in [ ]expanding_mean lambda xrolling_mean(xlen( )min_periods= calling rolling_mean and friends on dataframe applies the transformation to each column (see figure - )in [ ]pd rolling_mean(close_px plot(logy=truefigure - stocks prices -day ma (log -axissee table - for listing of related functions in pandas table - moving window and exponentially-weighted functions function description rolling_count returns number of non-na observations in each trailing window rolling_sum moving window sum rolling_mean moving window mean rolling_median moving window median rolling_varrolling_std moving window variance and standard deviationrespectively uses denominator rolling_skewrolling_kurt moving window skewness ( rd momentand kurtosis ( th moment)respectively rolling_minrolling_max moving window minimum and maximum rolling_quantile moving window score at percentile/sample quantile rolling_corrrolling_cov moving window correlation and covariance rolling_apply apply generic array function over moving window ewma exponentially-weighted moving average ewmvarewmstd exponentially-weighted moving variance and standard deviation ewmcorrewmcov exponentially-weighted moving correlation and covariance moving window functions www it-ebooks info