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How to count tickets with status change filtered byYear-To-Date in Pandas?
How do you change the size of figures drawn with matplotlib?How can I count the occurrences of a list item?How to change the order of DataFrame columns?How to drop rows of Pandas DataFrame whose value in a certain column is NaNChange data type of columns in PandasHow do I get the row count of a pandas DataFrame?How to iterate over rows in a DataFrame in Pandas?How to deal with SettingWithCopyWarning in Pandas?How to count the NaN values in a column in pandas DataFrameHow to check if any value is NaN in a Pandas DataFrame
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I have 2 snapshots of data set stored in 2 dataframes that contains JIRA tickets, there is a column called UpdatedDate which tells me when the snapshot was taken.
I want to calculate number of tickets which still remain open filter by Year-to-Date which basically means: how many tickets in total (combined snapshots) are still open util tomorrow (eg.2019-03-29).
But the problem is the both of my dataframes can contain the same JIRA issue, but the status of the ticket might or might not change.
# this df1 (Snapshot 1)
Issue key Project name Status UpdatedDate
111 Proj1 Analysis 2019-03-18
222 Proj1 Open 2019-03-18
# this df2 (Snapshot 2)
Issue key Project name Status UpdatedDate
111 Proj1 Done 2019-03-28
222 Proj1 Open 2019-03-28
So as the table indicated above, issue111's status has changed to Done on snapshot 2 where as issue222's status is still Open.
So if my Year-to-Date filter is set on 2019-03-29. it will show me 2 ticket with Status Open, but one of them will be a duplication.
How can I count number of ticket that are still open but without duplicates?
python pandas dataframe
asked Mar 28 at 17:38
bossangelobossangelo
273 bronze badges
add a comment
|
I have 2 snapshots of data set stored in 2 dataframes that contains JIRA tickets, there is a column called UpdatedDate which tells me when the snapshot was taken.
I want to calculate number of tickets which still remain open filter by Year-to-Date which basically means: how many tickets in total (combined snapshots) are still open util tomorrow (eg.2019-03-29).
But the problem is the both of my dataframes can contain the same JIRA issue, but the status of the ticket might or might not change.
# this df1 (Snapshot 1)
Issue key Project name Status UpdatedDate
111 Proj1 Analysis 2019-03-18
222 Proj1 Open 2019-03-18
# this df2 (Snapshot 2)
Issue key Project name Status UpdatedDate
111 Proj1 Done 2019-03-28
222 Proj1 Open 2019-03-28
So as the table indicated above, issue111's status has changed to Done on snapshot 2 where as issue222's status is still Open.
So if my Year-to-Date filter is set on 2019-03-29. it will show me 2 ticket with Status Open, but one of them will be a duplication.
How can I count number of ticket that are still open but without duplicates?
python pandas dataframe
asked Mar 28 at 17:38
bossangelobossangelo
273 bronze badges
add a comment
|
I have 2 snapshots of data set stored in 2 dataframes that contains JIRA tickets, there is a column called UpdatedDate which tells me when the snapshot was taken.
I want to calculate number of tickets which still remain open filter by Year-to-Date which basically means: how many tickets in total (combined snapshots) are still open util tomorrow (eg.2019-03-29).
But the problem is the both of my dataframes can contain the same JIRA issue, but the status of the ticket might or might not change.
# this df1 (Snapshot 1)
Issue key Project name Status UpdatedDate
111 Proj1 Analysis 2019-03-18
222 Proj1 Open 2019-03-18
# this df2 (Snapshot 2)
Issue key Project name Status UpdatedDate
111 Proj1 Done 2019-03-28
222 Proj1 Open 2019-03-28
So as the table indicated above, issue111's status has changed to Done on snapshot 2 where as issue222's status is still Open.
So if my Year-to-Date filter is set on 2019-03-29. it will show me 2 ticket with Status Open, but one of them will be a duplication.
How can I count number of ticket that are still open but without duplicates?
python pandas dataframe
asked Mar 28 at 17:38
bossangelobossangelo
273 bronze badges
I have 2 snapshots of data set stored in 2 dataframes that contains JIRA tickets, there is a column called UpdatedDate which tells me when the snapshot was taken.
I want to calculate number of tickets which still remain open filter by Year-to-Date which basically means: how many tickets in total (combined snapshots) are still open util tomorrow (eg.2019-03-29).
But the problem is the both of my dataframes can contain the same JIRA issue, but the status of the ticket might or might not change.
# this df1 (Snapshot 1)
Issue key Project name Status UpdatedDate
111 Proj1 Analysis 2019-03-18
222 Proj1 Open 2019-03-18
# this df2 (Snapshot 2)
Issue key Project name Status UpdatedDate
111 Proj1 Done 2019-03-28
222 Proj1 Open 2019-03-28
So as the table indicated above, issue111's status has changed to Done on snapshot 2 where as issue222's status is still Open.
So if my Year-to-Date filter is set on 2019-03-29. it will show me 2 ticket with Status Open, but one of them will be a duplication.
How can I count number of ticket that are still open but without duplicates?
python pandas dataframe
python pandas dataframe
asked Mar 28 at 17:38
bossangelobossangelo
273 bronze badges
asked Mar 28 at 17:38
bossangelobossangelo
273 bronze badges
asked Mar 28 at 17:38
bossangelobossangelo
273 bronze badges
asked Mar 28 at 17:38
bossangelobossangelo
273 bronze badges
asked Mar 28 at 17:38
bossangelobossangelo
273 bronze badges
273 bronze badges
add a comment
|
add a comment
|
1 Answer
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you can sort_values() and then drop_duplicates():
pd.concat([df1, df2])
.sort_values(['UpdatedDate'], ascending=[False])
.drop_duplicates(['Issue key'], keep='first')
.loc[lambda x: x.Status == 'Open']
answered Mar 28 at 18:16
jxcjxc
2,9602 gold badges4 silver badges17 bronze badges
add a comment
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
you can sort_values() and then drop_duplicates():
pd.concat([df1, df2])
.sort_values(['UpdatedDate'], ascending=[False])
.drop_duplicates(['Issue key'], keep='first')
.loc[lambda x: x.Status == 'Open']
answered Mar 28 at 18:16
jxcjxc
2,9602 gold badges4 silver badges17 bronze badges
add a comment
|
you can sort_values() and then drop_duplicates():
pd.concat([df1, df2])
.sort_values(['UpdatedDate'], ascending=[False])
.drop_duplicates(['Issue key'], keep='first')
.loc[lambda x: x.Status == 'Open']
answered Mar 28 at 18:16
jxcjxc
2,9602 gold badges4 silver badges17 bronze badges
add a comment
|
you can sort_values() and then drop_duplicates():
pd.concat([df1, df2])
.sort_values(['UpdatedDate'], ascending=[False])
.drop_duplicates(['Issue key'], keep='first')
.loc[lambda x: x.Status == 'Open']
answered Mar 28 at 18:16
jxcjxc
2,9602 gold badges4 silver badges17 bronze badges
you can sort_values() and then drop_duplicates():
pd.concat([df1, df2])
.sort_values(['UpdatedDate'], ascending=[False])
.drop_duplicates(['Issue key'], keep='first')
.loc[lambda x: x.Status == 'Open']
answered Mar 28 at 18:16
jxcjxc
2,9602 gold badges4 silver badges17 bronze badges
answered Mar 28 at 18:16
jxcjxc
2,9602 gold badges4 silver badges17 bronze badges
answered Mar 28 at 18:16
jxcjxc
2,9602 gold badges4 silver badges17 bronze badges
answered Mar 28 at 18:16
jxcjxc
2,9602 gold badges4 silver badges17 bronze badges
2,9602 gold badges4 silver badges17 bronze badges
add a comment
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add a comment
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