G2

Furkan A.

Data scientist

Computer Software

Mid-Market (51-1000 emp.)

6/9/2026

"A Beginner-Friendly Language with Powerful Libraries"

5/5

What do you like best about Python?

Python has become an essential part of my work. It allows me to analyze data efficiently, build machine learning solutions, automate routine tasks, and deliver insights faster. Its simplicity, versatility, and strong community support make it one of the best programming languages I have worked with and a tool I rely on every day. Python has helped me become much more productive in my work as a Data Scientist because it saves time, simplifies complex tasks, and provides powerful tools for working with data and machine learning. Out of all the programming languages I have used, Python is one of the most practical and efficient for solving real-world problems. I use Python regularly for data analysis, machine learning projects, data visualization, and personal automation tasks. One of the things I like most about Python is its readability. When I first started learning it, I was surprised by how quickly I could understand the syntax and begin writing useful programs. The code is clean and easy to follow, which makes development faster and reduces the time spent debugging. In my day-to-day work as a Data Scientist, Python plays a major role throughout the entire data lifecycle. I use it to collect data from different sources, clean and preprocess datasets, handle missing values, perform exploratory data analysis, and create visualizations that help identify trends and patterns. Python allows me to transform raw data into meaningful insights that can support better decision-making. I also use Python extensively for building machine learning models. Whether I am working on classification, regression, clustering, or predictive analytics problems, Python provides reliable libraries and frameworks that make the process efficient. From preparing training data to evaluating model performance and generating predictions, Python helps streamline the entire workflow. Another reason I enjoy using Python is its rich ecosystem of libraries. Tools such as Pandas and NumPy make data manipulation and numerical analysis much easier. Matplotlib and Seaborn help create clear visualizations that communicate insights effectively. Scikit-learn provides a wide range of machine learning algorithms and evaluation techniques that allow me to build models quickly without needing to implement everything from scratch. These libraries significantly reduce development time and allow me to focus more on solving business problems rather than writing repetitive code. Python is also extremely useful for automation. I often use it to automate repetitive tasks such as data cleaning, report generation, file processing, and workflow management. Automating these activities helps save valuable time and improves overall productivity. What stands out most about Python is that it is beginner-friendly while still being powerful enough for advanced projects. Its syntax is simple and intuitive, almost like reading plain English, which makes it easier for new programmers to learn. At the same time, it offers the flexibility and scalability needed for professional data science, machine learning, and artificial intelligence projects. Review collected by and hosted on G2.com.

What do you dislike about Python?

Python is very useful for my work, but there are a few areas where it could be improved. One challenge I have noticed while working as a Data Scientist is performance when handling very large datasets or training complex machine learning models. For most day-to-day analysis tasks, Python performs well, but when working with millions of records or computationally intensive models, processing can sometimes take longer than expected compared to lower-level programming languages. Another issue I occasionally face is debugging. In data science projects, code often consists of multiple data processing steps, feature engineering pipelines, and model training workflows. Sometimes an error does not appear immediately and only shows up after a long-running process has completed. Finding the root cause can take extra time, especially when working with large datasets or complex notebooks. Library and dependency management can also be challenging. One of Python's biggest strengths is its vast ecosystem of libraries, but keeping all dependencies compatible is not always straightforward. I have experienced situations where a project worked perfectly with one version of a library, but after an update, certain functions behaved differently or produced unexpected errors. Managing environments and package versions becomes very important when working on production-level projects. I have also noticed that memory usage can sometimes be high when processing large datasets. While libraries such as Pandas make data analysis very convenient, working with large amounts of data often requires additional optimization techniques to maintain good performance. Despite these challenges, Python remains my preferred programming language. The advantages far outweigh the drawbacks. Its simple syntax, extensive collection of libraries, strong community support, and versatility make it an excellent choice for data analysis, machine learning, data visualization, automation, and AI development. Python allows me to focus more on solving business problems and extracting insights from data rather than spending time dealing with unnecessary complexity. For these reasons, it continues to be one of the most valuable tools I use in my work every day. Review collected by and hosted on G2.com.

What problems is Python solving and how is that benefiting you?

Python has become one of the most important tools in my work. It helps me work faster, improve accuracy, automate repetitive processes, and build data-driven solutions more efficiently. Its simplicity, flexibility, and extensive ecosystem of libraries make it an excellent choice for data science, machine learning, and everyday problem-solving. Python has significantly improved my productivity by making data analysis, machine learning, and automation tasks much more efficient. It reduces manual effort and helps me complete complex tasks with less time and code compared to many other tools. In my daily work as a Data Scientist, I regularly use Python to handle data preparation, analysis, visualization, and machine learning workflows. Before using Python, many tasks required multiple manual steps and a significant amount of time. Now, I can automate much of that work through scripts, allowing me to complete projects faster and more accurately. One of the biggest benefits is in data analysis. I frequently work with datasets from different sources, and Python makes it easy to load, clean, transform, and analyze data. Libraries such as Pandas and NumPy help me process large amounts of information efficiently. Instead of spending hours manually organizing data, I can focus on understanding trends, identifying patterns, and generating meaningful insights. Python is also extremely valuable for machine learning projects. I use it to prepare training data, build predictive models, evaluate performance, and test different approaches. Libraries like Scikit-learn provide ready-to-use algorithms and tools that simplify the entire machine learning process. This allows me to focus more on solving business problems and improving model performance rather than writing complex code from scratch. Another area where Python has a major impact is automation. I often automate repetitive tasks such as file processing, data validation, report generation, and routine calculations. Tasks that previously required manual effort can now be completed automatically in a matter of minutes. This not only saves time but also reduces the possibility of human error. Python also helps me communicate insights more effectively through data visualization. Using libraries such as Matplotlib and Plotly, I can create charts and reports that make complex data easier to understand for both technical and non-technical audiences. Review collected by and hosted on G2.com.