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five ways that data scientists deal with missing values letamp;#39;s read some temperature data if we look closely at it we notice that some of these temperatures happen to be null which is also clear when we plot the data if we call drop n a all of those null rows will be removed and you can see here that theyamp;#39;re gone but now we have gaps in our time data if we call fill n a we can fill all the null values with a specific value as seen when plotted all the null values go to zero we can impute the average value by taking the mean of that temperature column in combination with fill in a and we could see that here in this plot because this is time series data we can use fill to forward fill any missing values with the prior value B Phil does the exact same thing but in Reverse by calling interpolate we will fill in the missing values based on an equation solved for the values that we have surrounding it and this is what interpolate looks like when plotted beautiful so now you kn
