danfo.Groupby.col(col_names) [source]
Parameters | Type | Description | Default |
col_names | Array | List of column | ​ |
Returns: Groupby Data structure
Note: This is similar to pandas df.groupby(["column"])["col_names"]
Examples
Obtain a column
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"])grp.col(["C"])​//for more coumnsgrp.col(["C","D"])
Apparently the output are not that useful unless you perform some operations like max(), count() and the likes.
//it returns the groupby data structureGroupBy {key_col: [ 'A' ],col_dict: {foo: [ [Array], [Array], [Array], [Array], [Array] ],bar: [ [Array], [Array], [Array] ]},data: [[ 'foo', 'one', 1, 3 ],[ 'bar', 'one', 3, 2 ],[ 'foo', 'two', 2, 4 ],[ 'bar', 'three', 4, 1 ],[ 'foo', 'two', 5, 5 ],[ 'bar', 'two', 2, 6 ],[ 'foo', 'one', 6, 7 ],[ 'foo', 'three', 7, 8 ]],column_name: [ 'A', 'B', 'C', 'D' ],data_tensors: {foo: DataFrame {kwargs: [Object],series: false,data: [Array],row_data_tensor: [Tensor],index_arr: [Array],columns: [Array],col_data: [Array],col_data_tensor: [Tensor],col_types: [Array],A: [Getter/Setter],B: [Getter/Setter],C: [Getter/Setter],D: [Getter/Setter]},bar: DataFrame {kwargs: [Object],series: false,data: [Array],row_data_tensor: [Tensor],index_arr: [Array],columns: [Array],col_data: [Array],col_data_tensor: [Tensor],col_types: [Array],A: [Getter/Setter],B: [Getter/Setter],C: [Getter/Setter],D: [Getter/Setter]}},group_col_name: [ 'C' ],group_col: { foo: [ [Series] ], bar: [ [Series] ] }}
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