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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

By : Idris
4 (4)
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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

4 (4)
By: 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 (16 chapters)
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13
A. Key Concepts
15
C. Online Resources

Social network analysis

Social network analysis studies social relations using network theory. Nodes represent participants in a network. Lines between nodes represent relationships. Formally, this is called a graph. Due to the constraints of this book, we will only have a quick look at a simple graph that comes with the popular NetworkX Python library. matplotlib will help with the visualization of the graph.

Install NetworkX with the following command:

$ pip3 install networkx

The import convention for NetworkX is as follows:

import networkx as nx 

NetworkX provides a number of sample graphs, which can be listed as follows:

print([s for s in dir(nx) if s.endswith('graph')]) 

Load the Davis Southern women graph and plot a histogram of the degree of connections:

G = nx.davis_southern_women_graph() 
plt.figure(1) 
plt.hist(nx.degree(G).values()) 

The resulting histogram is shown as follows:

Social network analysis

Draw the graph with node labels as follows:

plt.figure(2) 
pos = nx.spring_layout(G) 
nx.draw...

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