## NumPy Structured Arrays vs Record Arrays, NumPy Arrays Tutorial in Python Data Science

In this NumPy Python Data Science Tutorial, i discuss NumPy Structured arrays and NumPy Record arrays. Structured arrays use structured data type.
NumPy Structured arrays ( 1:20 ) are ndarrays whose datatype is a composition of simpler datatypes organized as a sequence of named fields.
NumPy Record Arrays ( 7:55 ) use a special datatype, numpy.record, that allows field access by attribute on the structured scalars obtained from the array.
NumPy is the ultimate package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object, tools for integrating C/C++ and Fortran code, sophisticated (broadcasting) functions, useful linear algebra, random number capabilities and Fourier transform.
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NumPy Data Science Essential Training introduces the beginning to intermediate data scientist to NumPy, the Python library that supports numerical, scientific, and statistical programming, including machine learning. The library supports several aspects of data science, providing multidimensional array objects, derived objects (matrixes and masked arrays), and routines for math, logic, sorting, statistics, and random number generation. You will learn how to work with NumPy and Python within Jupyter Notebook, a browser-based tool for creating interactive documents with live code, annotations, and even visualizations such as plots. Learn how to create NumPy arrays, use NumPy statements and snippets, and index, slice, iterate, and otherwise manipulate arrays. Plus, learn how to plot data and combine NumPy arrays with Python classes, and get examples of NumPy in action: solving linear equations, finding patterns, performing statistics, generating magic cubes, and more.

Topics include:
• Using Jupyter Notebook
• Creating NumPy arrays from Python structures – https://youtu.be/69ComsKKRvA
• Slicing arrays – https://youtu.be/z4vDLNMDFE4
• Plotting in Jupyter notebooks
• Joining and splitting arrays
• Rearranging array elements
• Creating universal functions
• Finding patterns
• Building magic squares and magic cubes with NumPy and Python
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### Comment List

• TheEngineeringWorld
November 30, 2020

Your channel is great. Thanks for the content.

• TheEngineeringWorld
November 30, 2020

This channel is a great one

• TheEngineeringWorld
November 30, 2020

Sir kindly make Machine Learning projects which are under mentioned, i really need ???

Traveling Salesman Problem

Heuristics

Informed Search

Uninformed Search

Pure Heuristics

• Natural Language Processing

• Expert Systems

• Robotics

• Agents and Environment

•Propositional logic

• First order predicate logic

• TheEngineeringWorld
November 30, 2020

I am getting the following error on executing this set of codes.

person_data_def=[('name','S6'),('height','f8'),('weight','f8'),('age','18')]
people_array = np.zeros((4) , dtype =person_data_def)
people_array
TypeError Traceback (most recent call last)
<ipython-input-19-92ba0015998b> in <module>
—-> 1 people_array = np.zeros((4) , dtype =person_data_def)
2 people_array

TypeError: data type "" not understood

• TheEngineeringWorld
November 30, 2020

hello sir…. what are these S6, i8 etc. I could not find any documentation for these stuff.

• TheEngineeringWorld
November 30, 2020

Sir – excellent video here on NumPy arrays with mixed data types and object-oriented programming; thank you . Your 'Data Science with Python' videos are always well-structured and informative. I also like your reference to the "arithmetic fountain of youth" — good one 🙂

• TheEngineeringWorld
November 30, 2020

I really needed record arrays for my implementation, thank you for the video. it really helped.