Great question! Let's build on your existing code to achieve this—here's a step-by-step solution that covers querying Jira issues by project and assignee, extracting relevant details, and exporting to Excel.

Step 1: Install Required Libraries #

You already have the jira library, but we'll need pandas and openpyxl for seamless Excel exports. Install them if you haven't:

pip install jira pandas openpyxl

Step 2: Initialize Jira Client & Query Issues with JQL #

Instead of fetching a single issue, we'll use JQL (Jira Query Language) to filter issues by your target project and assignee. This lets you pull all matching issues at once:

from jira import JIRA
import pandas as pd

# Update these with your actual Jira credentials and server URL
options = {'server': 'https://your-jira-instance.com', 'verify': False}
jira = JIRA(options, basic_auth=('your-username', 'your-password'))

# Define your filters (customize these values)
target_project = 'DWO'
target_assignee = 'john.doe'  # Use the assignee's Jira username/email

# JQL query to fetch matching issues (adjust filters like status if needed)
jql_query = f'project = {target_project} AND assignee = "{target_assignee}" AND status != Closed'
# maxResults=False fetches all matching issues (use a number if you want a limit)
issues = jira.search_issues(jql_query, maxResults=False)

Step 3: Extract Relevant Issue Details #

Loop through each issue to collect the fields you care about. Customize this list based on your specific needs:

issue_data = []

for issue in issues:
    # Extract core fields (add/remove fields as required)
    issue_details = {
        'Issue Key': issue.key,
        'Summary': issue.fields.summary,
        'Description': issue.fields.description or 'No description',
        'Status': issue.fields.status.name,
        'Assignee': issue.fields.assignee.displayName if issue.fields.assignee else 'Unassigned',
        'Created Date': issue.fields.created,
        'Updated Date': issue.fields.updated,
        'Priority': issue.fields.priority.name if issue.fields.priority else 'No priority',
        'Labels': ', '.join(issue.fields.labels) if issue.fields.labels else 'None'
    }
    issue_data.append(issue_details)

Step 4: Export to Excel #

Using pandas, converting our collected data to an Excel file is straightforward:

# Convert to a pandas DataFrame for easy manipulation
df = pd.DataFrame(issue_data)

# Export to Excel (customize the filename as you like)
output_filename = f'{target_project}_{target_assignee}_issues.xlsx'
df.to_excel(output_filename, index=False, engine='openpyxl')

print(f"Success! Exported {len(issues)} issues to {output_filename}")

Key Customizations & Tips #

• JQL Flexibility: Tweak the JQL query to add more filters (e.g., AND created >= "-30d" for issues from the last 30 days, or AND issuetype = Bug for only bug reports).
• Custom Fields: To extract custom Jira fields, use issue.fields.customfield_XXXX where XXXX is the field's ID. You can find this ID by inspecting the issue page's HTML or using Jira's REST API.
• Error Handling: Add try-except blocks if you're dealing with large datasets or optional fields (e.g., some issues might not have an assignee or priority).
• Pagination: For thousands of issues, replace maxResults=False with pagination using the startAt parameter to avoid timeouts.

内容的提问来源于stack exchange，提问作者Himanshu Pant