Python: Tips and Tricks!

Python one of the most popular programming language in world due to its:

• easiness
• super versatile
• huge range of modules and libraries.

I love python, actually I prefer this language over the others. I have been programming in python since last 5 yrs in my Data science and AI implementation job, I have found some useful tricks which really boost the productivity.

And here I am sharing few of them.

bashplotlib

You want to plot the graphs in console?

pip install bashplotlib

You can have graphs in console using this library.

collections

Python has may data types which are quiet useful, but sometimes you need something more!

And then the collection module come in handy.

This module has some add in data types.

from collections import OrderedDict, Counter# Remembers the order the keys are added!
x = OrderedDict(a=1, b=2, c=3)# Counts the frequency of each character
y = Counter("Hello World!")

dir

Want to know about the attributes of an object in python?

Yes!! you can do it using this dir module.

dir() 
dir("Hello World!") 
dir(dir)

emoji

Yeah, really…you can add emoji to your python script!

pip install emoji

Don’t just read it, try yourself!

from emoji import emojize
print(emojize(":thumbs_up:")

future

One consequence of Python’s popularity is that there are always new versions under development. New versions mean new features — unless your version is out-of-date.

Fear not, however. The __future__ module lets you import functionality from future versions of Python. It’s literally like time travel, or magic, or something.

from __future__ import print_function
print("Hello World!")

geopy

It works by abstracting the APIs of a range of different Geo coding services. It enables you to obtain a place’s full street address, latitude, longitude, and even altitude.

There’s also a useful distance class. It calculates the distance between two locations in your favorite unit of measurement.

pip install geopy

from geopy import GoogleV3

place = "Kedarnath"
location = GoogleV3().geocode(place)

print(location.address)
print(location.location)

howdoi

Stuck on a problem, but don’t want to leave the terminal?

Then checkout howdoi.

pip install howdoi
howdoi place image in tkinter frame
howdoi read csv file in pandas

Be aware though — it scrapes code from top answers from Stack Overflow. It might not always give the most helpful information…

inspect

Python’s inspect module is great for understanding what is happening behind the scenes. You can even call its methods on itself!

The code sample below uses inspect.getsource() to print its own source code. It also uses inspect.getmodule() to print the module in which it was defined.

The last line of code prints out its own line number.

import inspect
print(inspect.getsource(inspect.getsource))
print(inspect.getmodule(inspect.getmodule))
print(inspect.currentframe().f_lineno)

Of course, beyond these trivial uses, the inspect module can prove useful for understanding what your code is doing. You could also use it for writing self-documenting code.

Jedi

The Jedi library is an auto completion and code analysis library. It makes writing code quicker and more productive.

Unless you’re developing your own IDE, you’ll probably be most interested in using Jedi as an editor plugin. Luckily, there are already loads available!

You may already be using Jedi, however. The IPython project makes use of Jedi for its code auto completion functionality.

**kwargs

When learning any language, there are many milestones along the way. With Python, understanding the mysterious **kwargs syntax probably counts as one.

The double-asterisk in front of a dictionary object lets you pass the contents of that dictionary as named arguments to a function.

The dictionary’s keys are the argument names, and the values are the values passed to the function. You don’t even need to call it kwargs!

dictionary = {"a": 1, "b": 2}
def someFunction(a, b):
    print(a + b)
    return# these do the same thing:
someFunction(**dictionary)
someFunction(a=1, b=2)

This is useful when you want to write functions that can handle named arguments not defined in advance.

List comprehensions

One of my favorite things about programming in Python are its list comprehensions.

These expressions make it easy to write very clean code that reads almost like natural language.

numbers = [1,2,3,4,5,6,7]
evens = [x for x in numbers if x % 2 is 0]
odds = [y for y in numbers if y not in evens]cities = ['London', 'Dublin', 'Oslo']def visit(city):
    print("Welcome to "+city)for city in cities:
    visit(city)

map

Python supports functional programming through a number of inbuilt features. One of the most useful is the map() function — especially in combination with lambda functions.

x = [1, 2, 3]
y = map(lambda x : x + 1 , x)# prints out [2,3,4]
print(list(y))

In the example above, map() applies a simple lambda function to each element in x. It returns a map object, which can be converted to some iterable object such as a list or tuple.

newspaper3k

If you haven’t seen it already, then be prepared to have your mind blown by Python’s newspaper module.

It lets you retrieve news articles and associated meta-data from a range of leading international publications. You can retrieve images, text and author names.

It even has some inbuilt NLP functionality.

So if you were thinking of using BeautifulSoup or some other DIY web scraping library for your next project, save yourself the time and effort and $ pip install newspaper3k instead.

Operator overloading

Python provides support for operator overloading, which is one of those terms that make you sound like a legit computer scientist.

It’s actually a simple concept. Ever wondered why Python lets you use the + operator to add numbers and also to concatenate strings? That’s operator overloading in action.

You can define objects which use Python’s standard operator symbols in their own specific way. This lets you use them in contexts relevant to the objects you’re working with.

class Thing:
    def __init__(self, value):
        self.__value = value
    def __gt__(self, other):
        return self.__value > other.__value
    def __lt__(self, other):
        return self.__value < other.__value
something = Thing(100)
nothing = Thing(0)# True
something > nothing# False
something < nothing# Error
something + nothing

pprint

Python’s default print function does its job. But try printing out any large, nested object, and the result is rather ugly.

Here’s where the Standard Library’s pretty-print module steps in. This prints out complex structured objects in an easy-to-read format.

A must-have for any Python developer who works with non-trivial data structures.

import requests
import pprint
url = 'https://randomuser.me/api/?results=1'
users = requests.get(url).json()
pprint.pprint(users)

Queue

Python supports multi threading, and this is facilitated by the Standard Library’s Queue module.

This module lets you implement queue data structures. These are data structures that let you add and retrieve entries according to a specific rule.

‘First in, first out’ (or FIFO) queues let you retrieve objects in the order they were added. ‘Last in, first out’ (LIFO) queues let you access the most recently added objects first.

Finally, priority queues let you retrieve objects according to the order in which they are sorted.

Here’s an example of how to use queues for multi threaded programming in Python.

__repr__

When defining a class or an object in Python, it is useful to provide an ‘official’ way of representing that object as a string. For example:

>>> file = open('file.txt', 'r')
>>> print(file)
<open file 'file.txt', mode 'r' at 0x10d30aaf0>

This makes debugging code a lot easier. Add it to your class definitions as below:

class someClass:
    def __repr__(self):
        return "<some description here>"someInstance = someClass()# prints <some description here>
print(someInstance)

sh

Python makes a great scripting language. Sometimes using the standard os and sub process libraries can be a bit of a headache.

The sh library provides a neat alternative.

It lets you call any program as if it were an ordinary function — useful for automating workflows and tasks, all from within Python.

import shsh.pwd()
sh.mkdir('new_folder')
sh.touch('new_file.txt')
sh.whoami()
sh.echo('This is great!')

Type hints

Python is a dynamically-typed language. You don’t need to specify datatypes when you define variables, functions, classes etc.

This allows for rapid development times. However, there are few things more annoying than a runtime error caused by a simple typing issue.

Since Python 3.5, you have the option to provide type hints when defining functions.

def addTwo(x : Int) -> Int:
    return x + 2

You can also define type aliases:

from typing import ListVector = List[float]
Matrix = List[Vector]def addMatrix(a : Matrix, b : Matrix) -> Matrix:
  result = []
  for i,row in enumerate(a):
    result_row =[]
    for j, col in enumerate(row):
      result_row += [a[i][j] + b[i][j]]
    result += [result_row]
  return resultx = [[1.0, 0.0], [0.0, 1.0]]
y = [[2.0, 1.0], [0.0, -2.0]]z = addMatrix(x, y)

Although not compulsory, type annotations can make your code easier to understand.

They also allow you to use type checking tools to catch those stray TypeErrors before runtime. Probably worthwhile if you are working on large, complex projects!

uuid

A quick and easy way to generate Universally Unique IDs (or ‘UUIDs’) is through the Python Standard Library’s uuid module.

import uuiduser_id = uuid.uuid4()
print(user_id)

This creates a randomized 128-bit number that will almost certainly be unique.

In fact, there are over ²¹²² possible UUIDs that can be generated. That’s over five undecillion (or 5,000,000,000,000,000,000,000,000,000,000,000,000).

The probability of finding duplicates in a given set is extremely low. Even with a trillion UUIDs, the probability of a duplicate existing is much, much less than one-in-a-billion.

Pretty good for two lines of code.

Virtual environments

This is probably my favorite Python thing of all.

Chances are you are working on multiple Python projects at any one time. Unfortunately, sometimes two projects will rely on different versions of the same dependency. Which do you install on your system?

Luckily, Python’s support for virtual environments lets you have the best of both worlds. From the command line:

python -m venv my-project
source my-project/bin/activate
pip install all-the-modules

Now you can have standalone versions and installations of Python running on the same machine. Sorted!

wikipedia

Wikipedia has a great API that allows users programmatic access to an unrivalled body of completely free knowledge and information.

The wikipedia module makes accessing this API almost embarrassingly convenient.

import wikipediaresult = wikipedia.page('freeCodeCamp')
print(result.summary)
for link in result.links:
    print(link)

Like the real site, the module provides support for multiple languages, page disambiguation, random page retrieval, and even has a donate() method.

xkcd

Humor is a key feature of the Python language — after all, it is named after the British comedy sketch show Monty Python’s Flying Circus. Much of Python’s official documentation references the show’s most famous sketches.

The sense of humor isn’t restricted to the docs, though. Have a go running the line below:

import antigravity

Never change, Python. Never change.

YAML

YAML stands for ‘ YAML Ain’t Markup Language ‘. It is a data formatting language, and is a superset of JSON.

Unlike JSON, it can store more complex objects and refer to its own elements. You can also write comments, making it particularly suited to writing configuration files.

The PyYAML module lets you use YAML with Python. Install with:

$ pip install pyyaml

And then import into your projects:

import yaml

PyYAML lets you store Python objects of any datatype, and instances of any user-defined classes also.

zip

One last trick for ya, and it really is a cool one. Ever needed to form a dictionary out of two lists?

keys = ['a', 'b', 'c']
vals = [1, 2, 3]
zipped = dict(zip(keys, vals))

The zip() inbuilt function takes a number of iterable objects and returns a list of tuples. Each tuple groups the elements of the input objects by their positional index.

You can also ‘unzip’ objects by calling *zip() on them.

Thanks for reading, i hope you enjoyed it a lot.

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