1_ Introduction Python.pptx python is a data

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

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Learning objectives
1. Installing Python
2. Fundamentals of Python
3. Data Visualisation
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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
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Installation Process –
Step 1: Type https://www.anaconda.com at the address bar of web browser.
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Step 2: Click on download button
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Step 3: Download python 3.8 version for windows OS
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Step 4: Double click on the downloaded file to run the application
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Why Jupyter NoteBook?
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Why?
• Edit code on web browser
• Easy in documentation
• Easy in demonstration
• User- friendly Interface

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

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About Jupyter NoteBook
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Cell -> Access using Enter Key

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Input Field -> Green color indicates edit mode
Blue color indicates command mode

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-> It contains documentation
-> Text not executed as code

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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.
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o Ctrl +Enter (Output field can not be modified)
o Shift +Enter (Output field is modified)
o Run button on Jupyter interface

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About Jupyter Notebook
• Important shortcut keys
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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

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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
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Importing Different Files in Jupyter Notebook
• Importing text file
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• Importing tablular file
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• Importing excel file
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• Importing Zip file
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• Importing PDF file
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Loading a simple delimited data file
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• head method shows us only the first 5 rows
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Get the number of rows and columns
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get the dtype of each column
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Pandas Types Versus Python Types
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get more information about data
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Looking at Columns, Rows, and Cells
• # get the country column and save it to its own variable
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# show the first 5 observations
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# show the last 5 observations
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# Looking at country, continent, and year
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Looking at Columns, Rows, and Cells
• Subset Rows by Index Label: loc
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get the first row
• Python counts from 0
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• # get the 100th row
# Python counts from 0
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Subsetting Multiple Rows
• # select the first, 100th, and 1000th rows
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Subset Rows by Row Number: iloc
• # get the 2nd row
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• get the 100th row
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• # using -1 to get the last row
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With iloc, we can pass in the -1 to get the last row—something we couldn’t do with loc.
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• # get the first, 100th, and 1000th rows
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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).
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• # subset columns with loc
# note the position of the colon
# it is used to select all rows
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• # subset columns with iloc
• # iloc will alow us to use integers
• # -1 will select the last column
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Subsetting Columns by Range
• # create a range of integers from 0 to 4 inclusive
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• # subset the dataframe with the range
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Subsetting Rows and Columns
• # using loc
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Subsetting Multiple Rows and Columns
• #get the 1st, 100th, and 1000th rows
# from the 1st, 4th, and 6th columns
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• 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
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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
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• If you need to “flatten” the dataframe, you can use the
reset_index method.
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Grouped Frequency Counts
• use the nunique to get counts of unique values on a Pandas Series.
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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
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Methods of visual presentation of data
• Table
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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

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• Graphs
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20
30
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60
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80
90
East
West
North

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• Pie chart
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1st Qtr
2nd Qtr
3rd Qtr
4th Qtr

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• Multiple bar chart
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0 20 40 60 80 100
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
North
West
East

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• Simple pictogram
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20
40
60
80
100
East
North
West

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Frequency distributions
• Frequency tables
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Class Interval Frequency Cumulative Frequency
< 20 13 13
<40 18 31
<60 25 56
<80 15 71
<100 9 80
Observation Table

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Frequency diagrams
Frequency
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15
20
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< 20 <40 <60 <80 <100
Frequency
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Frequency
0
5
10
15
20
25
30
< 20 <40 <60 <80 <100
Frequency
Cumulative Frequency
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10
20
30
40
50
60
70
80
90
< 20 <40 <60 <80 <100
Cumulative Frequency

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Histogram
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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
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20
0 10 20 30 40 50 60 70 80
Years
Frequency

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Histogram Construction
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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
0
10
20
0 10 20 30 40 50 60 70 80
Years
Frequency

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Frequency Polygon
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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

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Ogive
Cumulative
20-under 30 6
30-under 40 24
40-under 50 35
50-under 60 46
60-under 70 49
70-under 80 50
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20
40
60
0 10 20 30 40 50 60 70 80
Years
Frequency

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

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Scatter Plot
Registered Vehicles (1000's) Gasoline Sales (1000's of Gallons)
5 60
15 120
9 90
15 140
7 60
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100
200
0 5 10 15 20
RegisteredVehicles
Gasoline
Sales
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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

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

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Graphical Errors:
Compressing the Vertical Axis
Good Presentation
Quarterly Sales Quarterly Sales
Bad Presentation
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25
50
Q1 Q2 Q3 Q4
$
0
100
200
Q1 Q2 Q3 Q4
$
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Graphical Errors: No Zero Point on the Vertical Axis
Monthly Sales
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J F M A M J
$
Graphing the first six months of sales
Monthly Sales
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42
45
J F M A M J
$
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Good Presentations
Bad Presentation

1_ Introduction Python.pptx python is a data

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