#data visualization python

[codewithnazam]

What is Pandas in Python?

Introduction What is Pandas? What does the name “Pandas” stand for? Why use Pandas? Getting Started with Pandas Installing Pandas Creating your first DataFrame Exploring and manipulating data in DataFrames Key Features of Pandas Data Structures Data Manipulation and Analysis Visualization Conclusion Summary of key benefits Why Pandas is essential for Python data science FAQs What is…

[gardenfractals]

Locally Linear Embedding (LLE) approaches

[davesanalytics]

CORRECTED: Causes of Death In Infants.

My apologies. The previous "Causes" plot had the data reversed. Here is the correct plot.

[localgeography]

Example map of my python script that calculates the total area of each place category that falls within a user-specified region.

This script takes a radius (in meters) and geographic coordinates (in decimal degrees) that are specified by the user as inputs and produces a .csv file of each place category with its corresponding area and a map showing the area of interest.

[uthra-krish]

Exploring Data Science Tools: My Adventures with Python, R, and More

Welcome to my data science journey! In this blog post, I'm excited to take you on a captivating adventure through the world of data science tools. We'll explore the significance of choosing the right tools and how they've shaped my path in this thrilling field.

Choosing the right tools in data science is akin to a chef selecting the finest ingredients for a culinary masterpiece. Each tool has its unique flavor and purpose, and understanding their nuances is key to becoming a proficient data scientist.

I. The Quest for the Right Tool

My journey began with confusion and curiosity. The world of data science tools was vast and intimidating. I questioned which programming language would be my trusted companion on this expedition. The importance of selecting the right tool soon became evident.

I embarked on a research quest, delving deep into the features and capabilities of various tools. Python and R emerged as the frontrunners, each with its strengths and applications. These two contenders became the focus of my data science adventures.

II. Python: The Swiss Army Knife of Data Science

Python, often hailed as the Swiss Army Knife of data science, stood out for its versatility and widespread popularity. Its extensive library ecosystem, including NumPy for numerical computing, pandas for data manipulation, and Matplotlib for data visualization, made it a compelling choice.

My first experiences with Python were both thrilling and challenging. I dove into coding, faced syntax errors, and wrestled with data structures. But with each obstacle, I discovered new capabilities and expanded my skill set.

III. R: The Statistical Powerhouse

In the world of statistics, R shines as a powerhouse. Its statistical packages like dplyr for data manipulation and ggplot2 for data visualization are renowned for their efficacy. As I ventured into R, I found myself immersed in a world of statistical analysis and data exploration.

My journey with R included memorable encounters with data sets, where I unearthed hidden insights and crafted beautiful visualizations. The statistical prowess of R truly left an indelible mark on my data science adventure.

IV. Beyond Python and R: Exploring Specialized Tools

While Python and R were my primary companions, I couldn't resist exploring specialized tools and programming languages that catered to specific niches in data science. These tools offered unique features and advantages that added depth to my skill set.

For instance, tools like SQL allowed me to delve into database management and querying, while Scala opened doors to big data analytics. Each tool found its place in my toolkit, serving as a valuable asset in different scenarios.

V. The Learning Curve: Challenges and Rewards

The path I took wasn't without its share of difficulties. Learning Python, R, and specialized tools presented a steep learning curve. Debugging code, grasping complex algorithms, and troubleshooting errors were all part of the process.

However, these challenges brought about incredible rewards. With persistence and dedication, I overcame obstacles, gained a profound understanding of data science, and felt a growing sense of achievement and empowerment.

VI. Leveraging Python and R Together

One of the most exciting revelations in my journey was discovering the synergy between Python and R. These two languages, once considered competitors, complemented each other beautifully.

I began integrating Python and R seamlessly into my data science workflow. Python's data manipulation capabilities combined with R's statistical prowess proved to be a winning combination. Together, they enabled me to tackle diverse data science tasks effectively.

VII. Tips for Beginners

For fellow data science enthusiasts beginning their own journeys, I offer some valuable tips:

Embrace curiosity and stay open to learning.

Work on practical projects while engaging in frequent coding practice.

Explore data science courses and resources to enhance your skills.

Seek guidance from mentors and engage with the data science community.

Remember that the journey is continuous—there's always more to learn and discover.

My adventures with Python, R, and various data science tools have been transformative. I've learned that choosing the right tool for the job is crucial, but versatility and adaptability are equally important traits for a data scientist.

As I summarize my expedition, I emphasize the significance of selecting tools that align with your project requirements and objectives. Each tool has a unique role to play, and mastering them unlocks endless possibilities in the world of data science.

I encourage you to embark on your own tool exploration journey in data science. Embrace the challenges, relish the rewards, and remember that the adventure is ongoing. May your path in data science be as exhilarating and fulfilling as mine has been.

Happy data exploring!

[tchaikovskym]

How's it going learning python? I needed to learn the very, very basics last year and it was fun, but at the same time so difficult to understand and remember all the codes, especially functions and all the stuff for diagrams like matplotlib etc. Hope it's going well for you :)

Listen bestie I'm at a print("Hello, World!") and add # before comments stage :D learned how to define structures and print(type(x)). I'm a baby.

But overall, I think it will be fun to use! If I manage to do anything :D because I have a shit ton to do on top of all that (full time work, writing an introduction to an article, making a med student a material for his cell bio microscope slides, and making an hour long lecture abt my thesis for school kids :D it's all due next week :D)

But overall I think I will be okay, because complicated things don't scare me as much as they used to. And besides I won't have to remember everything by heart. If it's me and stackoverflow against python let it be, I've been there with R.

And I'm actually very scared of matplotlib bc it was an extra credit task we had to perform on a hpc cluster and I had no idea how to do it (the rest of the code was given to us ready except the extra credit tasks). It was the end of the lesson so I just. Left.

[newsdataapi]

Get Free News API to scrape news articles

NewsData.io offered a free news API that developers could use to access news articles and headlines from various sources. This API provided endpoints for fetching news articles, headlines, and other related data. Get a Free News API Key in 3 steps:

Visit NewsData.io website

Create an account on it

Get Free API Keys from dashboard

[d0nutzgg]

Understanding IHD with Data Science

Ischemic Heart Disease (IHD), more commonly recognized as coronary artery disease, is a profound health concern that stems from a decreased blood supply to the heart. Such a decrease is typically due to fatty deposits or plaques narrowing the coronary arteries. These arteries, as vital conduits delivering oxygen-rich blood to the heart, play a paramount role in ensuring the heart's efficient functioning. An obstruction or reduced flow within these arteries can usher in adverse outcomes, with heart attacks being the most dire. Given the gravity of IHD, the global medical community emphasizes the essence of early detection and prompt intervention to manage its repercussions effectively.

A New Age in Healthcare: Embracing Data Science

As we stand on the cusp of the fourth industrial revolution, technology's intertwining with every domain is evident. The healthcare sector is no exception. The integration of data science in healthcare is not merely an augmentation; it's a paradigm shift. Data science, with its vast array of tools and methodologies, is fostering new avenues to understand, diagnose, and even predict various health conditions long before they manifest pronounced symptoms.

Machine Learning: The Vanguard of Modern Medical Research

Among the myriad of tools under the vast umbrella of data science, Machine Learning (ML) shines exceptionally bright. An essential offshoot of artificial intelligence, ML capitalizes on algorithms and statistical models, granting computers the capability to process vast amounts of data and discern patterns without being explicitly programmed.

In the healthcare realm, the applications of ML are manifold. From predicting potential disease outbreaks based on global health data trends to optimizing patient flow in bustling hospitals, ML is progressively becoming a linchpin in medical operations. One of its most lauded applications, however, is its prowess in early disease prediction, and IHD detection stands as a testament to this.

Drawn to the immense potential ML holds, I ventured into a research project aimed at harnessing the RandomForestClassifier model's capabilities. Within the medical research sphere, this model is celebrated for its robustness and adaptability, making it a prime choice for my endeavor.

Deep Dive into the Findings

The results from the ML model were heartening. With an accuracy rate of 90%, the model’s prowess in discerning the presence of IHD based on an array of parameters was evident. Such a high accuracy rate is pivotal, considering the stakes at hand – the very health of a human heart. 9 times out of 10 the model is correct at its predictions.

Breaking down the data, some correlations with IHD stood out prominently:

Moderate COPD (Chronic Obstructive Pulmonary Disease) – 15%: COPD's inclusion is noteworthy. While primarily a lung condition, its linkage with heart health has been a topic of numerous studies. A compromised respiratory system can inadvertently strain the heart, underscoring the interconnectedness of our bodily systems.

Diabetes – 18%: The correlation between diabetes and heart health isn't novel. Elevated blood sugar levels over extended periods can damage blood vessels, including the coronary arteries.

Age (segmented in quarterlies) – 15%: Age, as an immutable factor, plays a significant role. With age, several bodily systems gradually wear down, rendering individuals more susceptible to a plethora of conditions, IHD included.

Smoking habits – 14%: The deleterious effects of smoking on lung health are well-documented. However, its impact extends to the cardiovascular system, with nicotine and other chemicals adversely affecting heart functions.

MWT1 and MWT2 (indicators of physical endurance) – 13% and 14% respectively: Physical endurance and heart health share an intimate bond. These metrics, gauging one's physical stamina, can be precursors to potential heart-related anomalies.

Redefining Patient Care in the Machine Learning Era

Armed with these insights, healthcare can transcend its conventional boundaries. A deeper understanding of IHD's contributors empowers medical professionals to devise comprehensive care strategies that are both preventive and curative.

Moreover, the revelations from this study underscore the potential for proactive medical interventions. Instead of being reactive, waiting for symptoms to manifest, healthcare providers can now adopt a preventive stance. Patients exhibiting the highlighted risk factors can be placed under more meticulous observation, ensuring that potential IHD developments are nipped in the bud.

With the infusion of machine learning, healthcare is on the cusp of a personalized revolution. Gone are the days of one-size-fits-all medical approaches. Recognizing the uniqueness of each patient's health profile, machine learning models like the one employed in this study can pave the way for hyper-personalized care regimens.

As machine learning continues to entrench itself in healthcare, a future where disease predictions are accurate, interventions are timely, and patient care is unparalleled isn't merely a vision; it's an impending reality.

[igmpi]

Explore IGMPI’s Big Data Analytics program, designed for professionals seeking expertise in data-driven decision-making. Learn advanced analytics techniques, data mining, machine learning, and business intelligence tools to excel in the fast-evolving world of big data.

[tandeminformaticsmadurai]

Comprehensive Full Stack Development Course in Madurai

Are you looking to become a proficient full stack developer? This Full Stack Development Course in Madurai is designed to transform you into a versatile and job-ready developer skilled in both front-end and back-end technologies. Over the duration of the course, you will master essential programming languages, frameworks, and tools to create fully functional, dynamic websites and applications from scratch. This course covers popular front-end technologies like HTML, CSS, JavaScript, and React, along with server-side development using Node.js, Express.js, and MongoDB.

Website                      : https://tandeminformatics.com/

[detroitography]

Mapping Merged Sidewalk and Parcel Data in Detroit

by: Ted Tansley After mapping DDOT bus stop assets in Detroit, I revisited the sidewalk reporter data to see if there was a way to map it out too. I found that the web app had a parcel data layer that was separate from the reports itself. In looking at it closer, I found that the parcel layer was from 2018, but I figured that it would work for what I wanted to use it for. My Code Data…

[uegub]

Exploring the Key Facets of Data in Data Science

Data is at the heart of everything in data science. But it’s not just about having a lot of data; it’s about knowing how to handle it, process it, and extract meaningful insights from it. In data science, we work with data in many different forms, and each form has its own unique characteristics. In this article, we’re going to take a closer look at the important aspects of data that data scientists must understand to unlock its full potential. 

1. Types of Data: Understanding the Basics 

When you dive into data science, one of the first things you’ll encounter is understanding the different types of data. Not all data is created equal, and recognizing the type you’re working with helps you figure out the best tools and techniques to use. 

Structured Data: This is the kind of data that fits neatly into rows and columns, like what you'd find in a spreadsheet or a relational database. Structured data is easy to analyze, and tools like SQL are perfect for working with it. Examples include sales data, employee records, or product inventories. 

Unstructured Data: Unstructured data is a bit trickier. It’s not organized in any set format. Think about things like social media posts, emails, or videos. This type of data often requires advanced techniques like natural language processing (NLP) or computer vision to make sense of it. 

Semi-structured Data: This type of data is somewhere in the middle. It has some structure but isn’t as organized as structured data. Examples include XML or JSON files. You’ll often encounter semi-structured data when pulling information from websites or APIs. 

Knowing the type of data you’re dealing with is crucial. It will guide you in selecting the right tools and methods for analysis, which is the foundation of any data science project. 

2. Data Quality: The Foundation of Reliable Insights 

Good data is essential to good results. Data quality is a huge factor in determining how reliable your analysis will be. Without high-quality data, the insights you generate could be inaccurate or misleading. 

Accuracy: Data needs to be accurate. Even small errors can distort your analysis. If the numbers you're working with are wrong, the conclusions you draw won’t be reliable. This could be something like a typo in a database entry or an incorrect sensor reading. 

Consistency: Consistency means the data is uniform across different sources. For example, you don't want to have "NY" and "New York" both used to describe the same location in different parts of your dataset. Consistency makes it easier to work with the data without having to constantly fix small discrepancies. 

Completeness: Missing data is a common issue, but it can be a real problem. If you’re working with a dataset that has missing values, your model or analysis might not be as effective. Sometimes you can fill in missing data or remove incomplete rows, but it’s important to be mindful of how these missing pieces affect your overall results. 

Data scientists spend a lot of time cleaning and validating data because, at the end of the day, your conclusions are only as good as the data you work with. 

3. Transforming and Preprocessing Data 

Once you’ve got your data, it often needs some work before you can start drawing insights from it. Raw data usually isn’t in the best form for analysis, so transforming and preprocessing it is an essential step. 

Normalization and Scaling: When different features of your data are on different scales, it can cause problems with some machine learning algorithms. For example, if one feature is measured in thousands and another is measured in ones, the algorithm might give more importance to the larger values. Normalizing or scaling your data ensures that everything is on a comparable scale. 

Feature Engineering: This involves creating new features from the data you already have. It’s a way to make your data more useful for predictive modeling. For example, if you have a timestamp, you could extract features like day of the week, month, or season to see if they have any impact on your target variable. 

Handling Missing Data: Missing data happens. Sometimes it’s due to errors in data collection, and sometimes it’s just unavoidable. You might choose to fill in missing values based on the mean or median, or, in some cases, remove rows or columns that have too many missing values. 

These steps ensure that your data is clean, consistent, and ready for the next stage of analysis. 

4. Integrating and Aggregating Data 

In real-world projects, you rarely get all your data in one place. More often than not, data comes from multiple sources: databases, APIs, spreadsheets, or even data from sensors. This is where data integration comes in. 

Data Integration: This process involves bringing all your data together into one cohesive dataset. Sometimes, this means matching up similar records from different sources, and other times it means transforming data into a common format. Integration helps you work with a unified dataset that tells a full story. 

Data Aggregation: Sometimes you don’t need all the data. Instead, you might want to summarize it. Aggregation involves combining data points into a summary form. For example, if you're analyzing sales, you might aggregate the data by region or time period (like daily or monthly sales). This helps make large datasets more manageable and reveals high-level insights. 

When you're dealing with large datasets, integration and aggregation help you zoom out and see the bigger picture. 

5. The Importance of Data Visualization 

Data visualization is all about telling the story of your data. It’s one thing to find trends and patterns, but it’s another to be able to communicate those findings in a way that others can understand. Visualization is a powerful tool for data scientists because it makes complex information more accessible. 

Spotting Trends: Graphs and charts can help you quickly see patterns over time, like a rise in sales during the holiday season or a dip in customer satisfaction after a product launch. 

Identifying Outliers: Visualizations also help you identify anomalies in the data. For instance, a bar chart might highlight an unusually high spike in sales that warrants further investigation. 

Making Sense of Complex Data: Some datasets are just too large to process without visualization. A heatmap or scatter plot can distill complex datasets into something much easier to digest. 

Effective data visualization turns raw data into insights that are easier to communicate, which is a critical skill for any data scientist. 

6. Data Security and Privacy: Ethical Considerations 

In today’s data-driven world, privacy and security are huge concerns. As a data scientist, it’s your responsibility to ensure that the data you’re working with is handled ethically and securely. 

Data Anonymization: If you’re working with sensitive information, it’s crucial to remove personally identifiable information (PII) to protect people's privacy. Anonymizing data ensures that individuals’ identities remain secure. 

Secure Data Storage: Data should always be stored securely to prevent unauthorized access. This might mean using encrypted storage solutions or following best practices for data security. 

Ethical Use of Data: Finally, data scientists need to be mindful of how the data is being used. For example, if you’re using data to make business decisions or predictions, it’s essential to ensure that your models are not biased in ways that could negatively impact certain groups of people. 

Data security and privacy are not just legal requirements—they’re also ethical obligations that should be taken seriously in every data science project. 

Conclusion 

Data is the foundation of data science, but it's not just about having lots of it. The true power of data comes from understanding its many facets, from its structure and quality to how it’s processed, integrated, and visualized. By mastering these key elements, you can ensure that your data science projects are built on solid ground, leading to reliable insights and effective decision-making. 

If you’re ready to dive deeper into data science and learn more about handling data in all its forms, seeking out a data science course nearby might be a great way to gain hands-on experience and expand your skill set. 

[davesanalytics]

Top 10 Countries Providing Foreign Aid (ranked by % CNI)

[uthra-krish]

The Skills I Acquired on My Path to Becoming a Data Scientist

Data science has emerged as one of the most sought-after fields in recent years, and my journey into this exciting discipline has been nothing short of transformative. As someone with a deep curiosity for extracting insights from data, I was naturally drawn to the world of data science. In this blog post, I will share the skills I acquired on my path to becoming a data scientist, highlighting the importance of a diverse skill set in this field.

The Foundation — Mathematics and Statistics

At the core of data science lies a strong foundation in mathematics and statistics. Concepts such as probability, linear algebra, and statistical inference form the building blocks of data analysis and modeling. Understanding these principles is crucial for making informed decisions and drawing meaningful conclusions from data. Throughout my learning journey, I immersed myself in these mathematical concepts, applying them to real-world problems and honing my analytical skills.

Programming Proficiency

Proficiency in programming languages like Python or R is indispensable for a data scientist. These languages provide the tools and frameworks necessary for data manipulation, analysis, and modeling. I embarked on a journey to learn these languages, starting with the basics and gradually advancing to more complex concepts. Writing efficient and elegant code became second nature to me, enabling me to tackle large datasets and build sophisticated models.

Data Handling and Preprocessing

Working with real-world data is often messy and requires careful handling and preprocessing. This involves techniques such as data cleaning, transformation, and feature engineering. I gained valuable experience in navigating the intricacies of data preprocessing, learning how to deal with missing values, outliers, and inconsistent data formats. These skills allowed me to extract valuable insights from raw data and lay the groundwork for subsequent analysis.

Data Visualization and Communication

Data visualization plays a pivotal role in conveying insights to stakeholders and decision-makers. I realized the power of effective visualizations in telling compelling stories and making complex information accessible. I explored various tools and libraries, such as Matplotlib and Tableau, to create visually appealing and informative visualizations. Sharing these visualizations with others enhanced my ability to communicate data-driven insights effectively.

Machine Learning and Predictive Modeling

Machine learning is a cornerstone of data science, enabling us to build predictive models and make data-driven predictions. I delved into the realm of supervised and unsupervised learning, exploring algorithms such as linear regression, decision trees, and clustering techniques. Through hands-on projects, I gained practical experience in building models, fine-tuning their parameters, and evaluating their performance.

Database Management and SQL

Data science often involves working with large datasets stored in databases. Understanding database management and SQL (Structured Query Language) is essential for extracting valuable information from these repositories. I embarked on a journey to learn SQL, mastering the art of querying databases, joining tables, and aggregating data. These skills allowed me to harness the power of databases and efficiently retrieve the data required for analysis.

Domain Knowledge and Specialization

While technical skills are crucial, domain knowledge adds a unique dimension to data science projects. By specializing in specific industries or domains, data scientists can better understand the context and nuances of the problems they are solving. I explored various domains and acquired specialized knowledge, whether it be healthcare, finance, or marketing. This expertise complemented my technical skills, enabling me to provide insights that were not only data-driven but also tailored to the specific industry.

Soft Skills — Communication and Problem-Solving

In addition to technical skills, soft skills play a vital role in the success of a data scientist. Effective communication allows us to articulate complex ideas and findings to non-technical stakeholders, bridging the gap between data science and business. Problem-solving skills help us navigate challenges and find innovative solutions in a rapidly evolving field. Throughout my journey, I honed these skills, collaborating with teams, presenting findings, and adapting my approach to different audiences.

Continuous Learning and Adaptation

Data science is a field that is constantly evolving, with new tools, technologies, and trends emerging regularly. To stay at the forefront of this ever-changing landscape, continuous learning is essential. I dedicated myself to staying updated by following industry blogs, attending conferences, and participating in courses. This commitment to lifelong learning allowed me to adapt to new challenges, acquire new skills, and remain competitive in the field.

In conclusion, the journey to becoming a data scientist is an exciting and dynamic one, requiring a diverse set of skills. From mathematics and programming to data handling and communication, each skill plays a crucial role in unlocking the potential of data. Aspiring data scientists should embrace this multidimensional nature of the field and embark on their own learning journey. If you want to learn more about Data science, I highly recommend that you contact ACTE Technologies because they offer Data Science courses and job placement opportunities. Experienced teachers can help you learn better. You can find these services both online and offline. Take things step by step and consider enrolling in a course if you’re interested. By acquiring these skills and continuously adapting to new developments, they can make a meaningful impact in the world of data science.

[cacmsinsitute]

Why Data Analytics is the Skill of the Future (And How to Get Ahead)

In today's fast-paced digital landscape, the ability to analyse and interpret data is more important than ever. With the globe collecting data at an unprecedented rate, industries are turning to data analytics to drive decisions, enhance efficiency, and gain a competitive advantage. As a result, data analytics is rapidly becoming one of the most valued skills in almost every industry, and individuals who understand it are well-positioned for a prosperous career.

The Increasing Demand for Data Analytics

Data analytics is more than just a buzzword; it's a fast expanding field that is impacting industries around the world. According to the U.S. Bureau of Labour Statistics, demand for data science and analytics experts is predicted to increase by 35% between 2021 and 2031, greatly above the average for all occupations. This rapid expansion emphasizes the importance of data analytics as a vital business function, with organizations relying on data to make informed decisions and optimize operations.

Data-driven tactics are being adopted in a variety of industries, including healthcare, finance, marketing, and ecommerce. Companies seek experienced people who can use data to foresee trends, analyze customer behavior, streamline operations, and improve overall decision-making. As a result, data analytics specialists are in high demand, and mastering this ability can lead to a wide range of opportunities in this competitive area.

Why Data Analytics is Important for Future Careers

Developing data analytics abilities is one of the most effective strategies for students and professionals to future-proof their careers. As businesses increasingly rely on data-driven insights, people who can comprehend and analyze data are well-positioned for long-term success.

Data analytics is a broad field that applies to almost every sector. Understanding data is essential for anyone who wants to work in corporate planning, marketing, finance, or healthcare. The capacity to analyze and interpret massive data sets enables professionals to make better decisions, discover hidden possibilities, and deliver actionable insights. Businesses will increasingly prioritize data-driven strategies, making data analytics experts invaluable assets.

How to Advance in Data Analytics: Enroll in Offline Courses

To succeed in this competitive sector, hands-on experience is required. While there are several online courses accessible, offline learning provides the benefits of personalized instruction, engaging learning environments, and direct access to knowledgeable professors. CACMS Institute in Amritsar offers offline data analytics courses that educate students with the practical skills and knowledge required to succeed in this rapidly expanding sector.

CACMS Institute provides expert advice in a classroom setting where you may ask real-time questions, work on actual projects, and engage with peers on data-driven challenges. The curriculum is intended to emphasize the fundamentals of data analytics, covering important tools such as Python, SQL, Power BI, Tableau, and Excel. These tools are vital for anyone interested in pursuing a career in data analytics since they allow experts to manage, visualize, and analyze data efficiently.

Future-Proof Your Career with CACMS Institute

CACMS Institute provides an organized, offline learning environment that teaches more than just theory; it also teaches hands-on, practical skills. CACMS' courses focus on practical data analytics applications, ensuring that students not only learn the tools and techniques but also understand how to apply them in real-world corporate contexts.

If you want to advance in the field of data analytics, there's never been a better opportunity to participate in an offline course at CACMS Institute. The combination of professional instructors, a well crafted curriculum, and an engaging classroom atmosphere will prepare you for success in tomorrow's data-driven world.

Take the first step towards safeguarding your future now! Contact CACMS Institute at +91 8288040281 or visit cacms.in for more information and to enrol in our data analytics courses in Amritsar.

[newsdataapi]

In today's world creating a website and application has become very easy with the use of APIs. With the APIs, you can integrate your website or applications with other sources. Here we have a list of Top Free APIs you can use in your APP development.