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Acquiring and processing information on world's largest banks

python-project-for-data-engineering 최신버젼 다운로드

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# Acquiring and Processing Information on the World's Largest Banks

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## Disclaimer

This repository contains my submission for the ***Final Project: Acquiring and processing information on world's largest banks***. The original files were provided by the IBM Skills Network as part of the **[Python Project for Data Engineering](https://www.coursera.org/learn/python-project-for-data-engineering)** course on Coursera.

### Usage

* You are welcome to use this repository as a reference or starting point for your own project.

* If you choose to fork this repository, please ensure that you comply with the terms of the Apache License and give proper credit to the original authors.

## Objectives

* Extract real-world data from a public website using Webscraping and Requests API in Python
* Transform the data as per the problem statement
* Load the data in the required file format as well as a SQLite database
* Query the database to retrieve filtered information from the table

## Project Scenario

A multi-national firm has hired you as a data engineer. Your job is to access and process data as per requirements.

Your boss asked you to compile the list of the top 10 largest banks in the world ranked by market capitalization in billion USD. Further, you need to transform the data and store it in USD, GBP, EUR, and INR per the exchange rate information made available to you as a CSV file. You should save the processed information table locally in a CSV format and as a database table. Managers from different countries will query the database table to extract the list and note the market capitalization value in their own currency.

Particulars of the code to be made have been shared below.

| Parameter                               | Value                                                                                                                             |
| --------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- |
| Code name                               | `banks_project.py`                                                                                                                |
| Data URL                                | `https://web.archive.org/web/20230908091635/https://en.wikipedia.org/wiki/List_of_largest_banks`                                  |
| Exchange rate CSV path                  | `https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMSkillsNetwork-PY0221EN-Coursera/labs/v2/exchange_rate.csv` |
| Table Attributes (upon Extraction only) | `Name`, `MC_USD_Billion`                                                                                                          |
| Table Attributes (final)                | `Name`, `MC_USD_Billion`, `MC_GBP_Billion`, `MC_EUR_Billion`, `MC_INR_Billion`                                                    |
| Output CSV Path                         | `./Largest_banks_data.csv`                                                                                                        |
| Database name                           | `Banks.db`                                                                                                                        |
| Table name                              | `Largest_banks`                                                                                                                   |
| Log file                                | `code_log.txt`                                                                                                                    |

Important Note:

To maintain consistency of the project structure, the web page is routed through an archive database. Often, in case the archive server is busy, the users may encounter delayed execution and/or an error such as:
`requests.exceptions.ConnectionError: HTTPSConnectionPool(host='web.archive.org', port=443): Max retries exceeded with url`.
In such a situation, try executing the code again. In case the problem persists, you can change the URL to the live version, such as:
`https://en.wikipedia.org/wiki/List_of_largest_banks`

## Directions

1. Write a function to extract the tabular information from the given URL under the heading By Market Capitalization, and save it to a data frame.
1. Write a function to transform the data frame by adding columns for Market Capitalization in GBP, EUR, and INR, rounded to 2 decimal places, based on the exchange rate information shared as a CSV file.
1. Write a function to load the transformed data frame to an output CSV file.
1. Write a function to load the transformed data frame to an SQL database server as a table.
1. Write a function to run queries on the database table.
1. Run the following queries on the database table:
    - Extract the information for the London office, that is Name and MC_GBP_Billion
    - Extract the information for the Berlin office, that is Name and MC_EUR_Billion
    - Extract the information for New Delhi office, that is Name and MC_INR_Billion
1. Write a function to log the progress of the code.
1. While executing the data initialization commands and function calls, maintain appropriate log entries.

## Setup

Install the required libraries using the provided `requirements.txt` file. The command syntax is:

```bash
python3.11 -m pip install -r requirements.txt
```

Download the required exchange rate file using the terminal command:

```bash
wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMSkillsNetwork-PY0221EN-Coursera/labs/v2/exchange_rate.csv
```

Execute the code using the command:

```bash
python3.11 banks_project.py
```

## Learner

[Pravin Regismond](https://www.linkedin.com/in/pregismond)

## Acknowledgments

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