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<speak> Let's do one thing.<break strength="x-strong"/> We can replace missing values with <break strength="weak"/>either mean or median. <break strength="x-strong"/> Let's replace missing values. <break strength="x-strong"/> So here for the glucose column. <break strength="x-strong"/> So, we have created a new data frame diabetes_df_copy. <break strength="x-strong"/>We have copied our original data frame into diabetes_df_copy. <break strength="x-strong"/> We are modifying diabetes_df_copy.<break strength="x-strong"/> We replace all missing values with mean or median.<break strength="x-strong"/> Here for the glucose column, <break strength="strong"/>we are calculating the mean of the values in the glucose column. <break strength="x-strong"/>We are filling NAN values <break strength="weak"/>with this mean value.<break strength="x-strong"/> We are using the fill n a method<break strength="weak"/> to fill missing values.<break strength="x-strong"/> The fill N A function is used to Fill N A or N a N values<break strength="weak"/> using the specified method.<break strength="x-strong"/> Similarly, for the blood pressure column, <break strength="strong"/>we are filling with mean values. <break strength="x-strong"/>For skin thickness, <break strength="strong"/>we are filling N A or NAN values with the median.<break strength="x-strong"/> For the insulin column, <break strength="strong"/>we are filling the NAN values with the median value.<break strength="x-strong"/> For the BMI column, <break strength="strong"/>we are filling that with the median value.<break strength="x-strong"/> So, let's run the cell.<break strength="x-strong"/> We have modified the columns.<break strength="x-strong"/> Now all the columns are without missing values.<break strength="x-strong"/> Now let's create a histogram plot again. <break strength="x-strong"/> </speak>