Groupby.col
Obtain the column(s) per groups
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Obtain the column(s) per groups
Last updated
Was this helpful?
danfo.Groupby.col(col_names) []
col_names
Array
List of column
Returns: Groupby Data structure
Note: This is similar to pandas df.groupby(["column"])["colNames"]
Examples
Obtain the column to perform group aggregate operation on
const dfd = require("danfojs-node")
let data ={A: ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
B: ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
C: [1,3,2,4,5,2,6,7],
D: [3,2,4,1,5,6,7,8]
}
let df = new dfd.DataFrame(data)
let grp = df.groupby(["A"])
//select single column
let grpColumnC = grp.col(["C"])
// convert grouop internal data to dataFrame
grpColumnC.apply(x=> x).print()
//select multiple column
let grpColumnBD = grp.col(["B", "D"])
grpColumnBD.apply(x=> x).print()
Apparently the output are not that useful unless you perform some operations like max(), count() and the likes.
// select single column C
ββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ
β β A β C β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 0 β foo β 1 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 1 β foo β 2 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 2 β foo β 5 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 3 β foo β 6 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 4 β foo β 7 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 5 β bar β 3 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 6 β bar β 4 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 7 β bar β 2 β
ββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ
// select multiple column: B and D
ββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ€ββββββββββββββββββββ
β β A β B β D β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 0 β foo β one β 3 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 1 β foo β two β 4 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 2 β foo β two β 5 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 3 β foo β one β 7 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 4 β foo β three β 8 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 5 β bar β one β 2 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 6 β bar β three β 1 β
ββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββΌββββββββββββββββββββ’
β 7 β bar β two β 6 β
ββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ§ββββββββββββββββββββ