Makenzie  Pagac

Makenzie Pagac


A Complete Guide To Survival Analysis In Python

Survival Analysis Basics

Survival analysis is a set of statistical approaches used to find out the time it takes for an event of interest to occur. Survival analysis is used to study the time until some event of interest (often referred to as death) occurs. Time could be measured in years, months, weeks, days, etc. The event of interest could be anything of interest. It could be an actual death, a birth, a retirement, etc.

How it can be useful to analyze ongoing COVID-19 pandemic data?

(1) We can find the number of days until patients showed COVID-19 symptoms.

(2) We can find for which age group it’s deadlier.

(3) We can find which treatment has the highest survival probability.

(4) We can find whether a person’s sex has a significant effect on their survival time?

(5) We can also find the median number of days of survival for patients.

We are going to perform a thorough analysis of patients with lung cancer. Don’t worry once you understand the logic behind it, you’ll be able to perform it on any data set. Exciting, isn’t it?

Survival analysis is used in a variety of field such as:

  • Cancer studies for patients survival time analyses.
  • Sociology for “event-history analysis”.
  • In Engineering for “failure-time analysis”.
  • Time until product failure.
  • Time until a warranty claim.
  • Time until a process reaches a critical level.
  • Time from initial sales contact to a sale.
  • Time from employee hire to either termination or quit.
  • Time from a salesperson hire to their first sale.

In** cancer studies**, typical research questions include:

(1) What is the impact of certain clinical characteristics on patient’s survival? For example, is there any difference between the group of people who has higher blood sugar and those who don’t?

(2) What is the probability that an individual survives a specific period (years, months, days)? For example, given a set of cancer patients, we will be able to tell that if 300(random number) days after the diagnosis of cancer has been passed, then the probability of that person being alive at that time will be 0.7 (random number).

(3) Are there differences in survival between groups of patients? For example, let’s say there are 2 groups of people diagnosed with cancer. Those 2 groups were given 2 different kinds of treatments. Now our goal here will be to find out if there is a significant difference between the survival time for those 2 different groups based on the treatment they were given.

#overviews #python #statistics #survival analysis #programming

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Buddha Community

A Complete Guide To Survival Analysis 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

A Complete Guide To Survival Analysis In Python, part 2 - KDnuggets

In the first article of this three-part series, we saw the basics of the Kaplan-Meier Estimator. Now, it’s time to implement the theory we discussed in the first part.

Example 1: Kaplan-Meier Estimator (Without any groups)

Let’s code:

(1) Import required libraries:

(2) Read the dataset:

(3) Columns of our dataset:

(4) Additional info about dataset:

It gives us information about the data types and the number of rows in each column that has null values. It’s very important for us to remove the rows with a null value for some of the methods in survival analysis.

(5) Statistical info about dataset:

It gives us some statistical information like the total number of rows, mean, standard deviation, minimum value, 25th percentile, 50th percentile, 75th percentile, and maximum value for each column in our dataset.

(6) Find out sex distribution using histogram:

This gives us a general idea about how our data is distributed. In the following graph, you can see that around 139 values have a status of 1, and around 90 values have a status of 2. It means that in our dataset, there are 139 males and around 90 females.

(7) Create an object for KaplanMeierFitter:

(8) Organize the data:

Now we need to organize our data. We’ll add a new column in our dataset that is called “dead”. It stores the data about whether a person that is a part of our experiment is dead or alive (based on the status value). If our status value is 1 then that person is alive, and if our status value is 2 then the person is dead. It’s a very crucial step for what we need to do in the next step. As we are going to store our data in columns called censored and observed. Where observed data stores the value of dead persons in a specific timeline and censored data stores the value of alive persons or persons that we’re not going to investigate at that timeline.

(9) Fitting our data into object:

Here our goal is to find the number of days a patient survived before they died. So our event of interest will be “death”, which is stored in the “dead” column. The first argument it takes is the timeline for our experiment.

#2020 jul tutorials #overviews #python #statistics #survival analysis #data analysis

Biju Augustian

Biju Augustian


Guide to Python Programming Language

The course will lead you from beginning level to advance in Python Programming Language. You do not need any prior knowledge on Python or any programming language or even programming to join the course and become an expert on the topic.

The course is begin continuously developing by adding lectures regularly.

Please see the Promo and free sample video to get to know more.

Hope you will enjoy it.

Basic knowledge
An Enthusiast Mind
A Computer
Basic Knowledge To Use Computer
Internet Connection
What will you learn
Will Be Expert On Python Programming Language
Build Application On Python Programming Language

#uide to Python #Guide to Python Programming #Guide to Python Programming Language #Python Programming #Python Programming Language