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Step-by-step guide to building a career in Data Science

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Step-by-step guide to building a career in Data Science

With 2.5 quintillion bytes of data being created everyday companies are scrambling to build models and hire experts to extract information hidden in massive unstructured datasets and the data scientists have become the most sought-after professionals in the world.  The job portals are full of job postings looking for data scientists whose resume has the perfect combination of skill and experience. In this world which is being driven by the data revolution, achieving your big data career dreams need a little bit of planning and strategizing. So, here is a step-by-step guide for you.  

Grabbing a high paying and skilled data job is not going to be easy, industries will only invest money on individuals with the right skillset. Your job responsibility will involve wading through tons of unstructured data to find pattern and meaning, making forecasts regarding marketing trends, customer behavior and deliver the insight in a presentable format to the company on the basis of which they are going to be strategizing.

So, before you even begin make sure that you have the tenacity and enthusiasm required for the job. You would need to undergo Data science using python training, in order to gain the necessary skills and knowledge and since this is an evolving field you should be ready to constantly upskill yourself and stay updated about the latest developments in the field.

Are you ready? If it’s a resounding yes, then, without wasting any more time let’s get straight to the point and explore the steps that will lead you to become a data scientist.

Step 1: Complete education

Before you pursue data science, you must complete your bachelors degree, if you are coming from computer science, applied mathematics, or, economics that could give you a head start. However, you need to undergo Data Science training, post that to acquire the required skillset.

Step 2: Gain knowledge of Mathematics and statistics

You do not need to have a PHD in either, but, since both are at the core of the data science you must have a good grasp on applied mathematics and statistics. Your task would require you to have knowledge regarding linear algebra, probability & statistics. So, your first step would be to update yourself and be familiar with the concepts if you happen to hail from a non-science background so that you can sail through the rest of the journey.

 Step 3: Get ready to do programming

Just like mathematics and statistics, having a grip on a programming language preferably Python, is essential. Now, why do you need to learn coding? Well, coding is important as you have to work with large datasets comprising mostly unstructured data and coding will help you to clean, organize, read data and also process it. Now the stress is on Python because it is one of the widely used languages in the data science community and is comparatively easier to pick up.

Step 4: Learn Machine Learning

Machine learning plays a crucial role in data science as it helps finding patterns in data and making predictions. Mastering machine learning techniques would enable you develop algorithms for the models and create an automated system that enables you to make predictions in real-time. Consider undergoing a Machine Learning training gurgaon.

Step 5: Learn Data Munging, Visualization, and Reporting

It has been mentioned before that you would mostly be handling unstructured data, which means in order to process that data you must transform that data into a format that is easy to work with. Data munging helps you achieve that. Data visualization is again a must-have skill for a data scientist as it allows you to visually present your data findings that is easy to understand through graphs, charts, while data reporting lets you prepare and present reports for businesses.

Step 6: Be certified

Now that the field has advanced so much, there is a requirement for professionals who have undergone Data Science course. Doing a certification course would upskill you and arm you with industry knowledge. Reputed institutes like Dexlab Analytics offer cutting edge courses such as Python for data science training. If you just follow this step it would take care of the rest of the worries, the best part of getting your training is that here you will be taught everything from scratch so, no need to fret if you do not know programming language. Your learning would be aided by hands-on training.

Step 7: Practice your skills

You need to test the skills you have acquired and to hone the skills you must explore Kaggle, which lets your access resources you need and this platform also allows you to take part in competitions that further helps you sharpen your abilities. You should also keep on practicing by doing projects in order to put the theories into action.

Step 8: Work on your soft skills

In order to be a professional data scientist you must acquire soft skills as well. So along with working on your communication skills, you must also need to develop problem solving skills while learning how business organizations function to understand what would be required of you when you assume the role of a data scientist.

Step 9: Get an internship

Now that you have the skill and certification you need experience to get hired, build a resume stressing on the skills you have acquired and search the job portals to land an internship. It would not only enhance your resume, but, it also gives you exposures to real projects, the more projects you handle the better and you would also learn from the experts there.

Step 10: Apply for a job

Once you have gathered enough experience start applying for full-time positions as now you have both skill and experience. But, do not stop learning once you land a job, because this field is growing many changes will happen so you have to mold yourself accordingly. Be a part of the community, network with people, keep on exploring  GitHub and find out what other skills you require.

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So, those were the steps you need to follow to build a rewarding career in data science. The job opportunities are plenty and to grab the right job you must do big data training in gurgaon. These courses are aimed to prepare individuals for the industry, so get ready for an exciting career!


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Probability PART-I: Introducing The Concept Of Probability

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Probability PART-I: Introducing The Concept Of Probability

Today we will begin discussion about a significant concept, probability, which measures the likelihood of the occurrence of an event. This is the first part of the series, where you would be introduced to the core concept. So, let’s begin.

What is probability?

It is a measure of quantifying the likelihood that an event will occur and it is written as P(x).


Key concepts of probability


 
 
A union comprises of only unique values.
 
 
 
 
 

 
 
Intersection comprises of common values of the two sets

 

 

  • Mutually Exclusive Events:- If the occurrence of one event preludes the occurrence of the other event(s), then it is called mutually exclusive event.

P(A∩B) = 0

  • Independent Events:- If the occurrence or non-occurrence of an event does not have any effect on the occurrence or non-occurrence of other event(s), then it is called an independent event. For example drinking tea is independent of going for shopping.
  • Collectively Exhaustive Events:– A set of collectively exhaustive events comprises of all possible elementary events for an experiment. Therefore, all sample spaces are collectively exhaustive sets.
  • Complementary Events:– A complement of event A will be A` i.e. P(A`) = 1 ─ P(A)

Properties of probability

  • Probabilities are non-negative values ranging between 0 & 1.
  • Ω = 1 i.e. combined probability of sample is 1
  • If A & B are two mutually exclusive events then P(A U B)= P(A) +P(B)
  • Probability of not happening of an event is P(A)= 1 ─ P(A)

Rules of Counting the possibilities

  • The mn counting rule:- When a customer has a set of combinations to choose from like two different engines, five different paint colors and three different interior packages , how will he calculate the total number of options available to him? The answer to the question is “ mn counting rule”. Simply multiply the given options, like in our case 2 * 5 * 3 will give us 30.This means the customer has 30 combinations to choose from when it comes to purchasing a car.
  • Sampling from a population with replacement:- Suppose that you roll a dice three times i.e. the number of trials is 3, now if we want to check how many combinations are possible in this particular experiment we use Nn = 63 = 216
  • Sampling from a population without replacement:- When the sample space shrinks after each trial then you use the following formula :-

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Conclusion

There is a video covering the same concept attached down the blog, go through it to be more clear about this.

So, with this we wrap up our discussion on the concept of probability. If you want more informative blogs on Data Science training, then follow the Dexlab Analytics blog. Dexlab Analytics provides machine learning certification courses in gurgaon as well.


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What Is The Role Of Big Data In The Pharmaceutical Industry?

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What Is The Role Of Big Data In The Pharmaceutical Industry?

Big data is currently trending in almost all sectors as now the awareness of the hidden potential of data is on the rise. The pharmaceutical industry is a warehouse of valuable data that is constantly piling up for years and which if processed could unlock information that holds the key to the next level of innovation and help the industry save a significant amount of money in the process as well. Be it making the clinical trial process more efficient or, ensuring the safety of the patients, big data holds the clue to every issue bothering the industry. The industry has a big need for professionals who have Data science using Python training, because only they can handle the massive amount of data and channelize the information to steer the industry in the right direction.

We are here taking a look at different ways data is influencing the pharmaceutical industry.

Efficient clinical-trial procedure

Clinical trial holds so much importance as the effectiveness of a drug or, a procedure on a select group of patients is tested. The process involves many stages of testing and it could be time-consuming and not to mention the high level of risk factors involved in the process. The trials often go through delays that result in money loss and there is risk involved too as side effects of a specific drug or a component can be life-threatening. However, big data can help in so many ways here, to begin with, it could help filtering patients by analyzing several factors like genetics and select the ones who are eligible for the trials. Furthermore, the patients who are participating in clinical trials could also be monitored in real-time. Even the possible side effects could also be predicted and in turn, would save lives.

Successful sales and marketing efforts

The pharmaceutical industry can see a great difference in marketing efforts if only they use data-driven insight. Analyzing the data the companies could identify the locations and physicians ideal for the promotion of their new drug. They can also identify the needs of the patients and could target their sales representative teams towards that location. This would take the guesswork out of the process and increase the chance of getting a higher ROI. The data can also help them predict market trends as well as understand customer behavior. Another factor to consider here is monitoring the market response to a particular drug and also its performance, as this would help fine-tune marketing strategies.

Collaborative efforts

With the help of data, there could be better collaboration among the different segments that directly impact the industry. The companies could suggest different drugs that could be patient-specific and the physicians could use real-time patient data to decide whether the suggestions should be implemented in the treatment plan. There could be internal and external collaborations as well to improve the overall industry functioning. Be it reaching out to researchers or, CROs, establishing a strong link can help the industry move further.

Predictive analysis

A new drug might be effective in handling a particular health issue and could revolutionize the treatment procedure but, the presence of certain compounds might prove to be fatal for certain patients and drug toxicity if not detected at an early stage could endanger a particular patient. So, using predictive analysis a patient data could be analyzed to determine the genetic factors, disease history, as well as lifestyle. The smart algorithms thereby help identify the risk factors and makes it possible to take a personalized approach regarding medication that could prove to be more effective rather than some random medication.

Big data can increase the efficiency of the pharmaceutical industry in more ways than one, but compared to other industries somehow this industry still hasn’t been able to utilize the full potential of big data, due to factors like privacy and, monetary issues. The lack of trained professionals could also prove to be a big obstacle. Sending their select professionals for Data Science training, could prove to be a big boon for them in the future.


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How IoT analytics can help your business grow?

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How IoT analytics can help your business grow?

Internet of Things or IOT devices are a rage now, as these devices staying connected to the internet can procure data and exchange the same using the sensors embedded in those. Now the data which is being generated in copious amount needs to be processed and in comes IoT Analytics. This platform basically is concerned with analyzing the large amount of data generated by the devices. The interconnectivity of devices is helping different sectors be in sync with the world, and the timely extraction of data is of utmost significance now as it delivers actionable insights. This is a highly skilled job responsibility that could only be handled by professionals having done artificial intelligence course in delhi.

This particular domain is in the nascent stage and it is still growing, however, it is needless to point out that IoT analytics holds the clue to business success, as it enables the organizations to not only extract information from heterogeneous data but also helps in data integration. With the IoT devices generating almost 5 quintillion bytes of data, it is high time the organizations start investing in developing IoT analytics platform and building a data expert team comprising individuals having a background in Machine Learning Using Python. Now let’s have a look at the ways IoT analytics can boost business growth.

Optimized automated work environment

IoT analytics can optimize the automated work environment, especially the manufacturing companies can keep track of procedures without involving human employees and thereby lessening the chances of error and enhancing the accuracy of predicting machine failure, with the sensors monitoring the equipments and tracing every single issue in real-time and sending alerts to make way for predictive maintenance. The production flow goes on smoothly as a result without developing any glitch.

Increasing productivity

In an organization gauging the activity of the employees assumes huge significance as it directly impacts the productivity of the company, with sensors being strategically placed to monitor employee activity, performance, moods and other data points, this job gets easier. The data later gets analyzed to give the management valuable clues that enable them to make necessary modifications in policies.  

Bettering customer experience

Regardless of the nature of your business, you would want to make sure that your customers derive  utmost satisfaction. With IoT data analytics in place you are able to trace their preferences thanks to the data streaming from devices where they have already left a digital footprint of their shopping as well as searching patterns. This in turn enables you to offer tailor-made service or products. Monitoring of customer behavior could lead to devising marketing strategies that are information based.

Staying ahead by predicting trends

One of the crucial aspects of IoT analytics is its ability to predict future trends. As the smart sensors keep tracking data regarding customer behavior, product performance, it becomes easier for businesses to analyze future demands and also the way trends will change to make way for emerging ones and it enables the businesses to be ready. Having access to a future estimate prepares not just businesses but industries be future ready.

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Smarter resource management

Efficient utilization of resources is crucial to any business, and IoT analytics can help in a big way by making predictions on the basis of real-time data. It allows companies to measure their current resource allocation plan and make adjustments to make optimal usage of the available resources and channelizing that in the right direction. It also aids in disaster planning.

Ever since we went digital the streaming of large quantity of data has become a reality and this is going to continue in the coming decades. Since, most of the data generated this way is unstructured there needs to be cutting edge platforms like IoT analytics available to manage the data and processing it to enable industries make informed decisions. Accessing Data Science training, would help individuals planning on making a career in this field.


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Engineering To Data Science: What’s Causing The Professionals To Consider A Mid-Career Switch?

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Engineering To Data Science: What's Causing The Professionals To Consider A Mid-Career Switch?

Among all the decisions we make in our lives, choosing the right career path seems to be the most crucial one. Except for a couple of clueless souls, most students know by the time they clear their boards what they aspire to be. A big chunk of them veer towards engineering, MBA, even pursue masters degree in academics and post completion of their studies they settle for relevant jobs. So far that used to be the happily ever after career story, but, in the last couple of years there seems to be a big paradigm shift and it is causing a stir across industries. Professionals having an engineering background, or, masters degree are opting for a mid-career switch and a majority of them are opting for the data science domain by pursuing a Data Science course. So, what’s pushing them towards DS? Let’s investigate.

What’s causing the career switch?

No matter which field someone has chosen for career, achieving stability is a common goal. However, in many fields be it engineering, or, something else the job opportunities are not unlimited yet the number of job seekers is growing every year. So, thereby one can expect to face a stiff competition grabbing a well-paid job.

There have been many layoffs in recent times especially due to the unprecedented situation the world is going through. Even before that there were reports of job cuts and certain sectors not doing well would directly impact the career of thousands. Even if we do not concentrate on the extremes, the growth prospect in most places could be limited and achieving the desired salary or, promotion oftentimes becomes impossible. This leads to not only frustration but uncertainty as well.

The demand for big data

If you haven’t been living as a hermit, then you are aware of the data explosion that impacted nearly every industry. The moment everyone understood the power of big data they started investing in research and in building a system that can handle, store and process data which is a storehouse of information. Now, who is going to process data to extract the information? And here comes the new breed of data experts, namely the data scientists, who have mastered the technology having undergone Data Science training and are able to develop models and parse through data to deliver the insights companies are looking for to make informed decisions. The data trend is pushing the boundaries and as cutting edge technologies like AI, machine learning are percolating every aspect of the industries, the demand for avant-garde courses like natural language processing course in gurgaon, is skyrocketing.

Lack of trained industry ready data science professionals

Although big data has started trending as businesses started gathering data from multiple sources, there are not many professionals available to handle the data. The trend is only gaining momentum and if you just check the top job portals such as Glassdoor, Indeed and go through the ads seeking data scientists you would immediately know how far the field has traveled. With more and more industries turning to big data, the demand for qualified data scientists is shooting up.

Why data science is being chosen as the best option?

In the 21st century data science is a field which has plethora of opportunities for the right people and this is one field which is not only growing now but is also poised to grow in future as well. The data scientist is one of the most highest paid professional in today’s job market. According to the U.S. Bureau of Labor Statistics report by the year 2026 there is a possibility of creation of 11.5 million jobs in this field.

Now take a look at the Indian context, from agriculture to aviation the demand for data scientists would continue to grow as there is a severe shortage of professionals. As per a report the salary of a data scientist could hover around ₹1,052K per annum and remember the field is growing which means there is not going to be a dearth of job opportunities or, lucrative pay packages.

Data Science Machine Learning Certification

The shift

Considering all of these factors there has been a conscious shift in the mindset of the professionals, who are indeed making a beeline for institutes that offer data science certification. By doing so they hope to-

  • Access promising career opportunities
  • Achieve job satisfaction and financial stability
  • Earn more while enjoying job security
  • Work across industries and also be recruited by industry biggies
  • Gain valuable experience to be in demand for the rest of their career
  • Be a part of a domain that promises innovation and evolution instead of stagnation

Keeping in mind the growing demand for professionals and the dearth of trained personnel, premier institutes like DexLab Analytics have designed courses that are aimed to build industry-ready professionals. The best thing about such courses is that you can hail from any academic background, here you will be taught from scratch so that you can grasp the fundamentals before moving on to sophisticated modules.

Along with providing data science certification training, they also offer cutting edge courses  such as, artificial intelligence certification in delhi ncr, Machine Learning training gurgaon. Such courses enable the professionals enhance their skillset to make their mark in a world which is being dominated by big data and AI.  The faculty consists of skilled professionals who are armed with industry knowledge and hence are in a better position to shape students as per industry demands and standards.

The mid-career switch is happening and will continue to happen. There must be professionals who have the expertise to drive an organization towards the future by unlocking their data secrets. However, something must be kept in mind if you are considering a switch, you need to be ready to meet challenges,  along with knowledge of Python for data science training, you need to have a vision, a hunger and a love for data to be a successful data scientist.


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Regular Expression in Python Part III: Learn To Substitute With Re

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Regular Expression in Python Part III: Learn To Substitute With Re

This is the 3rd part of the ongoing Regex or, regular expression in Python series where we are discussing how to handle textual data. In the second part we introduced you to the re library and in this third segment, we are going to be discussing how to substitute characters or, words with re library.

Re library has a wide range of methods to deal with textual data, one such method is .sub() which helps us substitute alphabets or words based on the patterns we build. This method can be used with .match() method and .search() method, both having differences in the way they extract a pattern.

Difference between .match() & .search()

.match() :- This method extracts the required text only at the begging.

.search() :-This can extract the required string from the entire text but only at the first occurrence

In the above code you can see that even though the word “Hello” is in the middle of the text we are able to fetch it because of the special attribute of the .search() method. Here we are again using “Hello dexlab…!” as an example and .compile() is being used to create and apply our pattern.

Now suppose if we want to substitute the word “Hello” with another string “Hi” we will have to use .sub() method from the re library. But there are ways to use this method directly or indirectly.

 First let’s see the direct method.

In the above line of code we first mention what we want to substitute and with what and then we add the text.

Second way to do this is by first using .compile() method to build the pattern and then use that pattern to substitute the alphabet or word.

The above pattern in the .complie() method states that there is a word with the first alphabet in uppercase combined with lowercase alphabets to be substituted with the word “Hi”. This pattern can match any string with the same characteristics, for example:-

Look at the text used in the .sub() method, now instead of “Hello” we have “Pello” with the same characteristics substituted with the word “Hi”. But one must not forget this pattern can also be used in the .sub() method directly and the use of .compile() method is optional. .compile() method is used only to create an object based code.

So, this wraps up the discussion on how to substitute characters or, words with re library. Hopefully, you found this blog informative, if you wish to find more Python Certification course topics, keep following the Dexlab Analytics blog.

 


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Regular Expression in Python Part II: Using re library for textual data

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Regex Series Part-II: Using Re Library For Textual Data

This is the second part of the Regex series, where we will be continuing on by introducing you to another library in python, the re library. The previous blog introduced you to the Regex library in Python, and you learned about meta-characters and literals which could be used for creating patterns. This particular segment is about introducing the re library to help you with textual data analysis.

Re library in python holds the key to deal with all the problems relating to textual data analysis. This library provides a range of methods that can help you build patterns and extract or substitute the desired string. For example, suppose you want to change all the negative words to positive in a novel, for that all you need to have is a soft copy of the same and then you can import the re library and use its predefined methods first to make a pattern to extract the words and then substitute it to make the required changes.

Here one such method which we are going to use today from the re library is .compile() combined with .match() method to build and extract the pattern with the help of literals and meta-characters explained in the previous blog.

.compile() and .match() Methods

.complie() is used to build the pattern. You can use the meta-characters and literals within the parenthesis to build the pattern of the word which you want to extract or change. This is practically the first step without which not much can be done in re library. But why do we need a pattern and what is it that makes it necessary? To answer this question I am going to use few steps:-

  1. First thing to do is to observe the word or the string and see what is it that makes that specific word or words which you want to extract different from the rest of the text i.e. is the word a combination of digit or alphabet or special character, is there a special character before and after the word etc.
  2. Now recall all the meta-characters we have studied so far.
  3. Combine them with the .complie() method which basically helps us bring the meta-characters together.
  4. Now all there is left for us to do is apply the pattern on the text.

Therefore knowledge of meta-characters is necessary to form patterns to manipulate your textual data.

Now let’s see how to import re library and practically solve few of the Regex questions.

Use the above code to import the Regex library.

Question 1: How to make a pattern to extract the entire string “Hello dexlab…!!”?

In the above code we are using a combination of. (period)and * where

  • . (period) means match alpha-numeric or special character
  • * means match anything zero or more times

When combined together this means that you can match alpha-numeric or special characters zero or more times. Therefore the end result is:-

.match()method has a special property that it can match anything only at the beginning of the line. Suppose I want to extract “Hello” from a string, .match()method will only work when word is at the 0 index.

Question: How to extract and match only special characters?

The above code is matching only the space which is at the 0th index but not all the simultaneous special characters. To make a pattern that can match all the special characters we can use *

You can try ? and + to check the difference it makes on the output.

Question: How to extract numbers and special characters?

Now you must be wondering why the above code did not recognize the special characters after the numbers like @ and space. Here you must remember that the output you get is based on the pattern you make. In the above code we mentioned nothing that matches anything after the numbers. So we further need to expand our line of codes.

Question: How to extract only the output?

You can use the slice operator [] to extract only the text by using 0 index.

You must have picked up the fundamentals of the re library from the blog, watch the video attached below to follow the tutorial step by step. Follow the Regex series to gain expertise in textual data handling. Dexlab Analytics blog has more interesting and informative posts on Python Programming training.

 


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Regular Expression in Python Part I: Introducing Regex Library

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Regular Expression in Python Part I: Introducing Regex Library

Python is a versatile programming language and it has a rich library. In the visualization series we introduced you to different libraries used for data visualization purposes. Now, we introduce you to the Regex library in Python for handling textual data.

In Python to perform pattern recognition on textual data Regex is a library that provides a range of methods which when used with right pattern gives us the desired results. For example, if you want to change the spelling of colour to color in your text you can easily do so with the help of a given method provided that you form the pattern correctly.

Type of textual data in Regex

Literals:- In Python literals are the characters or words with their original meaning intact like the word dog means a literal dog and there is no hidden meaning behind that word.

Meta-characters:- These are the words or characters which hold special meaning for example \n means a new line or \t means tab separated values.

Given below are few of the meta-characters used in python with their meanings:-

\d – Matches a digit .i.e. \d= 1 ,\d\d= 23, \d\d\d = 345

\w – Matches alpha-numeric characters i.e. \w= 1, \w= a, \w\w= a1

\W– Matches special characters i.e. \W= %

Dog[ogn]– Matches a single character within the square bracketsi.e. Dogo, Dogg, Dogn

Dog(ogn) – Matches the entire string within the parenthesisi.e. Dogogn

Dog(ogn|aaa)– Matches either ogn or aaa i.e. Dogogn or Dogaaa

*– Matches 0 or more characters i.e. tre* = tree, tre*= tr, tre*= treeeeee

?– Matches 0 or 1 character i.e. colou?r= color, colou?r= colour

+ – Matches 1 or more character i.e. tre+= tree, tre+= treee, tre+≠tre

. – Matches alpha-numeric or special characters but only one time i.e. tre.= tree, tre.= tre#, tre.=tre1, tre.≠tre#1

The above meta-characters alone or in combination are used to form a pattern  which then are used for text mining for example tre.* means match anything 0 or more times that means now we can match tre#1 or tre.

Watch the video tutorial attached below to learn more about the fundamentals of this library. 

Hopefully you found the discussion on Regex library helpful and at the end of it you must have become familiar with the way this particular library works. To learn more about python for data analysis, keep on exploring Dexlab Analytics blog, where you will always find informative posts.

 


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Visualization with Python Part-V: Introducing the Pandas_bokeh library

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Visualization with Python Part-V: Introducing the Pandas_bokeh library

In our fifth installment of the visualization series using Python programming language, we introduce you to another powerful library in Python that is the Pandas_bokeh library. So, let’s find out what you can achieve with Pandas_bokeh library.

Pandas_bokeh is a library which can help you create interactive graphs in python. One can zoom in, zoom out, select a certain portion of the graph to see, move the plot left, right and center, create tabs in case they want to see a single plot at a time, create multiple plots at a time, create widgets like dropdown list, check boxes, radio buttons, slider etc. It is similar to the shiny app which is used in the r programming language  but simpler and faster.

How to install pandas_bokeh?

In the above code we are changing our jupyter notebook code cell into a command line by using ! and then we can use pip (python installation package) to install the library.

How to create a simple line plot using pandas_bokeh ?

The first thing to do is import the libraries which we will be using to create a line plot.

  • We will be creating our own dataset here and for that we need to import Numpy and Pandas libraries. Also we will be importing .figure() method from plotting module to create our canvas on which we will be building our graph from the scratch and we will also be importing .output_notebook() method to visualize our graph on jupyter notebook and to visualize our graph on a new tab and save at the same time we can use .output_file() method.
  • The Dataset we are creating here will have three columns ‘Days’, ‘Sales’ and ‘Date’.
  • We will be creating a dataset with hundred observations in each column so for that we are using .rand() method to generate hundred random numbers and that will be our ‘Sales’ column data. Now for our ‘Days’ column we will be creating a loop which will run hundred times and each time an array index value will be saved in a variable c which has an empty string and .split() method is then used to create a list of that string.
  • For creating a ‘Date’ column we will be using the following code

  • At last create a data frame we will be using .DataFrame() method.

  • Now to create two line graphs on a single canvas we will be using object-oriented programming.

  • To build the graph on the jupyter notebook we are using .output_notebook() method and in case you want to plot the graph on a new tab you can use .output_file(“filename.html”) method.

In the above line of codes we are creating two separate data frames df_d and df_d1 each containing Monday and Friday’s sales and dates separately now all we need to do is build a canvas using .figure() method and use few other arguments like x_axis_type to define the data type of the x axis and x_axis_label and y_axis_label to set graph labels, to adjust width and height of the canvas we have used plot_width and plot_height argument and to set title and title location we have used title and title_location. Once we have our canvas ready we can use .line() method and add x axis and y axis data to plot our graphs.

  • To interact with your graph you can use the icons on the right hand side corner which will help you zoom in and out, look at a certain part of the graph, scroll to zoom in and out, save your plot and reset the changes made by you using the side icons.

The video tutorial attached below will help you gain better understanding. 

At the end of this segment you must have become familiar with the nuances of the Pandas_bokeh library. As you continue on with the series, you will realize that you are becoming an expert in visualization. On Dexlab Analytics blog, you will find interesting blogs on various topics related to Python certification training.

 


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