This　paper　addresses　the　adaptive　wavelet　estimations　for　density　derivatives　by　using　data-driven　methods.　Based　on　the　classical　linear　wavelet　estimator　of　density　derivatives,　we　provide　a　point-wise　estimation　under　the　local　H　&　ouml;lder　condition　firstly.　Moreover,　we　introduce　a　data-driven　wavelet　estimator　for　adaptivity　and　prove　a　point-wise　oracle　inequality,　which　does　not　require　any　assumption　on　the　underlying　function.　Finally,　by　using　the　point-wise　oracle　inequality,　the　point-wise　estimation　under　the　local　Holder　condition　and　L-p-risk　(1　＜=　p　＜　infinity)　estimation　on　Besov　spaces　are　investigated　respectively.