displaying data in Jupyter pivottable.js
I have a pandas DataFrame with 3 columns : product, region, and cost.
I want to display a pivot table using pivottable.js in a Jupyter notebook such that product are rows, region are columns and cost are values.
I have tried :
from pivottablejs import pivot_ui
import pandas as pd
df = pd.DataFrame({'region':['N', 'S', 'W', 'E', 'N', 'S', 'W', 'E'],
'product':['P1', 'P1', 'P1', 'P1', 'P2', 'P2', 'P2', 'P2'],
'cost':[10, 13, 17, 28, 29, 23, 17, 18]})
pivot_ui(df, rows=['product'], cols=['region'], values=['cost'])
But this does not work, since there does not exist a values attribute for pivot_ui().
How to do that ?
python jupyter pivottable.js
asked Nov 25 '18 at 22:09
M. PageM. Page
1,9321921
add a comment |
I have a pandas DataFrame with 3 columns : product, region, and cost.
I want to display a pivot table using pivottable.js in a Jupyter notebook such that product are rows, region are columns and cost are values.
I have tried :
from pivottablejs import pivot_ui
import pandas as pd
df = pd.DataFrame({'region':['N', 'S', 'W', 'E', 'N', 'S', 'W', 'E'],
'product':['P1', 'P1', 'P1', 'P1', 'P2', 'P2', 'P2', 'P2'],
'cost':[10, 13, 17, 28, 29, 23, 17, 18]})
pivot_ui(df, rows=['product'], cols=['region'], values=['cost'])
But this does not work, since there does not exist a values attribute for pivot_ui().
How to do that ?
python jupyter pivottable.js
asked Nov 25 '18 at 22:09
M. PageM. Page
1,9321921
add a comment |
I have a pandas DataFrame with 3 columns : product, region, and cost.
I want to display a pivot table using pivottable.js in a Jupyter notebook such that product are rows, region are columns and cost are values.
I have tried :
from pivottablejs import pivot_ui
import pandas as pd
df = pd.DataFrame({'region':['N', 'S', 'W', 'E', 'N', 'S', 'W', 'E'],
'product':['P1', 'P1', 'P1', 'P1', 'P2', 'P2', 'P2', 'P2'],
'cost':[10, 13, 17, 28, 29, 23, 17, 18]})
pivot_ui(df, rows=['product'], cols=['region'], values=['cost'])
But this does not work, since there does not exist a values attribute for pivot_ui().
How to do that ?
python jupyter pivottable.js
asked Nov 25 '18 at 22:09
M. PageM. Page
1,9321921
I have a pandas DataFrame with 3 columns : product, region, and cost.
I want to display a pivot table using pivottable.js in a Jupyter notebook such that product are rows, region are columns and cost are values.
I have tried :
from pivottablejs import pivot_ui
import pandas as pd
df = pd.DataFrame({'region':['N', 'S', 'W', 'E', 'N', 'S', 'W', 'E'],
'product':['P1', 'P1', 'P1', 'P1', 'P2', 'P2', 'P2', 'P2'],
'cost':[10, 13, 17, 28, 29, 23, 17, 18]})
pivot_ui(df, rows=['product'], cols=['region'], values=['cost'])
But this does not work, since there does not exist a values attribute for pivot_ui().
How to do that ?
python jupyter pivottable.js
python jupyter pivottable.js
asked Nov 25 '18 at 22:09
M. PageM. Page
1,9321921
asked Nov 25 '18 at 22:09
M. PageM. Page
1,9321921
asked Nov 25 '18 at 22:09
M. PageM. Page
1,9321921
asked Nov 25 '18 at 22:09
M. PageM. Page
1,9321921
asked Nov 25 '18 at 22:09
M. PageM. Page
1,9321921
1,9321921
add a comment |
add a comment |
1 Answer
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oldest
votes
The first problem is that this function doesn't accept a values kwarg, but rather vals.
The second issue you'll face is that you'll need to specify an aggregation function (the default is Count) to summarize your values. This is sort of similar to the pandas pivot table's aggfunc argument. If you expect to only have a single value then something like pivot_ui(df, rows=['product'], cols=['region'], vals=['cost'], aggregatorName='First') should do the trick.
By way of explanation, your code above is just providing the Count of input records per cell. Count doesn't accept any arguments, so passing in vals on its own won't change that. First does accept arguments, so passing in vals=['cost'] will cause each cell to contain the first value of cost (ordered via "natural sort") per cell.
answered Nov 26 '18 at 3:58
nicolaskruchtennicolaskruchten
13.4k65071
works perfectly !
– M. Page
Nov 26 '18 at 7:01
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
The first problem is that this function doesn't accept a values kwarg, but rather vals.
The second issue you'll face is that you'll need to specify an aggregation function (the default is Count) to summarize your values. This is sort of similar to the pandas pivot table's aggfunc argument. If you expect to only have a single value then something like pivot_ui(df, rows=['product'], cols=['region'], vals=['cost'], aggregatorName='First') should do the trick.
By way of explanation, your code above is just providing the Count of input records per cell. Count doesn't accept any arguments, so passing in vals on its own won't change that. First does accept arguments, so passing in vals=['cost'] will cause each cell to contain the first value of cost (ordered via "natural sort") per cell.
answered Nov 26 '18 at 3:58
nicolaskruchtennicolaskruchten
13.4k65071
works perfectly !
– M. Page
Nov 26 '18 at 7:01
add a comment |
The first problem is that this function doesn't accept a values kwarg, but rather vals.
The second issue you'll face is that you'll need to specify an aggregation function (the default is Count) to summarize your values. This is sort of similar to the pandas pivot table's aggfunc argument. If you expect to only have a single value then something like pivot_ui(df, rows=['product'], cols=['region'], vals=['cost'], aggregatorName='First') should do the trick.
By way of explanation, your code above is just providing the Count of input records per cell. Count doesn't accept any arguments, so passing in vals on its own won't change that. First does accept arguments, so passing in vals=['cost'] will cause each cell to contain the first value of cost (ordered via "natural sort") per cell.
answered Nov 26 '18 at 3:58
nicolaskruchtennicolaskruchten
13.4k65071
works perfectly !
– M. Page
Nov 26 '18 at 7:01
add a comment |
The first problem is that this function doesn't accept a values kwarg, but rather vals.
The second issue you'll face is that you'll need to specify an aggregation function (the default is Count) to summarize your values. This is sort of similar to the pandas pivot table's aggfunc argument. If you expect to only have a single value then something like pivot_ui(df, rows=['product'], cols=['region'], vals=['cost'], aggregatorName='First') should do the trick.
By way of explanation, your code above is just providing the Count of input records per cell. Count doesn't accept any arguments, so passing in vals on its own won't change that. First does accept arguments, so passing in vals=['cost'] will cause each cell to contain the first value of cost (ordered via "natural sort") per cell.
answered Nov 26 '18 at 3:58
nicolaskruchtennicolaskruchten
13.4k65071
The first problem is that this function doesn't accept a values kwarg, but rather vals.
The second issue you'll face is that you'll need to specify an aggregation function (the default is Count) to summarize your values. This is sort of similar to the pandas pivot table's aggfunc argument. If you expect to only have a single value then something like pivot_ui(df, rows=['product'], cols=['region'], vals=['cost'], aggregatorName='First') should do the trick.
By way of explanation, your code above is just providing the Count of input records per cell. Count doesn't accept any arguments, so passing in vals on its own won't change that. First does accept arguments, so passing in vals=['cost'] will cause each cell to contain the first value of cost (ordered via "natural sort") per cell.
answered Nov 26 '18 at 3:58
nicolaskruchtennicolaskruchten
13.4k65071
answered Nov 26 '18 at 3:58
nicolaskruchtennicolaskruchten
13.4k65071
answered Nov 26 '18 at 3:58
nicolaskruchtennicolaskruchten
13.4k65071
answered Nov 26 '18 at 3:58
nicolaskruchtennicolaskruchten
13.4k65071
13.4k65071
works perfectly !
– M. Page
Nov 26 '18 at 7:01
add a comment |
works perfectly !
– M. Page
Nov 26 '18 at 7:01
works perfectly !
– M. Page
Nov 26 '18 at 7:01
works perfectly !
– M. Page
Nov 26 '18 at 7:01
add a comment |
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