A Tools in Python for Performing Calculations with Weather Data


MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.

MetPy follows semantic versioning in its version number. This means that any MetPy 1.x release will be backwards compatible with an earlier 1.y release. By “backward compatible”, we mean that correct code that works on a 1.y version will work on a future 1.x version.

For additional MetPy examples not included in this repository, please see the Unidata Python Gallery.

We support Python >= 3.7.

Need Help?

Need help using MetPy? Found an issue? Have a feature request? Checkout our support page.

Important Links


Other required packages:

  • Numpy
  • Scipy
  • Matplotlib
  • Pandas
  • Pint
  • Xarray

There is also an optional dependency on the pyproj library for geographic projections (used with cross sections, grid spacing calculation, and the GiniFile interface).

See the installation guide for more information.

Code of Conduct

We want everyone to feel welcome to contribute to MetPy and participate in discussions. In that spirit please have a look at our Code of Conduct.


Imposter syndrome disclaimer: We want your help. No, really.

There may be a little voice inside your head that is telling you that you’re not ready to be an open source contributor; that your skills aren’t nearly good enough to contribute. What could you possibly offer a project like this one?

We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one’s coding skills. Writing perfect code isn’t the measure of a good developer (that would disqualify all of us!); it’s trying to create something, making mistakes, and learning from those mistakes. That’s how we all improve, and we are happy to help others learn.

Being an open source contributor doesn’t just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you’re coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.

For more information, please read the see the contributing guide.


The space MetPy aims for is GEMPAK (and maybe NCL)-like functionality, in a way that plugs easily into the existing scientific Python ecosystem (numpy, scipy, matplotlib). So, if you take the average GEMPAK script for a weather map, you need to:

  • read data
  • calculate a derived field
  • show on a map/skew-T

One of the benefits hoped to achieve over GEMPAK is to make it easier to use these routines for any meteorological Python application; this means making it easy to pull out the LCL calculation and just use that, or re-use the Skew-T with your own data code. MetPy also prides itself on being well-documented and well-tested, so that on-going maintenance is easily manageable.

The intended audience is that of GEMPAK: researchers, educators, and any one wanting to script up weather analysis. It doesn’t even have to be scripting; all python meteorology tools are hoped to be able to benefit from MetPy. Conversely, it’s hoped to be the meteorological equivalent of the audience of scipy/scikit-learn/skimage.

Download Details:

Author: Unidata
The Demo/Documentation: View The Demo/Documentation
Download Link: Download The Source Code
Official Website: https://github.com/Unidata/MetPy
License: BSD-3-Clause

#python #metpy

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A Tools in Python for Performing Calculations with Weather Data
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Your Data Architecture: Simple Best Practices for Your Data Strategy

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If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

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‘Hello Python’

#single (') Quoted String

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“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

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The operator “+” is used to concatenate strings and “*” is used to repeat the string.


output**:****‘Hello python’**

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To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

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The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:

  1. Big Data Framework
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  5. Data Preprocessing Tools
  6. Data Wrangling Tools
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  8. Data Governance Tools
  9. Security Management Tools
  10. Real-Time Data Streaming Tools

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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:

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Instead of only gathering and storing information that can offer crucial insights to meet short-term goals, an increasing number of enterprises are storing much larger amounts of data gathered from multiple resources across business processes. However, all this data is meaningless on its own. It can add value only when it is processed and analyzed the right way to draw point insights that can improve decision-making.

Processing and analyzing big data is not an easy task. If not handled correctly, big data can turn into an obstacle rather than an effective solution for businesses. Effective handling of big data management  requires to use of tools that can steer you toward tangible, substantial results. For that, you need a set of great big data tools that will not only solve this problem but also help you in producing substantial results.

Data storage tools, warehouses, and data lakes all play a crucial role in helping companies store and sort vast amounts of information. However, the true power of big data lies in its analytics. There are a host of big data tools in the market today to aid a business’ journey from gathering data to storing, processing, analyzing, and reporting it. Let’s take a closer look at some of the top big data tools that can help you inch closer to your goal of establishing data-driven decision-making and workflow processes.

Apache Hadoop

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