Introduction to Python
Instructor : Rupal Mishra
Indian Institute of Quantitative Finance
Introduction to Python
Python is a high-level programming
language
⚫ Open source and community driven
⚫ standard distribution includes many modules
⚫ Dynamic typed
⚫ Source can be compiled or run just-in-time
⚫ Similar to perl, tcl, ruby
⚫
2
Why Python?
Unlike AML and Avenue, there is a
considerable base of developers already
using the language
⚫ “Tried and true” language that has been in
development since 1991
⚫ Can interface with the Component Object
Model (COM) used by Windows
⚫ Can interface with Open Source GIS toolsets
⚫
3
Python Interfaces
IDLE – a cross-platform Python
development environment
⚫ PythonWin – a Windows only interface to
Python
⚫ Python Shell – running 'python' from the
Command Line opens this interactive shell
⚫ For the exercises, we'll use IDLE, but you
can try them all and pick a favorite
⚫
4
IDLE – Development Environment
⚫
IDLE helps you
program in Python
by:
– color-coding your
program code
– debugging
– auto-indent
– interactive shell
5
Example Python
⚫
Hello World
print “hello
world”
Prints hello world to
standard out
⚫ Open IDLE and try it
out yourself
⚫ Follow along using
IDLE
⚫
6
More than just printing
Python is an object oriented language
⚫ Practically everything can be treated as an
object
⚫ “hello world” is a string
⚫ Strings, as objects, have methods that return
the result of a function on the string
⚫
7
String Methods
Assign a string to a
variable
⚫ In this case “hw”
⚫ hw.title()
⚫ hw.upper()
⚫ hw.isdigit()
⚫ hw.islower()
⚫
8
String Methods
The string held in your variable remains the
same
⚫ The method returns an altered string
⚫ Changing the variable requires reassignment
⚫
– hw = hw.upper()
– hw now equals “HELLO WORLD”
9
Keywords and identifiers
Cannot use keyword as variable
⚫ Keywords are case sensitive
⚫ In python 3.9, there were 36 keywords
⚫ All keywords except for True ,False and
None are in lowercase.
⚫ Identifier – Any combination of letter in
lowercase or uppercase or in digits or an
underscore _. Like ‘variable_2’ correct
identifier.
⚫ Identifier cannot start with a digit.1x is not
valid identifier.
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⚫
Coding Statements
Assignment is a coding statement like a=1
⚫ Multi-line assignment – use this (\) and then
go to next line.
⚫ Multiline can be achieved using (),[] or {}.
⚫ For indentation, use (: )
⚫
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Python Lists:
Lists are the most versatile of Python's
compound data types. A list contains items
separated by commas and enclosed within
square brackets ([]).
⚫ To some extent, lists are similar to arrays in
C. One difference between them is that all
the items belonging to a list can be of
different data type.
⚫
Python Lists:
The values stored in a list can be accessed
using the slice operator ( [ ] and [ : ] ) with
indexes starting at 0 in the beginning of the
list and working their way to end-1.
⚫ The plus ( + ) sign is the list concatenation
operator, and the asterisk ( * ) is the
repetition operator.
⚫
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Lists
Think of a list as a stack of cards, on which
your information is written
⚫ The information stays in the order you place
it in until you modify that order
⚫ Methods return a string or subset of the list or
modify the list to add or remove components
⚫ Written as var[index], index refers to order
within set (think card number, starting at 0)
⚫ You can step through lists as part of a loop
⚫
14
Flow of code to print alternate elements in
list:
⚫ Loop : for each element in list, if position/2
!=0 , print that element.
⚫
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Lists
list = ['iiqf', 243 , 3.9, 'Don', 28.9 ]
tinylist = [123, 'Don']
print(list)
#
print(list[0])
#
print(list[1:3])
#
3rd
print(list[2:])
#
element
print(tinylist * 2) #
print(list + tinylist)
Prints complete list
Prints first element of the list
Prints elements starting from 2nd till
Prints elements starting from 3rd
Prints list two times
# Prints concatenated lists
Output:
['iiqf', 243, 3.9, 'Don', 28.9]
iiqf
[243, 3.9]
[3.9, 'Don', 28.9]
[123, 'Don', 123, 'Don']
['iiqf', 243, 3.9, 'Don', 28.9, 123, 'Don']
List Methods
⚫
Adding to the List
– var[n] = object
⚫
replaces n with object
– var.append(object)
⚫
⚫
adds object to the end of the list
Removing from the List
– var[n] = []
⚫
empties contents of card, but preserves order
– var.remove(n)
⚫
removes card at n
– var.pop(n)
⚫
removes n and returns its value
17
List Methods
list[1] = 246
print(list)
list.append(‘xyz’)
print(list)
list[3]=[]
print(list)
list.remove(28.9)
print(list)
list.pop(0)
print(list)
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Python Tuples:
A tuple is another sequence data type that is
similar to the list. A tuple consists of a
number of values separated by commas.
Unlike lists, however, tuples are enclosed
within parentheses.
⚫ The main differences between lists and tuples
are: Lists are enclosed in brackets ( [ ] ), and
their elements and size can be changed, while
tuples are enclosed in parentheses ( ( ) ) and
cannot be updated. Tuples can be thought of
as read-only lists.
⚫
Tuple
tuple = ( 'iiqf', 243 , 2.23, 'sanjay', 70.2 )
tinytuple = (123, 'singhania')
print(tuple)
# Prints complete list
print(tuple[0])
# Prints first element of the list
print(tuple[1:3]) # Prints elements starting from 2nd till 3rd
print(tuple[2:])
# Prints elements starting from 3rd element
print(tinytuple * 2) # Prints list(two times
print(tuple + tinytuple) # Prints concatenated lists
Output:
List vs Tuple
Lists
Tuple
Lists are mutable like list. append will
work.
Tuples are immutable. There is no
method append here.
Iterations in list are slow
Iterations are comparatively faster
List is better for addition and removal of
elements
Tuple is fast, so better for accessing
elements.
Consumes more memory
Consumes less memory
Multiple methods in list
Less methods in list.
More prone to errors
Less prone to errors
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Python Dictionary:
Python 's dictionaries are hash table type.
They work like associative arrays or hashes
found in Perl and consist of key-value pairs.
⚫ Keys can be almost any Python type, but are
usually numbers or strings. Values, on the
other hand, can be any arbitrary Python
object.
⚫ Dictionaries are enclosed by curly braces ( {
} ) and values can be assigned and accessed
using square braces ( [] ).
⚫
Dictionaries
Dictionaries are sets of key & value pairs
⚫ Allows you to identify values by a
descriptive name instead of order in a list
⚫ Keys are unordered unless explicitly sorted
⚫ Keys should be immutable. Values can be
mutable or immutable.
⚫ Keys are unique:
⚫
– var[‘item’] = “apple”
– var[‘item’] = “banana”
– Print(var[‘item’]) prints just banana
23
Data Type Conversion:
Function
int(x [,base])
Description
Converts x to an integer. base specifies the base if x is a string.
long(x [,base] )
Converts x to a long integer. base specifies the base if x is a string.
float(x)
Converts x to a floating-point number.
complex(real
[,imag])
str(x)
Creates a complex number.
repr(x)
Converts object x to an expression string.
eval(str)
Evaluates a string and returns an object.
tuple(s)
Converts s to a tuple.
list(s)
Converts s to a list.
set(s)
Converts s to a set.
dict(d)
Creates a dictionary. d must be a sequence of (key,value) tuples.
frozenset(s)
Converts s to a frozen set.
chr(x)
Converts an integer to a character.
unichr(x)
Converts an integer to a Unicode character.
ord(x)
Converts a single character to its integer value.
hex(x)
Converts an integer to a hexadecimal string.
oct(x)
Converts an integer to an octal string.
Converts object x to a string representation.
4. Python - Basic Operators
Python language supports following type of
operators.
⚫ Arithmetic Operators
⚫ Comparision Operators
⚫ Logical (or Relational) Operators
⚫ Assignment Operators
⚫ Conditional (or ternary) Operators
⚫
Python Arithmetic Operators:
Operator
+
*
/
%
**
//
Description
Addition - Adds values on either side of the
operator
Subtraction - Subtracts right hand operand
from left hand operand
Multiplication - Multiplies values on either
side of the operator
Division - Divides left hand operand by
right hand operand
Modulus - Divides left hand operand by
right hand operand and returns remainder
Exponent - Performs exponential (power)
calculation on operators
Floor Division - The division of operands
where the result is the quotient in which
the digits after the decimal point are
removed.
Example
a + b will give 30
a - b will give -10
a * b will give 200
b / a will give 2
b % a will give 0
a**b will give 10 to
the power 20
9//2 is equal to 4 and
9.0//2.0 is equal to 4.0
Python Comparison
Operators:
Operato
Description
r
==
Checks if the value of two operands are equal or not, if
yes then condition becomes true.
!=
Checks if the value of two operands are equal or not, if
values are not equal then condition becomes true.
<>
Checks if the value of two operands are equal or not, if
values are not equal then condition becomes true.
>
Checks if the value of left operand is greater than the
value of right operand, if yes then condition becomes
true.
<
Checks if the value of left operand is less than the
value of right operand, if yes then condition becomes
true.
>=
Checks if the value of left operand is greater than or
equal to the value of right operand, if yes then
condition becomes true.
<=
Checks if the value of left operand is less than or equal
to the value of right operand, if yes then condition
becomes true.
Example
(a == b) is not true.
(a != b) is true.
(a <> b) is true. This is
similar to != operator.
(a > b) is not true.
(a < b) is true.
(a >= b) is not true.
(a <= b) is true.
Python Assignment
Operators:
Operator
=
+=
-=
*=
/=
%=
**=
//=
Description
Simple assignment operator, Assigns values from right
side operands to left side operand
Example
c = a + b will
assigne value of a +
b into c
Add AND assignment operator, It adds right operand to
c += a is equivalent
the left operand and assign the result to left operand
to c = c + a
Subtract AND assignment operator, It subtracts right
c -= a is equivalent
operand from the left operand and assign the result to left to c = c - a
operand
Multiply AND assignment operator, It multiplies right
c *= a is equivalent
operand with the left operand and assign the result to left to c = c * a
operand
Divide AND assignment operator, It divides left operand
c /= a is equivalent
with the right operand and assign the result to left
to c = c / a
operand
Modulus AND assignment operator, It takes modulus
c %= a is equivalent
using two operands and assign the result to left operand
to c = c % a
Exponent AND assignment operator, Performs exponential c **= a is
(power) calculation on operators and assign value to the
equivalent to c = c
left operand
** a
Floor Division and assigns a value, Performs floor division c //= a is equivalent
on operators and assign value to the left operand
to c = c // a
Python Bitwise Operators:
Operat
Description
or
&
Binary AND Operator copies a bit to the
result if it exists in both operands.
|
Binary OR Operator copies a bit if it exists
in either operand.
^
Binary XOR Operator copies the bit if it is
set in one operand but not both.
~
Binary Ones Complement Operator is unary
and has the effect of 'flipping' bits.
<<
Binary Left Shift Operator. The left
operands value is moved left by the
number of bits specified by the right
operand.
>>
Binary Right Shift Operator. The left
operands value is moved right by the
number of bits specified by the right
operand.
Example
(a & b) will give 12
which is 0000 1100
(a | b) will give 61
which is 0011 1101
(a ^ b) will give 49
which is 0011 0001
(~a ) will give -60
which is 1100 0011
a << 2 will give 240
which is 1111 0000
a >> 2 will give 15
which is 0000 1111
Python Logical Operators:
Operat
Description
or
and
Called Logical AND operator. If both the
operands are true then then condition
becomes true.
or
Called Logical OR Operator. If any of the two
operands are non zero then then condition
becomes true.
not
Called Logical NOT Operator. Use to
reverses the logical state of its operand. If a
condition is true then Logical NOT operator
will make false.
Example
(a and b) is true.
(a or b) is true.
not(a and b) is false.
Python Membership
Operators:
⚫
In addition to the operators discussed
previously, Python has membership
operators, which test for membership in a
sequence, such as strings, lists, or tuples.
Operator
Description
Example
in
Evaluates to true if it finds a variable in the x in y, here in results in a
specified sequence and false otherwise.
1 if x is a member of
sequence y.
not in
Evaluates to true if it does not finds a
x not in y, here not in
variable in the specified sequence and false results in a 1 if x is a
otherwise.
member of sequence y.
Python Operators
Precedence
Operator
Description
**
Exponentiation (raise to the power)
~+* / % //
Ccomplement, unary plus and minus (method names for
the last two are +@ and -@)
Multiply, divide, modulo and floor division
+-
Addition and subtraction
>> <<
Right and left bitwise shift
&
Bitwise 'AND'
^|
Bitwise exclusive `OR' and regular `OR'
<= < > >=
Comparison operators
<> == !=
Equality operators
= %= /= //= -= += Assignment operators
*= **=
is is not
Identity operators
in not in
Membership operators
not or and
Logical operators
Python - IF...ELIF...ELSE
Statement
The syntax of the if statement is:
if expression:
if expression:
statement(s)
statement(s)
else:
Example:
statement(s)
var1 = 100
if var1:
print ("1 - Got a true expression value“)
print (var1)
var2 = 0
if var2:
print ("2 - Got a true expression value“)
print (var2)
⚫
var1 = 100
if var1:
print("1 - Got a true expression value")
print(var1)
else:
print("1 - Got a false expression value")
print(var1)
var2 = 0
if var2:
print("2 - Got a true expression value")
print(var2)
else:
print("2 - Got a false expression value")
print(var2)
The Nested if...elif...else
Construct
var = 100
if var < 200:
print("Expression value is less than 200")
if var == 150:
print("Which is 150")
elif var == 100:
print("Which is 100")
elif var == 50:
print("Which is 50")
elif var < 50:
print("Expression value is less than 50")
else:
print("Could not find true expression")
Single Statement Suites:
If the suite of an if clause consists only of a single line, it may
go on the same line as the header statement:
x=1
if (x==1): print("x is 1")
5. Python - while Loop
Statements
The while loop is one of the looping constructs available in
Python. The while loop continues until the expression
becomes false. The expression has to be a logical expression
and must return either a true or a false value
The syntax of the while loop is:
while expression:
statement(s)
Example:
x = 0
while (x < 9):
print ('The count is:', x)
x = x + 1
The Infinite Loops:
You must use caution when using while loops because of the
possibility that this condition never resolves to a false value.
This results in a loop that never ends. Such a loop is called
an infinite loop.
An infinite loop might be useful in client/server programming
where the server needs to run continuously so that client
programs can communicate with it as and when required.
Following loop will continue till you enter CTRL+C :
FLIP OF COIN GAME:
Either you win or loose at flip of coin.
Single Statement Suites:
⚫
Similar to the if statement syntax, if your while clause consists only of a
single statement, it may be placed on the same line as the while header.
⚫
Here is the syntax of a one-line while clause:
while expression : statement
6. Python - for Loop
Statements
The for loop in Python has the ability to iterate over the items
of any sequence, such as a list or a string.
The syntax of the loop look is:
for iterating_var in sequence:
statements(s)
Example:
str = "Python"
for i in str:
print(i)
Python break,continue and
pass Statements
The break Statement:
⚫ The break statement in Python terminates the current loop
and resumes execution at the next statement, just like the
traditional break found in C.
Example:
for letter in 'IIQFNew':
if(letter == 'N'):
break
print('Current Letter :', letter)
var = 10
while var>0:
print('Current var value :', var)
var = var-1
if var == 5:
break
The continue Statement:
⚫ The continue statement in Python returns the control to the
beginning of the while loop. The continue statement rejects
all the remaining statements in the current iteration of the
loop and moves the control back to the top of the loop.
Example:
for letter in 'IIQFNew':
if letter == 'N':
continue
print('Current Letter :', letter)
var = 10
while var > 0:
var = var -1
if var == 5:
continue
print('Current variable value :', var)
The else Statement Used with Loops
Python supports to have an else statement associated with a loop
statements.
⚫ If the else statement is used with a for loop, the else statement
is executed when the loop has exhausted iterating the list.
⚫ If the else statement is used with a while loop, the else
statement is executed when the condition becomes false.
Example:
for num in range(10,20):
for i in range(2,num):
if num%i == 0:
j=num/i
print('%d equals %d * %d' % (num,i,j))
break
else:
print(num, 'is a prime number')
The pass Statement:
The pass statement in Python is used when a statement is required
syntactically but you do not want any command or code to execute.
⚫ The pass statement is a null operation; nothing happens when it
executes. The pass is also useful in places where your code will
eventually go, but has not been written yet (e.g., in stubs for example):
Example:
⚫
for letter in 'Python':
if letter == 'h':
pass
print('This is pass block')
print('Current Letter :', letter)
What is Numpy?
Numpy, Scipy, and Matplotlib provide
MATLAB-like functionality in python.
⚫ Numpy Features:
⚫
– Typed multidimentional arrays (matrices)
– Fast numerical computations (matrix math)
– High-level math functions
45
Why do we need NumPy
Python does numerical computations slowly.
⚫ 1000 x 1000 matrix multiply
⚫
– Python triple loop takes > 10 min.
– Numpy takes ~0.03 seconds
46
NumPy Overview
1.
2.
3.
4.
Arrays
Shaping and transposition
Mathematical Operations
Indexing and slicing
47
Arrays
Structured lists of numbers.
Vectors
⚫ Matrices
⚫ Images
⚫ Tensors
⚫ ConvNets
⚫
48
Arrays
Structured lists of numbers.
⚫
Vectors
⚫ Matrices
⚫ Images
⚫ Tensors
⚫ ConvNets
⚫
49
Arrays
Structured lists of numbers.
Vectors
⚫ Matrices
⚫ Images
⚫ Tensors
⚫ ConvNets
⚫
50
Arrays
Structured lists of numbers.
Vectors
⚫ Matrices
⚫ Images
⚫ Tensors
⚫ ConvNets
⚫
51
Arrays
Structured lists of numbers.
Vectors
⚫ Matrices
⚫ Images
⚫ Tensors
⚫ ConvNets
⚫
52
Arrays, Basic Properties
import numpy as np
a = np.array([[1,2,3],[4,5,6]],dtype=np.float32)
print a.ndim, a.shape, a.dtype
1.
2.
3.
Arrays can have any number of dimensions, including
zero (a scalar).
Arrays are typed: np.uint8, np.int64, np.float32,
np.float64
Arrays are dense. Each element of the array exists and
has the same type.
53
Arrays, creation
np.ones, np.zeros
⚫ np.arange
⚫ np.concatenate
⚫ np.astype
⚫ np.zeros_like,
np.ones_like
⚫ np.random.random
⚫
54
Arrays, creation
np.ones, np.zeros
⚫ np.arange
⚫ np.concatenate
⚫ np.astype
⚫ np.zeros_like,
np.ones_like
⚫ np.random.random
⚫
55
Arrays, creation
np.ones, np.zeros
⚫ np.arrange
arange([start,]stop[,step],[,dtype=None]
⚫ np.concatenate
⚫ np.astype
⚫ np.zeros_like, np.ones_like
⚫ np.random.random
⚫
56
Arrays, creation
np.ones, np.zeros
⚫ np.arange
⚫ np.concatenate
⚫ np.astype
⚫ np.zeros_like,
np.ones_like
⚫ np.random.random
⚫
57
Axis in Numpy
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np.ones, np.zeros
⚫ np.arange
⚫ np.concatenate
⚫ np.astype
⚫ np.zeros_like,
np.ones_like
⚫ np.random.random
⚫
59
Arrays, creation
np.ones, np.zeros
⚫ np.arange
⚫ np.concatenate
⚫ np.astype
⚫ np.zeros_like,
np.ones_like
⚫ np.random.random
⚫
60
Arrays, creation
np.ones, np.zeros
⚫ np.arange
⚫ np.concatenate
⚫ np.astype
⚫ np.zeros_like,
np.ones_like
⚫ np.random.random
⚫
61
Arrays, creation
np.ones, np.zeros
⚫ np.arange
⚫ np.concatenate
⚫ np.astype
⚫ np.zeros_like,
np.ones_like
⚫ np.random.rando
m
⚫
62
Arrays, danger zone
Must be dense, no holes.
⚫ Must be one type
⚫ Cannot combine arrays of different shape
⚫
63
Shaping
a =
np.array([1,2,3,4,5,6])
a = a.reshape(3,2)
a = a.reshape(2,-1)
a = a.ravel()
1. Total number of elements cannot
change.
2. Use -1 to infer axis shape
3. Row-major by default (MATLAB is
column-major)
64
Transposition
a = np.arange(10).reshape(5,2)
a = a.T
a = a.transpose((1,0))
np.transpose permutes axes.
a.T transposes the first two axes.
65
Mathematical operators
Arithmetic operations are element-wise
⚫ Logical operator return a bool array
⚫ In place operations modify the array
⚫
66
Mathematical operators
Arithmetic operations are element-wise
⚫ Logical operator return a bool array
⚫ In place operations modify the array
⚫
67
Mathematical operators
Arithmetic operations are element-wise
⚫ Logical operator return a bool array
⚫ In place operations modify the array
⚫
68
Mathematical operators
Arithmetic operations are element-wise
⚫ Logical operator return a bool array
⚫ In place operations modify the array
⚫
69
Math, universal functions
Also called ufuncs
Element-wise
Examples:
–
–
–
–
–
np.exp
np.sqrt
np.sin
np.cos
np.isnan
70
Quick Game
⚫
Create a game, where you guess a number
between 1 to 10 and check it with random
integer number generated by the code. If
number matches, you win else loose.
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Indexing
x[0,0]
# top-left element
x[0,-1]
# first row, last column
x[0,:]# first row (many entries)
x[:,0]# first column (many entries)
Notes:
– Zero-indexing
– Multi-dimensional indices are comma-separated
(i.e., a tuple)
72
Scipy
⚫
•
Scipy(https://www.scipy.org/):
Scientific Python
⚫
•
often mentioned in the same breath with NumPy
⚫
•
based on the data structures of Numpy and furthermore its basic creation and
manipulation functions
⚫
•
Both numpy and scipy has to be installed. Numpy has to be installed before
scipy
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Numpy vs Scipy
⚫
Numpy:
•
Numpy is written in C and use for
mathematical or numeric calculation.
•
It is faster than other Python Libraries
•
Numpy is the most useful library for Data
Science to perform basic calculations.
•
Numpy contains nothing but array data type
which performs the most basic operation
like sorting, shaping, indexing, etc.
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Numpy vs Scipy
⚫
SciPy:
•
SciPy is built in top of the NumPy
•
SciPy is a fully-featured version of Linear
Algebra while Numpy contains only a few
features.
•
Most new Data Science features are
available in Scipy rather than Numpy.
Indian Institute of Quantitative Finance
Statistics
•
The main public methods for continuous
random variables are:
•
rvs: Random Variates
1)
pdf: Probability Density Function
2)
cdf: Cumulative Distribution Function
3)
sf: Survival Function (1-CDF)
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Statistics
1)
ppf: Percent Point Function (Inverse of CDF)
2)
isf: Inverse Survival Function (Inverse of SF)
3)
stats: Return mean, variance, (Fisher’s)
skew, or (Fisher’s) kurtosis moment: noncentral moments of the distribution
Indian Institute of Quantitative Finance
Tips to avoid bugs
1.
2.
3.
4.
5.
Know what your datatypes are.
Check whether you have a view or
a copy.
Use matplotlib for sanity checks.
Use pdb to check each step of your
computation.
Know np.dot vs np.mult.
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Scipy
⚫
Statistics(Scipy.Stats) :
from scipy import stats
import numpy as np
from scipy.stats import uniform
print(stats.norm.cdf(0))
print(stats.norm.cdf(0.5))
print(stats.norm.cdf(np.array([-3.33, 0, 3.33])))
print(stats.norm.ppf(0.5))
print(stats.norm.ppf(3))
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Pandas
1.
fast and efficient Data Frame object for data
manipulation
2.
Tools for reading and writing data between
in-memory data structures and different
formats: CSV and text files, Microsoft
Excel, SQL databases
Indian Institute of Quantitative Finance
Pandas
1.
reshaping and pivoting of data sets
2.
Intelligent label-based slicing, fancy
indexing, and sub setting of large data sets;
3.
Columns can be inserted and deleted from
data structures for size mutability;
Indian Institute of Quantitative Finance
Exercise
⚫
Run a code for simulation using pandas and
numpy using GBM model.
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Indentation and Blocks
Python uses whitespace and indents to
denote blocks of code
⚫ Lines of code that begin a block end in a
colon:
⚫ Lines within the code block are indented at
the same level
⚫ To end a code block, remove the indentation
⚫ You'll want blocks of code that run only
when certain conditions are met
⚫
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Modules
Modules are additional pieces of code that
further extend Python’s functionality
⚫ A module typically has a specific function
⚫
– additional math functions, databases, network…
Python comes with many useful modules
⚫ arcgisscripting is the module we will use to
load ArcGIS toolbox functions into Python
⚫
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Modules
⚫
Modules are accessed using import
– import sys, os # imports two modules
⚫
Modules can have subsets of functions
– os.path is a subset within os
⚫
Modules are then addressed by
modulename.function()
– sys.argv # list of arguments
– filename = os.path.splitext("points.txt")
– filename[1] # equals ".txt"
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