![]() You can control the amount of context shown using the %xmode magic command, from Plain (same as the standard Python interpreter) to Verbose (which inlines function argument values and more). ![]() ![]() Having additional context by itself is a big advantage over the standard Python interpreter (which does not provide any additional context). In : %run examples /ipython_bug.py - AssertionError Traceback (most recent call last) /home /wesm /code /pydata -book /examples /ipython_bug.py in () 13 throws_an_exception() 14 -> 15 calling_things() /home /wesm /code /pydata -book /examples /ipython_bug.py in calling_things() 11 def calling_things(): 12 works_fine() -> 13 throws_an_exception() 14 15 calling_things() /home /wesm /code /pydata -book /examples /ipython_bug.py in throws_an_exception() 7 a = 5 8 b = 6 -> 9 assert(a + b = 10) 10 11 def calling_things(): AssertionError: As an example, Python’s float function is capable of casting a string to a floating-point number, but it fails with ValueError on improper inputs: In data analysis applications, many functions work only on certain kinds of input. Handling Python errors or exceptions gracefully is an important part of building robust programs. Generates the Cartesian product of the input iterables as tuples, similar to a nested for loop. Generates (key, sub-iterator) for each unique key. Generates a sequence of all possible k-tuples of elements in the iterable, respecting order. Generates a sequence of all possible k-tuples of elements in the iterable, ignoring order and without replacement (see also the companion function combinations_with_replacement). Once elements from the first iterator are exhausted, elements from the next iterator are returned, and so on. Generates a sequence by chaining iterators together. Table 3.2: Some useful itertools functions Function After the function is finished, the local namespace is destroyed (with some exceptions that are outside the purview of this chapter). The local namespace is created when the function is called and is immediately populated by the function’s arguments. Any variables that are assigned within a function by default are assigned to the local namespace. An alternative and more descriptive name describing a variable scope in Python is a namespace. Namespaces, Scope, and Local Functionsįunctions can access variables created inside the function as well as those outside the function in higher (or even global) scopes. You need to remember only what their names are. This frees you from having to remember the order in which the function arguments were specified. You can specify keyword arguments in any order. The main restriction on function arguments is that the keyword arguments must follow the positional arguments (if any). True if a and b have no elements in common True if the elements of b are all contained in a Set a to contain the elements in either a or b but not both Set a to the elements in a that are not in bĪll of the elements in either a or b but not both Set the contents of a to be the intersection of the elements in a and b Set the contents of a to be the union of the elements in a and b Remove an arbitrary element from set a, raising KeyError if the set is empty Reset set a to an empty state, discarding all of its elements Table 3.1: Python set operations Function The easiest way to create one is with a comma-separated sequence of values wrapped in parentheses: TupleĪ tuple is a fixed-length, immutable sequence of Python objects which, once assigned, cannot be changed. We start with tuple, list, and dictionary, which are some of the most frequently used sequence types. Mastering their use is a critical part of becoming a proficient Python programmer. Python’s data structures are simple but powerful. Finally, we'll look at the mechanics of Python file objects and interacting with your local hard drive. Then, we'll discuss creating your own reusable Python functions. We'll start with Python's workhorse data structures: tuples, lists, dictionaries, and sets. While add-on libraries like pandas and NumPy add advanced computational functionality for larger datasets, they are designed to be used together with Python's built-in data manipulation tools. This chapter discusses capabilities built into the Python language that will be used ubiquitously throughout the book. The code examples are MIT licensed and can be found on GitHub or Gitee. The content from this website may not be copied or reproduced. If you find the online edition of the book useful, please consider ordering a paper copy or a DRM-free eBook to support the author. If you encounter any errata, please report them here. This Open Access web version of Python for Data Analysis 3rd Edition is now available as a companion to the print and digital editions.
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