#Python basic data types code
#Python basic data types series

This translates into a 15-digit relative accuracy. In general, all platforms that Python runs on use the IEEE 754 double-precision standard (i.e., 64 bits), for internal representation. The precision is dependent on the number of bits used to represent the number. However, for b = 0.35 we get something different than the expected rational number : In : b. One half, i.e., 0.5, is stored exactly because it has an exact (finite) binary representation as. Consider the following example: In : c = 0.5 c.

Other numbers can be represented perfectly and are therefore stored exactly even with a finite number of bits available.

However, given a fixed number of bits used to represent such a number-i.e., a fixed number of terms in the representation series-inaccuracies are the consequence. For certain floating-point numbers the binary representation might involve a large number of elements or might even be an infinite series.

#Python basic data types series

The reason for this is that floats are internally represented in binary format that is, a decimal number 0 < n < 1 is represented by a series of the form. To illustrate what this implies, let us define another float object: In : b = 0.35 type ( b ) Out: floatįloat objects like this one are always represented internally up to a certain degree of accuracy only. 4 ) Out: floatĪ float is a bit more involved in that the computerized representation of rational or real numbers is in general not exact and depends on the specific technical approach taken. Expressions involving a float also return a float object in general: In : 1. or 1.0, causes Python to interpret the object as a float. Adding a dot to an integer value, like in 1. bit_length () Out: 333įor the last expression to return the generally desired result of 0.25, we must operate on float objects, which brings us naturally to the next basic data type. Consider, for example, the googol number 10 100. Alternatively, the Python built-in function dir gives a complete list of attributes and methods of any object.Ī specialty of Python is that integers can be arbitrarily large. This then provides a collection of methods you can call on the object. You simply type the object name followed by a dot (e.g., a.) and then press the Tab key, e.g., a. Advanced Python environments, like IPython, provide tab completion capabilities that show all methods attached to an object. In general, there are so many different methods that it is hard to memorize all methods of all classes and objects. You will see that the number of bits needed increases the higher the integer value is that we assign to the object: In : a = 100000 a. For example, you can get the number of bits needed to represent the int object in-memory by calling the method bit_length: In : a. There is a saying that “everything in Python is an object.” This means, for example, that even simple objects like the int object we just defined have built-in methods. In the latter case, the information provided depends on the description the programmer has stored with the class. The built-in function type provides type information for all objects with standard and built-in types as well as for newly created classes and objects. One of the most fundamental data types is the integer, or int: In : a = 10 type ( a ) Out: int The topics introduced here are all important and fundamental for the chapters to come. If you are equipped with a background from another programing language, say C or Matlab, you should be able to easily grasp the differences that Python usage might bring along. The spirit of this chapter is to provide a general introduction to Python specifics when it comes to data types and structures.

#Python basic data types code

The following section is devoted to the characteristics and capabilities of the NumPy ndarray class and illustrates some of the benefits of this class for scientific and financial applications.Īs the final section illustrates, thanks to NumPy’s array class vectorized code is easily implemented, leading to more compact and also better-performing code. The next section introduces the fundamental data structures of Python (e.g., list objects) and illustrates control structures, functional programming paradigms, and anonymous functions. The first section introduces basic data types such as int, float, and string.