AI News, What is Python – An Easy Explanation For Absolutely Anyone

What is Python – An Easy Explanation For Absolutely Anyone

Here is another post in which I try to disentangle some of the concepts that underpin today's big data world.  In this post I look at Python, which is an open source programming language commonly used for data manipulation in commercial Big Data operations.

In a nutshell I would say that there are three core strengths of Python which have contributed to its enthusiastic adoption by programmers working with Big Data, and they are: The software which allows us to create programs in Python is open source - meaning it is in the public domain and can be freely used by anyone.

A big advantage of open source software is that anyone can modify it and create their own versions to do specific tasks - this is one of the main reasons that the concept of open source has been enthusiastically embraced by Big Data fans.

Python is a high level language - meaning that the code which the programmer types into to create the program is more like natural human language than code written to control machines.

These are mostly extensions to the functionality that can be created in programs written in the language, and many programmers have created powerful and versatile tools and algorithms specifically designed at manipulating the large amounts of data that come with Big Data initiatives.

Quick Tip: Speed up your Python data processing scripts with Process Pools

Python is a great programming language for crunching data and automating repetitive tasks.

That means that 75% or more of your computer’s power is sitting there nearly idle while you are waiting for your program to finish running!

Let’s learn how to take advantage of the full processing power of your computer by running Python functions in parallel.

Thanks to Python’s concurrent.futures module, it only takes 3 lines of code to turn a normal program into one that can process data in parallel.

Here’s a short program that uses Python’s built-in glob function to get a list of all the jpeg files in a folder and then uses the Pillow image processing library to save out 128-pixel thumbnails of each photo: This program follows a simple pattern you’ll often see in data processing scripts: Let’s test this program on a folder with 1000 jpeg files and see how long it takes to run: The program took 8.9 seconds to run.

Here’s an approach we can use to process this data in parallel: Four copies of Python running on four separate CPUs should be able to do roughly 4 times as much work as one CPU, right?

The final step is to ask the Process Pool to execute our helper function on our list of data using those 4 processes.

We can do that by replacing the original for loop we had: With this new call to The function takes in the helper function to call and the list of data to process with it.

It does all the hard work of splitting up the list, sending the sub-lists off to each child process, running the child processes, and combining the results.

So I’ve used Python’s zip() function as a shortcut to grab the original filename and the matching result in one step.

Here’s how our program looks with those three changes: Let’s run the program and see if it finishes any faster: It finished in 2.2 seconds!

A Complete Tutorial to Learn Data Science with Python from Scratch

After working on SAS for more than 5 years, I decided to move out of my comfort zone. Being a data scientist, my hunt for other useful tools was ON!

But, over the years, with strong community support, this language got dedicated library for data analysis and predictive modeling.

Due to lack of resource on python for data science, I decided to create this tutorial to help many others to learn python faster. In this tutorial, we will take bite sized information about how to use Python for Data Analysis, chew it till we are comfortable and practice it at our own end.

There are 2 approaches to install Python: Second method provides a hassle free installation and hence I’ll recommend that to beginners.

The imitation of this approach is you have to wait for the entire package to be upgraded, even if you are interested in the latest version of a single library.

It provides a lot of good features for documenting while writing the code itself and you can choose to run the code in blocks (rather than the line by line execution) We will use iPython environment for this complete tutorial.

The most commonly used construct is if-else, with following syntax: For instance, if we want to print whether the number N is even or odd: Now that you are familiar with Python fundamentals, let’s take a step further.

What if you have to perform the following tasks: If you try to write code from scratch, its going to be a nightmare and you won’t stay on Python for more than 2 days!

Following are a list of libraries, you will need for any scientific computations and data analysis: Additional libraries, you might need: Now that we are familiar with Python fundamentals and additional libraries, lets take a deep dive into problem solving through Python.

We will now use Pandas to read a data set from an Analytics Vidhya competition, perform exploratory analysis and build our first basic categorization algorithm for solving this problem.

The essential difference being that column names and row numbers are known as column and row index, in case of dataframes.

To begin, start iPython interface in Inline Pylab mode by typing following on your terminal / windows command prompt: This opens up iPython notebook in pylab environment, which has a few useful libraries already imported.

You can check whether the environment has loaded correctly, by typing the following command (and getting the output as seen in the figure below):

describe() function would provide count, mean, standard deviation (std), min, quartiles and max in its output (Read this article to refresh basic statistics to understand population distribution) Here are a few inferences, you can draw by looking at the output of describe() function: Please note that we can get an idea of a possible skew in the data by comparing the mean to the median, i.e.

The frequency table can be printed by following command: Similarly, we can look at unique values of port of credit history.

Now we will look at the steps required to generate a similar insight using Python. Please refer to this article for getting a hang of the different data manipulation techniques in Pandas.

If you have not realized already, we have just created two basic classification algorithms here, one based on credit history, while other on 2 categorical variables (including gender).

Next let’s explore ApplicantIncome and LoanStatus variables further, perform data munging and create a dataset for applying various modeling techniques.

Let us look at missing values in all the variables because most of the models don’t work with missing data and even if they do, imputing them helps more often than not.

So, let us check the number of nulls / NaNs in the dataset This command should tell us the number of missing values in each column as isnull() returns 1, if the value is null.

the simplest being replacement by mean, which can be done by following code: The other extreme could be to build a supervised learning model to predict loan amount on the basis of other variables and then use age along with other variables to predict survival.

Since, the purpose now is to bring out the steps in data munging, I’ll rather take an approach, which lies some where in between these 2 extremes.

This can be done using the following code: Now, we will create a Pivot table, which provides us median values for all the groups of unique values of Self_Employed and Education features.

Next, we define a function, which returns the values of these cells and apply it to fill the missing values of loan amount: This should provide you a good way to impute missing values of loan amount.

So instead of treating them as outliers, let’s try a log transformation to nullify their effect: Looking at the histogram again:

For example, creating a column for LoanAmount/TotalIncome might make sense as it gives an idea of how well the applicant is suited to pay back his loan.

After, we have made the data useful for modeling, let’s now look at the python code to create a predictive model on our data set.

One way would be to take all the variables into the model but this might result in overfitting (don’t worry if you’re unaware of this terminology yet).

In simple words, taking all variables might result in the model understanding complex relations specific to the data and will not generalize well.

Accuracy : 80.945% Cross-Validation Score : 80.946% Accuracy : 80.945% Cross-Validation Score : 80.946% Generally we expect the accuracy to increase on adding variables.

Accuracy : 81.930% Cross-Validation Score : 76.656% Here the model based on categorical variables is unable to have an impact because Credit History is dominating over them.

Let’s try a few numerical variables: Accuracy : 92.345% Cross-Validation Score : 71.009% Here we observed that although the accuracy went up on adding variables, the cross-validation error went down.

Also, we will modify the parameters of random forest model a little bit: Accuracy : 82.899% Cross-Validation Score : 81.461% Notice that although accuracy reduced, but the cross-validation score is improving showing that the model is generalizing well.

You would have noticed that even after some basic parameter tuning on random forest, we have reached a cross-validation accuracy only slightly better than the original logistic regression model.

I am sure this not only gave you an idea about basic data analysis methods but it also showed you how to implement some of the more sophisticated techniques available today.

If you come across any difficulty while practicing Python, or you have any thoughts / suggestions / feedback on the post, please feel free to post them through comments below.

Python (programming language)

Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace.

It supports multiple programming paradigms, including object-oriented, imperative, functional and procedural, and has a large and comprehensive standard library.[30]

Python 2.0 was released on 16 October 2000 with many major new features, including a cycle-detecting garbage collector and support for Unicode.[36]

Releases of Python 3 include the 2to3 utility, which automates (at least partially) the translation of Python 2 code to Python 3.[39]

Python 2.7's end-of-life date was initially set at 2015 then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3.[40][41]

Object-oriented programming and structured programming are fully supported, and many of its features support functional programming and aspect-oriented programming (including by metaprogramming[43]

Python uses dynamic typing, and a combination of reference counting and a cycle-detecting garbage collector for memory management.

It also features dynamic name resolution (late binding), which binds method and variable names during program execution.

Van Rossum's vision of a small core language with a large standard library and easily extensible interpreter stemmed from his frustrations with ABC, which espoused the opposite approach.[32]

While offering choice in coding methodology, the Python philosophy rejects exuberant syntax (such as that of Perl) in favor of a simpler, less-cluttered grammar.

Python's philosophy rejects the Perl 'there is more than one way to do it' approach to language design in favor of 'there should be one—and preferably only one—obvious way to do it'.[50]

Python's developers strive to avoid premature optimization, and reject patches to non-critical parts of CPython that would offer marginal increases in speed at the cost of clarity.[52]

When speed is important, a Python programmer can move time-critical functions to extension modules written in languages such as C, or use PyPy, a just-in-time compiler.

This is reflected in the language's name—a tribute to the British comedy group Monty Python[53]—and in occasionally playful approaches to tutorials and reference materials, such as examples that refer to spam and eggs (from a famous Monty Python sketch) instead of the standard foo and bar.[54][55]

To say that code is pythonic is to say that it uses Python idioms well, that it is natural or shows fluency in the language, that it conforms with Python's minimalist philosophy and emphasis on readability.

From Python 2.5, it is possible to pass information back into a generator function, and from Python 3.3, the information can be passed through multiple stack levels.[66]

A particular case of this is that an assignment statement such as a = 1 cannot form part of the conditional expression of a conditional statement.

This has the advantage of avoiding a classic C error of mistaking an assignment operator = for an equality operator == in conditions: if (c = 1) { ...

Python methods have an explicit self parameter to access instance data, in contrast to the implicit self (or this) in some other object-oriented programming languages (e.g., C++, Java, Objective-C, or Ruby).[76]

Despite being dynamically typed, Python is strongly typed, forbidding operations that are not well-defined (for example, adding a number to a string) rather than silently attempting to make sense of them.

New instances of classes are constructed by calling the class (for example, SpamClass() or EggsClass()), and the classes are instances of the metaclass type (itself an instance of itself), allowing metaprogramming and reflection.

The syntax of both styles is the same, the difference being whether the class object is inherited from, directly or indirectly (all new-style classes inherit from object and are instances of type).

5**3 == 125 and 9**0.5 == 3.0, and a new matrix multiply @ operator is included in version 3.5.[81]

However, maintaining the validity of this equation means that while the result of a%b is, as expected, in the half-open interval [0, b), where b is a positive integer, it has to lie in the interval (b, 0] when b is negative.[85]

Integers are transparently switched from the machine-supported maximum fixed-precision (usually 32 or 64 bits), belonging to the python type int, to arbitrary precision, belonging to the Python type long, where needed.

this behavior is now entirely contained by the int class.) The Decimal type/class in module decimal (since version 2.4) provides decimal floating point numbers to arbitrary precision and several rounding modes.[91]

Due to Python's extensive mathematics library, and the third-party library NumPy that further extends the native capabilities, it is frequently used as a scientific scripting language to aid in problems such as numerical data processing and manipulation.[citation needed]

It includes modules for creating graphical user interfaces, connecting to relational databases, generating pseudorandom numbers, arithmetic with arbitrary precision decimals,[94]

Most Python implementations (including CPython) include a read–eval–print loop (REPL), permitting them to function as a command line interpreter for which the user enters statements sequentially and receives results immediately.

Python's development is conducted largely through the Python Enhancement Proposal (PEP) process, the primary mechanism for proposing major new features, collecting community input on issues and documenting Python design decisions.[110]

Python's development team monitors the state of the code by running the large unit test suite during development, and using the BuildBot continuous integration system.[115]

Since 2003, Python has consistently ranked in the top ten most popular programming languages in the TIOBE Programming Community Index where, as of January 2018[update], it is the fourth most popular language (behind Java, C, and C++).[121]

An empirical study found that scripting languages, such as Python, are more productive than conventional languages, such as C and Java, for programming problems involving string manipulation and search in a dictionary, and determined that memory consumption was often 'better than Java and not much worse than C or C++'.[123]

SageMath is a mathematical software with a 'notebook' programmable in Python: its library covers many aspects of mathematics, including algebra, combinatorics, numerical mathematics, number theory, and calculus.

Python has been successfully embedded in many software products as a scripting language, including in finite element method software such as Abaqus, 3D parametric modeler like FreeCAD, 3D animation packages such as 3ds Max, Blender, Cinema 4D, Lightwave, Houdini, Maya, modo, MotionBuilder, Softimage, the visual effects compositor Nuke, 2D imaging programs like GIMP,[135]

As a scripting language with modular architecture, simple syntax and rich text processing tools, Python is often used for natural language processing.[147]

If you want to learn Data Science, start with one of these programming classes

started creating my own data science master’s degree using online courses shortly afterwards, after realizing it was a better fit for me than computer science.

For this guide, I spent 20+ hours trying to find every single online introduction to programming course offered as of August 2016, extracting key bits of information from their syllabi and reviews, and compiling their ratings.

Borrowing this answer from Programmers Stack Exchange: The course we are looking for introduces programming and optionally touches on relevant aspects of computer science that would benefit a new programmer in terms of awareness.

The professors kindly and promptly sent me detailed course syllabi upon request, which were difficult to find online prior to the course’s official restart in September 2016.

Learn to Program: The Fundamentals (LTP1) Timeline: 7 weeks Estimated time commitment: 6–8 hours per week This course provides an introduction to computer programming intended for people with no programming experience.

It covers the basics of programming in Python including elementary data types (numeric types, strings, lists, dictionaries, and files), control flow, functions, objects, methods, fields, and mutability.

Modules Learn to Program: Crafting Quality Code (LTP2) Timeline: 5 weeks Estimated time commitment: 6–8 hours per week You know the basics of programming in Python: elementary data types (numeric types, strings, lists, dictionaries, and files), control flow, functions, objects, methods, fields, and mutability.

There are two programming assignments in LTP2 of similar size.” He emphasized that the estimate of 6–8 hours per week is a rough guess: “Estimating time spent is incredibly student-dependent, so please take my estimates in that context.

Sometimes someone will get stuck on a concept for a couple of hours, while they might breeze through on other concepts … That’s one of the reasons the self-paced format is so appealing to us.” In total, the University of Toronto’s Learn to Program series runs an estimated 12 weeks at 6–8 hours per week, which is about standard for most online courses created by universities.

With 6,000+ reviews and the highest weighted average rating of 4.93/5 stars, this popular course is noted for its engaging videos, challenging quizzes, and enjoyable mini projects.

The condensed course description and full syllabus are as follows: “This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications … To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple.

For students interested in some light preparation prior to the start of class, we recommend a self-paced Python learning site such as” Timeline: 5 weeks Estimated time commitment: 7–10 hours per week Week 0 — statements, expressions, variables  Understand the structure of this class, and explore Python as a calculator.

Week 2 — event-driven programming, local/global variables Learn the basics of event-driven programming, understand the difference between local and global variables, and create an interactive program that plays a simple guessing game.

Week 4 — lists, keyboard input, the basics of modeling motion Learn the basics of lists in Python, model moving objects in Python, and recreate the classic arcade game “Pong.” Week 5 — mouse input, list methods, dictionaries Read mouse input, learn about list methods and dictionaries, and draw images. Week 6 — classes and object-oriented programming Learn the basics of object-oriented programming in Python using classes, and work with tiled images.

Though the latter three come at a price point of $25/month, DataCamp is best in category for covering the programming fundamentals and R-specific topics, which is reflected in its average rating of 4.29/5 stars.

The series breakdown is as follows: Estimated time commitment: 4 hours Chapters: Estimated time commitment: 6 hours Chapters: Estimated time commitment: 4 hours This follow-up course on intermediate R does not cover new programming concepts.

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