Matplotlib is the “grandfather” library of data visualization with Python. It was created by John Hunter. He created it to try replicating MatLab’s (another programming language) plotting capabilities in Python. So, if you are already familiar with matlab, matplotlib will feel natural to you.
This library gives you the flexibility to plot the graphs the way you want. You can start with a blank canvas and plot the graph on that canvas wherever you want. You can make multiple plots on top of each other, change the line type, change the line color using predefined colors or hex codes, line width etc.
Installation
Before you begin you’ll need to install matplotlib first by using the following code:-
There are two ways in which you can built matplotlib graphs:-
Method based graphs
Object-oriented graphs
Method Based Graphs in Matplotlib:-
There are pre-defined methods in matplotlib library which you can use to create graphs directly using python language for example:-
Where, import matplotlib.pyplot as plt this code is used to import the library, %matplotlib inline is used to keep the plot within the parameters of the jupyter notebook, import numpy as np and x = np.array([1,3,4,6,8,10]) is used to import the numpy (numerical python) library and create an array x and plt.plot(x) is used to plot the distribution of the x variable.
We can also use .xlabel(), .ylabel() and .title() methods print x axis and y axis labels and title on the graph.
If you want to add text within your graph you can either use .annotate() method or .text() method.
Creating Multiplots
You can also create multiple plots by using .subplot() method by mentioning the number of rows and columns in which you want your graphs to be plotted. It works similar to the way you mention the number of rows and columns in a matrix.
You can add title, axis labels, texts etc., on each plot separately. In the end you can add .tight_layout() to solve the problem of overlapping of the graphs and to make the labels and scales visible.
Check out the video attached below to get an in-depth understanding of how Matplotlib works. This is a part of a visualization series using Python programming language. So, stay tuned for more updates. You can discover more such informative posts on the Dexlab Analytics blog.
The growing significance of big data and the insight it imparts is of utmost significance. Data scientists are working round the clock to process the massive amount of data generated every day. However, unless you have been through Data Science training, it would be impossible for you to grasp even an iota of what is being communicated through data.
The patterns, outliers every single important factor that emerged through decoding must be presented in a coherent format for the untrained eyes. Data visualization enables the researchers to present data findings visually via different techniques and tools to enable people to grasp that information easily.
Why data visualization is so vital?
The complicated nuances of data analysis is not easier for anybody to understand. As we humans are programmed to gravitate towards a visual representation of any information, it makes sense to convey the findings through charts, graphs, or, some other way. This way it takes only a couple of moments for the marketing heads to process what is the trend to watch out for.
We are used to seeing and processing the information presented through bars and pie charts in company board meetings, people use these conventional models to represent company sales data.
It only makes sense to narrate what the scientists have gathered from analyzing complex raw data sets, via visual techniques to an audience who needs that information to form data-driven decisions for the future.
So what are the different formats and tools of data visualization?
Data visualization can take myriad forms which may vary in the format but, these all have one purpose to serve representing data in an easy to grasp manner. The data scientist must be able to choose the right technique to relate his data discovery which should not only enlighten the audience but, also entertain them.
The popular data visualization formats are as follows
Area Chart Bubble Cloud/Chart Scatter Plot Funnel Chart Heat Map The formats should be adopted in accordance with the information to be communicated
Data scientists also have access to smart visualization tools which are
Qlikview
Datawrapper
Sisense
FusionCharts
Plotly
Looker
Tableau
A data scientist must be familiar with the tools available and be able to decide on which suits his line of work better.
What are the advantages of data visualization?
Data visualization is a tricky process while ensuring that the audience does not fall asleep during a presentation, data scientists also need to identify the best visualization techniques, which they can learn during big data training in gurgaon to represent the relationship, comparison or, some other data dynamic. If and when done right data visualization has several benefits to offer
Enables efficient analysis of data
In business, efficient data interpretation can help companies understand trends. Data visualization allows them quickly identify and grasp the information regarding company performance hidden in the data and enables them to make necessary changes to the strategy.
Identify connections faster
While representing information regarding the operational issues of an organization, data visualization technique can be of immense help as it allows to show connections among different data sets with more clarity. Thereby enabling the management to quickly identify the connecting factors.
Better performance analysis
Using certain visualizing techniques it is easier to present a product or, customer-related data in a multi-dimensional manner. This could provide the marketing team with the insight to understand the obstacles they are facing. Such as the reaction of a certain demographic to a particular product, or, it could also be the demand for certain products in different areas. They are able to act faster to solve the niggling issues this way.
Adopt the latest trends
Data processing can quickly identify the emerging trends, and with the help of data visualization techniques, the findings could be quickly represented in an appealing manner to the team. The visual element can immediately communicate which trends are to watch out for and which might no longer work.
Encourages interaction
Visual representation of data allows the strategists to not just look at numbers but, actually understand the story being told through the patterns. It encourages interaction and allows them to delve deeper into the patterns, instead of just merely looking at some numbers and making assumptions.
Data visualization is certainly aiding the businesses to gain an insight that was lost to them earlier. A data scientist needs to be familiar with the sophisticated data visualization tools and must strike a balance between the data and its representation. Identifying what is unimportant and which needs to be communicated as well as finding an engaging visual technique to quickly narrate the story is what makes him an asset for the company. A premier Data analyst training institute can help hone the skills of an aspiring data scientist through carefully designed courses.
The wave of digital transformation is ravaging all industry verticals. Big Data coupled with AI and ML is driving the force, with data being at the bull’s eye.
But, what if we say most of the data in the world is hardly used? What if it becomes a hefty liability? Yes, data can become a liability if we fail to understand it properly. For that, we’ve data visualization – it’s the best way to present your data to the world in order to gain meaningful insights.
Fortunately, data visualization is evolving rapidly. Charts, graphs, infographics, videos and AR/VR presentations have taken the channels of communication to an entirely different level. In this blog, we’ve compiled tip 3 most popular and effective data visualization tools – they are easy to use, do their job well and highly compatible with major software and programming languages. However, they are all paid, although they offer free-trials.
Tableau
With a huge customer base of 57000+accounts spread across diverse industry verticals, Tableau is the father of data visualization software and for the right reasons! Along with having the ability to generate interactive visualizations, Tableau is relatively easy to use and offers more than generic BI solutions.
Tableau is ideal for handling vast and fast-changing datasets that are used mainly for Big Data operations, such as ML applications and AI implementations. Developers and data scientists look up to Tableau as it integrates seamlessly with high-end database solutions, including My SQL, Hadoop, SAP, Amazon AWS and Teradata. Also, a wide number of third-party resources online are on offer plus a powerful community to aid and assist new users about how to integrate the tool seamlessly with their projects.
Interested in arming yourself with the skills of Tableau? Worry not; DexLab Analytics is a top-notch Tableau training institute in the heart of Delhi excelling in many other in-demand skill training courses.
Plotly
For highly advanced and complex data visualizations, Plotly is the key. All thanks to how well it homogenizes with cutting-edge programming languages, such as Matlab, Python and R! All of them being extremely analytics oriented.
Developed above the open source d3.js visualization libraries for JavaScript, this high-valued commercial package is extremely user-friendly, along with providing inbuilt support for APIs, like Salesforce.
QlikView
Touted as the biggest rival of Tableau, QlikView boasts of 40000 clients’ accounts across 100 countries. It is one of the most terrific players in the space of data visualization, and why not?! The customers who have used it have lauded QlikView because of its customized setup and versatile range of functionalities. However, this could also mean it takes some time to be familiar with entirely and then only it can be leveraged to its full potential.
Along with providing superior data viz capabilities, the tool excels in some of the best BI and analytics reporting capabilities. It’s simple, effective and non-clumsy user interface scores extra brownie points. Interestingly, customers use it in collaboration with its sister package, QlikSense – it manages data discovery and exploration to derive maximum benefits.
Every customer demand needs to be fulfilled, and CEO’s expect marketing analysts to deliver them. Being a key marketing initiative, optimizing every customer experience is a significant deal to seal for marketers all around the globe.
Data, of course, plays a crucial role in marketing endeavors – but only the data that is interpretable makes sense, rendering other data useless. To turn data into actionable, organizations need to understand the accuracy of data and in the process should be successful in turning insights into action.
Tableau plays a very crucial role in data intelligence, data representations, data visualizations, and data modelling. The time has gone when folks used to draw graph, charts, on excel and variant types of resources or tool. Because provide us live data source accessibility with number of data sources like – spark, Google Products, Social network, programming language (R,Python,JAVA) whereas tableau uses VIZQL in background to speed up the performance. So now the time is to move towards Business Intelligence.As of now, you don’t need to worry about either data or data sources.
Data Visualization can be your bow and arrow provided you know the exact way to use it.
In modern day scenario, data visualization has become the crux of efforts – raw data in various forms and statistics tends to be incredibly powerful, but only if you decide to work with them as a whole. After all, it’s not just the numbers but the story behind those numerical figures that reveals something. So, you require data visualization to brush up these notions and turn them into something more compelling to target audience. Data Visualization makes your messages more attractive, lively and enhances the impact, along with keeping your audience hooked.
Each year, pronouncements are made. And each year, a particular job field rides high above the tides of fortune. For 2017, Data Scientist jobs seem to be #1 Best Job in India. Several magazines and research associates have put Data Scientist jobs at #1 position. No wonder, data science jobs are the hottest jobs in today’s market, hopefully in future too. So, how do you become a good data scientist? Affordable Data Science Training Course in Gurgaon is now available in India that too quite easily. DexLab Analytics is one such institute that offers state-of-the-art data science training facilities for young aspiring candidates.
Get hold of SAS skills
If you are aware of the top data science skills, you must have known that statistical analysis and data mining calls for SAS specialization. SAS plays an important role in all these disciplines. It has been the pioneer and the most reliable software suit, and for a long time enjoying the monopoly position.
However, since the advent of R and Python, the powerful open source competitors, it is true that the growth curve of SAS has been little but hampered. Nevertheless SAS skills still boast of astounding demand all over the world.
SAS training courses help you understand the nuances of data science. Nowadays, these training’s are not too difficult to find, myriad institutes offer online and classroom training for its students on a regular basis. It is no more too difficult to get a grip on the fundamentals of this subject matter.
The number speaks of positivity
It would be like mine 11th commandment – there is a shortage of data science jobs. It is being predicted that there could be a shortage of 200,000 data scientists by 2020, and this is for real. Indian market is an emerging economy, though data science may not be so famous here as it is in the US, yet I am proud to say that the importance of this field is on the rise.
The survey says – the global demand for data scientists grew by more than 50% in between 2014 and 2015, while the searches have increased by 73%.
The skills you require to possess
By analyzing a whole lot of LinkedIn job postings, we have come to a conclusion that there are 5 high-in demand skills that you need to master in order to ace in data analytics – SQL, Hadoop, Python, Java, and R. Apart from these five, you also need to be quite proficient in Data Visualization and statistics, and try to bring out your creative side to the front.
How much difficult is it to choose a data analytics course?
Make sure, you know what you want, very clearly. Prepare yourself well, before getting into any course. Experience matters, but before that you need encompassing training on the subject matter that can only be offered by a pioneering institute of data science. However, before investing money and your time, check properly if the curriculum satisfies your needs. The material needs to be crisp, to the point and in line with the current industry standards.
To learn more about Machine Learning Using Python and Spark – click here. To learn more about Data Analyst with Advanced excel course – click here. To learn more about Data Analyst with SAS Course – click here. To learn more about Data Analyst with R Course – click here. To learn more about Big Data Course – click here.
We are happy to announce our month-long corporate training session for the representatives of WHO, who will be joining us to discuss data analytics all the way from Bhutan. The team of delegates who have come to seek training from our expert in-house trainers are for the Central of Disease Control, Ministry of Health Royal Government of Bhutan.
The training is on the concepts of R Programming, Data Science using R and Statistical Modelling using R, and will go on from the 8th of February 2017 to the 8th of March 2017. We are hosting this training session at our headquarters in Gurgaon, Delhi NCR. It is a matter of great pride and honour for the team of seasoned industry expert trainers at DexLab Analytics to be hosting the representatives from WHO.
