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Now, let's start for missing value treatment in Python.

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So this photo, what observations from Oord EBD.

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We have discussed point two and three in our outlier video.

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Now we will be discussing about this other missing we are losing and Hause, but it's variable.

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The best way to identify missing value is just to take in full of our data frame will be if not in full.

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Here you will see the total number of values for all the variables.

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There is only one variable, which is an Herzberg's, where the total number of values is less than

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five zero six.

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Therefore, this confirms that and Horsburgh contain missing values.

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You can.

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Also, look at duty to identify missing values.

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But then a duty you only get.

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Values of numerical variables.

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This will give you details of categorical variables as one.

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Greeting missing, well, it was fairly easy in Biton.

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To impute missing values, we will use.

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Fail and they function.

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So we'll rate be, if not our column name, which is and also birds.

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And.

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We.

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Equal to since we want our imputed data to again change the original values in our data.

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So.

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All right, B.F. Dorte and his words.

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Don't philander.

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And since we want to replace this value with the mean of our hospital beds.

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All right, B.F. Dorte and husbands.

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Not mean what this function is actually doing is.

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Plus, it is calculating the mean awful what really well, which is B.F. and Herzberg's.

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Then whenever I would be, if Dorte and Oz Bags is Zettl or and then it is filling those blanks with

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this mean value.

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And after filling all the missing values, we are again saving this day and Bill are or reasonably Dufrene.

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If on this.

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Missing values are not imputed.

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If we take in full of our day Dufrene food, I'd be if not in full.

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You can see.

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Now, the count is five 06 and set off for 98.

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So we have faith, all of that missing, we're loose with the meaning of that column.

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Now, if you want to do missing, well, you're imputation for all of the columns.

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You have to right, B.F. Dot, fill in there and then record just right.

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F dot mean

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and again, we have to save this data frame again and go out business little frame.

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So we'll read BFE.

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Way to be if.

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Not feel this way, Lou, do for all the columns, but since we want a specific solution for each of

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our columns, as we mentioned, they know what to re elected, that for some variables, we won zero.

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For some variables we won.

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We mean for some variables.

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We want more.

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That's why we have we have used specific variables.

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They impute our missing value.

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This statement is not needed yet, as we have already imputed a lot of missing values.

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Now we'll see what to do about the notebook.

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Go on, change their title of offor notebook.

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And click on, say, streaming, and then you can see that so you're missing well, the imputation and

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Biton.

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Thanks.