. . . . 772 2.18.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773 2.18.2 Iterating through groups . . . . . . . . . . . . . . . . . . . . . . . More general, this fits in the more general split-apply-combine pattern: • Split the data into groups 38 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.3.4 • Apply data into subsets and compute the mean for each. Using a data.frame called df and splitting it into groups by1 and by2: df <- data.frame( v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9), v2 = c(11,33,55,77,88,33,55,NA

. . . . 742 2.18.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743 2.18.2 Iterating through groups . . . . . . . . . . . . . . . . . . . . . . . More general, this fits in the more general split-apply-combine pattern: • Split the data into groups • Apply a function to each group independently • Combine the results into a data structure The data into subsets and compute the mean for each. Using a data.frame called df and splitting it into groups by1 and by2: df <- data.frame( v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9), v2 = c(11,33,55,77,88,33,55,NA

. . . . 771 2.18.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 772 2.18.2 Iterating through groups . . . . . . . . . . . . . . . . . . . . . . . Getting started pandas: powerful Python data analysis toolkit, Release 1.3.3 • Split the data into groups • Apply a function to each group independently • Combine the results into a data structure The data into subsets and compute the mean for each. Using a data.frame called df and splitting it into groups by1 and by2: df <- data.frame( v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9), v2 = c(11,33,55,77,88,33,55,NA

. . . . 679 2.17.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 680 2.17.2 Iterating through groups . . . . . . . . . . . . . . . . . . . . . . . More general, this fits in the more general split-apply-combine pattern: • Split the data into groups • Apply a function to each group independently • Combine the results into a data structure The data into subsets and compute the mean for each. Using a data.frame called df and splitting it into groups by1 and by2: df <- data.frame( v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9), v2 = c(11,33,55,77,88,33,55,NA

. . . . 679 2.17.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 680 2.17.2 Iterating through groups . . . . . . . . . . . . . . . . . . . . . . . More general, this fits in the more general split-apply-combine pattern: • Split the data into groups • Apply a function to each group independently • Combine the results into a data structure The data into subsets and compute the mean for each. Using a data.frame called df and splitting it into groups by1 and by2: df <- data.frame( v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9), v2 = c(11,33,55,77,88,33,55,NA

. . . . 777 2.18.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 778 2.18.2 Iterating through groups . . . . . . . . . . . . . . . . . . . . . . . Getting started pandas: powerful Python data analysis toolkit, Release 1.4.2 • Split the data into groups • Apply a function to each group independently • Combine the results into a data structure The data into subsets and compute the mean for each. Using a data.frame called df and splitting it into groups by1 and by2: df <- data.frame( v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9), v2 = c(11,33,55,77,88,33,55,NA

. . . . 777 2.18.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 778 2.18.2 Iterating through groups . . . . . . . . . . . . . . . . . . . . . . . Getting started pandas: powerful Python data analysis toolkit, Release 1.4.4 • Split the data into groups • Apply a function to each group independently • Combine the results into a data structure The data into subsets and compute the mean for each. Using a data.frame called df and splitting it into groups by1 and by2: df <- data.frame( v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9), v2 = c(11,33,55,77,88,33,55,NA

Tutorials 39 pandas: powerful Python data analysis toolkit, Release 1.5.0rc0 • Split the data into groups • Apply a function to each group independently • Combine the results into a data structure The data into subsets and compute the mean for each. Using a data.frame called df and splitting it into groups by1 and by2: df <- data.frame( v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9), v2 = c(11,33,55,77,88,33,55,NA refers to a process where we’d like to split a dataset into groups, apply some function (typically aggregation) , and then combine the groups together. A common SQL operation would be getting the count

. . . . . . . . . . . . 717 16 Group By: split-apply-combine 721 16.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722 16.1.1 GroupBy sorting selection in GroupBy . . . . . . . . . . . . . . . . . . . . . . . . . . . 728 16.2 Iterating through groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 16.3 Selecting items . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751 16.9.8 Enumerate groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 752 16.9.9 Plotting

. . . . . . . . . . . . 748 16 Group By: split-apply-combine 751 16.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 752 16.1.1 GroupBy sorting selection in GroupBy . . . . . . . . . . . . . . . . . . . . . . . . . . . 758 16.2 Iterating through groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759 16.3 Selecting items . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 781 16.9.8 Enumerate groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 782 16.9.9 Plotting