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

Pony ORM


Pony ORM is another Python ORM package. Pony ORM is written in pure Python and has automatic query optimization and a GUI database schema editor. It also supports automatic transaction management, automatic caching, and composite keys. Pony ORM uses Python generator expressions, which are translated in SQL. Install it as follows:

$ pip3 install pony

Import the packages we will need in this example. Refer to the pony_ride.py file in this book's code bundle:

from pony.orm import Database, db_session  
import statsmodels.api as sm 

Create an in-memory SQLite database:

db = Database('sqlite', ':memory:') 

Load the sunspots data and write it to the database with the Pandas DataFrame.to_sql function:

with db_session: 
    data_loader = sm.datasets.sunspots.load_pandas() 
    df = data_loader.data 
    df.to_sql("sunspots", db.get_connection()) 
    print(db.select("count(*) FROM sunspots")) 

The number of rows in the sunspots table is printed as follows:

[309]