From b7c7230358deb4076b21a7677ae79357dd5a49c4 Mon Sep 17 00:00:00 2001 From: Ivanildo Barauna de Souza Junior Date: Tue, 18 Jun 2024 00:39:53 -0300 Subject: [PATCH] Initial Commit --- .gitignore | 2 +- README.md | 91 +- SECURITY.md | 4 +- docs/index.html | 8772 ---------------- etl/common/utils/__init__.py | 0 etl/common/utils/test_logs.py | 51 - etl/config/logFile.py | 6 - etl/controller/__init__.py | 0 etl/controller/pipeline.py | 75 - etl/controller/test_controller_pipeline.py | 25 - etl/models/__init__.py | 0 etl/models/extract/__init__.py | 0 etl/models/extract/api_data_extractor.py | 55 - etl/models/extract/params_validator.py | 33 - etl/models/extract/test_api_data_extractor.py | 7 - etl/models/extract/test_params_validator.py | 37 - etl/models/load/__init__.py | 0 etl/models/load/parquet_loader.py | 44 - etl/models/load/test_parquet_loader.py | 20 - etl/models/transform/__init__.py | 0 etl/models/transform/publisher.py | 18 - etl/models/transform/test_publisher.py | 54 - etl/run.py | 21 - etl/test_run.py | 14 - etl/views/dataset.pkl | Bin 626092 -> 0 bytes etl/views/index.html | 8946 ----------------- etl/views/make_dataset.py | 25 - notebooks/data_explorer.ipynb | 1306 --- poetry.lock | 2744 ----- pyproject.toml | 18 +- src/currency_quote/__init__.py | 1 + .../currency_quote}/common/utils/common.py | 15 +- .../currency_quote}/common/utils/logs.py | 1 - .../currency_quote}/config/datasource.py | 0 .../currency_quote/controller/controller.py | 0 .../currency_quote/models/model.py | 0 .../currency_quote/tests/test.py | 0 37 files changed, 27 insertions(+), 22358 deletions(-) delete mode 100644 docs/index.html delete mode 100644 etl/common/utils/__init__.py delete mode 100644 etl/common/utils/test_logs.py delete mode 100644 etl/config/logFile.py delete mode 100644 etl/controller/__init__.py delete mode 100644 etl/controller/pipeline.py delete mode 100644 etl/controller/test_controller_pipeline.py delete mode 100644 etl/models/__init__.py delete mode 100644 etl/models/extract/__init__.py delete mode 100644 etl/models/extract/api_data_extractor.py delete mode 100644 etl/models/extract/params_validator.py delete mode 100644 etl/models/extract/test_api_data_extractor.py delete mode 100644 etl/models/extract/test_params_validator.py delete mode 100644 etl/models/load/__init__.py delete mode 100644 etl/models/load/parquet_loader.py delete mode 100644 etl/models/load/test_parquet_loader.py delete mode 100644 etl/models/transform/__init__.py delete mode 100644 etl/models/transform/publisher.py delete mode 100644 etl/models/transform/test_publisher.py delete mode 100644 etl/run.py delete mode 100644 etl/test_run.py delete mode 100644 etl/views/dataset.pkl delete mode 100644 etl/views/index.html delete mode 100644 etl/views/make_dataset.py delete mode 100644 notebooks/data_explorer.ipynb delete mode 100644 poetry.lock create mode 100644 src/currency_quote/__init__.py rename {etl => src/currency_quote}/common/utils/common.py (51%) rename {etl => src/currency_quote}/common/utils/logs.py (97%) rename {etl => src/currency_quote}/config/datasource.py (100%) rename data/ETH-EUR-1713658884.example => src/currency_quote/controller/controller.py (100%) rename etl/__init__.py => src/currency_quote/models/model.py (100%) rename etl/common/__init__.py => src/currency_quote/tests/test.py (100%) diff --git a/.gitignore b/.gitignore index d612341..4e25e89 100644 --- a/.gitignore +++ b/.gitignore @@ -163,7 +163,7 @@ cython_debug/ .idea/ poetry.lock -etl/common/logs/* +src/currency_quote/common/logs/* *.DS_Store diff --git a/README.md b/README.md index 086739f..898dcbe 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,11 @@ -# Awesome Project: ETL Process for Currency Quotes Data +# currency-quote-wrapper: Complete solution for extracting currency pair quotes data ![Project Status](https://img.shields.io/badge/status-done-brightgreen) -![License](https://img.shields.io/badge/license-MIT-blue) ![GitHub release (latest by date)](https://img.shields.io/github/v/release/IvanildoBarauna/ETL-awesome-api) ![Python Version](https://img.shields.io/badge/python-3.9-blue) ![GitHub Workflow Status](https://github.com/IvanildoBarauna/ETL-awesome-api/actions/workflows/CI-CD.yaml/badge.svg) -[![codecov](https://codecov.io/gh/IvanildoBarauna/ETL-awesome-api/branch/main/graph/badge.svg?token=GEGNHFM6P)](https://codecov.io/gh/IvanildoBarauna/ETL-awesome-api) - -## Code Coverage KPI Graph - -[![codecov](https://codecov.io/gh/IvanildoBarauna/ETL-awesome-api/graphs/sunburst.svg?token=GEGNHFM6PS)](https://codecov.io/gh/IvanildoBarauna/ETL-awesome-api) +![License](https://img.shields.io/badge/license-MIT-blue) +![GitHub release (latest by date)](https://img.shields.io/github/v/release/IvanildoBarauna/currency-quote-wrapper) +![Python Version](https://img.shields.io/badge/python-3.9-blue) +![GitHub Workflow Status](https://github.com/IvanildoBarauna/currency-quote-wrapper/actions/workflows/CI-CD.yaml/badge.svg) +[![codecov](https://codecov.io/gh/IvanildoBarauna/currency-quote-wrapper/branch/main/graph/badge.svg?token=GEGNHFM6P)](https://codecov.io/gh/IvanildoBarauna/currency-quote-wrapper) ## Project Stack @@ -14,19 +13,17 @@ ## Project description -The "Awesome Project: ETL Process for Currency Quotes Data" project is a complete solution dedicated to extracting, transforming and loading (ETL) currency quote data. This project uses several advanced techniques and architectures to ensure the efficiency and robustness of the ETL process. +* ADD DESCRIPTION ## Contributing See the following docs: -- [Contributing Guide](https://github.com/IvanildoBarauna/ETL-awesome-api/blob/main/CONTRIBUTING.md) -- [Code Of Conduct](https://github.com/IvanildoBarauna/ETL-awesome-api/blob/main/CODE_OF_CONDUCT.md) +- [Contributing Guide](https://github.com/IvanildoBarauna/currency-quote-wrapper/blob/main/CONTRIBUTING.md) +- [Code Of Conduct](https://github.com/IvanildoBarauna/currency-quote-wrapper/blob/main/CODE_OF_CONDUCT.md) ## Project Highlights: -- MVC Architecture: Implementation of the Model-View-Controller (MVC) architecture, separating business logic, user interface and data manipulation for better organization and code maintenance. - - Comprehensive Testing: Development of tests to ensure the quality and robustness of the code at various stages of the ETL process - Parallelism in Models: Use of parallelism in the data transformation and loading stages, increasing efficiency and reducing processing time. @@ -37,67 +34,15 @@ See the following docs: - Configuration Management: Use of a configuration module to manage endpoints, retry times and number of attempts, providing flexibility and ease of adjustment. -- Common Module: Implementation of a common module for code reuse across the project, promoting consistency and reducing redundancies. - -- Dynamic Views: Generation of views with index.html using nbConvert, based on consolidated data from a Jupyter Notebook that integrates the generated files into a single dataset for exploration and analysis. - -# ETL Process: - -- Extraction: A single request is made to a specific endpoint to obtain quotes from multiple currencies. -- Transformation: The request response is processed, separating each currency quote and storing it in individual files in Parquet format, facilitating data organization and retrieval. -- Upload: Individual Parquet files are consolidated into a single dataset using a Jupyter Notebook, allowing for comprehensive analysis and valuable insights into currency quotes. - -In summary, "Awesome Project: ETL Process for Currency Quotes Data" offers a robust and efficient solution for collecting, processing and analyzing currency quote data, using advanced architecture and parallelism techniques to optimize each step of the ETL process. - -
- Repository structure - -- [`data/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/data): Stores raw data in Parquet format. - - ETH-EUR-1713658884.parquet: Example: Raw data for ETH-EUR quotes. file_name = symbol + extraction unix timestamp -- [`notebooks/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/notebooks): Contains the `data_explorer.ipynb` notebook for data exploration. -- [`etl/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/etl): Contains the project's source code. - - [`run.py`](https://github.com/IvanildoBarauna/ETL-awesome-api/blob/main/etl/run.py): Entrypoint of the application - - [`common/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/etl/common): Library for code reuse and standardization. - - [`utils/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/etl/utils) - - [`logs.py`](https://github.com/IvanildoBarauna/ETL-awesome-api/blob/main/etl/utils/logs.py): Package for log management. - - [`common.py`](https://github.com/IvanildoBarauna/ETL-awesome-api/blob/main/etl/utils/common.py): Package for common code tasks like output directory retrieval or default timestamp. - - [`logs/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/etl/common/logs): For storing debug logs. - - [`controller/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/etl/controller) - - [`pipeline.py`](https://github.com/IvanildoBarauna/ETL-awesome-api/blob/main/etl/controller/pipeline.py): Receives data extraction requests and orchestrates ETL models . - - [`models/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/etl/models): - - [`extract/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/etl/models/extract) - - [`api_data_extractor.py`](https://github.com/IvanildoBarauna/ETL-awesome-api/blob/main/etl/models/extract/api_data_extractor.py): Receives the parameters from the controller, sends the request and returns in JSON. - - [`transform/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/etl/models/transform) - - [`publisher.py`](https://github.com/IvanildoBarauna/ETL-awesome-api/blob/main/etl/models/extract/publisher.py): Receives the JSON from the extractor, separates the dictionary by currency and publishes each of them to a queue to be processed individually. - - [`load/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/etl/models/load) - - [`parquet_loader.py`](https://github.com/IvanildoBarauna/ETL-awesome-api/blob/main/etl/models/extract/parquet_loader.py): In a separate thread, receive a new dictionary from queue that the transformer is publishing and generates .parquet files in the default directory. - - [`views/`](https://github.com/IvanildoBarauna/ETL-awesome-api/tree/main/etl/views): For storing data analysis and visualization. - -
- -
- How to run the application locally - - ## Step by Step - -Ensure Python 3.9 or higher is installed on your machine - - - Clone the repository: - ```sh - $ git clone https://github.com/IvanildoBarauna/ETL-awesome-api.git - ``` -- Go to directory -```sh -$ cd ETL-awesome-api -``` -- Install dependencies and execute project - - ```sh - $ poetry install && poetry run python etl/run.py - ``` +## How to use it -Learn more about [`poetry`](https://python-poetry.org/) +``` python +## Importing library +from currency_quote import GetData, GetDataTypes -## ETL and Data Analysis Results: +last_quote = GetData("USD-BRL", GetDataTypes.LastCotation) -You can see the complete data analysis, the Jupyter Notebook is deployed in [GitHub Pages](https://ivanildobarauna.github.io/ETL-awesome-api/) +x = 2024-01-01 +y = 2024-02-02 + +quote_by_period = GetData("USD-BRL", GetDataTypes.Period, start_date=x, end_date=y) \ No newline at end of file diff --git a/SECURITY.md b/SECURITY.md index f58a768..f504689 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -4,8 +4,8 @@ | Version | Supported | |---------| ------------------ | -| 4.4.* | :white_check_mark: | -| < 3.0.* | :x: | +| **** | :white_check_mark: | +| < *.*.* | :x: | ## Reporting a Vulnerability diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index 6128744..0000000 --- a/docs/index.html +++ /dev/null @@ -1,8772 +0,0 @@ - - - - - -data_explorer - - - - - - - - - - - - -
- - - - - - - - - - - - -
- - diff --git a/etl/common/utils/__init__.py b/etl/common/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/etl/common/utils/test_logs.py b/etl/common/utils/test_logs.py deleted file mode 100644 index f3df1bf..0000000 --- a/etl/common/utils/test_logs.py +++ /dev/null @@ -1,51 +0,0 @@ -import pytest -from etl.common.utils.logs import CustomLogger -from etl.common.utils.common import DefaultOutputLogFolder -import logging -import os - - -@pytest.fixture -def setup_logger(): - return CustomLogger("test") - - -def test_custom_logger_contructor(setup_logger): - new_logger = setup_logger - assert new_logger.module == "test" - - -def test_custom_logger(setup_logger): - new_logger = setup_logger - logger = new_logger._logger() - assert isinstance(logger, logging.Logger) - - -def test_make_file_log(setup_logger): - default_folder = DefaultOutputLogFolder() - expected_file_log = "test.log" - expected_path = f"{default_folder}{expected_file_log}" - - new_logger = setup_logger - result_path = new_logger._make_file_log() - - assert result_path == expected_path - assert os.path.isfile(expected_path) - - -def test_log_info(setup_logger): - new_logger = setup_logger - - assert new_logger.info("This test message.") - - -def test_log_error(setup_logger): - new_logger = setup_logger - - assert new_logger.error("This test message.") - - -def test_log_warning(setup_logger): - new_logger = setup_logger - - assert new_logger.warning("This test message.") diff --git a/etl/config/logFile.py b/etl/config/logFile.py deleted file mode 100644 index a93fcf1..0000000 --- a/etl/config/logFile.py +++ /dev/null @@ -1,6 +0,0 @@ -import os - - -def log_file_name(file: str) -> str: - current_dir = os.path.dirname(os.path.relpath(file)) - return current_dir.split("/")[-1:][0] diff --git a/etl/controller/__init__.py b/etl/controller/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/etl/controller/pipeline.py b/etl/controller/pipeline.py deleted file mode 100644 index 2e495e5..0000000 --- a/etl/controller/pipeline.py +++ /dev/null @@ -1,75 +0,0 @@ -import time -import threading -import queue - -from tqdm import tqdm - -from etl.models.extract.api_data_extractor import extraction -from etl.models.transform.publisher import transformation -from etl.models.load.parquet_loader import load -from etl.views.make_dataset import DatasetSerializer - - -class PipelineExecutor: - def __init__(self, *xargs): - self.params = list(xargs) - self.files_to_dataset = [] - self.controller_queue = queue.Queue() - - def pipeline_run(self): - total_invalid_params = 0 - for arg in self.params: - if not isinstance(arg, str): - total_invalid_params += 1 - - if total_invalid_params == len(self.params): - raise TypeError(f"Invalid parameters >>>> {self.params}") - - extractor = extraction(self.params) - response, valid_params = extractor.run - - try: - def produce(): - transformer = transformation( - json_response=response, - params=valid_params, - queue=self.controller_queue, - ) - transformer.publish() - # The production is finished - self.controller_queue.put(None) - - def consume(): - with tqdm( - desc="Consuming Data", - unit=" item", - total=len(valid_params), - ) as pbar: - while True: - time.sleep(0.2) - item = self.controller_queue.get() - if item is None: - self.controller_queue.task_done() - break - loader = load(item) - self.files_to_dataset.append(loader.run()[0]) - self.controller_queue.task_done() - pbar.update() - - thread_producer = threading.Thread(target=produce) - thread_consumer = threading.Thread(target=consume) - - thread_producer.start() - - thread_consumer.start() - - thread_producer.join() - thread_consumer.join() - self.controller_queue.join() - - DatasetSerializer(self.files_to_dataset).serialize() - - return valid_params - - except Exception as e: - raise e diff --git a/etl/controller/test_controller_pipeline.py b/etl/controller/test_controller_pipeline.py deleted file mode 100644 index 9852927..0000000 --- a/etl/controller/test_controller_pipeline.py +++ /dev/null @@ -1,25 +0,0 @@ -import pytest -from etl.controller.pipeline import PipelineExecutor - -def test_pipelines_params_with_two_param(): - expected_result = ["USD-BRL", "USD-BRLT"] - new_executor = PipelineExecutor("USD-BRL", "USD-BRLT") - result = new_executor.pipeline_run() - assert result == expected_result - -def test_pipelines_params_with_two_param_and_one_invalid(): - expected_result = ["USD-BRL", "USD-BRLT"] - new_executor = PipelineExecutor("USD-BRL", "USD-BRLT", "invalid_param") - result = new_executor.pipeline_run() - assert result == expected_result - -def test_pipelines_params_with_all_invalid_params(): - with pytest.raises(TypeError): - new_executor = PipelineExecutor(1, 1, 1) - new_executor.pipeline_run() - - -def test_pipelines_params_with_all_invalid_keys_params(): - with pytest.raises(KeyError): - validator = PipelineExecutor("INVALID-QUOTE", "INVALID-QUOTE2") - validator.pipeline_run() diff --git a/etl/models/__init__.py b/etl/models/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/etl/models/extract/__init__.py b/etl/models/extract/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/etl/models/extract/api_data_extractor.py b/etl/models/extract/api_data_extractor.py deleted file mode 100644 index 80eb158..0000000 --- a/etl/models/extract/api_data_extractor.py +++ /dev/null @@ -1,55 +0,0 @@ -import time -from typing import Tuple - - -import requests -from tqdm import tqdm - -from etl.common.utils.logs import CustomLogger -from etl.config.logFile import log_file_name -from etl.config.datasource import API -from etl.models.extract import params_validator - -logger = CustomLogger(log_file_name(file=__file__)) - -class extraction: - def __init__(self, params: list) -> None: - self.params = params - - @property - def run(self) -> Tuple[dict, list]: - validator = params_validator.ParamsValidator(self.params) - valid_params = validator.valid_params_for_call() - - url_endpoint = API.ENDPOINT_LAST_COTATION + ",".join(valid_params) - logger.info( - f"Sending request to: {API.ENDPOINT_LAST_COTATION} :: 1 of {API.RETRY_ATTEMPTS}", - ) - response = requests.get(url_endpoint) - - for try_number in range(API.RETRY_ATTEMPTS): - if response.ok: - logger.info( - f"Request finished with status {response.status_code}") - json_data = response.json() - return json_data, valid_params - else: - if try_number < API.RETRY_ATTEMPTS - 1: - logger.warning( - f"""response error, status_code {response.status_code}. - Retrying in {API.RETRY_TIME_SECONDS} seconds...""" - ) - for _ in tqdm(range(100), total=100, desc=f"loading"): - time.sleep(API.RETRY_TIME_SECONDS / 100) - logger.info( - f"Sending request to: {API.ENDPOINT_LAST_COTATION} :: {try_number + 2} of {API.RETRY_ATTEMPTS}", - ) - else: - logger.warning("Attempt limits exceeded") - raise ConnectionError( - f"""Could not connect to the server after 3 attempts. - Please try again later. - Response status code: {response.status_code}""" - ) - - return {}, [] diff --git a/etl/models/extract/params_validator.py b/etl/models/extract/params_validator.py deleted file mode 100644 index e50b990..0000000 --- a/etl/models/extract/params_validator.py +++ /dev/null @@ -1,33 +0,0 @@ -import requests -from etl.common.utils.logs import CustomLogger -from etl.config.datasource import API -from etl.config.logFile import log_file_name - -logger = CustomLogger(log_file_name(file=__file__)) -class ParamsValidator: - def __init__(self, params: list) -> None: - self.params = params - - def valid_params_for_call(self) -> list: - """ - Returns a list of valid parameters for the pipeline execution. - - Returns: - list: List of valid parameters. - - """ - valParams = [] - AvaliableList = requests.get(API.ENDPOINT_AVALIABLE_PARITIES).json() - - for param in self.params: - if param in AvaliableList: - valParams.append(param) - else: - logger.warning(f"Param: {param} is not valid for call") - - if valParams: - return valParams - else: - raise KeyError( - f"The informed params: {self.params} are not avaliable for extract, see available list in: {API.ENDPOINT_AVALIABLE_PARITIES}" - ) diff --git a/etl/models/extract/test_api_data_extractor.py b/etl/models/extract/test_api_data_extractor.py deleted file mode 100644 index bdf5b33..0000000 --- a/etl/models/extract/test_api_data_extractor.py +++ /dev/null @@ -1,7 +0,0 @@ -from etl.models.extract.api_data_extractor import extraction - - -def test_extraction_constructor(): - params = ["USD-BRL", "USD-BRLT", "CAD-BRL"] - extractor = extraction(params) - assert extractor.params == params diff --git a/etl/models/extract/test_params_validator.py b/etl/models/extract/test_params_validator.py deleted file mode 100644 index 2c95f30..0000000 --- a/etl/models/extract/test_params_validator.py +++ /dev/null @@ -1,37 +0,0 @@ -import pytest -from etl.models.extract.params_validator import ParamsValidator - - -@pytest.fixture -def valid_params() -> list: - return ["BRL-COP", "BRL-PEN"] - - -@pytest.fixture -def invalid_params() -> list: - return ["param1", "param2"] - - -@pytest.fixture -def mixed_params(): - return ["BRL-COP", "BRL-PEN", "param1", "param2"] - - -def test_valid_params(valid_params): - validator = ParamsValidator(valid_params) - valid = validator.valid_params_for_call() - assert validator.params == valid_params - assert valid_params == valid - - -def test_invalid_params(invalid_params): - validator = ParamsValidator(invalid_params) - with pytest.raises(KeyError): - validator.valid_params_for_call() - - -def test_mixed_params(mixed_params, valid_params): - validator = ParamsValidator(mixed_params) - validated = validator.valid_params_for_call() - assert validator.params == mixed_params - assert validated == valid_params diff --git a/etl/models/load/__init__.py b/etl/models/load/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/etl/models/load/parquet_loader.py b/etl/models/load/parquet_loader.py deleted file mode 100644 index c823a40..0000000 --- a/etl/models/load/parquet_loader.py +++ /dev/null @@ -1,44 +0,0 @@ -import pandas as pd - -from etl.config.logFile import log_file_name -from etl.common.utils.logs import CustomLogger -from etl.common.utils.common import ( - DefaultTimestampStr, - DefaultOutputFolder, - DefaultUTCDatetime, -) - -logger = CustomLogger(log_file_name(file=__file__)) - -class load: - def __init__(self, item) -> None: - self.dic = item - - def run(self): - extracted_files = [] - param = self.dic["code"] + "-" + self.dic["codein"] - ts = DefaultTimestampStr() - df = pd.DataFrame([self.dic]) - - if df.empty: - logger.error("DataFrame is empty") - raise ValueError("DataFrame is empty") - - # Add new columns to the DataFrame - df["symbol"] = param - - # Add two columns with the current date and time - df["extracted_at"] = DefaultUTCDatetime() - - df["id"] = f"{param}-{ts}" - - # Write the DataFrame to a Parquet file - try: - df.to_parquet(f"{DefaultOutputFolder()}{param}-{ts}.parquet") - except Exception as e: - logger.error(f"Error writing parquet file: {e}") - - # Append list with the file path - extracted_files.append(f"{param}-{ts}.parquet") - - return extracted_files diff --git a/etl/models/load/test_parquet_loader.py b/etl/models/load/test_parquet_loader.py deleted file mode 100644 index f7cf93d..0000000 --- a/etl/models/load/test_parquet_loader.py +++ /dev/null @@ -1,20 +0,0 @@ -import pytest -from etl.models.load.parquet_loader import load - - -def test_load_to_parquet(): - # Create a sample item - item = {"code": "USD", "codein": "BRL", "value": 5.0} - - # Instantiate the loadToParquet class - loader = load(item) - - # Call the load method - extracted_files = loader.run() - - # Assert that the extracted_files list contains the expected file path - assert len(extracted_files) == 1 - - for extracted_file in extracted_files: - assert extracted_file.startswith("USD-") - assert extracted_file.endswith(".parquet") diff --git a/etl/models/transform/__init__.py b/etl/models/transform/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/etl/models/transform/publisher.py b/etl/models/transform/publisher.py deleted file mode 100644 index a2a9460..0000000 --- a/etl/models/transform/publisher.py +++ /dev/null @@ -1,18 +0,0 @@ -import time -import queue - -from tqdm import tqdm - -class transformation: - def __init__(self, json_response: dict, params, queue: queue.Queue): - self.json_response = json_response - self.valid_params = params - self.queue = queue - - def publish(self): - for param in tqdm( - self.valid_params, total=len(self.valid_params), desc="Producing Data" - ): - dic = self.json_response[param.replace("-", "")] - time.sleep(0.2) - self.queue.put(dic) diff --git a/etl/models/transform/test_publisher.py b/etl/models/transform/test_publisher.py deleted file mode 100644 index 9dbdac9..0000000 --- a/etl/models/transform/test_publisher.py +++ /dev/null @@ -1,54 +0,0 @@ -import pytest -from etl.models.transform.publisher import transformation -import queue - -fila = queue.Queue() - - -@pytest.fixture -def json_response() -> dict: - return { - "BRLCOP": { - "code": "BRL", - "codein": "COP", - "name": "Real Brasileiro/Peso Colombiano", - "high": "746.23", - "low": "741.33", - "varBid": "-8.36", - "pctChange": "-1.11", - "bid": "742.44", - "ask": "743.12", - "timestamp": "1716386175", - "create_date": "2024-05-22 10:56:15", - }, - "BRLPEN": { - "code": "BRL", - "codein": "PEN", - "name": "Real Brasileiro/Sol do Peru", - "high": "0.7319", - "low": "0.7253", - "varBid": "-0.0098", - "pctChange": "-1.33", - "bid": "0.7116", - "ask": "0.7403", - "timestamp": "1716386118", - "create_date": "2024-05-22 10:55:18", - }, - } - - -@pytest.fixture -def valid_params() -> list: - return ["BRL-COP", "BRL-PEN"] - - -def test_transformation_process_param(json_response, valid_params): - # Create an instance of the transformation class - transform = transformation(json_response, valid_params, fila) - transform.publish() - - assert fila.qsize() == 2 - - # for extracted_file in transform.extracted_files: - # assert extracted_file.startswith("BRL-") - # assert extracted_file.endswith(".parquet") diff --git a/etl/run.py b/etl/run.py deleted file mode 100644 index 5f1e17b..0000000 --- a/etl/run.py +++ /dev/null @@ -1,21 +0,0 @@ -import time -import sys - -from etl.controller.pipeline import PipelineExecutor - -start = time.time() - - -def main(params: str = "USD-BRL"): - new_exec = PipelineExecutor(params) - new_exec.pipeline_run() - - -if __name__ == "__main__": # pragma: no cover - if len(sys.argv) > 1: - params = str(sys.argv[1]) - main(params) - else: - main() - -print("Elapsed Time: ", round(time.time() - start, 2), "seconds") diff --git a/etl/test_run.py b/etl/test_run.py deleted file mode 100644 index 890af6a..0000000 --- a/etl/test_run.py +++ /dev/null @@ -1,14 +0,0 @@ -from unittest.mock import patch -from etl.run 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-class DatasetSerializer: - def __init__(self, source_files: list) -> None: - self.files = source_files - - def serialize(self): - serialized_df = pd.read_pickle("etl/views/dataset.pkl") - print("Total rows before execution: ", serialized_df.shape[0]) - dfs = [] - for file in self.files: - df = pd.read_parquet(DefaultFolder() + file) - dfs.append(df) - - delta_df = pd.concat(dfs, ignore_index=True) - - new_df = pd.concat([delta_df, serialized_df], ignore_index=True) - - pd.to_pickle(new_df, "etl/views/dataset.pkl") - print("Total rows after execution:: ", new_df.shape[0]) - print("New rows added: ", - new_df.shape[0] - serialized_df.shape[0]) diff --git a/notebooks/data_explorer.ipynb b/notebooks/data_explorer.ipynb deleted file mode 100644 index 911689b..0000000 --- a/notebooks/data_explorer.ipynb +++ /dev/null @@ -1,1306 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Consolidated files in the unique DataFrame and show the total files extracted" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2872BRLOMRReal Brasileiro/Rial Omanense0.07470.0747-0.0005-0.620.07440.074917164381812024-05-23 01:23:01BRL-OMR2024-05-23 04:26:21BRL-OMR-1716438381NaNNaNNaNNaN
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" - ], - "text/plain": [ - " code codein name high low varBid \\\n", - "2868 BRL NPR Real Brasileiro/Rúpia Nepalesa 26.03 26.01 -0.11 \n", - "2869 USD BRL Dólar Americano/Real Brasileiro 5.1764 5.1304 0.0207 \n", - "2870 USD UGX Dólar Americano/Shilling Ugandês 3808.67 3805.9 1.45 \n", - "2871 USD QAR Dólar Americano/Rial Catarense 3.6415 3.6415 0 \n", - "2872 BRL OMR Real Brasileiro/Rial Omanense 0.0747 0.0747 -0.0005 \n", - "\n", - " pctChange bid ask timestamp create_date symbol \\\n", - "2868 -0.4 25.72 26.33 1716342722 2024-05-21 22:52:02 BRL-NPR \n", - "2869 0.4 5.1645 5.1652 1716584393 2024-05-24 17:59:53 USD-BRL \n", - "2870 0.04 3765.92 3848.79 1716347415 2024-05-22 00:10:15 USD-UGX \n", - "2871 0 3.64 3.643 1716347432 2024-05-22 00:10:32 USD-QAR \n", - "2872 -0.62 0.0744 0.0749 1716438181 2024-05-23 01:23:01 BRL-OMR \n", - "\n", - " extracted_at id value key1 key2 key3 \n", - "2868 2024-05-22 03:10:35 BRL-NPR-1716347434 NaN NaN NaN NaN \n", - "2869 2024-05-25 03:27:19 USD-BRL-1716607639 NaN NaN NaN NaN \n", - "2870 2024-05-22 03:11:44 USD-UGX-1716347504 NaN NaN NaN NaN \n", - "2871 2024-05-22 03:11:10 USD-QAR-1716347469 NaN NaN NaN NaN \n", - "2872 2024-05-23 04:26:21 BRL-OMR-1716438381 NaN NaN NaN NaN " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "all_files = pd.read_pickle(\"../etl/views/\" + \"dataset.pkl\")\n", - "\n", - "all_files.shape\n", - "\n", - "# the last 5 rows\n", - "all_files.tail()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.1 Data set sample, list 5 files" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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codecodeinnamehighlowvarBidpctChangebidasktimestampcreate_datesymbolextracted_atidvaluekey1key2key3
0USDBRLDólar Americano/Real Brasileiro5.43075.37310.04750.885.42245.42417186506712024-06-17 15:57:51USD-BRL2024-06-17 18:58:30USD-BRL-1718650710NaNNaNNaNNaN
1USDBRLDólar Americano/Real Brasileiro5.25855.19360.04170.85.24435.245917171891932024-05-31 17:59:53USD-BRL2024-06-02 00:07:59USD-BRL-1717286879NaNNaNNaNNaN
2USDBRLTDólar Americano/Real Brasileiro Turismo5.2855.220.0551.055.135.4417171838002024-05-31 16:30:00USD-BRLT2024-06-02 00:08:00USD-BRLT-1717286880NaNNaNNaNNaN
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" - ], - "text/plain": [ - " code codein name high low \\\n", - "0 USD BRL Dólar Americano/Real Brasileiro 5.4307 5.3731 \n", - "1 USD BRL Dólar Americano/Real Brasileiro 5.2585 5.1936 \n", - "2 USD BRLT Dólar Americano/Real Brasileiro Turismo 5.285 5.22 \n", - "\n", - " varBid pctChange bid ask timestamp create_date \\\n", - "0 0.0475 0.88 5.4224 5.424 1718650671 2024-06-17 15:57:51 \n", - "1 0.0417 0.8 5.2443 5.2459 1717189193 2024-05-31 17:59:53 \n", - "2 0.055 1.05 5.13 5.44 1717183800 2024-05-31 16:30:00 \n", - "\n", - " symbol extracted_at id value key1 key2 key3 \n", - "0 USD-BRL 2024-06-17 18:58:30 USD-BRL-1718650710 NaN NaN NaN NaN \n", - "1 USD-BRL 2024-06-02 00:07:59 USD-BRL-1717286879 NaN NaN NaN NaN \n", - "2 USD-BRLT 2024-06-02 00:08:00 USD-BRLT-1717286880 NaN NaN NaN NaN " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_files.head(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data transformation\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "# Contributed by @Marian Gobea Alcoba ⭐\n", - "\n", - "all_files[\"create_date\"] = pd.to_datetime(all_files[\"create_date\"])\n", - "all_files[\"extracted_at\"] = pd.to_datetime(all_files[\"extracted_at\"])\n", - "all_files = all_files.astype({\n", - " \"code\": \"category\",\n", - " \"codein\": \"category\",\n", - " \"name\": \"category\",\n", - " \"high\": \"float64\", \n", - " \"low\": \"float64\", \n", - " \"varBid\": \"float64\",\n", - " \"pctChange\": \"float64\",\n", - " \"bid\": \"float64\",\n", - " \"ask\": \"float64\",\n", - " })" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Contributed by @Marian Gobea Alcoba ⭐\n", - "# @title Missing values\n", - "import missing_mga as missing \n", - "\n", - "# @title Apago warnings\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "all_files.missing.number_missing()\n", - "all_files.missing.number_missing_by_column()\n", - "# # @title Las columnas \"value\", \"key1\", \"key2\" y \"key3\" tienen muchos valores faltantes por lo que podrían quitarse del dataset\n", - "# all_files = all_files.drop(columns=[\"value\", \"key1\", \"key2\", \"key3\"])\n", - "all_files.missing.missing_value_heatmap()\n", - "# @title La inmensa mayoria ya no tienen faltantes. \n", - "all_files.missing.missing_case_plot()\n", - "all_files.missing.missing_upsetplot()\n", - "# @title Eliminamos las filas con faltantes dado que falta practicamente el 100% de los datos\n", - "all_files = all_files.dropna()\n", - "all_files.missing.number_missing()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Change DataTypes and Reorder columns" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2USDBRLTDólar Americano/Real Brasileiro Turismo5.28505.22000.05501.055.13005.440017171838002024-05-31 16:30:00USD-BRLT2024-06-02 00:08:00USD-BRLT-1717286880
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" - ], - "text/plain": [ - " code codein name high low \\\n", - "0 USD BRL Dólar Americano/Real Brasileiro 5.4307 5.3731 \n", - "1 USD BRL Dólar Americano/Real Brasileiro 5.2585 5.1936 \n", - "2 USD BRLT Dólar Americano/Real Brasileiro Turismo 5.2850 5.2200 \n", - "\n", - " varBid pctChange bid ask timestamp create_date \\\n", - "0 0.0475 0.88 5.4224 5.4240 1718650671 2024-06-17 15:57:51 \n", - "1 0.0417 0.80 5.2443 5.2459 1717189193 2024-05-31 17:59:53 \n", - "2 0.0550 1.05 5.1300 5.4400 1717183800 2024-05-31 16:30:00 \n", - "\n", - " symbol extracted_at id \n", - "0 USD-BRL 2024-06-17 18:58:30 USD-BRL-1718650710 \n", - "1 USD-BRL 2024-06-02 00:07:59 USD-BRL-1717286879 \n", - "2 USD-BRLT 2024-06-02 00:08:00 USD-BRLT-1717286880 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Change data types\n", - "df = all_files.astype({'ask': float, 'bid': float, 'varBid': float, 'pctChange': float})\n", - "\n", - "# Show the dataframe\n", - "df.head(3)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Using SQL for Data Exploration\n", - " 3.1 What is the currency with the highest ask value?" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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codecodeinnamehighlowvarBidpctChangebidasktimestampcreate_datesymbolextracted_atid
0USDBRLDólar Americano/Real Brasileiro5.43075.37310.04750.885.42245.42417186506712024-06-17 15:57:51.000000USD-BRL2024-06-17 18:58:30.000000USD-BRL-1718650710
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" - ], - "text/plain": [ - " code codein name high low varBid \\\n", - "0 USD BRL Dólar Americano/Real Brasileiro 5.4307 5.3731 0.0475 \n", - "\n", - " pctChange bid ask timestamp create_date symbol \\\n", - "0 0.88 5.4224 5.424 1718650671 2024-06-17 15:57:51.000000 USD-BRL \n", - "\n", - " extracted_at id \n", - "0 2024-06-17 18:58:30.000000 USD-BRL-1718650710 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pandasql import sqldf\n", - "\n", - "query = \"\"\"\n", - " SELECT * FROM df order by extracted_at desc limit 1\n", - "\"\"\"\n", - "\n", - "newDf = sqldf(query, locals())\n", - "\n", - "newDf\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " 3.1 Disponible Data" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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codecodeinnamehighlowvarBidpctChangebidasktimestampcreate_datesymbolextracted_atid
0USDBRLDólar Americano/Real Brasileiro5.43075.37310.04750.885.42245.424017186506712024-06-17 15:57:51.000000USD-BRL2024-06-17 18:58:30.000000USD-BRL-1718650710
1USDBRLDólar Americano/Real Brasileiro5.25855.19360.04170.805.24435.245917171891932024-05-31 17:59:53.000000USD-BRL2024-06-02 00:07:59.000000USD-BRL-1717286879
2USDBRLTDólar Americano/Real Brasileiro Turismo5.28505.22000.05501.055.13005.440017171838002024-05-31 16:30:00.000000USD-BRLT2024-06-02 00:08:00.000000USD-BRLT-1717286880
3USDBRLDólar Americano/Real Brasileiro5.25855.19360.04170.805.24435.245917171891932024-05-31 17:59:53.000000USD-BRL2024-06-02 00:07:59.000000USD-BRL-1717286879
4USDJPYDólar Americano/Iene Japonês157.3700156.56000.40000.26157.2000157.220017171793282024-05-31 15:15:28.000000USD-JPY2024-05-31 18:15:36.000000USD-JPY-1717179336
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2852BRLNPRReal Brasileiro/Rúpia Nepalesa26.030026.0100-0.1100-0.4025.720026.330017163427222024-05-21 22:52:02.000000BRL-NPR2024-05-22 03:10:35.000000BRL-NPR-1716347434
2853USDBRLDólar Americano/Real Brasileiro5.17645.13040.02070.405.16455.165217165843932024-05-24 17:59:53.000000USD-BRL2024-05-25 03:27:19.000000USD-BRL-1716607639
2854USDUGXDólar Americano/Shilling Ugandês3808.67003805.90001.45000.043765.92003848.790017163474152024-05-22 00:10:15.000000USD-UGX2024-05-22 03:11:44.000000USD-UGX-1716347504
2855USDQARDólar Americano/Rial Catarense3.64153.64150.00000.003.64003.643017163474322024-05-22 00:10:32.000000USD-QAR2024-05-22 03:11:10.000000USD-QAR-1716347469
2856BRLOMRReal Brasileiro/Rial Omanense0.07470.0747-0.0005-0.620.07440.074917164381812024-05-23 01:23:01.000000BRL-OMR2024-05-23 04:26:21.000000BRL-OMR-1716438381
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2857 rows × 14 columns

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" - ], - "text/plain": [ - " code codein name high \\\n", - "0 USD BRL Dólar Americano/Real Brasileiro 5.4307 \n", - "1 USD BRL Dólar Americano/Real Brasileiro 5.2585 \n", - "2 USD BRLT Dólar Americano/Real Brasileiro Turismo 5.2850 \n", - "3 USD BRL Dólar Americano/Real Brasileiro 5.2585 \n", - "4 USD JPY Dólar Americano/Iene Japonês 157.3700 \n", - "... ... ... ... ... \n", - "2852 BRL NPR Real Brasileiro/Rúpia Nepalesa 26.0300 \n", - "2853 USD BRL Dólar Americano/Real Brasileiro 5.1764 \n", - "2854 USD UGX Dólar Americano/Shilling Ugandês 3808.6700 \n", - "2855 USD QAR Dólar Americano/Rial Catarense 3.6415 \n", - "2856 BRL OMR Real Brasileiro/Rial Omanense 0.0747 \n", - "\n", - " low varBid pctChange bid ask timestamp \\\n", - "0 5.3731 0.0475 0.88 5.4224 5.4240 1718650671 \n", - "1 5.1936 0.0417 0.80 5.2443 5.2459 1717189193 \n", - "2 5.2200 0.0550 1.05 5.1300 5.4400 1717183800 \n", - "3 5.1936 0.0417 0.80 5.2443 5.2459 1717189193 \n", - "4 156.5600 0.4000 0.26 157.2000 157.2200 1717179328 \n", - "... ... ... ... ... ... ... \n", - "2852 26.0100 -0.1100 -0.40 25.7200 26.3300 1716342722 \n", - "2853 5.1304 0.0207 0.40 5.1645 5.1652 1716584393 \n", - "2854 3805.9000 1.4500 0.04 3765.9200 3848.7900 1716347415 \n", - "2855 3.6415 0.0000 0.00 3.6400 3.6430 1716347432 \n", - "2856 0.0747 -0.0005 -0.62 0.0744 0.0749 1716438181 \n", - "\n", - " create_date symbol extracted_at \\\n", - "0 2024-06-17 15:57:51.000000 USD-BRL 2024-06-17 18:58:30.000000 \n", - "1 2024-05-31 17:59:53.000000 USD-BRL 2024-06-02 00:07:59.000000 \n", - "2 2024-05-31 16:30:00.000000 USD-BRLT 2024-06-02 00:08:00.000000 \n", - "3 2024-05-31 17:59:53.000000 USD-BRL 2024-06-02 00:07:59.000000 \n", - "4 2024-05-31 15:15:28.000000 USD-JPY 2024-05-31 18:15:36.000000 \n", - "... ... ... ... \n", - "2852 2024-05-21 22:52:02.000000 BRL-NPR 2024-05-22 03:10:35.000000 \n", - "2853 2024-05-24 17:59:53.000000 USD-BRL 2024-05-25 03:27:19.000000 \n", - "2854 2024-05-22 00:10:15.000000 USD-UGX 2024-05-22 03:11:44.000000 \n", - "2855 2024-05-22 00:10:32.000000 USD-QAR 2024-05-22 03:11:10.000000 \n", - "2856 2024-05-23 01:23:01.000000 BRL-OMR 2024-05-23 04:26:21.000000 \n", - "\n", - " id \n", - "0 USD-BRL-1718650710 \n", - "1 USD-BRL-1717286879 \n", - "2 USD-BRLT-1717286880 \n", - "3 USD-BRL-1717286879 \n", - "4 USD-JPY-1717179336 \n", - "... ... \n", - "2852 BRL-NPR-1716347434 \n", - "2853 USD-BRL-1716607639 \n", - "2854 USD-UGX-1716347504 \n", - "2855 USD-QAR-1716347469 \n", - "2856 BRL-OMR-1716438381 \n", - "\n", - "[2857 rows x 14 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pandasql import sqldf\n", - "\n", - "query = \"\"\"\n", - " SELECT * FROM df \n", - "\"\"\"\n", - "\n", - "newDf = sqldf(query, locals())\n", - " \n", - "newDf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Using SQL + Matplotlib for Data Viz\n", - " 4.1 What is the TOP 10 Most Value Currency considering BRL?" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "query = \"\"\"\n", - " SELECT \n", - " name\n", - " ,round(avg(ask),2) AvgAsk\n", - " FROM df \n", - " where codein = 'BRL'\n", - " and not code in ('BTC', 'ETH', 'LTC', 'DOGE')\n", - " group by name\n", - " order by avg(ask) desc limit 10\n", - "\"\"\"\n", - "\n", - "newDf = sqldf(query, locals())\n", - "newDf.sort_values(by='AvgAsk', ascending=True, inplace=True)\n", - "\n", - "AvgAskByCurrency = newDf.plot(\n", - " kind='barh', x='name', y='AvgAsk', \n", - " figsize=(15, 10), \n", - " legend=False, \n", - " color='blue', title='Top 10 Most Valuable Currencies by Ask Price', xlabel='Ask Price', ylabel='Symbol')\n", - "\n", - "\n", - "# Adicionando rótulos aos dados\n", - "for index, value in enumerate(newDf['AvgAsk']):\n", - " plt.text(value, index, str(value))\n", - "\n", - "# Exibir o gráfico\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4.2 What is the TOP 10 locations BRL has + value?" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "query = \"\"\"\n", - " SELECT \n", - " name\n", - " ,round(avg(ask),2) AvgAsk\n", - " FROM df \n", - " where codein = 'BRL'\n", - " and not code in ('BTC', 'ETH', 'LTC')\n", - " group by name\n", - " order by avg(ask) limit 10\n", - "\"\"\"\n", - "\n", - "newDf = sqldf(query, locals())\n", - "newDf.sort_values(by='AvgAsk', ascending=False, inplace=True)\n", - "\n", - "AvgAskByCurrency = newDf.plot(\n", - " kind='barh', x='name', y='AvgAsk', \n", - " figsize=(15, 10), \n", - " legend=False, \n", - " color='blue', title='Top 10 Most Valuable Currencies by Ask Price', xlabel='Ask Price', ylabel='Symbol')\n", - "\n", - "\n", - "# Adicionando rótulos aos dados\n", - "for index, value in enumerate(newDf['AvgAsk']):\n", - " plt.text(value, index, str(value))\n", - "\n", - "# Exibir o gráfico\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4.3 What the top 10 like BRL in value?" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "query = \"\"\"\n", - " SELECT \n", - " name\n", - " ,round(avg(ask),2) AvgAsk\n", - " FROM df \n", - " where codein = 'BRL'\n", - " and not code in ('BTC', 'ETH', 'LTC', 'DOGE')\n", - " and ask >=1\n", - " group by name\n", - " order by avg(ask) desc limit 5\n", - "\"\"\"\n", - "\n", - "newDf = sqldf(query, locals())\n", - "newDf.sort_values(by='AvgAsk', ascending=False, inplace=True)\n", - "\n", - "AvgAskByCurrency = newDf.plot(\n", - " kind='barh', x='name', y='AvgAsk', \n", - " figsize=(15, 10), \n", - " legend=False, \n", - " color='blue', title='Top 10 Most Valuable Currencies by Ask Price', xlabel='Ask Price', ylabel='Symbol')\n", - "\n", - "\n", - "# Adicionando rótulos aos dados\n", - "for index, value in enumerate(newDf['AvgAsk']):\n", - " plt.text(value, index, str(value))\n", - "\n", - "# Exibir o gráfico\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4.4 Average Ask By Day" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "## Query to get the average ASK and BID by day\n", - "query = \"\"\"\n", - " SELECT \n", - " create_date DT_REF\n", - " ,round(avg(ask),2) AvgAsk\n", - " ,round(avg(bid),2) Avgbid\n", - " FROM df \n", - " where not code in ('BTC', 'ETH', 'LTC', 'DOGE')\n", - " group by 1\n", - " order by 1 \n", - "\"\"\"\n", - "\n", - "newDf = sqldf(query, locals())\n", - "newDf.sort_values(by='DT_REF', ascending=True, inplace=True)\n", - "\n", - "cht = newDf.plot(\n", - " kind='line', x='DT_REF', y='AvgAsk',\n", - " figsize=(15, 10), \n", - " legend=False, \n", - " color='blue', title='Average ASK tendence by Day', xlabel='Date', ylabel='AvgAsk')\n", - "\n", - "#exibir o grafico\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4.5 Average Bid By Day" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "query = \"\"\"\n", - " SELECT \n", - " create_date DT_REF\n", - " ,round(avg(ask),2) AvgAsk\n", - " ,round(avg(bid),2) Avgbid\n", - " FROM df \n", - " where not code in ('BTC', 'ETH', 'LTC', 'DOGE')\n", - " group by 1\n", - " order by 1 \n", - "\"\"\"\n", - "\n", - "newDf = sqldf(query, locals())\n", - "newDf.sort_values(by='DT_REF', ascending=True, inplace=True)\n", - "\n", - "cht = newDf.plot(\n", - " kind='line', x='DT_REF', y='Avgbid',\n", - " figsize=(15, 10), \n", - " legend=False, \n", - " color='blue', title='Average BID tendence by Day', xlabel='Date', ylabel='Avgbid')\n", - "\n", - "#exibir o grafico\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "awesome-data-ingestion-bT26QLZB-py3.9", - "language": "python", - "name": "python3" - 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-def DefaultOutputFolder(): - """ - Returns the default output folder path. - """ - return os.path.join(os.path.dirname(__file__), "../../../data/") - - -def DefaultOutputLogFolder() -> str: - """ - Returns the default output log folder path. - """ - return os.path.join(os.path.dirname(__file__), "../logs/") +from datetime import datetime, timezone def DefaultTimestampStr() -> str: diff --git a/etl/common/utils/logs.py b/src/currency_quote/common/utils/logs.py similarity index 97% rename from etl/common/utils/logs.py rename to src/currency_quote/common/utils/logs.py index 3ad9532..40ce755 100644 --- a/etl/common/utils/logs.py +++ b/src/currency_quote/common/utils/logs.py @@ -1,6 +1,5 @@ import logging import os -from etl.common.utils.common import DefaultOutputLogFolder class CustomLogger(): diff --git a/etl/config/datasource.py b/src/currency_quote/config/datasource.py similarity index 100% rename from etl/config/datasource.py rename to src/currency_quote/config/datasource.py diff --git a/data/ETH-EUR-1713658884.example b/src/currency_quote/controller/controller.py similarity index 100% rename from data/ETH-EUR-1713658884.example rename to src/currency_quote/controller/controller.py diff --git a/etl/__init__.py b/src/currency_quote/models/model.py similarity index 100% rename from etl/__init__.py rename to src/currency_quote/models/model.py diff --git a/etl/common/__init__.py b/src/currency_quote/tests/test.py similarity index 100% rename from etl/common/__init__.py rename to src/currency_quote/tests/test.py
- - - - - - - - - - - - -