for exampleyou can plot normal probability density function of continuous data on the black curve shown in the graphbut if we were to quantize that into discrete dataset like we would do with histogramwe can say the number occurs some set number of timesand you can actually say the number has little over chance of occurring so probability mass function is the way that we visualize the probability of discrete data occurringand it looks lot like histogram because it basically is histogram terminology differencea probability density function is solid curve that describes the probability of range of values happening with continuous data probability mass function is the probabilities of given discrete values occurring in dataset types of data distributions let' look at some real examples of probability distribution functions and data distributions in general and wrap your head little bit more around data distributions and how to visualize them and use them in python go ahead and open up the %jtusjcvujpotjqzoc from the book materialsand you can follow along with me here if you' like uniform distribution let' start off with really simple exampleuniform distribution uniform distribution just means there' flat constant probability of value occurring within given range jnqpsuovnqzbtoq *nqpsunbuqmpumjcqzqmpubtqmu wbmvft oqsboepnvojgpsn qmuijtu wbmvftqmutipx