max count mean - std min - - - - max - will explain in more detail what has happened here in the next major section on groupwise operations and transformations you may notice that custom aggregation functions are much slower than the optimized functions found in table - this is because there is significant overhead (function callsdata rearrangementin constructing the intermediate group data chunks table - optimized groupby methods function name description count number of non-na values in the group sum sum of non-na values mean mean of non-na values median arithmetic median of non-na values stdvar unbiased ( denominatorstandard deviation and variance minmax minimum and maximum of non-na values prod product of non-na values firstlast first and last non-na values to illustrate some more advanced aggregation featuresi'll use less trivial dataseta dataset on restaurant tipping obtained it from the reshape packageit was originally found in bryant smith' text on business statistics (and found in the book' github repositoryafter loading it with read_csvi add tipping percentage column tip_pct in [ ]tips pd read_csv('ch /tips csv'add tip percentage of total bill in [ ]tips['tip_pct'tips['tip'tips['total_bill'in [ ]tips[: out[ ]total_bill tip sex smoker female no male no day sun sun time size tip_pct dinner dinner data aggregation www it-ebooks info