What is Pandas?

Pandas is an open source library, providing high-performance, easy-to-use data structures and data analysis tools for Python.

The DataFrame is one of Pandas' most important data structures. It's basically a way to store tabular data where you can label the rows and the columns. One way to build a DataFrame is from a dictionary and also importing from CSV(comma-separated value).
#KeyNote #Pandas #DataFrame #DataScience

Watch "A Python tutorial: A Python code to create a file Part II" on YouTube
https://youtu.be/woqHFEtYk-E

Don't miss it out?
There are three tracks in this scholarship
1. Cloud
2. Data
3. Artificial Intelligence

The application period for 15.000 free Udacity Scholarships in Data Science is now open! Please apply by November 16th, 2020 via https://www.udacity.com/bertelsmann-tech-scholarships.
Application will be open from September 15th, 2020 until November 16th, 2020. Scholarship recipients will be selected in November 2020 and will start the online course in December 2020.

Object oriented Programming is an important concept that must be known by all computer coders.
#MULTISELECTQUIZ #OOPTERMINOLOGY
Q: Please select the true concepts of the OOP terminology?

Methods encode behavior of an object and are represented by functions - 29
👍👍👍👍👍👍👍👍 59%

A programming language can be either object-oriented or procedural, but not both - 11
👍👍👍 22%

Attributes encode the state of an object and are represented by variables - 11
👍👍👍 22%

Object and class are different terms describing the same concept - 7
👍 14%

Object is an abstract template describing the general states and behaviors - 7
👍 14%

Encapsulation is a software design practice of bundling the data and the methods that operate on that data. - 11
👍👍👍 22%

👥 49 people voted so far.

N.B: You can select more than two answers in which you think there are more than 1 answers in the options.

Could you solve this problem at Hackerrank?
#Python #SolveProblem #BookDrawing
https://www.hackerrank.com/challenges/drawing-book/problem

A python tutorial to read and write JSON in python with demonstration.

https://youtu.be/7pD9GpiIxgM

Watch "A hackerank Challenge: Book drawing problem solution in Python 3" on YouTube
https://youtu.be/tbWwPdi-gNY

The correct answers are only option 1, 3, and 6.

#ProblemSolving #Python3 #HurdleRace

A video player plays a game in which the character competes in a hurdle race. Hurdles are of varying heights, and the characters have a maximum height they can jump. There is a magic potion they can take that will increase their maximum jump height by unit for each dose. How many doses of the potion must the character take to be able to jump all of the hurdles. If the character can already clear all of the hurdles, return

.Example
height = [1, 2, 3, 3, 2]
k = 1

The character can jump 1 unit high initially and must take 3- 1 = 2 doses of potion to be able to jump all of the hurdles.

Function Description

Create the hurdleRace function.

hurdleRace has the following parameter(s):

int k: the height the character can jump naturally
int height[n]: the heights of each hurdle

Returns

int: the minimum number of doses required, always 0 or more

Sample Input 0

5 4
1 6 3 5 2

Sample Output

2
Post your solution @PythonETHBot
Also you can submit your solution at #Hackerank

How to scrapping telegram users data and analyzing the data using Pandas

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https://youtu.be/NQlyhKBrMY0

#Names #Variable #Python
Q: Explain the difference between name and variable in Python?

Send your answer to @pythonethbot

#Keynote #Variables #Names
variables are objects stored in memory.
Names are labels that we assign to them. Names are how we refers to variables through code.
Ex: a = 5
a is the name that points to a variable in a memory. There can be possible that multiple names points to the same variable.
Variables keep track of the information we need to successfully execute a program. Variables can be used to store a variety of types of information in computer memory.

IoT
# part 1
What is IOT ❓
The Internet of Thing is network of physical object or '' things '' embedded with electronics, software, sensors & network connectivity. which enables this objects the collect or exchange data.
✅''Things'' in the IoT sense can refer in wide variety of device such as heart monitoring implants, biochip transponders on farm animal, electrical clams in costal waters, automobiles built in sensors.
✅This device collect useful data the help of variety existing technologies and then autonomously flow the data b/n other devices.
How it's work ❓
✅ IoT is not the result of single novel technology instead several complementary technical developments provides capabilities that taken together help to Bering the gap b/n virtual and physical world. The capabilities include :-
-communication & cooperation
- addressability
- identification
- sensing
- actuation
- embedded information processing
- localization
- user interface
#The structure of IoT
The IoT can be viewed as a gigantic network consisting of network of device and computers connected a series of intimidate technologies where numerous technology RFID(radio frequency identification ) wireless connection may act as enables of connectivity.
°Tagging Things: real time items traceability and addressability by RFID
°Feeling Things: sense act as primary device to collect data from the environment.
°Shrinking Things: miniaturization and nanotechnology has providing the ability of small things to interact and connect with '' things '' or '' smaller device ''
°Thinking Things: embedded intelligence in device through sensors has formed the network connection to internet. It can make '' things '' realizing the intelligent control.
© @FTYOTORAWI

# part 2
IoT
How big is the IoT ❓
✅Big and getting bigger -- there are already more connected things than people in the world.

✅Tech analyst company IDC predicts that in total there will be 41.6 billion 👀 connected IoT devices by 2025, or "things." It also suggests industrial and automotive equipment represent the largest opportunity of connected "things,", but it also sees strong adoption of smart home and wearable devices in the near term.  
Benefit of IoT for business ❓
✅it's depend on the particular implementation; agility and efficiency are usually top considerations. The idea is that enterprises should have access to more data about their own products and their own internal systems, and a greater ability to make changes as a result.
For ex:Manufacturers are adding sensors to the components of their products so that they can transmit data back about how they are performing.
Security on IoT ❓
✅Security is one the biggest issues with the IoT. These sensors are collecting in many cases extremely sensitive data -- what you say and do in your own home, for example. Keeping that secure is vital to consumer trust, but so far the IoT's security track record has been extremely poor. Too many IoT devices give little thought to the basics of security, like encrypting data in transit and at rest
The IoT and cyberwarfare ❓
✅The IoT makes computing physical. So if things go wrong with IoT devices, there can be major real-world consequences -- something that nations planning their cyberwarfare strategies are now taking into account.

© @FTYOTORAWI

#KeyNote #DataScience #Methodology #DataMining

Data Science Methodologies

What is CRISP-DM(Cross industries - Data Mining)?

The CRISP-DM methodology is a process aimed at increasing the use of data mining over a wide variety of business applications and industries. The intent is to take case specific scenarios and general behaviors to make them domain neutral. CRISP-DM is comprised of six steps with an entity that has to implement in order to have a reasonable chance of success.

The six steps are shown in the following diagram:

1. Business Understanding:- This stage is the most important because this is where the intention of the project is outlined. Foundational Methodology and CRISP-DM are aligned here. It requires communication and clarity. The difficulty here is that stakeholders have different objectives, biases, and modalities of relating information. They don’t all see the same things or in the same manner. Without clear, concise, and complete perspective of what the project goals are resources will be needlessly expended.

2. Data Understanding:- Data understanding relies on business understanding. Data is collected at this stage of the process. The understanding of what the business wants and needs will determine what data is collected, from what sources, and by what methods. CRISP-DM combines the stages of Data Requirements, Data Collection, and Data Understanding from the Foundational Methodology outline.

3. Data Preparation:- Once the data has been collected, it must be transformed into a useable subset unless it is determined that more data is needed. Once a dataset is chosen, it must then be checked for questionable, missing, or ambiguous cases. Data Preparation is common to CRISP-DM and Foundational Methodology.

4. Modeling:- Once prepared for use, the data must be expressed through whatever appropriate models, give meaningful insights, and hopefully new knowledge. This is the purpose of data mining: to create knowledge information that has meaning and utility. The use of models reveals patterns and structures within the data that provide insight into the features of interest. Models are selected on a portion of the data and adjustments are made if necessary. Model selection is an art and science. Both Foundational Methodology and CRISP-DM are required for the subsequent stage.

5. Evaluation:- The selected model must be tested. This is usually done by having a pre-selected test, set to run the trained model on. This will allow you to see the effectiveness of the model on a set it sees as new. Results from this are used to determine efficacy of the model and foreshadows its role in the next and final stage.

6. Deployment:- In the deployment step, the model is used on new data outside of the scope of the dataset and by new stakeholders. The new interactions at this phase might reveal the new variables and needs for the dataset and model. These new challenges could initiate revision of either business needs and actions, or the model and data, or both.

CRISP-DM is a highly flexible and cyclical model. Flexibility is required at each step along with communication to keep the project on track. At any of the six stages, it may be necessary to revisit an earlier stage and make changes. The key point of this process is that it’s cyclical; therefore, even at the finish you are having another business understanding encounter to discuss the viability after deployment. The journey continues.

For more information on CRISP-DM, go to: IBM Knowledge Center – CRISP-DM Help Overview

“Python 3.9.0 is the newest feature release of the #python3 language” that comes with numerous features, and optimizations.