Image by Author | CanvaDespite the rapid advancements in data science, many universities and institutions still rely heavily on tools like Excel and SPSS for statistical analysis and reporting. While these platforms have served their purpose for decades, sticking solely to them means missing out on the simplicity, power, and flexibility that modern Python tools offer.
In this article, we will explore 7 essential Python tools that data scientists are actually using in 2025. These tools are transforming the way analytical reports are created, statistical problems are solved, research papers are written, and advanced data analyses are performed.
7 Python Statistics Tools
If you are still living in the past with legacy software, it is time to discover what Python can do for your workflow.
1. Python’s Built-in Statistics Module: Quick and Easy Stats
Python’s built-in statistics module provides simple functions for calculating mean, median, mode, variance, and more. It is perfect for quick statistical analysis without any external dependencies, making it a handy tool for small datasets and basic exploratory work.
import statistics as stats
2. NumPy: The Foundation of Numerical Computing
NumPy is the backbone of scientific computing in Python. It is the most widely used package, and most machine learning and data analytics Python packages depend on it. NumPy offers powerful array operations, mathematical functions, and random number capabilities, making it essential for statistical analysis and data manipulation.
Learn more: https://numpy.org/
3. Pandas: Data Analysis and Manipulation Made Simple
Pandas is the go-to library for data manipulation and analysis. While working as a data scientist, I use it every day for loading data, processing it, cleaning it, and performing data analysis. With its intuitive DataFrame structure, Pandas makes it easy to clean, transform, and analyze data, including powerful groupby operations and built-in statistical methods.
Learn more: https://pandas.pydata.org/
4. SciPy: Advanced Statistical Functions and More
SciPy builds on NumPy and provides a wide range of advanced statistical functions, probability distributions, and hypothesis testing capabilities. It is essential for anyone performing scientific or statistical computing in Python.
Learn more: https://scipy.org/
5. Statsmodels: In-Depth Statistical Modeling
Statsmodels is designed for statistical modeling and hypothesis testing. It offers tools for linear and nonlinear regression, time series analysis, and statistical tests. While NumPy and Pandas are great, to get the most out of them, you should also use Statsmodels for tasks like simple linear regressions, forecasting, time series analysis, and more.
Learn more: https://www.statsmodels.org/
6. Scikit-learn: Machine Learning Meets Statistics
Scikit-learn is one of the most popular libraries for machine learning, but it also provides a suite of statistical tools for data preprocessing, feature selection, and model evaluation. Its user-friendly API and integration with NumPy and Pandas make it a go-to tool for various workflows. Even in simple analytical projects, we often use Scikit-learn to convert categorical features into numerical ones, normalize the data, and more.
Learn more: https://scikit-learn.org/
7. Matplotlib: Visualizing Statistical Insights
Matplotlib is the standard Python library for data visualization. It allows you to create a wide range of plots and charts, making it easy to visualize statistical distributions, trends, and relationships in your data. As a core Python package, it is heavily relied upon by other visualization libraries like Seaborn and Plotly.
Learn more: https://matplotlib.org/
Final Thoughts
In the age of AI, statistical analysis is far from obsolete, in fact, it’s more important than ever. Data scientists and analysts still rely on statistical tools to deeply understand data, interpret results, and create highly valuable reports. While AI-powered platforms can automate and accelerate many aspects of data analysis, the backbone of these systems remains the tried-and-true Python libraries and statistical methods that experts have trusted for years.
So, while the landscape of data analysis is rapidly changing, Python’s statistical tools are here to stay, and mastering them will keep you at the forefront of data science.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
