data aggregation and group operations categorizing data set and applying function to each groupwhether an aggregation or transformationis often critical component of data analysis workflow after loadingmergingand preparing data seta familiar task is to compute group statistics or possibly pivot tables for reporting or visualization purposes pandas provides flexible and high-performance groupby facilityenabling you to slice and diceand summarize data sets in natural way one reason for the popularity of relational databases and sql (which stands for "structured query language"is the ease with which data can be joinedfilteredtransformedand aggregated howeverquery languages like sql are rather limited in the kinds of group operations that can be performed as you will seewith the expressiveness and power of python and pandaswe can perform much more complex grouped operations by utilizing any function that accepts pandas object or numpy array in this you will learn how tosplit pandas object into pieces using one or more keys (in the form of functionsarraysor dataframe column namescomputing group summary statisticslike countmeanor standard deviationor user-defined function apply varying set of functions to each column of dataframe apply within-group transformations or other manipulationslike normalizationlinear regressionrankor subset selection compute pivot tables and cross-tabulations perform quantile analysis and other data-derived group analyses www it-ebooks info