Python includes a profiler called cProfile. It not only gives the total running time, but also times each function separately, and tells you how many times each function was called, making it easy to determine where you should make optimizations. You can call it from within your code, or from the interpreter, like this: import … Read more
Is there any reason for a class declaration to inherit from object? In Python 3, apart from compatibility between Python 2 and 3, no reason. In Python 2, many reasons. Python 2.x story: In Python 2.x (from 2.2 onwards) there’s two styles of classes depending on the presence or absence of object as a base-class: … Read more
The column names (which are strings) cannot be sliced in the manner you tried. Here you have a couple of options. If you know from context which variables you want to slice out, you can just return a view of only those columns by passing a list into the __getitem__ syntax (the []’s). df1 = … Read more
For Python 3.5+ use (docs): import importlib.util import sys spec = importlib.util.spec_from_file_location(“module.name”, “/path/to/file.py”) foo = importlib.util.module_from_spec(spec) sys.modules[“module.name”] = foo spec.loader.exec_module(foo) foo.MyClass() For Python 3.3 and 3.4 use: from importlib.machinery import SourceFileLoader foo = SourceFileLoader(“module.name”, “/path/to/file.py”).load_module() foo.MyClass() (Although this has been deprecated in Python 3.4.) For Python 2 use: import imp foo = imp.load_source(‘module.name’, ‘/path/to/file.py’) foo.MyClass() … Read more
Like this: keys = [‘a’, ‘b’, ‘c’] values = [1, 2, 3] dictionary = dict(zip(keys, values)) print(dictionary) # {‘a’: 1, ‘b’: 2, ‘c’: 3} Voila 🙂 The pairwise dict constructor and zip function are awesomely useful.
For a dataframe df, one can use any of the following: len(df.index) df.shape[0] df[df.columns[0]].count() (== number of non-NaN values in first column) Code to reproduce the plot: import numpy as np import pandas as pd import perfplot perfplot.save( “out.png”, setup=lambda n: pd.DataFrame(np.arange(n * 3).reshape(n, 3)), n_range=[2**k for k in range(25)], kernels=[ lambda df: len(df.index), lambda … Read more
Looks like you haven’t properly installed the header files and static libraries for python dev. Use your package manager to install them system-wide. For apt (Ubuntu, Debian…): sudo apt-get install python-dev # for python2.x installs sudo apt-get install python3-dev # for python3.x installs For yum (CentOS, RHEL…): sudo yum install python-devel # for python2.x installs … Read more
Answer in one line: ”.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(N)) or even shorter starting with Python 3.6 using random.choices(): ”.join(random.choices(string.ascii_uppercase + string.digits, k=N)) A cryptographically more secure version: see this post ”.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(N)) In details, with a clean function for further reuse: >>> import string >>> import random >>> … Read more
Maybe I missed the question, but why not: class MyException(Exception): pass To override something (or pass extra args), do this: class ValidationError(Exception): def __init__(self, message, errors): # Call the base class constructor with the parameters it needs super().__init__(message) # Now for your custom code… self.errors = errors That way you could pass dict of error … Read more
Use isinstance to check if o is an instance of str or any subclass of str: if isinstance(o, str): To check if the type of o is exactly str, excluding subclasses of str: if type(o) is str: Another alternative to the above: if issubclass(type(o), str): See Built-in Functions in the Python Library Reference for relevant … Read more