":81.8,"trailing_week_ppc":10.34,"image_path":"","start_date":"2017-02-10"},{"id":"13cdb397-a45e-4241-a17e-65a9f20235ef","name":"Development - TEST TEST","account":"test company - Pilot","account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","ea_project_id":"13cdb397-a45e-4241-a17e-65a9f20235ef","ea_account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","project_ppc_to_date":0,"trailing_week_ppc":0,"image_path":"","start_date":"2017-02-15"},{"id":"2dbd8b6e-a096-4f31-96ca-82662b44dc34","name":"sean0007 - Test","account":"test company - Pilot","account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","ea_project_id":"2dbd8b6e-a096-4f31-96ca-82662b44dc34","ea_account_id":"704fbc55-0565-496f-94e6-2e0c95b98327","project_ppc_to_date":0,"trailing_week_ppc":0,"image_path":"\/api\/v1\/projects\/2dbd8b6e-a096-4f31-96ca-82662b44dc34\/image","start_date":"2018-02-02"}];gon.highcharts_path="\/assets\/highcharts-34b694afdf78e0fec0852867d3cfe0f1b54e302677f832887d978180c39ddd99.js";gon.urls={"logout":"\/logout","profile":"https:\/\/accounts.TEST.com","help":"http:\/\/help.TEST.com\/view\/BIM360P\/ENU\/","community":"http:\/\/forums..com/t5\/bim-360\/ct-p\/2025","ideas":"http:\/\/forums.TEST.com\/t5\/bim-360-ideastation\/idb-p\/2032","support":"https:\/\/sso..com\/idp\/startSSO.ping?PartnerSpId=https:\/\/saml_jit.salesforce.com\u0026TargetResource=%2Fcustomer%2Fapex%\u0026cparam1=00D300000008uIL\u0026cparam2=0DM3A000000PDsL\u0026cparam3=https:\/\/TEST-communities.force.com\/customer\u0026cparam4=Plan","whats_new":"http:\/\/help.TEST.com\/view\/BIM360P\/ENU\/?guid=GUID-8970143C-3A63-4E6A-855D-585927C8EACD","facebook":"https:\/\/www..com\/","twitter":"https:\/\/.com\/","youtube":"https:\/\/www..com\/user\/","privacy":"http:\/\/www..com\/privacy","terms":"http:\/\/www..com\/termsofservice"};//]]>'

try:
    found = re.findall(r'gon.projects=\[(.+?)]\;gon.highcharts_path', output)
except AttributeError:
    found = ''

f = StringIO(found[0])
L = list(generate_tokens(f.readline))

tester = []
for tok in L:
    if (tok.type == 3) or (tok.type == 2):
        output = tok.string.replace('\"\"', '\"Blank\"')
        output2 = output.replace('\"', '')
        tester.append(output2)

keys = tester[0:][::2]
values = tester[1:][::2]

任何帮助都将不胜感激。你知道吗

Answer 1

将该行写入CSV以代替print

import numpy as np
tester=['id', '0c9d534a-5af4-4fa5-8cd7-668432775317', 'name', 'test company UK Sandbox', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '0c9d534a-5af4-4fa5-8cd7-668432775317', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '26.22', 'trailing_week_ppc', '0', 'image_path', 'Blank', 'start_date', '2015-06-01', 'id', '3c47d67b-d2b5-494e-8c37-0552dfb1449a', 'name', 'XXMA0069 - TEST', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '3c47d67b-d2b5-494e-8c37-0552dfb1449a', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '65.79', 'trailing_week_ppc', '0', 'image_path', '\\/api\\/v1\\/projects\\/3c47d67b-d2b5-494e-8c37-0552dfb1449a\\/image', 'start_date', '2015-10-05', 'id', 'ae326656-0523-4c8f-894d-18d6f615c4d4', 'name', 'XXMA006', 'account', 'test company', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', 'ae326656-0523-4c8f-894d-18d6f615c4d4', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '45.41', 'trailing_week_ppc', '33.33', 'image_path', 'Blank', 'start_date', '2015-10-05', 'id', '944036a2-0aaf-42bf-851b-9b0ced44df45', 'name', 'XXMA0076 - TEST', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '944036a2-0aaf-42bf-851b-9b0ced44df45', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '72.04', 'trailing_week_ppc', '23.08', 'image_path', '\\/api\\/v1\\/projects\\/944036a2-0aaf-42bf-851b-9b0ced44df45\\/image', 'start_date', '2016-10-24', 'id', '11fa1215-0b4a-4437-9c5e-31dbe9599b52', 'name', 'XXMA0082 - QEHB PFP Project', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '11fa1215-0b4a-4437-9c5e-31dbe9599b52', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '66.67', 'trailing_week_ppc', '0.0', 'image_path', 'Blank', 'start_date', '2017-01-03', 'id', 'da199d7f-b145-4c5f-89ec-3f9dcacf98d7', 'name', 'XXXX0536 - Audley Coopers Hill', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', 'da199d7f-b145-4c5f-89ec-3f9dcacf98d7', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '81.8', 'trailing_week_ppc', '10.34', 'image_path', 'Blank', 'start_date', '2017-02-10', 'id', '13cdb397-a45e-4241-a17e-65a9f20235ef', 'name', 'Development - TEST TEST', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '13cdb397-a45e-4241-a17e-65a9f20235ef', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '0', 'trailing_week_ppc', '0', 'image_path', 'Blank', 'start_date', '2017-02-15', 'id', '2dbd8b6e-a096-4f31-96ca-82662b44dc34', 'name', 'sean0007 - Test', 'account', 'test company - Pilot', 'account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'ea_project_id', '2dbd8b6e-a096-4f31-96ca-82662b44dc34', 'ea_account_id', '704fbc55-0565-496f-94e6-2e0c95b98327', 'project_ppc_to_date', '0', 'trailing_week_ppc', '0', 'image_path', '\\/api\\/v1\\/projects\\/2dbd8b6e-a096-4f31-96ca-82662b44dc34\\/image', 'start_date', '2018-02-02']
f = open('csvfile.csv','w')
keys = tester[0:][::2]
seen = set()
seen_add = seen.add
keysu= [x for x in keys if not (x in seen or seen_add(x))]
values = tester[1:][::2]
f.write(','.join(keysu)+'\n')
a = np.array(values).reshape(int(len(values)/len(keysu)),len(keysu))
for i in a:
  f.write(','.join(i)+'\n')

输出

id,name,account,account_id,ea_project_id,ea_account_id,project_ppc_to_date,trailing_week_ppc,image_path,start_date
0c9d534a-5af4-4fa5-8cd7-668432775317,test company UK Sandbox,test company - Pilot,704fbc55-0565-496f-94e6-2e0c95b98327,0c9d534a-5af4-4fa5-8cd7-668432775317,704fbc55-0565-496f-94e6-2e0c95b98327,26.22,0,Blank,2015-06-01
3c47d67b-d2b5-494e-8c37-0552dfb1449a,XXMA0069 - TEST,test company - Pilot,704fbc55-0565-496f-94e6-2e0c95b98327,3c47d67b-d2b5-494e-8c37-0552dfb1449a,704fbc55-0565-496f-94e6-2e0c95b98327,65.79,0,\/api\/v1\/projects\/3c47d67b-d2b5-494e-8c37-0552dfb1449a\/image,2015-10-05
ae326656-0523-4c8f-894d-18d6f615c4d4,XXMA006,test company,704fbc55-0565-496f-94e6-2e0c95b98327,ae326656-0523-4c8f-894d-18d6f615c4d4,704fbc55-0565-496f-94e6-2e0c95b98327,45.41,33.33,Blank,2015-10-05
944036a2-0aaf-42bf-851b-9b0ced44df45,XXMA0076 - TEST,test company - Pilot,704fbc55-0565-496f-94e6-2e0c95b98327,944036a2-0aaf-42bf-851b-9b0ced44df45,704fbc55-0565-496f-94e6-2e0c95b98327,72.04,23.08,\/api\/v1\/projects\/944036a2-0aaf-42bf-851b-9b0ced44df45\/image,2016-10-24
11fa1215-0b4a-4437-9c5e-31dbe9599b52,XXMA0082 - QEHB PFP Project,test company - Pilot,704fbc55-0565-496f-94e6-2e0c95b98327,11fa1215-0b4a-4437-9c5e-31dbe9599b52,704fbc55-0565-496f-94e6-2e0c95b98327,66.67,0.0,Blank,2017-01-03
da199d7f-b145-4c5f-89ec-3f9dcacf98d7,XXXX0536 - Audley Coopers Hill,test company - Pilot,704fbc55-0565-496f-94e6-2e0c95b98327,da199d7f-b145-4c5f-89ec-3f9dcacf98d7,704fbc55-0565-496f-94e6-2e0c95b98327,81.8,10.34,Blank,2017-02-10
13cdb397-a45e-4241-a17e-65a9f20235ef,Development - TEST TEST,test company - Pilot,704fbc55-0565-496f-94e6-2e0c95b98327,13cdb397-a45e-4241-a17e-65a9f20235ef,704fbc55-0565-496f-94e6-2e0c95b98327,0,0,Blank,2017-02-15
2dbd8b6e-a096-4f31-96ca-82662b44dc34,sean0007 - Test,test company - Pilot,704fbc55-0565-496f-94e6-2e0c95b98327,2dbd8b6e-a096-4f31-96ca-82662b44dc34,704fbc55-0565-496f-94e6-2e0c95b98327,0,0,\/api\/v1\/projects\/2dbd8b6e-a096-4f31-96ca-82662b44dc34\/image,2018-02-02

Answer 2

我建议您将列表转换为数据帧，然后使用pandas保存为csv。你知道吗

pandas_to_csv

Answer 3

为什么不使用CSV module。你知道吗

例如：

import csv

with open("Tester.csv", 'w') as myfile:
    wr = csv.writer(myfile, delimiter=';')
    wr.writerow(keys)
    wr.writerow(values)