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Python Data Analysis Second Edition by Packt

This is the code repository for Python Data Analysis - Second Edition, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

Go toData analysis allows making sense of heaps of data. Python, with its strong set of libraries, is a popular language used today to conduct various data analysis, machine learning and visualization tasks.

With this book, you will learn about data analysis with Python in the broadest sense possible, covering everything from data retrieval, cleaning, manipulation, visualization, and storage to complex analysis and modeling. It focuses on a plethora of open source Python modules such as NumPy, SciPy, matplotlib, pandas, IPython, Cython, scikit-learn, and NLTK. In later chapters, the book covers topics such as data visualization, signal processing, and time-series analysis, databases, predictive analytics and machine learning. This book will turn you into an ace data analyst in no time. Mapt

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

import pandas as pd
from numpy.random import seed from numpy.random import rand from numpy.random import rand_int import numpy as np

seed(42)

df = pd.DataFrame({'Weather' : ['cold', 'hot', 'cold',
'hot', 'cold', 'hot', 'cold'],
'Food' : ['soup', 'soup', 'icecream', 'chocolate', 'icecream', 'icecream', 'soup'],
'Price' : 10 * rand(7), 'Number' : rand_int(1, 9,)}) print(df)

The code examples in this book should work on most modern operating systems. For all chapters, Python > 3.5.0 and pip3 is required. You can download Python 3.5.x from https://www.python.org/downloads/. On this webpage, you can find installers for Windows and Mac OS X as well as source archives for Linux, Unix, and Mac OS X. You can find instructions for installing and using python for various operating systems on this webpage: https://docs.python.org/3/using/index.html.

• Mastering Python Data Analysis

• Getting Started with Python Data Analysis

• Data Mining with Python: Implementing Classification and Regression

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