the result dataframe depicted in figure - clearly shows the quantile based bin range and corresponding label assigned for each developer income value in the income_quantile_range and income_quantile_labels featuresrespectively statistical transformations let' look at different strategy of feature engineering on numerical data by using statistical or mathematical transformations in this sectionwe will look at the log transform as well as the box-cox transform both of these transform functions belong to the power transform family of functions these functions are typically used to create monotonic data transformationsbut their main significance is that they help in stabilizing varianceadhering closely to the normal distribution and making the data independent of the mean based on its distribution several transformations are also used as part of feature scalingwhich we cover in future section log transform the log transform belongs to the power transform family of functions this function can be defined as logb(xwhich reads as log of to the base is equal to this translates to by xwhich indicates as to what power must the base be raised to in order to get the natural logarithm uses the base where popularly known as euler' number you can also use base used popularly in the decimal system log transforms are useful when applied to skewed distributions as they tend to expand the values which fall in the range of lower magnitudes and tend to compress or reduce the values which fall in the range of higher magnitudes this tends to make the skewed distribution as normal-like as possible let' use log transform on our developer income feature from our coder survey dataset in [ ]fcc_survey_df['income_log'np log(( fcc_survey_df['income'])fcc_survey_df[['id ''age''income''income_log']iloc[ : out[ ]id age income income_log cdb bec dd eab bbf aa fd ffede dff db bddc dc ed eb ab bb the dataframe obtained in this output depicts the log transformed income feature in the income_log field let' now plot the data distribution of this transformed feature using the following code in [ ]income_log_mean np round(np mean(fcc_survey_df['income_log']) figax plt subplots(fcc_survey_df['income_log'hist(bins= color='# 'plt axvline(income_log_meancolor=' 'ax set_title('developer income histogram after log transform'fontsize= ax set_xlabel('developer income (log scale)'fontsize= ax set_ylabel('frequency'fontsize= ax text( '$\mu$='+str(income_log_mean)fontsize=