1561177675

So, let’s get started!

NumPy is a Python package which stands for ‘Numerical Python’. It is the core library for scientific computing, which contains a powerful n-dimensional array object, provide tools for integrating C, C++ etc. It is also useful in linear algebra, random number capability etc. NumPy array can also be used as an efficient multi-dimensional container for generic data. Now, let me tell you what exactly is a python numpy array.

**NumPy Array: **Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. We can initialize numpy arrays from nested Python lists and access it elements. In order to perform these numpy operations, the next question which will come in your mind is:

To install Python NumPy, go to your command prompt and type “pip install numpy”. Once the installation is completed, go to your IDE (For example: PyCharm) and simply import it by typing: “import numpy as np”

Moving ahead in** python numpy tutorial**, let us understand what exactly is a multi-dimensional **numPy array**.

Here, I have different elements that are stored in their respective memory locations. It is said to be two dimensional because it has rows as well as columns. In the above image, we have 3 columns and 4 rows available.

Let us see how it is implemented in **PyCharm**:

```
import numpy as np
a=np.array([1,2,3])
print(a)
```

```
Output – [1 2 3]
```

```
a=np.array([(1,2,3),(4,5,6)])
print(a)
```

```
O/P – [[ 1 2 3]
[4 5 6]]
```

Many of you must be wondering that why do we use python numpy if we already have python list? So, let us understand with some examples in this python numpy tutorial.

We use **python numpy array** instead of a list because of the below three reasons:

Less MemoryFastConvenient

The very first reason to choose **python numpy array** is that it occupies less memory as compared to list. Then, it is pretty fast in terms of execution and at the same time it is very convenient to work with numpy. So these are the major advantages that python numpy array has over list. Don’t worry, I am going to prove the above points one by one practically in **PyCharm**. Consider the below example:

```
import numpy as np
import time
import sys
S= range(1000)
print(sys.getsizeof(5)*len(S))
D= np.arange(1000)
print(D.size*D.itemsize)
```

```
O/P – 14000
4000
```

The above output shows that the memory allocated by list (denoted by S) is 14000 whereas the memory allocated by the numpy array is just 4000. From this, you can conclude that there is a major difference between the two and this makes python numpy array as the preferred choice over list.

Next, let’s talk **how python numpy array** is faster and more convenient when compared to list.

```
import time
import sys
SIZE = 1000000
L1= range(SIZE)
L2= range(SIZE)
A1= np.arange(SIZE)
A2=np.arange(SIZE)
start= time.time()
result=[(x,y) for x,y in zip(L1,L2)]
print((time.time()-start)*1000)
start=time.time()
result= A1+A2
print((time.time()-start)*1000)
```

```
O/P – 380.9998035430908
49.99995231628418
```

In the above code, we have defined two lists and two numpy arrays. Then, we have compared the time taken in order to find the sum of lists and sum of numpy arrays both. If you see the output of the above program, there is a significant change in the two values. List took 380ms whereas the numpy array took almost 49ms. Hence, numpy array is faster than list. Now, if you noticed we had run a ‘for’ loop for a list which returns the concatenation of both the lists whereas for numpy arrays, we have just added the two array by simply printing A1+A2. That’s why working with numpy is much easier and convenient when compared to the lists.

Therefore, the above examples proves the point as to why you should go for python numpy array rather than a list!

Moving forward in python numpy tutorial, let’s focus on some of its operations.

**ndim**:

You can find the dimension of the array, whether it is a two-dimensional array or a single dimensional array. So, let us see this practically how we can find the dimensions. In the below code, with the help of ‘ndim’ function, I can find whether the array is of single dimension or multi dimension.

```
import numpy as np
a = np.array([(1,2,3),(4,5,6)])
print(a.ndim)
```

```
Output – 2
```

Since the output is 2, it is a two-dimensional array (multi dimension).

**itemsize**:

You can calculate the byte size of each element. In the below code, I have defined a single dimensional array and with the help of ‘itemsize’ function, we can find the size of each element.

```
import numpy as np
a = np.array([(1,2,3)])
print(a.itemsize)
```

```
Output – 4
```

So every element occupies 4 byte in the above numpy array.

**dtype:**

You can find the data type of the elements that are stored in an array. So, if you want to know the data type of a particular element, you can use ‘dtype’ function which will print the datatype along with the size. In the below code, I have defined an array where I have used the same function.

```
import numpy as np
a = np.array([(1,2,3)])
print(a.dtype)
```

```
Output – int32
```

As you can see, the data type of the array is integer 32 bits. Similarly, you can find the size and shape of the array using ‘size’ and ‘shape’ function respectively.

```
import numpy as np
a = np.array([(1,2,3,4,5,6)])
print(a.size)
print(a.shape)
```

```
Output – 6 (1,6)
```

Next, let us move forward and see what are the other operations that you can perform with **python numpy module**. We can also perform reshape as well as slicing operation using** python numpy operation**. But, what exactly is reshape and slicing? So let me explain this one by one in this **python numpy tutorial**.

**reshape:**

Reshape is when you change the number of rows and columns which gives a new view to an object. Now, let us take an example to reshape the below array:

As you can see in the above image, we have 3 columns and 2 rows which has converted into 2 columns and 3 rows. Let me show you practically how it’s done.

```
import numpy as np
a = np.array([(8,9,10),(11,12,13)])
print(a)
a=a.reshape(3,2)
print(a)
```

```
Output – [[ 8 9 10] [11 12 13]] [[ 8 9] [10 11] [12 13]]
```

**slicing:**

As you can see the ‘reshape’ function has showed its magic. Now, let’s take another operation i.e Slicing. Slicing is basically extracting particular set of elements from an array. This slicing operation is pretty much similar to the one which is there in the list as well. Consider the following example:

Before getting into the above example, let’s see a simple one. We have an array and we need a particular element (say 3) out of a given array. Let’s consider the below example:

```
import numpy as np
a=np.array([(1,2,3,4),(3,4,5,6)])
print(a[0,2])
```

Output – 3

Here, the array(1,2,3,4) is your index 0 and (3,4,5,6) is index 1 of the python numpy array. Therefore, we have printed the second element from the zeroth index.

Taking one step forward, let’s say we need the 2nd element from the zeroth and first index of the array. Let’s see how you can perform this operation:

```
import numpy as np
a=np.array([(1,2,3,4),(3,4,5,6)])
print(a[0:,2])
```

Output – [3 5]

Here colon represents all the rows, including zero. Now to get the 2nd element, we’ll call index 2 from both of the rows which gives us the value 3 and 5 respectively.

Next, just to remove the confusion, let’s say we have one more row and we don’t want to get its 2nd element printed just as the image above. What we can do in such case?

Consider the below code:

```
import numpy as np
a=np.array([(8,9),(10,11),(12,13)])
print(a[0:2,1])
```

Output – [9 11]

As you can see in the above code, only 9 and 11 gets printed. Now when I have written 0:2, this does not include the second index of the third row of an array. Therefore, only 9 and 11 gets printed else you will get all the elements i.e [9 11 13].

**linspace**

This is another operation in python numpy which returns evenly spaced numbers over a specified interval. Consider the below example:

```
import numpy as np
a=np.linspace(1,3,10)
print(a)
```

Output – [ 1. 1.22222222 1.44444444 1.66666667 1.88888889 2.11111111 2.33333333 2.55555556 2.77777778 3. ]

As you can see in the result, it has printed 10 values between 1 to 3.

**max/ min**

Next, we have some more operations in numpy such as to find the minimum, maximum as well the sum of the numpy array. Let’s go ahead in python numpy tutorial and execute it practically.

```
import numpy as np
a= np.array([1,2,3])
print(a.min())
print(a.max())
print(a.sum())
```

Output – 1 3 6

You must be finding these pretty basic, but with the help of this knowledge you can perform a lot bigger tasks as well. Now, lets understand the concept of **axis** in python numpy.

As you can see in the figure, we have a **numpy array** 2*3. Here the rows are called as axis 1 and the columns are called as axis 0. Now you must be wondering what is the use of these axis?

Suppose you want to calculate the sum of all the columns, then you can make use of axis. Let me show you practically, how you can implement axis in your **PyCharm**:

```
a= np.array([(1,2,3),(3,4,5)])
print(a.sum(axis=0))
```

Output – [4 6 8]

Therefore, the sum of all the columns are added where 1+3=4, 2+4=6 and 3+5=8. Similarly, if you replace the axis by 1, then it will print [6 12] where all the rows get added.

**Square Root & Standard Deviation**

There are various mathematical functions that can be performed using **python numpy**. You can find the square root, standard deviation of the array. So, let’s implement these operations:

```
import numpy as np
a=np.array([(1,2,3),(3,4,5,)])
print(np.sqrt(a))
print(np.std(a))
```

Output – [[ 1. 1.41421356 1.73205081]

[ 1.73205081 2. 2.23606798]]

1.29099444874

As you can see the output above, the square root of all the elements are printed. Also, the standard deviation is printed for the above array i.e how much each element varies from the mean value of the **python numpy array**.

**Addition Operation**

You can perform more operations on numpy array i.e addition, subtraction,multiplication and division of the two matrices. Let me go ahead in python numpy tutorial, and show it to you practically:

```
import numpy as np
x= np.array([(1,2,3),(3,4,5)])
y= np.array([(1,2,3),(3,4,5)])
print(x+y)
```

Output – [[ 2 4 6] [ 6 8 10]]

This is extremely simple! Right? Similarly, we can perform other operations such as subtraction, multiplication and division. Consider the below example:

```
import numpy as np
x= np.array([(1,2,3),(3,4,5)])
y= np.array([(1,2,3),(3,4,5)])
print(x-y)
print(x*y)
print(x/y)
```

Output – [[0 0 0] [0 0 0]]

[[ 1 4 9] [ 9 16 25]]

[[ 1. 1. 1.] [ 1. 1. 1.]]

**Vertical & Horizontal Stacking**

Next, if you want to concatenate two arrays and not just add them, you can perform it using two ways – *vertical stacking* and *horizontal stacking*. Let me show it one by one in this python numpy tutorial.

```
import numpy as np
x= np.array([(1,2,3),(3,4,5)])
y= np.array([(1,2,3),(3,4,5)])
print(np.vstack((x,y)))
print(np.hstack((x,y)))
```

Output – [[1 2 3] [3 4 5] [1 2 3] [3 4 5]]

[[1 2 3 1 2 3] [3 4 5 3 4 5]]

**ravel**

There is one more operation where you can convert one numpy array into a single column i.e *ravel*. Let me show how it is implemented practically:

```
import numpy as np
x= np.array([(1,2,3),(3,4,5)])
print(x.ravel())
```

Output – [ 1 2 3 3 4 5]

Let’s move forward in python numpy tutorial, and look at some of its special functions.

There are various special functions available in numpy such as sine, cosine, tan, log etc. First, let’s begin with sine function where we will learn to plot its graph. For that, we need to import a module called *matplotlib*. To understand the basics and practical implementations of this module, you can refer **Matplotlib Tutorial**. Moving ahead with **python numpy tutorial**, let’s see how these graphs are plotted.

```
import numpy as np
import matplotlib.pyplot as plt
x= np.arange(0,3*np.pi,0.1)
y=np.sin(x)
plt.plot(x,y)
plt.show()
```

Output –

Similarly, you can plot a graph for any trigonometric function such as cos, tan etc. Let me show you one more example where you can plot a graph of another function, let’s say * tan**.*

```
import numpy as np
import matplotlib.pyplot as plt
x= np.arange(0,3*np.pi,0.1)
y=np.tan(x)
plt.plot(x,y)
plt.show()
```

Output –

Moving forward with python numpy tutorial, let’s see some other special functionality in numpy array such as exponential and logarithmic function. Now in exponential, the *e *value is somewhere equal to 2.7 and in log, it is actually *log base 10*. When we talk about natural log i.e log base e, it is referred as Ln. So let’s see how it is implemented practically:

```
a= np.array([1,2,3])
print(np.exp(a))
```

Output – [ 2.71828183 7.3890561 20.08553692]

As you can see the above output, the exponential values are printed i.e *e* raise to the power 1 is *e,* which gives the result as 2.718… Similarly, *e* raise to the power of 2 gives the value somewhere near 7.38 and so on. Next, in order to calculate log, let’s see how you can implement it:

```
import numpy as np
import matplotlib.pyplot as plt
a= np.array([1,2,3])
print(np.log(a))
```

Output – [ 0. 0.69314718 1.09861229]

Here, we have calculated natural log which gives the value as displayed above. Now, if we want log base 10 instead of Ln or natural log, you can follow the below code:

```
import numpy as np
import matplotlib.pyplot as plt
a= np.array([1,2,3])
print(np.log10(a))
```

Output – [ 0. 0.30103 0.47712125]

By this, we come to the end of this python numpy tutorial. We have covered all the basics of python numpy, so you can start practicing now. The more you practice, the more you will learn.

#python #numpy

1666082925

This tutorialvideo on 'Arrays in Python' will help you establish a strong hold on all the fundamentals in python programming language. Below are the topics covered in this video:

1:15 What is an array?

2:53 Is python list same as an array?

3:48 How to create arrays in python?

7:19 Accessing array elements

9:59 Basic array operations

- 10:33 Finding the length of an array

- 11:44 Adding Elements

- 15:06 Removing elements

- 18:32 Array concatenation

- 20:59 Slicing

- 23:26 Looping

**Python Array Tutorial – Define, Index, Methods**

In this article, you'll learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

The artcile covers arrays that you create by importing the `array module`

. We won't cover NumPy arrays here.

- Introduction to Arrays
- The differences between Lists and Arrays
- When to use arrays

- How to use arrays
- Define arrays
- Find the length of arrays
- Array indexing
- Search through arrays
- Loop through arrays
- Slice an array

- Array methods for performing operations
- Change an existing value
- Add a new value
- Remove a value

- Conclusion

Let's get started!

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are **not** the same thing.

**Lists** store items that are of **various data types**. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, **arrays** store only items that are of the **same single data type**. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the `array module`

in order to be used.

Arrays of the `array module`

are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

In order to create Python arrays, you'll first have to import the `array module`

which contains all the necassary functions.

There are three ways you can import the `array module`

:

- By using
`import array`

at the top of the file. This includes the module`array`

. You would then go on to create an array using`array.array()`

.

```
import array
#how you would create an array
array.array()
```

- Instead of having to type
`array.array()`

all the time, you could use`import array as arr`

at the top of the file, instead of`import array`

alone. You would then create an array by typing`arr.array()`

. The`arr`

acts as an alias name, with the array constructor then immediately following it.

```
import array as arr
#how you would create an array
arr.array()
```

- Lastly, you could also use
`from array import *`

, with`*`

importing all the functionalities available. You would then create an array by writing the`array()`

constructor alone.

```
from array import *
#how you would create an array
array()
```

Once you've imported the `array module`

, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

`variable_name = array(typecode,[elements])`

Let's break it down:

`variable_name`

would be the name of the array.- The
`typecode`

specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type. - Inside square brackets you mention the
`elements`

that would be stored in the array, with each element being separated by a comma. You can also create an*empty*array by just writing`variable_name = array(typecode)`

alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

TYPECODE | C TYPE | PYTHON TYPE | SIZE |
---|---|---|---|

'b' | signed char | int | 1 |

'B' | unsigned char | int | 1 |

'u' | wchar_t | Unicode character | 2 |

'h' | signed short | int | 2 |

'H' | unsigned short | int | 2 |

'i' | signed int | int | 2 |

'I' | unsigned int | int | 2 |

'l' | signed long | int | 4 |

'L' | unsigned long | int | 4 |

'q' | signed long long | int | 8 |

'Q' | unsigned long long | int | 8 |

'f' | float | float | 4 |

'd' | double | float | 8 |

Tying everything together, here is an example of how you would define an array in Python:

```
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers)
#output
#array('i', [10, 20, 30])
```

Let's break it down:

- First we included the array module, in this case with
`import array as arr`

. - Then, we created a
`numbers`

array. - We used
`arr.array()`

because of`import array as arr`

. - Inside the
`array()`

constructor, we first included`i`

, for signed integer. Signed integer means that the array can include positive*and*negative values. Unsigned integer, with`H`

for example, would mean that no negative values are allowed. - Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of `i`

typecode, meaning they were not integer values, you would get an error:

```
import array as arr
numbers = arr.array('i',[10.0,20,30])
print(numbers)
#output
#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
# numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer
```

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

```
from array import *
#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])
print(numbers)
#output
#array('d', [10.0, 20.0, 30.0])
```

The example above imported the `array module`

via `from array import *`

and created an array `numbers`

of float data type. This means that it holds only floating point numbers, which is specified with the `'d'`

typecode.

To find out the exact number of elements contained in an array, use the built-in `len()`

method.

It will return the integer number that is equal to the total number of elements in the array you specify.

```
import array as arr
numbers = arr.array('i',[10,20,30])
print(len(numbers))
#output
# 3
```

In the example above, the array contained three elements – `10, 20, 30`

– so the length of `numbers`

is `3`

.

Each item in an array has a specific address. Individual items are accessed by referencing their *index number*.

Indexing in Python, and in all programming languages and computing in general, starts at `0`

. It is important to remember that counting starts at `0`

and **not** at `1`

.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

`array_name[index_value_of_item]`

Here is how you would access each individual element in an array:

```
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element
#output
#10
#20
#30
```

Remember that the index value of the last element of an array is always one less than the length of the array. Where `n`

is the length of the array, `n - 1`

will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of `-1`

, the second to last element would have an index of `-2`

, and so on.

Here is how you would get each item in an array using that method:

```
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
#output
#30
#20
#10
```

You can find out an element's index number by using the `index()`

method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

```
import array as arr
numbers = arr.array('i',[10,20,30])
#search for the index of the value 10
print(numbers.index(10))
#output
#0
```

If there is more than one element with the same value, the index of the first instance of the value will be returned:

```
import array as arr
numbers = arr.array('i',[10,20,30,10,20,30])
#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))
#output
#0
```

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the `print()`

method. That method gives the following result:

```
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers)
#output
#array('i', [10, 20, 30])
```

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple `for`

loop:

```
import array as arr
numbers = arr.array('i',[10,20,30])
for number in numbers:
print(number)
#output
#10
#20
#30
```

You could also use the `range()`

function, and pass the `len()`

method as its parameter. This would give the same result as above:

```
import array as arr
values = arr.array('i',[10,20,30])
#prints each individual value in the array
for value in range(len(values)):
print(values[value])
#output
#10
#20
#30
```

To access a specific range of values inside the array, use the slicing operator, which is a colon `:`

.

When using the slicing operator and you only include one value, the counting starts from `0`

by default. It gets the first item, and goes up to but not including the index number you specify.

```
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#get the values 10 and 20 only
print(numbers[:2]) #first to second position
#output
#array('i', [10, 20])
```

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

```
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#get the values 20 and 30 only
print(numbers[1:3]) #second to third position
#output
#rray('i', [20, 30])
```

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

You can change the value of a specific element by speficying its position and assigning it a new value:

```
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40
print(numbers)
#output
#array('i', [40, 20, 30])
```

To add one single value at the end of an array, use the `append()`

method:

```
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integer 40 to the end of numbers
numbers.append(40)
print(numbers)
#output
#array('i', [10, 20, 30, 40])
```

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

```
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integer 40 to the end of numbers
numbers.append(40.0)
print(numbers)
#output
#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
# numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer
```

But what if you want to add more than one value to the end an array?

Use the `extend()`

method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

```
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets
numbers.extend([40,50,60])
print(numbers)
#output
#array('i', [10, 20, 30, 40, 50, 60])
```

And what if you don't want to add an item to the end of an array? Use the `insert()`

method, to add an item at a specific position.

The `insert()`

function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

```
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integer 40 in the first position
#remember indexing starts at 0
numbers.insert(0,40)
print(numbers)
#output
#array('i', [40, 10, 20, 30])
```

To remove an element from an array, use the `remove()`

method and include the value as an argument to the method.

```
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
numbers.remove(10)
print(numbers)
#output
#array('i', [20, 30])
```

With `remove()`

, only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

```
import array as arr
#original array
numbers = arr.array('i',[10,20,30,10,20])
numbers.remove(10)
print(numbers)
#output
#array('i', [20, 30, 10, 20])
```

Only the first occurence of `10`

is removed.

You can also use the `pop()`

method, and specify the position of the element to be removed:

```
import array as arr
#original array
numbers = arr.array('i',[10,20,30,10,20])
#remove the first instance of 10
numbers.pop(0)
print(numbers)
#output
#array('i', [20, 30, 10, 20])
```

And there you have it - you now know the basics of how to create arrays in Python using the `array module`

. Hopefully you found this guide helpful.

Thanks for reading and happy coding!

#python #programming

1595467140

The most important feature of NumPy is the homogeneous high-performance n-dimensional array object. Data manipulation in Python is nearly equivalent to the manipulation of NumPy arrays. NumPy array manipulation is basically related to accessing data and sub-arrays. It also includes array splitting, reshaping, and joining of arrays. Even the other external libraries in Python relate to NumPy arrays.

**_Keeping you updated with latest technology trends, _***Join DataFlair on Telegram*

Arrays in NumPy are synonymous with lists in Python with a homogenous nature. The homogeneity helps to perform smoother mathematical operations. These arrays are mutable. NumPy is useful to perform basic operations like finding the dimensions, the bite-size, and also the data types of elements of the array.

NumPy has a variety of built-in functions to create an array.

For 1-D arrays the most common function is np.arange(…), passing any value create an array from 0 to that number.

- import numpy as np
- array=np.
**arange**(20) - array

Output

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19])

We can check the dimensions by using array.shape.

#numpy tutorials #array in numpy #numpy array #python numpy array

1619667660

Up until now, we have been discussing some of the basic nuts and bolts of Numpy ; in this section we will dive deep into the reasons that Numpy is so important in the Python Data Science world.

The key to make the computation on Numpy arrays fast is to use vectorized operations, generally implemented through Numpy’s Universal functions (ufuncs). The vectorized approach is designed to push loop into the compiled layer that underlies Numpy, leading to much faster execution. Vectorized operations in Numpy are implemented via ufuncs, whose main purpose is to quickly execute repeated operations on values in Numpy arrays.

In order to read previous articles on Numpy, follow the below links:

#numpy #numpy-array #learning #data-science #learning-to-code

1616074200

Data manipulation in Python is nearly synonymous with Numpy array manipulation, even newer tools like Pandas are built around the Numpy array. This section will present several examples using Numpy and manipulation to access data and subarrays, and to split, reshape and join arrays.

Let’s start by defining three random arrays: a one-dimensional, two-dimensional, and three dimensional array. We’ll use Numpy’s random number generator, which we will seed with a set value in order to ensure that the same random arrays are generated each time this code is run.

#data-science #numpy #numpy-tutorial #numpy-array #learning

1619660285

A geek in Machine Learning with a Master’s degree in…

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NumPy is an essential Python library to perform mathematical and scientific computations. NumPy offers Python’s array-like data structures with exclusive operations and methods. Many data science libraries and frameworks, including Pandas, Scikit-Learn, Statsmodels, Matplotlib and SciPy, are built on top of NumPy with Numpy arrays in their building blocks. Some frameworks, including TensorFlow and PyTorch, introduce NumPy arrays or NumPy-alike arrays as their fundamental data structure in the name of tensors.

How NumPy becomes the base of Data Science computing system (source)

Data Science relies heavily on Linear Algebra. NumPy is famous for its Linear Algebra operations. This article discusses methods available in the NumPy library to perform various Linear Algebra operations with examples. These examples assume that the readers have a basic understanding of NumPy arrays. Check out the following articles to have a better understanding of NumPy fundamentals:

#developers corner #linear algebra #matrices #numpy #numpy array #numpy dot product #numpy matrix multiplication #numpy tutorial #svd #vectors