Python-ING-bank-reports

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Python-ING-bank-reports

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This project intend to create a small lightweight sqlite database with multiple everyday accounts from ING BANK Australia and run SQL queries to analyse it and print in PDF charts to visualize it.

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# Python ING bank reports

This project intend to create a small lightweight sqlite database with the everyday accounts from ING BANK Australia and run SQL queries to analyse it and print in PDF charts to vizualize it.

This generate a report of big picture about transactions, monthly income and where the money go in expenses **for multiple accounts together and separated automatically.**

Check screenshots in the end to see how the report looks like!

# Requirements
Python 3.7 or Above

matplotlib 3.5.3

# How to use it:

You can use with mock data to play with the script and see results or with your real data and personalize it.

## With mock up data
- On the "CONFIG.py" change "IS_TEST" to True.
- Select the amount of accounts you want to simulate at 'AMOUNT_TEST'
- Run the main :)
- Check for erros in the console or the final report on the "Reports" folder
For example, create automatically mockup data and run reports for 2 accounts:
```py
IS_TEST = True
AMOUNT_TEST = 2
```

## Real Data
- On the "CONFIG.py" change "IS_TEST" to False.
- Go to the ING everyday account that you want the reports and download the transactions in csv format
- Change the name to "{anythingyouwant}-ING.csv" and add inside a folder "Input" in the same directory as the source code
- Add as much everyday accounts as you want!
- Run the main :)
- Check for erros in the console or the final report on the "Reports" folder
- Have insights and/or query more infomration with tools like POWER BI

# Personalize your results!
Change the file "CONFIG_CATEGORIES.py" - list of categories, regex matching and "if function" have your own filter about your expenses :)

The regex is as simple as it can be:
Add "|" and the unique name that appear on description between "()"

Change the color of the chars on CONFIG.py

# Goals

- Import ING bank files in CSV format
- Personalize your classification of the descriptions in a way useful to you
- Create a SQLite database with the information and category
- Do analyses of of the information
- Vizualization in PDF

# Obs
Right now the code produce the following reports:
- A Bar chart showing difference between income and expeses durring all the years that are in the csv
- A Pie chart showing the expenses of which category + 10 highest expenses that month (can change in config)
- A monthly analyses of the expeses through the current year (can change in cofig)

# Screenshots - Report and Meaning
This is a report generated with mock data and the CONFIG "IS_TEST = True" and "TEST_AMOUT = 1"

Difference Income/Expenses between years:
![example1](https://raw.githubusercontent.com/Victorhpinheiro/Python-ING-bank-reports/main/imgs/example1.PNG)

Income view by month
![example2](https://raw.githubusercontent.com/Victorhpinheiro/Python-ING-bank-reports/main/imgs/example2.PNG)

Pie chart categories per year + top 10 other category by year
![example3](https://raw.githubusercontent.com/Victorhpinheiro/Python-ING-bank-reports/main/imgs/example3.PNG)

Montlhy expenses + top 5 other category by month
![example4](https://raw.githubusercontent.com/Victorhpinheiro/Python-ING-bank-reports/main/imgs/example4.PNG)

P.S: the Other expenses are random everyday things like a concert or sending to a friend some money, some random store that doesn't match the categories configuration

# How it works

The program have the following flow:

![flow](https://raw.githubusercontent.com/Victorhpinheiro/Python-ING-bank-reports/main/imgs/flow_chart.png)

It works by reading and formating the information in a way that will be useful in the db.
Then it procede to do a series of queries and add the information in a list.
The information is than saved in a pdf for all the accounts CSV present in the thing.

## Note

If you have any sugestion/question please let me know so I can improve the quality of reports and insights.
Anyone is welcome to use it and adapt the CONFIG.py and CONFIG_CATEGORIES.py files and play with the results and queries.
You can also connect the banking.sqlite to PowerBI and do your onw dashboards.

## Note 2

I know I could use pandas for this. I am very familiar with it and already do those analyses using it :)

I want to practice my SQL best practices and interaction with database when I did this project, it was also a personal project for myselft and my fiance that I decided to share.

## Note 3

It can read also files from commonweath but the results are not good and there are a few challanges I was not willing to make:
- CBA does not offer csv files with more than 600 interactions and/or older than 2 years old
- CBA records internal transactions as "Trasfer to Transfer to xx<4_digits_bank>" making the classifying of internal movements hard

However, it will create the banking.sqlite file that you can access through a tool to query (i.e DB Browser (SQLCipher))
If you are also felling motivated chaging the CONFIG_CATEGORIES for your personal use 
or also use a tool for vizualization like PowerBI.

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