application
├── app
│   └── folder
│       └── file.py
└── app2
    └── some_folder
        └── some_file.py

from application.app.folder.file import func_name

from extra import * 

def gap():
    print
    print

glob(os.path.join("src","*.c"))

glob(os.path.join("src","*.c"))
glob(os.path.join("src","*","*.c"))
glob(os.path.join("src","*","*","*.c"))
glob(os.path.join("src","*","*","*","*.c"))

try:
  with open("a", "w") as a and open("b", "w") as b:
    do_something()
except IOError as e:
  print "Operation failed: %s" % e.strerror

import os.path

from flask import Flask, Response


app = Flask(__name__)
app.config.from_object(__name__)


def root_dir():  # pragma: no cover
    return os.path.abspath(os.path.dirname(__file__))


def get_file(filename):  # pragma: no cover
    try:
        src = os.path.join(root_dir(), filename)
        # Figure out how flask returns static files
        # Tried:
        # - render_template
        # - send_file
        # This should not be so non-obvious
        return open(src).read()
    except IOError as exc:
        return str(exc)


@app.route("/", methods=["GET"])
def metrics():  # pragma: no cover
    content = get_file("jenkins_analytics.html")
    return Response(content, mimetype="text/html")


@app.route("/", defaults={"path": ""})
@app.route("/<path:path>")
def get_resource(path):  # pragma: no cover
    mimetypes = {
        ".css": "text/css",
        ".html": "text/html",
        ".js": "application/javascript",
    }
    complete_path = os.path.join(root_dir(), path)
    ext = os.path.splitext(path)[1]
    mimetype = mimetypes.get(ext, "text/html")
    content = get_file(complete_path)
    return Response(content, mimetype=mimetype)


if __name__ == "__main__":  # pragma: no cover
    app.run(port=80)

I read through the zipfile documentation, but couldn"t understand how to unzip a file, only how to zip a file. How do I unzip all the contents of a zip file into the same directory?

Answer #1

Recommendation for beginners:

This is my personal recommendation for beginners: start by learning virtualenv and pip, tools which work with both Python 2 and 3 and in a variety of situations, and pick up other tools once you start needing them.

PyPI packages not in the standard library:

• virtualenv is a very popular tool that creates isolated Python environments for Python libraries. If you"re not familiar with this tool, I highly recommend learning it, as it is a very useful tool, and I"ll be making comparisons to it for the rest of this answer.

It works by installing a bunch of files in a directory (eg: env/), and then modifying the PATH environment variable to prefix it with a custom bin directory (eg: env/bin/). An exact copy of the python or python3 binary is placed in this directory, but Python is programmed to look for libraries relative to its path first, in the environment directory. It"s not part of Python"s standard library, but is officially blessed by the PyPA (Python Packaging Authority). Once activated, you can install packages in the virtual environment using pip.

• pyenv is used to isolate Python versions. For example, you may want to test your code against Python 2.7, 3.6, 3.7 and 3.8, so you"ll need a way to switch between them. Once activated, it prefixes the PATH environment variable with ~/.pyenv/shims, where there are special files matching the Python commands (python, pip). These are not copies of the Python-shipped commands; they are special scripts that decide on the fly which version of Python to run based on the PYENV_VERSION environment variable, or the .python-version file, or the ~/.pyenv/version file. pyenv also makes the process of downloading and installing multiple Python versions easier, using the command pyenv install.

• pyenv-virtualenv is a plugin for pyenv by the same author as pyenv, to allow you to use pyenv and virtualenv at the same time conveniently. However, if you"re using Python 3.3 or later, pyenv-virtualenv will try to run python -m venv if it is available, instead of virtualenv. You can use virtualenv and pyenv together without pyenv-virtualenv, if you don"t want the convenience features.

• virtualenvwrapper is a set of extensions to virtualenv (see docs). It gives you commands like mkvirtualenv, lssitepackages, and especially workon for switching between different virtualenv directories. This tool is especially useful if you want multiple virtualenv directories.

• pyenv-virtualenvwrapper is a plugin for pyenv by the same author as pyenv, to conveniently integrate virtualenvwrapper into pyenv.

• pipenv aims to combine Pipfile, pip and virtualenv into one command on the command-line. The virtualenv directory typically gets placed in ~/.local/share/virtualenvs/XXX, with XXX being a hash of the path of the project directory. This is different from virtualenv, where the directory is typically in the current working directory. pipenv is meant to be used when developing Python applications (as opposed to libraries). There are alternatives to pipenv, such as poetry, which I won"t list here since this question is only about the packages that are similarly named.

Standard library:

• pyvenv (not to be confused with pyenv in the previous section) is a script shipped with Python 3 but deprecated in Python 3.6 as it had problems (not to mention the confusing name). In Python 3.6+, the exact equivalent is python3 -m venv.

• venv is a package shipped with Python 3, which you can run using python3 -m venv (although for some reason some distros separate it out into a separate distro package, such as python3-venv on Ubuntu/Debian). It serves the same purpose as virtualenv, but only has a subset of its features (see a comparison here). virtualenv continues to be more popular than venv, especially since the former supports both Python 2 and 3.

Answer #2

os.listdir() - list in the current directory

With listdir in os module you get the files and the folders in the current dir

 import os
 arr = os.listdir()
 print(arr)
 
 >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

Looking in a directory

arr = os.listdir("c:\files")

glob from glob

with glob you can specify a type of file to list like this

import glob

txtfiles = []
for file in glob.glob("*.txt"):
    txtfiles.append(file)

glob in a list comprehension

mylist = [f for f in glob.glob("*.txt")]

get the full path of only files in the current directory

import os
from os import listdir
from os.path import isfile, join

cwd = os.getcwd()
onlyfiles = [os.path.join(cwd, f) for f in os.listdir(cwd) if 
os.path.isfile(os.path.join(cwd, f))]
print(onlyfiles) 

["G:\getfilesname\getfilesname.py", "G:\getfilesname\example.txt"]

Getting the full path name with os.path.abspath

You get the full path in return

 import os
 files_path = [os.path.abspath(x) for x in os.listdir()]
 print(files_path)
 
 ["F:\documentiapplications.txt", "F:\documenticollections.txt"]

Walk: going through sub directories

os.walk returns the root, the directories list and the files list, that is why I unpacked them in r, d, f in the for loop; it, then, looks for other files and directories in the subfolders of the root and so on until there are no subfolders.

import os

# Getting the current work directory (cwd)
thisdir = os.getcwd()

# r=root, d=directories, f = files
for r, d, f in os.walk(thisdir):
    for file in f:
        if file.endswith(".docx"):
            print(os.path.join(r, file))

os.listdir(): get files in the current directory (Python 2)

In Python 2, if you want the list of the files in the current directory, you have to give the argument as "." or os.getcwd() in the os.listdir method.

 import os
 arr = os.listdir(".")
 print(arr)
 
 >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

To go up in the directory tree

# Method 1
x = os.listdir("..")

# Method 2
x= os.listdir("/")

Get files: os.listdir() in a particular directory (Python 2 and 3)

 import os
 arr = os.listdir("F:\python")
 print(arr)
 
 >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

Get files of a particular subdirectory with os.listdir()

import os

x = os.listdir("./content")

os.walk(".") - current directory

 import os
 arr = next(os.walk("."))[2]
 print(arr)
 
 >>> ["5bs_Turismo1.pdf", "5bs_Turismo1.pptx", "esperienza.txt"]

next(os.walk(".")) and os.path.join("dir", "file")

 import os
 arr = []
 for d,r,f in next(os.walk("F:\_python")):
     for file in f:
         arr.append(os.path.join(r,file))

 for f in arr:
     print(files)

>>> F:\_python\dict_class.py
>>> F:\_python\programmi.txt

next(os.walk("F:\") - get the full path - list comprehension

 [os.path.join(r,file) for r,d,f in next(os.walk("F:\_python")) for file in f]
 
 >>> ["F:\_python\dict_class.py", "F:\_python\programmi.txt"]

os.walk - get full path - all files in sub dirs**

x = [os.path.join(r,file) for r,d,f in os.walk("F:\_python") for file in f]
print(x)

>>> ["F:\_python\dict.py", "F:\_python\progr.txt", "F:\_python\readl.py"]

os.listdir() - get only txt files

 arr_txt = [x for x in os.listdir() if x.endswith(".txt")]
 print(arr_txt)
 
 >>> ["work.txt", "3ebooks.txt"]

Using glob to get the full path of the files

If I should need the absolute path of the files:

from path import path
from glob import glob
x = [path(f).abspath() for f in glob("F:\*.txt")]
for f in x:
    print(f)

>>> F:acquistionline.txt
>>> F:acquisti_2018.txt
>>> F:ootstrap_jquery_ecc.txt

Using os.path.isfile to avoid directories in the list

import os.path
listOfFiles = [f for f in os.listdir() if os.path.isfile(f)]
print(listOfFiles)

>>> ["a simple game.py", "data.txt", "decorator.py"]

Using pathlib from Python 3.4

import pathlib

flist = []
for p in pathlib.Path(".").iterdir():
    if p.is_file():
        print(p)
        flist.append(p)

 >>> error.PNG
 >>> exemaker.bat
 >>> guiprova.mp3
 >>> setup.py
 >>> speak_gui2.py
 >>> thumb.PNG

With list comprehension:

flist = [p for p in pathlib.Path(".").iterdir() if p.is_file()]

Alternatively, use pathlib.Path() instead of pathlib.Path(".")

Use glob method in pathlib.Path()

import pathlib

py = pathlib.Path().glob("*.py")
for file in py:
    print(file)

>>> stack_overflow_list.py
>>> stack_overflow_list_tkinter.py

Get all and only files with os.walk

import os
x = [i[2] for i in os.walk(".")]
y=[]
for t in x:
    for f in t:
        y.append(f)
print(y)

>>> ["append_to_list.py", "data.txt", "data1.txt", "data2.txt", "data_180617", "os_walk.py", "READ2.py", "read_data.py", "somma_defaltdic.py", "substitute_words.py", "sum_data.py", "data.txt", "data1.txt", "data_180617"]

Get only files with next and walk in a directory

 import os
 x = next(os.walk("F://python"))[2]
 print(x)
 
 >>> ["calculator.bat","calculator.py"]

Get only directories with next and walk in a directory

 import os
 next(os.walk("F://python"))[1] # for the current dir use (".")
 
 >>> ["python3","others"]

Get all the subdir names with walk

for r,d,f in os.walk("F:\_python"):
    for dirs in d:
        print(dirs)

>>> .vscode
>>> pyexcel
>>> pyschool.py
>>> subtitles
>>> _metaprogramming
>>> .ipynb_checkpoints

os.scandir() from Python 3.5 and greater

import os
x = [f.name for f in os.scandir() if f.is_file()]
print(x)

>>> ["calculator.bat","calculator.py"]

# Another example with scandir (a little variation from docs.python.org)
# This one is more efficient than os.listdir.
# In this case, it shows the files only in the current directory
# where the script is executed.

import os
with os.scandir() as i:
    for entry in i:
        if entry.is_file():
            print(entry.name)

>>> ebookmaker.py
>>> error.PNG
>>> exemaker.bat
>>> guiprova.mp3
>>> setup.py
>>> speakgui4.py
>>> speak_gui2.py
>>> speak_gui3.py
>>> thumb.PNG

Examples:

Ex. 1: How many files are there in the subdirectories?

In this example, we look for the number of files that are included in all the directory and its subdirectories.

import os

def count(dir, counter=0):
    "returns number of files in dir and subdirs"
    for pack in os.walk(dir):
        for f in pack[2]:
            counter += 1
    return dir + " : " + str(counter) + "files"

print(count("F:\python"))

>>> "F:\python" : 12057 files"

Ex.2: How to copy all files from a directory to another?

A script to make order in your computer finding all files of a type (default: pptx) and copying them in a new folder.

import os
import shutil
from path import path

destination = "F:\file_copied"
# os.makedirs(destination)

def copyfile(dir, filetype="pptx", counter=0):
    "Searches for pptx (or other - pptx is the default) files and copies them"
    for pack in os.walk(dir):
        for f in pack[2]:
            if f.endswith(filetype):
                fullpath = pack[0] + "\" + f
                print(fullpath)
                shutil.copy(fullpath, destination)
                counter += 1
    if counter > 0:
        print("-" * 30)
        print("	==> Found in: `" + dir + "` : " + str(counter) + " files
")

for dir in os.listdir():
    "searches for folders that starts with `_`"
    if dir[0] == "_":
        # copyfile(dir, filetype="pdf")
        copyfile(dir, filetype="txt")


>>> _compiti18Compito Contabilità 1conti.txt
>>> _compiti18Compito Contabilità 1modula4.txt
>>> _compiti18Compito Contabilità 1moduloa4.txt
>>> ------------------------
>>> ==> Found in: `_compiti18` : 3 files

Ex. 3: How to get all the files in a txt file

In case you want to create a txt file with all the file names:

import os
mylist = ""
with open("filelist.txt", "w", encoding="utf-8") as file:
    for eachfile in os.listdir():
        mylist += eachfile + "
"
    file.write(mylist)

Example: txt with all the files of an hard drive

"""
We are going to save a txt file with all the files in your directory.
We will use the function walk()
"""

import os

# see all the methods of os
# print(*dir(os), sep=", ")
listafile = []
percorso = []
with open("lista_file.txt", "w", encoding="utf-8") as testo:
    for root, dirs, files in os.walk("D:\"):
        for file in files:
            listafile.append(file)
            percorso.append(root + "\" + file)
            testo.write(file + "
")
listafile.sort()
print("N. of files", len(listafile))
with open("lista_file_ordinata.txt", "w", encoding="utf-8") as testo_ordinato:
    for file in listafile:
        testo_ordinato.write(file + "
")

with open("percorso.txt", "w", encoding="utf-8") as file_percorso:
    for file in percorso:
        file_percorso.write(file + "
")

os.system("lista_file.txt")
os.system("lista_file_ordinata.txt")
os.system("percorso.txt")

All the file of C: in one text file

This is a shorter version of the previous code. Change the folder where to start finding the files if you need to start from another position. This code generate a 50 mb on text file on my computer with something less then 500.000 lines with files with the complete path.

import os

with open("file.txt", "w", encoding="utf-8") as filewrite:
    for r, d, f in os.walk("C:\"):
        for file in f:
            filewrite.write(f"{r + file}
")

How to write a file with all paths in a folder of a type

With this function you can create a txt file that will have the name of a type of file that you look for (ex. pngfile.txt) with all the full path of all the files of that type. It can be useful sometimes, I think.

import os

def searchfiles(extension=".ttf", folder="H:\"):
    "Create a txt file with all the file of a type"
    with open(extension[1:] + "file.txt", "w", encoding="utf-8") as filewrite:
        for r, d, f in os.walk(folder):
            for file in f:
                if file.endswith(extension):
                    filewrite.write(f"{r + file}
")

# looking for png file (fonts) in the hard disk H:
searchfiles(".png", "H:\")

>>> H:4bs_18Dolphins5.png
>>> H:4bs_18Dolphins6.png
>>> H:4bs_18Dolphins7.png
>>> H:5_18marketing htmlassetsimageslogo2.png
>>> H:7z001.png
>>> H:7z002.png

(New) Find all files and open them with tkinter GUI

I just wanted to add in this 2019 a little app to search for all files in a dir and be able to open them by doubleclicking on the name of the file in the list.

import tkinter as tk
import os

def searchfiles(extension=".txt", folder="H:\"):
    "insert all files in the listbox"
    for r, d, f in os.walk(folder):
        for file in f:
            if file.endswith(extension):
                lb.insert(0, r + "\" + file)

def open_file():
    os.startfile(lb.get(lb.curselection()[0]))

root = tk.Tk()
root.geometry("400x400")
bt = tk.Button(root, text="Search", command=lambda:searchfiles(".png", "H:\"))
bt.pack()
lb = tk.Listbox(root)
lb.pack(fill="both", expand=1)
lb.bind("<Double-Button>", lambda x: open_file())
root.mainloop()

Answer #3

-----> pip install gensim config --global http.sslVerify false

Just install any package with the "config --global http.sslVerify false" statement

You can ignore SSL errors by setting pypi.org and files.pythonhosted.org as trusted hosts.

$ pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org <package_name>

Note: Sometime during April 2018, the Python Package Index was migrated from pypi.python.org to pypi.org. This means "trusted-host" commands using the old domain no longer work.

Permanent Fix

Since the release of pip 10.0, you should be able to fix this permanently just by upgrading pip itself:

$ pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org pip setuptools

Or by just reinstalling it to get the latest version:

$ curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py

(‚Ķ¬†and then running get-pip.py with the relevant Python interpreter).

pip install <otherpackage> should just work after this. If not, then you will need to do more, as explained below.

You may want to add the trusted hosts and proxy to your config file.

pip.ini (Windows) or pip.conf (unix)

[global]
trusted-host = pypi.python.org
               pypi.org
               files.pythonhosted.org

Alternate Solutions (Less secure)

Most of the answers could pose a security issue.

Two of the workarounds that help in installing most of the python packages with ease would be:

• Using easy_install: if you are really lazy and don"t want to waste much time, use easy_install <package_name>. Note that some packages won"t be found or will give small errors.
• Using Wheel: download the Wheel of the python package and use the pip command pip install wheel_package_name.whl to install the package.

Answer #4

It helps to install a python package foo on your machine (can also be in virtualenv) so that you can import the package foo from other projects and also from [I]Python prompts.

It does the similar job of pip, easy_install etc.,

Using setup.py

Let"s start with some definitions:

Package - A folder/directory that contains __init__.py file.
Module - A valid python file with .py extension.
Distribution - How one package relates to other packages and modules.

Let"s say you want to install a package named foo. Then you do,

$ git clone https://github.com/user/foo  
$ cd foo
$ python setup.py install

Instead, if you don"t want to actually install it but still would like to use it. Then do,

$ python setup.py develop  

This command will create symlinks to the source directory within site-packages instead of copying things. Because of this, it is quite fast (particularly for large packages).

Creating setup.py

If you have your package tree like,

foo
├── foo
│   ├── data_struct.py
│   ├── __init__.py
│   └── internals.py
├── README
├── requirements.txt
└── setup.py

Then, you do the following in your setup.py script so that it can be installed on some machine:

from setuptools import setup

setup(
   name="foo",
   version="1.0",
   description="A useful module",
   author="Man Foo",
   author_email="[email protected]",
   packages=["foo"],  #same as name
   install_requires=["wheel", "bar", "greek"], #external packages as dependencies
)

Instead, if your package tree is more complex like the one below:

foo
├── foo
│   ├── data_struct.py
│   ├── __init__.py
│   └── internals.py
├── README
├── requirements.txt
├── scripts
│   ├── cool
│   └── skype
└── setup.py

Then, your setup.py in this case would be like:

from setuptools import setup

setup(
   name="foo",
   version="1.0",
   description="A useful module",
   author="Man Foo",
   author_email="[email protected]",
   packages=["foo"],  #same as name
   install_requires=["wheel", "bar", "greek"], #external packages as dependencies
   scripts=[
            "scripts/cool",
            "scripts/skype",
           ]
)

Add more stuff to (setup.py) & make it decent:

from setuptools import setup

with open("README", "r") as f:
    long_description = f.read()

setup(
   name="foo",
   version="1.0",
   description="A useful module",
   license="MIT",
   long_description=long_description,
   author="Man Foo",
   author_email="[email protected]",
   url="http://www.foopackage.com/",
   packages=["foo"],  #same as name
   install_requires=["wheel", "bar", "greek"], #external packages as dependencies
   scripts=[
            "scripts/cool",
            "scripts/skype",
           ]
)

The long_description is used in pypi.org as the README description of your package.

And finally, you"re now ready to upload your package to PyPi.org so that others can install your package using pip install yourpackage.

At this point there are two options.

• publish in the temporary test.pypi.org server to make oneself familiarize with the procedure, and then publish it on the permanent pypi.org server for the public to use your package.
• publish straight away on the permanent pypi.org server, if you are already familiar with the procedure and have your user credentials (e.g., username, password, package name)

Once your package name is registered in pypi.org, nobody can claim or use it. Python packaging suggests the twine package for uploading purposes (of your package to PyPi). Thus,

(1) the first step is to locally build the distributions using:

# prereq: wheel (pip install wheel)  
$ python setup.py sdist bdist_wheel   

(2) then using twine for uploading either to test.pypi.org or pypi.org:

$ twine upload --repository testpypi dist/*  
username: ***  
password: ***  

It will take few minutes for the package to appear on test.pypi.org. Once you"re satisfied with it, you can then upload your package to the real & permanent index of pypi.org simply with:

$ twine upload dist/*  

Optionally, you can also sign the files in your package with a GPG by:

$ twine upload dist/* --sign 

Bonus Reading:

• See a sample setup.py from a real project here: torchvision-setup.py

• PEP 517, setuptools

• why twine? using twine

Answer #5

tl;dr / quick fix

• Don"t decode/encode willy nilly
• Don"t assume your strings are UTF-8 encoded
• Try to convert strings to Unicode strings as soon as possible in your code
• Fix your locale: How to solve UnicodeDecodeError in Python 3.6?
• Don"t be tempted to use quick reload hacks

Unicode Zen in Python 2.x - The Long Version

Without seeing the source it"s difficult to know the root cause, so I"ll have to speak generally.

UnicodeDecodeError: "ascii" codec can"t decode byte generally happens when you try to convert a Python 2.x str that contains non-ASCII to a Unicode string without specifying the encoding of the original string.

In brief, Unicode strings are an entirely separate type of Python string that does not contain any encoding. They only hold Unicode point codes and therefore can hold any Unicode point from across the entire spectrum. Strings contain encoded text, beit UTF-8, UTF-16, ISO-8895-1, GBK, Big5 etc. Strings are decoded to Unicode and Unicodes are encoded to strings. Files and text data are always transferred in encoded strings.

The Markdown module authors probably use unicode() (where the exception is thrown) as a quality gate to the rest of the code - it will convert ASCII or re-wrap existing Unicodes strings to a new Unicode string. The Markdown authors can"t know the encoding of the incoming string so will rely on you to decode strings to Unicode strings before passing to Markdown.

Unicode strings can be declared in your code using the u prefix to strings. E.g.

>>> my_u = u"my ünicôdé strįng"
>>> type(my_u)
<type "unicode">

Unicode strings may also come from file, databases and network modules. When this happens, you don"t need to worry about the encoding.

Gotchas

Conversion from str to Unicode can happen even when you don"t explicitly call unicode().

The following scenarios cause UnicodeDecodeError exceptions:

# Explicit conversion without encoding
unicode("€")

# New style format string into Unicode string
# Python will try to convert value string to Unicode first
u"The currency is: {}".format("€")

# Old style format string into Unicode string
# Python will try to convert value string to Unicode first
u"The currency is: %s" % "€"

# Append string to Unicode
# Python will try to convert string to Unicode first
u"The currency is: " + "€"         

Examples

In the following diagram, you can see how the word café has been encoded in either "UTF-8" or "Cp1252" encoding depending on the terminal type. In both examples, caf is just regular ascii. In UTF-8, é is encoded using two bytes. In "Cp1252", √© is 0xE9 (which is also happens to be the Unicode point value (it"s no coincidence)). The correct decode() is invoked and conversion to a Python Unicode is successfull:

In this diagram, decode() is called with ascii (which is the same as calling unicode() without an encoding given). As ASCII can"t contain bytes greater than 0x7F, this will throw a UnicodeDecodeError exception:

The Unicode Sandwich

It"s good practice to form a Unicode sandwich in your code, where you decode all incoming data to Unicode strings, work with Unicodes, then encode to strs on the way out. This saves you from worrying about the encoding of strings in the middle of your code.

Input / Decode

Source code

If you need to bake non-ASCII into your source code, just create Unicode strings by prefixing the string with a u. E.g.

u"Zürich"

To allow Python to decode your source code, you will need to add an encoding header to match the actual encoding of your file. For example, if your file was encoded as "UTF-8", you would use:

# encoding: utf-8

This is only necessary when you have non-ASCII in your source code.

Files

Usually non-ASCII data is received from a file. The io module provides a TextWrapper that decodes your file on the fly, using a given encoding. You must use the correct encoding for the file - it can"t be easily guessed. For example, for a UTF-8 file:

import io
with io.open("my_utf8_file.txt", "r", encoding="utf-8") as my_file:
     my_unicode_string = my_file.read() 

my_unicode_string would then be suitable for passing to Markdown. If a UnicodeDecodeError from the read() line, then you"ve probably used the wrong encoding value.

CSV Files

The Python 2.7 CSV module does not support non-ASCII characters üò©. Help is at hand, however, with https://pypi.python.org/pypi/backports.csv.

Use it like above but pass the opened file to it:

from backports import csv
import io
with io.open("my_utf8_file.txt", "r", encoding="utf-8") as my_file:
    for row in csv.reader(my_file):
        yield row

Databases

Most Python database drivers can return data in Unicode, but usually require a little configuration. Always use Unicode strings for SQL queries.

In the connection string add:

charset="utf8",
use_unicode=True

E.g.

>>> db = MySQLdb.connect(host="localhost", user="root", passwd="passwd", db="sandbox", use_unicode=True, charset="utf8")

Add:

psycopg2.extensions.register_type(psycopg2.extensions.UNICODE)
psycopg2.extensions.register_type(psycopg2.extensions.UNICODEARRAY)

HTTP

Web pages can be encoded in just about any encoding. The Content-type header should contain a charset field to hint at the encoding. The content can then be decoded manually against this value. Alternatively, Python-Requests returns Unicodes in response.text.

Manually

If you must decode strings manually, you can simply do my_string.decode(encoding), where encoding is the appropriate encoding. Python 2.x supported codecs are given here: Standard Encodings. Again, if you get UnicodeDecodeError then you"ve probably got the wrong encoding.

The meat of the sandwich

Work with Unicodes as you would normal strs.

Output

stdout / printing

print writes through the stdout stream. Python tries to configure an encoder on stdout so that Unicodes are encoded to the console"s encoding. For example, if a Linux shell"s locale is en_GB.UTF-8, the output will be encoded to UTF-8. On Windows, you will be limited to an 8bit code page.

An incorrectly configured console, such as corrupt locale, can lead to unexpected print errors. PYTHONIOENCODING environment variable can force the encoding for stdout.

Files

Just like input, io.open can be used to transparently convert Unicodes to encoded byte strings.

Database

The same configuration for reading will allow Unicodes to be written directly.

Python 3

Python 3 is no more Unicode capable than Python 2.x is, however it is slightly less confused on the topic. E.g the regular str is now a Unicode string and the old str is now bytes.

The default encoding is UTF-8, so if you .decode() a byte string without giving an encoding, Python 3 uses UTF-8 encoding. This probably fixes 50% of people"s Unicode problems.

Further, open() operates in text mode by default, so returns decoded str (Unicode ones). The encoding is derived from your locale, which tends to be UTF-8 on Un*x systems or an 8-bit code page, such as windows-1251, on Windows boxes.

Why you shouldn"t use sys.setdefaultencoding("utf8")

It"s a nasty hack (there"s a reason you have to use reload) that will only mask problems and hinder your migration to Python 3.x. Understand the problem, fix the root cause and enjoy Unicode zen. See Why should we NOT use sys.setdefaultencoding("utf-8") in a py script? for further details

Answer #6

You can"t.

One workaround is to create clone a new environment and then remove the original one.

First, remember to deactivate your current environment. You can do this with the commands:

• deactivate on Windows or
• source deactivate on macOS/Linux.

Then:

conda create --name new_name --clone old_name
conda remove --name old_name --all # or its alias: `conda env remove --name old_name`

Notice there are several drawbacks of this method:

1. It redownloads packages (you can use --offline flag to disable it)
2. Time consumed on copying environment"s files
3. Temporary double disk usage

There is an open issue requesting this feature.

Answer #7

Explanation

From PEP 328

Relative imports use a module"s __name__ attribute to determine that module"s position in the package hierarchy. If the module"s name does not contain any package information (e.g. it is set to "__main__") then relative imports are resolved as if the module were a top level module, regardless of where the module is actually located on the file system.

At some point PEP 338 conflicted with PEP 328:

... relative imports rely on __name__ to determine the current module"s position in the package hierarchy. In a main module, the value of __name__ is always "__main__", so explicit relative imports will always fail (as they only work for a module inside a package)

and to address the issue, PEP 366 introduced the top level variable __package__:

By adding a new module level attribute, this PEP allows relative imports to work automatically if the module is executed using the -m switch. A small amount of boilerplate in the module itself will allow the relative imports to work when the file is executed by name. [...] When it [the attribute] is present, relative imports will be based on this attribute rather than the module __name__ attribute. [...] When the main module is specified by its filename, then the __package__ attribute will be set to None. [...] When the import system encounters an explicit relative import in a module without __package__ set (or with it set to None), it will calculate and store the correct value (__name__.rpartition(".")[0] for normal modules and __name__ for package initialisation modules)

(emphasis mine)

If the __name__ is "__main__", __name__.rpartition(".")[0] returns empty string. This is why there"s empty string literal in the error description:

SystemError: Parent module "" not loaded, cannot perform relative import

The relevant part of the CPython"s PyImport_ImportModuleLevelObject function:

if (PyDict_GetItem(interp->modules, package) == NULL) {
    PyErr_Format(PyExc_SystemError,
            "Parent module %R not loaded, cannot perform relative "
            "import", package);
    goto error;
}

CPython raises this exception if it was unable to find package (the name of the package) in interp->modules (accessible as sys.modules). Since sys.modules is "a dictionary that maps module names to modules which have already been loaded", it"s now clear that the parent module must be explicitly absolute-imported before performing relative import.

Note: The patch from the issue 18018 has added another if block, which will be executed before the code above:

if (PyUnicode_CompareWithASCIIString(package, "") == 0) {
    PyErr_SetString(PyExc_ImportError,
            "attempted relative import with no known parent package");
    goto error;
} /* else if (PyDict_GetItem(interp->modules, package) == NULL) {
    ...
*/

If package (same as above) is empty string, the error message will be

ImportError: attempted relative import with no known parent package

However, you will only see this in Python 3.6 or newer.

Solution #1: Run your script using -m

Consider a directory (which is a Python package):

.
├── package
│   ├── __init__.py
│   ├── module.py
│   └── standalone.py

All of the files in package begin with the same 2 lines of code:

from pathlib import Path
print("Running" if __name__ == "__main__" else "Importing", Path(__file__).resolve())

I"m including these two lines only to make the order of operations obvious. We can ignore them completely, since they don"t affect the execution.

__init__.py and module.py contain only those two lines (i.e., they are effectively empty).

standalone.py additionally attempts to import module.py via relative import:

from . import module  # explicit relative import

We"re well aware that /path/to/python/interpreter package/standalone.py will fail. However, we can run the module with the -m command line option that will "search sys.path for the named module and execute its contents as the __main__ module":

[email protected]:~$ python3 -i -m package.standalone
Importing /home/vaultah/package/__init__.py
Running /home/vaultah/package/standalone.py
Importing /home/vaultah/package/module.py
>>> __file__
"/home/vaultah/package/standalone.py"
>>> __package__
"package"
>>> # The __package__ has been correctly set and module.py has been imported.
... # What"s inside sys.modules?
... import sys
>>> sys.modules["__main__"]
<module "package.standalone" from "/home/vaultah/package/standalone.py">
>>> sys.modules["package.module"]
<module "package.module" from "/home/vaultah/package/module.py">
>>> sys.modules["package"]
<module "package" from "/home/vaultah/package/__init__.py">

-m does all the importing stuff for you and automatically sets __package__, but you can do that yourself in the

Solution #2: Set __package__ manually

Please treat it as a proof of concept rather than an actual solution. It isn"t well-suited for use in real-world code.

PEP 366 has a workaround to this problem, however, it"s incomplete, because setting __package__ alone is not enough. You"re going to need to import at least N preceding packages in the module hierarchy, where N is the number of parent directories (relative to the directory of the script) that will be searched for the module being imported.

Thus,

1. Add the parent directory of the Nth predecessor of the current module to sys.path

2. Remove the current file"s directory from sys.path

3. Import the parent module of the current module using its fully-qualified name

4. Set __package__ to the fully-qualified name from 2

5. Perform the relative import

I"ll borrow files from the Solution #1 and add some more subpackages:

package
├── __init__.py
├── module.py
└── subpackage
    ├── __init__.py
    └── subsubpackage
        ├── __init__.py
        └── standalone.py

This time standalone.py will import module.py from the package package using the following relative import

from ... import module  # N = 3

We"ll need to precede that line with the boilerplate code, to make it work.

import sys
from pathlib import Path

if __name__ == "__main__" and __package__ is None:
    file = Path(__file__).resolve()
    parent, top = file.parent, file.parents[3]

    sys.path.append(str(top))
    try:
        sys.path.remove(str(parent))
    except ValueError: # Already removed
        pass

    import package.subpackage.subsubpackage
    __package__ = "package.subpackage.subsubpackage"

from ... import module # N = 3

It allows us to execute standalone.py by filename:

[email protected]:~$ python3 package/subpackage/subsubpackage/standalone.py
Running /home/vaultah/package/subpackage/subsubpackage/standalone.py
Importing /home/vaultah/package/__init__.py
Importing /home/vaultah/package/subpackage/__init__.py
Importing /home/vaultah/package/subpackage/subsubpackage/__init__.py
Importing /home/vaultah/package/module.py

A more general solution wrapped in a function can be found here. Example usage:

if __name__ == "__main__" and __package__ is None:
    import_parents(level=3) # N = 3

from ... import module
from ...module.submodule import thing

Solution #3: Use absolute imports and setuptools

The steps are -

1. Replace explicit relative imports with equivalent absolute imports

2. Install package to make it importable

For instance, the directory structure may be as follows

.
├── project
│   ├── package
│   │   ├── __init__.py
│   │   ├── module.py
│   │   └── standalone.py
│   └── setup.py

where setup.py is

from setuptools import setup, find_packages
setup(
    name = "your_package_name",
    packages = find_packages(),
)

The rest of the files were borrowed from the Solution #1.

Installation will allow you to import the package regardless of your working directory (assuming there"ll be no naming issues).

We can modify standalone.py to use this advantage (step 1):

from package import module  # absolute import

Change your working directory to project and run /path/to/python/interpreter setup.py install --user (--user installs the package in your site-packages directory) (step 2):

[email protected]:~$ cd project
[email protected]:~/project$ python3 setup.py install --user

Let"s verify that it"s now possible to run standalone.py as a script:

[email protected]:~/project$ python3 -i package/standalone.py
Running /home/vaultah/project/package/standalone.py
Importing /home/vaultah/.local/lib/python3.6/site-packages/your_package_name-0.0.0-py3.6.egg/package/__init__.py
Importing /home/vaultah/.local/lib/python3.6/site-packages/your_package_name-0.0.0-py3.6.egg/package/module.py
>>> module
<module "package.module" from "/home/vaultah/.local/lib/python3.6/site-packages/your_package_name-0.0.0-py3.6.egg/package/module.py">
>>> import sys
>>> sys.modules["package"]
<module "package" from "/home/vaultah/.local/lib/python3.6/site-packages/your_package_name-0.0.0-py3.6.egg/package/__init__.py">
>>> sys.modules["package.module"]
<module "package.module" from "/home/vaultah/.local/lib/python3.6/site-packages/your_package_name-0.0.0-py3.6.egg/package/module.py">

Note: If you decide to go down this route, you"d be better off using virtual environments to install packages in isolation.

Solution #4: Use absolute imports and some boilerplate code

Frankly, the installation is not necessary - you could add some boilerplate code to your script to make absolute imports work.

I"m going to borrow files from Solution #1 and change standalone.py:

1. Add the parent directory of package to sys.path before attempting to import anything from package using absolute imports:

   import sys
   from pathlib import Path # if you haven"t already done so
   file = Path(__file__).resolve()
   parent, root = file.parent, file.parents[1]
   sys.path.append(str(root))
   
   # Additionally remove the current file"s directory from sys.path
   try:
       sys.path.remove(str(parent))
   except ValueError: # Already removed
       pass
   

2. Replace the relative import by the absolute import:

   from package import module  # absolute import
   

standalone.py runs without problems:

[email protected]:~$ python3 -i package/standalone.py
Running /home/vaultah/package/standalone.py
Importing /home/vaultah/package/__init__.py
Importing /home/vaultah/package/module.py
>>> module
<module "package.module" from "/home/vaultah/package/module.py">
>>> import sys
>>> sys.modules["package"]
<module "package" from "/home/vaultah/package/__init__.py">
>>> sys.modules["package.module"]
<module "package.module" from "/home/vaultah/package/module.py">

I feel that I should warn you: try not to do this, especially if your project has a complex structure.

As a side note, PEP 8 recommends the use of absolute imports, but states that in some scenarios explicit relative imports are acceptable:

Absolute imports are recommended, as they are usually more readable and tend to be better behaved (or at least give better error messages). [...] However, explicit relative imports are an acceptable alternative to absolute imports, especially when dealing with complex package layouts where using absolute imports would be unnecessarily verbose.

Answer #8

Is this the correct use of conftest.py?

Yes it is. Fixtures are a potential and common use of conftest.py. The fixtures that you will define will be shared among all tests in your test suite. However, defining fixtures in the root conftest.py might be useless and it would slow down testing if such fixtures are not used by all tests.

Does it have other uses?

Yes it does.

• Fixtures: Define fixtures for static data used by tests. This data can be accessed by all tests in the suite unless specified otherwise. This could be data as well as helpers of modules which will be passed to all tests.

• External plugin loading: conftest.py is used to import external plugins or modules. By defining the following global variable, pytest will load the module and make it available for its test. Plugins are generally files defined in your project or other modules which might be needed in your tests. You can also load a set of predefined plugins as explained here.

  pytest_plugins = "someapp.someplugin"

• Hooks: You can specify hooks such as setup and teardown methods and much more to improve your tests. For a set of available hooks, read Hooks link. Example:

    def pytest_runtest_setup(item):
         """ called before ``pytest_runtest_call(item). """
         #do some stuff`
  

• Test root path: This is a bit of a hidden feature. By defining conftest.py in your root path, you will have pytest recognizing your application modules without specifying PYTHONPATH. In the background, py.test modifies your sys.path by including all submodules which are found from the root path.

Can I have more than one conftest.py file?

Yes you can and it is strongly recommended if your test structure is somewhat complex. conftest.py files have directory scope. Therefore, creating targeted fixtures and helpers is good practice.

When would I want to do that? Examples will be appreciated.

Several cases could fit:

Creating a set of tools or hooks for a particular group of tests.

root/mod/conftest.py

def pytest_runtest_setup(item):
    print("I am mod")
    #do some stuff


test root/mod2/test.py will NOT produce "I am mod"

Loading a set of fixtures for some tests but not for others.

root/mod/conftest.py

@pytest.fixture()
def fixture():
    return "some stuff"

root/mod2/conftest.py

@pytest.fixture()
def fixture():
    return "some other stuff"

root/mod2/test.py

def test(fixture):
    print(fixture)

Will print "some other stuff".

Overriding hooks inherited from the root conftest.py.

root/mod/conftest.py

def pytest_runtest_setup(item):
    print("I am mod")
    #do some stuff

root/conftest.py

def pytest_runtest_setup(item):
    print("I am root")
    #do some stuff

By running any test inside root/mod, only "I am mod" is printed.

You can read more about conftest.py here.

EDIT:

What if I need plain-old helper functions to be called from a number of tests in different modules - will they be available to me if I put them in a conftest.py? Or should I simply put them in a helpers.py module and import and use it in my test modules?

You can use conftest.py to define your helpers. However, you should follow common practice. Helpers can be used as fixtures at least in pytest. For example in my tests I have a mock redis helper which I inject into my tests this way.

root/helper/redis/redis.py

@pytest.fixture
def mock_redis():
    return MockRedis()

root/tests/stuff/conftest.py

pytest_plugin="helper.redis.redis"

root/tests/stuff/test.py

def test(mock_redis):
    print(mock_redis.get("stuff"))

This will be a test module that you can freely import in your tests. NOTE that you could potentially name redis.py as conftest.py if your module redis contains more tests. However, that practice is discouraged because of ambiguity.

If you want to use conftest.py, you can simply put that helper in your root conftest.py and inject it when needed.

root/tests/conftest.py

@pytest.fixture
def mock_redis():
    return MockRedis()

root/tests/stuff/test.py

def test(mock_redis):
    print(mock_redis.get(stuff))

Another thing you can do is to write an installable plugin. In that case your helper can be written anywhere but it needs to define an entry point to be installed in your and other potential test frameworks. See this.

If you don"t want to use fixtures, you could of course define a simple helper and just use the plain old import wherever it is needed.

root/tests/helper/redis.py

class MockRedis():
    # stuff

root/tests/stuff/test.py

from helper.redis import MockRedis

def test():
    print(MockRedis().get(stuff))

However, here you might have problems with the path since the module is not in a child folder of the test. You should be able to overcome this (not tested) by adding an __init__.py to your helper

root/tests/helper/init.py

from .redis import MockRedis

Or simply adding the helper module to your PYTHONPATH.

Answer #9

I would suggest reading PEP 483 and PEP 484 and watching this presentation by Guido on type hinting.

In a nutshell: Type hinting is literally what the words mean. You hint the type of the object(s) you"re using.

Due to the dynamic nature of Python, inferring or checking the type of an object being used is especially hard. This fact makes it hard for developers to understand what exactly is going on in code they haven"t written and, most importantly, for type checking tools found in many IDEs (PyCharm and PyDev come to mind) that are limited due to the fact that they don"t have any indicator of what type the objects are. As a result they resort to trying to infer the type with (as mentioned in the presentation) around 50% success rate.

To take two important slides from the type hinting presentation:

Why type hints?

1. Helps type checkers: By hinting at what type you want the object to be the type checker can easily detect if, for instance, you"re passing an object with a type that isn"t expected.
2. Helps with documentation: A third person viewing your code will know what is expected where, ergo, how to use it without getting them TypeErrors.
3. Helps IDEs develop more accurate and robust tools: Development Environments will be better suited at suggesting appropriate methods when know what type your object is. You have probably experienced this with some IDE at some point, hitting the . and having methods/attributes pop up which aren"t defined for an object.

Why use static type checkers?

• Find bugs sooner: This is self-evident, I believe.
• The larger your project the more you need it: Again, makes sense. Static languages offer a robustness and control that dynamic languages lack. The bigger and more complex your application becomes the more control and predictability (from a behavioral aspect) you require.
• Large teams are already running static analysis: I"m guessing this verifies the first two points.

As a closing note for this small introduction: This is an optional feature and, from what I understand, it has been introduced in order to reap some of the benefits of static typing.

You generally do not need to worry about it and definitely don"t need to use it (especially in cases where you use Python as an auxiliary scripting language). It should be helpful when developing large projects as it offers much needed robustness, control and additional debugging capabilities.

Type hinting with mypy:

In order to make this answer more complete, I think a little demonstration would be suitable. I"ll be using mypy, the library which inspired Type Hints as they are presented in the PEP. This is mainly written for anybody bumping into this question and wondering where to begin.

Before I do that let me reiterate the following: PEP 484 doesn"t enforce anything; it is simply setting a direction for function annotations and proposing guidelines for how type checking can/should be performed. You can annotate your functions and hint as many things as you want; your scripts will still run regardless of the presence of annotations because Python itself doesn"t use them.

Anyways, as noted in the PEP, hinting types should generally take three forms:

• Function annotations (PEP 3107).
• Stub files for built-in/user modules.
• Special # type: type comments that complement the first two forms. (See: What are variable annotations? for a Python 3.6 update for # type: type comments)

Additionally, you"ll want to use type hints in conjunction with the new typing module introduced in Py3.5. In it, many (additional) ABCs (abstract base classes) are defined along with helper functions and decorators for use in static checking. Most ABCs in collections.abc are included, but in a generic form in order to allow subscription (by defining a __getitem__() method).

For anyone interested in a more in-depth explanation of these, the mypy documentation is written very nicely and has a lot of code samples demonstrating/describing the functionality of their checker; it is definitely worth a read.

Function annotations and special comments:

First, it"s interesting to observe some of the behavior we can get when using special comments. Special # type: type comments can be added during variable assignments to indicate the type of an object if one cannot be directly inferred. Simple assignments are generally easily inferred but others, like lists (with regard to their contents), cannot.

Note: If we want to use any derivative of containers and need to specify the contents for that container we must use the generic types from the typing module. These support indexing.

# Generic List, supports indexing.
from typing import List

# In this case, the type is easily inferred as type: int.
i = 0

# Even though the type can be inferred as of type list
# there is no way to know the contents of this list.
# By using type: List[str] we indicate we want to use a list of strings.
a = []  # type: List[str]

# Appending an int to our list
# is statically not correct.
a.append(i)

# Appending a string is fine.
a.append("i")

print(a)  # [0, "i"]

If we add these commands to a file and execute them with our interpreter, everything works just fine and print(a) just prints the contents of list a. The # type comments have been discarded, treated as plain comments which have no additional semantic meaning.

By running this with mypy, on the other hand, we get the following response:

(Python3)[email protected]: mypy typeHintsCode.py
typesInline.py:14: error: Argument 1 to "append" of "list" has incompatible type "int"; expected "str"

Indicating that a list of str objects cannot contain an int, which, statically speaking, is sound. This can be fixed by either abiding to the type of a and only appending str objects or by changing the type of the contents of a to indicate that any value is acceptable (Intuitively performed with List[Any] after Any has been imported from typing).

Function annotations are added in the form param_name : type after each parameter in your function signature and a return type is specified using the -> type notation before the ending function colon; all annotations are stored in the __annotations__ attribute for that function in a handy dictionary form. Using a trivial example (which doesn"t require extra types from the typing module):

def annotated(x: int, y: str) -> bool:
    return x < y

The annotated.__annotations__ attribute now has the following values:

{"y": <class "str">, "return": <class "bool">, "x": <class "int">}

If we"re a complete newbie, or we are familiar with Python¬†2.7 concepts and are consequently unaware of the TypeError lurking in the comparison of annotated, we can perform another static check, catch the error and save us some trouble:

(Python3)[email protected]: mypy typeHintsCode.py
typeFunction.py: note: In function "annotated":
typeFunction.py:2: error: Unsupported operand types for > ("str" and "int")

Among other things, calling the function with invalid arguments will also get caught:

annotated(20, 20)

# mypy complains:
typeHintsCode.py:4: error: Argument 2 to "annotated" has incompatible type "int"; expected "str"

These can be extended to basically any use case and the errors caught extend further than basic calls and operations. The types you can check for are really flexible and I have merely given a small sneak peak of its potential. A look in the typing module, the PEPs or the mypy documentation will give you a more comprehensive idea of the capabilities offered.

Stub files:

Stub files can be used in two different non mutually exclusive cases:

• You need to type check a module for which you do not want to directly alter the function signatures
• You want to write modules and have type-checking but additionally want to separate annotations from content.

What stub files (with an extension of .pyi) are is an annotated interface of the module you are making/want to use. They contain the signatures of the functions you want to type-check with the body of the functions discarded. To get a feel of this, given a set of three random functions in a module named randfunc.py:

def message(s):
    print(s)

def alterContents(myIterable):
    return [i for i in myIterable if i % 2 == 0]

def combine(messageFunc, itFunc):
    messageFunc("Printing the Iterable")
    a = alterContents(range(1, 20))
    return set(a)

We can create a stub file randfunc.pyi, in which we can place some restrictions if we wish to do so. The downside is that somebody viewing the source without the stub won"t really get that annotation assistance when trying to understand what is supposed to be passed where.

Anyway, the structure of a stub file is pretty simplistic: Add all function definitions with empty bodies (pass filled) and supply the annotations based on your requirements. Here, let"s assume we only want to work with int types for our Containers.

# Stub for randfucn.py
from typing import Iterable, List, Set, Callable

def message(s: str) -> None: pass

def alterContents(myIterable: Iterable[int])-> List[int]: pass

def combine(
    messageFunc: Callable[[str], Any],
    itFunc: Callable[[Iterable[int]], List[int]]
)-> Set[int]: pass

The combine function gives an indication of why you might want to use annotations in a different file, they some times clutter up the code and reduce readability (big no-no for Python). You could of course use type aliases but that sometime confuses more than it helps (so use them wisely).

This should get you familiarized with the basic concepts of type hints in Python. Even though the type checker used has been mypy you should gradually start to see more of them pop-up, some internally in IDEs (PyCharm,) and others as standard Python modules.

I"ll try and add additional checkers/related packages in the following list when and if I find them (or if suggested).

Checkers I know of:

• Mypy: as described here.
• PyType: By Google, uses different notation from what I gather, probably worth a look.

Related Packages/Projects:

• typeshed: Official Python repository housing an assortment of stub files for the standard library.

The typeshed project is actually one of the best places you can look to see how type hinting might be used in a project of your own. Let"s take as an example the __init__ dunders of the Counter class in the corresponding .pyi file:

class Counter(Dict[_T, int], Generic[_T]):
        @overload
        def __init__(self) -> None: ...
        @overload
        def __init__(self, Mapping: Mapping[_T, int]) -> None: ...
        @overload
        def __init__(self, iterable: Iterable[_T]) -> None: ...

Where _T = TypeVar("_T") is used to define generic classes. For the Counter class we can see that it can either take no arguments in its initializer, get a single Mapping from any type to an int or take an Iterable of any type.

Notice: One thing I forgot to mention was that the typing module has been introduced on a provisional basis. From PEP 411:

A provisional package may have its API modified prior to "graduating" into a "stable" state. On one hand, this state provides the package with the benefits of being formally part of the Python distribution. On the other hand, the core development team explicitly states that no promises are made with regards to the the stability of the package"s API, which may change for the next release. While it is considered an unlikely outcome, such packages may even be removed from the standard library without a deprecation period if the concerns regarding their API or maintenance prove well-founded.

So take things here with a pinch of salt; I"m doubtful it will be removed or altered in significant ways, but one can never know.

^** Another topic altogether, but valid in the scope of type-hints: PEP 526: Syntax for Variable Annotations is an effort to replace # type comments by introducing new syntax which allows users to annotate the type of variables in simple varname: type statements.

See What are variable annotations?, as previously mentioned, for a small introduction to these.

Answer #10

Whatever is assigned to the files variable is incorrect. Use the following code.

import glob
import os

list_of_files = glob.glob("/path/to/folder/*") # * means all if need specific format then *.csv
latest_file = max(list_of_files, key=os.path.getctime)
print(latest_file)

Downloading files from the Internet using Python: StackOverflow Questions

How do I merge two dictionaries in a single expression (taking union of dictionaries)?

Question by Carl Meyer

I have two Python dictionaries, and I want to write a single expression that returns these two dictionaries, merged (i.e. taking the union). The update() method would be what I need, if it returned its result instead of modifying a dictionary in-place.

>>> x = {"a": 1, "b": 2}
>>> y = {"b": 10, "c": 11}
>>> z = x.update(y)
>>> print(z)
None
>>> x
{"a": 1, "b": 10, "c": 11}

How can I get that final merged dictionary in z, not x?

(To be extra-clear, the last-one-wins conflict-handling of dict.update() is what I"m looking for as well.)

Accessing the index in "for" loops?

Question by Joan Venge

How do I access the index in a for loop like the following?

ints = [8, 23, 45, 12, 78]
for i in ints:
    print("item #{} = {}".format(???, i))

I want to get this output:

item #1 = 8
item #2 = 23
item #3 = 45
item #4 = 12
item #5 = 78

When I loop through it using a for loop, how do I access the loop index, from 1 to 5 in this case?

Iterating over dictionaries using "for" loops

I am a bit puzzled by the following code:

d = {"x": 1, "y": 2, "z": 3} 
for key in d:
    print (key, "corresponds to", d[key])

What I don"t understand is the key portion. How does Python recognize that it needs only to read the key from the dictionary? Is key a special word in Python? Or is it simply a variable?

Using global variables in a function

How can I create or use a global variable in a function?

If I create a global variable in one function, how can I use that global variable in another function? Do I need to store the global variable in a local variable of the function which needs its access?

Manually raising (throwing) an exception in Python

How can I raise an exception in Python so that it can later be caught via an except block?

Calling a function of a module by using its name (a string)

What is the best way to go about calling a function given a string with the function"s name in a Python program. For example, let"s say that I have a module foo, and I have a string whose content is "bar". What is the best way to call foo.bar()?

I need to get the return value of the function, which is why I don"t just use eval. I figured out how to do it by using eval to define a temp function that returns the result of that function call, but I"m hoping that there is a more elegant way to do this.

What is the meaning of single and double underscore before an object name?

Can someone please explain the exact meaning of having single and double leading underscores before an object"s name in Python, and the difference between both?

Also, does that meaning stay the same regardless of whether the object in question is a variable, a function, a method, etc.?

Save plot to image file instead of displaying it using Matplotlib

I am writing a quick-and-dirty script to generate plots on the fly. I am using the code below (from Matplotlib documentation) as a starting point:

from pylab import figure, axes, pie, title, show

# Make a square figure and axes
figure(1, figsize=(6, 6))
ax = axes([0.1, 0.1, 0.8, 0.8])

labels = "Frogs", "Hogs", "Dogs", "Logs"
fracs = [15, 30, 45, 10]

explode = (0, 0.05, 0, 0)
pie(fracs, explode=explode, labels=labels, autopct="%1.1f%%", shadow=True)
title("Raining Hogs and Dogs", bbox={"facecolor": "0.8", "pad": 5})

show()  # Actually, don"t show, just save to foo.png

I don"t want to display the plot on a GUI, instead, I want to save the plot to a file (say foo.png), so that, for example, it can be used in batch scripts. How do I do that?

What are the differences between type() and isinstance()?

What are the differences between these two code fragments?

Using type():

import types

if type(a) is types.DictType:
    do_something()
if type(b) in types.StringTypes:
    do_something_else()

Using isinstance():

if isinstance(a, dict):
    do_something()
if isinstance(b, str) or isinstance(b, unicode):
    do_something_else()

How can I install packages using pip according to the requirements.txt file from a local directory?

Here is the problem:

I have a requirements.txt file that looks like:

BeautifulSoup==3.2.0
Django==1.3
Fabric==1.2.0
Jinja2==2.5.5
PyYAML==3.09
Pygments==1.4
SQLAlchemy==0.7.1
South==0.7.3
amqplib==0.6.1
anyjson==0.3
...

I have a local archive directory containing all the packages + others.

I have created a new virtualenv with

bin/virtualenv testing

Upon activating it, I tried to install the packages according to requirements.txt from the local archive directory.

source bin/activate
pip install -r /path/to/requirements.txt -f file:///path/to/archive/

I got some output that seems to indicate that the installation is fine:

Downloading/unpacking Fabric==1.2.0 (from -r ../testing/requirements.txt (line 3))
  Running setup.py egg_info for package Fabric
    warning: no previously-included files matching "*" found under directory "docs/_build"
    warning: no files found matching "fabfile.py"
Downloading/unpacking South==0.7.3 (from -r ../testing/requirements.txt (line 8))
  Running setup.py egg_info for package South
....

But a later check revealed none of the package is installed properly. I cannot import the package, and none is found in the site-packages directory of my virtualenv. So what went wrong?

Answer #1

The Python 3 range() object doesn"t produce numbers immediately; it is a smart sequence object that produces numbers on demand. All it contains is your start, stop and step values, then as you iterate over the object the next integer is calculated each iteration.

The object also implements the object.__contains__ hook, and calculates if your number is part of its range. Calculating is a (near) constant time operation ^*. There is never a need to scan through all possible integers in the range.

From the range() object documentation:

The advantage of the range type over a regular list or tuple is that a range object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores the start, stop and step values, calculating individual items and subranges as needed).

So at a minimum, your range() object would do:

class my_range:
    def __init__(self, start, stop=None, step=1, /):
        if stop is None:
            start, stop = 0, start
        self.start, self.stop, self.step = start, stop, step
        if step < 0:
            lo, hi, step = stop, start, -step
        else:
            lo, hi = start, stop
        self.length = 0 if lo > hi else ((hi - lo - 1) // step) + 1

    def __iter__(self):
        current = self.start
        if self.step < 0:
            while current > self.stop:
                yield current
                current += self.step
        else:
            while current < self.stop:
                yield current
                current += self.step

    def __len__(self):
        return self.length

    def __getitem__(self, i):
        if i < 0:
            i += self.length
        if 0 <= i < self.length:
            return self.start + i * self.step
        raise IndexError("my_range object index out of range")

    def __contains__(self, num):
        if self.step < 0:
            if not (self.stop < num <= self.start):
                return False
        else:
            if not (self.start <= num < self.stop):
                return False
        return (num - self.start) % self.step == 0

This is still missing several things that a real range() supports (such as the .index() or .count() methods, hashing, equality testing, or slicing), but should give you an idea.

I also simplified the __contains__ implementation to only focus on integer tests; if you give a real range() object a non-integer value (including subclasses of int), a slow scan is initiated to see if there is a match, just as if you use a containment test against a list of all the contained values. This was done to continue to support other numeric types that just happen to support equality testing with integers but are not expected to support integer arithmetic as well. See the original Python issue that implemented the containment test.

* Near constant time because Python integers are unbounded and so math operations also grow in time as N grows, making this a O(log N) operation. Since it’s all executed in optimised C code and Python stores integer values in 30-bit chunks, you’d run out of memory before you saw any performance impact due to the size of the integers involved here.

Answer #2

Recommendation for beginners:

This is my personal recommendation for beginners: start by learning virtualenv and pip, tools which work with both Python 2 and 3 and in a variety of situations, and pick up other tools once you start needing them.

PyPI packages not in the standard library:

• virtualenv is a very popular tool that creates isolated Python environments for Python libraries. If you"re not familiar with this tool, I highly recommend learning it, as it is a very useful tool, and I"ll be making comparisons to it for the rest of this answer.

It works by installing a bunch of files in a directory (eg: env/), and then modifying the PATH environment variable to prefix it with a custom bin directory (eg: env/bin/). An exact copy of the python or python3 binary is placed in this directory, but Python is programmed to look for libraries relative to its path first, in the environment directory. It"s not part of Python"s standard library, but is officially blessed by the PyPA (Python Packaging Authority). Once activated, you can install packages in the virtual environment using pip.

• pyenv is used to isolate Python versions. For example, you may want to test your code against Python 2.7, 3.6, 3.7 and 3.8, so you"ll need a way to switch between them. Once activated, it prefixes the PATH environment variable with ~/.pyenv/shims, where there are special files matching the Python commands (python, pip). These are not copies of the Python-shipped commands; they are special scripts that decide on the fly which version of Python to run based on the PYENV_VERSION environment variable, or the .python-version file, or the ~/.pyenv/version file. pyenv also makes the process of downloading and installing multiple Python versions easier, using the command pyenv install.

• pyenv-virtualenv is a plugin for pyenv by the same author as pyenv, to allow you to use pyenv and virtualenv at the same time conveniently. However, if you"re using Python 3.3 or later, pyenv-virtualenv will try to run python -m venv if it is available, instead of virtualenv. You can use virtualenv and pyenv together without pyenv-virtualenv, if you don"t want the convenience features.

• virtualenvwrapper is a set of extensions to virtualenv (see docs). It gives you commands like mkvirtualenv, lssitepackages, and especially workon for switching between different virtualenv directories. This tool is especially useful if you want multiple virtualenv directories.

• pyenv-virtualenvwrapper is a plugin for pyenv by the same author as pyenv, to conveniently integrate virtualenvwrapper into pyenv.

• pipenv aims to combine Pipfile, pip and virtualenv into one command on the command-line. The virtualenv directory typically gets placed in ~/.local/share/virtualenvs/XXX, with XXX being a hash of the path of the project directory. This is different from virtualenv, where the directory is typically in the current working directory. pipenv is meant to be used when developing Python applications (as opposed to libraries). There are alternatives to pipenv, such as poetry, which I won"t list here since this question is only about the packages that are similarly named.

Standard library:

• pyvenv (not to be confused with pyenv in the previous section) is a script shipped with Python 3 but deprecated in Python 3.6 as it had problems (not to mention the confusing name). In Python 3.6+, the exact equivalent is python3 -m venv.

• venv is a package shipped with Python 3, which you can run using python3 -m venv (although for some reason some distros separate it out into a separate distro package, such as python3-venv on Ubuntu/Debian). It serves the same purpose as virtualenv, but only has a subset of its features (see a comparison here). virtualenv continues to be more popular than venv, especially since the former supports both Python 2 and 3.

Answer #3

You have four main options for converting types in pandas:

1. to_numeric() - provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)

2. astype() - convert (almost) any type to (almost) any other type (even if it"s not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).

3. infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible.

4. convert_dtypes() - convert DataFrame columns to the "best possible" dtype that supports pd.NA (pandas" object to indicate a missing value).

Read on for more detailed explanations and usage of each of these methods.

1. to_numeric()

The best way to convert one or more columns of a DataFrame to numeric values is to use pandas.to_numeric().

This function will try to change non-numeric objects (such as strings) into integers or floating point numbers as appropriate.

Basic usage

The input to to_numeric() is a Series or a single column of a DataFrame.

>>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
>>> s
0      8
1      6
2    7.5
3      3
4    0.9
dtype: object

>>> pd.to_numeric(s) # convert everything to float values
0    8.0
1    6.0
2    7.5
3    3.0
4    0.9
dtype: float64

As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:

# convert Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

You can also use it to convert multiple columns of a DataFrame via the apply() method:

# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame

# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)

As long as your values can all be converted, that"s probably all you need.

Error handling

But what if some values can"t be converted to a numeric type?

to_numeric() also takes an errors keyword argument that allows you to force non-numeric values to be NaN, or simply ignore columns containing these values.

Here"s an example using a Series of strings s which has the object dtype:

>>> s = pd.Series(["1", "2", "4.7", "pandas", "10"])
>>> s
0         1
1         2
2       4.7
3    pandas
4        10
dtype: object

The default behaviour is to raise if it can"t convert a value. In this case, it can"t cope with the string "pandas":

>>> pd.to_numeric(s) # or pd.to_numeric(s, errors="raise")
ValueError: Unable to parse string

Rather than fail, we might want "pandas" to be considered a missing/bad numeric value. We can coerce invalid values to NaN as follows using the errors keyword argument:

>>> pd.to_numeric(s, errors="coerce")
0     1.0
1     2.0
2     4.7
3     NaN
4    10.0
dtype: float64

The third option for errors is just to ignore the operation if an invalid value is encountered:

>>> pd.to_numeric(s, errors="ignore")
# the original Series is returned untouched

This last option is particularly useful when you want to convert your entire DataFrame, but don"t not know which of our columns can be converted reliably to a numeric type. In that case just write:

df.apply(pd.to_numeric, errors="ignore")

The function will be applied to each column of the DataFrame. Columns that can be converted to a numeric type will be converted, while columns that cannot (e.g. they contain non-digit strings or dates) will be left alone.

Downcasting

By default, conversion with to_numeric() will give you either a int64 or float64 dtype (or whatever integer width is native to your platform).

That"s usually what you want, but what if you wanted to save some memory and use a more compact dtype, like float32, or int8?

to_numeric() gives you the option to downcast to either "integer", "signed", "unsigned", "float". Here"s an example for a simple series s of integer type:

>>> s = pd.Series([1, 2, -7])
>>> s
0    1
1    2
2   -7
dtype: int64

Downcasting to "integer" uses the smallest possible integer that can hold the values:

>>> pd.to_numeric(s, downcast="integer")
0    1
1    2
2   -7
dtype: int8

Downcasting to "float" similarly picks a smaller than normal floating type:

>>> pd.to_numeric(s, downcast="float")
0    1.0
1    2.0
2   -7.0
dtype: float32

2. astype()

The astype() method enables you to be explicit about the dtype you want your DataFrame or Series to have. It"s very versatile in that you can try and go from one type to the any other.

Basic usage

Just pick a type: you can use a NumPy dtype (e.g. np.int16), some Python types (e.g. bool), or pandas-specific types (like the categorical dtype).

Call the method on the object you want to convert and astype() will try and convert it for you:

# convert all DataFrame columns to the int64 dtype
df = df.astype(int)

# convert column "a" to int64 dtype and "b" to complex type
df = df.astype({"a": int, "b": complex})

# convert Series to float16 type
s = s.astype(np.float16)

# convert Series to Python strings
s = s.astype(str)

# convert Series to categorical type - see docs for more details
s = s.astype("category")

Notice I said "try" - if astype() does not know how to convert a value in the Series or DataFrame, it will raise an error. For example if you have a NaN or inf value you"ll get an error trying to convert it to an integer.

As of pandas 0.20.0, this error can be suppressed by passing errors="ignore". Your original object will be return untouched.

Be careful

astype() is powerful, but it will sometimes convert values "incorrectly". For example:

>>> s = pd.Series([1, 2, -7])
>>> s
0    1
1    2
2   -7
dtype: int64

These are small integers, so how about converting to an unsigned 8-bit type to save memory?

>>> s.astype(np.uint8)
0      1
1      2
2    249
dtype: uint8

The conversion worked, but the -7 was wrapped round to become 249 (i.e. 2⁸ - 7)!

Trying to downcast using pd.to_numeric(s, downcast="unsigned") instead could help prevent this error.

3. infer_objects()

Version 0.21.0 of pandas introduced the method infer_objects() for converting columns of a DataFrame that have an object datatype to a more specific type (soft conversions).

For example, here"s a DataFrame with two columns of object type. One holds actual integers and the other holds strings representing integers:

>>> df = pd.DataFrame({"a": [7, 1, 5], "b": ["3","2","1"]}, dtype="object")
>>> df.dtypes
a    object
b    object
dtype: object

Using infer_objects(), you can change the type of column "a" to int64:

>>> df = df.infer_objects()
>>> df.dtypes
a     int64
b    object
dtype: object

Column "b" has been left alone since its values were strings, not integers. If you wanted to try and force the conversion of both columns to an integer type, you could use df.astype(int) instead.

4. convert_dtypes()

Version 1.0 and above includes a method convert_dtypes() to convert Series and DataFrame columns to the best possible dtype that supports the pd.NA missing value.

Here "best possible" means the type most suited to hold the values. For example, this a pandas integer type if all of the values are integers (or missing values): an object column of Python integer objects is converted to Int64, a column of NumPy int32 values will become the pandas dtype Int32.

With our object DataFrame df, we get the following result:

>>> df.convert_dtypes().dtypes                                             
a     Int64
b    string
dtype: object

Since column "a" held integer values, it was converted to the Int64 type (which is capable of holding missing values, unlike int64).

Column "b" contained string objects, so was changed to pandas" string dtype.

By default, this method will infer the type from object values in each column. We can change this by passing infer_objects=False:

>>> df.convert_dtypes(infer_objects=False).dtypes                          
a    object
b    string
dtype: object

Now column "a" remained an object column: pandas knows it can be described as an "integer" column (internally it ran infer_dtype) but didn"t infer exactly what dtype of integer it should have so did not convert it. Column "b" was again converted to "string" dtype as it was recognised as holding "string" values.

Answer #4

Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with Python with map and pool.

The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks. I"ll summarize below - it ends up being just a few lines of code:

from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(4)
results = pool.map(my_function, my_array)

Which is the multithreaded version of:

results = []
for item in my_array:
    results.append(my_function(item))

Description

Map is a cool little function, and the key to easily injecting parallelism into your Python code. For those unfamiliar, map is something lifted from functional languages like Lisp. It is a function which maps another function over a sequence.

Map handles the iteration over the sequence for us, applies the function, and stores all of the results in a handy list at the end.

Implementation

Parallel versions of the map function are provided by two libraries:multiprocessing, and also its little known, but equally fantastic step child:multiprocessing.dummy.

multiprocessing.dummy is exactly the same as multiprocessing module, but uses threads instead (an important distinction - use multiple processes for CPU-intensive tasks; threads for (and during) I/O):

multiprocessing.dummy replicates the API of multiprocessing, but is no more than a wrapper around the threading module.

import urllib2
from multiprocessing.dummy import Pool as ThreadPool

urls = [
  "http://www.python.org",
  "http://www.python.org/about/",
  "http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html",
  "http://www.python.org/doc/",
  "http://www.python.org/download/",
  "http://www.python.org/getit/",
  "http://www.python.org/community/",
  "https://wiki.python.org/moin/",
]

# Make the Pool of workers
pool = ThreadPool(4)

# Open the URLs in their own threads
# and return the results
results = pool.map(urllib2.urlopen, urls)

# Close the pool and wait for the work to finish
pool.close()
pool.join()

And the timing results:

Single thread:   14.4 seconds
       4 Pool:   3.1 seconds
       8 Pool:   1.4 seconds
      13 Pool:   1.3 seconds

Passing multiple arguments (works like this only in Python 3.3 and later):

To pass multiple arrays:

results = pool.starmap(function, zip(list_a, list_b))

Or to pass a constant and an array:

results = pool.starmap(function, zip(itertools.repeat(constant), list_a))

If you are using an earlier version of Python, you can pass multiple arguments via this workaround).

(Thanks to user136036 for the helpful comment.)

Answer #5

How to iterate over rows in a DataFrame in Pandas?

Answer: DON"T^*!

Iteration in Pandas is an anti-pattern and is something you should only do when you have exhausted every other option. You should not use any function with "iter" in its name for more than a few thousand rows or you will have to get used to a lot of waiting.

Do you want to print a DataFrame? Use DataFrame.to_string().

Do you want to compute something? In that case, search for methods in this order (list modified from here):

1. Vectorization
2. Cython routines
3. List Comprehensions (vanilla for loop)
4. DataFrame.apply(): i) ¬†Reductions that can be performed in Cython, ii) Iteration in Python space
5. DataFrame.itertuples() and iteritems()
6. DataFrame.iterrows()

iterrows and itertuples (both receiving many votes in answers to this question) should be used in very rare circumstances, such as generating row objects/nametuples for sequential processing, which is really the only thing these functions are useful for.

Appeal to Authority

The documentation page on iteration has a huge red warning box that says:

Iterating through pandas objects is generally slow. In many cases, iterating manually over the rows is not needed [...].

_{* It"s actually a little more complicated than "don"t". df.iterrows() is the correct answer to this question, but "vectorize your ops" is the better one. I will concede that there are circumstances where iteration cannot be avoided (for example, some operations where the result depends on the value computed for the previous row). However, it takes some familiarity with the library to know when. If you"re not sure whether you need an iterative solution, you probably don"t. PS: To know more about my rationale for writing this answer, skip to the very bottom.}

Faster than Looping: Vectorization, Cython

A good number of basic operations and computations are "vectorised" by pandas (either through NumPy, or through Cythonized functions). This includes arithmetic, comparisons, (most) reductions, reshaping (such as pivoting), joins, and groupby operations. Look through the documentation on Essential Basic Functionality to find a suitable vectorised method for your problem.

If none exists, feel free to write your own using custom Cython extensions.

Next Best Thing: List Comprehensions^*

List comprehensions should be your next port of call if 1) there is no vectorized solution available, 2) performance is important, but not important enough to go through the hassle of cythonizing your code, and 3) you"re trying to perform elementwise transformation on your code. There is a good amount of evidence to suggest that list comprehensions are sufficiently fast (and even sometimes faster) for many common Pandas tasks.

The formula is simple,

# Iterating over one column - `f` is some function that processes your data
result = [f(x) for x in df["col"]]
# Iterating over two columns, use `zip`
result = [f(x, y) for x, y in zip(df["col1"], df["col2"])]
# Iterating over multiple columns - same data type
result = [f(row[0], ..., row[n]) for row in df[["col1", ...,"coln"]].to_numpy()]
# Iterating over multiple columns - differing data type
result = [f(row[0], ..., row[n]) for row in zip(df["col1"], ..., df["coln"])]

If you can encapsulate your business logic into a function, you can use a list comprehension that calls it. You can make arbitrarily complex things work through the simplicity and speed of raw Python code.

Caveats

List comprehensions assume that your data is easy to work with - what that means is your data types are consistent and you don"t have NaNs, but this cannot always be guaranteed.

1. The first one is more obvious, but when dealing with NaNs, prefer in-built pandas methods if they exist (because they have much better corner-case handling logic), or ensure your business logic includes appropriate NaN handling logic.
2. When dealing with mixed data types you should iterate over zip(df["A"], df["B"], ...) instead of df[["A", "B"]].to_numpy() as the latter implicitly upcasts data to the most common type. As an example if A is numeric and B is string, to_numpy() will cast the entire array to string, which may not be what you want. Fortunately zipping your columns together is the most straightforward workaround to this.

_{*Your mileage may vary for the reasons outlined in the Caveats section above.}

An Obvious Example

Let"s demonstrate the difference with a simple example of adding two pandas columns A + B. This is a vectorizable operaton, so it will be easy to contrast the performance of the methods discussed above.

Benchmarking code, for your reference. The line at the bottom measures a function written in numpandas, a style of Pandas that mixes heavily with NumPy to squeeze out maximum performance. Writing numpandas code should be avoided unless you know what you"re doing. Stick to the API where you can (i.e., prefer vec over vec_numpy).

I should mention, however, that it isn"t always this cut and dry. Sometimes the answer to "what is the best method for an operation" is "it depends on your data". My advice is to test out different approaches on your data before settling on one.

Further Reading

• 10 Minutes to pandas, and Essential Basic Functionality - Useful links that introduce you to Pandas and its library of vectorized*/cythonized functions.

• Enhancing Performance - A primer from the documentation on enhancing standard Pandas operations

• Are for-loops in pandas really bad? When should I care? - a detailed writeup by me on list comprehensions and their suitability for various operations (mainly ones involving non-numeric data)

• When should I (not) want to use pandas apply() in my code? - apply is slow (but not as slow as the iter* family. There are, however, situations where one can (or should) consider apply as a serious alternative, especially in some GroupBy operations).

_{* Pandas string methods are "vectorized" in the sense that they are specified on the series but operate on each element. The underlying mechanisms are still iterative, because string operations are inherently hard to vectorize.}

Why I Wrote this Answer

A common trend I notice from new users is to ask questions of the form "How can I iterate over my df to do X?". Showing code that calls iterrows() while doing something inside a for loop. Here is why. A new user to the library who has not been introduced to the concept of vectorization will likely envision the code that solves their problem as iterating over their data to do something. Not knowing how to iterate over a DataFrame, the first thing they do is Google it and end up here, at this question. They then see the accepted answer telling them how to, and they close their eyes and run this code without ever first questioning if iteration is not the right thing to do.

The aim of this answer is to help new users understand that iteration is not necessarily the solution to every problem, and that better, faster and more idiomatic solutions could exist, and that it is worth investing time in exploring them. I"m not trying to start a war of iteration vs. vectorization, but I want new users to be informed when developing solutions to their problems with this library.

Answer #6

In Python, what is the purpose of __slots__ and what are the cases one should avoid this?

TLDR:

The special attribute __slots__ allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:

1. faster attribute access.
2. space savings in memory.

The space savings is from

1. Storing value references in slots instead of __dict__.
2. Denying __dict__ and __weakref__ creation if parent classes deny them and you declare __slots__.

Quick Caveats

Small caveat, you should only declare a particular slot one time in an inheritance tree. For example:

class Base:
    __slots__ = "foo", "bar"

class Right(Base):
    __slots__ = "baz", 

class Wrong(Base):
    __slots__ = "foo", "bar", "baz"        # redundant foo and bar

Python doesn"t object when you get this wrong (it probably should), problems might not otherwise manifest, but your objects will take up more space than they otherwise should. Python 3.8:

>>> from sys import getsizeof
>>> getsizeof(Right()), getsizeof(Wrong())
(56, 72)

This is because the Base"s slot descriptor has a slot separate from the Wrong"s. This shouldn"t usually come up, but it could:

>>> w = Wrong()
>>> w.foo = "foo"
>>> Base.foo.__get__(w)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: foo
>>> Wrong.foo.__get__(w)
"foo"

The biggest caveat is for multiple inheritance - multiple "parent classes with nonempty slots" cannot be combined.

To accommodate this restriction, follow best practices: Factor out all but one or all parents" abstraction which their concrete class respectively and your new concrete class collectively will inherit from - giving the abstraction(s) empty slots (just like abstract base classes in the standard library).

See section on multiple inheritance below for an example.

Requirements:

• To have attributes named in __slots__ to actually be stored in slots instead of a __dict__, a class must inherit from object (automatic in Python 3, but must be explicit in Python 2).

• To prevent the creation of a __dict__, you must inherit from object and all classes in the inheritance must declare __slots__ and none of them can have a "__dict__" entry.

There are a lot of details if you wish to keep reading.

Why use __slots__: Faster attribute access.

The creator of Python, Guido van Rossum, states that he actually created __slots__ for faster attribute access.

It is trivial to demonstrate measurably significant faster access:

import timeit

class Foo(object): __slots__ = "foo",

class Bar(object): pass

slotted = Foo()
not_slotted = Bar()

def get_set_delete_fn(obj):
    def get_set_delete():
        obj.foo = "foo"
        obj.foo
        del obj.foo
    return get_set_delete

and

>>> min(timeit.repeat(get_set_delete_fn(slotted)))
0.2846834529991611
>>> min(timeit.repeat(get_set_delete_fn(not_slotted)))
0.3664822799983085

The slotted access is almost 30% faster in Python 3.5 on Ubuntu.

>>> 0.3664822799983085 / 0.2846834529991611
1.2873325658284342

In Python 2 on Windows I have measured it about 15% faster.

Why use __slots__: Memory Savings

Another purpose of __slots__ is to reduce the space in memory that each object instance takes up.

My own contribution to the documentation clearly states the reasons behind this:

The space saved over using __dict__ can be significant.

SQLAlchemy attributes a lot of memory savings to __slots__.

To verify this, using the Anaconda distribution of Python 2.7 on Ubuntu Linux, with guppy.hpy (aka heapy) and sys.getsizeof, the size of a class instance without __slots__ declared, and nothing else, is 64 bytes. That does not include the __dict__. Thank you Python for lazy evaluation again, the __dict__ is apparently not called into existence until it is referenced, but classes without data are usually useless. When called into existence, the __dict__ attribute is a minimum of 280 bytes additionally.

In contrast, a class instance with __slots__ declared to be () (no data) is only 16 bytes, and 56 total bytes with one item in slots, 64 with two.

For 64 bit Python, I illustrate the memory consumption in bytes in Python 2.7 and 3.6, for __slots__ and __dict__ (no slots defined) for each point where the dict grows in 3.6 (except for 0, 1, and 2 attributes):

       Python 2.7             Python 3.6
attrs  __slots__  __dict__*   __slots__  __dict__* | *(no slots defined)
none   16         56 + 272†   16         56 + 112† | †if __dict__ referenced
one    48         56 + 272    48         56 + 112
two    56         56 + 272    56         56 + 112
six    88         56 + 1040   88         56 + 152
11     128        56 + 1040   128        56 + 240
22     216        56 + 3344   216        56 + 408     
43     384        56 + 3344   384        56 + 752

So, in spite of smaller dicts in Python 3, we see how nicely __slots__ scale for instances to save us memory, and that is a major reason you would want to use __slots__.

Just for completeness of my notes, note that there is a one-time cost per slot in the class"s namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use data descriptors like properties, called "members".

>>> Foo.foo
<member "foo" of "Foo" objects>
>>> type(Foo.foo)
<class "member_descriptor">
>>> getsizeof(Foo.foo)
72

Demonstration of __slots__:

To deny the creation of a __dict__, you must subclass object. Everything subclasses object in Python 3, but in Python 2 you had to be explicit:

class Base(object): 
    __slots__ = ()

now:

>>> b = Base()
>>> b.a = "a"
Traceback (most recent call last):
  File "<pyshell#38>", line 1, in <module>
    b.a = "a"
AttributeError: "Base" object has no attribute "a"

Or subclass another class that defines __slots__

class Child(Base):
    __slots__ = ("a",)

and now:

c = Child()
c.a = "a"

but:

>>> c.b = "b"
Traceback (most recent call last):
  File "<pyshell#42>", line 1, in <module>
    c.b = "b"
AttributeError: "Child" object has no attribute "b"

To allow __dict__ creation while subclassing slotted objects, just add "__dict__" to the __slots__ (note that slots are ordered, and you shouldn"t repeat slots that are already in parent classes):

class SlottedWithDict(Child): 
    __slots__ = ("__dict__", "b")

swd = SlottedWithDict()
swd.a = "a"
swd.b = "b"
swd.c = "c"

and

>>> swd.__dict__
{"c": "c"}

Or you don"t even need to declare __slots__ in your subclass, and you will still use slots from the parents, but not restrict the creation of a __dict__:

class NoSlots(Child): pass
ns = NoSlots()
ns.a = "a"
ns.b = "b"

And:

>>> ns.__dict__
{"b": "b"}

However, __slots__ may cause problems for multiple inheritance:

class BaseA(object): 
    __slots__ = ("a",)

class BaseB(object): 
    __slots__ = ("b",)

Because creating a child class from parents with both non-empty slots fails:

>>> class Child(BaseA, BaseB): __slots__ = ()
Traceback (most recent call last):
  File "<pyshell#68>", line 1, in <module>
    class Child(BaseA, BaseB): __slots__ = ()
TypeError: Error when calling the metaclass bases
    multiple bases have instance lay-out conflict

If you run into this problem, You could just remove __slots__ from the parents, or if you have control of the parents, give them empty slots, or refactor to abstractions:

from abc import ABC

class AbstractA(ABC):
    __slots__ = ()

class BaseA(AbstractA): 
    __slots__ = ("a",)

class AbstractB(ABC):
    __slots__ = ()

class BaseB(AbstractB): 
    __slots__ = ("b",)

class Child(AbstractA, AbstractB): 
    __slots__ = ("a", "b")

c = Child() # no problem!

Add "__dict__" to __slots__ to get dynamic assignment:

class Foo(object):
    __slots__ = "bar", "baz", "__dict__"

and now:

>>> foo = Foo()
>>> foo.boink = "boink"

So with "__dict__" in slots we lose some of the size benefits with the upside of having dynamic assignment and still having slots for the names we do expect.

When you inherit from an object that isn"t slotted, you get the same sort of semantics when you use __slots__ - names that are in __slots__ point to slotted values, while any other values are put in the instance"s __dict__.

Avoiding __slots__ because you want to be able to add attributes on the fly is actually not a good reason - just add "__dict__" to your __slots__ if this is required.

You can similarly add __weakref__ to __slots__ explicitly if you need that feature.

Set to empty tuple when subclassing a namedtuple:

The namedtuple builtin make immutable instances that are very lightweight (essentially, the size of tuples) but to get the benefits, you need to do it yourself if you subclass them:

from collections import namedtuple
class MyNT(namedtuple("MyNT", "bar baz")):
    """MyNT is an immutable and lightweight object"""
    __slots__ = ()

usage:

>>> nt = MyNT("bar", "baz")
>>> nt.bar
"bar"
>>> nt.baz
"baz"

And trying to assign an unexpected attribute raises an AttributeError because we have prevented the creation of __dict__:

>>> nt.quux = "quux"
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: "MyNT" object has no attribute "quux"

You can allow __dict__ creation by leaving off __slots__ = (), but you can"t use non-empty __slots__ with subtypes of tuple.

Biggest Caveat: Multiple inheritance

Even when non-empty slots are the same for multiple parents, they cannot be used together:

class Foo(object): 
    __slots__ = "foo", "bar"
class Bar(object):
    __slots__ = "foo", "bar" # alas, would work if empty, i.e. ()

>>> class Baz(Foo, Bar): pass
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: Error when calling the metaclass bases
    multiple bases have instance lay-out conflict

Using an empty __slots__ in the parent seems to provide the most flexibility, allowing the child to choose to prevent or allow (by adding "__dict__" to get dynamic assignment, see section above) the creation of a __dict__:

class Foo(object): __slots__ = ()
class Bar(object): __slots__ = ()
class Baz(Foo, Bar): __slots__ = ("foo", "bar")
b = Baz()
b.foo, b.bar = "foo", "bar"

You don"t have to have slots - so if you add them, and remove them later, it shouldn"t cause any problems.

Going out on a limb here: If you"re composing mixins or using abstract base classes, which aren"t intended to be instantiated, an empty __slots__ in those parents seems to be the best way to go in terms of flexibility for subclassers.

To demonstrate, first, let"s create a class with code we"d like to use under multiple inheritance

class AbstractBase:
    __slots__ = ()
    def __init__(self, a, b):
        self.a = a
        self.b = b
    def __repr__(self):
        return f"{type(self).__name__}({repr(self.a)}, {repr(self.b)})"

We could use the above directly by inheriting and declaring the expected slots:

class Foo(AbstractBase):
    __slots__ = "a", "b"

But we don"t care about that, that"s trivial single inheritance, we need another class we might also inherit from, maybe with a noisy attribute:

class AbstractBaseC:
    __slots__ = ()
    @property
    def c(self):
        print("getting c!")
        return self._c
    @c.setter
    def c(self, arg):
        print("setting c!")
        self._c = arg

Now if both bases had nonempty slots, we couldn"t do the below. (In fact, if we wanted, we could have given AbstractBase nonempty slots a and b, and left them out of the below declaration - leaving them in would be wrong):

class Concretion(AbstractBase, AbstractBaseC):
    __slots__ = "a b _c".split()

And now we have functionality from both via multiple inheritance, and can still deny __dict__ and __weakref__ instantiation:

>>> c = Concretion("a", "b")
>>> c.c = c
setting c!
>>> c.c
getting c!
Concretion("a", "b")
>>> c.d = "d"
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: "Concretion" object has no attribute "d"

Other cases to avoid slots:

• Avoid them when you want to perform __class__ assignment with another class that doesn"t have them (and you can"t add them) unless the slot layouts are identical. (I am very interested in learning who is doing this and why.)
• Avoid them if you want to subclass variable length builtins like long, tuple, or str, and you want to add attributes to them.
• Avoid them if you insist on providing default values via class attributes for instance variables.

You may be able to tease out further caveats from the rest of the __slots__ documentation (the 3.7 dev docs are the most current), which I have made significant recent contributions to.

Critiques of other answers

The current top answers cite outdated information and are quite hand-wavy and miss the mark in some important ways.

Do not "only use __slots__ when instantiating lots of objects"

I quote:

"You would want to use __slots__ if you are going to instantiate a lot (hundreds, thousands) of objects of the same class."

Abstract Base Classes, for example, from the collections module, are not instantiated, yet __slots__ are declared for them.

Why?

If a user wishes to deny __dict__ or __weakref__ creation, those things must not be available in the parent classes.

__slots__ contributes to reusability when creating interfaces or mixins.

It is true that many Python users aren"t writing for reusability, but when you are, having the option to deny unnecessary space usage is valuable.

__slots__ doesn"t break pickling

When pickling a slotted object, you may find it complains with a misleading TypeError:

>>> pickle.loads(pickle.dumps(f))
TypeError: a class that defines __slots__ without defining __getstate__ cannot be pickled

This is actually incorrect. This message comes from the oldest protocol, which is the default. You can select the latest protocol with the -1 argument. In Python 2.7 this would be 2 (which was introduced in 2.3), and in 3.6 it is 4.

>>> pickle.loads(pickle.dumps(f, -1))
<__main__.Foo object at 0x1129C770>

in Python 2.7:

>>> pickle.loads(pickle.dumps(f, 2))
<__main__.Foo object at 0x1129C770>

in Python 3.6

>>> pickle.loads(pickle.dumps(f, 4))
<__main__.Foo object at 0x1129C770>

So I would keep this in mind, as it is a solved problem.

Critique of the (until Oct 2, 2016) accepted answer

The first paragraph is half short explanation, half predictive. Here"s the only part that actually answers the question

The proper use of __slots__ is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. This saves the overhead of one dict for every object that uses slots

The second half is wishful thinking, and off the mark:

While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.

Python actually does something similar to this, only creating the __dict__ when it is accessed, but creating lots of objects with no data is fairly ridiculous.

The second paragraph oversimplifies and misses actual reasons to avoid __slots__. The below is not a real reason to avoid slots (for actual reasons, see the rest of my answer above.):

They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies.

It then goes on to discuss other ways of accomplishing that perverse goal with Python, not discussing anything to do with __slots__.

The third paragraph is more wishful thinking. Together it is mostly off-the-mark content that the answerer didn"t even author and contributes to ammunition for critics of the site.

Memory usage evidence

Create some normal objects and slotted objects:

>>> class Foo(object): pass
>>> class Bar(object): __slots__ = ()

Instantiate a million of them:

>>> foos = [Foo() for f in xrange(1000000)]
>>> bars = [Bar() for b in xrange(1000000)]

Inspect with guppy.hpy().heap():

>>> guppy.hpy().heap()
Partition of a set of 2028259 objects. Total size = 99763360 bytes.
 Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
     0 1000000  49 64000000  64  64000000  64 __main__.Foo
     1     169   0 16281480  16  80281480  80 list
     2 1000000  49 16000000  16  96281480  97 __main__.Bar
     3   12284   1   987472   1  97268952  97 str
...

Access the regular objects and their __dict__ and inspect again:

>>> for f in foos:
...     f.__dict__
>>> guppy.hpy().heap()
Partition of a set of 3028258 objects. Total size = 379763480 bytes.
 Index  Count   %      Size    % Cumulative  % Kind (class / dict of class)
     0 1000000  33 280000000  74 280000000  74 dict of __main__.Foo
     1 1000000  33  64000000  17 344000000  91 __main__.Foo
     2     169   0  16281480   4 360281480  95 list
     3 1000000  33  16000000   4 376281480  99 __main__.Bar
     4   12284   0    987472   0 377268952  99 str
...

This is consistent with the history of Python, from Unifying types and classes in Python 2.2

If you subclass a built-in type, extra space is automatically added to the instances to accomodate __dict__ and __weakrefs__. (The __dict__ is not initialized until you use it though, so you shouldn"t worry about the space occupied by an empty dictionary for each instance you create.) If you don"t need this extra space, you can add the phrase "__slots__ = []" to your class.

Answer #7

os.listdir() - list in the current directory

With listdir in os module you get the files and the folders in the current dir

 import os
 arr = os.listdir()
 print(arr)
 
 >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

Looking in a directory

arr = os.listdir("c:\files")

glob from glob

with glob you can specify a type of file to list like this

import glob

txtfiles = []
for file in glob.glob("*.txt"):
    txtfiles.append(file)

glob in a list comprehension

mylist = [f for f in glob.glob("*.txt")]

get the full path of only files in the current directory

import os
from os import listdir
from os.path import isfile, join

cwd = os.getcwd()
onlyfiles = [os.path.join(cwd, f) for f in os.listdir(cwd) if 
os.path.isfile(os.path.join(cwd, f))]
print(onlyfiles) 

["G:\getfilesname\getfilesname.py", "G:\getfilesname\example.txt"]

Getting the full path name with os.path.abspath

You get the full path in return

 import os
 files_path = [os.path.abspath(x) for x in os.listdir()]
 print(files_path)
 
 ["F:\documentiapplications.txt", "F:\documenticollections.txt"]

Walk: going through sub directories

os.walk returns the root, the directories list and the files list, that is why I unpacked them in r, d, f in the for loop; it, then, looks for other files and directories in the subfolders of the root and so on until there are no subfolders.

import os

# Getting the current work directory (cwd)
thisdir = os.getcwd()

# r=root, d=directories, f = files
for r, d, f in os.walk(thisdir):
    for file in f:
        if file.endswith(".docx"):
            print(os.path.join(r, file))

os.listdir(): get files in the current directory (Python 2)

In Python 2, if you want the list of the files in the current directory, you have to give the argument as "." or os.getcwd() in the os.listdir method.

 import os
 arr = os.listdir(".")
 print(arr)
 
 >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

To go up in the directory tree

# Method 1
x = os.listdir("..")

# Method 2
x= os.listdir("/")

Get files: os.listdir() in a particular directory (Python 2 and 3)

 import os
 arr = os.listdir("F:\python")
 print(arr)
 
 >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

Get files of a particular subdirectory with os.listdir()

import os

x = os.listdir("./content")

os.walk(".") - current directory

 import os
 arr = next(os.walk("."))[2]
 print(arr)
 
 >>> ["5bs_Turismo1.pdf", "5bs_Turismo1.pptx", "esperienza.txt"]

next(os.walk(".")) and os.path.join("dir", "file")

 import os
 arr = []
 for d,r,f in next(os.walk("F:\_python")):
     for file in f:
         arr.append(os.path.join(r,file))

 for f in arr:
     print(files)

>>> F:\_python\dict_class.py
>>> F:\_python\programmi.txt

next(os.walk("F:\") - get the full path - list comprehension

 [os.path.join(r,file) for r,d,f in next(os.walk("F:\_python")) for file in f]
 
 >>> ["F:\_python\dict_class.py", "F:\_python\programmi.txt"]

os.walk - get full path - all files in sub dirs**

x = [os.path.join(r,file) for r,d,f in os.walk("F:\_python") for file in f]
print(x)

>>> ["F:\_python\dict.py", "F:\_python\progr.txt", "F:\_python\readl.py"]

os.listdir() - get only txt files

 arr_txt = [x for x in os.listdir() if x.endswith(".txt")]
 print(arr_txt)
 
 >>> ["work.txt", "3ebooks.txt"]

Using glob to get the full path of the files

If I should need the absolute path of the files:

from path import path
from glob import glob
x = [path(f).abspath() for f in glob("F:\*.txt")]
for f in x:
    print(f)

>>> F:acquistionline.txt
>>> F:acquisti_2018.txt
>>> F:ootstrap_jquery_ecc.txt

Using os.path.isfile to avoid directories in the list

import os.path
listOfFiles = [f for f in os.listdir() if os.path.isfile(f)]
print(listOfFiles)

>>> ["a simple game.py", "data.txt", "decorator.py"]

Using pathlib from Python 3.4

import pathlib

flist = []
for p in pathlib.Path(".").iterdir():
    if p.is_file():
        print(p)
        flist.append(p)

 >>> error.PNG
 >>> exemaker.bat
 >>> guiprova.mp3
 >>> setup.py
 >>> speak_gui2.py
 >>> thumb.PNG

With list comprehension:

flist = [p for p in pathlib.Path(".").iterdir() if p.is_file()]

Alternatively, use pathlib.Path() instead of pathlib.Path(".")

Use glob method in pathlib.Path()

import pathlib

py = pathlib.Path().glob("*.py")
for file in py:
    print(file)

>>> stack_overflow_list.py
>>> stack_overflow_list_tkinter.py

Get all and only files with os.walk

import os
x = [i[2] for i in os.walk(".")]
y=[]
for t in x:
    for f in t:
        y.append(f)
print(y)

>>> ["append_to_list.py", "data.txt", "data1.txt", "data2.txt", "data_180617", "os_walk.py", "READ2.py", "read_data.py", "somma_defaltdic.py", "substitute_words.py", "sum_data.py", "data.txt", "data1.txt", "data_180617"]

Get only files with next and walk in a directory

 import os
 x = next(os.walk("F://python"))[2]
 print(x)
 
 >>> ["calculator.bat","calculator.py"]

Get only directories with next and walk in a directory

 import os
 next(os.walk("F://python"))[1] # for the current dir use (".")
 
 >>> ["python3","others"]

Get all the subdir names with walk

for r,d,f in os.walk("F:\_python"):
    for dirs in d:
        print(dirs)

>>> .vscode
>>> pyexcel
>>> pyschool.py
>>> subtitles
>>> _metaprogramming
>>> .ipynb_checkpoints

os.scandir() from Python 3.5 and greater

import os
x = [f.name for f in os.scandir() if f.is_file()]
print(x)

>>> ["calculator.bat","calculator.py"]

# Another example with scandir (a little variation from docs.python.org)
# This one is more efficient than os.listdir.
# In this case, it shows the files only in the current directory
# where the script is executed.

import os
with os.scandir() as i:
    for entry in i:
        if entry.is_file():
            print(entry.name)

>>> ebookmaker.py
>>> error.PNG
>>> exemaker.bat
>>> guiprova.mp3
>>> setup.py
>>> speakgui4.py
>>> speak_gui2.py
>>> speak_gui3.py
>>> thumb.PNG

Examples:

Ex. 1: How many files are there in the subdirectories?

In this example, we look for the number of files that are included in all the directory and its subdirectories.

import os

def count(dir, counter=0):
    "returns number of files in dir and subdirs"
    for pack in os.walk(dir):
        for f in pack[2]:
            counter += 1
    return dir + " : " + str(counter) + "files"

print(count("F:\python"))

>>> "F:\python" : 12057 files"

Ex.2: How to copy all files from a directory to another?

A script to make order in your computer finding all files of a type (default: pptx) and copying them in a new folder.

import os
import shutil
from path import path

destination = "F:\file_copied"
# os.makedirs(destination)

def copyfile(dir, filetype="pptx", counter=0):
    "Searches for pptx (or other - pptx is the default) files and copies them"
    for pack in os.walk(dir):
        for f in pack[2]:
            if f.endswith(filetype):
                fullpath = pack[0] + "\" + f
                print(fullpath)
                shutil.copy(fullpath, destination)
                counter += 1
    if counter > 0:
        print("-" * 30)
        print("	==> Found in: `" + dir + "` : " + str(counter) + " files
")

for dir in os.listdir():
    "searches for folders that starts with `_`"
    if dir[0] == "_":
        # copyfile(dir, filetype="pdf")
        copyfile(dir, filetype="txt")


>>> _compiti18Compito Contabilità 1conti.txt
>>> _compiti18Compito Contabilità 1modula4.txt
>>> _compiti18Compito Contabilità 1moduloa4.txt
>>> ------------------------
>>> ==> Found in: `_compiti18` : 3 files

Ex. 3: How to get all the files in a txt file

In case you want to create a txt file with all the file names:

import os
mylist = ""
with open("filelist.txt", "w", encoding="utf-8") as file:
    for eachfile in os.listdir():
        mylist += eachfile + "
"
    file.write(mylist)

Example: txt with all the files of an hard drive

"""
We are going to save a txt file with all the files in your directory.
We will use the function walk()
"""

import os

# see all the methods of os
# print(*dir(os), sep=", ")
listafile = []
percorso = []
with open("lista_file.txt", "w", encoding="utf-8") as testo:
    for root, dirs, files in os.walk("D:\"):
        for file in files:
            listafile.append(file)
            percorso.append(root + "\" + file)
            testo.write(file + "
")
listafile.sort()
print("N. of files", len(listafile))
with open("lista_file_ordinata.txt", "w", encoding="utf-8") as testo_ordinato:
    for file in listafile:
        testo_ordinato.write(file + "
")

with open("percorso.txt", "w", encoding="utf-8") as file_percorso:
    for file in percorso:
        file_percorso.write(file + "
")

os.system("lista_file.txt")
os.system("lista_file_ordinata.txt")
os.system("percorso.txt")

All the file of C: in one text file

This is a shorter version of the previous code. Change the folder where to start finding the files if you need to start from another position. This code generate a 50 mb on text file on my computer with something less then 500.000 lines with files with the complete path.

import os

with open("file.txt", "w", encoding="utf-8") as filewrite:
    for r, d, f in os.walk("C:\"):
        for file in f:
            filewrite.write(f"{r + file}
")

How to write a file with all paths in a folder of a type

With this function you can create a txt file that will have the name of a type of file that you look for (ex. pngfile.txt) with all the full path of all the files of that type. It can be useful sometimes, I think.

import os

def searchfiles(extension=".ttf", folder="H:\"):
    "Create a txt file with all the file of a type"
    with open(extension[1:] + "file.txt", "w", encoding="utf-8") as filewrite:
        for r, d, f in os.walk(folder):
            for file in f:
                if file.endswith(extension):
                    filewrite.write(f"{r + file}
")

# looking for png file (fonts) in the hard disk H:
searchfiles(".png", "H:\")

>>> H:4bs_18Dolphins5.png
>>> H:4bs_18Dolphins6.png
>>> H:4bs_18Dolphins7.png
>>> H:5_18marketing htmlassetsimageslogo2.png
>>> H:7z001.png
>>> H:7z002.png

(New) Find all files and open them with tkinter GUI

I just wanted to add in this 2019 a little app to search for all files in a dir and be able to open them by doubleclicking on the name of the file in the list.

import tkinter as tk
import os

def searchfiles(extension=".txt", folder="H:\"):
    "insert all files in the listbox"
    for r, d, f in os.walk(folder):
        for file in f:
            if file.endswith(extension):
                lb.insert(0, r + "\" + file)

def open_file():
    os.startfile(lb.get(lb.curselection()[0]))

root = tk.Tk()
root.geometry("400x400")
bt = tk.Button(root, text="Search", command=lambda:searchfiles(".png", "H:\"))
bt.pack()
lb = tk.Listbox(root)
lb.pack(fill="both", expand=1)
lb.bind("<Double-Button>", lambda x: open_file())
root.mainloop()

Answer #8

This is the behaviour to adopt when the referenced object is deleted. It is not specific to Django; this is an SQL standard. Although Django has its own implementation on top of SQL. (1)

There are seven possible actions to take when such event occurs:

• CASCADE: When the referenced object is deleted, also delete the objects that have references to it (when you remove a blog post for instance, you might want to delete comments as well). SQL equivalent: CASCADE.
• PROTECT: Forbid the deletion of the referenced object. To delete it you will have to delete all objects that reference it manually. SQL equivalent: RESTRICT.
• RESTRICT: (introduced in Django 3.1) Similar behavior as PROTECT that matches SQL"s RESTRICT more accurately. (See django documentation example)
• SET_NULL: Set the reference to NULL (requires the field to be nullable). For instance, when you delete a User, you might want to keep the comments he posted on blog posts, but say it was posted by an anonymous (or deleted) user. SQL equivalent: SET NULL.
• SET_DEFAULT: Set the default value. SQL equivalent: SET DEFAULT.
• SET(...): Set a given value. This one is not part of the SQL standard and is entirely handled by Django.
• DO_NOTHING: Probably a very bad idea since this would create integrity issues in your database (referencing an object that actually doesn"t exist). SQL equivalent: NO ACTION. (2)

Source: Django documentation

See also the documentation of PostgreSQL for instance.

In most cases, CASCADE is the expected behaviour, but for every ForeignKey, you should always ask yourself what is the expected behaviour in this situation. PROTECT and SET_NULL are often useful. Setting CASCADE where it should not, can potentially delete all of your database in cascade, by simply deleting a single user.

Additional note to clarify cascade direction

It"s funny to notice that the direction of the CASCADE action is not clear to many people. Actually, it"s funny to notice that only the CASCADE action is not clear. I understand the cascade behavior might be confusing, however you must think that it is the same direction as any other action. Thus, if you feel that CASCADE direction is not clear to you, it actually means that on_delete behavior is not clear to you.

In your database, a foreign key is basically represented by an integer field which value is the primary key of the foreign object. Let"s say you have an entry comment_A, which has a foreign key to an entry article_B. If you delete the entry comment_A, everything is fine. article_B used to live without comment_A and don"t bother if it"s deleted. However, if you delete article_B, then comment_A panics! It never lived without article_B and needs it, and it"s part of its attributes (article=article_B, but what is article_B???). This is where on_delete steps in, to determine how to resolve this integrity error, either by saying:

• "No! Please! Don"t! I can"t live without you!" (which is said PROTECT or RESTRICT in Django/SQL)
• "All right, if I"m not yours, then I"m nobody"s" (which is said SET_NULL)
• "Good bye world, I can"t live without article_B" and commit suicide (this is the CASCADE behavior).
• "It"s OK, I"ve got spare lover, and I"ll reference article_C from now" (SET_DEFAULT, or even SET(...)).
• "I can"t face reality, and I"ll keep calling your name even if that"s the only thing left to me!" (DO_NOTHING)

I hope it makes cascade direction clearer. :)

Footnotes

(1) Django has its own implementation on top of SQL. And, as mentioned by @JoeMjr2 in the comments below, Django will not create the SQL constraints. If you want the constraints to be ensured by your database (for instance, if your database is used by another application, or if you hang in the database console from time to time), you might want to set the related constraints manually yourself. There is an open ticket to add support for database-level on delete constrains in Django.

(2) Actually, there is one case where DO_NOTHING can be useful: If you want to skip Django"s implementation and implement the constraint yourself at the database-level.

Answer #9

Label vs. Location

The main distinction between the two methods is:

• loc gets rows (and/or columns) with particular labels.

• iloc gets rows (and/or columns) at integer locations.

To demonstrate, consider a series s of characters with a non-monotonic integer index:

>>> s = pd.Series(list("abcdef"), index=[49, 48, 47, 0, 1, 2]) 
49    a
48    b
47    c
0     d
1     e
2     f

>>> s.loc[0]    # value at index label 0
"d"

>>> s.iloc[0]   # value at index location 0
"a"

>>> s.loc[0:1]  # rows at index labels between 0 and 1 (inclusive)
0    d
1    e

>>> s.iloc[0:1] # rows at index location between 0 and 1 (exclusive)
49    a

Here are some of the differences/similarities between s.loc and s.iloc when passed various objects:

loc"s label-querying capabilities extend well-beyond integer indexes and it"s worth highlighting a couple of additional examples.

Here"s a Series where the index contains string objects:

>>> s2 = pd.Series(s.index, index=s.values)
>>> s2
a    49
b    48
c    47
d     0
e     1
f     2

Since loc is label-based, it can fetch the first value in the Series using s2.loc["a"]. It can also slice with non-integer objects:

>>> s2.loc["c":"e"]  # all rows lying between "c" and "e" (inclusive)
c    47
d     0
e     1

For DateTime indexes, we don"t need to pass the exact date/time to fetch by label. For example:

>>> s3 = pd.Series(list("abcde"), pd.date_range("now", periods=5, freq="M")) 
>>> s3
2021-01-31 16:41:31.879768    a
2021-02-28 16:41:31.879768    b
2021-03-31 16:41:31.879768    c
2021-04-30 16:41:31.879768    d
2021-05-31 16:41:31.879768    e

Then to fetch the row(s) for March/April 2021 we only need:

>>> s3.loc["2021-03":"2021-04"]
2021-03-31 17:04:30.742316    c
2021-04-30 17:04:30.742316    d

Rows and Columns

loc and iloc work the same way with DataFrames as they do with Series. It"s useful to note that both methods can address columns and rows together.

When given a tuple, the first element is used to index the rows and, if it exists, the second element is used to index the columns.

Consider the DataFrame defined below:

>>> import numpy as np 
>>> df = pd.DataFrame(np.arange(25).reshape(5, 5),  
                      index=list("abcde"), 
                      columns=["x","y","z", 8, 9])
>>> df
    x   y   z   8   9
a   0   1   2   3   4
b   5   6   7   8   9
c  10  11  12  13  14
d  15  16  17  18  19
e  20  21  22  23  24

Then for example:

>>> df.loc["c": , :"z"]  # rows "c" and onwards AND columns up to "z"
    x   y   z
c  10  11  12
d  15  16  17
e  20  21  22

>>> df.iloc[:, 3]        # all rows, but only the column at index location 3
a     3
b     8
c    13
d    18
e    23

Sometimes we want to mix label and positional indexing methods for the rows and columns, somehow combining the capabilities of loc and iloc.

For example, consider the following DataFrame. How best to slice the rows up to and including "c" and take the first four columns?

>>> import numpy as np 
>>> df = pd.DataFrame(np.arange(25).reshape(5, 5),  
                      index=list("abcde"), 
                      columns=["x","y","z", 8, 9])
>>> df
    x   y   z   8   9
a   0   1   2   3   4
b   5   6   7   8   9
c  10  11  12  13  14
d  15  16  17  18  19
e  20  21  22  23  24

We can achieve this result using iloc and the help of another method:

>>> df.iloc[:df.index.get_loc("c") + 1, :4]
    x   y   z   8
a   0   1   2   3
b   5   6   7   8
c  10  11  12  13

get_loc() is an index method meaning "get the position of the label in this index". Note that since slicing with iloc is exclusive of its endpoint, we must add 1 to this value if we want row "c" as well.

Answer #10

Quick Answer:

The simplest way to get row counts per group is by calling .size(), which returns a Series:

df.groupby(["col1","col2"]).size()

Usually you want this result as a DataFrame (instead of a Series) so you can do:

df.groupby(["col1", "col2"]).size().reset_index(name="counts")

If you want to find out how to calculate the row counts and other statistics for each group continue reading below.

Detailed example:

Consider the following example dataframe:

In [2]: df
Out[2]: 
  col1 col2  col3  col4  col5  col6
0    A    B  0.20 -0.61 -0.49  1.49
1    A    B -1.53 -1.01 -0.39  1.82
2    A    B -0.44  0.27  0.72  0.11
3    A    B  0.28 -1.32  0.38  0.18
4    C    D  0.12  0.59  0.81  0.66
5    C    D -0.13 -1.65 -1.64  0.50
6    C    D -1.42 -0.11 -0.18 -0.44
7    E    F -0.00  1.42 -0.26  1.17
8    E    F  0.91 -0.47  1.35 -0.34
9    G    H  1.48 -0.63 -1.14  0.17

First let"s use .size() to get the row counts:

In [3]: df.groupby(["col1", "col2"]).size()
Out[3]: 
col1  col2
A     B       4
C     D       3
E     F       2
G     H       1
dtype: int64

Then let"s use .size().reset_index(name="counts") to get the row counts:

In [4]: df.groupby(["col1", "col2"]).size().reset_index(name="counts")
Out[4]: 
  col1 col2  counts
0    A    B       4
1    C    D       3
2    E    F       2
3    G    H       1

Including results for more statistics

When you want to calculate statistics on grouped data, it usually looks like this:

In [5]: (df
   ...: .groupby(["col1", "col2"])
   ...: .agg({
   ...:     "col3": ["mean", "count"], 
   ...:     "col4": ["median", "min", "count"]
   ...: }))
Out[5]: 
            col4                  col3      
          median   min count      mean count
col1 col2                                   
A    B    -0.810 -1.32     4 -0.372500     4
C    D    -0.110 -1.65     3 -0.476667     3
E    F     0.475 -0.47     2  0.455000     2
G    H    -0.630 -0.63     1  1.480000     1

The result above is a little annoying to deal with because of the nested column labels, and also because row counts are on a per column basis.

To gain more control over the output I usually split the statistics into individual aggregations that I then combine using join. It looks like this:

In [6]: gb = df.groupby(["col1", "col2"])
   ...: counts = gb.size().to_frame(name="counts")
   ...: (counts
   ...:  .join(gb.agg({"col3": "mean"}).rename(columns={"col3": "col3_mean"}))
   ...:  .join(gb.agg({"col4": "median"}).rename(columns={"col4": "col4_median"}))
   ...:  .join(gb.agg({"col4": "min"}).rename(columns={"col4": "col4_min"}))
   ...:  .reset_index()
   ...: )
   ...: 
Out[6]: 
  col1 col2  counts  col3_mean  col4_median  col4_min
0    A    B       4  -0.372500       -0.810     -1.32
1    C    D       3  -0.476667       -0.110     -1.65
2    E    F       2   0.455000        0.475     -0.47
3    G    H       1   1.480000       -0.630     -0.63

Footnotes

The code used to generate the test data is shown below:

In [1]: import numpy as np
   ...: import pandas as pd 
   ...: 
   ...: keys = np.array([
   ...:         ["A", "B"],
   ...:         ["A", "B"],
   ...:         ["A", "B"],
   ...:         ["A", "B"],
   ...:         ["C", "D"],
   ...:         ["C", "D"],
   ...:         ["C", "D"],
   ...:         ["E", "F"],
   ...:         ["E", "F"],
   ...:         ["G", "H"] 
   ...:         ])
   ...: 
   ...: df = pd.DataFrame(
   ...:     np.hstack([keys,np.random.randn(10,4).round(2)]), 
   ...:     columns = ["col1", "col2", "col3", "col4", "col5", "col6"]
   ...: )
   ...: 
   ...: df[["col3", "col4", "col5", "col6"]] = 
   ...:     df[["col3", "col4", "col5", "col6"]].astype(float)
   ...: 

Disclaimer:

If some of the columns that you are aggregating have null values, then you really want to be looking at the group row counts as an independent aggregation for each column. Otherwise you may be misled as to how many records are actually being used to calculate things like the mean because pandas will drop NaN entries in the mean calculation without telling you about it.