cs 115 lecture 4
play

CS 115 Lecture 4 More Python; testing software Neil Moore - PowerPoint PPT Presentation

Nov 03, 2023 •318 likes •2.06k views

CS 115 Lecture 4 More Python; testing software Neil Moore Department of Computer Science University of Kentucky Lexington, Kentucky 40506 neil@cs.uky.edu 8 September 2015 Syntax: Statements A statement is the smallest unit of code that can

  1. Floating-point Type float represents floating-point numbers : numbers with a decimal point. Limited precision and range. Two forms of literal floats: ◮ Number with a decimal point: 3.14 , .027 , 0.001 , 3. , 1.0 ⋆ Must have a decimal point! ⋆ 1.0 or 1. is a float, but 1 is an integer! ◮ “E”-notation (scientific notation): ⋆ 6.022e23 , 1.0E9 , 31e-2 ⋆ Write e for “times 10 to the” ⋆ Does not need a decimal point: the e is enough. ⋆ Exponent must be an integer. In some languages these are called “doubles” Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 6 / 26

  2. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  3. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  4. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  5. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. ◮ Try: 10000000000000002.0 - 10000000000000001.0 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  6. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. ◮ Try: 10000000000000002.0 - 10000000000000001.0 ⋆ Python’s answer is 2.0 : the 1 was rounded down! Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  7. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. ◮ Try: 10000000000000002.0 - 10000000000000001.0 ⋆ Python’s answer is 2.0 : the 1 was rounded down! The exponent can go from about -300 to 300. ◮ Limits the range of a float. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  8. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. ◮ Try: 10000000000000002.0 - 10000000000000001.0 ⋆ Python’s answer is 2.0 : the 1 was rounded down! The exponent can go from about -300 to 300. ◮ Limits the range of a float. ◮ Try: 1e309 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  9. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. ◮ Try: 10000000000000002.0 - 10000000000000001.0 ⋆ Python’s answer is 2.0 : the 1 was rounded down! The exponent can go from about -300 to 300. ◮ Limits the range of a float. ◮ Try: 1e309 —gives inf (infinity) Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  10. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. ◮ Try: 10000000000000002.0 - 10000000000000001.0 ⋆ Python’s answer is 2.0 : the 1 was rounded down! The exponent can go from about -300 to 300. ◮ Limits the range of a float. ◮ Try: 1e309 —gives inf (infinity) ◮ Try: 1e-324 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  11. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. ◮ Try: 10000000000000002.0 - 10000000000000001.0 ⋆ Python’s answer is 2.0 : the 1 was rounded down! The exponent can go from about -300 to 300. ◮ Limits the range of a float. ◮ Try: 1e309 —gives inf (infinity) ◮ Try: 1e-324 —gives 0.0 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  12. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. ◮ Try: 10000000000000002.0 - 10000000000000001.0 ⋆ Python’s answer is 2.0 : the 1 was rounded down! The exponent can go from about -300 to 300. ◮ Limits the range of a float. ◮ Try: 1e309 —gives inf (infinity) ◮ Try: 1e-324 —gives 0.0 The exact limits are on the number of bits, not digits. ◮ Even 0.1 can’t be represented exactly ⋆ Try: 0.1 + 0.1 + 0.1 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  13. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. ◮ Try: 10000000000000002.0 - 10000000000000001.0 ⋆ Python’s answer is 2.0 : the 1 was rounded down! The exponent can go from about -300 to 300. ◮ Limits the range of a float. ◮ Try: 1e309 —gives inf (infinity) ◮ Try: 1e-324 —gives 0.0 The exact limits are on the number of bits, not digits. ◮ Even 0.1 can’t be represented exactly ⋆ Try: 0.1 + 0.1 + 0.1 —gives 0.30000000000000004 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  14. Floating-point limitations Floats are stored in a binary form of scientific notation: ◮ Mantissa : the digits (in binary). ◮ Exponent : how far to move the decimal (binary) point. In Python, the mantissa holds about 15 significant digits. ◮ Any digits past that are lost (rounding error). ⋆ (leading and trailing zeros don’t count) ◮ Limits the precision of a float. ◮ Try: 10000000000000002.0 - 10000000000000001.0 ⋆ Python’s answer is 2.0 : the 1 was rounded down! The exponent can go from about -300 to 300. ◮ Limits the range of a float. ◮ Try: 1e309 —gives inf (infinity) ◮ Try: 1e-324 —gives 0.0 The exact limits are on the number of bits, not digits. ◮ Even 0.1 can’t be represented exactly ⋆ Try: 0.1 + 0.1 + 0.1 —gives 0.30000000000000004 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 7 / 26

  15. Arithmetic on integers and floats You can perform arithmetic on both ints and floats. For most arithmetic operators ( + - * ** ) the rules are: If both operands are ints, the result is an int. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 8 / 26

  16. Arithmetic on integers and floats You can perform arithmetic on both ints and floats. For most arithmetic operators ( + - * ** ) the rules are: If both operands are ints, the result is an int. ◮ 3 + 5 → 8 ◮ 2 ** 100 → 1267650600228229401496703205376 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 8 / 26

  17. Arithmetic on integers and floats You can perform arithmetic on both ints and floats. For most arithmetic operators ( + - * ** ) the rules are: If both operands are ints, the result is an int. ◮ 3 + 5 → 8 ◮ 2 ** 100 → 1267650600228229401496703205376 If one or both operand is a float, the result is a float. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 8 / 26

  18. Arithmetic on integers and floats You can perform arithmetic on both ints and floats. For most arithmetic operators ( + - * ** ) the rules are: If both operands are ints, the result is an int. ◮ 3 + 5 → 8 ◮ 2 ** 100 → 1267650600228229401496703205376 If one or both operand is a float, the result is a float. ◮ 3.0 + 0.14 → 3.14 ◮ 100 - 1.0 → 99.0 ◮ 2.0 ** 100 → 1.2676506002282294e+30 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 8 / 26

  19. Arithmetic on integers and floats You can perform arithmetic on both ints and floats. For most arithmetic operators ( + - * ** ) the rules are: If both operands are ints, the result is an int. ◮ 3 + 5 → 8 ◮ 2 ** 100 → 1267650600228229401496703205376 If one or both operand is a float, the result is a float. ◮ 3.0 + 0.14 → 3.14 ◮ 100 - 1.0 → 99.0 ◮ 2.0 ** 100 → 1.2676506002282294e+30 There is one exception. . . Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 8 / 26

  20. Arithmetic on integers and floats You can perform arithmetic on both ints and floats. For most arithmetic operators ( + - * ** ) the rules are: If both operands are ints, the result is an int. ◮ 3 + 5 → 8 ◮ 2 ** 100 → 1267650600228229401496703205376 If one or both operand is a float, the result is a float. ◮ 3.0 + 0.14 → 3.14 ◮ 100 - 1.0 → 99.0 ◮ 2.0 ** 100 → 1.2676506002282294e+30 There is one exception. . . ◮ What is 1/2 ? Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 8 / 26

  21. Arithmetic on integers and floats You can perform arithmetic on both ints and floats. For most arithmetic operators ( + - * ** ) the rules are: If both operands are ints, the result is an int. ◮ 3 + 5 → 8 ◮ 2 ** 100 → 1267650600228229401496703205376 If one or both operand is a float, the result is a float. ◮ 3.0 + 0.14 → 3.14 ◮ 100 - 1.0 → 99.0 ◮ 2.0 ** 100 → 1.2676506002282294e+30 There is one exception. . . ◮ What is 1/2 ? Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 8 / 26

  22. Division Python actually has two division operators, / and // . / always gives a float. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 9 / 26

  23. Division Python actually has two division operators, / and // . / always gives a float. ◮ 1 / 2 → 0.5 ◮ 6 / 3 → 2.0 ◮ 3.0 / 0.5 → 6.0 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 9 / 26

  24. Division Python actually has two division operators, / and // . / always gives a float. ◮ 1 / 2 → 0.5 ◮ 6 / 3 → 2.0 ◮ 3.0 / 0.5 → 6.0 // does floor division : rounds the answer down to a whole number. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 9 / 26

  25. Division Python actually has two division operators, / and // . / always gives a float. ◮ 1 / 2 → 0.5 ◮ 6 / 3 → 2.0 ◮ 3.0 / 0.5 → 6.0 // does floor division : rounds the answer down to a whole number. ◮ If both operands are integers, so is the result. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 9 / 26

  26. Division Python actually has two division operators, / and // . / always gives a float. ◮ 1 / 2 → 0.5 ◮ 6 / 3 → 2.0 ◮ 3.0 / 0.5 → 6.0 // does floor division : rounds the answer down to a whole number. ◮ If both operands are integers, so is the result. ⋆ 22 // 7 → 3 ⋆ 1 // 2 → 0 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 9 / 26

  27. Division Python actually has two division operators, / and // . / always gives a float. ◮ 1 / 2 → 0.5 ◮ 6 / 3 → 2.0 ◮ 3.0 / 0.5 → 6.0 // does floor division : rounds the answer down to a whole number. ◮ If both operands are integers, so is the result. ⋆ 22 // 7 → 3 ⋆ 1 // 2 → 0 ◮ If either operand is a float, so is the result. ⋆ But it still has a whole-number value. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 9 / 26

  28. Division Python actually has two division operators, / and // . / always gives a float. ◮ 1 / 2 → 0.5 ◮ 6 / 3 → 2.0 ◮ 3.0 / 0.5 → 6.0 // does floor division : rounds the answer down to a whole number. ◮ If both operands are integers, so is the result. ⋆ 22 // 7 → 3 ⋆ 1 // 2 → 0 ◮ If either operand is a float, so is the result. ⋆ But it still has a whole-number value. ⋆ 22 // 7.0 → 3.0 ⋆ 3.1 // 0.5 → 6.0 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 9 / 26

  29. Division Python actually has two division operators, / and // . / always gives a float. ◮ 1 / 2 → 0.5 ◮ 6 / 3 → 2.0 ◮ 3.0 / 0.5 → 6.0 // does floor division : rounds the answer down to a whole number. ◮ If both operands are integers, so is the result. ⋆ 22 // 7 → 3 ⋆ 1 // 2 → 0 ◮ If either operand is a float, so is the result. ⋆ But it still has a whole-number value. ⋆ 22 // 7.0 → 3.0 ⋆ 3.1 // 0.5 → 6.0 In either case, dividing by zero is a run-time error! Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 9 / 26

  30. Division Python actually has two division operators, / and // . / always gives a float. ◮ 1 / 2 → 0.5 ◮ 6 / 3 → 2.0 ◮ 3.0 / 0.5 → 6.0 // does floor division : rounds the answer down to a whole number. ◮ If both operands are integers, so is the result. ⋆ 22 // 7 → 3 ⋆ 1 // 2 → 0 ◮ If either operand is a float, so is the result. ⋆ But it still has a whole-number value. ⋆ 22 // 7.0 → 3.0 ⋆ 3.1 // 0.5 → 6.0 In either case, dividing by zero is a run-time error! Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 9 / 26

  31. Remainder (modulo) The % operator ( modulo or mod ) finds the remainder of a division. Between 0 (inclusive) and the right hand side (exclusive). Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 10 / 26

  32. Remainder (modulo) The % operator ( modulo or mod ) finds the remainder of a division. Between 0 (inclusive) and the right hand side (exclusive). ◮ 6 % 3 → 0 ◮ 7 % 3 → 1 ◮ 8 % 3 → 2 ◮ 9 % 3 → 0 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 10 / 26

  33. Remainder (modulo) The % operator ( modulo or mod ) finds the remainder of a division. Between 0 (inclusive) and the right hand side (exclusive). ◮ 6 % 3 → 0 ◮ 7 % 3 → 1 ◮ 8 % 3 → 2 ◮ 9 % 3 → 0 Uses of modulo: ◮ Even/odd: n is even if n % 2 is zero. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 10 / 26

  34. Remainder (modulo) The % operator ( modulo or mod ) finds the remainder of a division. Between 0 (inclusive) and the right hand side (exclusive). ◮ 6 % 3 → 0 ◮ 7 % 3 → 1 ◮ 8 % 3 → 2 ◮ 9 % 3 → 0 Uses of modulo: ◮ Even/odd: n is even if n % 2 is zero. ◮ Digits: n % 10 is the last digit of n . Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 10 / 26

  35. Remainder (modulo) The % operator ( modulo or mod ) finds the remainder of a division. Between 0 (inclusive) and the right hand side (exclusive). ◮ 6 % 3 → 0 ◮ 7 % 3 → 1 ◮ 8 % 3 → 2 ◮ 9 % 3 → 0 Uses of modulo: ◮ Even/odd: n is even if n % 2 is zero. ◮ Digits: n % 10 is the last digit of n . ◮ “Clock arithmetic” ⋆ Minutes are mod 60: 3: 58 + 15 minutes = 4: 13 ⋆ Hours are mod 12: 10 :00 + 4 hours = 2 :00 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 10 / 26

  36. Remainder (modulo) The % operator ( modulo or mod ) finds the remainder of a division. Between 0 (inclusive) and the right hand side (exclusive). ◮ 6 % 3 → 0 ◮ 7 % 3 → 1 ◮ 8 % 3 → 2 ◮ 9 % 3 → 0 Uses of modulo: ◮ Even/odd: n is even if n % 2 is zero. ◮ Digits: n % 10 is the last digit of n . ◮ “Clock arithmetic” ⋆ Minutes are mod 60: 3: 58 + 15 minutes = 4: 13 ⋆ Hours are mod 12: 10 :00 + 4 hours = 2 :00 Python can do modulo on floats. ◮ 5 % 2.4 → 0.2 ◮ Far more common with ints, though. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 10 / 26

  37. Remainder (modulo) The % operator ( modulo or mod ) finds the remainder of a division. Between 0 (inclusive) and the right hand side (exclusive). ◮ 6 % 3 → 0 ◮ 7 % 3 → 1 ◮ 8 % 3 → 2 ◮ 9 % 3 → 0 Uses of modulo: ◮ Even/odd: n is even if n % 2 is zero. ◮ Digits: n % 10 is the last digit of n . ◮ “Clock arithmetic” ⋆ Minutes are mod 60: 3: 58 + 15 minutes = 4: 13 ⋆ Hours are mod 12: 10 :00 + 4 hours = 2 :00 Python can do modulo on floats. ◮ 5 % 2.4 → 0.2 ◮ Far more common with ints, though. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 10 / 26

  38. Booleans Type bool represents boolean values . Named after George Boole, English mathematician and logician. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 11 / 26

  39. Booleans Type bool represents boolean values . Named after George Boole, English mathematician and logician. George Boole, English computer scientist. Image: Computer History Museum Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 11 / 26

  40. Booleans Type bool represents boolean values . Named after George Boole, English mathematician and logician. Exactly two values: True , False Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 11 / 26

  41. Booleans Type bool represents boolean values . Named after George Boole, English mathematician and logician. Exactly two values: True , False ◮ No quotes! They aren’t strings. ◮ Case-sensitive as always: capital T and F. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 11 / 26

  42. Booleans Type bool represents boolean values . Named after George Boole, English mathematician and logician. Exactly two values: True , False ◮ No quotes! They aren’t strings. ◮ Case-sensitive as always: capital T and F. Boolean values are the basis of computer circuits: EE 280. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 11 / 26

  43. Booleans Type bool represents boolean values . Named after George Boole, English mathematician and logician. Exactly two values: True , False ◮ No quotes! They aren’t strings. ◮ Case-sensitive as always: capital T and F. Boolean values are the basis of computer circuits: EE 280. Can’t do arithmetic on them. ◮ Can do and , or , and not . Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 11 / 26

  44. Booleans Type bool represents boolean values . Named after George Boole, English mathematician and logician. Exactly two values: True , False ◮ No quotes! They aren’t strings. ◮ Case-sensitive as always: capital T and F. Boolean values are the basis of computer circuits: EE 280. Can’t do arithmetic on them. ◮ Can do and , or , and not . ◮ Most often used with if and while statements. ◮ More about boolean operations in chapter 4. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 11 / 26

  45. Booleans Type bool represents boolean values . Named after George Boole, English mathematician and logician. Exactly two values: True , False ◮ No quotes! They aren’t strings. ◮ Case-sensitive as always: capital T and F. Boolean values are the basis of computer circuits: EE 280. Can’t do arithmetic on them. ◮ Can do and , or , and not . ◮ Most often used with if and while statements. ◮ More about boolean operations in chapter 4. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 11 / 26

  46. Strings Type str represents strings : sequences of characters. Literal strings: a sequence of characters in single or double quotes. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 12 / 26

  47. Strings Type str represents strings : sequences of characters. Literal strings: a sequence of characters in single or double quotes. ◮ ’hello’ , "world" , "" ◮ Use whichever quote isn’t in the string: ’some "quotes"’ , "a’postrophe" Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 12 / 26

  48. Strings Type str represents strings : sequences of characters. Literal strings: a sequence of characters in single or double quotes. ◮ ’hello’ , "world" , "" ◮ Use whichever quote isn’t in the string: ’some "quotes"’ , "a’postrophe" ◮ If you have to include the quote character, escape it with a backslash: msg = ’the word "don \ ’t" is 5 characters long’ ◮ Have to escape backslashes, too: folder = "C: \\ Python 3.4" Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 12 / 26

  49. Strings Type str represents strings : sequences of characters. Literal strings: a sequence of characters in single or double quotes. ◮ ’hello’ , "world" , "" ◮ Use whichever quote isn’t in the string: ’some "quotes"’ , "a’postrophe" ◮ If you have to include the quote character, escape it with a backslash: msg = ’the word "don \ ’t" is 5 characters long’ ◮ Have to escape backslashes, too: folder = "C: \\ Python 3.4" ◮ Special characters: tab \ t , newline \ n . Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 12 / 26

  50. Strings Type str represents strings : sequences of characters. Literal strings: a sequence of characters in single or double quotes. ◮ ’hello’ , "world" , "" ◮ Use whichever quote isn’t in the string: ’some "quotes"’ , "a’postrophe" ◮ If you have to include the quote character, escape it with a backslash: msg = ’the word "don \ ’t" is 5 characters long’ ◮ Have to escape backslashes, too: folder = "C: \\ Python 3.4" ◮ Special characters: tab \ t , newline \ n . Can perform a few operations on strings: ◮ Concatenate (join) strings with + : greeting = "Hello, " + name Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 12 / 26

  51. Strings Type str represents strings : sequences of characters. Literal strings: a sequence of characters in single or double quotes. ◮ ’hello’ , "world" , "" ◮ Use whichever quote isn’t in the string: ’some "quotes"’ , "a’postrophe" ◮ If you have to include the quote character, escape it with a backslash: msg = ’the word "don \ ’t" is 5 characters long’ ◮ Have to escape backslashes, too: folder = "C: \\ Python 3.4" ◮ Special characters: tab \ t , newline \ n . Can perform a few operations on strings: ◮ Concatenate (join) strings with + : greeting = "Hello, " + name ◮ Repeat a string by “multiplying” with an integer: rating = ’ ⋆ ’ * 4 # ⋆ ⋆ ⋆⋆ bird = 2 * ’do’ # dodo Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 12 / 26

  52. Converting between types Converting between types is also called type casting . Write the name of the type you are converting to, then the expression to convert in parentheses Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 13 / 26

  53. Converting between types Converting between types is also called type casting . Write the name of the type you are converting to, then the expression to convert in parentheses: ◮ float(2) → 2.0 ◮ int(3.14) → 3 ◮ str(1.2e3) → "1200.0" ◮ int("02") → 2 ◮ float("0") → 0.0 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 13 / 26

  54. Converting between types Converting between types is also called type casting . Write the name of the type you are converting to, then the expression to convert in parentheses: ◮ float(2) → 2.0 ◮ int(3.14) → 3 ◮ str(1.2e3) → "1200.0" ◮ int("02") → 2 ◮ float("0") → 0.0 Converting double to int rounds towards zero (+ down, − up) Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 13 / 26

  55. Converting between types Converting between types is also called type casting . Write the name of the type you are converting to, then the expression to convert in parentheses: ◮ float(2) → 2.0 ◮ int(3.14) → 3 ◮ str(1.2e3) → "1200.0" ◮ int("02") → 2 ◮ float("0") → 0.0 Converting double to int rounds towards zero (+ down, − up) Run-time error if a string could not be converted: ◮ n = int("hello") # CRASHES with ValueError Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 13 / 26

  56. Converting between types Converting between types is also called type casting . Write the name of the type you are converting to, then the expression to convert in parentheses: ◮ float(2) → 2.0 ◮ int(3.14) → 3 ◮ str(1.2e3) → "1200.0" ◮ int("02") → 2 ◮ float("0") → 0.0 Converting double to int rounds towards zero (+ down, − up) Run-time error if a string could not be converted: ◮ n = int("hello") # CRASHES with ValueError ◮ p = int("3.2") # CRASHES, but int(float("3.2")) is OK Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 13 / 26

  57. Converting between types Converting between types is also called type casting . Write the name of the type you are converting to, then the expression to convert in parentheses: ◮ float(2) → 2.0 ◮ int(3.14) → 3 ◮ str(1.2e3) → "1200.0" ◮ int("02") → 2 ◮ float("0") → 0.0 Converting double to int rounds towards zero (+ down, − up) Run-time error if a string could not be converted: ◮ n = int("hello") # CRASHES with ValueError ◮ p = int("3.2") # CRASHES, but int(float("3.2")) is OK Converting a string does not do arithmetic: ◮ half = float("1/2") # CRASHES Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 13 / 26

  58. Converting between types Converting between types is also called type casting . Write the name of the type you are converting to, then the expression to convert in parentheses: ◮ float(2) → 2.0 ◮ int(3.14) → 3 ◮ str(1.2e3) → "1200.0" ◮ int("02") → 2 ◮ float("0") → 0.0 Converting double to int rounds towards zero (+ down, − up) Run-time error if a string could not be converted: ◮ n = int("hello") # CRASHES with ValueError ◮ p = int("3.2") # CRASHES, but int(float("3.2")) is OK Converting a string does not do arithmetic: ◮ half = float("1/2") # CRASHES Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 13 / 26

  59. Output: print Every program needs to do output of some kind: to the screen, a file, etc. In Python, we use the print function. Sends output to “standard output”. ◮ This is usually the shell window. ◮ Or the window that appears when you double-click a Python program. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 14 / 26

  60. Output: print Every program needs to do output of some kind: to the screen, a file, etc. In Python, we use the print function. Sends output to “standard output”. ◮ This is usually the shell window. ◮ Or the window that appears when you double-click a Python program. Syntax: print( arguments ) ◮ arguments is a comma-separated list of things to print. ⋆ Can have zero, one, or more arguments. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 14 / 26

  61. Output: print Every program needs to do output of some kind: to the screen, a file, etc. In Python, we use the print function. Sends output to “standard output”. ◮ This is usually the shell window. ◮ Or the window that appears when you double-click a Python program. Syntax: print( arguments ) ◮ arguments is a comma-separated list of things to print. ⋆ Can have zero, one, or more arguments. ◮ Each argument can be a literal, variable, expression, . . . ◮ Arguments can be any type: string, integer, float, . . . Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 14 / 26

  62. Output: print Every program needs to do output of some kind: to the screen, a file, etc. In Python, we use the print function. Sends output to “standard output”. ◮ This is usually the shell window. ◮ Or the window that appears when you double-click a Python program. Syntax: print( arguments ) ◮ arguments is a comma-separated list of things to print. ⋆ Can have zero, one, or more arguments. ◮ Each argument can be a literal, variable, expression, . . . ◮ Arguments can be any type: string, integer, float, . . . ⋆ print("Welcome to my program") Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 14 / 26

  63. Output: print Every program needs to do output of some kind: to the screen, a file, etc. In Python, we use the print function. Sends output to “standard output”. ◮ This is usually the shell window. ◮ Or the window that appears when you double-click a Python program. Syntax: print( arguments ) ◮ arguments is a comma-separated list of things to print. ⋆ Can have zero, one, or more arguments. ◮ Each argument can be a literal, variable, expression, . . . ◮ Arguments can be any type: string, integer, float, . . . ⋆ print("Welcome to my program") ⋆ print(6 * 7) Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 14 / 26

  64. Output: print Every program needs to do output of some kind: to the screen, a file, etc. In Python, we use the print function. Sends output to “standard output”. ◮ This is usually the shell window. ◮ Or the window that appears when you double-click a Python program. Syntax: print( arguments ) ◮ arguments is a comma-separated list of things to print. ⋆ Can have zero, one, or more arguments. ◮ Each argument can be a literal, variable, expression, . . . ◮ Arguments can be any type: string, integer, float, . . . ⋆ print("Welcome to my program") ⋆ print(6 * 7) ⋆ print("Hello", name) Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 14 / 26

  65. Output: print Every program needs to do output of some kind: to the screen, a file, etc. In Python, we use the print function. Sends output to “standard output”. ◮ This is usually the shell window. ◮ Or the window that appears when you double-click a Python program. Syntax: print( arguments ) ◮ arguments is a comma-separated list of things to print. ⋆ Can have zero, one, or more arguments. ◮ Each argument can be a literal, variable, expression, . . . ◮ Arguments can be any type: string, integer, float, . . . ⋆ print("Welcome to my program") ⋆ print(6 * 7) ⋆ print("Hello", name) ⋆ print() Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 14 / 26

  66. Output: print Every program needs to do output of some kind: to the screen, a file, etc. In Python, we use the print function. Sends output to “standard output”. ◮ This is usually the shell window. ◮ Or the window that appears when you double-click a Python program. Syntax: print( arguments ) ◮ arguments is a comma-separated list of things to print. ⋆ Can have zero, one, or more arguments. ◮ Each argument can be a literal, variable, expression, . . . ◮ Arguments can be any type: string, integer, float, . . . ⋆ print("Welcome to my program") ⋆ print(6 * 7) ⋆ print("Hello", name) ⋆ print() Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 14 / 26

  67. Semantics of print Evaluates the arguments (computes their values). Prints values to standard output, starting at the cursor. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 15 / 26

  68. Semantics of print Evaluates the arguments (computes their values). Prints values to standard output, starting at the cursor. If multiple arguments are given, puts a space between. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 15 / 26

  69. Semantics of print Evaluates the arguments (computes their values). Prints values to standard output, starting at the cursor. If multiple arguments are given, puts a space between. Outputs a “newline” character at the end. ◮ Moves the cursor to the beginning of the next line. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 15 / 26

  70. Semantics of print Evaluates the arguments (computes their values). Prints values to standard output, starting at the cursor. If multiple arguments are given, puts a space between. Outputs a “newline” character at the end. ◮ Moves the cursor to the beginning of the next line. ◮ No-argument print() prints just a newline. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 15 / 26

  71. Semantics of print Evaluates the arguments (computes their values). Prints values to standard output, starting at the cursor. If multiple arguments are given, puts a space between. Outputs a “newline” character at the end. ◮ Moves the cursor to the beginning of the next line. ◮ No-argument print() prints just a newline. The print function does not return a value. ◮ That means you can’t (usefully) use it in an expression: x = print(2) # BAD Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 15 / 26

  72. Semantics of print Evaluates the arguments (computes their values). Prints values to standard output, starting at the cursor. If multiple arguments are given, puts a space between. Outputs a “newline” character at the end. ◮ Moves the cursor to the beginning of the next line. ◮ No-argument print() prints just a newline. The print function does not return a value. ◮ That means you can’t (usefully) use it in an expression: x = print(2) # BAD ⋆ This isn’t a syntax error, but x ’s value will be None . ⋆ Not very useful: usually a semantic error. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 15 / 26

  73. Semantics of print Evaluates the arguments (computes their values). Prints values to standard output, starting at the cursor. If multiple arguments are given, puts a space between. Outputs a “newline” character at the end. ◮ Moves the cursor to the beginning of the next line. ◮ No-argument print() prints just a newline. The print function does not return a value. ◮ That means you can’t (usefully) use it in an expression: x = print(2) # BAD ⋆ This isn’t a syntax error, but x ’s value will be None . ⋆ Not very useful: usually a semantic error. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 15 / 26

  74. Extra arguments to print Sometimes you don’t want spaces between the arguments, or don’t want a newline at the end. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 16 / 26

  75. Extra arguments to print Sometimes you don’t want spaces between the arguments, or don’t want a newline at the end. You can control these with so-called keyword arguments . sep= string : Use string to separate arguments instead of space. Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 16 / 26

  76. Extra arguments to print Sometimes you don’t want spaces between the arguments, or don’t want a newline at the end. You can control these with so-called keyword arguments . sep= string : Use string to separate arguments instead of space. ◮ print(month, day, year, sep = "/") ⋆ Might output: 1/27/2015 Neil Moore (UK CS) CS 115 Lecture 4 Fall 2015 16 / 26

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Lecture # 5 - Monday, Aug 30th In this lecture I reviewed the previous lecture 4, and then

Lecture # 5 - Monday, Aug 30th In this lecture I reviewed the previous lecture 4, and then

Lecture # 5 - Monday, Aug 30th In this lecture I reviewed the previous lecture 4, and then reviewed the final point of lecture #3: Hennigs logical approach to tree inference is valid as logic, but we have a hard time using it because our data

660 views • 7 slides
Algorithms (2IL15) Lecture 13 Wrap-up lecture 1 TU/e Algorithms (2IL15) Lecture 13

Algorithms (2IL15) Lecture 13 Wrap-up lecture 1 TU/e Algorithms (2IL15) Lecture 13

TU/e Algorithms (2IL15) Lecture 13 Algorithms (2IL15) Lecture 13 Wrap-up lecture 1 TU/e Algorithms (2IL15) Lecture 13 Part I of the course: Optimization Problems we want to find valid solution minimizing cost (or maximizing

904 views • 20 slides
In 2020SP, this lecture and lecture 20 are both optional extra material CS 5412/LECTURE 17 Ken

In 2020SP, this lecture and lecture 20 are both optional extra material CS 5412/LECTURE 17 Ken

In 2020SP, this lecture and lecture 20 are both optional extra material CS 5412/LECTURE 17 Ken Birman LEAVE NO TRACE BEHIND Spring, 2020 HTTP://WWW.CS.CORNELL.EDU/COURSES/CS5412/2020SP 1 THE PRIVACY PUZZLE FOR I O T We have sensors

1.04k views • 65 slides
Recall last lecture ... Lecture 8 Also last lecture: Painter's Algorithm More Hidden Surface

Recall last lecture ... Lecture 8 Also last lecture: Painter's Algorithm More Hidden Surface

Recall last lecture ... Lecture 8 Also last lecture: Painter's Algorithm More Hidden Surface Removal Sort polygons in depth. Use the farthest vertex in each polygon as the sorting key. Efficient Painter - binary space partition (BSP)

563 views • 6 slides
Plan Lecture 1 - String diagrams and symmetric monoidal categories Lecture 2 -

Plan Lecture 1 - String diagrams and symmetric monoidal categories Lecture 2 -

Plan Lecture 1 - String diagrams and symmetric monoidal categories Lecture 2 - Resource-sensitive algebraic theories Lecture 3 - Interacting Hopf monoids and graphical linear algebra Lecture 4 - Signal Flow Graphs and recurrence

1.2k views • 61 slides
Where are we at - Topic overview Lecture 1A: Security requirements/features Lecture 7A

Where are we at - Topic overview Lecture 1A: Security requirements/features Lecture 7A

Where are we at - Topic overview Lecture 1A: Security requirements/features Lecture 7A Threatens Privacy Threatens Try to achieve Lecture 2B: Network threats Lecture 3A: Attacks on Lecture 6A: Security Protocols Web servers, malware

381 views • 25 slides
Lecture Capture Introduction to Lecture Capture Learning Outcomes What will lecture capture

Lecture Capture Introduction to Lecture Capture Learning Outcomes What will lecture capture

ISDS & Digital Business Skills Lecture Capture Introduction to Lecture Capture Learning Outcomes What will lecture capture actually do? What happens before a lecture? What happens during a lecture? What does the button do?

527 views • 18 slides
CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How can we predict binary or categorical variables? {0,1}, {True, False} {1, , N} Last lecture Will I purchase this product? (yes) Will I click

1.74k views • 60 slides
CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How can we predict binary or categorical variables? {0,1}, {True, False} {1, , N} Last lecture Will I purchase this product? (yes) Will I click

850 views • 46 slides
CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How can we predict binary or categorical variables? {0,1}, {True, False} {1, , N} Last lecture Will I purchase this product? (yes) Will I click

839 views • 58 slides
Usability of Programming Languages Lecture 4 - directed by your research interests Lecture

Usability of Programming Languages Lecture 4 - directed by your research interests Lecture

Overview Practical experimental course lectures are only introductory Lecture 1 - theoretical principles classic approaches l h current trends in leading research. Lecture 2 - candidate research methods advantages and

499 views • 15 slides
Introduction to AI & Intelligent Agents This Lecture Chapters 1 and 2 Next Lecture

Introduction to AI & Intelligent Agents This Lecture Chapters 1 and 2 Next Lecture

Introduction to AI & Intelligent Agents This Lecture Chapters 1 and 2 Next Lecture Chapter 3.1 to 3.4 (Please read lecture topic material before and after each lecture on that topic) What is Artificial Intelligence? Thought

636 views • 31 slides
Introduction to Numerical Optimization Biostatistics 615/815 Lecture 14 Lecture 14 Course is

Introduction to Numerical Optimization Biostatistics 615/815 Lecture 14 Lecture 14 Course is

Introduction to Numerical Optimization Biostatistics 615/815 Lecture 14 Lecture 14 Course is More Than Half Done! If you have comments they are very welcome y y Lectures Lecture notes Weekly Homework Midterm

795 views • 42 slides
Lecture 12: Clustering 1 6.0002 LECTURE 12 Re Reading Chapter 23 6.0002 LECTURE 12 2 Mach Ma

Lecture 12: Clustering 1 6.0002 LECTURE 12 Re Reading Chapter 23 6.0002 LECTURE 12 2 Mach Ma

Lecture 12: Clustering 1 6.0002 LECTURE 12 Re Reading Chapter 23 6.0002 LECTURE 12 2 Mach Ma chine e Lea earn rning Paradigm Observe set of examples: training data Infer something about process that generated that data Use

847 views • 36 slides
Lecture Outline Regeltechniek Previous lecture: Stability and transient response. Lecture 4

Lecture Outline Regeltechniek Previous lecture: Stability and transient response. Lecture 4

Lecture Outline Regeltechniek Previous lecture: Stability and transient response. Lecture 4 Basics of Feedback Control Robert Babu ska Today: Steady-state response. Delft Center for Systems and Control Faculty of Mechanical

388 views • 7 slides
Psycholinguistics Lecture 2 By Dr.Chelli Lecture Objectives At the end of this lecture, students

Psycholinguistics Lecture 2 By Dr.Chelli Lecture Objectives At the end of this lecture, students

Psycholinguistics Lecture 2 By Dr.Chelli Lecture Objectives At the end of this lecture, students will be able to: Discuss the different stages in the history of psycholinguistics Identify the scholars in the field of psycholinguistics

589 views • 28 slides
Acknowledgement Jeremy Gow jennifer george 1 Last weeks lecture Measuring digital

Acknowledgement Jeremy Gow jennifer george 1 Last weeks lecture Measuring digital

FY04: Introduction to the use of computers jennifer george Acknowledgement Jeremy Gow jennifer george 1 Last weeks lecture Measuring digital data Bits Bytes Kilobytes Megabytes ... SI and Binary units

325 views • 17 slides
What does the data tell us about outcomes of EVAR in challenging anatomy? UCSF Vascular Surgery

What does the data tell us about outcomes of EVAR in challenging anatomy? UCSF Vascular Surgery

What does the data tell us about outcomes of EVAR in challenging anatomy? UCSF Vascular Surgery Symposium 2018 Sukgu M Han, MD, MS Assistant Professor of Clinical Surgery Co-director, Comprehensive Aortic Center Division of Vascular Sugery

693 views • 21 slides
Building a better NetFlow (to appear in SIGCOMM 2004) Cristian Estan, Ken Keys, David Moore,

Building a better NetFlow (to appear in SIGCOMM 2004) Cristian Estan, Ken Keys, David Moore,

Building a better NetFlow (to appear in SIGCOMM 2004) Cristian Estan, Ken Keys, David Moore, George Varghese University of California, San Diego IETF60 Aug 4, 2004 IPFIX WG UCSD CSE Disclaimers "NetFlow" used

510 views • 26 slides
It All Adds Up TODAY'S TAKEAWAYS KEY CONCEPTS ABOUT THE PRESENTERS OUR STORY Over 25 Years

It All Adds Up TODAY'S TAKEAWAYS KEY CONCEPTS ABOUT THE PRESENTERS OUR STORY Over 25 Years

Troubleshooting Melissa Moore, Certified Prevention 2020 ALCOHOL POLICY CONFERENCE Strengthen Relationships Strategies to Identify & Tips to Assess & Engage Finding What Works Our Story Specialist, Marathon County Health 2020

475 views • 12 slides
Introduction to Parallel Computing Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar To

Introduction to Parallel Computing Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar To

Introduction to Parallel Computing Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar To accompany the text Introduction to Parallel Computing, Addison Wesley, 2003. Topic Overview Motivating Parallelism Scope of Parallel

421 views • 15 slides
Chapter 3 Boolean Algebra and Digital Logic Chapter 3 Objectives Understand the relationship

Chapter 3 Boolean Algebra and Digital Logic Chapter 3 Objectives Understand the relationship

Chapter 3 Boolean Algebra and Digital Logic Chapter 3 Objectives Understand the relationship between Boolean logic and digital computer circuits. Learn how to design simple logic circuits. Understand how digital circuits work

898 views • 73 slides
Structured Predic+on for Language and Other Discrete Data

Structured Predic+on for Language and Other Discrete Data

Structured Predic+on for Language and Other Discrete Data (10-710 and 11-763) Introductory Lecture A LiAle Bit of History 1935: Zipf s law

268 views • 26 slides
Stifel Nicolaus Transportation Conference February 15, 2011 Wick Moorman Chairman, President

Stifel Nicolaus Transportation Conference February 15, 2011 Wick Moorman Chairman, President

Stifel Nicolaus Transportation Conference February 15, 2011 Wick Moorman Chairman, President & CEO Norfolk Southern Corporation Everybody talks about the weather, but nobody does anything about it. -- Charles Dudley Warner

300 views • 17 slides

More recommend