Implement DataFrame.idxmin and DataFrame.idxmax

[V1NAY8]

I am thinking this should be implementable.

Reference for pandas idxmin: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.idxmin.html
Reference for pandas idxmax: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.idxmax.html

[V1NAY8]

I'll do this.

This can be broken into two:
for axis = "index" and axis = "columns"

[V1NAY8]

For index the following can be done. To match output with pandas

This can be achievable using top hits aggregation,
Input Query:

curl -X GET "localhost:9200/flights/_search?pretty" -H 'Content-Type: application/json' -d'
{
    "size": 0,
    "aggs": {
        "top_AvgTicketPrice": {
            "top_hits": {
                "sort": [
                    {
                        "AvgTicketPrice": {
                            "order": "desc"
                        }
                    }
                ],
                "_source": {
                    "includes": [
                        "_id",
                        "AvgTicketPrice"
                    ]
                },
                "size": 1
            }
        },
        "top_FlightDelayMin": {
            "top_hits": {
                "sort": [
                    {
                        "FlightDelayMin": {
                            "order": "desc"
                        }
                    }
                ],
                "_source": {
                    "includes": [
                        "_id",
                        "FlightDelayMin"
                    ]
                },
                "size": 1
            }
        },
        "top_dayOfWeek": {
            "top_hits": {
                "sort": [
                    {
                        "dayOfWeek": {
                            "order": "desc"
                        }
                    }
                ],
                "_source": {
                    "includes": [
                        "_id",
                        "dayOfWeek"
                    ]
                },
                "size": 1
            }
        }
    }
}
'

Output:

{
  "took" : 13,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10000,
      "relation" : "gte"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "top_dayOfWeek" : {
      "hits" : {
        "total" : {
          "value" : 13059,
          "relation" : "eq"
        },
        "max_score" : null,
        "hits" : [
          {
            "_index" : "flights",
            "_type" : "_doc",
            "_id" : "1988",
            "_score" : null,
            "_source" : {
              "dayOfWeek" : 6
            },
            "sort" : [
              6
            ]
          }
        ]
      }
    },
    "top_AvgTicketPrice" : {
      "hits" : {
        "total" : {
          "value" : 13059,
          "relation" : "eq"
        },
        "max_score" : null,
        "hits" : [
          {
            "_index" : "flights",
            "_type" : "_doc",
            "_id" : "1843",
            "_score" : null,
            "_source" : {
              "AvgTicketPrice" : 1199.7290528077556
            },
            "sort" : [
              1199.729
            ]
          }
        ]
      }
    },
    "top_FlightDelayMin" : {
      "hits" : {
        "total" : {
          "value" : 13059,
          "relation" : "eq"
        },
        "max_score" : null,
        "hits" : [
          {
            "_index" : "flights",
            "_type" : "_doc",
            "_id" : "109",
            "_score" : null,
            "_source" : {
              "FlightDelayMin" : 360
            },
            "sort" : [
              360
            ]
          }
        ]
      }
    }
  }
}

[sethmlarson]

Closed in #353