import jira.client
from jira.client import JIRA
from jira import JIRA
import pandas as pd
import numpy as np

options  = {'server':'your server URL here'}
jira = JIRA(options, basic_auth=('your user name here','your password here'))


#extracting number of issues loop because of Jira limitation.
block_size=100
block_num=0
allissues = []
while True:
    start_idx = block_num*block_size
    issues =  jira.search_issues('project=your project name here', start_idx, block_size)
    if len(issues) == 0:
        break
    block_num += 1
    for issue in issues:
        allissues.append(issue)


#Create a dataframe and assign issues one by one.

issuesdf=pd.DataFrame()
for issue in allissues:

    issue = jira.issue(issue.key, expand='changelog')
    changelog = issue.changelog

    for history in changelog.histories:
        for item in history.items:
            if item.field == 'status':
                if "Blocked" in item.toString or "Blocked" in item.fromString:
                    d={
                        'Key' : issue.key,
                        'Assignee': issue.fields.assignee,
                        'Components': issue.fields.components,
                        'Summary': issue.fields.summary,
                        'Issuetype': issue.fields.issuetype.name,
                        'Priority': issue.fields.priority.name,
                        'Current Status': issue.fields.status.name,
                        'Date': history.created,
                        'Transition': item.fromString+' - '+item.toString,


                    }


                    issuesdf=issuesdf.append(d,ignore_index=True)



issuesdf['Date']=pd.to_datetime(issuesdf['Date'])
issuesdf['Date']=issuesdf['Date'].dt.normalize()

blockeddf = issuesdf[issuesdf['Transition'].str.match('In Progress - Blocked')]
blockeddf['Counter']=blockeddf.groupby((blockeddf['Key'] != blockeddf['Key'].shift(1)).cumsum()).cumcount()+1
resolveddf = issuesdf[issuesdf['Transition'].str.match('Blocked - ')]
resolveddf['Counter']=resolveddf.groupby((resolveddf['Key'] != resolveddf['Key'].shift(1)).cumsum()).cumcount()+1
newdf = pd.merge(blockeddf,resolveddf,how='left', on=['Key','Counter'])
newdf['Number of Days'] = newdf['Date_y'] - newdf['Date_x']
newdf['Number of Days'] = newdf['Number of Days']/np.timedelta64(1,'D')

newdf.to_csv(r'your csv path here')


print(issuesdf)
issuesdf.to_csv(r'your csv path here')