Over my career I’ve worked on many geospatial related projects using the ArcGIS platform, which I absolutely love. That means I get to consult in projects with cutting-edge geospatial technologies, like Multidimensional Rasters, Deep Learning, and spatial IoT automation. With that in mind, I always try to keep track of how to perform the same operations I’m working on without Esri’s beautifully crafted systems.
Over the last few weekends during an exceptionally tedious quarantine I’ve worked on this little script that would reproduce something I’ve been developing with ESRI’s Living Atlas to achieve NDVI Zonal Statistics (Normalized Difference Vegetation Index, a classic in Remote Sensing) for rural land plots.
The plan here was to perform an entire geoprocessing and remote sensing routine without having to resort to any GIS Desktop software. We’re starting out with a layer that contains some parcels in which we’re intersted and a layer with protected areas where special legislation applies. Nothing else is allowed outside python! This is our plan du jour:
So. First things first. We have to import the geojson file that has the land plots stored in it. For that, we’ll be using the geopandas library. If you’re not familiar with it, my advice is get acquainted with it. Geopandas basically emulates the functions we’ve been using in classic GIS Desktop softwares (GRASS, GDAL, ArcGIS, QGIS…) in python in a way consistent with pandas — a very popular tool among data scientists — to allow spatial operations on geometric types.
In order to import our geojson, the first thing we must note are the data types in geopandas. Basically, when we ran the
.read_file method, we assigned a
geodataframe type to the
polygons variable. Inside every
geodataframe there will always be a
geoseries , which we can access via the
.geometry method. Once we find the
geoseries , we can make use of the
.isvalid method, that produces a list of True/False values for each record in our series.
And of course there are invalid geometries hanging in our dataset. It comes as no surprise, since those land plots came from the CAR Registry, where every rural land owner in Brazil have to self-declare the extent of their own properties.
So, how do we fix that? Maybe you’re used to running the excelent invalid geometries checking tools present in ArcGIS or QGIS, which generate even a report on what the problem was with each record in a table. But we don’t have access to those in geopandas. Instead, we’ll do a little trick to correct the geometries, by applying a 0 meter buffer to all geometries.
And now we’ll finally get to take a look at our polygons, by using the
.plot method, which is actually inherited from the
matplotlib components in
This is a fast and useful way to get a quick notion of what our data looks like spatially, but it’s not the same as a map.
#python #towards-data-science #geopandas #google-earth-engine #geospatial
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
#python development services #python development company #python app development #python development #python in web development #python software development
Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.
Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:
Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.
#python #packages or libraries #python 20 modules #python 20 most usefull modules #python intersting modules #top 20 python libraries #top 20 python modules #top 20 python packages
python is one of the most go-for languages among the developers due to the availability of open-source libraries and frameworks. According to a survey report, Python is the top language preferred for Statistical Modelling, and an overwhelming majority of practitioners prefer Python as the language for statistical works.
Python has become a favourite language for hackers these days. The reason is the presence of pre-built tools and libraries, which makes hacking easy. In fact, the language is adequate for ethical hacking as ethical hackers need to develop smaller scripts, and Python fulfils this criterion.
Below here, we listed down the top 7 Python libraries used in hacking.
**About: **Requests is a simple HTTP library for Python that allows a user to send HTTP/1.1 requests extremely easily. This library helps in building robust HTTP applications and includes intuitive features such as automatic content decompression and decoding, connection timeouts, basic & digits authentication, among others.
Know more here.
About: Scapy is a powerful Python-based interactive packet manipulation program and library. This library is able to forge or decode packets of a wide number of protocols, send them on the wire, capture them, store or read them using pcap files, match requests, and more. It allows the construction of tools that can easily scan or attack networks. It is designed to allow fast packet prototyping by using default values that work. It can also perform tasks such as sending invalid frames, injecting your own 802.11 frames, combining techniques, such as VLAN hopping with ARP cache poisoning, VOIP decoding on WEP encrypted channel, etc., which most other tools cannot.
Know more here.
**About: **IMpacket is a library that includes a collection of Python classes for working with network protocols. It is focused on providing low-level programmatic access to network packets. It allows Python developers to craft and decode network packets in a simple and consistent manner. The library provides a set of tools as examples of what can be done within the context of this library.
Know more here.
**About: **Cryptography is a package which provides cryptographic recipes and primitives to Python developers. It includes both high-level recipes and low-level interfaces to common cryptographic algorithms such as symmetric ciphers, message digests and key derivation functions. This library is broadly divided into two levels. One is with safe cryptographic recipes that require little to no configuration choices. The other level is low-level cryptographic primitives, which are often dangerous and can be used incorrectly.
Know more here.
#developers corner #hacking tools #libraries for hacking #python #python libraries #python libraries used for hacking #python tools
Some of my most popular blogs are about Python libraries. I believe that they are so popular because Python libraries have the power to save us a lot of time and headaches. The problem is that most people focus on those most popular libraries but forget that multiple less-known Python libraries are just as good as their most famous cousins.
Finding new Python libraries can also be problematic. Sometimes we read about these great libraries, and when we try them, they don’t work as we expected. If this has ever happened to you, fear no more. I got your back!
In this blog, I will show you four Python libraries and why you should try them. Let’s get started.
#python #coding #programming #cool python libraries #python libraries #4 cool python libraries