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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 52 MB·14,348 Downloads·New! that show you how to solve a broad set of data analysis problems effectively.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. You’ll learn the latest. Data Analysis From Scratch With Python: Beginner Guide using Python, Pandas, NumPy, Scikit-Learn. 2018·2 Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging. 51 MB·18,324 Downloads.

Python for Data Analysis. Data Wrangling with Pandas, NumPy, and IPython. Beijing Boston Farnham Sebastopol Tokyo ww. llitebooks. Python for Data Analysis. Printed in the United States of America. Published by O’Reilly Media, In. 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. The first one is mostly used for regular analysis using R style formulas, while scikit-learn is more tailored for Machine Learning. Overview of Python Libraries for Data Scientists. statsmodels:, linear regressions, ANOVA tests, hypothesis testings, many more. scikit-learn:, kmeans, support vector machines, random forests, many more.

Python for Data Analysis, 2e Paperback – 3 Nov 2017. While 'data analysis' is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis. by Wes McKinney (Author).

Python for Data Analysis, 2e Paperback – 3 November 2017. Wes McKinney is the main author of pandas, the popular open sourcePython library for data analysis. Wes is an active speaker andparticipant in the Python and open source communities.

summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples Python for Data Analysis, 2e Ready Get complete.

summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples Python for Data Analysis, 2e Ready Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python.

Dignities: ➕ relevance of the topic; ➕ the presence of a large number of examples

PDF Continuing application of Python within the USGS Astrogeology Center focusing on high performance computing . IPython notebooks for exploratory data analysis in the. context of model development and validation, local.

PDF Continuing application of Python within the USGS Astrogeology Center focusing on high performance computing, legacy code bases, and interactive.


Publisher: Packt Publishing; 2nd Revised edition edition (27 Mar. 2017)
Author: Armando Fandango
Language: English
ISBN-10: 1787127486
ISBN-13: 9781787127487
ASIN: B01MQYK5G2
Key Features:
> Find, manipulate, and analyze your data using the Python 3.5 libraries
> Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code
> An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects.
Book Description:
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.
What you will learn:
> Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms
> Prepare and clean your data, and use it for exploratory analysis
> Manipulate your data with Pandas
> Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5
> Visualize your data with open source libraries such as matplotlib, bokeh, and plotly
> Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian
> Understand signal processing and time series data analysis
> Get to grips with graph processing and social network analysis
About the Author:
Armando Fandango is Chief Data Scientist at Epic Engineering and Consulting Group, and works on confidential projects related to defense and government agencies. Armando is an accomplished technologist with hands-on capabilities and senior executive-level experience with startups and large companies globally. His work spans diverse industries including FinTech, stock exchanges, banking, bioinformatics, genomics, AdTech, infrastructure, transportation, energy, human resources, and entertainment.
Armando has worked for more than ten years in projects involving predictive analytics, data science, machine learning, big data, product engineering, high performance computing, and cloud infrastructures. His research interests spans machine learning, deep learning, and scientific computing.
Table of Contents:
1. Getting Started 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 Data and Social Media
10. Predictive Analytics and Machine Learning
11. Environments Outside the Python Ecosystem and Cloud Computing
12. Performance Tuning, Profiling, and Concurrency
13. Key Concepts
14. Useful Functions
15. Online Resources

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Python Data Analysis - 2E 2017 Pdf