Wes McKinney - Python for Data Analysis
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Finding great data analysts is difficult. Despite the explosive growth of data in industries ranging from manufacturing and retail to high technology, finance, and healthcare, learning and accessing data analysis tools has remained a challenge. This pragmatic guide will help train you in one of the most important tools in the fieldPython.
Filled with practical case studies, Python for Data Analysis demonstrates the nuts and bolts of manipulating, processing, cleaning, and crunching data with Python. It also serves as a modern introduction to scientific computing in Python for data-intensive applications. Learn about the growing field of data analysis from an expert in the community.
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The scientific Python ecosystem of open source libraries has grown substantially over the last 10 years. By late 2011, I had long felt that the lack of centralized learning resources for data analysis and statistical applications was a stumbling block for new Python programmers engaged in such work. Key projects for data analysis (especially NumPy, IPython, matplotlib, and pandas) had also matured enough that a book written about them would likely not go out-of-date very quickly. Thus, I mustered the nerve to embark on this writing project. This is the book that I wish existed when I started using Python for data analysis in 2007. I hope you find it useful and are able to apply these tools productively in your work.
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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 youll 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
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.
For many people (myself among them), the Python language is easy to fall in love with. Since its first appearance in 1991, Python has become one of the most popular dynamic, programming languages, along with Perl, Ruby, and others. Python and Ruby have become especially popular in recent years for building websites using their numerous web frameworks, like Rails (Ruby) and Django (Python). Such languages are often called
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