Python is a popular programming language in the field of data science and data analytics due to its simplicity and versatile applications. Its vast community of developers provides ample support and resources for users to tackle any issues they may face. Here are the 10 essential Python libraries for data science in 2023:
NumPy:
This library allows for the manipulation of multi-dimensional arrays, which is a crucial aspect of data analysis. It is used to optimize RAM usage and runtime performance.
SciPy:
SciPy is a computing package that includes tools for mathematical operations such as linear algebra, optimization, and statistics. It is built on NumPy and is commonly used in scientific computation jobs.
Theano:
This Python package is based on NumPy and allows for the manipulation and analysis of mathematical expressions, particularly matrix-valued expressions. It is used in computer vision and intensive learning.
Pandas:
One of the most popular libraries for data analysis, Pandas provides data manipulation and analysis features for data structures. It is used in recommendation systems, advertising, and natural language processing.
Matplotlib:
Matplotlib is a tool for data analysis and plotting, allowing for the creation of static, animated, and live displays. It is heavily used in data visualization with third-party modules.
Plotly:
Plotly is a charting and graphing library that allows for the creation of low-code applications for building and deploying data apps in Python.
Seaborn:
Seaborn is a high-level interface built on top of Matplotlib that provides statistical diagrams and appealing visualizations. It is used in various IDEs to visualize data.
Ggplot:
Ggplot is a tool designed for creating fast graphics, regardless of the complexity of the source data. It is available in Python as part of the plotnine module.
Altair:
Altair is a declarative statistical visualization tool built on the Vega visualization language. It is used to display graphs for data sets with fewer than 5,000 rows.
Autoviz:
Autoviz automatically visualizes collections of data, making it easier to comprehend data in a variety of fields.
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