Python Programming Language Reference -...

Python Programming Language Reference

Daniel ToppoPictet Asset Management

20192 – Financial Econometrics 2

Spring 2017

Python

2Intro Lecture

The language basics

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Python language – Basics

3Intro Lecture

Sample code #1# This is a comment>>> s1 = "Hello" # 's1' holds 'Hello'>>> s2 = " World" # 's2' holds ' World'>>> print (s1 + s2) # string concatenation'Hello World'

>>> x = (1+2)*3.4 # simple arithmetic operation>>> print (x) # let's print x in the terminal10.2

>>> for i in [1,2,3]: # loop! statement ends with a semi-colon… print (i) # we are in the loop block -> take care of indentation!123>>> if i==3: # condition! statement ends with a semi-colon… print ("bingo!") # we are in the condition block -> indentation again!'bingo'


4Intro Lecture

Sample code #1 – Explained! Comments: start with a dash (#)

print will print values on the terminal

Variables are assigned with the = identifier

Compare variables with the == identifier

Boolean (aka logical) operators: and | or | not

Arithmetic operators: + | - | * | / | %

Strings operators:o + concatenationo * repetitiono % when formatting strings


5Intro Lecture

Comments Starts with a dash (#) Everything that is right to the # is ignored by the interpreter To span a comment on multiple lines, every line must start

with a dash (#)

# This is a comment – completely ignored by the interpreterprint("Hello World!") # this is another comment

# This is a comment which# is spanned over multiple# lines


6Intro Lecture

Identifiers and Keywords A python identifier is a name used to define

o a variableo a functiono a module (file containing Python statements & definitions)o a Class (i.e. a template, specification representing an object)

An identifier starts with o a letter [a-z], or an underscore (_) ([A-Z] for Classes)o followed by letters | underscores | digitso punctuation characters are NOT allowed within an identifier

x = 1 # valid1abd = "Rainbow" # invalid – an identifier cannot start with a digit_a_string_ = "Hello" # validlistA1 = ['a', x, "aaa"] # validx@3 = 0 # invalid – @ is not allowed in an identifier


7Intro Lecture

Identifiers and Keywords

False assert del for in or while

None break elif from is pass with

True class else global lambda raise yield

and continue except if nonlocal return

as def finally import not try

The table below lists the Python Keywords These cannot be used as ordinary identifiers (i.e., variable |

constant | function | class, etc.) in your programs

Python language – Datatypes & Operators

8Intro Lecture

Python Datatypes – Overview

Data Type ExamplesNumbers int (signed integers) float (real values) complex

103.453.5 + 7.2j

String "Hello"List [1, 2, 3]Tuple (1, "London", 2.2, 6j)Dictionary {"Name" : "Tony", "Age" : 15}


9Intro Lecture

Numbers – Types

Type DescriptionInteger Limited precision

Positive | Negative whole number (no fractional part)

Float Real numbers Integer and fractional parts

Complex Real and imaginary parts (a + bj) Parts are floating point numbers j represents the squared root of -1

See Python Reference in the Appendix to this presentation for detailed information about numbers


10Intro Lecture

Numbers – FunctionsFunction DescriptionComparison functions int (x) float (x) complex (x, y)

convert x to a integer number convert x to a floating point number convert x and y to a complex number

(real part = x; imaginary part = y)Mathematical functions abs (x) | fabs(x) ceil (x) | floor (x) cmp (x, y) exp (x) | log (x) | log10 (x) min(x1,x2,…) |

max(x1,x2,…) pow (x, y) round (x[,n]) sqrt (x)

absolute value of x: 𝑥𝑥 ceiling of x: 𝑥𝑥 | floor of x: 𝑥𝑥 comparison 𝑒𝑒𝑥𝑥| ln(𝑥𝑥)| 𝑙𝑙𝑙𝑙𝑙𝑙10(𝑥𝑥) min | max of arguments 𝑥𝑥𝑦𝑦 round x to n digits from the decimal

point 𝑥𝑥


11Intro Lecture

Numbers – FunctionsFunction DescriptionTrigonometric functions cos (x) | sin (x) | tan (x) acos (x) | asin (x) | atan (x) hypot (x, y) degrees (x) radians (x)

cosine, sine, tangent arccos, arcsin, arctan Euclidian norm: 𝑥𝑥2 + 𝑦𝑦2 convert x from radians to degrees convert x from degrees to radians

Random functions choice (seq) random() seed ([x])

shuffle (list)

pick a random item from sequence generates a random float: 0 ≤ 𝑟𝑟 ≤ 1 Sets the integer starting value for

generating a random floating number Shuffles the items of a list


12Intro Lecture

Strings Strings are immutable ordered sequences of characters Represented in a variety of ways:

o Single quoteso Double quoteso Triple quotes (they can span multi lines)

See Python Reference in the Appendix to this presentation for detailed information about strings

aString = "I'm double-quoted!" # Note the embedded 'single' quotesanotherString = 'I am "single-quoted"! ‘ # Note the embedded "double" quotesspannedString = '''I am spanned over

multiple lines!'''


13Intro Lecture

String – OperatorsOperators DescriptionSpecific string operators + * [i] [ : ]

in | not in

concatenation repetition slicing : returns the ith character range slicing: returns the characters

within the given range True if the character is | is not in the

given string


14Intro Lecture

String – Formatting A string can be formatted using the % operator

>>> print ("Hello! My name is %s and I have %d legs" %("John", 5))Hello! My name is John and I have 5 legs

Operators Description %c %d | %i %e | %E %s %f %u %x | %X

character signed decimal integer exponential notation string floating point number unsigned decimal integer hexadecimal integer


16Intro Lecture

Sequences & Dictionaries

aString = "Bob" aList = [1, aString, "toto"] # [1, 'Bob', 'toto']aTuple = (1, 2, aString) # (1, 2, 'Bob')aDictionary = {"Name" : aString, "Age" : 52} # {'Age': 52, 'Name': 'Bob'}

Type Description

String Ordered sequence of characters Defined using quotes " " or ' ' Immutable

List Ordered sequence of mixed types Defined using square brackets [ ] Mutable

Tuple Ordered sequence of mixed types Defined using parenthesis ( ) Immutable

Dictionary Unordered sequence of key-value items Defined using curly braces { } Mutable


17Intro Lecture

List & Tuple – OperatorsOperators DescriptionSpecific string operators

+ * [i] [ : ]

in | not in

concatenation repetition slicing : returns the ith character range slicing: returns the characters

within the given range True if the character is | is not in

the given string


18Intro Lecture

List & Tuple – FunctionsFunction DescriptionList & Tuple methods

cmp (list1, list2) len (list) max (list) | min (list) list (seq) | tuple (seq)

compares element of list1 and list2 returns the total length of list return the item of list with the max | min

value converts a tuple into a list & vice-versa

List only methods

append (obj) | extend (seq) insert (obj, index) count (obj) index (obj) remove (obj) | pop ()

reverse () | sort ([func])

appends obj | seq to the list insert obj at the given index counts how many time obj is in the list returns the lowest index of obj in list removes obj from the list | removes and

returns the last object from the list reverses elements of the list | sorts the list

using a comparing function func if given


19Intro Lecture

Dictionary – FunctionsOperators Description cmp (dict1, dict2) len (dict) str (dict) list (seq) | tuple (seq)

dict.clear() dict.copy() dict.get(key)

dict.has_key(key) dict.keys() | dict.values() dict.items()

dict.update(dict2)

compares element of dict1 and dict2 returns the total length of dict returns a string representation of dict converts a tuple into a list & vice-versa

removes all elements of of dict returns a copy of dict returns the value of dict associated to

the key key checks if dict has key as key returns a list of dict's keys | dict's values returns a list of dict's elements (keys,

values) adds dict2 elements to dict


20Intro Lecture

Python Datatypes – Mutability Immutable : the values of the sequence (tuple | string) CANNOT

be modified Mutable : the values of a list CAN be modified

o the list references the SAME memory location after modification

o list are slower than immutable sequences>>> aString = "Hello" # 'aString' is immutable, it CANNOT be changed!>>> aString[0] = "h" # the interpreter will not let you do that…

Traceback (most recent call last):TypeError : 'str' object does not support item assignment

>>> aList = [1, aString, "toto"]>>> anotherList = aList # 'anotherList' points to 'aList' -> [1, 'Hello', 'toto']>>> aList[2] = "titi" # [1, 'Hello', 'titi']>>> anotherList # [1, 'Hello', 'titi']


21Intro Lecture

Python Operators – Overview

Type of operator OperatorsArithmetic + (Addition) | - (Substration)

* (Multiplication) | / (Division)% (Modulus) | ** (Exponent)

Boolean (Logical) and | or | not

Comparison == (is equal) | != (not equal)>= (bigger or equal than) | <= (smaller or equal)> (strictly bigger than) | < (strictly smaller than)

Assignment = (main assignment operator)+= (addition assignment) | += (substr. assignment)*= (mult. assignment) | /= (division assignment)%= (modulus assignment) | **= (exp. assignment)


22Intro Lecture


Type of operator OperatorsMembership in (a value is in a specified sequence)

not in (a value is NOT in a specified sequence)

Identity is (two objects are equal, i.e. point to the same memory location)is not (two objects are NOT equal)


24Intro Lecture

Sample code #2>>> a = 2 # 'a' holds (i.e. points to) '2'>>> a == 2 # compares 'a' to '2'True>>> a += a**3 # a = a + a^3 = 10>>> a % 4 # 2 (10 = 4*2 + 2)2

>>> s = "Hello">>> 'H' in s # membership operatorTrue>>> s[1:4] # extracts the 1st (included) to the 4th (excluded) characters'ell'>>> s[:2] + s[2:] # slicing and concatenation'Hello'>>> s[-1] # Negative lookup -> from right, starting with -1'o'>>> "H" in s and "s" not in s or ("H" + "e" + "l"*2 + "o") in sTrue


25Intro Lecture

Assignment Assignment means binding a name (variable) to a reference

to some objecto A reference is a location in the computer memory that is assigned

to the variableo The variable points to the memory place of the referenced object

When changing the value of the variable, a new object is created into the memory, and the variable points to that new object

variable: a

a = 10a = a + 12

12

MemoryValue: 10

Type: Integer

Value: 22Type: Integer

1

2


26Intro Lecture

Assignment Python automatically "knows" the type of the variable based on

the assigned objecto there is no need to define the type when defining a variableo this process is called type inference

If you try to access a variable before having created it -> error!

x = 1 # 'x' is of type 'integer'_a_string_ = "Hello" # a 'string'listA1 = ['a', x, "aaa"] # a 'list't = (1, 2, 3) # a 'tuple'aDict = {"Name" : "Bob", "Age" : 52} # a 'dictionary'

>>> zTraceback (most recent call last):File "<ipython-input-4-a8a78d0ff555>", line 1, in <module>

NameError: name 'z' is not defined

Python language – Control flows

27Intro Lecture

Conditional execution The if statement is used for conditional execution The statements within the if expression belong to a new group of

statements The group of statements

o starts with a semi-colon ":"o has indentation (using the <tab> key)

x = random()if (x < 0.5): # take care of the semi-colon ':' after the statement!

print ("x is lower than 0.5!") # and of the indentation

if (x > 0.5): # let's start a new block of statementsif (x < 0.75): # a nested 'if ' condition!!

print ("x is lower than 0.75!") # only executed if x < 0.75!!print ("x > 0.5") # executed if x > 0.5


28Intro Lecture

Conditional execution An if statement can be followed by

o one (or many) optional elif statement(s), which evaluate a new boolean expression and execute when the previous booleanexpression is False

o one (or many) optional else statement(s), which execute when the last boolean expression is evaluated to False

The (optional) else statement is the last element of the if-elif-else chain

else and elif statements cannot live on their own, they must follow an if statement!

if (x < 0.5) : # take care of the semi-colon ':' after the statement!print ("x is lower than 0.5!") # and of the indentation

elif (x >= 0.5 and x < 0.75) : # evaluated when 0.5 <= x < 0.75print ("x is greater than or equal to 0.5 but smaller than 0.75!")

else :print ("x is big!") # evaluated when x >= 0.75


29Intro Lecture

Loops Loops are blocks of code executed several times Like conditional statements, the loop block

o starts with a semi-colon ":"o has indentation (using the <tab> key)

Type of loop Descriptionfor loop Executes a block of statements a

specific number of times depending on the value of a counter

while loop Executes a block of instructions while a certain condition is being True

The condition is tested before executing the block of statements


30Intro Lecture

Loops

# a 'while' loopx = 1while x < 10 : # semi-colon ':' after the statement -> start of a new block

print ("x is lower than 10") # indentation!x += 1 # increment x -> the condition is met

while x<10

# a 'for' loopaRange = ['a', 'b', 'c', 'd']for x in aRange : # semi-colon ':' after the statement -> start of a new block

print ("Current element: " + x)


31Intro Lecture

Loops Loops also have specific clauses:

o break : terminates the nearest enclosed loop, skipping the optional else clause

o continue: continues with the next cycle of the nearest enclosed loopo else: executed when the loop terminates through exhaustion of the

condition, or when the condition becomes False, but not when the loop is terminated by the break statement

x = 1while x < 10 :

print ("x is " + str (x))x += 1if (x > 5) :

break # loop stops if x > 5!

for s in ['H', 'e', 'l', 'zzzz', 'l', 'o'] : if (s == 'zzzz') :

continue # the loop will skip the 'zzzz' element of the listprint (s)

Python language – Functions

32Intro Lecture

Functions - Definition A function is a named section containing a block of statements

o reusableo performs a specific tasko may return (using the return statement) or not some value

Provides modularity for an application:o defined only onceo can be used when required at several different points in the program

The keyword def defines a function:o followed by the function nameo and the parenthesized list of parameterso and with a final semi-colon ':' indicating the start of the function bodyo the body must be indented (like loops or conditional statements)

An optional string literal as first statement of the body will be interpreted as the function documentation (see example below)


33Intro Lecture

Functions - Examplesdef even(n) : # check whether a number is even

"""Checks if a number is even""" # function documentationif (n % 2 == 0) :

print (str (n) + " is even!")else :

print (str (n) + " is not even!")

>>> even (19)19 is not even!

>>> even (224)224 is even!

And here is how to call the function:


34Intro Lecture

Functions argumentsA function can be called using the following types of arguments:

Type of argument Descriptionrequired argument mandatory arguments that must match the

function definition must be passed in correct order

keyword argument the arguments are identified by their name arguments can be passed in an arbitrary

order

default argument arguments may have default value if the value is not provided for a default

argument, the default value is used

variable-length arguments arbitrary argument list – used to call the function with an arbitrary list of arguments

defined with an asterisk (*) before their name


35Intro Lecture

Functions arguments - Examplesdef letsTalk (name, *args) : # required and variable-length parameters

print (name + " is my name")for arg in args :

print (arg)

>>> letsTalk ("Gigi", 'Hi', 'there!') Gigi is my nameHithere!

def sayMyNameAndAge (name, age=25) : # age has a default valueprint (name + " is " + str (age) + " years old")

>>> sayMyNameAndAge (age=30, name="Gigio") # keyword parametersGigio is my 30 years old

>>> sayMyNameAndAge ("Gigio") # as age is optional, we can skip itGigio is my 25 years old


36Intro Lecture

Functions – Scope of variables There are two scopes of variables in Python

o Global variableo Local variable

A variable defined inside the body of a function is only accessible from within the function; it is a local variable

A variable declared outside any function is a global variable; it can be accessed from within any declared function

x = 5 # 'x' is a global variable – defined outside any function, but # available within the body of defined functions below

def f (y) :z = x**2 + y**2 # 'z' and 'y' are local variablesreturn z

def exploreVariables (y) :result = f (4) # 'result' is a local variable

Python language – Modules

37Intro Lecture

Modules – Definition A module is a file grouping Python definitions and statements

of related functionalities

The file can be composed ofo variableso functionso classeso runnable code

A module can then be imported in a script or in an interactive instance of the interpreter

A module called moduleA is stored in a file named moduleA.py

It is sometimes called library in other programming languages


38Intro Lecture

Modules – Examples

def positiveSum (*values) : # sums up positive values only result = 0for value in values :

if (value > 0) :result += value

return result

def negatePositiveSum (*values) : # sums up positive values and negates resultreturn -positiveSum(*values)

utils.py # utils module is stored in filename called 'utils.py'

>>> import utils # Here, we import the 'utils' module that is stored in the # 'utils.py' file created above. We can then call any # function declared in the 'utils' module

>>> utils.positiveSum (1, 2, -5)3>>> utils.negatePositiveSum (1, 2, -5)-3


39Intro Lecture

Modules – Imports A module can contain executable code (i.e. code not stored in a function),

o these statements are intended to initialize the moduleo they are executed only the first time the module is imported

It is possible to import only specific names from a module, or everything using the * wildcard, or give a specific name to an imported module:

When importing a module, Python searches for the module in the following order:o the current directory;o if the module is not found, Python searches in the directories defined in the

PYTHONPATH environment variable. Usually: Windows: C:\python\lib *IX : /usr/local/lib/python

>>> from utils import positiveSum # Only 'positiveSum' is available>>> from utils import * # Everything is available>>> import utils as utl # Names in utils can now be accessed using

the 'utl' namespace (see below)


40Intro Lecture

Modules – Namespaces In Python, a name is an identifier that maps to an object; a name can

be:o a variableo a functiono a classo etc.

A namespace is a space that holds a certain number of names

A module acts as a global namespaceo you CANNOT have two objects in a module that share the same nameo however, you CAN have two objects with the same name in two different

modules>>> import FloatingNumbers as fn>>> import IntegerNumbers as in

>>> fn.sum (1.0, 2.1) # Here, the two modules have distinct namespaces: 'fn', >>> in.sum (1, 2) # and 'in', with a function called 'sum' having the same

# name in both namespaces

Python

41Intro Lecture

Useful External Libraries



Python language – External Libraries

42Intro Lecture

NumPy– Scientific computing The NumPy library allows to compute scientific numerical

calculations with Python

It provides easy management for lists and numbers

Official website: http://www.numpy.org/>>> import numpy as np>>> np.pi # 3.141592653589793

>>> np.array([[1, 2], [3, 4], [5, 6]]) # Create a multidimensional array [[1 2][3 4][5 6]]

>>> np.arange(0, 10, 2) # Create a range that starts from 0 to 10, with step = 2 [0 2 4 6 8]

>>> np.linspace(0, 10, 6) # Create a range that starts from 0 to 10, having 6 elements[ 0. 2. 4. 6. 8. 10.]

http://www.numpy.org/


43Intro Lecture

NumPy– Arrays creation & manipulations>>> A = np.arange(1, 6, 1) # array([1, 2, 3, 4, 5])>>> B = np.zeros((5, 5)) # a (5x5) matrix composed of zeros >>> C = np.ones((4, 6)) # a (4x6) matrix composed of ones>>> D = np.eye(3) # a (3x3) identity matrix>>> E = np.random.rand(3, 2) # a (3x2) matrix composed of random numbers

# IMPORTANT! Arrays (lists) start with index = 0!>>> A[2:4] # from elements 3 (inclusive) to 5 (exclusive)[3 4]>>> A[3:] # from elements 4 (inclusive) to the end (inclusive)[4 5]

>>> E[2] # third row of 'E'>>> E[:,0] # first column of 'E'>>> E[2, 1] # element at 3rd row, 2nd col of 'E'>>> E[0:2, :] # 1st and 2nd rows, with all columns


44Intro Lecture

NumPy– Linear algebra>>> import numpy as np>>> A = np.array([[1, 2], [3, 4], [5, 6]])>>> B = np.array([[2, 3], [4, 5], [6, 7]])>>> np.size (A) # Returns the number of elements in the matrix 6>>> np.shape (A) # Returns the 'shape' of the matrix, i.e. the size of the matrix(3, 2)

>>> A*B # Element-wise multiplication! 'A' and 'B' must have the same shape[[ 2 6][12 20][30 42]]

>>> C = np.array([[1, 2, 3], [4, 5, 6]])>>> np.dot(A,C) # Matrix multiplication![[ 9 12 15][19 26 33][29 40 51]]

>>> A.T # Transpose 'A'[[1 3 5][2 4 6]]


45Intro Lecture

NumPy– Linear algebra>>> A = np.array([[1, 2], [3, 4]]) # Let 'A' be a squared matrix

>>> np.linalg.det(A) # 'A' determinant-2

>>> np.linalg.inv(A) # Inverse of 'A'[[-2. 1. ][ 1.5 -0.5]]

>>> D, V = np.linalg.eig(A) # Eigenvalues and Eigenvector of 'A'[-0.37228132 5.37228132] # Eigenvalues [[-0.82456484 -0.41597356] # Eigenvector [ 0.56576746 -0.90937671]]

>>> A = np.array([[1, 2], [3, 4]])>>> B = np.array([[3, 5], [8, 9]]) >>> covmat = np.cov(A, B) # Covariance Matrix


46Intro Lecture

Matplotlib – Python plotting Matplotlib is a 2D / 3D Python plotting library

It makes heavy use of NumPy.

Large number of types of plots available:o Line ploto Histogramso Bar chartso Pie chartso Polar plotso Error chartso Scatter plotso Path plotso Streamplotso Etc.

o Official website: http://matplotlib.org/

http://matplotlib.org/


47Intro Lecture

Matplotlib – Plot simple lineimport numpy as npimport matplotlib.pyplot as plt # Let's import the plotting library

x = np.linspace(0, 2*np.pi, 100)y = np.sin(x)plt.plot(x, y) # plots sin(x) between 0 and 2*pi


48Intro Lecture

Matplotlib – Format plot

x = np.linspace(0, 2*np.pi, 100)y = np.sin(x)plt.plot(x, y, "r:o") # format the line (red dots)

A plot can be formatted in multiple different ways Details on how to format the plot can be found at:

http://matplotlib.org/api/lines_api.html

x = np.linspace(0, 2*np.pi, 100)y = np.sin(x)plt.plot(x, y, color="purple", linewidth = 10)

http://matplotlib.org/api/lines_api.html


49Intro Lecture

Matplotlib – Axes, title and legendx = np.linspace(0, 2*np.pi, 100)y = np.sin(x)plt.plot(x, y, label="sin(x)") # plots sin(x) between 0 and 2*pi, and adds a legend

plt.title("sin(x) function plot") # a titleplt.legend() # actually plots the legend! plt.xlabel("x values") # x axis labelplt.ylabel("sin(x) values") # y axis label

plt.xlim(2, 5) # fixes x axis values to [2, 5] rangeplt.ylim(-2, 2) # fixes y axis values to [2, 2] range


50Intro Lecture

Matplotlib – Multiple plotsx = np.linspace(0, 2*np.pi, 100)y1 = np.sin(x)y2 = np.cos(x)plt.plot(x, y1, label="sin(x)") # plots sin(x) plt.plot(x, y2, label="cos(x)") # plots cos(x) plt.legend()

x = np.linspace(0, 2*np.pi, 100)y1 = np.sin(x)y2 = np.cos(x)plt.subplot(211) # 2 rows x 1 col subplotsplt.plot(x, y1, "ro") # plots sin(x) in 1st subplotplt.subplot(212) # 2 rows x 1 col subplotsplt.plot(x, y2, "b--") # plots cos(x) in 2nd subplot


51Intro Lecture

Pandas – Data Analysis Library Pandas provides high-performance, easy-to-use data structures

and data analysis tools for Python

Libraries functionalities:o DataFrame object: to manipulate data with indices that can be dates,

strings, etc.o Tools allowing to read / write data from / to Microsoft Excel, CSV files,

text files, SQL database, etc.o Direct data plottingo Intelligent data alignment on index, along with management of

missing datao Creation of pivot tableso Fusing and joining large datasetso Time series analysis

Official website: http://pandas.pydata.org/

http://pandas.pydata.org/


52Intro Lecture

Pandas – Read data from Excel fileimport pandas as pd

dataFile = "salaries.xlsx" # data is defined in Excel filexslx = pd.ExcelFile(dataFile) # creates an xslx object

sheetName = "salaries" # The sheet name where the data is stored

data = xslx.parse(sheetName) # Actually parse the sheet and store thedata in a DataFrame object

dataFile2 = "sales.csv" # data is defined in 'CSV' filedata2 = pd.read_csv(dataFile2)


53Intro Lecture

Pandas – View datadates = pd.date_range('20161231', periods=31) # creates an array of dates starting

31 dec, 2016 of 31 days

data = pd.DataFrame(np.random.randn(31, 3), index=dates, columns=["A", "B", "C"])

data.head() # Prints the first 5th elements of 'data'A B C

2016-12-31 0.329 1.750 -0.6482017-01-01 -0.619 0.614 -0.0492017-01-02 -0.967 -0.196 0.8282017-01-03 0.796 -0.291 -0.7632017-01-04 -0.661 0.110 -0.284

data.tail() # Prints the last 5th elements of 'data'data.describe() # Prints statistic summary of 'data'data.sort_index(axis=1, ascending=False) # Sort by axis=1 (i.e. by columns)data.sort_values(by='B') # Sort column 'B' values ascendingdata.T # Transpose the data


54Intro Lecture

Pandas – Select datadata["A"] # Selects the column 'A' from the data

data[0:6] # Slices rows (from 1st row to 5th row)

data.loc["20161231" : "20170102", ['B']] # From 31 dec 2016 to 2 jan 2017, column BB

2016-12-31 1.7502017-01-01 0.6142017-01-02 -0.196

data[data.C > 0] # Boolean indexing -> filters by positive values of column 'C'A B C

2017-01-02 -0.967 -0.286 0.8282017-01-05 0.665 -0.572 0.5022017-01-06 -1.557 -1.305 1.5592017-01-07 0.786 -0.164 1.1522017-01-08 -1.411 0.445 1.075


55Intro Lecture

StatsModels – Statistics Library StatsModels allows to explore data, estimate statistical models,

and perform statistical tests

Libraries functionalities:o Linear regression modelso Generalized linear modelso Discrete choice modelso Robust linear modelso Nonparametric estimatorso Statistical testso Plotting functionso etc.

Official website: http://statsmodels.sourceforge.net/

http://statsmodels.sourceforge.net/


56Intro Lecture

StatsModels – Ordinary Least Squares (OLS)import statsmodels.api as sm

data = pd.read_csv("salaries.csv") # Pandas DataFrame from 'CSV' file

y = data["salary"] # Regressor – dependent variableX = data["yearsOfService"] # Predictor – independent variable

model = sm.OLS(y, X).fit() # Fits a simple ordinary least squares modelmodel.summary() # Display summary of regression results


57Intro Lecture

SciPy – Scientific Python SciPy is a library for scientific computing

Libraries functionalities:o Numerical integrationo Optimizationo Interpolationo Fourrier Transformso Signal Processingo Statisticso etc.

Official website: http://scipy.org/

http://scipy.org/


58Intro Lecture

SciPy– Optimizationfrom scipy import optimize

x_root = np.random.random()y_root = np.random.random()

def f (params) : # defines the function to optimizex, y = paramsreturn (x-x_root)**2 + (y-y_root)**2

initParams = np.array([0.5, 0.5]) # initial guess

optimize.minimize(f, initParams, method="SLSQP", bounds=bnds) # let's minimize f!

fun: 4.5442939897139043e-09message: 'Optimization terminated successfully.'success: Truex: array([ 0.12439444, 0.61272226])


59Intro Lecture

SciPy– Optimization with constraintsfrom scipy import optimize

initParams = np.array([0.5, 0.5]) # initial guess

bnds = ((0, 1), (0, 1)) # bounds for x and y : [0, 1] (inclusive!)

def constraints(params): # A constraint function: 0.5 - (x + y) >= 0x, y = paramsreturn 0.5 - (x + y)

cons = {'type' : 'ineq', 'fun' : constraints} # Here, we define the type of constraint - an# inequality - along with the function# defining the constraint

optimize.minimize(f, initParams, method="SLSQP", bounds=bnds, constraints=cons)

x: array([ 0.48928464, 0.01071536])

Python

60Intro Lecture

Installing and Running



Installing and running

61Intro Lecture

Anaconda Environment Open data science platform Includes Python distribution Available platforms:

o Windowso Mac OSXo Linux

Includes 100+ scientific packages Spyder IDE (Integrated Development Environment) Download at https://www.continuum.io/downloads

https://www.continuum.io/downloads

Python Shell - Interactive Interpreter

62Intro Lecture

Launched via any Windows / *IX terminalo Type "python" to launch the interpretero To exit the interpreter, type "exit()", or

in *IX terminal: CTRL-D in Windows terminal: CTRL-C or CTRL-Z

The interpreter welcomes you with the ">>>" prompto Allows to evaluate user inputs

Use the terminal to launch python scripts (".py" files):o $ python script.py

Spyder IDE

63Intro Lecture

Text editor with syntax color highlighting and smart indenting -> eases and speeds developments

Shell (Python and IPyhton consoles) for displaying results and interactive evaluation

Data / Files explorers Code analyzer Contextual help Code debugging Etc.

Python

64Intro Lecture

References



Python - References

65Intro Lecture

Online documentation Official Python website: https://docs.python.org/3/ Wiki-Book: https://en.wikibooks.org/wiki/Python_Programming Byte of Python: http://www.swaroopch.com/notes/Python Python Bibliotheca: http://openbookproject.net/pybiblio/ Tutorialspoint: https://www.tutorialspoint.com/python Google: https://developers.google.com/edu/python/ Pythonspot: https://pythonspot.com/ Pythonlearn: http://www.pythonlearn.com/book.php Python for Fun: http://www.openbookproject.net/py4fun/ Pandas: http://pandas.pydata.org/ Numpy: http://www.numpy.org/ Scipy: https://www.scipy.org/

https://docs.python.org/3/

https://en.wikibooks.org/wiki/Python_Programming

http://www.swaroopch.com/notes/Python

http://openbookproject.net/pybiblio/

https://www.tutorialspoint.com/python

https://developers.google.com/edu/python/

https://pythonspot.com/

http://www.pythonlearn.com/book.php

http://www.openbookproject.net/py4fun/

http://pandas.pydata.org/

http://www.numpy.org/

https://www.scipy.org/

Python - References

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BooksMark LutzLearning Python, 5th EditionO'Reilly Media; 5 edition (July 6, 2013)

Paul JonesPython: The Fundamentals Of Python ProgrammingCreateSpace Publishing Platform (Oct. 26, 2016)

Eric MatthesPython Crash Course: A Hands-On, Project-Based Introduction to ProgrammingNo Starch Press; 1 edition (November 30, 2015)

Python - References

67Intro Lecture

Books

Yves HilpischPython for FinanceO′Reilly (Dec. 30, 2014)

Wes McKinneyPython for Data AnalysisO'Reilly Media (Jul. 31, 2016)

http://www.google.ch/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwi_8sHz_rnSAhXIC5oKHfwfC28QjRwIBw&url=http://tpq.io/&bvm=bv.148747831,d.bGg&psig=AFQjCNGwbA4qjrU0JQh787lUDO4HOLyLWw&ust=1488618687936468

http://www.google.ch/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwi_8sHz_rnSAhXIC5oKHfwfC28QjRwIBw&url=http://tpq.io/&bvm=bv.148747831,d.bGg&psig=AFQjCNGwbA4qjrU0JQh787lUDO4HOLyLWw&ust=1488618687936468

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