Learning data analytics is never easy, and there are countless tools and resources available. As a result, it can sometimes be difficult to figure out what skills to learn and which tools to use.
In this article, we’ll give you an overview of the 10 most commonly used Python libraries for data analysis. See if you have used any of these libraries? Like this article, like to support, welcome to the end of the technical exchange learning.
01、Pandas
In the daily work of data analysts, 70% to 80% involve understanding and cleaning data, that is, data exploration and data mining.
Pandas is mainly used for data analysis, which is one of the most commonly used Python libraries. It provides you with some of the most useful tools for exploring, cleaning and analyzing data. Using Pandas, you can load, prepare, manipulate, and analyze all kinds of structured data.
02、NumPy
NumPy is mainly used to support N-dimensional arrays. These multi-dimensional arrays are 50 times more robust than Python lists, which makes NumPy a favorite of many data scientists.
NumPy is used by other libraries such as TensorFlow for internal computation of tensors. NumPy provides fast pre-compiled functions for numerical routines that can be difficult to solve manually. For better efficiency, NumPy uses array-oriented computation, which enables easy handling of multiple classes.
03、Scikit-learn
Scikit-learn is arguably the most important machine learning library in Python. After cleaning and processing data with Pandas or NumPy, Scikit-learn can be used to build machine learning models, due to the fact that Scikit-learn contains a large number of tools for predictive modeling and analysis.
There are many advantages to using Scikit-learn. For example, you can use Scikit-learn to build several types of machine learning models, including supervised and unsupervised models, cross-validate the accuracy of models, and perform feature importance analysis.
04. Gradio
Gradio allows you to build and deploy web applications for machine learning models with just three lines of code. It serves the same purpose as Streamlight or Flask, but deploying models is much faster and easier.
The advantages of Gradio are the following.
Allows further model validation. Specifically, different inputs in the model can be tested in an interactive way
Easy to demonstrate
Easy to implement and distribute, anyone can access the web application through a public link.
05. TensorFlow
TensorFlow is one of the most popular Python libraries for implementing neural networks. It uses multidimensional arrays, also known as tensors, to perform multiple operations on specific inputs.
This feature of TensorFlow is also known as pipelining.
06. Keras
Keras is mainly used to create deep learning models, especially neural networks. It is built on top of TensorFlow and Theano and can be used to simply build neural networks. However, since Keras uses back-end infrastructure to generate computational graphs, it is relatively slow compared to other libraries.
07、SciPy
SciPy is mainly used for its scientific functions and mathematical functions derived from NumPy. The library provides functions such as statistical functions, optimization functions and signal processing functions. In order to solve differential equations and provide optimization, it includes functions for numerical calculation of the integral. sciPy’s advantages are
Multidimensional image processing
the ability to solve Fourier transforms and differential equations
Very robust and efficient linear algebra calculations thanks to its optimization algorithms
08. Statsmodels
Statsmodels is a library that excels in core statistics. This versatile library mixes the functionality of many Python libraries, such as graphical properties and functions from Matplotlib; data processing; using Pandas, for R-like formulas; using Pasty, and is built on NumPy and SciPy.
Specifically, it is very useful for creating statistical models such as OLS and for performing statistical tests.
09. Plotly
Plotly is an absolutely essential tool for building visualizations, it is very powerful, easy to use, and can interact with visualizations.
Dash is a web-based Python interface that addresses the need for JavaScript in such analytical web applications and allows you to plot both online and offline.
10、Seaborn
Seaborn is built on Matplotlib, a library that enables the creation of different visualizations.
One of Seaborn’s most important features is the creation of zoomed-in data visualizations. This allows initially obscure and relevant properties to be highlighted, enabling data workers to understand the model more correctly.
Seaborn also has customizable themes and interfaces, and provides design-aware data visualizations for better data reporting.
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