Hello, everyone, and welcome to this by tend to go.
Here we will understand some useful operations in data frames.
Let us begin.
First, we will import these two labor added numpty and Bendat.
Great.
After that, we have to define a data frame B F to define this data frame.
First, define a dictionary.
B.
No defined data, frame B F and take the same grade.
So this is our data frame.
B.
F in this data frame, they're out of food rules and three columns.
No, baby.
Select Golomb to dot unique.
Execute.
So did that the unit values in column to this matter, unique returns, unique values in a column like
these.
Similarly, we can take lente also.
Type 80 and.
Three.
So there are three unique values in this column.
Column two, to get the unique values, we can use one more method that is and unique.
Three.
So this method, any unique returns?
Number of unique values.
So this is all about getting unique values and unique value count.
Let's see one more method.
Column to dot value cound.
It returns number of values.
So there are two values for fortify you.
One value for fifty for you and one value for 60 for you.
So this method retains value cound like these.
Doesn't this stand a blind method?
To understand the Uplay method, we have to define a function name of the function ese times for you,
specify argument as X. then return.
X into five, you execute.
No, call this function.
13.
Fifty.
So this function is working fine.
Let us understand a planned method, type D f.
Column one dog.
Some.
Ben.
US take B f.
So some of the column VUN is in here, we have applied a method, some on this column.
Notes, electic, welcome once again.
Dot metal apply.
And in parentheses aims for you.
Execute.
So this is detailed here, we how applied this function times for you to column one.
So this is the result.
One in two for you.
Two in two for you.
Three in two for you.
And four in two for you.
So this way we can use apply function or apply method here.
We have specified a user defined function.
This one aims for you.
Similarly, we can use built-In function B F enter column three.
Don't apply.
No integrity built in function.
Lynn.
So this is the output.
These are the length of the column.
Three, three, three three three.
You can see three, three, three and three.
So this is all about the apply method.
Let us understand, dropped method APF dot drop.
Tape labels is equal to column one.
And Axis is equal to one.
Great.
So column one is the little here to save this change permanently specify in place is equal do true.
This one, by default, in place is equal to falls.
We can take Golomb names.
Also A, B, F dot columns.
Execute.
So these are the column names called One Call to end call.
Three.
No, no.
Down here.
This is not a method.
It is an attribute.
Similarly, we can do four indexes, A, B, F dot.
Index.
Execute.
So these are the details about index stock at zero.
Stop at Ford.
And stay safe.
It equal to one.
That means zero.
One, two, three.
This one.
Zero.
One, two, three.
And upper bound is excluded.
Let us understand one more method.
A, b, f dort sort underscore values.
Gholam Van.
So here values are sorted along the column.
One, we can't specify any column name.
Bilyk van and specified to.
Now the values are assaulted on the basis of column two.
That is along the column to other values are changed.
You can see index zero, then three, then one and then two.
This is along the column to let us understand one more method, A, B, F, dot.
Is null.
This method returns boolean output, all the values are faulty or false.
Means no null value and true means there is a null value.
So there is no null value in this data frame.
The F.
So these are the some basic operations or methods that we help.
Understood.
Enlisted Odille.
Operations.
Foot.
We have defined a data frame, the F.
After that, we help understood the unique method.
Unique Len and N unique.
Then VLT understood a method.
In applying method, we can specify user defined functions as well as built-In functions.
Then be out understood these two attributes, columns and index.
Then we have understood sort values matured.
And at the end we understood is Nele mattered.
So despite tend to do deal on data frame operations entier.
I will see you in the next one.
buildOn, happy learning.