Mixing
pandas to find earliest occurrence of statement and set to starter
up vote
2
down vote
favorite
Consider the following df
data = {'Name' : ['John','John','Lucy','Lucy','Lucy'],
'Payroll' : [15,15,75,75,75],
'Week' : [1,2,1,2,3]}
df = pd.DataFrame(data)
Name Payroll Week
0 John 15 1
1 John 15 2
2 Lucy 75 1
3 Lucy 75 2
4 Lucy 75 3
What I'm attempting to do is true apply a Boolean throughout a DataFrame very similar to this one with 2m+ rows and 20+ columns to find out when someone started
To find if someone is active or not I pass a condition to another df:
df2 = df.loc[df.Week == df.Week.max()]
This gives me the final week i then use an isin filter to find out if the person is active or has left
df['Status'] = np.where(df['Payroll'].isin(df2['Payroll']), 'Active','Leaver')
So using the above code I get the following which is great, which tells me that since John is not in the latest week he has left the company
Name Payroll Week Status
0 John 15 1 Leaver
1 John 15 2 Leaver
2 Lucy 75 1 Active
3 Lucy 75 2 Active
4 Lucy 75 3 Active
What I'm trying to achieve is to know when John started with us, I could try a mask for each week of the year and an isin to check for when they first appeared but I figured there must be a more pythonic way do this!
Desired output:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Leaver
2 Lucy 75 1 Starter
3 Lucy 75 2 Active
4 Lucy 75 3 Active
Any help is much appreciated.
Edit for Clarity :
data = {'Name' : ['John','John','John','John','Lucy','Lucy','Lucy','Lucy','Lucy'],
'Payroll' : [15,15,15,15,75,75,75,75,75],
'Week' : [1,2,3,4,1,2,3,4,5]}
df = pd.DataFrame(data)
desired output:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Active
2 John 15 3 Active
3 John 15 4 Leaver
4 Lucy 75 1 Starter
5 Lucy 75 2 Active
6 Lucy 75 3 Active
7 Lucy 75 4 Active
8 Lucy 75 5 Active
things to note:
Max week is 5 so anyone not in week 5 is a leaver
first week of person in df makes them a starter.
all weeks in between are set to Active.
python pandas
asked yesterday
Datanovice
508211
add a comment |
up vote
2
down vote
favorite
Consider the following df
data = {'Name' : ['John','John','Lucy','Lucy','Lucy'],
'Payroll' : [15,15,75,75,75],
'Week' : [1,2,1,2,3]}
df = pd.DataFrame(data)
Name Payroll Week
0 John 15 1
1 John 15 2
2 Lucy 75 1
3 Lucy 75 2
4 Lucy 75 3
What I'm attempting to do is true apply a Boolean throughout a DataFrame very similar to this one with 2m+ rows and 20+ columns to find out when someone started
To find if someone is active or not I pass a condition to another df:
df2 = df.loc[df.Week == df.Week.max()]
This gives me the final week i then use an isin filter to find out if the person is active or has left
df['Status'] = np.where(df['Payroll'].isin(df2['Payroll']), 'Active','Leaver')
So using the above code I get the following which is great, which tells me that since John is not in the latest week he has left the company
Name Payroll Week Status
0 John 15 1 Leaver
1 John 15 2 Leaver
2 Lucy 75 1 Active
3 Lucy 75 2 Active
4 Lucy 75 3 Active
What I'm trying to achieve is to know when John started with us, I could try a mask for each week of the year and an isin to check for when they first appeared but I figured there must be a more pythonic way do this!
Desired output:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Leaver
2 Lucy 75 1 Starter
3 Lucy 75 2 Active
4 Lucy 75 3 Active
Any help is much appreciated.
Edit for Clarity :
data = {'Name' : ['John','John','John','John','Lucy','Lucy','Lucy','Lucy','Lucy'],
'Payroll' : [15,15,15,15,75,75,75,75,75],
'Week' : [1,2,3,4,1,2,3,4,5]}
df = pd.DataFrame(data)
desired output:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Active
2 John 15 3 Active
3 John 15 4 Leaver
4 Lucy 75 1 Starter
5 Lucy 75 2 Active
6 Lucy 75 3 Active
7 Lucy 75 4 Active
8 Lucy 75 5 Active
things to note:
Max week is 5 so anyone not in week 5 is a leaver
first week of person in df makes them a starter.
all weeks in between are set to Active.
python pandas
asked yesterday
Datanovice
508211
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
Consider the following df
data = {'Name' : ['John','John','Lucy','Lucy','Lucy'],
'Payroll' : [15,15,75,75,75],
'Week' : [1,2,1,2,3]}
df = pd.DataFrame(data)
Name Payroll Week
0 John 15 1
1 John 15 2
2 Lucy 75 1
3 Lucy 75 2
4 Lucy 75 3
What I'm attempting to do is true apply a Boolean throughout a DataFrame very similar to this one with 2m+ rows and 20+ columns to find out when someone started
To find if someone is active or not I pass a condition to another df:
df2 = df.loc[df.Week == df.Week.max()]
This gives me the final week i then use an isin filter to find out if the person is active or has left
df['Status'] = np.where(df['Payroll'].isin(df2['Payroll']), 'Active','Leaver')
So using the above code I get the following which is great, which tells me that since John is not in the latest week he has left the company
Name Payroll Week Status
0 John 15 1 Leaver
1 John 15 2 Leaver
2 Lucy 75 1 Active
3 Lucy 75 2 Active
4 Lucy 75 3 Active
What I'm trying to achieve is to know when John started with us, I could try a mask for each week of the year and an isin to check for when they first appeared but I figured there must be a more pythonic way do this!
Desired output:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Leaver
2 Lucy 75 1 Starter
3 Lucy 75 2 Active
4 Lucy 75 3 Active
Any help is much appreciated.
Edit for Clarity :
data = {'Name' : ['John','John','John','John','Lucy','Lucy','Lucy','Lucy','Lucy'],
'Payroll' : [15,15,15,15,75,75,75,75,75],
'Week' : [1,2,3,4,1,2,3,4,5]}
df = pd.DataFrame(data)
desired output:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Active
2 John 15 3 Active
3 John 15 4 Leaver
4 Lucy 75 1 Starter
5 Lucy 75 2 Active
6 Lucy 75 3 Active
7 Lucy 75 4 Active
8 Lucy 75 5 Active
things to note:
Max week is 5 so anyone not in week 5 is a leaver
first week of person in df makes them a starter.
all weeks in between are set to Active.
python pandas
asked yesterday
Datanovice
508211
Consider the following df
data = {'Name' : ['John','John','Lucy','Lucy','Lucy'],
'Payroll' : [15,15,75,75,75],
'Week' : [1,2,1,2,3]}
df = pd.DataFrame(data)
Name Payroll Week
0 John 15 1
1 John 15 2
2 Lucy 75 1
3 Lucy 75 2
4 Lucy 75 3
What I'm attempting to do is true apply a Boolean throughout a DataFrame very similar to this one with 2m+ rows and 20+ columns to find out when someone started
To find if someone is active or not I pass a condition to another df:
df2 = df.loc[df.Week == df.Week.max()]
This gives me the final week i then use an isin filter to find out if the person is active or has left
df['Status'] = np.where(df['Payroll'].isin(df2['Payroll']), 'Active','Leaver')
So using the above code I get the following which is great, which tells me that since John is not in the latest week he has left the company
Name Payroll Week Status
0 John 15 1 Leaver
1 John 15 2 Leaver
2 Lucy 75 1 Active
3 Lucy 75 2 Active
4 Lucy 75 3 Active
What I'm trying to achieve is to know when John started with us, I could try a mask for each week of the year and an isin to check for when they first appeared but I figured there must be a more pythonic way do this!
Desired output:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Leaver
2 Lucy 75 1 Starter
3 Lucy 75 2 Active
4 Lucy 75 3 Active
Any help is much appreciated.
Edit for Clarity :
data = {'Name' : ['John','John','John','John','Lucy','Lucy','Lucy','Lucy','Lucy'],
'Payroll' : [15,15,15,15,75,75,75,75,75],
'Week' : [1,2,3,4,1,2,3,4,5]}
df = pd.DataFrame(data)
desired output:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Active
2 John 15 3 Active
3 John 15 4 Leaver
4 Lucy 75 1 Starter
5 Lucy 75 2 Active
6 Lucy 75 3 Active
7 Lucy 75 4 Active
8 Lucy 75 5 Active
things to note:
Max week is 5 so anyone not in week 5 is a leaver
first week of person in df makes them a starter.
all weeks in between are set to Active.
python pandas
python pandas
asked yesterday
Datanovice
508211
asked yesterday
Datanovice
508211
edited yesterday
asked yesterday
Datanovice
508211
asked yesterday
Datanovice
508211
asked yesterday
Datanovice
508211
508211
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
up vote
3
down vote
accepted
Use numpy.select with new condition by duplicated:
a = df.loc[df.Week == df.Week.max(), 'Payroll']
m1 = ~df['Payroll'].isin(a)
m2 = ~df['Payroll'].duplicated()
m3 = ~df['Payroll'].duplicated(keep='last')
df['Status'] = np.select([m2, m1 & m3], ['Starter', 'Leaver'], 'Active')
print (df)
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Active
2 John 15 3 Active
3 John 15 4 Leaver
4 Lucy 75 1 Starter
5 Lucy 75 2 Active
6 Lucy 75 3 Active
7 Lucy 75 4 Active
8 Lucy 75 5 Active
answered yesterday
jezrael
307k20243317
Hey Jozi! this is close but Lucy would be set as Active as she's present in the df in the final week
– Datanovice
yesterday
1
I don't think so as it's possible for people to have the same names, however the payroll column has unique values per person
– Datanovice
yesterday
1
@Datanovice - exactly, you are right.
– jezrael
yesterday
1
@Datanovice - I think the best should be changed sample data, is it possible? Can you add some 2-3 new rows?
– jezrael
yesterday
1
beautiful, you are the man!
– Datanovice
yesterday
|
show 9 more comments
up vote
2
down vote
The simplest way that I have come across is using groupby and finding minimal index for the name in the group:
for _, dfg in df.groupby(df['Name']):
gidx = min(dfg.index)
df.loc[df.index == gidx,'Status'] = 'Starter'
print(df)
And the df is then:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Leaver
2 Lucy 75 1 Starter
3 Lucy 75 2 Active
4 Lucy 75 3 Active
answered yesterday
sophros
1,6831418
awesome! let me test this, currently running on my lovely work laptop!
– Datanovice
yesterday
Mind the correction (proper indexing this time) :)
– sophros
yesterday
additionally, could this be used to find the last occurrence of an item in a df? I'd like to note someone as active until they are a leaver in their max week if its less than the dataframes max week
– Datanovice
yesterday
and final comment, anyway to do this without a for loop, I've always been reprimanded here for using them as pandas uses 2d vector arrays and doesn't work well with loops (especially as my df is 2million rows +)
– Datanovice
yesterday
1
You can change themintomaxand you have the last entry. With this the changes are minimal. In terms of the for loop there is another approach I will post in a moment.
– sophros
yesterday
|
show 3 more comments
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
3
down vote
accepted
Use numpy.select with new condition by duplicated:
a = df.loc[df.Week == df.Week.max(), 'Payroll']
m1 = ~df['Payroll'].isin(a)
m2 = ~df['Payroll'].duplicated()
m3 = ~df['Payroll'].duplicated(keep='last')
df['Status'] = np.select([m2, m1 & m3], ['Starter', 'Leaver'], 'Active')
print (df)
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Active
2 John 15 3 Active
3 John 15 4 Leaver
4 Lucy 75 1 Starter
5 Lucy 75 2 Active
6 Lucy 75 3 Active
7 Lucy 75 4 Active
8 Lucy 75 5 Active
answered yesterday
jezrael
307k20243317
Hey Jozi! this is close but Lucy would be set as Active as she's present in the df in the final week
– Datanovice
yesterday
1
I don't think so as it's possible for people to have the same names, however the payroll column has unique values per person
– Datanovice
yesterday
1
@Datanovice - exactly, you are right.
– jezrael
yesterday
1
@Datanovice - I think the best should be changed sample data, is it possible? Can you add some 2-3 new rows?
– jezrael
yesterday
1
beautiful, you are the man!
– Datanovice
yesterday
|
show 9 more comments
up vote
3
down vote
accepted
Use numpy.select with new condition by duplicated:
a = df.loc[df.Week == df.Week.max(), 'Payroll']
m1 = ~df['Payroll'].isin(a)
m2 = ~df['Payroll'].duplicated()
m3 = ~df['Payroll'].duplicated(keep='last')
df['Status'] = np.select([m2, m1 & m3], ['Starter', 'Leaver'], 'Active')
print (df)
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Active
2 John 15 3 Active
3 John 15 4 Leaver
4 Lucy 75 1 Starter
5 Lucy 75 2 Active
6 Lucy 75 3 Active
7 Lucy 75 4 Active
8 Lucy 75 5 Active
answered yesterday
jezrael
307k20243317
Hey Jozi! this is close but Lucy would be set as Active as she's present in the df in the final week
– Datanovice
yesterday
1
I don't think so as it's possible for people to have the same names, however the payroll column has unique values per person
– Datanovice
yesterday
1
@Datanovice - exactly, you are right.
– jezrael
yesterday
1
@Datanovice - I think the best should be changed sample data, is it possible? Can you add some 2-3 new rows?
– jezrael
yesterday
1
beautiful, you are the man!
– Datanovice
yesterday
|
show 9 more comments
up vote
3
down vote
accepted
up vote
3
down vote
accepted
Use numpy.select with new condition by duplicated:
a = df.loc[df.Week == df.Week.max(), 'Payroll']
m1 = ~df['Payroll'].isin(a)
m2 = ~df['Payroll'].duplicated()
m3 = ~df['Payroll'].duplicated(keep='last')
df['Status'] = np.select([m2, m1 & m3], ['Starter', 'Leaver'], 'Active')
print (df)
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Active
2 John 15 3 Active
3 John 15 4 Leaver
4 Lucy 75 1 Starter
5 Lucy 75 2 Active
6 Lucy 75 3 Active
7 Lucy 75 4 Active
8 Lucy 75 5 Active
answered yesterday
jezrael
307k20243317
Use numpy.select with new condition by duplicated:
a = df.loc[df.Week == df.Week.max(), 'Payroll']
m1 = ~df['Payroll'].isin(a)
m2 = ~df['Payroll'].duplicated()
m3 = ~df['Payroll'].duplicated(keep='last')
df['Status'] = np.select([m2, m1 & m3], ['Starter', 'Leaver'], 'Active')
print (df)
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Active
2 John 15 3 Active
3 John 15 4 Leaver
4 Lucy 75 1 Starter
5 Lucy 75 2 Active
6 Lucy 75 3 Active
7 Lucy 75 4 Active
8 Lucy 75 5 Active
answered yesterday
jezrael
307k20243317
edited yesterday
answered yesterday
jezrael
307k20243317
answered yesterday
jezrael
307k20243317
answered yesterday
jezrael
307k20243317
307k20243317
Hey Jozi! this is close but Lucy would be set as Active as she's present in the df in the final week
– Datanovice
yesterday
1
I don't think so as it's possible for people to have the same names, however the payroll column has unique values per person
– Datanovice
yesterday
1
@Datanovice - exactly, you are right.
– jezrael
yesterday
1
@Datanovice - I think the best should be changed sample data, is it possible? Can you add some 2-3 new rows?
– jezrael
yesterday
1
beautiful, you are the man!
– Datanovice
yesterday
|
show 9 more comments
Hey Jozi! this is close but Lucy would be set as Active as she's present in the df in the final week
– Datanovice
yesterday
1
I don't think so as it's possible for people to have the same names, however the payroll column has unique values per person
– Datanovice
yesterday
1
@Datanovice - exactly, you are right.
– jezrael
yesterday
1
@Datanovice - I think the best should be changed sample data, is it possible? Can you add some 2-3 new rows?
– jezrael
yesterday
1
beautiful, you are the man!
– Datanovice
yesterday
Hey Jozi! this is close but Lucy would be set as Active as she's present in the df in the final week
– Datanovice
yesterday
Hey Jozi! this is close but Lucy would be set as Active as she's present in the df in the final week
– Datanovice
yesterday
1
1
I don't think so as it's possible for people to have the same names, however the payroll column has unique values per person
– Datanovice
yesterday
I don't think so as it's possible for people to have the same names, however the payroll column has unique values per person
– Datanovice
yesterday
1
1
@Datanovice - exactly, you are right.
– jezrael
yesterday
@Datanovice - exactly, you are right.
– jezrael
yesterday
1
1
@Datanovice - I think the best should be changed sample data, is it possible? Can you add some 2-3 new rows?
– jezrael
yesterday
@Datanovice - I think the best should be changed sample data, is it possible? Can you add some 2-3 new rows?
– jezrael
yesterday
1
1
beautiful, you are the man!
– Datanovice
yesterday
beautiful, you are the man!
– Datanovice
yesterday
|
show 9 more comments
up vote
2
down vote
The simplest way that I have come across is using groupby and finding minimal index for the name in the group:
for _, dfg in df.groupby(df['Name']):
gidx = min(dfg.index)
df.loc[df.index == gidx,'Status'] = 'Starter'
print(df)
And the df is then:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Leaver
2 Lucy 75 1 Starter
3 Lucy 75 2 Active
4 Lucy 75 3 Active
answered yesterday
sophros
1,6831418
awesome! let me test this, currently running on my lovely work laptop!
– Datanovice
yesterday
Mind the correction (proper indexing this time) :)
– sophros
yesterday
additionally, could this be used to find the last occurrence of an item in a df? I'd like to note someone as active until they are a leaver in their max week if its less than the dataframes max week
– Datanovice
yesterday
and final comment, anyway to do this without a for loop, I've always been reprimanded here for using them as pandas uses 2d vector arrays and doesn't work well with loops (especially as my df is 2million rows +)
– Datanovice
yesterday
1
You can change themintomaxand you have the last entry. With this the changes are minimal. In terms of the for loop there is another approach I will post in a moment.
– sophros
yesterday
|
show 3 more comments
up vote
2
down vote
The simplest way that I have come across is using groupby and finding minimal index for the name in the group:
for _, dfg in df.groupby(df['Name']):
gidx = min(dfg.index)
df.loc[df.index == gidx,'Status'] = 'Starter'
print(df)
And the df is then:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Leaver
2 Lucy 75 1 Starter
3 Lucy 75 2 Active
4 Lucy 75 3 Active
answered yesterday
sophros
1,6831418
awesome! let me test this, currently running on my lovely work laptop!
– Datanovice
yesterday
Mind the correction (proper indexing this time) :)
– sophros
yesterday
additionally, could this be used to find the last occurrence of an item in a df? I'd like to note someone as active until they are a leaver in their max week if its less than the dataframes max week
– Datanovice
yesterday
and final comment, anyway to do this without a for loop, I've always been reprimanded here for using them as pandas uses 2d vector arrays and doesn't work well with loops (especially as my df is 2million rows +)
– Datanovice
yesterday
1
You can change themintomaxand you have the last entry. With this the changes are minimal. In terms of the for loop there is another approach I will post in a moment.
– sophros
yesterday
|
show 3 more comments
up vote
2
down vote
up vote
2
down vote
The simplest way that I have come across is using groupby and finding minimal index for the name in the group:
for _, dfg in df.groupby(df['Name']):
gidx = min(dfg.index)
df.loc[df.index == gidx,'Status'] = 'Starter'
print(df)
And the df is then:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Leaver
2 Lucy 75 1 Starter
3 Lucy 75 2 Active
4 Lucy 75 3 Active
answered yesterday
sophros
1,6831418
The simplest way that I have come across is using groupby and finding minimal index for the name in the group:
for _, dfg in df.groupby(df['Name']):
gidx = min(dfg.index)
df.loc[df.index == gidx,'Status'] = 'Starter'
print(df)
And the df is then:
Name Payroll Week Status
0 John 15 1 Starter
1 John 15 2 Leaver
2 Lucy 75 1 Starter
3 Lucy 75 2 Active
4 Lucy 75 3 Active
answered yesterday
sophros
1,6831418
edited yesterday
answered yesterday
sophros
1,6831418
answered yesterday
sophros
1,6831418
answered yesterday
sophros
1,6831418
1,6831418
awesome! let me test this, currently running on my lovely work laptop!
– Datanovice
yesterday
Mind the correction (proper indexing this time) :)
– sophros
yesterday
additionally, could this be used to find the last occurrence of an item in a df? I'd like to note someone as active until they are a leaver in their max week if its less than the dataframes max week
– Datanovice
yesterday
and final comment, anyway to do this without a for loop, I've always been reprimanded here for using them as pandas uses 2d vector arrays and doesn't work well with loops (especially as my df is 2million rows +)
– Datanovice
yesterday
1
You can change themintomaxand you have the last entry. With this the changes are minimal. In terms of the for loop there is another approach I will post in a moment.
– sophros
yesterday
|
show 3 more comments
awesome! let me test this, currently running on my lovely work laptop!
– Datanovice
yesterday
Mind the correction (proper indexing this time) :)
– sophros
yesterday
additionally, could this be used to find the last occurrence of an item in a df? I'd like to note someone as active until they are a leaver in their max week if its less than the dataframes max week
– Datanovice
yesterday
and final comment, anyway to do this without a for loop, I've always been reprimanded here for using them as pandas uses 2d vector arrays and doesn't work well with loops (especially as my df is 2million rows +)
– Datanovice
yesterday
1
You can change themintomaxand you have the last entry. With this the changes are minimal. In terms of the for loop there is another approach I will post in a moment.
– sophros
yesterday
awesome! let me test this, currently running on my lovely work laptop!
– Datanovice
yesterday
awesome! let me test this, currently running on my lovely work laptop!
– Datanovice
yesterday
Mind the correction (proper indexing this time) :)
– sophros
yesterday
Mind the correction (proper indexing this time) :)
– sophros
yesterday
additionally, could this be used to find the last occurrence of an item in a df? I'd like to note someone as active until they are a leaver in their max week if its less than the dataframes max week
– Datanovice
yesterday
additionally, could this be used to find the last occurrence of an item in a df? I'd like to note someone as active until they are a leaver in their max week if its less than the dataframes max week
– Datanovice
yesterday
and final comment, anyway to do this without a for loop, I've always been reprimanded here for using them as pandas uses 2d vector arrays and doesn't work well with loops (especially as my df is 2million rows +)
– Datanovice
yesterday
and final comment, anyway to do this without a for loop, I've always been reprimanded here for using them as pandas uses 2d vector arrays and doesn't work well with loops (especially as my df is 2million rows +)
– Datanovice
yesterday
1
1
You can change the
min to max and you have the last entry. With this the changes are minimal. In terms of the for loop there is another approach I will post in a moment.– sophros
yesterday
You can change the
min to max and you have the last entry. With this the changes are minimal. In terms of the for loop there is another approach I will post in a moment.– sophros
yesterday
|
show 3 more comments
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});
Sign up using Google
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Post as a guest
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Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
