Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
Online Resources

The Pandas Series


The Pandas Series data structure is a one-dimensional, heterogeneous array with labels. We can create a Pandas Series data structure as follows:

  • Using a Python dict

  • Using a NumPy array

  • Using a single scalar value

When creating a Series, we can hand the constructor a list of axis labels, which is commonly referred to as the index. The index is an optional parameter. By default, if we use a NumPy array as the input data, Pandas will index values by autoincrementing the index commencing from 0. If the data handed to the constructor is a Python dict, the sorted dict keys will become the index. In the case of a scalar value as the input data, we are required to supply the index. For each new value in the index, the scalar input value will be reiterated. The Pandas Series and DataFrame interfaces have features and behaviors borrowed from NumPy arrays and Python dictionaries, such as slicing, a lookup function that uses a key, and vectorized operations. Performing a lookup on a DataFrame...