All language subtitles for 4. Numpy Indexing Slicing Transformation

Hi, Ron, and welcome in this new video in this video, we're going to see a lot of think about Nampai

library like indexing, slicing and some function very useful.

So let's get started now by show you how to do indexing with Nampai.

It is very easy because it is exactly as for their lists when we had lists of lists.

So we need to do a double indexation.

For example, if I want the coefficient at the first row and the first column I just put Urbis and Parallel

broke it with the index of the row and a number of brackets with the index of the current.

Then if I run the crowd, I have this number.

So it is that we want, so we have a good indexing variant is another way to do exactly the same thing

using just one pair of brackets.

But with the number of the index for the row and for the column, the limited by a comma.

So you choose the syntax that you prefer, then if you want to choose just, for example, a sub matrix.

You can do it

using exactly the same syntax as here, but with a range of coefficient.

For example, I can take all the rope and just the first column, for example, to take all the rule.

I can also just put the dual point total to Python that I want all the number of.

I can, for example, take just the tool and one column, so you can really do what you want with this

syntax.

You can also choose, for example, one columns.

So to do it, we need to combine a little bit.

The two previous way to do indexing and slicing.

So we need the first row and all the column.

So it is very easy to use the indexing and the slicing.

But again, I will invite you to try by yourself to really master it.

Then let me show you some very useful function from the Nampai Library.

For example, if I want the maximum value of an array, I can find it using the max function.

So I read this point max and then I have the max value of this.

All right?

But sometimes, for example, we want to have the maximum value by zero or the maximum value by the

column and not just, for example, the maximum value between all of the value in the art.

So

to have the maximum value for the role for the column, we are going to use exactly the same function,

but we need to specify the access for the rule.

Usually, we always need to put axis equal zero.

But as we want the maximum value further rule, we need to find the maximum value for each column.

So it is a little bit tricky to understand, but I will show you the example and you will understand

very quickly.

So I just.

Put this mattress here to a better understand, think so the best value, the maximum sorry value is

not OK.

And if we want the maximum value by Rome, so the maximum value of this rule, then the maximum value

of this world, then the maximum value of this rule, etc. We need to pass by the column.

OK.

And tell to Python that I want.

The best value between death row, death row and disrupt, and as we can see, we have three, seven

and nine.

So put axis evil one is really the things to do, and it is not and never for the columns.

It is exactly the same.

We need to check the road and we see that for the first column, the best value is nine for a second

column when we check all the road.

We find that the best value is six and exactly the same for the third column.

So with this syntax, we can have a lot of function, like find the minimum value to do it.

We just need to put me in the seat of Max.

We can have the mean, so the average of the value in the rent.

So again, we just need to change the function, so it is very powerful.

If you want the standard deviation, you can also do exactly the same, so vested interests on the deviation.

And we have the standard deviation, so this intact is very powerful.

There are a lot of other friction, so to run all of the above.

I invite you to go on the Nampai documentation, but this function already the most important, I think.

Now I will show you another way to do exactly the same thing this way is to use, for example, this

syntax so important.

The standard deviation function from them.

And you need to put the array inside this.

And now we can.

Specified in the right or not.

Like with this function, for example, if I specify X is equal zero, I have exactly the same number

here and here, so it is just another way to do exactly the same thing.

That little difference is that here we have a list and here we have a one dimensional.

All right.

So it is very the only difference between the results of this two function.

Now I will show you how to use very specific function and very used function in finance.

First, the lung function.

So to do it, we call the lung function from the pilot and we just have to put the right inside this

function to do the exponent show of this of all coefficient in the matrix.

We do exactly the same, but using the function e p.

And if I want the square root of all of the co-efficient in The Matrix again, I will do exactly the

same thing, but with the function square root.

So actually, we have seen a lot of things and we have to see a little thing, which is the concatenation.

So how to merge some power between them?

And it is very also important to know it.

I know that I felt that all thing is important because it is a very literal crash course and I have

really put all the necessary things.

So actually, all the things in this course, in the Python course is very necessary to master our future

project.

So.

I just copy some art, and I will show you how to concatenate it to concatenate this all right.

I will use the concatenate function from Mumbai.

This function needs a tipple of.

All right.

So

we put our two array and we need to specify the axis.

By which you want to concatenate.

You are right here.

We don't really not have a choice because we want to concatenate one dimension that RNA.

So we just have one dimension in our.

So automatically we can just concatenate by this dimension, which is zero because we have only one.

And as we can see, we have merged this RNA with discovery here.

So we can do exactly the same

with the two dimension.

All right.

So I have just transformed this one dimension that I read in two dimensional arrays.

So

I take exactly the same syntax so we can do this concatenation this time.

So by the third.

Or we can do a concatenation by the column.

So we need to specify axes in one one because it is the second axis, the first axis is the axis of

the row and the second axis is the axis of the column, sometimes with some project.

Not in this course, but I think it's very important to tell you that you can have, for example, matrix

of three dimension, etc. But.

Just know that it exists, and I think it's nuts, recommend to try to work with it.

If you are a beginner, so now we want to join this array by the column, so we put Axis Evil one.

It is all for the Nampai narrowly.

And again, I will invite you to play with this to a better understanding of all the function.