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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

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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.

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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

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Publication date : May 25, 2015
Length: 298 pages
Edition : 1st
Language : English
ISBN-13 : 9781783985111
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Publication date : May 25, 2015
Length: 298 pages
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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

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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%
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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
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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
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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
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ok
Amazon Verified review Amazon
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