An edition of Python Basics: With Illustrations from the Financial Markets
(2019)
Python Basics
With Illustrations from the Financial Markets
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The material presented here is a condensed introduction to Python and its data science related libraries such as NumPy, Pandas, and Matplotlib. The illustrative examples we use are associated with the financial markets.
We think it should also be useful to
- Anyone who wants a brief introduction to Python and the key components
of its data science stack, and
- Python programmers who want a quick refresher on using Python for
data analysis.
We do not expect any of our readers to have a formal background in computer science, although some familiarity with programming would be nice to have. The concepts and ideas here are covered with several examples to help connect theory to practice.
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Previews available in: English
Subjects
Python, Programming, Financial markets, algorithmic trading, trading, algorithm, coding| Edition | Availability |
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Python Basics: With Illustrations from the Financial Markets
2019, QuantInsti Quantitative Learning Pvt. Ltd.
in English
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Book Details
Table of Contents
Contents
1 Introduction
1.1 What is Python?
1.2 Where is Python used?
1.3 Why Python?
1.4 History of Python
1.5 Python 3 versus Python 2
1.6 Key Takeaways
2 Getting Started with Python
2.1 Python as a Calculator
2.1.1 Floating Point Expressions
2.2 Python Basics
2.2.1 Literal Constants
2.2.2 Numbers
2.2.3 Strings
2.2.4 Comments
2.2.5 print() function
2.2.6 format() function
2.2.7 Escape Sequence
2.2.8 Indentation
2.3 Key Takeaways
3 Variables and Data Types in Python
3.1 Variables
3.1.1 Variable Declaration and Assignment
3.1.2 Variable Naming Conventions
3.2 Data Types
3.2.1 Integer
3.2.2 Float
3.2.3 Boolean
3.2.4 String
3.2.5 Operations on String
3.2.6 type() function
3.3 Type Conversion
3.4 Key Takeaways
4 Modules, Packages and Libraries
4.1 Standard Modules
4.2 Packages
4.3 Installation of External Libraries
4.3.1 Installing pip
4.3.2 Installing Libraries
4.4 Importing modules
4.4.1 import statement
4.4.2 Selective imports
4.4.3 The Module Search Path
4.5 dir()function
4.6 Key Takeaways
5 Data Structures
5.1 Indexing and Slicing
5.2 Array
5.2.1 Visualizing an Array
5.2.2 Accessing Array Element
5.2.3 Manipulating Arrays
5.3 Tuples
5.3.1 Accessing tuple elements
5.3.2 Immutability
5.3.3 Concatenating Tuples
5.3.4 Unpacking Tuples
5.3.5 Tuple methods
5.4 Lists
5.4.1 Accessing List Items
5.4.2 Updating Lists
5.4.3 List Manipulation
5.4.4 Stacks and Queues
5.5 Dictionaries
5.5.1 Creating and accessing dictionaries
5.5.2 Altering dictionaries
5.5.3 Dictionary Methods
5.6 Sets
5.7 Key Takeaways
6 Keywords & Operators
6.1 Python Keywords
6.2 Operators
6.2.1 Arithmetic operators
6.2.2 Comparison operators
6.2.3 Logical operators
6.2.4 Bitwise operator
6.2.5 Assignment operators
6.2.6 Membership operators
6.2.7 Identity operators
6.2.8 Operator Precedence
6.3 Key Takeaways
7 Control Flow Statements
7.1 Conditional Statements
7.1.1 The if statement
7.1.2 The elif clause
7.1.3 The else clause
7.2 Loops
7.2.1 The while statement
7.2.2 The for statement
7.2.3 The range() function
7.2.4 Looping through lists
7.2.5 Looping through strings
7.2.6 Looping through dictionaries
7.2.7 Nested loops
7.3 Loop control statements
7.3.1 The break keyword
7.3.2 The continue keyword
7.3.3 The pass keyword
7.4 List comprehensions
7.5 Key Takeaways
8 Iterators & Generators
8.1 Iterators
8.1.1 Iterables
8.1.2 enumerate() function
8.1.3 The zip()function
8.1.4 Creating a custom iterator
8.2 Generators
8.3 Key Takeaways
9 Functions in Python
9.1 Recapping built-in functions
9.2 User defined functions
9.2.1 Functions with a single argument
9.2.2 Functions with multiple arguments and a return statement
9.2.3 Functions with default arguments
9.2.4 Functions with variable length arguments
9.2.5 DocStrings
9.2.6 Nested functions and non-local variable
9.3 Variable Namespace and Scope
9.3.1 Names in the Python world
9.3.2 Namespace
9.3.3 Scopes
9.4 Lambda functions
9.4.1 map() Function
9.4.2 filter() Function
9.4.3 zip() Function 177
9.5 Key Takeaways
10 NumPy Module
10.1 NumPy Arrays
10.1.1 N-dimensional arrays
10.2 Array creation using built-in functions
10.3 Random Sampling in NumPy
10.4 Array Attributes and Methods
10.5 Array Manipulation
10.6 Array Indexing and Iterating
10.6.1 Indexing and Subsetting
10.6.2 Boolean Indexing
10.6.3 Iterating Over Arrays
10.7 Key Takeaways
11 Pandas Module
11.1 Pandas Installation
11.1.1 Installing with pip
11.1.2 Installing with Conda environments
11.1.3 Testing Pandas installation
11.2 What problem does Pandas solve?
11.3 Pandas Series
11.3.1 Simple operations with Pandas Series
11.4 Pandas DataFrame
11.5 Importing data in Pandas
11.5.1 Importing data from CSV file
11.5.2 Customizing pandas import
11.5.3 Importing data from Excel files
11.6 Indexing and Subsetting
11.6.1 Selecting a single column
11.6.2 Selecting multiple columns
11.6.3 Selecting rows via []
11.6.4 Selecting via .loc[] (By label)
11.6.5 Selecting via .iloc[] (By position)
11.6.6 Boolean indexing
11.7 Manipulating a DataFrame
11.7.1 Transpose using .T
11.7.2 The .sort_index() method
11.7.3 The .sort_values() method
11.7.4 The .reindex() function
11.7.5 Adding a new column
11.7.6 Delete an existing column
11.7.7 The .at[] (By label)
11.7.8 The .iat[] (By position)
11.7.9 Conditional updating of values
11.7.10 The .dropna() method
11.7.11 The .fillna() method
11.7.12 The .apply() method
11.7.13 The .shift() function
11.8 Statistical Exploratory data analysis
11.8.1 The info() function
11.8.2 The describe() function
11.8.3 The value_counts() function
11.8.4 The mean() function
11.8.5 The std() function
11.9 Filtering Pandas DataFrame
11.10Iterating Pandas DataFrame
11.11Merge, Append and Concat Pandas DataFrame
11.12TimeSeries in Pandas
11.12.1 Indexing Pandas TimeSeries
11.12.2 Resampling Pandas TimeSeries
11.12.3 Manipulating TimeSeries
11.13Key Takeaways
12 Data Visualization with Matplotlib
12.1 Basic Concepts
12.1.1 Axes
12.1.2 Axes method v/s pyplot
12.1.3 Multiple Axes
12.2 Plotting
12.2.1 Line Plot
12.2.2 Scatter Plot
12.2.3 Histogram Plots
12.3 Customization
12.4 Key Takeaways
Contributors
ID Numbers
Links outside Open Library
Community Reviews (0)
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| August 22, 2019 | Edited by Rekhit | Edited without comment. |
| August 21, 2019 | Created by Rekhit | Added new book. |