1_ Introduction Python.pptx python is a data

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1. Lecture 1: Python – Fundamentals Dr. A. Ramesh DEPARTMENT OF MANAGEMENT IIT ROORKEE

2. Learning objectives Installing Python Fundamentals of Python Data Visualisation 2

3. Python Installation Process Installation Process – Step 1: Type https://www.anaconda.com at the address bar of web browser. Step 2: Click on download button Step 3: Download python 3.8 version for windows OS Step 4: Double click on file to run the application Step 5: Follow the instructions until completion of installation process 3

4. Python Installation Process Installation Process – Step 1: Type https://www.anaconda.com at the address bar of web browser. 4

5. Python Installation Process Step 2: Click on download button 5

6. Python Installation Process Step 3: Download python 3.8 version for windows OS 6

7. Python Installation Process Step 4: Double click on the downloaded file to run the application 7

8. Python Installation Process 8

9. Python Installation Process 9

10. Python Installation Process 10

11. Python Installation Process 11

12. Python Installation Process 12

13. Python Installation Process 13

14. Python Installation Process 14

15. Python Installation Process 15

16. Python Installation Process 16

17. Why Jupyter NoteBook ? 17 Why? Edit code on web browser Easy in documentation Easy in demonstration User- friendly Interface

18. Python and Jupyter 18 Python Programming Language Jupyter Application Software Package contains both python and jupyter application

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20. About Jupyter NoteBook 20 Cell -> Access using Enter Key

21. About Jupyter NoteBook 21 Input Field -> Green color indicates edit mode Blue color indicates command mode

22. About Jupyter NoteBook 22 -> It contains documentation -> Text not executed as code

23. About Jupyter Notebook Command mode allow to edit notebook as whole To close edit mode (Press Escape key) Execution (Three ways) Comment line is written preceding with # symbol. 23 Ctrl +Enter (Output field can not be modified) Shift +Enter (Output field is modified) Run button on Jupyter interface

24. About Jupyter Notebook Important shortcut keys 24 A -> To create cell above B -> To create cell below D + D -> For deleting cell M -> For markdown cell Y -> For code cell

25. Fundamentals of Python Loading a simple delimited data file Counting how many rows and columns were loaded Determining which type of data was loaded Looking at different parts of the data by subsetting rows and columns 25

26. Importing Different Files in Jupyter Notebook Importing text file 26

27. Importing Different Files in Jupyter Notebook Importing tablular file 27

28. Importing Different Files in Jupyter Notebook Importing excel file 28

29. Importing Different Files in Jupyter Notebook Importing Zip file 29

30. Importing Different Files in Jupyter Notebook Importing PDF file 30

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32. Loading a simple delimited data file 32

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34. head method shows us only the first 5 rows 34

35. Get the number of rows and columns 35

36. get column names 36

37. get the dtype of each column 37

38. Pandas Types Versus Python Types 38

39. get more information about data 39

40. Looking at Columns, Rows, and Cells # get the country column and save it to its own variable 40

41. # show the first 5 observations 41

42. # show the last 5 observations 42

43. # Looking at country, continent, and year 43

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45. Looking at Columns, Rows, and Cells Subset Rows by Index Label: loc 45

46. get the first row Python counts from 0 46

47. # get the 100th row # Python counts from 0 47

48. get the last row 48

49. Subsetting Multiple Rows # select the first, 100th, and 1000th rows 49

50. Subset Rows by Row Number: iloc # get the 2nd row 50

51. get the 100th row 51

52. # using -1 to get the last row 52

53. With iloc , we can pass in the -1 to get the last row—something we couldn’t do with loc . 53

54. # get the first, 100th, and 1000th rows 54

55. Subsetting Columns The Python slicing syntax uses a colon, : If we have just a colon, the attribute refers to everything. So, if we just want to get the first column using the loc or iloc syntax, we can write something like df.loc [:, [columns]] to subset the column(s). 55

56. # subset columns with loc # note the position of the colon # it is used to select all rows 56

57. 57

58. # subset columns with iloc # iloc will alow us to use integers # -1 will select the last column 58

59. Subsetting Columns by Range # create a range of integers from 0 to 4 inclusive 59

60. # subset the dataframe with the range 60

61. Subsetting Rows and Columns # using loc 61

62. # using iloc 62

63. Subsetting Multiple Rows and Columns #get the 1st, 100th, and 1000th rows # from the 1st, 4th, and 6th columns 63

64. if we use the column names directly, # it makes the code a bit easier to read # note now we have to use loc , instead of iloc 64

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67. Grouped Means # For each year in our data, what was the average life expectancy? # To answer this question, # we need to split our data into parts by year; # then we get the ' lifeExp ' column and calculate the mean 67

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70. If you need to “flatten” the dataframe , you can use the reset_index method. 70

71. Grouped Frequency Counts use the nunique to get counts of unique values on a Pandas Series . 71

72. Basic Plot 72

73. 73

74. Visual Representation of the Data Histogram -- vertical bar chart of frequencies Frequency Polygon -- line graph of frequencies Ogive -- line graph of cumulative frequencies Pie Chart -- proportional representation for categories of a whole Stem and Leaf Plot Pareto Chart Scatter Plot 74

75. Methods of visual presentation of data Table 75

76. Methods of visual presentation of data Graphs 76

77. Methods of visual presentation of data Pie chart 77

78. Methods of visual presentation of data Multiple bar chart 78

79. Methods of visual presentation of data Simple pictogram 79 West

80. Frequency distributions Frequency tables 80

81. Frequency diagrams 81

82. Histogram 82 Class Interval Frequency 20-under 30 6 30-under 40 18 40-under 50 11 50-under 60 11 60-under 70 3 70-under 80 1

83. Histogram Construction 83 Class Interval Frequency 20-under 30 6 30-under 40 18 40-under 50 11 50-under 60 11 60-under 70 3 70-under 80 1

84. Frequency Polygon 84 Class Interval Frequency 20-under 30 6 30-under 40 18 40-under 50 11 50-under 60 11 60-under 70 3 70-under 80 1

85. Ogive Cumulative Class Interval Frequency 20-under 30 6 30-under 40 24 40-under 50 35 50-under 60 46 60-under 70 49 70-under 80 50 85

86. Relative Frequency Ogive Cumulative Relative Class Interval Frequency 20-under 30 .12 30-under 40 .48 40-under 50 .70 50-under 60 .92 60-under 70 .98 70-under 80 1.00 86

87. Pareto Chart 87 10 20 30 40 50 60 70 80 90 100 Poor Wiring Short in Coil Defective Plug Other Frequency 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

88. Scatter Plot Registered Vehicles (1000's) Gasoline Sales (1000's of Gallons) 5 60 15 120 9 90 15 140 7 60 88

89. Principles of Excellent Graphs The graph should not distort the data The graph should not contain unnecessary adornments (sometimes referred to as chart junk) The scale on the vertical axis should begin at zero All axes should be properly labeled The graph should contain a title The simplest possible graph should be used for a given set of data

90. Graphical Errors: Chart Junk 1960: $1.00 1970: $1.60 1980: $3.10 1990: $3.80 Minimum Wage Bad Presentation Minimum Wage 2 4 1960 1970 1980 1990 $  Good Presentation

91. Graphical Errors: Compressing the Vertical Axis Good Presentation Quarterly Sales Quarterly Sales Bad Presentation 25 50 Q1 Q2 Q3 Q4 $ 100 200 Q1 Q2 Q3 Q4 $ 

92. Graphical Errors: No Zero Point on the Vertical Axis Monthly Sales 36 39 42 45 J F M A M J $ Graphing the first six months of sales Monthly Sales 39 42 45 J F M A M J $ 36  Good Presentations Bad Presentation