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Data Warehouse: Concept and Benefits

Data Warehouse: Concept and Benefits

A business organization has to deal with a massive amount of data streaming from myriad sources, and data warehousing refers to the process of collection and storage of that data that needs to be analyzed to glean valuable business insight.  Data warehousing plays a crucial role in business intelligence. The concept originated in the 1980s, it basically involves data extraction from disparate sources which later gets processed and post formatting the data stays in the system ready to be utilized for taking important decisions.

Data warehouse basically performs the task of running an analysis on the stored data which could be both structured and unstructured even semi-structured, however, the data that is in the warehouse cannot be modified. Data warehousing basically helps companies gain insight regarding factors influencing business, and they could use the data insight to formulate new strategies, developing products and so on. This highly skilled task demands professionals who have a background in Data science using python training.

What are the different steps in data warehousing?

Data warehousing involves the following steps

Transactional data extraction: In this step, the data is extracted from multiple sources available and loaded into the system.

Data transformation: The transactional data extracted from different sources need to be transformed and it would need relating as well.

Building a dimensional model: A dimensional model comprising fact and dimension tables are built and the data gets loaded.

Getting a front-end reporting tool: The tool could be built or, purchased, a crucial decision that needs much deliberation.

Benefits of data warehousing

An edge over the competition

This is undeniably one benefit every business would be eager to reap from data warehousing.  The data that is untapped could be the source of valuable information regarding risk factors, trends, customers and so many other factors that could impact the business. Data warehousing collates the data and arranges them in a contextual manner that is easy for a company to access and utilize to make informed decisions.

Enhanced data quality

Since data pooled from different sources could be structured or, unstructured and in different formats, working with such data inconsistency could be problematic and data warehousing takes care of the issue by transforming the data into a consistent format. The standardized data that easily conforms to the analytics platform can be of immense value.

Historical data analysis

A data warehouse basically stores a big amount of data and that includes historical data as well. Such data are basically old records of the company regarding sales, employee data, or, product-related information. Now the historical data belonging to different time periods need to be analyzed to predict upcoming trends.

Smarter business intelligence

Since businesses now rely on data-driven insight to devise strategies, they need access to data that is consistent, error-free, and high quality. However, data coming from numerous sources could be erroneous and irrelevant. But, data warehousing takes care of this issue by formatting the data to make it consistent and free from any error and could be analyzed to offer valuable insight that could help the management take decisions regarding sales, marketing, finance.

High ROI

Building a data warehouse requires significant investment but in the long term, the revenue that it generates can be significant. In fact, keen business intelligence now plays a crucial role in determining the success of an organization and with data warehousing the organizations can have access to data that is consistent and high quality thus enabling the company to derive actionable intel.  When a company implements such insight in making smarter strategies, they do gain in the long run.

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Data warehousing plays a significant role in collating and storing valuable data that fuels a company’s business decisions. However,  given the specialized nature of the task, one must undergo Data Science training, to learn the nuances. The field of big data has plenty of opportunities for the right candidates.


A Quick Guide to Data Mining

A Quick Guide to Data Mining

Data mining refers to processing mountainous amount of data that pile up, to detect patterns and offer useful insight to businesses to strategize better. The data in question could be both structured and unstructured datasets containing valuable information and which if and when processed using the right technique could lead towards solutions.

Enrolling in a Data analyst training institute, can help the professionals involved in this field hone their skills. Now that we have learned what data mining is, let’s have a look at the data mining techniques employed for refining data.  

Data cleaning

Since the data we are talking about is mostly unstructured data it could be erroneous, corrupt data. So, before the data processing can even begin it is essential to rectify or, eliminate such data from the data sets and thus preparing the ground for the next phases of operations. Data cleaning enhances data quality and ensures faster processing of data to generate insight. Data Science training is essential to be familiar with the process of data mining.

Classification analysis

Classification analysis is a complicated data mining technique which basically is about data segmentation. To be more precise it is decided which category an observation might belong to. While working with various data different attributes of the data are analyzed and the class or, segments they belong to are identified, then using algorithms further information is extracted.   

Regression analysis

Regression analysis basically refers to the method of deciding the correlation between variables. Using this method how one variable influences the other could be decided. It basically allows the data analyst to decide which variable is of importance and which could be left out. Regression analysis basically helps to predict.  

Anomaly detection

Anomaly detection is the technique that detects data points, observations in a dataset, that deviate from an expected or, normal pattern or behavior. This anomaly could point to some fault or, could lead towards the discovery of an exception that might offer new potential. In fields like health monitoring, or security this could be invaluable.


This data mining technique is somewhat similar to classification analysis, but, different in the way that here data objects are grouped together in a cluster. Now objects belonging to one particular cluster will share some common thread while they would be completely different from objects in other clusters. In this technique visual presentation of data is important, for profiling customers this technique comes in handy.  


This data mining technique is employed to find some hidden relationhip patterns among variables, mostly dependent variables belonging to a dataset. The recurring relationships of variables are taken into account in this process. This comes in handy in predicting customer behavior, such as when they shop what items are they likely to purchase together could be predicted.

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Tracking patterns

This technique is especially useful while sorting out data for the businesses. In this process while working with big datasets, certain trends or, patterns are recognized and these patterns are then monitored to draw a conclusion. This pattern tracking technique could also aid in identifying some sort of anomaly in the dataset that might otherwise go undetected.

Big data is accumulating every day and the more efficiently the datasets get processed and sorted, the better would be the chances of businesses and other sectors be accurate in predicting trends and be prepared for it. The field of data science is full of opportunities now, learning Data science using python training could help the younger generation make it big in this field.



Top Python Libraries to Know About in 2020

Top Python Libraries To Know About In 2020

Python today is one of the most sought after programming languages in the world. As per Python’s Executive Summary, “Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance.”

The most advantageous facet of Python is the richness of its library sources and the myriad uses and applications of these libraries in programming. This essay is dedicated to studying some of the best Python libraries available.

Tensor Flow

Tensor Flow is a highly popular open source library built by Google and Brain Team. It is used in almost all Google projects for machine learning. Tensor Flow 

works as a computational library for writing fresh algorithms that require vast amounts of tensor operations.


Unarguably one of the most competent libraries for working with complex data, Scikit-learn is a python library associated with Numpy and SciPy. This library facilitates cross validation or the ability to use more than one metric.


Keras is one of the most revolutionary libraries in Python in that it makes it easy to express neural networks. Keras provides some of the most competent utilities for compiling models, processing datasets and more.


It is the largest machine learning library that permits developers to perform tensor computation, create dynamic graphs and calculate gradients automatically. Also, it offers a rich repository of APIs for solving applications related to neural networks.

Light GBM

Gradient Boosting is one of the best machine learning libraries that helps developers build new algorithms using elementary models like decision trees. This library is highly scalable and optimal for fast implementation of gradient boosting.


This library helps overcome the problem of inaccuracy in machine learning model predictions. It is used in mathematical operations that consume less computational time and it is important when it comes to depending on other Python libraries.


This library is built using Numpy and it is used in high-level computations in data science. It is used extensively for scientific and computations, solving differential equations, linear algebra and optimization algorithms.

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Python Data Analysis or Pandas is another highly popular library that is crucial to a data science life cycle in a data science project. Pandas provides super fast and flexible data structures such as data frame CDs that are specifically designed to work with structured data intuitively.

There are many more libraries like Theano and Librosa that are lesser known but very very important for machine learning, the most revolutionary scientific development of our century. To know more on the subject, do peruse the DexLab Analytics website today. DexLab Analytics is a premier Machine Learning institute in Gurgaon.



Why Python is Preferred in AI and Machine Learning?

Why Python is Preferred in AI and Machine Learning?

Python has become one of the leading coding languages across the globe and for more reasons than one. In this article, we evaluate why Python is beneficial in the use of Machine Learning and Artificial Intelligence applications.

Artificial intelligence and Machine Learning are profoundly shaping the world we live in, with new applications mushrooming by the day. Competent designers are choosing Python as their go-to programming language for designing AI and ML programs.

Artificial Intelligence enables music platforms like Spotify to prescribe melodies to users and streaming platforms like Netflix to understand what shows viewers would like to watch based on their tastes and preferences. The science is widely being used to power organizations with worker efficiency and self-administration. 

Machine-driven intelligence ventures are different from traditional programming languages in that they have innovation stack and the ability to accommodate an AI-based experiment. Python has these features and more. It is a steady programming language, it is adaptable and has accessible instruments.

Here are some features of Python that enable AI engineers to build gainful products.

  • An exemplary library environment 

“An extraordinary selection of libraries is one of the primary reasons Python is the most mainstream programming language utilized for AI”, a report says. Python libraries are very extensive in nature and enable designers to perform useful activities without the need to code them from scratch.

Machine Learning demands incessant information preparation, and Python’s libraries allows you to access, deal with and change information. These are libraries can be used for ML and AI: Pandas, Keras, TensorFlow, Matplotlib, NLTK, Scikit-picture, PyBrain, Caffe, Stats models and in the PyPI storehouse, you can find and look at more Python libraries. 

  • Basic and predictable 

Python has on offer short and decipherable code. Python’s effortless built allows engineers to make and design robust frameworks. Designers can straightway concentrate on tackling an ML issue rather concentrating on the subtleties of the programming language. 

Moreover, Python is easy to learn and therefore being adopted by more and more designers who can easily construct models for AI. Also, many software engineers feel Python is more intuitive than other programming languages.

  • A low entry barrier 

Working in the ML and AI industry means an engineer will have to manage tons of information in a prodigious way. The low section hindrance or low entry barrier allows more information researchers to rapidly understand Python and begin using it for AI advancement without wasting time or energy learning the language.

Moreover, Python programming language is in simple English with a straightforward syntax which makes it very readable and easy to understand.

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Thus, we have seen how advantageous Python is as a programming language which can be used to build AI models with ease and agility. It has a broad choice of AI explicit libraries and its basic grammar and readability make the language accessible to non-developers.

It is being widely adopted by developers across institutions working in the field of AI. It is no surprise then that artificial intelligence courses in Delhi and Machine Learning institutes in Gurgaon are enrolling more and more developers who want to be trained in the science of Python.


8 Skills a Python Programmer Should Master

8 Skills a Python Programmer Should Master

Python has become the lingua franca of the computing world. It has come to become the most sought after programming language for deep learning, machine learning and artificial intelligence. It is a favourite with programmers because it is easy to understand and learn and it achieves a lot more in terms of productivity as compared to other languages.

Python is a dynamic, high-level, general-purpose programming language that is useful for developing desktop, web and mobile applications that can also be used for complex scientific and numeric applications, data science, AI etc. Python focuses a lot on code readability.

From web and game development to machine learning, from AI to scientific computing and academic research, Data science and analysis, python is regarded as the real deal. Python is useful in domains like finance, social media, biotech etc. Developing large software applications in Python is also simpler due to its large amount of available libraries.

The Python developer usually deals with backend components, apps connection with third-party web services and giving support to frontend developers in web applications. Of course, one might create applications with use of different languages but pretty often Python is the language chosen for it – and there are several reasons for that.

In this article, we will walk through a structured approach to top 8 skills required to become a Python Developer. These skills are:

  • Core Python
  • Good grasp of Web Frameworks
  • Front-End Technologies
  • Data Science
  • Machine Learning and AI
  • Python Libraries
  • Multi-Process Architecture
  • Communication Skills

Core Python

This is the foundation of any Python developer. If one wants to achieve success in this career, he/she needs to understand the core python concepts. These include the following:

  • Iterators
  • Data Structures
  • Generators
  • OOPs concepts
  • Exception Handling
  • File handling concepts
  • Variables and data types

However, learning the core language (as mentioned above) is only the first step in mastering this language and becoming a successful Python developer.

Good grasp of Web Frameworks

By automating the implementation of redundant tasks, frameworks cut development time and enable developers to focus greatly on application logic rather than routine elements.

Because it is one of the leading programming languages, there is no scarcity of frameworks for Python. Different frameworks have their own set of advantages and issues. Hence, the selection needs to be made on the basis of project requirements and developer preference. There are primarily three types of Python frameworks, namely full-stack, micro-framework, and asynchronous.

A good Python web developer has incredible honing over either of the two web frameworks Django or Flask or both. Django is a high-level Python Web Framework that encourages a good, clean and pragmatic design and Flask is also widely used Python micro web framework.

Front-End Technologies (JavaScript, CSS3, HTML5)

Sometimes, Python developers must work with the frontend team to match together the server-side and the client-side. This means Python developers need a basic understanding of how the frontend works, what’s possible and what’s not, and how the application will appear.

While there is likely a UX team, SCRUM master, and project or product manager to coordinate the workflow, it’s still good to have a basic understanding of front-end tasks.

Data Science

Data science offers a world of new opportunities. Being a Python developer, there are several prerequisites you need to know starting with things you learn in high school mathematics, such as statistics, probability, etc. Some of the other parts of data science you need to understand, and use include SQL knowledge; the use of Python packages, data wrangling and data cleanup, analysis of data, and visualization of data.

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning (as well as Deep Learning) are constantly growing. Python is the perfect programming language which is used in all the frameworks of Machine Learning and Deep Learning. This will be a huge plus for someone if he/she knows about this domain. If someone is into data science, then definitely digging in the Machine Learning topic would be a great idea.

Python Libraries

Python libraries certainly deserve a place in every Python Developer’s toolbox. Python has a massive collection of libraries, both native and third-party libraries. With so many Python libraries out there, though, it’s no surprise that some don’t get all the attention they deserve. Plus, programmers who work exclusively in one domain don’t always know about the goodies available to them for other kinds of work.

Python libraries are extensively used in simplifying everything from file system access, database programming, and working with cloud services to building lightweight web apps, creating GUIs, and working with images, ebooks, and Word files—and much more.

Multiprocessing Architecture

Multiprocessing refers to the ability of a system to support more than one processor at the same time. Applications in a multiprocessing system are broken to smaller routines that run independently. The operating system allocates these threads to the processors improving performance of the system. As a Python-Developer one should definitely know about the MVC (Model View Controller) and MVT (Model View Template) Architecture. Once you understand the Multi-Processing Architecture you can solve issues related to the core framework etc.

Communication Skills

In best software development firms the teams are made out of amazing programmers which work together to achieve the final goal – no matter if it means to finish the project, to create a new app or maybe to help a startup. However, working in a team means that a developer has to communicate well – not only to get the stuff done but also to keep the documentation clear so others can easily read and follow the thinking path to fully understand the idea.

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In this write-up, we have elaborated on the top skills one needs to have to be a successful Python Developer. One must have a working knowledge of Core Python and a good grasp of Web Frameworks, Front-End Technologies, Data Science, Machine Learning and AI, Python Libraries, Multi-Process Architecture and Communication skills. Though there are a few more skills not listed in this blog, one can achieve success in developing large software applications by mastering all the above skills only.

As delineated in the article, Python is the new rage in the computing world. And it is no surprise then that more and more professionals are opting to take up courses teaching Machine learning using Python and python for data analysis.


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A Handbook of the Basic Data Types in Python 3: Strings

A Handbook of the Basic Data Types in Python 3: Strings

In general, a data type defines the format, sets the upper & lower bounds of the data so that a program could use it appropriately. Data types are the classification or categorization of data items which describes the character of a variable. The most used data types are numeric, non-numeric and Boolean (true/false).

Python has the following standard Data Types:

  • Booleans
  • Numbers
  • String
  • List
  • Tuple
  • Set
  • Dictionary

Mutable and Immutable Objects

Data objects of the above types are stored in a computer’s memory for processing. Some of these values can be modified during processing, but the contents of the others can’t be altered once they are created in the memory.

Number values, strings, and tuple are immutable, which means their contents can’t be altered after creation.

On the other hand, the collection of items in a List or Dictionary object can be modified. It is possible to add, delete, insert, and rearrange items in a list or dictionary. Hence, they are mutable objects.


A Boolean is such a data type that almost every programming language has, and so does Python. Boolean in Python can have two values – True or False. These values can be used for assigning and comparison.


Numbers are one of the most prominent Python data types. In Numbers, there are mainly 3 types which include Integer, Float, and Complex.


A sequence of one or more characters enclosed within either single quotes ‘or double quotes” is considered as String in Python. Any letter, a number or a symbol could be a part of the string. Multi-line strings can be represented using triple quotes,”’ or “””.

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Python list is an array-like construct which stores a heterogeneous collection of items of varied data typed objects in an ordered sequence. It is very flexible and does not have a fixed size. The Index in a list begins with a zero in Python.


A tuple is a sequence of Python objects separated by commas. Tuples are immutable, which means tuples once created cannot be modified. Tuples are defined using parentheses ().


A set is an unordered collection of items. Set is defined by values separated by a comma inside braces { }. Amongst all the Python data types, the set is one which supports mathematical operations like union, intersection, symmetric difference etc. Since the set derives its implementation from the “Set” in mathematics, so it can’t have multiple occurrences of the same element.


A dictionary in Python is an unordered collection of key-value pairs. It’s a built-in mapping type in Python where keys map to values. These key-value pairs provide an intuitive way to store data. To retrieve the value we must know the key. In Python, dictionaries are defined within braces {}.

This article is about one specific data type, which is a string. The String is a sequence of characters enclosed in single (”) or double quotation (“”) marks.

Here are examples of creating strings in Python.

Counting Number of Characters Using LEN () Function

The LEN () built-in function counts the number of characters in the string.

Creating Empty Strings

Although variables S3 and S4 do not contain any characters they are still valid strings. S3 and S4 both represent empty strings here.

We can verify this fact by using the type () function.

String Concatenation

String concatenation means joining one or more strings together. To concatenate strings in Python we use + operator.

String Repetition Operator (*)

Just like in numbers, * operator can also be used with strings. When used with strings * operator repeats the string n number of times. Its general format is: 1 string * n,

where n is a number of type int.

Membership Operators – in and not in

The in or not in operators are used to check the existence of a string inside another string. For example:

Indexing in a String

In Python, characters in a string are stored in a sequence. We can access individual characters inside a string by using an index.

An index refers to the position of a character inside a string. In Python, strings are 0 indexed. This means that the first character is at index 0; the second character is at index 1 and so on. The index position of the last character is one less than the length of the string.

To access the individual characters inside a string we type the name of the variable, followed by the index number of the character inside the square brackets [].

Instead of manually counting the index position of the last character in the string, we can use the LEN () function to calculate the string and then subtract 1 from it to get the index position of the last character.

We can also use negative indexes. A negative index allows us to access characters from the end of the string. Negative index starts from -1, so the index position of the last character is -1, for the second last character it is -2 and so on.

Slicing Strings

String slicing allows us to get a slice of characters from the string. To get a slice of string we use the slicing operator. Its syntax is:


str_name[start_index:end_index] returns a slice of string starting from index start_index to the end_index. The character at the end_index will not be included in the slice. If end_index is greater than the length of the string then the slice operator returns a slice of string starting from start_index to the end of the string. The start_index and end_index are optional. If start_index is not specified then slicing begins at the beginning of the string and if end_index is not specified then it goes on to the end of the string. For example:

Apart from these functionalities, there are so many built-in methods for strings which make the string as the useful data type of Python. Some of the common built-in methods are as follows: –

capitalize ()

Capitalizes the first letter of the string

join (seq)

Merges (concatenates) the string representations of elements in sequence seq into a string, with separator string.

lower ()

Converts all the letters in a string that are in uppercase to lowercase.

max (str)

Returns the max alphabetical character from the string str.

min (str)

Returns the min alphabetical character from the string str.

replace (old, new [, max])

Replaces all the occurrences of old in a string with new or at most max occurrences if max gave.

 split (str=””, num=string.count(str))

Splits string according to delimiter str (space if not provided) and returns list of substrings; split into at most num substrings if given.


Converts lowercase letters in a string to uppercase.


So in this article, firstly, we have seen a brief introduction of all the data types of python. Later in this article, we focused on the strings. We have seen several Python operations on strings as well as the most common useful built-in methods of strings.

Python is the language of the present age, wherein almost every field there is a need for Python. For example, Python for data analysisMachine Learning Using Python has been easy and comprehensible than they were ever before. Thus, if you are also interested in Python and looking for promising courses Computer Vision Course PythonRetail Analytics using PythonNeural Network Machine Learning Python, then get in touch with Dexlab Analytics now and step into the world of opportunities!


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Automation is to Highly Impact the Knowledge Workers

Automation is to Highly Impact the Knowledge Workers

Automation will mainly target the knowledge workers, who are highly paid and educated and involved in thinking and analytical jobs.

The robot revolution is anticipated for quite some time now and with the ongoing advancements in Machine Learning, Artificial Intelligence and Data Science, the future is near. However, it is also one of the most dreaded events for the workers going forward, who would be vulnerable to losing their respective jobs.

Going back to the 2017 McKinsey study, around 50% of the jobs in the manufacturing industries are automatable using the latest technology. However, according to the latest report, the white-collar workers, who are well-read and engaged in thinking and analytical jobs, are more likely to suffer the most.

According to a new study conducted by Michael Webb, Stanford University Economist, the powerful technologies of computer science like Artificial Intelligence and Machine Learning, which can make human-like decisions and grow using real-time data, will eventually target the white-collar workers. Artificial Intelligence has already made marked intrusions in the white-collar jobs, like telemarketing, which are primarily overseen by the bots. However, with the tireless efforts of the Data Scientists, along with the expansion of the Machine Learning course in India, it is believed to oust the majority of the knowledge workers, like chemical engineers, market researchers, market analysts, physicists, librarians and more.

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The new research focuses on the intersecting subject-noun pairs in AI patents and job descriptions to find out the jobs that will be heavily affected by the Ai technology. For example, the job descriptions of market research analysts comprise of “data analysis”, “identifying markets” and “track market trends”, which are in fact, all covered by the AI patents that are existing. This new study looks far more progressive than the previous ones because it analyzes patents for the technology which are yet to develop completely.

With the rising trends of Data Science and Machine Learning, Artificial Intelligence has really come a long way from what an imaginary concept. Thus, courses like Machine Learning Using Python and Python for Data Analysis, are in heavy demands. 


This article has been sourced


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Python Statistics Fundamentals: How to Describe Your Data? (Part I)

Python Statistics Fundamentals: How to Describe Your Data?

Statistics is a branch of mathematics which deals with the collection, analysis, interpretation and presentation of masses of numerical data. Statistics is a tool used to communicate our understanding of data. It helps us understand the world better, make assertions, and communicate our confidence in the statements we are making.

Two main statistical methods are used in data analysis:

  1. Descriptive statistics: This method is used to summarize data from a sample using measures such as the mean or standard deviation
  2. Inferential statistics: With this method, you can conclude data that are subject to random variation (e.g., observational errors, sampling variation).

This article is about the descriptive statistics which are used to describe and summarize the datasets. We are also going to see the available Python libraries to get those numerical quantities.

This whole topic will be covered in a series of two blogs. This first blog is about the types of measures in descriptive statistics. Furthermore, we will also see the built-in Python “Statistics” library, which has a relatively small number of the most important statistics functions.

Descriptive statistics can be defined as the measures that summarize a given data, and these measures can be broken down further into the measures of central tendency and the measures of dispersion. Measures of central tendency include mean, median, and the mode, while the measures of dispersion include standard deviation and variance.

We will cover the following topics in descriptive statistics:

  • Measures of Central Tendency
  1. Mean
  2. Median
  3. Mode
  • Measures of Dispersion
  1. Variation
  2. Standard Deviation

First, we need to import the Python statistics module.


The arithmetic mean is the sum of data divided by the number of data-points. It is a measure of the central location of data in a set of values that vary in range. In Python, we usually do this by dividing the sum of given numbers with the count of the number present. Python mean function can be used to calculate the mean/average of the given list of numbers. It returns the mean of the data set passed as parameters.

mean( ): Arithmetic mean (“average”) of data.

harmonic_mean( ): It is the reciprocal of the arithmetic mean of the reciprocals of the data (say for three numbers a, b and c, 1/mean = 3/(1/a + 1/b + 1/c)).


median( ): Median or middle value of data is calculated as the mean of middle two. When the number of data points is odd, the middle data point is returned. The median is a robust measure of a central location and is less affected by the presence of outliers in your data compared to the mean.

median_low( ): Low median of data is calculated when the number of data points is odd. Here the middle value is usually returned. When it is even, the smaller of the two middle values is returned.

median_high( ): High median of data is calculated when the number of data points is odd. Here, the middle value is usually returned. When it is even, the larger of the two middle values is returned.


mode( ): Mode (most common value) of discrete data. The mode (when it exists) is the most typical value and is a robust measure of central location.

Measures of Dispersion

Measures of dispersion are statistics that describe how data varies, usually relative to the typical value. While measures of centre give us an idea of the typical value, measures of spread give us a sense of how much the data tends to diverge from the typical value.

These following functions (from the statistics module in python) calculate a measure of how much the population or sample tends to deviate from the typical or average values.

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Population Variance

pvariance( ): Returns the population variance of data. Use this function to calculate the variance from the entire population. To estimate the variance from a sample, the variance ( ) function is usually a better choice. When called with the entire population, this gives the population variance σ². When called on a sample instead, this is the biased sample variance s², also known as variance with N degrees of freedom.

Population Standard Deviation

pstdev( ): Return the population standard deviation (the square root of the population variance)

Sample Variance

variance ( ): Returns the sample variance of data, an iterable of at least two real-valued numbers. Variance, or second moment about the mean, is a measure of the variability (spread or dispersion) of data. A large variance indicates that the data is spread out; a small variance indicates it is clustered closely around the mean. If the optional second argument is given to the function, it should be the mean of data. This is the sample variance s² with Bessel’s correction, also known as variance with N-1 degrees of freedom.

Sample Standard Deviation

stdev( ): Returns the sample standard deviation (the square root of the sample variance)


So, this article focuses on describing and summarizing the datasets, also helping you to calculate numerical quantities in Python. It’s possible to get descriptive statistics with pure Python code, but that’s rarely necessary. In the next series of this blog we will see the Python statistics libraries which are comprehensive, popular, and widely used especially for this purpose.

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A Step-by-Step Guide on Python Variables

A Step-by-Step Guide on Python Variables

Variable is the name given to the memory location where data is stored. Once a variable is stored, space is allocated in memory. Variables are named locations that are used to store references to the object stored in memory.

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Now, coming to this article, here are some of the topics that will be covered in this article:

  • Rules to Define a Variable
  • Assigning Values to a Variable
  • Re-declaring a Variable in Python
  • Variable Scope
  • Deleting a Variable

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Rules to Define a Variable

These are the few rules to define a python variable:

  1. Python variable name can contain small case letters (a-z), upper case letters (A-Z), numbers (0-9), and underscore (_).
  2. A variable name can’t start with a number.
  3. We can’t use reserved keywords as a variable name.
  4. The variable name can be of any length.
  5. Python variable can’t contain only digits.
  6. The variable names are case sensitive.

Assigning Values to a Variable

There is no need for an explicit declaration to reserve memory. The assignment is done using the equal to (=) operator.

Multiple Assignment in Python

Multiple variables can be assigned to the same variable.

Multi-value Assignment in Python

Multiple variables can be assigned to multiple objects.

Re-declaring a Variable in Python

After declaring a variable, one can again declare it and assign a new value to it. Python interpreter discards the old value and only considers the new value. The type of the new value can be different than the type of the old value.

Variable Scope

A variable scope defines the area of accessibility of the variable in the program. A Python variable has two scopes:

  1. Local Scope
  2. Global Scope

Python Local Variable

When a variable is defined inside a function or a class, then it’s accessible only inside it. They are called local variables and their scope is only limited to that function or class boundary.

If we try to access a local variable outside its scope, we get an error that the variable is not defined.

Python Global Variable

When the variable is not inside a function or a class, it’s accessible from anywhere in the program. These variables are called global variables.

Deleting a Variable

One can delete variable using the command “del”.

In the example below, the variable “d” is deleted by using command Del and when it is further proceeded to print, we get an error “variable name is not defined” which means the variable is already deleted.


In this article we have learned the concepts of Python variables which are used in every program. We also learned the rules associated to the naming of a variable, assigning value to a variable, scope of a variable and deleting a variable.

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This technical blog is sourced from: and


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