Pandas : a Python Data Analysis LibraryMcKinney, Wes (Aqr)
1、Python Data Analysis Library 或 pandas 是基于NumPy 的一种工具,该工具是为了解决数据分析任务而创建的。Pandas 纳入了大量库和一些标准的数据模型,提供了高效地操作大型数据集所需的工具。 pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现,它是使Python成为强大而高效的数据分析环境的重...
SciPy: This is a scientific computing library that provides science and engineering related functions. SciPy supplements and slightly overlaps NumPy. NumPy and SciPy historically shared their code base but were later separated. Pandas: This is a data-manipulation library that provides data structures an...
Python Data Analysis Library可根据需要帮助组织各种参数的数据。 各种内置数据类型(如serie,frame和panels)使Pandas成为数据科学家中最受欢迎的库。 帧表格格式允许对数据进行类似数据库的添加/删除操作,分组很容易。 此外,Pandas提供了三维面板数据结构,有助于更好地可视化数据类型。 灵活支持多种数据格式,包括缺少数...
Pandas(Python Data Analysis Library)是基于NumPy 的一种工具,该工具是为了解决数据分析任务而创建的(让以Numpy为中心的应用变得更加简单)。Pandas 纳入了大量库和一些标准的数据模型,提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现,它是使Python成为强...
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more - pandas-dev/pandas
Numerical computation is a critical aspect of scientific computing and data analysis, and NumPy works as a reliable Python library equipped with robust data structures (ndarray or n-dimensional array) to support the same. NumPy provides fast and efficient processing of multidimensional arr...
上QQ阅读APP,阅读体验更流畅 领看书特权 Summary In this chapter, we focused on Pandas--a Python data analysis library. This was an introductory tutorial about the basic Pandas features and data structures. We saw how a great deal of the Pandas functionality mimics relational database tables, allo...
scikit-learn provides a library of transformers, which may clean (see Preprocessing data), reduce (see Unsupervised dimensionality reduction), expand (see Kernel Approximation) or generate (see Feature extraction) feature representations. scikit-learn 提供了数据转换的模块,包括数据清理、降维、扩展和特征提...
Making informative visualizations (sometimes calledplots) is one of the most important tasks in data analysis. It may be a part of the exploratory process—for example, to help identify outliers or needed data transformations, or as a way of generating ideas for models. For others, building an...