Python Data Analysis - Second Edition - download pdf or read online

By Armando Fandango

ISBN-10: 1787127486

ISBN-13: 9781787127487

Key Features

  • Find, manage, and study your information utilizing the Python 3.5 libraries
  • Perform complex, high-performance linear algebra and mathematical calculations with fresh and effective Python code
  • An easy-to-follow consultant with life like examples which are usually utilized in real-world info research projects.

Book Description

Data research thoughts generate helpful insights from small and massive volumes of information. Python, with its robust set of libraries, has turn into a well-liked platform to behavior a number of information research and predictive modeling tasks.

With this e-book, you'll the way to approach and manage facts with Python for advanced research and modeling. We research info manipulations equivalent to aggregating, concatenating, appending, cleansing, and dealing with lacking values, with NumPy and Pandas. The booklet covers the right way to shop and retrieve information from a variety of information assets comparable to SQL and NoSQL, CSV fies, and HDF5. We how you can visualize information utilizing visualization libraries, besides complex issues comparable to sign processing, time sequence, textual information research, computing device studying, and social media analysis.

The publication covers a plethora of Python modules, reminiscent of matplotlib, statsmodels, scikit-learn, and NLTK. It additionally covers utilizing Python with exterior environments corresponding to R, Fortran, C/C++, and strengthen libraries.

What you'll learn

  • Install open resource Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on a variety of platforms
  • Prepare and fresh your facts, and use it for exploratory analysis
  • Manipulate your information with Pandas
  • Retrieve and shop your info from RDBMS, NoSQL, and dispensed filesystems akin to HDFS and HDF5
  • Visualize your information with open resource libraries reminiscent of matplotlib, bokeh, and plotly
  • Learn approximately quite a few computer studying equipment comparable to supervised, unsupervised, probabilistic, and Bayesian
  • Understand sign processing and time sequence information analysis
  • Get to grips with graph processing and social community analysis

About the Author

Armando Fandango is leader information Scientist at Epic Engineering and Consulting staff, and works on exclusive initiatives regarding protection and govt organisations. Armando is an complete technologist with hands-on features and senior executive-level adventure with startups and big businesses globally. His paintings spans different industries together with FinTech, inventory exchanges, banking, bioinformatics, genomics, AdTech, infrastructure, transportation, power, human assets, and entertainment.

Armando has labored for greater than ten years in tasks regarding predictive analytics, facts technological know-how, desktop studying, mammoth facts, product engineering, excessive functionality computing, and cloud infrastructures. His examine pursuits spans computing device studying, deep studying, and medical computing.

Table of Contents

  1. Getting began with Python Libraries
  2. NumPy Arrays
  3. The Pandas Primer
  4. Statistics and Linear Algebra
  5. Retrieving, Processing, and Storing Data
  6. Data Visualization
  7. Signal Processing and Time Series
  8. Working with Databases
  9. Analyzing Textual information and Social Media
  10. Predictive Analytics and computing device Learning
  11. Environments outdoor the Python surroundings and Cloud Computing
  12. Performance Tuning, Profiling, and Concurrency
  13. Key Concepts
  14. Useful Functions
  15. Online Resources

Show description

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