{
  "context": "This evaluation assesses how effectively the engineer uses influxdb-client-python's DataFrame write capabilities to ingest sensor data. The focus is on utilizing the library's optimized DataFrame serialization features rather than implementing manual row-by-row conversion.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Uses pandas.read_csv",
      "description": "Uses pandas.read_csv() or equivalent pandas method to read the CSV file into a DataFrame",
      "max_score": 10
    },
    {
      "name": "Proper datetime conversion",
      "description": "Converts the timestamp column to pandas datetime objects using pd.to_datetime() or parse_dates parameter in read_csv()",
      "max_score": 15
    },
    {
      "name": "Gets write API",
      "description": "Obtains a write API instance from the InfluxDB client using client.write_api() with appropriate write options",
      "max_score": 15
    },
    {
      "name": "DataFrame write method",
      "description": "Uses write_api.write() method with the DataFrame directly as the record parameter, leveraging the optimized DataFrame serialization",
      "max_score": 25
    },
    {
      "name": "Measurement name parameter",
      "description": "Specifies the measurement name using the data_frame_measurement_name parameter in the write() call",
      "max_score": 10
    },
    {
      "name": "Tag columns parameter",
      "description": "Specifies tag columns (sensor_id and location) using the data_frame_tag_columns parameter to correctly map columns to tags",
      "max_score": 15
    },
    {
      "name": "Timestamp column handling",
      "description": "Properly handles the timestamp column by ensuring it's set as the DataFrame index or using data_frame_timestamp_column parameter",
      "max_score": 10
    }
  ]
}