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What is the difference bet w een a N u mP y arra y and a list ? P R AC TIC IN G C OD IN G IN TE R VIE W QU E STION S IN P YTH ON Kirill Smirno v Data Science Cons u ltant , Altran N u mP y arra y import numpy as np num_array = np.array([1, 2,

  1. What is the difference bet w een a N u mP y arra y and a list ? P R AC TIC IN G C OD IN G IN TE R VIE W QU E STION S IN P YTH ON Kirill Smirno v Data Science Cons u ltant , Altran

  2. N u mP y arra y import numpy as np num_array = np.array([1, 2, 3, 4, 5]) print(num_array) [1 2 3 4 5] num_list = [1, 2, 3, 4, 5] print(num_list) [1, 2, 3, 4, 5] PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  3. Similarities bet w een an arra y and a list num_array = np.array([1, 2, 3, 4, 5]) num_list = [1, 2, 3, 4, 5] for item in num_array: for item in num_list: print(item) print(item) 1 1 2 2 3 3 4 4 5 5 PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  4. Similarities bet w een an arra y and a list num_array = np.array([1, 2, 3, 4, 5]) num_list = [1, 2, 3, 4, 5] num_array[1] num_list[1] 2 2 num_array[1:4] num_list[1:4] array([2, 3, 4]) [2, 3, 4] PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  5. Similarities bet w een an arra y and a list num_array = np.array([1, 2, 3, 4, 5]) num_list = [1, 2, 3, 4, 5] num_array[3] = 40 num_list[3] = 40 print(num_array) print(num_list) [1 2 3 40 5] [1, 2, 3, 40, 5] num_array[0:3] = [10, 20, 30] num_list[0:3] = [10, 20, 30] print(num_array) print(num_list) [10 20 30 40 5] [10, 20, 30, 40, 5] PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  6. Difference bet w een an arra y an a list N u mP y arra y s are designed for high e � cienc y comp u tations N u mP y arra y s store v al u es of the same t y pe PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  7. . dt y pe propert y num_array = np.array([1, 2, 3, 4, 5]) num_array.dtype dtype('int64') PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  8. Changing the data t y pe of an element num_array = np.array([1, 2, 3, 4, 5]) num_list = [1, 2, 3, 4, 5] num_list[2] = 'three' num_array[2] = 'three' print(num_list) ValueError [1, 2, 'three', 4, 5] PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  9. Specif y ing the data t y pe e x plicitl y num_array = np.array([1, 2, 3, 4, 5]) num_array = np.array([1, 2, 3, 4, 5], dtype = np.dtype('int64')) print(num_array) [1 2 3 4 5] num_array.dtype dtype('int64') PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  10. Specif y ing the data t y pe e x plicitl y num_array = np.array([1, 2, 3, 4, 5]) num_array = np.array([1, 2, 3, 4, 5], dtype = np.dtype('str')) print(num_array) ['1' '2' '3' '4' '5'] num_array.dtype dtype('<U1') PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  11. Object as a data t y pe num_array = np.array([1, 2, 3, 4, 5], dtype = np.dtype('O')) num_array[2] = 'three' print(num_array) [1 2 'three' 4 5] PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  12. Difference bet w een an arra y and a list N u mP y arra y s are designed for high e � cienc y comp u tations N u mP y arra y s store v al u es of a concrete data t y pe N u mP y arra y s ha v e a special w a y to access its elements PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  13. Accessing items list2d = [ array2d = np.array([ [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15] [11, 12, 13, 14, 15] ] ]) # Retrieve 8 # Retrieve 8 list2d[1][2] array2d[1][2] 8 8 PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  14. Accessing items list2d = [ array2d = np.array([ [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15] [11, 12, 13, 14, 15] ] ]) # Retrieve 8 # Retrieve 8 list2d[1][2] array2d[1, 2] 8 8 PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  15. Accessing items list2d = [ array2d = np.array([ [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15] [11, 12, 13, 14, 15] ] ]) # Retrieve [[2, 3, 4], [7, 8, 9]] # Retrieve [[2, 3, 4], [7, 8, 9]] PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  16. Accessing items list2d = [ array2d = np.array([ [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15] [11, 12, 13, 14, 15] ] ]) # Retrieve [[2, 3, 4], [7, 8, 9]] # Retrieve [[2, 3, 4], [7, 8, 9]] [ [list2d[j][1:4] for j in range(0, 2)] ] [[2, 3, 4], [7, 8, 9]] PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  17. Accessing items list2d = [ array2d = np.array([ [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15] [11, 12, 13, 14, 15] ] ]) # Retrieve [[2, 3, 4], [7, 8, 9]] # Retrieve [[2, 3, 4], [7, 8, 9]] [ array2d[0:2, 1:4] [list2d[j][1:4] for j in range(0, 2)] ] array([[2, 3, 4], [7, 8, 9]]) [[2, 3, 4], [7, 8, 9]] PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  18. Difference bet w een an arra y and a list N u mP y arra y s are designed for high e � cienc y comp u tations N u mP y arra y s store v al u es of a concrete data t y pe N u mP y arra y s ha v e a special w a y to access its elements N u mP y arra y s ha v e e � cient w a y to perform operations on them . PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  19. Operations +, -, *, / w ith lists num_list1 = [1, 2, 3] num_list1 * num_list2 num_list2 = [10, 20, 30] TypError num_list1 + num_list2 num_list2 / num_list1 [1, 2, 3, 10, 20, 30] TypeError num_list2 - num_list1 TypeError PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  20. Operations +, -, *, / w ith arra y s num_array1 = np.array([1, 2, 3]) num_array1 * num_array2 num_array2 = np.array([10, 20, 30]) array([10, 40, 90]) num_array1 + num_array2 num_array2 / num_array1 array([11, 22, 33]) array([10, 10, 10]) num_array2 - num_array1 array([9, 18, 27]) PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  21. Operations +, -, *, / w ith m u ltidimensional arra y s num_array1 = np.array([ num_array1 + num_array2 [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], array([[ 11, 22, 33, 44, 55], [11, 12, 13,14, 15] [ 66, 77, 88, 99, 110], ]) [121, 132, 143, 154, 165]]) num_array2 = np.array([ [10, 20, 30, 40, 50], num_array2 / num_array1 [60, 70, 80, 90, 100], [110, 120, 130,140, 150] array([[10., 10., 10., 10., 10.], ]) [10., 10., 10., 10., 10.], [10., 10., 10., 10., 10.]]) PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  22. Conditional operations > , < , >= , <= , == , != num_array = np.array([-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]) num_array < 0 array([True, True, True, False, False, False, False]) num_array[num_array < 0] array([-5, -4, -3, -2, -1]) PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  23. Broadcasting num_array = np.array([1, 2, 3]) num_list = [1, 2, 3] num_array * 3 num_list * 3 array([3, 6, 9]) [1, 2, 3, 1, 2, 3, 1, 2, 3] num_array + 3 array([4, 5, 6]) PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  24. Broadcasting w ith m u ltidimensional arra y s array2d 3 x 4 ( ) array2d / array1d array2d = np.array([ array([[1., 1., 1., 1.], [1, 2, 3, 4], [1., 1., 1., 1.], [1, 2, 3, 4], [1., 1., 1., 1.]]) [1, 2, 3, 4] ]) array1d 1 x 4 ( ) array1d = np.array([1, 2, 3, 4]) PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  25. Broadcasting w ith m u ltidimensional arra y s array2d 3 x 4 ( ) array2d / array1d array2d = np.array([ array([[1. , 2. , 3. , 4. ], [1, 2, 3, 4], [0.5 , 1. , 1.5 , 2. ], [1, 2, 3, 4], [0.333, 0.667, 1. , 1.333]]) [1, 2, 3, 4] ]) array1d 3 x 1 ( ) array1d = np.array([[1], [2], [3]]) PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  26. Let ' s practice P R AC TIC IN G C OD IN G IN TE R VIE W QU E STION S IN P YTH ON

  27. Ho w to u se the . appl y() method on a DataFrame ? P R AC TIC IN G C OD IN G IN TE R VIE W QU E STION S IN P YTH ON Kirill Smirno v Data Science Cons u ltant , Altran

  28. Dataset import pandas as pd scores = pd.read_csv('exams.csv') scores = scores[['math score', 'reading score', 'writing score']] print(scores.head()) math score reading score writing score 0 74 86 82 1 44 49 53 2 54 46 43 3 88 95 92 4 85 81 81 PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  29. Defa u lt . appl y() df.apply(function) import numpy as np scores_new = scores.apply(np.sqrt) print(scores.head()) print(score_new) math score reading score writing score math score reading score writing score 0 74 86 82 0 8.602325 9.273618 9.055385 1 44 49 53 1 6.633250 7.000000 7.280110 2 54 46 43 2 7.348469 6.782330 6.557439 3 88 95 92 3 9.380832 9.746794 9.591663 4 85 81 81 4 9.219544 9.000000 9.000000 ... PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

  30. Defa u lt . appl y() df.apply(function) import numpy as np scores_new = scores.apply(np.mean) print(scores.head()) print(score_new.head()) math score reading score writing score math score 65.18 0 74 86 82 reading score 69.28 1 44 49 53 writing score 67.96 2 54 46 43 dtype: float64 3 88 95 92 4 85 81 81 type(scores_new) pandas.core.series.Series PRACTICING CODING INTERVIEW QUESTIONS IN PYTHON

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