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![55 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](https://image.slidesharecdn.com/1introductionpython-240808131344-607b8d22/75/1_-Introduction-Python-pptx-python-is-a-data-55-2048.jpg)
1_ Introduction Python.pptx python is a data
- 1. INDIAN INSTITUTE OFTECHNOLOGY ROORKEE Lecture 1: Python – Fundamentals Dr. A. Ramesh DEPARTMENT OF MANAGEMENT IIT ROORKEE
- 2. 2 2 Learning objectives 1. InstallingPython 2. Fundamentals of Python 3. Data Visualisation 2
- 3. 3 3 Python Installation Process InstallationProcess – 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. 4 4 Python Installation Process InstallationProcess – Step 1: Type https://www.anaconda.com at the address bar of web browser. 4
- 5. 5 5 Python Installation Process Step2: Click on download button 5
- 6. 6 6 Python Installation Process Step3: Download python 3.8 version for windows OS 6
- 7. 7 7 Python Installation Process Step4: Double click on the downloaded file to run the application 7
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- 17. 17 17 Why Jupyter NoteBook? 17 Why? •Edit code on web browser • Easy in documentation • Easy in demonstration • User- friendly Interface
- 18. 18 18 Python and Jupyter 18 PythonProgramming Language Jupyter Application Software Package contains both python and jupyter application
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- 20. 20 20 About Jupyter NoteBook 20 Cell-> Access using Enter Key
- 21. 21 21 About Jupyter NoteBook 21 InputField -> Green color indicates edit mode Blue color indicates command mode
- 22. 22 22 About Jupyter NoteBook 22 ->It contains documentation -> Text not executed as code
- 23. 23 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 o Ctrl +Enter (Output field can not be modified) o Shift +Enter (Output field is modified) o Run button on Jupyter interface
- 24. 24 24 About Jupyter Notebook •Important shortcut keys 24 o A -> To create cell above o B -> To create cell below o D + D -> For deleting cell o M -> For markdown cell o Y -> For code cell
- 25. 25 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 Filesin Jupyter Notebook • Importing text file 26
- 27. Importing Different Filesin Jupyter Notebook • Importing tablular file 27
- 28. Importing Different Filesin Jupyter Notebook • Importing excel file 28
- 29. Importing Different Filesin Jupyter Notebook • Importing Zip file 29
- 30. Importing Different Filesin Jupyter Notebook • Importing PDF file 30
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- 32. 32 32 Loading a simpledelimited data file 32
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- 34. 34 34 • head methodshows us only the first 5 rows 34
- 35. 35 35 Get the numberof rows and columns 35
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- 37. 37 37 get the dtypeof each column 37
- 38. 38 38 Pandas Types VersusPython Types 38
- 39. 39 39 get more informationabout data 39
- 40. 40 40 Looking at Columns,Rows, and Cells • # get the country column and save it to its own variable 40
- 41. 41 41 # show thefirst 5 observations 41
- 42. 42 42 # show thelast 5 observations 42
- 43. 43 43 # Looking atcountry, continent, and year 43
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- 45. 45 45 Looking at Columns,Rows, and Cells • Subset Rows by Index Label: loc 45
- 46. 46 46 get the firstrow • Python counts from 0 46
- 47. 47 47 • # getthe 100th row # Python counts from 0 47
- 48. 48 48 • get thelast row 48
- 49. 49 49 Subsetting Multiple Rows •# select the first, 100th, and 1000th rows 49
- 50. 50 50 Subset Rows byRow Number: iloc • # get the 2nd row 50
- 51. 51 51 • get the100th row 51
- 52. 52 52 • # using-1 to get the last row 52
- 53. 53 53 With iloc, wecan pass in the -1 to get the last row—something we couldn’t do with loc. 53
- 54. 54 54 • # getthe first, 100th, and 1000th rows 54
- 55. 55 55 Subsetting Columns • ThePython 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. 56 56 • # subsetcolumns with loc # note the position of the colon # it is used to select all rows 56
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- 58. 58 58 • # subsetcolumns with iloc • # iloc will alow us to use integers • # -1 will select the last column 58
- 59. 59 59 Subsetting Columns byRange • # create a range of integers from 0 to 4 inclusive 59
- 60. 60 60 • # subsetthe dataframe with the range 60
- 61. 61 61 Subsetting Rows andColumns • # using loc 61
- 62. 62 62 • # usingiloc 62
- 63. 63 63 Subsetting Multiple Rowsand Columns • #get the 1st, 100th, and 1000th rows # from the 1st, 4th, and 6th columns 63
- 64. 64 64 • if weuse 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. 67 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. 70 70 • If youneed to “flatten” the dataframe, you can use the reset_index method. 70
- 71. 71 71 Grouped Frequency Counts •use the nunique to get counts of unique values on a Pandas Series. 71
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- 74. 74 74 Visual Representation ofthe 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. 75 75 Methods of visualpresentation of data • Table 75 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr East 20.4 27.4 90 20.4 West 30.6 38.6 34.6 31.6 North 45.9 46.9 45 43.9
- 76. 76 76 Methods of visualpresentation of data • Graphs 76 0 10 20 30 40 50 60 70 80 90 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr East West North
- 77. 77 77 Methods of visualpresentation of data • Pie chart 77 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
- 78. 78 78 Methods of visualpresentation of data • Multiple bar chart 78 0 20 40 60 80 100 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr North West East
- 79. 79 79 Methods of visualpresentation of data • Simple pictogram 79 0 20 40 60 80 100 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr East North West
- 80. 80 80 Frequency distributions • Frequencytables 80 Class Interval Frequency Cumulative Frequency < 20 13 13 <40 18 31 <60 25 56 <80 15 71 <100 9 80 Observation Table
- 81. 81 81 Frequency diagrams Frequency 0 5 10 15 20 25 30 < 20<40 <60 <80 <100 Frequency 81 Frequency 0 5 10 15 20 25 30 < 20 <40 <60 <80 <100 Frequency Cumulative Frequency 0 10 20 30 40 50 60 70 80 90 < 20 <40 <60 <80 <100 Cumulative Frequency
- 82. 82 82 Histogram 82 Class Interval Frequency 20-under 306 30-under 40 18 40-under 50 11 50-under 60 11 60-under 70 3 70-under 80 1 0 10 20 0 10 20 30 40 50 60 70 80 Years Frequency
- 83. 83 83 Histogram Construction 83 Class IntervalFrequency 20-under 30 6 30-under 40 18 40-under 50 11 50-under 60 11 60-under 70 3 70-under 80 1 0 10 20 0 10 20 30 40 50 60 70 80 Years Frequency
- 84. 84 84 Frequency Polygon 84 Class IntervalFrequency 20-under30 6 30-under 40 18 40-under 50 11 50-under 60 11 60-under 70 3 70-under 80 1 0 10 20 0 10 20 30 40 50 60 70 80 Years Frequency
- 85. 85 85 Ogive Cumulative Class Interval Frequency 20-under30 6 30-under 40 24 40-under 50 35 50-under 60 46 60-under 70 49 70-under 80 50 85 0 20 40 60 0 10 20 30 40 50 60 70 80 Years Frequency
- 86. 86 86 Relative Frequency Ogive Cumulative Relative ClassInterval 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 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 10 20 30 40 50 60 70 80 Years Cumulative Relative Frequency
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- 88. 88 88 Scatter Plot Registered Vehicles(1000's) Gasoline Sales (1000's of Gallons) 5 60 15 120 9 90 15 140 7 60 0 100 200 0 5 10 15 20 RegisteredVehicles Gasoline Sales 88
- 89. 89 89 Principles of ExcellentGraphs • 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. 90 90 Graphical Errors: ChartJunk 1960: $1.00 1970: $1.60 1980: $3.10 1990: $3.80 Minimum Wage Bad Presentation Minimum Wage 0 2 4 1960 1970 1980 1990 $ Good Presentation
- 91. 91 91 Graphical Errors: Compressing theVertical Axis Good Presentation Quarterly Sales Quarterly Sales Bad Presentation 0 25 50 Q1 Q2 Q3 Q4 $ 0 100 200 Q1 Q2 Q3 Q4 $
- 92. 92 92 Graphical Errors: NoZero 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 0 39 42 45 J F M A M J $ 36 Good Presentations Bad Presentation