Hello and welcome to this patented Odean here we will understand how to deal with missing data.
Let us begin first import these two liabilities numpty and pend.
Great.
Now define a dictionary.
The.
He as a den value, as a least one to the null value and P doc and a n.
Then key B and value as a list.
Three.
And B dot.
And A N again.
And B dot and a..
And these two are the NL values.
Then one more key that it c and value as a list.
Four for you and six.
No execute.
Take this dictionary.
Great.
So there are three key value pairs in this dictionary.
The.
Now, using this dictionary to define A data frame B F.
PD dot data frame specified dictionary be executed.
Take this data from D.F..
So this is the data frame.
There are three columns and three rows, column A, column B and column C.
And these three are the rows zero, one and two.
These are the indexes.
Let us understand.
Drop and then method.
A, b, f.
Dot drop and a.
So this is the output.
Let us understand this out to the drop in a meter drops Dinello values.
As you can see here, all the roads with null values are dropped by default.
Access is equal to zero.
Hayne's ordinal values are dropped on the basis of rudes.
We can drop the null values on the basis of columns.
Also.
To do that, a, b, f didn't drop and a method.
Specify access is equal to one.
By default, access is equal to zero.
So this is the output Nele values are dropped on the basis of columns.
Let us understand one more parameter in drop and a method.
The F dot drop.
And they know putting place is equal to two.
Dequeue.
Let us understand this output.
Place is equal to two means, at least two non and they valued.
So there are at least two non in their values in a row.
We can make access is equal to one.
Also.
At the barometer, axis is equal to one.
Great to not in their values in a column.
And when access is equal to zero.
Do none.
And their values in OTRO.
Let us see one more example.
Drop and then nematode.
Specified three showed it equal to one.
At least one nun and a value in Uru.
Similarly, when access is equal to one.
At least one nun in a value in a column.
So this is all about the drop in a method.
Let us understand one more method.
Fill in a method.
Jack dee dee, dee dee dee f.
No, use the Method B F don't feel any.
In parentheses, value is equal to.
New value executed.
So this is the output.
You can see here all the Nele values are replaced by new value.
Previously, there were three NL values.
Now there are three new valued.
So using this method to fill in, they we can replace the null values.
We can add a mean of a column.
Also, let us see how type data from the F select the column A, this one column A.
There are three values in column A, one, two and new value.
This is the null value.
Now you fill.
Their method
enter.
Parameter value is equal to column A.
Does it mean.
So here we are replacing the value by means of this column.
So this he did, he told the metal value is replaced by me enough column.
That is one point five.
You mean off these two values is one point for you.
So this is all about Dicle and a method.
Using the film and a method we can replace the values as well as we can replace the null values with
mean of the column.
There are other methods also that which we can replace the null values.
Let us divide what we have learned in this Teito deal.
In this tutorial, if we help understood how to deal with missing data.
First, we have created a dictionary.
B, then we have created a data frame, B, F, and we have created this data frame using this dictionary.
B, then we help understood drop a method using the drop and then method.
We can drop null values on the basis of rows or on the basis of columns.
And after that, we help understood, fill and then method with this method.
We can replace the values.
So this patented odille on missing data ends here.
I will see you in the next one.
buildOn, happy learning.