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Python For Data Analysis: Data Wrangling With Pandas, NumPy, And IPython

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it’s specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

Paperback: 466 pages

Publisher: O'Reilly Media; 1 edition (November 1, 2012)

Language: English

ISBN-10: 1449319793

ISBN-13: 978-1449319793

Product Dimensions: 7 x 0.9 x 9.2 inches

Shipping Weight: 1.8 pounds (View shipping rates and policies)

Average Customer Review: 4.2 out of 5 stars  See all reviews (134 customer reviews)

Best Sellers Rank: #4,768 in Books (See Top 100 in Books) #3 in Books > Computers & Technology > Databases & Big Data > Data Processing #8 in Books > Computers & Technology > Programming > Languages & Tools > Python #12 in Books > Textbooks > Computer Science > Programming Languages

View larger This is by no means a complete list. Even though it may not always be obvious, a large percentage of data sets can be transformed into a structured form that is more suitable for analysis and modeling. If not, it may be possible to extract features from a data set into a structured form. As an example, a collection of news articles could be processed into a word frequency table which could then be used to perform sentiment analysis. Most users of spreadsheet programs like Microsoft Excel, perhaps the most widely used data analysis tool in the world, will not be strangers to these kinds of data. What Is This Book About? This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language. When I say 'data', what am I referring to exactly? The primary focus is on structured data, a deliberately vague term that encompasses many different common forms of data, such as: Multidimensional arrays (matrices). Tabular or spreadsheet-like data in which each column may be a different type (string, numeric, date, or otherwise). This includes most kinds of data commonly stored in relational databases or tab- or comma-delimited text files. Multiple tables of data interrelated by key columns (what would be primary or foreign keys for a SQL user). Evenly or unevenly spaced time series.

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