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ARRAY Presentation

Learn with Learnbay about Array.<br>For more details visit: www.learnbay.co

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ARRAY Presentation

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  1. BASIC OPERATIONS ON AN ARRAY www.Learnbay.co

  2. Basic Operations on an Array • Arithmetic operations: • These operations are basic arithmetic operations like addition,subtraction,division,power etc. • The operation takes place element wise. • Eg: arr1=np.arange(6) O/P:array([0, 1, 2, 3, 4, 5]) print(arr1+3)....every no. is added by 3 print(arr1*2).... 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: lst=[10,20,30] print(lst*3) • This will repeat the list thrice.O/P: =[10,20,30,10,20,30,10,20,30]

  3. Arithmetic operations between 2 Arrays • Even between two arrays the operations take place element wise. • Eg 1 : arr1=np.arange(6).....[0,1,2,3,4,5] arr2=np.arange(6) ).....[0,1,2,3,4,5] print(arr1+arr2) O/P: [ 0 2 4 6 8 10] • Eg 2: print(arr1>arr2)Here element wise comparison takes place and returns array of booleanvalues.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]

  4. 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. • Eg:2D array: arr1=np.arange(6).reshape(2,3) arr2=np.arange(7,13).reshape(2,3) arr1=array([[ 1,2,3], [4, 5, 6]]) arr2=array([[ 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.

  5. 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. • arr=np.array([10,20,30])arr+=2............# arr=arr+2 print(arr) • O/P: [12 22 32] • This will make changes to the existing array.

  6. 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 arr1=[10,20,30]1. sum():finding sum of all the elements in the array. arr1.sum()........O/P:60 2.min(): finding minimum value from all the elements in the array arr1.sum()........ O/P:10 3.max(): finding maximum value from all the elements in the array arr1.sum()........ O/P:30

  7. 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: arr1=np.arange(12).reshape(6,2) arr1 • O/P: array([[ 0, 1], [ 2, 3], [ 4, 5], [ 6, 7], [ 8, 9], [10, 11]]) np.sum(arr1,axis=0/1) np.max(arr1,axis=0/1) np.min(arr1,axis=0/1) axis=0 :column wise addition/min/max value axis=1:row wise addition/min/max value

  8. Universal Functions MATHS FINCTIONS • np.add()-adds valuesnp.add(2,3)......O/P: 5 • np.multiply()-multiplication of the valuesnp.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

  9. 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. • Eg:np.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

  10. Bitwise operators • and (&): (int,int) -> int :0011 & 0101 returns the result 0001 inclusive • Or (|): (int,int) -> int: 0011 & 0101 returns the result 0111 • not (~): (int) -> int: ~01 returns the result 10 • exclusive or(^): (int,int) -> int :0011 & 0101 returns the result 0110 • shift left (<<): (int,int) -> int: 101 << 2 returns the result 10100 For shifting left, 0s are added on the right • shift right (>>): (int,int) -> int: 101 >> 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

  11. Statistical functions • arr_pop=np.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/P:402.5 • Standard deviation: finding standard deviation of elements in the array print(np.std(arr_pop)) ......O/P:268.762 • Variance: finding variance of elements in the array print(np.var(arr_pop)) ......O/P:72233.333

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