# Assignment 1




## Task 1: Working with Mongo - Advanced


## Areas covered:
- Timeseries Operation
- Working with NoSQL
- Working with Pandas

## Description:
You are given with a dataset of an inspection company in the form of a JSON file. The end goal of the project is to create an API interface that will provide the following: 

1. Business name with maximum number of "`Violation Issued`".
2. Business name that has no violation.
3. Generate Excel Report based on `result`, `business name` and `date`

Sample Document:
```json
{
  "id": "10021-2015-ENFO",
  "certificate_number": 9278806,
  "business_name": "ATLIXCO DELI GROCERY INC.",
  "date": "Feb 20 2015",
  "result": "No Violation Issued",
  "sector": "Cigarette Retail Dealer - 127",
  "address": {
    "city": "RIDGEWOOD",
    "zip": 11385,
    "street": "MENAHAN ST",
    "number": 1712
  }
}
```

Bonus Points: Use Mongo Aggregate framework

### Tools to use: 
1. Pycharm / VSCode
2. Robo3T / Studio3T / MongoDB Compass
3. PyMongo


### Reference:
https://www.mongodb.com/docs/manual/tutorial/query-documents/
https://www.mongodb.com/docs/manual/reference/operator/aggregation-pipeline/
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_excel.html
https://fastapi.tiangolo.com/advanced/custom-response/#fileresponse
https://pymongo.readthedocs.io/en/stable/


_________________________________

## Task 2: Working with Timeseries


## Areas covered:
- Timeseries Operation
- Working with Timeseries
- Working with Pandas

## Description:
You are given with a dataset of weather in the form of a CSV file. The end goal of the project is to create an API interface that will provide the following: 

1. Get daily, monthly, weekly and monthly aggregate (min, max, and average) of the data and generate report in Excel format.

Sample Document:

|Date                         |Time         |CO(GT)|PT08.S1(CO)|NMHC(GT)   |C6H6(GT)|PT08.S2(NMHC)|NOx(GT)|PT08.S3(NOx)|NO2(GT)|PT08.S4(NO2)|PT08.S5(O3)|T   |RH  |AH    |
|-----------------------------|-------------|------|-----------|-----------|--------|-------------|-------|------------|-------|------------|-----------|----|----|------|
|10/03/2004                   |18:00:00     |2.6   |1360       |150        |11.9    |1046         |166    |1056        |113    |1692        |1268       |13.6|48.9|0.7578|


### Tools to use: 
1. Pycharm / VSCode
2. Pandas 

### Reference:
https://kairosdb.github.io/docs/restapi/QueryMetrics.html
https://pypi.org/project/kairosdb-python/


