Today, the majority of organizations frequently use data-driven decision-making. Businesses gather a lot of data and use it to conduct in-depth analyses of their clients and goods, enabling them to prepare growth, product, and marketing strategies for the future. First, data must be extracted from all platforms used by the company and transformed into an analysis-ready format in order to conduct a proper study.

QuickBooks is one of the most widely utilized platforms today that many small businesses use to manage their financial data. You will gain an overview of how to set up QuickBooks Python integration in this post, as well as how to utilize Python to standardize the retrieved data for analysis.

Introduction to Python

In 1991, Guido Van Rossum introduced Python, one of the most well-liked general-purpose programming languages. It can be applied to numerous different tasks, including server-side web development, system scripting, data science and analytics, software development, etc.

The Python programming language is a high-level, general-purpose, interpreted language that primarily promotes code readability. Professional programmers and developers use it extensively in a variety of industries, including Web Development and Machine Learning.

High-level, all-purpose programming languages like Python are available. With the off-side rule and substantial indentation, its design philosophy prioritizes readability of the code.

Its extensive standard library has earned it the moniker “batteries included” language.

Incorporating Exceptions, Modules, Dynamic Typing, Dynamic Binding, Classes, High-level Dynamic Data Types, etc., Python is an interactive, interpreted, object-oriented programming language. Additionally, it can be used to send system calls to virtually all well-known operating systems.

Introduction to QuickBooks Desktop/Online

The top financial software provider, Intuit, created the well-known accounting program QuickBooks. Small and medium-sized organizations are the ones who utilize it most often to handle business money. Users may manage sales and costs, submit taxes, create reports based on their needs, and more with QuickBooks. The accounting software also provides several built-in reports that are easily customizable and enable firms to fully oversee their finances. QuickBooks has features that let organizations save vendor invoices and plan regular payments for when they are due. QuickBooks has features that let organizations save vendor invoices and plan regular payments for when they are due.

Businesses can carry out the following tasks using QuickBooks:

• Tracking of the Income and the Expenses.
• Tracking of the transaction which is done daily.
• Generating of reports for the planning.
• Preparing of Bills.
• Managing of Payroll.

QuickBooks Desktop

Embedded analytics provide configurable dashboards and reports that allow you to quickly assess how your organization is operating.

QuickBooks Online

QuickBooks users may manage their businesses and access their finances from any location using a mobile device, PC, or tablet.

Financial management software on the cloud is known as a QuickBooks Online. It helps you with chores like creating estimates and invoicing, which will reduce the amount of time you have to spend handling your company’s money. You can track cash flow and sales. Statements and transactions will be updated instantly thanks to a function of the QuickBooks Online Software’s banking interface.

Need of Python QuickBooks Integration

Below are some unique features of Python which makes the Python QuickBooks integration:

Free & Open-Source

Anyone may easily download and install Python for free from the official website. The term “open-source” denotes that the source code is freely accessible. Users with sufficient knowledge can now alter the code to suit particular business use cases and product specifications.

Easy to Read and do Coding

Python is thought to be a relatively beginner-friendly language, making it simple to read and write code. As a result, most people with a rudimentary understanding of programming can quickly pick up Python syntax.

High-Level Language

Python developers do not require any knowledge of the System Architecture or the requirement to manually monitor memory use. The Python Interpreter automatically handles all of this.

Code is Portable

Python code is portable when it has been written on one system because it can be readily moved to another and executed without any problems.

Interpreted

Python code is interpreted by the interpreter at runtime. This indicates that users are not required to compile the code before running it as they would in the case of other programming languages like Java, C/C++, etc.

Object Oriented

Python also supports object-oriented programming, which enables the users to create readable and reusable code.

Key Benefits of using Python

Following are some of the key benefits of using the Python programming language.

Improves the Productivity

Another advantage of Python is that it is a very productive language, and due to its simplicity.

Open Source

You can easily download any source code in Python, edit it and then share it in your own version of the code. When you want to reuse the code and save your time to create creative applications, this capability can be extremely helpful. Additionally, you have the option to modify its behavior and use an earlier version of the code for development.

Portable

The majority of programming languages, including the ones you have learned to read, write, and use like C, C++, etc., require changes to the code in order to run a given program on other platforms. With Python programming, though, such is not the case. Python has adopted the “write once, run anywhere” principle, allows you to create your code just once and have it work on any platform. However, you must take care to avoid including any functionality that rely on a certain operating system.

Massive Library

You can find all the necessary functions you need for any work in the extensive standard library of Python. Python is no longer dependent on outside libraries as a result. But if you do want to use some external libraries, you may quickly import a number of packages from the enormous Python Package Index (PyPi), which has more than 200,000 packages.

Integration with other Language

Another distinctive characteristic of Python allows it to be integrated for cross-platform development not only with libraries like Jython and Cython but also with other programming languages like Java, C, and C++. Python is therefore considerably more powerful. It is not generally recommended to utilize a single programming language for all applications and innovations because no programming language is comprehensive. Additionally, Python’s strength, like that of any other language, is to give you access to a sizable library so that you can benefit from the robust and potent features of other languages as well.

Working of Python with QuickBooks

• The process of connecting to QuickBooks data is identical to connecting to any other relational data source. The required connection properties should be used to create a connection string.
• You don’t need to set any connection properties when connecting to a local QuickBooks instance.
• The Remote Connector is used to submit requests to QuickBooks. On the same computer as QuickBooks, the Remote Connector takes connections via a small, embedded Web server. Users can securely connect from distant workstations thanks to the server’s support for SSL/TLS.
• You must provide the Remote Connector permission to access QuickBooks the first time you connect. For instructions, refer to the help documentation’s “Getting Started” chapter.
• After installing the CData QuickBooks Connector, follow the instructions below to install the remaining necessary modules and begin using Python objects to access QuickBooks.

Steps of Python QuickBooks Integration

Singer allows users to set up Python QuickBooks connectivity. Data extraction and consolidation are made simpler by Singer, a community-maintained library of Open-source extractors and loaders. Targets (data loading scripts) and Taps (data extraction scripts) are the fundamental components of Singer. Taps extract data from any source and output the data over stdout in a JSON-based format. Targets ingest data from the Taps and use it to carry out various activities, such as loading information into a file, an API, a database, etc.

To transport JSON-formatted data across databases, Web APIs, files, queues, etc., the Singer project has 150–200 Taps and Targets available. The steps listed below can be used to configure Python QuickBooks integration:

Python QuickBooks Integration Step 1: Installing of Python Package

The following Python packages must be installed in order to configure the Python QuickBooks integration:

• Tap-QuickBooks is a Singer Tap data extraction script that pulls information from the QuickBooks API. More details on GitHub.
• A Singer Target (data ingestion script) called target-csv accepts JSON data and converts it to CSV format. Tabular data is frequently stored in the CSV file format. More details on GitHub.
• Streams and fields from a Singer catalogue can be chosen using the open-source tool singer-discover. More details on GitHub.
• An Open-Source Python Library for Data Analysis and Manipulation is called pandas.
• Gluestick is an open-source Python library that includes ETL utility functions. Details about PyPi.

Python QuickBooks Integration Step 2: Python Virtual Environment

Creating an isolated environment for Python projects is the main goal of setting up a Python Virtual Environment. Singer Taps frequently get into dependency issues with one another. By encapsulating each dependency in a project-specific virtual environment, you may prevent problems brought on by discrepancies between dependencies.

• Installation of virtualenv library
• Creating of virtual environment
• Activation of virtual environment
• Installing of Jupyter Lab
• Installing of Tap-QuickBooks
• Installing of Target CSV
• Installing of Gluestick
• Installing of Pandas
• Installing of Dependencies
• Making of the virtual environment which is available to the Jupyter lab by installing ipykernel
• Creating of workspace
• Directory to be entre

Python QuickBooks Integration Step 3: Singer Tap to be configured

In this step you will want your QuickBooks OAuth credentials that is Client ID and Client Secret, to configure the Python QuickBooks integration. Your application’s Keys and OAuth section, either under Development or Production, is where you may obtain the OAuth credentials.

Following is the example of Client ID and its Secret

The next step is to construct a Singer configuration that contains your OAuth login information as well as some Singer-specific options. Here is an illustration of a configuration file:

{
“client_id”: “your_client_id”,
“client_secret”: “your_client_secret”,
“refresh_token”: “abcdef123456”,
“start_date”: “2020-10-06T00:00:00Z”,
“api_type”: “BULK”,
“select_fields_by_default”: true,
“sandbox”: true,
“realmId”: “1234567890”
}

In a file called config.json in your local directory, replace the values with your login information.

Singer Taps provides a command called discover that prints a JSON object containing the data that is accessible from QuickBooks. You must store the result in a catalog.json file. Execute the pursuing discover command:

tap-quickbooks --config config.json --discover > catalog.json

The following format will be used for the command’s output:

less catalog.json

{
“streams”: [
{
“stream”: “Invoice”,
“tap_stream_id”: “Invoice”,
“schema”: {
“type”: “object”,
“additionalProperties”: false,
“properties”: {
“AllowIPNPayment”: {
“type”: [
“boolean”,
“null”
]
},
…

The objects that you want to sync must be chosen from here. For this, you can utilize the Singer-discover tool. To switch from the version of singer-python to the version of singer-discover, enter the following command:

pip install https://github.com/chrisgoddard/singer-discover/archive/master.zip singer-python==5.4.1 prompt_toolkit==1.0.14

The catalogue you just choose should then be created.

singer-discover --input catalog.json --output properties.json

An interactive tool will then be launched so you may choose the streams (objects) from QuickBooks you desire. Enter after selecting Invoice. You will be given the choice to select certain fields when this happens. Press enter after accepting the preset selections.

The output from this should be as follows:

INFO Catalog configuration starting…
? Select Streams [Invoice]
? Select fields from stream: Invoice done (18 selections)
INFO Catalog configuration saved.

Finally, you can use the files you just created and the following commands to get data from QuickBooks:

pip install singer-python==5.3.1

To create a CSV file, download the invoice data from QuickBooks.

tap-quickbooks –config config.json –properties properties.json | target-csv > state.json

This will result in two output files:

The CSV file (Invoice-20210605T387782.csv, for instance) that contains the data from QuickBooks a JSON file called state.json that informs tap-QuickBooks of the most recent sync. To avoid synchronizing the same data again in the future, use this technique.

Your Python QuickBooks integration is now complete after you have extracted the necessary data from QuickBooks. Now that the procedure is contained within a Docker container, it can be automated to provide continuous and automatic sync. The platform for Power BI integration with QuickBooks is another option.

Python QuickBooks Integration Step 4: Data Standardizing

The following format will be used for the data that is extracted using tap-QuickBooks:

Before any relevant analysis can be done on the data, it must first be standardized. You can accomplish this by first adding the data to a Jupyter Notebook. Run the following command to launch a Jupyter Lab instance:

jupyter lab

In addition to launching the browser, this will start a Jupyter Lab instance in the current directory.

You can now access tap-QuickBooks from the Notebook sections. Make a fresh Jupyter Notebook with a meaningful name. To load and explore the data, utilize the pandas and gluestick Python packages. Here, the objective is to quickly transform the output from tap-QuickBooks.

import gluestick as gs
import pandas as pd

Read input data

input_data = gs.read_csv_folder(“.”, index_cols={‘Invoice’: ‘Id’})

View the input data

input_df = input_data[‘Invoice’]
input_df.head()

Once the data is in a Pandas data frame, you can change it to meet the needs of your research.

invoices = input_df[[“CustomerRef__name”, “TotalAmt”, “Balance”, “DueDate”]]

You were given a step-by-step tutorial in this post on how to set up Python QuickBooks integration. The use of this approach is subject to several restrictions. This includes the fact that this approach is not deemed suitable for users of Python with insufficient technical proficiency. The manual Python QuickBooks integration approach is the ideal choice for you if the constraints are not a worry for your requirements, but if they are, you might think about employing automated Data Integration services.

Without having to write any code, we assist you with fully automated and secure direct data transfer from a source of your choosing to a data warehouse, business intelligence tools, or any desired destination. It will simplify your life and make data movement painless. It is convenient, dependable, and secure.

Accounting Professionals, CPA, Enterprises, Owners

Looking for a professional expert to get the right assistance for your problems? Here, we have a team of professional and experienced team members to fix your technical, functional, data transfer, installation, update, upgrade, or data migrations errors. We are here at Dancing Numbers available to assist you with all your queries. To fix these queries you can get in touch with us via a toll-free number
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