Suppose that you want to reuse your PixieApp in a third-party library that you have been using for a while in order to perform a certain task, such as, for example, computing clusters with the scikit-learn machine learning library (http://scikit-learn.org) and displaying them as a graph. The problem is that most of the time, you are calling a high-level method that doesn't return data, but rather directly draws something on the cell output area, such as a chart or a report table. Calling this method from a PixieApp route will not work because the contract for routes is to return an HTML fragment string that will be processed by the framework. In this case, the method most likely doesn't return anything since it is writing the results directly in the cell output. The solution is to use the @captureOutput decorator—which is part of the PixieApp framework—in the route method.
Data Analysis with Python
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Data Analysis with Python
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Overview of this book
Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects.
Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.
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Current Title:
Data Analysis with Python
Table of Contents (16 chapters)
Data Analysis with Python
Contributors
Preface
Other Books You May Enjoy
Free Chapter
Programming and Data Science – A New Toolset
Python and Jupyter Notebooks to Power your Data Analysis
Accelerate your Data Analysis with Python Libraries
Publish your Data Analysis to the Web - the PixieApp Tool
Python and PixieDust Best Practices and Advanced Concepts
Analytics Study: AI and Image Recognition with TensorFlow
Analytics Study: NLP and Big Data with Twitter Sentiment Analysis
Analytics Study: Prediction - Financial Time Series Analysis and Forecasting
Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis
The Future of Data Analysis and Where to Develop your Skills
PixieApp Quick-Reference
Index
Customer Reviews