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One Dimensional Numpy
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From the course by IBM
Python for Data Science
5082 ratings
Meet the Instructors
Joseph SantarcangeloPh.D., Data Scientist at IBM
IBM Developer Skills Network
Rav AhujaData Science Program Manager
IBM
In this video we will be covering NumPy in 1D,
in particular ND arrays.
NumPy is a library for scientific computing.
It has many useful functions.
There are many other advantages like speed and memory.
NumPy is also the basis for pandas.
So check out our pandas video.
In this video we will be covering
the basics and array creation,
indexing and slicing,
basic operations, universal functions.
Let's go over how to create a NumPy array.
A Python list is a container that
allows you to store and access data.
Each element is associated with an index.
We can access each element using
a square bracket as follows.
A NumPy array or ND array is similar to a list.
It's usually fixed in size
and each element is of the same type,
in this case integers.
We can cast a list to
a NumPy array by first importing NumPy.
We then cast the list as follows;
we can access the data via an index.
As with the list, we can access
each element with an integer and a square bracket.
The value of a is stored as follows.
If we check the type of the array we get, Numpy.ndarray.
As NumPy arrays contain data of the same type,
we can use the attribute
dtype to obtain the data type of the array's elements.
In this case a 64-bit integer.
Let's review some basic array attributes
using the array a.
The attribute size is
the number of elements in the array.
As there are five elements the result is five.
The next two attributes will make
more sense when we get to higher dimensions,
but let's review them.
The attribute ndim represents
the number of array dimensions or the rank of the array,
in this case one.
The attribute shape is a tuple of
integers indicating the size of
the array in each dimension.
We can create a NumPy array with real numbers.
When we check the type of the array,
we get numpy.ndarray.
If we examine the attribute D type,
we see float64 as the elements are not integers.
There were many other attributes, check out numpy.org.
Let's review some indexing and slicing methods.
We can change the first element of
the array to 100 as follows.
The array's first value is now 100.
We can change the fifth element of the array as follows.
The fifth element is now zero.
Like lists and tuples we can slice a NumPy array.
The elements of the array correspond
to the following index.
We can select the elements from one to three and assign
it to a new NumPy array d as follows.
The elements in d correspond to the index.
Like lists, we do not count
the element corresponding to the last index.
We can assign the corresponding indices
to new values as follows.
The array c now has new values.
See the labs or numpy.org for
more examples of what you can do with NumPy.
NumPy makes it easier to do
many operations that are
commonly performed in data science.
The same operations are
usually computationally faster and
require less memory in NumPy compared to regular Python.
Let's review some of these operations
on one-dimensional arrays.
We will look at many of the operations in the context of
Euclidian vectors to make things more interesting.
Vector addition is a widely used operation
in data science.
Consider the vector u with two elements,
the elements are distinguished by the different colors.
Similarly, consider the vector v with two components.
In vector addition, we create
a new vector in this case z.
The first component of z
is the addition of the first component
of vectors u and v. Similarly,
the second component is
the sum of the second components of
u and v. This new vector z is now
a linear combination of the vector u and
v. Representing vector addition
with line segment or arrows is helpful.
The first vector is represented in red,
the vector will point in
the direction of the two components.
The first component of the vector is one.
As a result the arrow is offset
one unit from the origin in the horizontal direction.
The second component is zero,
we represent this component in the vertical direction.
As this component is zero,
the vector does not point in the horizontal direction.
We represent the second vector in blue.
The first component is zero,
therefore the arrow does not
point to the horizontal direction.
The second component is one.
As a result the vector points in
the vertical direction one unit.
When we add the vector u and v,
we get the new vector z.
We add the first component,
this corresponds to the horizontal direction.
We also add the second component.
It's helpful to use the tip to
tail method when adding vectors,
placing the tail of the vector v on the tip of vector u.
The new vector z is constructed by connecting
the base of the first vector u with the tail of the
second v. The following three lines of code
we'll add the two lists and place
the result in the list z.
We can also perform
vector addition with one line of NumPy code.
It would require multiple lines to perform
vector subtraction on two lists
as shown on the right side of the screen.
In addition, the NumPy code will run much faster.
This is important if you have lots of data.
We can also perform vector subtraction by changing
the addition sign to a subtraction sign.
It would require multiple lines
perform vector subtraction
on two lists as shown on the right side of the screen.
Vector multiplication with a scalar is
another commonly performed operation.
Consider the vector y,
each component is specified by a different color.
We simply multiply the vector by
a scalar value in this case two.
Each component of the vector is multiplied by two,
in this case each component is doubled.
We can use the line segment or
arrows to visualize what's going on.
The original vector y is in purple.
After multiplying it by a scalar value of two,
the vector is stretched out by two units as shown in red.
The new vector is twice as long in each direction.
Vector multiplication with a scalar only
requires one line of code using NumPy.
It would require multiple lines
to perform the same task as
shown with Python lists
as shown on the right side of the screen.
In addition, the operation would also be much slower.
Hadamard product is
another widely used operation in data science.
Consider the following two vectors,
u and v. The Hadamard product
of u and v is a new vector z.
The first component of z is the product of
the first element of u and v. Similarly,
the second component is
the product of the second element of
u and v. The resultant vector consists of
the entry wise product of u and v. We can
also perform hadamard product
with one line of code in NumPy.
It would require multiple lines to perform
hadamard product on two lists
I shown on the right side of the screen.
The dot product is
another widely used operation in data science.
Consider the vector u and v,
the dot product is a single number given by
the following term and
represents how similar two vectors are.
We multiply the first component from v and u,
we then multiply the second component
and add the result together.
The result is a number that represents
how similar the two vectors are.
We can also perform dot product using the NumPy function
dot and assign it with the variable result as follows.
Consider the array u,
the array contains the following elements.
If we add a scalar value to the array,
NumPy will add that value to each element.
This property is known as broadcasting.
A universal function is a function that
operates on ND arrays.
We can apply a universal function to a NumPy array.
Consider the arrays a,
we can calculate the mean or average value of
all the elements in a using the method mean.
This corresponds to the average of all the elements.
In this case the result is zero.
There are many other functions.
For example, consider the NumPy arrays b.
We can find the maximum value using the method five.
We see the largest value is five,
therefore the method max returns a five.
We can use NumPy to create functions that
map NumPy arrays to new NumPy arrays.
Let's implement some code on the left side of the screen
and use the right side of
the screen to demonstrate what's going on.
We can access the value of pie in NumPy as follows.
We can create the following NumPy array in radians.
This array corresponds to the following vector.
We can apply the function sin to the array
x and assign the values to the array y.
This applies the sine function
to each element in the array,
this corresponds to applying
the sine function to each component of the vector.
The result is a new array y,
where each value corresponds to
a sine function being applied to
each element in the array x.
A useful function for plotting
mathematical functions is line space.
Line space returns evenly
spaced numbers over specified interval.
We specify the starting point of the sequence,
the ending point of the sequence.
The parameter num indicates
the number of samples to generate,
in this case five.
The space between samples is one.
If we change the parameter num to nine,
we get nine evenly spaced numbers
over the integral from negative two to two.
The result is the difference
between subsequent samples is
0.5 as opposed to one as before.
We can use the function line space to generate 100
evenly spaced samples from the interval zero to two pie.
We can use the NumPy function sine to
map the array x to a new array y.
We can import the library pyplot as
plt to help us plot the function.
As we are using a Jupiter notebook,
we use the command matplotlib inline to display the plot.
The following command plots a graph.
The first input corresponds to
the values for the horizontal or x-axis.
The second input corresponds to
the values for the vertical or y-axis.
There's a lot more you can do with NumPy.
Check out the labs and NumPy.org for more.
Thanks for watching this video.
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