importance of python

Why is Python so Important for your Company’s Business

The business analysis flow is about collecting and analyzing data to obtain business insights. It is the job of every business analyst to use data to draw relevant conclusions about the efficiency of business operations, how to attract new customers, how to monitor customer behavior, the success of marketing activities, and product usage trends.

This data helps identify and optimize future results, such as developing new revenue streams and streamlining business processes.

Business analysis is a versatile tool for any industry: global technology businesses, nonprofits, education, etc.

Learn why Python is so important and how it plays a role in all industries and all areas of business analysis.

Why is Python so Important?

Python is replacing Excel to scale business decisions

Python and other open-source programming languages like R are quickly replacing Excel, which isn’t scalable for modern business needs.

Excel has been the de facto decision engine for companies for years. But it was built for a world where datasets were small, real-time information wasn’t needed, and collaboration wasn’t as important. Open-source programming languages can help companies make better use of their data, and many roles now require coding knowledge to be data fluent.

One of our data scientists, Chris Cardillo, says, “If somebody spends more than four hours a day in Excel, they will undoubtedly benefit from learning Python/R.”

Evaluating your workforce through this lens is useful—it compares the need for programming to a pattern easily identifiable in the workplace. In fact, the ability to drastically improve work outcomes with open-source code is exactly why Chris became a data scientist.

source: xkcd

Python improves work for everyone

Python is widely used and is one of the leading programming languages ​​in the fields of data science, web development, system management, and automated scripting.

Python out of the box allows users to store, access, and manipulate data. It also has a large and growing ecosystem that contains various open-source packages and libraries. This means that different roles can use Python for different purposes.

Python is suitable for almost all industries, including healthcare, finance, technology, and consulting.

The healthcare industry is using machine learning algorithms in Python to prevent and diagnose diseases and optimize hospital operations. Farmers use Python with the help of IoT technology to predict yield and manage crop pests and diseases.

Python is one of the most popular business analysis languages ​​today, and it continues to grow at an alarming rate. It is generally regarded as one of the easiest programming languages ​​to read and learn; its programming syntax is very simple, and its commands imitate English.

Why is Python useful for business analytics?

For organizations and data analysts who spend most of their time in Excel, switching to Python is a good idea.

Why use Python when any programming language can achieve the goal?

IT enthusiasts know that Python is the best language to use for automating scripting, web development, data science, and systems administration applications.

Python also allows users to access, store, and manipulate data.

This allows Python to be used to write applications for different verticals and fields: finance, healthcare, consulting, RPA, artificial intelligence, and technology.

Here are some reasons why you can switch to Python to write business analytics applications.

Ability to Automate and Replicate

ility to Automate and Replicate

Business analysts do not like to manually repeat complex analysis tasks.

They are more willing to delegate these tasks to dedicated assistants. This is where Python comes in.

Python helps to easily automate and replicate the process in the shortest time possible.

The Python application can be effectively used for tasks such as web scraping or online data collection, handling text errors when combining different data sets, and repeating complex analysis tasks multiple times.

The Python script is simple and easy to read, keeping everyone on the team consistent.


The Python language
has a simple syntax and can write commands like English. This is a great attraction for programmers.

You can learn Python through online courses.


Python is a general-purpose programming language, which means that it can be used to create web and desktop applications.

Language can also be used to code complex scientific and digital applications.

This shows its versatility.

The data is too large to fit in the Excel spreadsheet.

This data type has a million rows of information (SKU details, customer information, etc.) and can be easily parsed using Python routines.

Advanced Modeling Requirements

You can use Python’s ML function to study business knowledge that is too complex to articulate manually.

  • Price predictions with econometric models
  • Market segmentation using clustering algorithms
  • Product classification using tree-based algorithms
  • Estimation of product price elasticity

Python is the language of choice for creating advanced and flexible models required for these situations.

Make key time-bound decisions (normative analysis)

Predict the content, timing, and reason why certain outcomes may occur, as well as the actions to be taken using this information, which forms the basis of the science of normative analysis .

Decision scientists work in much the same way as data scientists and use the same tools and techniques.

These professionals have created deep learning tools designed to use neural networks to optimize prediction results.

Evaluation of Metrics (Descriptive Analytics) Evaluating business metrics to understand trends is called descriptive analysis, which is a job that is usually performed by data analysts in business organizations. Analysts can do this effectively using Python.

Python’s ability to access, sort, and display data is unparalleled.

Analyzing data, manipulating data with the help of libraries (Pandas), using Matplotlib to create suggestive visualizations, and optimizing workflows, all of which can be performed easily and efficiently. Exploratory data analysis, or EDA, can help companies carefully review the data before making assumptions.

EDA ensures that it can help analysts produce effective results that can be applied to the required business results and goals.

Availability of learning resources

Python is an open-source language, and many skilled programmers have made valuable contributions to existing resources.

Therefore, when a programmer encounters a problem, it is likely that others have already faced the same challenge.

Python is well documented and there are many resources to learn from. For coders, help is always available, and they are likely to find a ready-made solution soon.

The availability of excellent out-of-the-box analysis tools is different from developers choosing to design new tools. As we all know, analysts use many Python libraries that provide ready-made analysis tools. For example, there are many ML and DL tools that are readily available and can be used like

. This open-source Python coding system helps to significantly improve work results.

Predicting performance and results (predictive analysis)

The next important job of any analyst is to be able to predict what will happen to the business in the future.

Statistical models and optimization algorithms are used here. Forecasting the future based on current data and available information can help identify important relationships and gain insight into the direction of business development.

Machine learning is important here, and Python is undoubtedly a language that can be used to write useful applications such as decision trees and Bayesian networks.

TensorFlow (Google) is a popular Python library that allows business analysts and coders to access machine learning algorithms (supervised and unsupervised).

All the preceding paragraphs are sufficient to show that Python has become one of the most popular languages ​​for writing business analysis applications.

Today, Python has become a potential data management and processing solution for building applications that help businesses quickly understand artificial intelligence and machine learning, neural networks, deep learning, and big data.

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