setup
: This is an optional method implemented by the PixieApp to initialize its state. Will be automatically invoked before the PixieApp runs.Arguments: None
Example:
def setup(self): self.var1 = “some initial value” self.pandas_dataframe = pandas.DataFrame(data)
Note
You can find the code file here:
https://github.com/DTAIEB/Thoughtful-Data-Science/blob/master/chapter%205/sampleCode39.py
run
: This starts the PixieApp.Arguments:
entity: [Optional] Dataset passed as input to the PixieApp. Can be referenced with the
pd_entity
attribute or directly as a field calledpixieapp_entity
.**kwargs: Keyword arguments to be passed to the PixieApp when it runs. For example, using
runInDialog=”true”
will start the PixieApp in a dialog.app = MyPixieApp() app.run(runInDialog=”true”)
Example:
invoke_route
: This is used to programmatically invoke a route.Arguments:
Route method: Method to be invoked.
**kwargs: Keyword arguments to be passed to the route method.
app.invoke_route(app.route_method, arg1 = “value1”, arg2 = “value2”)
Example:
getPixieAppEntity
: This is used to retrieve the current PixieApp entity (which can be None) passed when calling therun()
method.getPixieAppEntity()
is typically called from within the PixieApp itself, that is:self.getPixieAppEntity()
Data Analysis with Python
By :
Data Analysis with Python
By:
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.
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