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dataframe.pivot-example2.test.ts
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dataframe.pivot-example2.test.ts
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import { assert, expect } from 'chai';
import 'mocha';
import { Index } from '../lib/index';
import { DataFrame } from '../lib/dataframe';
import { ArrayIterable } from '../lib/iterables/array-iterable';
import { Series } from '../lib/series';
//
// Replicate the following pandas examples:
//
// http://pbpython.com/pandas-pivot-table-explained.html
//
describe('DataFrame pivot examples 2', () => {
var df: DataFrame<number, any>;
beforeEach(() => {
df = new DataFrame({
columnNames:
["Account", "Name", "Rep", "Manager", "Product", "Quantity", "Price", "Status"],
rows: [
[ 714466, "Trantow-Barrows", "Craig Booker", "Debra Henley", "CPU", 1,30000, "presented" ],
[ 714466, "Trantow-Barrows", "Craig Booker", "Debra Henley", "Software", 1,10000, "presented" ],
[ 714466, "Trantow-Barrows", "Craig Booker", "Debra Henley", "Maintenance", 2,5000, "pending" ],
[ 737550, "Fritsch, Russel and Anderson", "Craig Booker", "Debra Henley", "CPU", 1,35000, "declined" ],
[ 146832, "Kiehn-Spinka", "Daniel Hilton", "Debra Henley", "CPU",2,65000, "won" ],
[ 218895, "Kulas Inc", "Daniel Hilton", "Debra Henley", "CPU",2,40000, "pending" ],
[ 218895, "Kulas Inc", "Daniel Hilton", "Debra Henley", "Software",1,10000, "presented" ],
[ 412290, "Jerde-Hilpert", "John Smith", "Debra Henley", "Maintenance",2,5000, "pending" ],
[ 740150, "Barton LLC", "John Smith", "Debra Henley", "CPU",1,35000, "declined" ],
[ 141962, "Herman LLC", "Cedric Moss", "Fred Anderson", "CPU",2,65000, "won" ],
[ 163416, "Purdy-Kunde", "Cedric Moss", "Fred Anderson", "CPU",1,30000, "presented" ],
[ 239344, "Stokes LLC", "Cedric Moss", "Fred Anderson", "Maintenance",1,5000, "pending" ],
[ 239344, "Stokes LLC", "Cedric Moss", "Fred Anderson", "Software",1,10000, "presented" ],
[ 307599, "Kassulke, Ondricka and Metz", "Wendy Yule", "Fred Anderson", "Maintenance", 3,7000, "won" ],
[ 688981, "Keeling LLC", "Wendy Yule", "Fred Anderson", "CPU",5,100000, "won" ],
[ 729833, "Koepp Ltd", "Wendy Yule", "Fred Anderson", "CPU",2,65000, "declined" ],
[ 729833, "Koepp Ltd", "Wendy Yule", "Fred Anderson", "Monitor",2,5000, "presented" ],
]
});
})
//
// Pandas example:
//
// pd.pivot_table(df,index=["Name"])
//
it('Can pivot on Name column and sum values', () => {
const pivotted = df.pivot("Name", {
Account: values => values.sum(),
Price: values => values.sum(),
Quantity: values => values.sum(),
});
expect(pivotted.getColumnNames()).to.eql(["Name", "Account", "Price", "Quantity"]);
expect(pivotted.toArray()).to.eql([
{
"Name": "Barton LLC",
"Account": 740150,
"Price": 35000,
"Quantity": 1
},
{
"Name": "Fritsch, Russel and Anderson",
"Account": 737550,
"Price": 35000,
"Quantity": 1
},
{
"Name": "Herman LLC",
"Account": 141962,
"Price": 65000,
"Quantity": 2
},
{
"Name": "Jerde-Hilpert",
"Account": 412290,
"Price": 5000,
"Quantity": 2
},
{
"Name": "Kassulke, Ondricka and Metz",
"Account": 307599,
"Price": 7000,
"Quantity": 3
},
{
"Name": "Keeling LLC",
"Account": 688981,
"Price": 100000,
"Quantity": 5
},
{
"Name": "Kiehn-Spinka",
"Account": 146832,
"Price": 65000,
"Quantity": 2
},
{
"Name": "Koepp Ltd",
"Account": 1459666,
"Price": 70000,
"Quantity": 4
},
{
"Name": "Kulas Inc",
"Account": 437790,
"Price": 50000,
"Quantity": 3
},
{
"Name": "Purdy-Kunde",
"Account": 163416,
"Price": 30000,
"Quantity": 1
},
{
"Name": "Stokes LLC",
"Account": 478688,
"Price": 15000,
"Quantity": 2
},
{
"Name": "Trantow-Barrows",
"Account": 2143398,
"Price": 45000,
"Quantity": 4
}
]);
});
//
// Pandas example:
//
// pd.pivot_table(df,index=["Name","Rep","Manager"])
//
it('Can pivot on multiple columns and sum values', () => {
const pivotted = df.pivot(["Name","Rep","Manager"], {
Account: values => values.sum(),
Price: values => values.sum(),
Quantity: values => values.sum(),
});
expect(pivotted.getColumnNames()).to.eql(["Name", "Rep", "Manager", "Account", "Price", "Quantity"]);
expect(pivotted.toArray()).to.eql([
{
"Name": "Barton LLC",
"Rep": "John Smith",
"Manager": "Debra Henley",
"Account": 740150,
"Price": 35000,
"Quantity": 1
},
{
"Name": "Fritsch, Russel and Anderson",
"Rep": "Craig Booker",
"Manager": "Debra Henley",
"Account": 737550,
"Price": 35000,
"Quantity": 1
},
{
"Name": "Herman LLC",
"Rep": "Cedric Moss",
"Manager": "Fred Anderson",
"Account": 141962,
"Price": 65000,
"Quantity": 2
},
{
"Name": "Jerde-Hilpert",
"Rep": "John Smith",
"Manager": "Debra Henley",
"Account": 412290,
"Price": 5000,
"Quantity": 2
},
{
"Name": "Kassulke, Ondricka and Metz",
"Rep": "Wendy Yule",
"Manager": "Fred Anderson",
"Account": 307599,
"Price": 7000,
"Quantity": 3
},
{
"Name": "Keeling LLC",
"Rep": "Wendy Yule",
"Manager": "Fred Anderson",
"Account": 688981,
"Price": 100000,
"Quantity": 5
},
{
"Name": "Kiehn-Spinka",
"Rep": "Daniel Hilton",
"Manager": "Debra Henley",
"Account": 146832,
"Price": 65000,
"Quantity": 2
},
{
"Name": "Koepp Ltd",
"Rep": "Wendy Yule",
"Manager": "Fred Anderson",
"Account": 1459666,
"Price": 70000,
"Quantity": 4
},
{
"Name": "Kulas Inc",
"Rep": "Daniel Hilton",
"Manager": "Debra Henley",
"Account": 437790,
"Price": 50000,
"Quantity": 3
},
{
"Name": "Purdy-Kunde",
"Rep": "Cedric Moss",
"Manager": "Fred Anderson",
"Account": 163416,
"Price": 30000,
"Quantity": 1
},
{
"Name": "Stokes LLC",
"Rep": "Cedric Moss",
"Manager": "Fred Anderson",
"Account": 478688,
"Price": 15000,
"Quantity": 2
},
{
"Name": "Trantow-Barrows",
"Rep": "Craig Booker",
"Manager": "Debra Henley",
"Account": 2143398,
"Price": 45000,
"Quantity": 4
}
]);
});
//
// Pandas example:
//
// pd.pivot_table(df,index=["Manager","Rep"])
//
it('Can pivot on multiple columns and sum values 2', () => {
const pivotted = df.pivot([ "Manager", "Rep" ], {
Account: values => values.sum(),
Price: values => values.sum(),
Quantity: values => values.sum(),
});
expect(pivotted.getColumnNames()).to.eql([ "Manager", "Rep", "Account", "Price", "Quantity" ]);
expect(pivotted.toArray()).to.eql([
{
"Manager": "Debra Henley",
"Rep": "Craig Booker",
"Account": 2880948,
"Price": 80000,
"Quantity": 5
},
{
"Manager": "Debra Henley",
"Rep": "Daniel Hilton",
"Account": 584622,
"Price": 115000,
"Quantity": 5
},
{
"Manager": "Debra Henley",
"Rep": "John Smith",
"Account": 1152440,
"Price": 40000,
"Quantity": 3
},
{
"Manager": "Fred Anderson",
"Rep": "Cedric Moss",
"Account": 784066,
"Price": 110000,
"Quantity": 5
},
{
"Manager": "Fred Anderson",
"Rep": "Wendy Yule",
"Account": 2456246,
"Price": 177000,
"Quantity": 12
}
]);
});
//
// Pandas example:
//
// pd.pivot_table(df,index=["Manager","Rep"],values=["Price"])
//
it('Can pivot on multiple columns and avg price', () => {
const pivotted = df.pivot([ "Manager", "Rep" ], {
Price: values => values.mean(),
});
expect(pivotted.getColumnNames()).to.eql([ "Manager", "Rep", "Price" ]);
expect(pivotted.toArray()).to.eql([
{
"Manager": "Debra Henley",
"Rep": "Craig Booker",
"Price": 20000,
},
{
"Manager": "Debra Henley",
"Rep": "Daniel Hilton",
"Price": 38333.333333333336,
},
{
"Manager": "Debra Henley",
"Rep": "John Smith",
"Price": 20000,
},
{
"Manager": "Fred Anderson",
"Rep": "Cedric Moss",
"Price": 27500,
},
{
"Manager": "Fred Anderson",
"Rep": "Wendy Yule",
"Price": 44250,
}
]);
});
//
// Pandas example:
//
// pd.pivot_table(df,index=["Manager","Rep"],values=["Price"],aggfunc=np.sum)
//
it('Can pivot on multiple columns and sum price', () => {
const pivotted = df.pivot([ "Manager", "Rep" ], {
Price: values => values.sum(),
});
expect(pivotted.getColumnNames()).to.eql([ "Manager", "Rep", "Price" ]);
expect(pivotted.toArray()).to.eql([
{
"Manager": "Debra Henley",
"Rep": "Craig Booker",
"Price": 80000,
},
{
"Manager": "Debra Henley",
"Rep": "Daniel Hilton",
"Price": 115000,
},
{
"Manager": "Debra Henley",
"Rep": "John Smith",
"Price": 40000,
},
{
"Manager": "Fred Anderson",
"Rep": "Cedric Moss",
"Price": 110000,
},
{
"Manager": "Fred Anderson",
"Rep": "Wendy Yule",
"Price": 177000,
}
]);
});
//
// Pandas example:
//
// pd.pivot_table(df,index=["Manager","Rep"],values=["Price"],aggfunc=[np.mean,len])
//
it('Can pivot on multiple columns and aggregate multiple times over same colmmn', () => {
const pivotted = df.pivot([ "Manager", "Rep" ], {
Price: {
Price_Average: values => values.mean(),
Price_Count: values => values.count(),
},
});
expect(pivotted.getColumnNames()).to.eql([ "Manager", "Rep", "Price_Average", "Price_Count" ]);
expect(pivotted.toArray()).to.eql([
{
"Manager": "Debra Henley",
"Rep": "Craig Booker",
"Price_Average": 20000,
"Price_Count": 4
},
{
"Manager": "Debra Henley",
"Rep": "Daniel Hilton",
"Price_Average": 38333.333333333336,
"Price_Count": 3
},
{
"Manager": "Debra Henley",
"Rep": "John Smith",
"Price_Average": 20000,
"Price_Count": 2
},
{
"Manager": "Fred Anderson",
"Rep": "Cedric Moss",
"Price_Average": 27500,
"Price_Count": 4
},
{
"Manager": "Fred Anderson",
"Rep": "Wendy Yule",
"Price_Average": 44250,
"Price_Count": 4
}
]);
});
});