Nina Diana

Nina Diana


Analysis of Tweets about the Joker in Python

In this post, we will analyze Twitter tweets related to the Joker (2019 film). To get started you need to apply for a Twitter developer account:

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After your developer account has been approved you need to create a Twitter application:

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The steps for applying for a Twitter developer account and creating a Twitter application are outlined here.

We will be using the free python library tweepy in order to access the Twitter API. Documentation for tweepy can be found here.


First, make sure you have tweepy installed. Open up a command line and type:

pip install tweepy 


Next, open up your favorite editor and import the tweepy and pandas libraries:

import tweepy
import pandas as pd


Next we need our consumer key and access token:

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Notice that the site suggests that you keep your key and token private! Here we define a fake key and token but you should use your real key and token upon creating the Twitter application as shown above:

consumer_key = '5GBi0dCerYpy2jJtkkU3UwqYtgJpRd' 
consumer_secret = 'Q88B4BDDAX0dCerYy2jJtkkU3UpwqY'
access_token = 'X0dCerYpwi0dCerYpwy2jJtkkU3U'
access_token_secret = 'kly2pwi0dCerYpjJtdCerYkkU3Um'

The next step is creating an OAuthHandler instance. We pass our consumer key and access token which we defined above:

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

Next, we pass the OAuthHandler instance into the API method:

api = tweepy.API(auth)


Next, we initialize lists for fields we are interested in analyzing. For now, we can look at the tweet strings, users, and the time of the tweet. Next, we write a for loop over a tweepy ‘Cursor’ object. Within the ‘Cursor’ object we pass the ‘’ method, set the query string (q= “Joker” ) for what we would like to search for, and set ‘count’ = 1000 so that we don’t exceed the twitter rate limit. We also use the ‘item()’ method to convert the ‘Cursor’ object into an iterable.

In order to simplify the query we can remove retweets and only include tweets in English. To get a sense of what this request returns we can print the values being appended to each list as well:

twitter_users = []
tweet_time = []
tweet_string = []
for tweet in tweepy.Cursor(,q="Joker", count=1000).items(1000):
        if (not tweet.retweeted) and ('RT @' not in tweet.text):
            if tweet.lang == "en":

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We can also play around with the query string. Let’s change it from “Joker” to “Joaquin”, the first name of the lead actor in Joker:

for tweet in tweepy.Cursor(,q="Joaquin", count=1000).items(1000):
        if (not tweet.retweeted) and ('RT @' not in tweet.text):
            if tweet.lang == "en":

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The next thing we can do is store the query results in a dataframe. To do this let’s define a function that takes a key word as an argument and returns a dataframe with 1000 tweets related to the keyword:

def get_related_tweets(key_word):
    twitter_users = []
    tweet_time = []
    tweet_string = [] 
    for tweet in tweepy.Cursor(,q=key_word, count=1000).items(1000):
            if (not tweet.retweeted) and ('RT @' not in tweet.text):
                if tweet.lang == "en":
    df = pd.DataFrame({'name':twitter_users, 'time': tweet_time, 'tweet': tweet_string})
    return df

When we call the function with “Joker”, define a dataframe as the function’s return value and print its first five rows we get :

df_joker = get_related_tweets("Joker")

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And if we do the same for “Joaquin”:

df_joaquin = get_related_tweets("Joaquin")

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We can also search for tweets with “Joker” and “bad movie” (let’s comment out the print to ‘.csv’ line):

def get_related_tweets(key_word):
    twitter_users = []
    tweet_time = []
    tweet_string = [] 
    for tweet in tweepy.Cursor(,q=key_word, count=1000).items(1000):
            if (not tweet.retweeted) and ('RT @' not in tweet.text):
                if tweet.lang == "en":
    df = pd.DataFrame({'name':twitter_users, 'time': tweet_time, 'tweet': tweet_string})
    return df
df_bad = get_related_tweets("Joker bad movie")

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Let’s take a closer look at a few lines by looping over the dataframe index and selecting values from the tweet column:

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And for tweets with “Joker” and “good movie”:

df_good = get_related_tweets("Joker good movie")

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In the next post, we will use a python library called TextBlob to perform sentiment analysis on some of these tweets. From there we can build a sentiment analyzer that classifies a tweet as having negative or positive sentiment. The code from this post is available on Github. Thank you for reading! Good luck and happy machine learning!

#python #programming

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Analysis of Tweets about the Joker  in Python
Shardul Bhatt

Shardul Bhatt


Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.


Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Art  Lind

Art Lind


Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind


How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.


In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

Arvel  Parker

Arvel Parker


Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).


The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.


output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type