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# Introduction to Google Colab and the NumPy library

What is Google Colab? Colaboratory, or “ Colab ” for brief, is a product from Google Research. Colab permits anyone to type in and execute bumptious python code through the browser, and is peculiarly well suited to car eruditeness, data analysis and direction. More in fact, Colab is a host Jupyter notebook servicing that requires no apparatus to use, while providing free access to computing resources including GPUs.

• Work from any computers. All notebooks are saved in Google Drive.
• Don’t need to worry that conda create env will clutter your directories
• Share to someone, or everyone easily. Just like a Google Doc.
• Automatic history and versioning
• Free GPU
• Form widgets are simple and easier to use.

# NumPy

NumPy ( brief for Numerical Python ) is “ the principal pile for logical computing with Python ” and it is the library on which Pandas, Matplotlib and Scikit-learn builds on.

Installation NumPy does not come with Python by default so it should be installed. The most casual way to get NumPy is to install Anaconda. If you do not want to install anaconda and just install NumPy, you can download the interpretation for your operate system from this page or you can barely use the python facility package to download it .

` pip install numpy`

After this you equitable need to import it in any IDE that you are using by

` significance numpy as neptunium`

NumPy Array A numpy array is a grid of values, all of the same classify, and is ordered by a tuple of nonnegative integers. The number of dimensions is the rank and file of the array ; the shape of an array may be a tuple of integers giving the measure of the align along each dimension. We can initialize numpy arrays from nested Python records, and get to components using public square brackets :

` a = np.array ( [ 1,2,3 ] )  boron = np.array ( [ [ 1,2,3 ], [ 4,5,6 ] ] )  photographic print ( type ( a ) )  print ( a.shape )  print ( type ( bacillus ) )  print ( b.shape )  (3,)(2, 3)`

NumPy besides has many functions for creating different types of arrays :

` # Creates a 2X2 zero align  c=np.zeros ( ( 2,2 ) ) # Creates a 2X4 ones array  d=np.ones ( ( 2,4 ) ) # Create a 2X2 constant align of number 7  e = np.full ( ( 2,2 ), 7 ) # Creates a 3X3 identity matrix  fluorine = np.eye ( 3 ) # Creates a 3X2 array filled with random values  guanine = np.random.random ( ( 3,2 ) ) # Creates a range of elements till 7  heat content = np.arange ( 7 )`

Slicing similar to Python lists, numpy arrays can be sliced. Since arrays may be multidimensional, you must specify a slice for each property of the align :

` # barn is the substitute align dwell of the foremost 2 rows and columns 1 and 2 of array a a = np.array ( [ [ 1,2,3,4 ], [ 5,6,7,8 ], [ 9,10,11,12 ] ] )  b = a [ :2, 1:3 ]  print ( b-complex vitamin ) # A slice of an array is a watch into the same datum, so modifying it will modify the original array. print ( a [ 0, 1 ] )  barn [ 0, 0 ] = 77 # barn [ 0, 0 ] is the lapp piece of data as a [ 0, 1 ]  print ( a [ 0, 1 ] ) [ [ 2 3 ]  [ 6 7 ] ]  2  77`

Adding and removing the elements The numpy.append() appends values along the mention axis at the end of the array. The syntax is numpy.append ( range, values, axis = none ) .

` # adds 1,2,3,4 at the end  a = [ 0 ]  a = np.append ( a, [ 1,2,3,4 ] )  print ( a ) [ 0 1 2 3 4 ]`

The numpy.delete() function returns a new range with the omission of sub-arrays along with the note axis. The syntax is numpy.delete ( array, values, axis = none ) .

` # Deletes elements 2 and 3 from the array  a = np.delete ( a, [ 2,3 ] )  print ( a ) [ 0 1 4 ]`

Sorting numpy.sort() function returns a screen copy of an array. Parameters :

arr : Array to be sorted.
axis : Axis along which we need range to be started.
order : This argumentation specifies which fields to compare first.
kind : [ ‘ quicksort ’ { default }, ‘ mergesort ’, ‘ heapsort ’ ] Sorting algorithm .

` # To sort an range, the kind ( range, axis, kind, orderby ) function is used  a = np.array ( [ [ 3,2,1 ], [ 6,5,4 ], [ 9,8,7 ] ] )  print ( a )  a = np.sort ( a, axis=1, kind = ‘ quicksort ’ )  print ( a ) [ [ 3 2 1 ]  [ 6 5 4 ]  [ 9 8 7 ] ] [ [ 1 2 3 ]  [ 4 5 6 ]  [ 7 8 9 ] ]`

Reshaping the array The numpy.reshape() routine shapes an range without changing data of array. Its syntax is numpy.reshape ( range, supreme headquarters allied powers europe, order = ‘ C ’ ) Parameters :

array : [ array_like ] Input array
shape : [ int or tuples of int ] e.g. if we are aranging an array with 10 elements then shaping it like numpy.reshape ( 4, 8 ) is wrong ; we can
order : [ C-contiguous, F-contiguous, A-contiguous ; optional ]
C-contiguous order in memory ( last index varies the fastest )
C order means that operating row-rise on the range will be slightly quicker
FORTRAN-contiguous holy order in memory ( first index varies the fastest ) .

` a = np.arange ( 10 )  print ( a )  a = a.reshape ( 2,5 )  a [ 0 1 2 3 4 5 6 7 8 9 ]  array ( [ [ 0, 1, 2, 3, 4 ],  [ 5, 6, 7, 8, 9 ] ] )`

Concatenation numpy.concatenate function concatenate a sequence of arrays along an existing axis .

` # Concatenate — Arrays are joined on the basis of their axis  bel = [ 1,2 ]  c = [ 3,4 ]  d= [ bacillus, c ]  d = np.concatenate ( five hundred )  print ( five hundred ) [ 1 2 3 4 ]`

Mathematical Functions

NumPy contains a big number of respective mathematical operations. NumPy provides standard trigonometric functions, functions for arithmetic operations, handling building complex numbers, etc .

` einsteinium = np.add ( bel, speed of light )  photographic print ( e )  degree fahrenheit = np.subtract ( b, speed of light )  print ( degree fahrenheit )  deoxyguanosine monophosphate = np.multiply ( b, cytosine )  print ( thousand )  henry = np.divide ( b, degree centigrade )  print ( henry )  iodine = np.power ( b, c )  print ( i ) [ 4 6 ]  [ -2 -2 ]  [ 3 8 ]  [ 0.33333333 0.5 ]  [ 1 16 ]`
source : https://thefartiste.com
Category : Tech