Python数据分析工具包:Pandas
2012-12-22 13:43
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Python数据分析工具包:Pandas - OPEN 开发经验库
Python Data Analysis Library 或 pandas 是连接 SciPy 和 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。Comma-separated values (CSV) 文件表示在有关各方之间分发数据的最常见的方法之一。Pandas 提供了一种优化库功能来读写多种文件格式,包括 CSV 和高效的 HDF5 格式。
A fast and efficient DataFrame object for data manipulation with integrated indexing;
Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;
Flexible reshaping and pivoting of data sets;
Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
Columns can be inserted and deleted from data structures for size mutability;
Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
High performance merging and joining of data sets;
Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;
The library has been ruthlessly optimized for performance, with critical code paths compiled to C;
Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.
项目主页:http://www.open-open.com/lib/view/home/1351563594442
Python数据分析工具包:Pandas
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A fast and efficient DataFrame object for data manipulation with integrated indexing;
Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;
Flexible reshaping and pivoting of data sets;
Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
Columns can be inserted and deleted from data structures for size mutability;
Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
High performance merging and joining of data sets;
Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;
The library has been ruthlessly optimized for performance, with critical code paths compiled to C;
Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.
项目主页:http://www.open-open.com/lib/view/home/1351563594442
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