Now, let's start for missing value treatment in Python.
So this photo, what observations from Oord EBD.
We have discussed point two and three in our outlier video.
Now we will be discussing about this other missing we are losing and Hause, but it's variable.
The best way to identify missing value is just to take in full of our data frame will be if not in full.
Here you will see the total number of values for all the variables.
There is only one variable, which is an Herzberg's, where the total number of values is less than
five zero six.
Therefore, this confirms that and Horsburgh contain missing values.
You can.
Also, look at duty to identify missing values.
But then a duty you only get.
Values of numerical variables.
This will give you details of categorical variables as one.
Greeting missing, well, it was fairly easy in Biton.
To impute missing values, we will use.
Fail and they function.
So we'll rate be, if not our column name, which is and also birds.
And.
We.
Equal to since we want our imputed data to again change the original values in our data.
So.
All right, B.F. Dorte and his words.
Don't philander.
And since we want to replace this value with the mean of our hospital beds.
All right, B.F. Dorte and husbands.
Not mean what this function is actually doing is.
Plus, it is calculating the mean awful what really well, which is B.F. and Herzberg's.
Then whenever I would be, if Dorte and Oz Bags is Zettl or and then it is filling those blanks with
this mean value.
And after filling all the missing values, we are again saving this day and Bill are or reasonably Dufrene.
If on this.
Missing values are not imputed.
If we take in full of our day Dufrene food, I'd be if not in full.
You can see.
Now, the count is five 06 and set off for 98.
So we have faith, all of that missing, we're loose with the meaning of that column.
Now, if you want to do missing, well, you're imputation for all of the columns.
You have to right, B.F. Dot, fill in there and then record just right.
F dot mean
and again, we have to save this data frame again and go out business little frame.
So we'll read BFE.
Way to be if.
Not feel this way, Lou, do for all the columns, but since we want a specific solution for each of
our columns, as we mentioned, they know what to re elected, that for some variables, we won zero.
For some variables we won.
We mean for some variables.
We want more.
That's why we have we have used specific variables.
They impute our missing value.
This statement is not needed yet, as we have already imputed a lot of missing values.
Now we'll see what to do about the notebook.
Go on, change their title of offor notebook.
And click on, say, streaming, and then you can see that so you're missing well, the imputation and
Biton.
Thanks.