用于数据分析、时间序列和统计的强大数据结构
workbenchdata-pandas的Python项目详细描述
^ {STR 1 } $熊猫/<强>是一个Python程序包，提供快速、灵活和有表现力的数据。 设计用于与结构化（表格，多维， 潜在的异构）和时间序列数据既简单又直观。IT 目标是成为实践的基础性高层建筑， real worldpython中的数据分析。此外，它还有一个更广泛的目标 成为^ {STR 1 } $最强大和最灵活的开源数据分析/ 任何语言都提供操作工具。已经在路上了 为了这个目标。
熊猫非常适合多种不同类型的数据：
- Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
- Ordered and unordered (not necessarily fixed-frequency) time series data.
- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
- Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure
熊猫系列（一维）和数据框架的两种主要数据结构 （二维），处理金融领域的绝大多数典型用例， 统计学、社会科学和许多工程领域。对于r用户， dataframe提供了r的data.frame所提供的一切 更多。熊猫是建立在NumPy之上的 旨在与许多 其他第三方库。
以下是一些熊猫做得很好的事情：
- Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
- Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
- Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
- Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
- Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
- Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
- Intuitive merging and joining data sets
- Flexible reshaping and pivoting of data sets
- 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.
这些原则中有许多是为了经常解决这些缺点 有使用其他语言/科学研究环境的经验。对于数据 科学家处理数据通常分为多个阶段： 咀嚼和清理数据，对其进行分析/建模，然后组织结果 使分析成为适合于绘图或表格显示的形式。熊猫是 所有这些任务的理想工具。
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