Why You Should Use Python For Your Next Project

• gooddata-python-sdk

  2 25 9.6 Python

  GoodData Cloud Python SDK

  

Our product contains a Headless BI engine where you can connect any application, data platform, or visualization tool to the engine’s semantic layer and consume the same consistent analytics anywhere. Connecting to a semantic layer with the help of GoodData Python libraries is very straightforward! GoodData Pandas allows creating pandas series and data frames from the computations done against a semantic model in GoodData.CN. As mentioned above, imagine you have a database with many tables, and you want to get a data frame consisting of columns from various joined tables. Usually, you have to do many joins manually in SQL before getting the desired data frame in Pandas. But if you connect the database to GoodData.CN, you can forget joins — with the help of a semantic model and GoodData Pandas, you will get your desired data frame with much less hassle. Just for demonstration, compare the two following code snippets. The first one is just pure pandas:

• examples

  143 7,763 5.3 Jupyter Notebook

  TensorFlow examples (by tensorflow)

  

If you are interested in tech, you probably noticed that people in the field like to call data the new oil. That would make data scientists, data engineers, and other data personas the oil magnates of the future, and they in turn rely on Python. These people spend most of their time preparing and working with data with the help of tools like Pandas, SciPy, TensorFlow, etc.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• Django

  484 77,009 9.8 Python

  The Web framework for perfectionists with deadlines.

  

Python is a high-level, dynamically typed programming language that focuses on rapid and robust development. It can be used in many areas, from web scraping to writing algorithms and data structures. Thanks to its versatility and simplicity, it has become a trendy language for data scientists in the last few years. Also, web developers are increasingly using Python thanks to frameworks such as Django, Flask, or FastAPI. World’s largest companies, such as Google, Facebook, Microsoft, and Spotify rely on Python every day, ensuring a lively community and a plethora of libraries for you to use for years to come.

• Pandas

  399 42,039 10.0 Python

  Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

  

If you are interested in tech, you probably noticed that people in the field like to call data the new oil. That would make data scientists, data engineers, and other data personas the oil magnates of the future, and they in turn rely on Python. These people spend most of their time preparing and working with data with the help of tools like Pandas, SciPy, TensorFlow, etc.

• NumPy

  272 26,459 10.0 Python

  The fundamental package for scientific computing with Python.

  

Still, it is worth mentioning that some libraries like NumPy, which is a library for scientific computing with Python, have some parts written in C, making it fast. It is always better to do the heavy computation in low-level languages such as C or C++, but their syntax and complexity might not be suitable for large projects. Therefore, some parts of Python libraries are written in C/C++ to make them faster and much more straightforward for data scientists, data analysts, and others. If you are looking for high performance in your code, consider Cython, which gives you C-like performance with code mostly written in Python.

• fastapi

  470 71,223 9.8 Python

  FastAPI framework, high performance, easy to learn, fast to code, ready for production

Python is a high-level, dynamically typed programming language that focuses on rapid and robust development. It can be used in many areas, from web scraping to writing algorithms and data structures. Thanks to its versatility and simplicity, it has become a trendy language for data scientists in the last few years. Also, web developers are increasingly using Python thanks to frameworks such as Django, Flask, or FastAPI. World’s largest companies, such as Google, Facebook, Microsoft, and Spotify rely on Python every day, ensuring a lively community and a plethora of libraries for you to use for years to come.

• Cython

  79 8,958 9.8 Python

  The most widely used Python to C compiler

  

Still, it is worth mentioning that some libraries like NumPy, which is a library for scientific computing with Python, have some parts written in C, making it fast. It is always better to do the heavy computation in low-level languages such as C or C++, but their syntax and complexity might not be suitable for large projects. Therefore, some parts of Python libraries are written in C/C++ to make them faster and much more straightforward for data scientists, data analysts, and others. If you are looking for high performance in your code, consider Cython, which gives you C-like performance with code mostly written in Python.

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

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