becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many different kinds 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 nested dict into DataFrame (GH212) • VBENCH Significantly speed up DataFrame __repr__ and count on large mixed-type DataFrame objects 1.6 v.0.4.3 through v0.4.1 (September 25 - October 9, 2011) 1.6.1 New

becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many different kinds 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 nested dict into DataFrame (GH212) • VBENCH Significantly speed up DataFrame __repr__ and count on large mixed-type DataFrame objects 1.7 v.0.4.3 through v0.4.1 (September 25 - October 9, 2011) 1.7.1 New

becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many different kinds 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 evaluation was made that propagating NA everywhere, including in numerical arrays, would cause a large amount of problems for users. Thus, a “practicality beats purity” approach was taken. This issue may

high-performance, easy-to-use data structures and data analysis tools for the Python programming language. See the overview for more detail about whats in the library. CONTENTS 1 pandas: powerful Python the Anaconda distribution is built upon. It is a package manager that is both cross-platform and language agnostic (it can play a similar role to a pip and virtualenv combination). 5 pandas: powerful achieve large speedups. If installed, must be Version 2.6.2 or higher. • bottleneck: for accelerating certain types of nan evaluations. bottleneck uses specialized cython routines to achieve large speedups

high-performance, easy-to-use data structures and data analysis tools for the Python programming language. See the overview for more detail about what’s in the library. CONTENTS 1 pandas: powerful Python maximum of 60 rows (the display.max_rows option). However, this still gives a repr that takes up a large part of the vertical screen estate. Therefore, a new option display.min_rows is introduced with a content of relatively small objects (e.g. df.head(20) shows all 20 rows), while giving a brief repr for large objects. To restore the previous behaviour of a single threshold, set pd.options.display.min_rows

becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many different kinds 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 register_matplotlib_converters`() (GH18301) 1.1.4 Performance Improvements • Improved performance of plotting large series/dataframes (GH18236). 1.1.5 Bug Fixes 1.1.5.1 Conversion • Bug in TimedeltaIndex subtraction

high-performance, easy-to-use data structures and data analysis tools for the Python programming language. See the overview for more detail about what’s in the library. CONTENTS 1 pandas: powerful Python maximum of 60 rows (the display.max_rows option). However, this still gives a repr that takes up a large part of the vertical screen estate. Therefore, a new option display.min_rows is introduced with a content of relatively small objects (e.g. df.head(20) shows all 20 rows), while giving a brief repr for large objects. To restore the previous behaviour of a single threshold, set pd.options.display.min_rows

evaluation via eval() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 942 2.24 Scaling to large datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 951 2.24 high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Getting started New to pandas? Check out the getting started guides. They contain an introduction Learn the pandas-equivalent operations compared to software you already know: The R programming language provides the data.frame data structure and multiple packages, such as tidyverse use and extend data

evaluation via eval() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904 2.24 Scaling to large datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 912 2.24 high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Getting started New to pandas? Check out the getting started guides. They contain an introduction Learn the pandas-equivalent operations compared to software you already know: The R programming language provides the data.frame data structure and multiple packages, such as tidyverse use and extend data

evaluation via eval() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 943 2.24 Scaling to large datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 952 2.24 high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Getting started New to pandas? Check out the getting started guides. They contain an introduction Learn the pandas-equivalent operations compared to software you already know: The R programming language provides the data.frame data structure and multiple packages, such as tidyverse use and extend data