Python offers more than 137000 libraries, but a data scientist must learn these 6 libraries to excel in this field. Python offers libraries for Data analysis, Data visualization, and machine learning.
So I have classified libraries into three Categories:
Libraries For Data Analysis
Libraries For Data Visualization
Libraries For Machine Learning
This blog is basically telling you that these are the libraries you must not miss and definitely excel in it. So let’s begin.
Python Libraries For Data Analysis:
NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use.
Numpy library is used for scientifical computing in python. It is a powerful library responsible for calculations on data. The “data” in Data Analysis typically refers to numerical data, e.g., stock prices, sales figures, sensor measurements, sports scores, database tables, etc.
A tutorial For Numpy: How To Use Numpy For Analysing The Data – Console Flare
Pandas is an open-source Python library that allows the handling of tabular data (i.e. explore, clean, and process).
pandas aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open-source data analysis/manipulation tool available in any language.
A tutorial For Pandas: Introduction To Pandas – Console Flare
Python Libraries For Data Visualization:
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.
- Create publication-quality plots.
- Make interactive figures that can zoom, pan, and update.
- Customize visual style and layout.
- Export to many file formats.
- Embed in JupyterLab and Graphical User Interfaces.
- Use a rich array of third-party packages built on Matplotlib.
Learn more about matplotlib: Plot types — Matplotlib 3.5.1 documentation
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Python Libraries For Machine Learning
Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy, and Matplotlib.
SciPy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics, and many other classes of problems.
Data Science mainly consists of Data analysis, visualization, and machine learning. So these are the libraries that you need to learn about.
If you want to learn to analyze data and become a data scientist, we are offering our courses here.
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