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Hands-On Financial Trading with Python

You're reading from   Hands-On Financial Trading with Python A practical guide to using Zipline and other Python libraries for backtesting trading strategies

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781838982881
Length 360 pages
Edition 1st Edition
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Authors (2):
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Sourav Ghosh Sourav Ghosh
Author Profile Icon Sourav Ghosh
Sourav Ghosh
Jiri Pik Jiri Pik
Author Profile Icon Jiri Pik
Jiri Pik
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to Algorithmic Trading FREE CHAPTER
2. Chapter 1: Introduction to Algorithmic Trading 3. Section 2: In-Depth Look at Python Libraries for the Analysis of Financial Datasets
4. Chapter 2: Exploratory Data Analysis in Python 5. Chapter 3: High-Speed Scientific Computing Using NumPy 6. Chapter 4: Data Manipulation and Analysis with pandas 7. Chapter 5: Data Visualization Using Matplotlib 8. Chapter 6: Statistical Estimation, Inference, and Prediction 9. Section 3: Algorithmic Trading in Python
10. Chapter 7: Financial Market Data Access in Python 11. Chapter 8: Introduction to Zipline and PyFolio 12. Chapter 9: Fundamental Algorithmic Trading Strategies 13. Other Books You May Enjoy Appendix A: How to Setup a Python Environment

Introducing pandas Series, pandas DataFrames, and pandas Indexes

pandas Series, pandas DataFrames, and pandas Indexes are the fundamental pandas data structures.

pandas.Series

The pandas.Series data structure represents a one-dimensional series of homogenous values (integer values, string values, double values, and so on). Series are a type of list and can contain only a single list with an index. A Data Frame, on the other hand, is a collection of one or more series.

Let's create a pandas.Series data structure:

import pandas as pd
ser1 = pd.Series(range(1, 6)); 
ser1

That series contains the index in the first column, and in the second column, the index's corresponding values:

0    1
1    2
2    3
3    4
4    5
dtype: int64

We can specify custom index names by specifying the index parameter:

ser2 = pd.Series(range(1, 6), 
     ...
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