#typescript#csv#diagrams#graph#json#nextjs#react#tool#visualization#yaml
JSON Crack is a free, open-source tool that instantly turns complex JSON, YAML, CSV, XML, or TOML data into clear, interactive graphs, making it easier to explore and understand your information. It lets you convert between formats, validate data, generate code (like TypeScript interfaces), run queries, and export visuals as images—all while keeping your data private since everything processes locally on your device[1][2][5].
https://github.com/AykutSarac/jsoncrack.com
https://docs.python.org/3.5/library/csv.html
The so-called #CSV (Comma Separated Values) format is the most common import and export format for spreadsheets and databases. CSV format was used for many years prior to attempts to describe the format in a standardized way in RFC 4180. The lack of a well-defined standard means that subtle differences often exist in the data produced and consumed by different applications. These differences can make it annoying to process CSV files from multiple sources. Still, while the delimiters and quoting characters vary, the overall format is similar enough that it is possible to write a single module which can efficiently manipulate such data, hiding the details of reading and writing the data from the programmer.
# The standard string repr for dicts is hard to read:
»> my_mapping = {'a': 23, 'b': 42, 'c': 0xc0ffee}
»> my_mapping
{'b': 42, 'c': 12648430. 'a': 23} # 😞
# The "#json" module can do a much better job:
»> import json
»> print(json.dumps(my_mapping, indent=4, sort_keys=True))
{
"a": 23,
"b": 42,
"c": 12648430
}
# Note this only works with dicts containing
# primitive types (check out the "pprint" module):
»> json.dumps({all: 'yup'})
TypeError: keys must be a string
http://www.csestack.org/python-libraries-for-data-science/
As per the DIKW Pyramid Model, #Data_Science job revolves around finding the information, knowledge from Raw Data. And it can be bundled into the stack of 4 entities:
source of #data
manage and store data
analyze the data
display analyzed output (#visualization, statistics)
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
#Scikit_learn is an open source Python library that implements a range of #machine_learning , preprocessing, cross-#validation and #visualization algorithms using a unified interface...
http://v1k45.com/blog/modern-django-part-1-setting-up-django-and-react/
Modern Django: Part 1: Setting up #Django and #React
This will be a multi part tutorial series on how to create a "Modern" web application or SPA using Django and React.js.
https://github.com/scottwoodall/django-react-template
A Django & React Template
A collection of #Django and #React modules that will help bootstrap your next Django web application.