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

Mean shift


Mean shift is another clustering algorithm that doesn't require an estimate for the number of clusters. It has been successfully applied to image processing. The algorithm tries to iteratively find the maxima of a density function. Before demonstrating mean shift, we will average the rain data on a day-of-the-year basis using a Pandas DataFrame. Create the DataFrame and average its data as follows:

df = pd.DataFrame.from_records(x.T, columns=['dates', 'rain']) 
df = df.groupby('dates').mean() 
 
df.plot() 

The following plot is the result:

Cluster the data with the mean shift algorithm as follows:

x = np.vstack((np.arange(1, len(df) + 1) , df.as_matrix().ravel())) 
x = x.T 
ms = cluster.MeanShift() 
ms.fit(x) 
labels = ms.predict(x) 

If we visualize the data with different line widths and shading for the three resulting clusters, the following figure is obtained:

As you can see, we have three clusters based on the average rainfall...