Pandas 0.25.3: Python数据分析工具库新功能及更新

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"Pandas 是一个强大的 Python 数据分析工具包，版本为 0.25.3，由 Wes McKinney 和 PyData 开发团队于 2019 年 11 月 2 日发布。这个开源软件基于 BSD 许可证，提供了高效且易用的数据结构和数据分析工具，专为 Python 编程语言设计。Pandas 提供了丰富的功能，包括数据清洗、处理、转换和可视化，适用于各种数据分析任务。在 0.25.3 版本中，重点更新了对 Python 3.8 的兼容性（GH28147），修复了一些关键问题。其中，DataFrame.reindex() 方法的使用得到了改进，确保了它能正确遵循限制参数（GH28631）。此外，RangeIndex.get_indexer() 对于递减的 RangeIndex，在目标值可能被错误识别的情况下也进行了修正。第 1 章介绍了 0.25.2 版本的新特性，包括但不限于已修复的bug，这些修复有助于提升用户在实际项目中的体验。为了获取更全面的变更历史和对其他版本的比较，建议查阅完整的 release notes。如果你正在寻找使用 pandas 进行数据分析的最佳实践，可以从概述部分开始，了解库内提供的核心数据结构（如 Series 和 DataFrame）、数据清洗工具（如 dropna 和 merge）、数据操作函数（如 groupby 和 apply）以及高级统计分析功能。同时，文档还包含了如何下载文档的不同格式（PDF 和 Zipped HTML）、安装指南、社区支持资源（如 Binary Installers、Source Repository、Issue Tracker、Q&A、邮件列表等），这对于新用户和开发者来说都是非常有用的。 pandas 0.25.3 版本的发布标志着该工具包在性能和功能上的一次重要迭代，旨在更好地服务于 Python 数据分析社区。无论你是初学者还是经验丰富的数据分析师，掌握并利用好 pandas 将极大地提升你的数据分析能力。"

pandas: powerful Python data analysis toolkit, Release 0.25.3

• Hierarchical labeling of axes (possible to have multiple labels per tick)

• Robust IO tools for loading data from ﬂat ﬁles (CSV and delimited), Excel ﬁles, databases, and saving / loading

data from the ultrafast HDF5 format

• Time series-speciﬁc functionality: date range generation and frequency conversion, moving window statistics,

moving window linear regressions, date shifting and lagging, etc.

Many of these principles are here to address the shortcomings frequently experienced using other languages / scientiﬁc

research environments. For data scientists, working with data is typically divided into multiple stages: munging and

cleaning data, analyzing / modeling it, then organizing the results of the analysis into a form suitable for plotting or

tabular display. pandas is the ideal tool for all of these tasks.

Some other notes

• pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However,

as with anything else generalization usually sacriﬁces performance. So if you focus on one feature for your

application you may be able to create a faster specialized tool.

• pandas is a dependency of statsmodels, making it an important part of the statistical computing ecosystem in

Python.

• pandas has been used extensively in production in ﬁnancial applications.

3.1.1 Data structures

Dimensions Name Description

1 Series 1D labeled homogeneously-typed array

2 DataFrame General 2D labeled, size-mutable tabular structure with potentially

heterogeneously-typed column

Why more than one data structure?

The best way to think about the pandas data structures is as ﬂexible containers for lower dimensional data. For

example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert

and remove objects from these containers in a dictionary-like fashion.

Also, we would like sensible default behaviors for the common API functions which take into account the typical

orientation of time series and cross-sectional data sets. When using ndarrays to store 2- and 3-dimensional data, a

burden is placed on the user to consider the orientation of the data set when writing functions; axes are considered

more or less equivalent (except when C- or Fortran-contiguousness matters for performance). In pandas, the axes are

intended to lend more semantic meaning to the data; i.e., for a particular data set there is likely to be a right way to

orient the data. The goal, then, is to reduce the amount of mental effort required to code up data transformations in

downstream functions.

For example, with tabular data (DataFrame) it is more semantically helpful to think of the index (the rows) and the

columns rather than axis 0 and axis 1. Iterating through the columns of the DataFrame thus results in more readable

code:

for col in df.columns:

series = df[col]

# do something with series

12 Chapter 3. Getting started

pandas: powerful Python data analysis toolkit, Release 0.25.3

3.1.2 Mutability and copying of data

All pandas data structures are value-mutable (the values they contain can be altered) but not always size-mutable. The

length of a Series cannot be changed, but, for example, columns can be inserted into a DataFrame. However, the vast

majority of methods produce new objects and leave the input data untouched. In general we like to favor immutability

where sensible.

3.1.3 Getting support

The ﬁrst stop for pandas issues and ideas is the Github Issue Tracker. If you have a general question, pandas community

experts can answer through Stack Overﬂow.

3.1.4 Community

pandas is actively supported today by a community of like-minded individuals around the world who contribute their

valuable time and energy to help make open source pandas possible. Thanks to all of our contributors.

If youre interested in contributing, please visit the contributing guide.

pandas is a NumFOCUS sponsored project. This will help ensure the success of development of pandas as a world-

class open-source project, and makes it possible to donate to the project.

3.1.5 Project governance

The governance process that pandas project has used informally since its inception in 2008 is formalized in Project

Governance documents. The documents clarify how decisions are made and how the various elements of our commu-

nity interact, including the relationship between open source collaborative development and work that may be funded

by for-proﬁt or non-proﬁt entities.

Wes McKinney is the Benevolent Dictator for Life (BDFL).

3.1.6 Development team

The list of the Core Team members and more detailed information can be found on the peoples page of the governance

repo.

3.1.7 Institutional partners

The information about current institutional partners can be found on pandas website page.

3.1.8 License

BSD 3-Clause License

,→Development Team

Redistribution and use in source and binary forms, with or without

modification, are permitted provided that the following conditions are met:

(continues on next page)

3.1. Package overview 13

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