figure - custom age binning for developer ages we can see from the dataframe output in figure - that the custom bins based on our scheme have been assigned for each developer' age try out some of your own binning schemesa daptive binning so farwe have decided the bin width and ranges in fixed-width binning howeverthis technique can lead to irregular bins that are not uniform based on the number of data points or values which fall in each bin some of the bins might be densely populated and some of them might be sparsely populated or even be emptyadaptive binning is safer and better approach where we use the data distribution itself to decide what should be the appropriate bins quantile based binning is good strategy to use for adaptive binning quantiles are specific values or cut-points which help in partitioning the continuous valued distribution of specific numeric field into discrete contiguous bins or intervals thusq-quantiles help in partitioning numeric attribute into equal partitions popular examples of quantiles include the -quantile known as the median which divides the data distribution into two equal bins -quantiles known as the quartileswhich divide the data into four equal bins and -quantiles also known as the deciles which create equal width bins let' now look at slice of data pertaining to developer income values in our coder survey dataset in [ ]fcc_survey_df[['id ''age''income']iloc[ : out[ ]id age income cdb bec dd eab bbf aa fd ffede dff db bddc dc ed eb ab bb the slice of data depicted by the dataframe shows us the income values for each developer in our dataset let' look at the whole data distribution for this income variable now using the following code in [ ]figax plt subplots(fcc_survey_df['income'hist(bins= color='# 'ax set_title('developer income histogram'fontsize= ax set_xlabel('developer income'fontsize= ax set_ylabel('frequency'fontsize=