Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Mastering Pandas for Finance
Mastering Pandas for Finance

Mastering Pandas for Finance: Master pandas, an open source Python Data Analysis Library, for financial data analysis

eBook
R$80 R$218.99
Paperback
R$272.99
Subscription
Free Trial
Renews at R$50p/m

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing
Table of content icon View table of contents Preview book icon Preview Book

Mastering Pandas for Finance

Chapter 2. Introducing the Series and DataFrame

pandas provides a comprehensive set of data structures for working with and manipulating data and performing various statistical and financial analyses. The two primary data structures in pandas are Series and DataFrame. In this chapter, we will examine the Series object and how it extends a NumPy ndarray to provide operations such as indexed data retrieval, axis labeling, and automatic alignment. Then, we will move on to examine how DataFrame extends the capabilities of Series to use columnar/tabular data, which can be of more than one data type.

The intention of this chapter is to be not only a refresher for those with basic familiarity with pandas, but also a means by which someone who is not initiated with pandas can gain enough familiarity with the two data structures and have a good foundation as we move into more finance-related subjects in later chapters. We will not cover all the details of using Series and DataFrame but...

Notebook setup

To utilize the examples in this chapter, we will need to include the following imports and settings in either your IPython or IPython Notebook environment:

In [1]:
   import pandas as pd
   import numpy as np

   pd.set_option('display.notebook_repr_html', False)
   pd.set_option('display.max_columns', 8)
   pd.set_option('display.max_rows', 8)

The main pandas data structures – Series and DataFrame

Several classes for manipulating data are provided by pandas. Of those, we are interested in Series and more interested in DataFrame.

The Series

The Series is the primary building block of pandas and represents a one-dimensional labeled array based on the NumPy ndarray. The Series extends the functionality of the NumPy ndarray by adding an associated set of labels that are used to index the elements of the array. A Series can hold zero or more instances of any single data type.

This labeled index adds significant power to access the elements of the Series over a NumPy array. Instead of simply accessing elements by position, a Series allows access to items through the associated index labels. The index also assists in a feature of pandas referred to as alignment, where operations between two Series are applied to values with identical labels.

The DataFrame

The Series is the basis for data representation and manipulation in pandas,...

The basics of the Series and DataFrame objects

Now let's examine using the Series and DataFrame objects, building up an understanding of their capabilities that will assist us in working with financial data.

Creating a Series and accessing elements

A Series can be created by passing a scalar value, a NumPy array, or a Python dictionary/list to the constructor of the Series object. The following command creates a Series from 100 normally distributed random numbers:

In [2]:
   np.random.seed(1)
   s = pd.Series(np.random.randn(100))
   s

Out[2]:
   0     1.624345
   1    -0.611756
   2    -0.528172
   3    -1.072969
           ...   
   96   -0.343854
   97    0.043597
   98   -0.620001
   99    0.698032
   Length: 100, dtype: float64

Individual elements of a Series can be retrieved using the [] operator of the Series object. The item with the index label 2 can be retrieved using the following code:

In [3]:
   s[2]

Out[3]:
   -0.528171752263

Multiple values can be retrieved using an array...

Reindexing the Series and DataFrame objects

Reindexing in pandas is a process that makes the data present in a Series or DataFrame match with a given set of labels along a particular axis. This is core to the functionalities of pandas as it enables label alignment across multiple objects.

The process of performing a reindex does the following:

  • Reorders existing data to match a set of labels
  • Inserts NaN markers where no data exists for a label
  • Fills missing data for a label using a type of logic (defaulting to adding NaNs)

The following is a simple example of reindexing a Series. The following Series has an index with numerical values, and the index is modified to be alphabetic by simply assigning a list of characters to the .index property, making the values able to be accessed via the character labels in the new index:

In [60]:
   np.random.seed(1)
   s = pd.Series(np.random.randn(5))
   s

Out[60]:
   0    1.624345
   1   -0.611756
   2   -0.528172
   3   -1.072969
   4    0.865408
   dtype...

Summary

In this chapter, we briefly overviewed the pandas Series and DataFrame objects, how they are used to represent data, and how to select data in both via queries, columns, and indices. The concept of reindexing both classes of objects is also introduced, and as we get into the later chapters, it will be common to perform reindexing of time-series data.

In the next chapter, we will examine indexing in more depth with an eye towards how performing various aggregations of data can derive results from the information represented in pandas. As we progress into more specific financial analysis, this combination of reindexing and aggregation will form the basis of much of the analysis performed later in the book.

Left arrow icon Right arrow icon
Download code icon Download Code

Description

If you are interested in quantitative finance, financial modeling, and trading, or simply want to learn how Python and pandas can be applied to finance, then this book is ideal for you. Some knowledge of Python and pandas is assumed. Interest in financial concepts is helpful, but no prior knowledge is expected.

Who is this book for?

If you are interested in quantitative finance, financial modeling, and trading, or simply want to learn how Python and pandas can be applied to finance, then this book is ideal for you. Some knowledge of Python and pandas is assumed. Interest in financial concepts is helpful, but no prior knowledge is expected.

What you will learn

  • Modeling and manipulating financial data using the pandas DataFrame
  • Indexing, grouping, and calculating statistical results on financial information
  • Timeseries modeling, frequency conversion, and deriving results on fixed and moving windows
  • Calculating cumulative returns and performing correlations with index and social data
  • Algorithmic trading and backtesting using momentum and mean reversion strategies
  • Option pricing and calculation of Value at Risk
  • Modeling and optimization of financial portfolios

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : May 25, 2015
Length: 298 pages
Edition : 1st
Language : English
ISBN-13 : 9781783985104
Category :
Languages :
Concepts :
Tools :

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Product Details

Publication date : May 25, 2015
Length: 298 pages
Edition : 1st
Language : English
ISBN-13 : 9781783985104
Category :
Languages :
Concepts :
Tools :

Packt Subscriptions

See our plans and pricing
Modal Close icon
R$50 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
R$500 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just R$25 each
Feature tick icon Exclusive print discounts
R$800 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just R$25 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total R$ 852.97
Mastering Pandas for Finance
R$272.99
Python for Finance
R$272.99
Mastering Python for Finance
R$306.99
Total R$ 852.97 Stars icon

Table of Contents

10 Chapters
1. Getting Started with pandas Using Wakari.io Chevron down icon Chevron up icon
2. Introducing the Series and DataFrame Chevron down icon Chevron up icon
3. Reshaping, Reorganizing, and Aggregating Chevron down icon Chevron up icon
4. Time-series Chevron down icon Chevron up icon
5. Time-series Stock Data Chevron down icon Chevron up icon
6. Trading Using Google Trends Chevron down icon Chevron up icon
7. Algorithmic Trading Chevron down icon Chevron up icon
8. Working with Options Chevron down icon Chevron up icon
9. Portfolios and Risk Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(6 Ratings)
5 star 0%
4 star 66.7%
3 star 33.3%
2 star 0%
1 star 0%
Filter icon Filter
Top Reviews

Filter reviews by




Amazon Customer Jul 01, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book brings together several important concepts in finance and explains how to implement them using a core of Python and pandas. Some of the key topics included in the books are:Wakari.io - a collaborative data analytics platform that allows to explore data and create analytic scripts in collaboration with IPython Notebooks.Introduction to the Series and DataFrame objectsA chapter on Reshaping, Reorganizing, and Aggregating DataCorrelations of Google trends with stock movements, creating algorithmic trading systemsCalculating options payoffs, prices, and behaviorsConstructing an efficient portfolioAn overview of modern portfolio theory and Computing Value at Risk (VaR)I liked that the provided code is in the form of ipython notebook.Disclaimer: I received this eBook as a complementary copy.
Amazon Verified review Amazon
yatgonewest Aug 30, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I would recommend this book as a good basic intro to the fundamentals of Pandas even if you're not particularly interested in applying it to finance. I have read portions of a few others and this authors style is very concise and to the point on Series and DataFrames, and in particular on indexing of both.
Amazon Verified review Amazon
Al Krinker Oct 07, 2016
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I wish I could give it 5 stars, but since this book was poorly edited by tech reviewer that missed many errors, I can't go above 3... The content is great and it def will get you going, but some formulas for example Value at Risk at the very end is incorrect and even in example it is wrong (they forgot the mean!)
Amazon Verified review Amazon
ajk251 Jul 11, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I found this book to be a useful one - it nicely covers all the pandas/finance basics. If your're a bit of a novice in either one, this book provides a nice introduction. It doesn't waste time with a Python introduction or other book-filling topics. It shows how to do all of the most useful operations in pandas as they relate to finance. Then it goes into options, portfolio theory, and applying pandas to a Google Trends project. I particularly liked the section on Zipline, a back-testing library. It was a little clearer than the documentation. The part where Quandl is used is also nice.That said, if you dug around the internet, you could probably cobble these examples together. Also, it doesn't really go into either pandas or finance too deeply. I don't hold those against the book - it can't be all things to all people. The book makes for a nice overview of the relevant topics - but expect an overview.Note: I was asked to give a review of the book by the publisher in exchange for a free ebook (I purchased the book from the publisher).
Amazon Verified review Amazon
Ho Yan Chan May 02, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
ok
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is included in a Packt subscription? Chevron down icon Chevron up icon

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription? Chevron down icon Chevron up icon

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits? Chevron down icon Chevron up icon

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled? Chevron down icon Chevron up icon

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title? Chevron down icon Chevron up icon

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team. 

Can I download the code files for Early Access titles? Chevron down icon Chevron up icon

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date? Chevron down icon Chevron up icon

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready? Chevron down icon Chevron up icon

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access? Chevron down icon Chevron up icon

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered? Chevron down icon Chevron up icon

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content? Chevron down icon Chevron up icon

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access? Chevron down icon Chevron up icon

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.