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  What is Pandas?  pandas provide rich data structures and functions designed to make working with structured data fast, easy, and expressive. It is, as you will see, one of the critical ingredients enabling Python to be a powerful and productive data analysis environment. The primary object in pandas that will be used in this book is the DataFrame, a twodimensional tabular, column-oriented data structure with both row and column labels: >>>  frame total_bill tip sex smoker day time size  1 16.99 1.01 Female No Sun Dinner 2  2 10.34 1.66 Male No Sun Dinner 3  3 21.01 3.5 Male No Sun Dinner 3  4 23.68 3.31 Male No Sun Dinner 2  5 24.59 3.61 Female No Sun Dinner 4  6 25.29 4.71 Male No Sun Dinner 4  7 8.77 2 Male No Sun Dinner 2  8 26.88 3.12 Male No Sun Dinner 4  9 15.04 1.96 Male No Sun Dinner 2  10 14.78 3.23 Male No Sun Dinner 2  pandas combine the high-performance array-computing features of NumPy with t...

What is NumPy ??  NumPy, short for Numerical Python, is the foundational package for scientific computing in Python. The majority of this book will be based on NumPy and libraries built on top of NumPy. It provides, among other things:  • A fast and efficient multidimensional array object ndarray  • Functions for performing element-wise computations with arrays or mathematical operations between arrays  • Tools for reading and writing array-based data sets to disk  • Linear algebra operations, Fourier transform, and random number generation  • Tools for integrating connecting C, C++, and Fortran code to Python  Beyond the fast array-processing capabilities that NumPy adds to Python, one of its primary purposes with regards to data analysis is as the primary container for data to be passed between algorithms. For numerical data, NumPy arrays are a much more efficient way of storing and manipulating data than the other built-in Python data structures. Al...

Why Not Python? While Python is an excellent environment for building computationally-intensive scientific applications and building most kinds of general-purpose systems, there are a number of uses for which Python may be less suitable. As Python is an interpreted programming language, in general, most Python code will run substantially slower than code written in a compiled language like Java or C++. As programmer time is typically more valuable than CPU time, many are happy to make this tradeoff. However, in an application with very low latency requirements (for example, a high-frequency trading system), the time spent programming in a lower-level, lower-productivity language like C++ to achieve the maximum possible performance might be time well spent. Python is not an ideal language for highly concurrent, multithreaded applications, particularly applications with many CPU-bound threads. The reason for this is that it has what is known as the global interpreter lock (GIL), a mechan...

  Why Python for Data Analysis?  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 scripting languages as they can be used to write quick-and-dirty small programs or scripts. I don’t like the term “scripting language” as it carries a connotation that it cannot be used for building mission-critical software. Among interpreted languages, Python is distinguished by its large and active scientific computing community. Adopttion of Python for scientific computing in both industrial applications and academic research has increased significantly since the early 2000s.   For data analysis and interactive, explo...

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