需求背景： 所在公司，员工的任务和工时都登记在jira上，每个季度需要拉取数据。
实现如下：
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import requests
from requests.auth import HTTPBasicAuth
import json
import copy
import pandas as pd
url = "https://your-domain/rest/api/2/search"
auth = HTTPBasicAuth("your-account", "your-password")
headers = {"Accept": "application/json", "Content-Type": "application/json"}
users = ['zhangsan', 'lisi']
payload = {
"expand": ["names", "schema", "operations"],
"jql": "created >= 2022-01-01 AND created <= 2022-03-31 AND assignee in (user) ORDER BY created DESC",
"maxResults": 1000,
# "fieldsByKeys": False,
"fields": [
"issuetype",
"summary",
"key",
"assignee",
"timespent"
],
"startAt": 0
}
print('开始获取数据...')
issues_total = []
for i in users:
tmp = copy.deepcopy(payload)
tmp['jql'] = tmp['jql'].replace('user', i)
response = requests.post(url, data=json.dumps(tmp), headers=headers, auth=auth)
# print(response.text)
issues = list(map(lambda x: {'issuetype': x['fields']['issuetype']['name'],
'summary': x['fields']['summary'],
'key': x['key'],
'assignee': x['fields']['assignee']['displayName'],
'timespent': round(x['fields']['timespent'] / 3600, 2) if x['fields']['timespent'] is not None else 0},
json.loads(response.text)['issues']))
print(issues)
issues_total += issues
print(len(issues_total))
final_df = pd.DataFrame(issues_total, columns=['issuetype','summary','key', 'assignee', 'timespent'])
final_df.to_excel("统计.xlsx")
备注：
使用该接口，一次最多只能返回1000条数据。哪怕jql查出来的数据超过了1000个。 如果遇到超过1000个的情况，比如2000个，可以分2次来下载，类似于分页。
第1次：
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"maxResults": 2000,
"startAt": 0
第2次：
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2
"maxResults": 2000,
"startAt": 1000
