ARRAY PRESENTATION

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BASIC OPERATIONS ON AN ARRAY
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Basic Operations on an Array

• Arithmetic operations
• These operations are basic arithmetic
  operations like addition,subtraction,division,powe
  r etc.
• The operation takes place element wise.
• Eg arr1np.arange(6) O/Parray(0, 1,
  2, 3, 4, 5) print(arr13)....every no.
  is added by 3 print(arr12).... every
  no. is multiplied by 2
  print(arr1/3).... every no. is divided by 3
• Hence element wise operations are performed
  whereas in the list it will repeat the same list
  .
• Eg lst10,20,30 print(lst3)
• This will repeat the list thrice.O/P
  10,20,30,10,20,30,10,20,30

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Arithmetic operations between 2 Arrays

• Even between two arrays the operations take place
  element wise.
• Eg 1 arr1np.arange(6).....0,1,2,3,4,5
  arr2np.arange(6) ).....0,1,2,3,4,5
  print(arr1arr2) O/P 0 2 4 6 8 10
• Eg 2 print(arr1gtarr2)Here element wise
  comparison takes place and returns array of
  boolean values.As the values in arr1 and arr2
  are equal it will return an array of boolean
  values.
• O/P False False False False False False

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NOTE

• If number of elements in both the arrays are not
  same then it will throw an error . Hence in this
  case as the broadcasting is not possible number
  of elements in the array needs to be equal.
• In case of scalar multiplication element wise
  multiplication takes place while in vector
  product dot product is considered . Hence number
  of rows should be equal to number of columns.
• Eg2D array arr1np.arange(6).reshape(2,3)
• arr2np.arange(7,13).
  reshape(2,3)
• arr1array( 1,2,3,
  4, 5, 6)
• arr2array( 7, 8, 9,
  10, 11, 12)
• np.dot(arr1,arr2) OR print(arr1.dot(arr2)
• O/P array( 31, 34, 112, 124)
• In all these examples every time new array is
  created and the original array remains as it
  is.So the original array is not discarded.

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2.Update/Modify the existing array

• When an operation is performed on the array
  always new array is created and original remains
  as it is.To make changes in the orignal array we
  use the below methd.
• arrnp.array(10,20,30)
  arr2............ arrarr2 print(arr)
• O/P 12 22 32
• This will make changes to the existing array.

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3.Unary Operations

• A single operator  or unary operation
  is one which takes and performs an operation with 
  a single operand / argument.
• Aggregation functions such as min(),max(),etc
  also used to perform various operations on an
  array.
• Let arr110,20,301. sum()finding sum of all
  the elements in the array.
• arr1.sum()........O/P60
• 2.min() finding minimum value from all
  the elements in the array
• arr1.sum()........ O/P10
• 3.max() finding maximum value from all
  the elements in the array
• arr1.sum()........ O/P30

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Finding the aggregation of elements along row or
column

• These functions can also be used to find the
  values along the rows or columns.
• Eg arr1np.arange(12).reshape(6,2) arr1
• O/P array( 0, 1, 2, 3, 4, 5, 6, 7,
  8, 9, 10, 11)
• np.sum(arr1,axis0/1)
• np.max(arr1,axis0/1)
• np.min(arr1,axis0/1)
• axis0 column wise addition/min/max value
• axis1row wise addition/min/max value

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Universal Functions

• MATHS FINCTIONS
• np.add()-adds valuesnp.add(2,3)......O/P 5
• np.multiply()-multiplication of the values
  np.multiply(2,3)......O/P 6
• np.sqrt()-to find square root of elements in
  array
• np.sqrt(9,16) )......O/P 3,4
• np.log()-to find log of a value
• np.log(1) .....O/P 0.0
• np.square()-to find square of a value
• np.log(5) .....O/P 25

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Trignometric functions

• np.sin/cos/tan(angle)-to find value of specific
  angle
• np.sin (np.pi/2) .....O/P 1.0
• To convert values into angles that is, to
  understand that it is value in degrees multiply
  the angle by pi/180 else it will consider them as
  usual numerical values.
• Egnp.sin(np.array(0,30,60,90,120))(np.pi/180)
  O/P array( 0. , -0.0172444 , -0.00531995,
  0.01560319, 0.01013358)
• To converting an array of values of angles to
  array values in radians use np.radians(angles).
• To find inverse trignometric functions use
  arcsin(),arccos(),arctan() functions

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Bitwise operators

• and () (int,int) -gt int 0011 0101 returns
  the result 0001 inclusive
• Or () (int,int) -gt int 0011 0101 returns
  the result 0111
• not () (int) -gt int 01 returns the result 10
  
• exclusive or() (int,int) -gt int 0011 0101
  returns the result 0110
• shift left (ltlt) (int,int) -gt int 101 ltlt 2
  returns the result 10100 For shifting left, 0s
  are added on the right
• shift right (gtgt) (int,int) -gt int 101 gtgt 2
  returns the result 1 for shifting right, bits
  are removed from the right
• Examples print(np.bitwise_and(10,20))
  print(np.bitwise_or(10,20))
• To get the binary representation use below
  method
• print(np.binary_repr(30))........O/P
  11110

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Statistical functions

• arr_popnp.array(200,300,707,505,50,800)
• Mean
• finding mean of elements in the array
• print(np.mean(arr_pop)).......O/P
  427.0
• Median
• finding median of elements in the array
• print(np.median(arr_pop))
  ......O/P402.5
• Standard deviation
• finding standard deviation of elements in
  the array
• print(np.std(arr_pop))
  ......O/P268.762
• Variance
• finding variance of elements in the array
• print(np.var(arr_pop))
  ......O/P72233.333