Simple real time visualisation of the execution of a Python program.
This library offers a simple real time visualisation of the execution of a Python program:
The numbers on the left are how many times each line has been hit. The bars show the lines that have been hit recently - longer bars mean more hits, lighter colours mean more recent.
Calls that are currently being executed are highlighted thanks to the
It also shows a live stacktrace:
pip install --user heartrate
Supports Python 3.5+.
import heartrate; heartrate.trace(browser=True)
In the file view, the stacktrace is at the bottom. In the stacktrace, you can click on stack entries for files that are being traced to open the visualisation for that file at that line.
trace only traces the thread where it is called. To trace multiple threads, you must call it in each thread, with a different port each time.
filesdetermines which files get traced in addition to the one where
tracewas called. It must be a callable which accepts one argument: the path to a file, and returns True if the file should be traced. For convenience, a few functions are supplied for use, e.g.:
from heartrate import trace, files trace(files=files.path_contains('my_app', 'my_library'))
files.all: trace all files.
files.path_contains(*substrings)trace all files where the path contains any of the given substrings.
files.contains_regex(pattern)trace all files which contain the given regex in the file itself, so you can mark files to be traced in the source code, e.g. with a comment.
# heartrate" (spaces optional).
host: HTTP host for the server. To run a remote server accessible from anywhere, use
port: HTTP port for the server. Default
browser: if True, automatically opens a browser tab displaying the visualisation for the file where
traceis called. False by default.
Thanks for reading ❤
If you liked this post, share it with all of your programming buddies!
At smaller companies access to and control of data is one of the biggest challenges faced by data analysts and data scientists. The same is true at larger companies when an analytics team is forced to navigate bureaucracy, cybersecurity and over-taxed IT, rather than benefit from a team of data engineers dedicated to collecting and making good data available.
Creative, persistent analysts find ways to get access to at least some of this data. Through a combination of daily processes to save email attachments, run database queries, and copy and paste from internal web pages one might build up a mighty collection of data sets on a personal computer or in a team shared drive or even a database.
But this solution does not scale well, and is rarely documented and understood by others who could take it over if a particular analyst moves on to a different role or company. In addition, it is a nightmare to maintain. One may spend a significant part of each day executing these processes and troubleshooting failures; there may be little time to actually use this data!
I lived this for years at different companies. We found ways to be effective but data management took up way too much of our time and energy. Often, we did not have the data we needed to answer a question. I continued to learn from the ingenuity of others and my own trial and error, which led me to the theoretical framework that I will present in this blog series: building a self-managed data library.
A data library is _not _a data warehouse, data lake, or any other formal BI architecture. It does not require any particular technology or skill set (coding will not be required but it will greatly increase the speed at which you can build and the degree of automation possible). So what is a data library and how can a small data analytics team use it to overcome the challenges I’ve described?
#big data #cloud & devops #data libraries #small data science teams #introduction to data libraries for small data science teams #data science
In this tutorial, we’ll be talking about what a library is and how they are useful. We will be looking at some examples in C, including the C Standard I/O Library and the C Standard Math Library, but these concepts can be applied to many different languages. Thank you for watching and happy coding!
Need some new tech gadgets or a new charger? Buy from my Amazon Storefront https://www.amazon.com/shop/blondiebytes
Also check out…
What is a Framework? https://youtu.be/HXqBlAywTjU
What is a JSON Object? https://youtu.be/nlYiOcMNzyQ
What is an API? https://youtu.be/T74OdSCBJfw
What are API Keys? https://youtu.be/1yFggyk--Zo
Using APIs with Postman https://youtu.be/0LFKxiATLNQ
Check out my courses on LinkedIn Learning!
REFERRAL CODE: https://linkedin-learning.pxf.io/blondiebytes
Support me on Patreon!
Check out my Python Basics course on Highbrow!
Check out behind-the-scenes and more tech tips on my Instagram!
Free HACKATHON MODE playlist:
MY FAVORITE THINGS:
Stitch Fix Invite Code: https://www.stitchfix.com/referral/10013108?sod=w&som=c
FabFitFun Invite Code: http://xo.fff.me/h9-GH
Uber Invite Code: kathrynh1277ue
Postmates Invite Code: 7373F
SoulCycle Invite Code: https://www.soul-cycle.com/r/WY3DlxF0/
Rent The Runway: https://rtr.app.link/e/rfHlXRUZuO
Want to BINGE?? Check out these playlists…
Intermediate Web Dev Tutorials: https://www.youtube.com/watch?v=LFa9fnQGb3g&index=1&list=PLcLMSci1ZoPubx8doMzttR2ROIl4uzQbK
GitHub | https://github.com/blondiebytes
Twitter | https://twitter.com/blondiebytes
LinkedIn | https://www.linkedin.com/in/blondiebytes
#blondiebytes #c library #code tutorial #library
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
SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:
1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.
2. Every database seller needs an approach to separate its item from others.
Right now, contrasts are noted where fitting.
#programming books #beginning sql pdf #commands sql #download free sql full book pdf #introduction to sql pdf #introduction to sql ppt #introduction to sql #practical sql pdf #sql commands pdf with examples free download #sql commands #sql free bool download #sql guide #sql language #sql pdf #sql ppt #sql programming language #sql tutorial for beginners #sql tutorial pdf #sql #structured query language pdf #structured query language ppt #structured query language
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