Build feature analyzer in Python

Create a Python-based feature analyzer that calculates basic feature information available through /data/features/{id} endpoint, e.g.:

{
  "data_features": {
    "feature": [
      {
        "index": "0",
        "name": "sepallength",
        "data_type": "numeric",
        "is_target": "false",
        "is_ignore": "false",
        "is_row_identifier": "false",
        "number_of_missing_values": "0"
      },
      {
        "index": "1",
        "name": "sepalwidth",
        "data_type": "numeric",
        "is_target": "false",
        "is_ignore": "false",
        "is_row_identifier": "false",
        "number_of_missing_values": "0"
      },
      {
        "index": "2",
        "name": "petallength",
        "data_type": "numeric",
        "is_target": "false",
        "is_ignore": "false",
        "is_row_identifier": "false",
        "number_of_missing_values": "0"
      },
      {
        "index": "3",
        "name": "petalwidth",
        "data_type": "numeric",
        "is_target": "false",
        "is_ignore": "false",
        "is_row_identifier": "false",
        "number_of_missing_values": "0"
      },
      {
        "index": "4",
        "name": "class",
        "data_type": "nominal",
        "nominal_value": [
          "Iris-setosa",
          "Iris-versicolor",
          "Iris-virginica"
        ],
        "is_target": "true",
        "is_ignore": "false",
        "is_row_identifier": "false",
        "number_of_missing_values": "0"
      }
    ]
  }
}

Start by tracing the existing /data/features/{id} endpoint and how feature metadata is currently obtained. Use the example response as the expected output, including feature names, types, flags, nominal values, and missing-value counts. Done means the endpoint exposes the requested basic feature information.