#Python is a multi-paradigm, dynamically typed, multipurpose programming language, designed to be quick (to learn, to use, and to understand), and to enforce a clean and uniform syntax. Please note that Python 2 is officially out of support as of January 1, 2020. Therefore automatically shift yourself from Python-2 to the latest and most improved version of python [python-3.x] .

#QuarantineYourself #LearnPython #LearnDataScience

Reading a File using Python

Computers use file systems to store and retrieve data. Each file is an individual container of related information. If you’ve ever saved a document, downloaded a song, or even sent an email you’ve created a file on some computer somewhere. Even script.py, the Python program you’re editing in the learning environment, is a file.
So, how do we interact with files using Python?
Let’s say we had a file called hello_python.txt with these contents:

Python is a powerful tool for Data Ananlysis. So, stay at home and learn python for future Data Science.

We could read that file like this:

script.py

with open('hello_python.txt') as python_file:
python_contents = python_file.read()
print(python_contents)

This
opens a file object called python_file and creates a new indented block where you can read the contents of the opened file. We then read the contents of the file python_file using python_file.read() and save the resulting string into the variable python_contents. Then we print python_contents, which outputs the statement written in the above!.

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Introduction to Machine Learning

The year is 2049…
But learn Python then Data Science(Data Analyze, Data Manipulation, Visualization, and Hypothesis testing), and then Learn Machine Learning.
Finally, learn AI

Python is a general-purpose programming language. It can do almost all of what other languages can do with comparable, or faster, speed. It is often chosen by Data Analysts and Data Scientists for prototyping, visualization, and execution of data analyses on datasets.

There’s an important question here. Plenty of other programming languages, like R, can be useful in the field of data science. Why are so many people choosing Python?

One major factor is Python’s versatility. There are over 125,000 third-party Python libraries. These libraries make Python more useful for specific purposes, from the traditional (e.g. web development, text processing) to the cutting edge (e.g. AI and machine learning). For example, a biologist might use the Biopython library to aid their work in genetic sequencing.

Additionally, Python has become a go-to language for data analysis. With data-focused libraries like pandas, NumPy, and Matplotlib, anyone familiar with Python’s syntax and rules can use it as a powerful tool to process, manipulate, and visualize data.

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Machine Learning

Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions. It has taken the Data Science world by storm.

It can be branched out into the following categories:

Supervised Learning
Unsupervised Learning

Supervised Learning is when the data is labeled and the program learns to predict the output from the input data. For instance, a supervised learning algorithm for credit card fraud detection would take as input a set of recorded transactions. It would learn what makes a transaction likely to be fraudulent. Then, for each new transaction, the program would predict if it is fraudulent or not.

Unsupervised Learning is where the data is unlabeled and the program learns to recognize the inherent structure of the input data. For the same fraud example, the model would take in a bunch of transactions with no indication of if they are fraudulent or not, and it would group them based on patterns it sees. It might discover two groups, fraudulent and legitimate.
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#DataScience #MachineLearning

Writing a File using Python

In the previous post we have seen that how to open and read a file using python script. Today, I have posting about how to write a file or create your own file using the script.

Reading a file is all well and good, but what if we want to create a file of our own? With Python we can do just that. It turns out that our open() function that we’re using to open a file to read needs another argument to open a file to write to.

script.py

with open('generated_file.txt', 'w') as gen_file:
  gen_file.write("I love python!")

Here we pass the argument 'w' to open() **in order to indicate to open the file in write-mode. The default argument is 'r' and passing 'r' to **open() opens the file in read-mode as we’ve been doing.

This code creates a new file in the same folder as script.py and gives it the text What an incredible file!. It’s important to note that if there is already a file called generated_file.txt it will completely overwrite that file, erasing whatever its contents were before.

#QuarantineYourself #LearnPython #LearnDataScience

#challenge1
level #very_easy

Write a a script that asks a user to enter two integer and returns the addition of them

#rule

1 your should handle an exeception if the user input is invalid

#input_output_example

#ex1
input a number: 5
input another number: 4
the sum is 9
#ex2
input a number: abc
input another number: def
your input is invaild
#ex3
input a number: 1.2
input another number: 4.6
the sum is 5.8

What Is a CSV File?

Text files aren’t the only thing that Python can read, but they’re the only thing that we don’t need any additional parsing library to understand. CSV files are an example of a text file that impose a structure to their data. CSV stands for Comma-Separated Values and CSV files are usually the way that data from spreadsheet software (like Microsoft Excel or Google Sheets) is exported into a portable format. A spreadsheet that looks like the following
Name Username Email
Asibeh Tenager asibeh asibeh@yahoo.com
Asibeh Tenager asibeh asibeh@yahoo.com



In a CSV file that same exact data would be rendered like this:

users.csv

Name,Username,Email, Asibeh Tenager, asibeh, asibeh@yahoo.com

Notice that the first row of the CSV file doesn’t actually represent any data, just the labels of the data that’s present in the rest of the file. The rest of the rows of the file are the same as the rows in the spreadsheet software, just instead of being separated into different cells they’re separated by… well I suppose it’s fair to say they’re separated by commas.


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Learn Python for

✅ Data Science
✅ Artificial Intelligence
✅ Machine Learning

✍ Mode of Delivery: Short notes, Books, Articles, Exercises, Challenges etc.

🪐 Plan to build a big Dataset in Ethiopia which helps Developers and Researchers abroad.

Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code (2020)

@python4fds

The pie chart shows that 4.5 % of the total confirmed cases are died.

#EssayQuestion1

Why do you want to learn python?

Post your answer @pyDiscussion

Jupyter Notebooks

Jupyter Notebooks are an extremely powerful tool for data analysis because they allow you to run python commands and see outputs within the structure of a notebook, which is helpful because in Data Analysis you are often running short commands to produce the data/visualizations you need for a certain investigation.

To know how to install Jupyter notebook, looking at the following short video

https://www.youtube.com/watch?v=5Yx6h7Mgiv0

Do you know the difference between Data Science vs Machine Learning?

What are the main differences and similarities between data scientists and machine learning engineers?

Anyone can explain? @pythonEthbot

Data Science Vs Machine Learning


Introduction

It seems as though even companies along with their job descriptions have some confusion on what constitutes a data scientist and machine learning engineer.

At first, studying to become a data scientist. Data science is the researching, building, and interpretation of the model you have built, while machine learning is the production of that model.

Data Scientist

A statistician? Kind of. Data science, in it’s simplest terms, can be described as a field of automated statistics in the form of models that aide in classifying and predicting outcomes. Here are the top skills that are required to be a data scientist:

✅Python or R

✅SQL

✅Jupyter Notebook

Python — To expound on the skills above, most companies are looking for Python more than R. Some job descriptions list both; however, most people you are working with like the machine learning engineers, data engineers, and software engineers will not have familiarity with R. Therefore, to be a more holistic data scientist, Python will be more beneficial for you.

SQL, at first, can seem more like a data analyst skill — it is, but it should still be a skill you employ for data science. Most datasets are not given to you in the business setting (as opposed to academia), and you will have to make your own — via SQL. Now, there are plenty of subtypes of SQL; like PostgreSQL, MySQL, Microsoft SQL Server T-SQL, and Oracle SQL. They are similar forms of the same querying language, hosted by different platforms. Because these are so similar, having any of these is useful and can be translated easily to a slightly different form of SQL.

Jupyter Notebook could almost be the exact opposite of a machine learning engineer’s toolkit. A Jupyter Notebook is a data scientist’s playground for both coding and modeling. A research environment, if you will, allowing quick and easy Python coding that can incorporate commenting out of code, the code itself, and a platform to build and test models from useful libraries like sklearn, pandas, and numpy.

Overall, a data scientist can be many things, but the main functions are to

— meet with stakeholders to define the business problem

— pull data (SQL)

— EDA, feature engineering, model building, & prediction (Python and Jupyter Notebook)

— depending on workplace, compile code to .py format and/or pickled model

✍... Will continue next time... You can put your comment or feedback @pythonethbot

11 Deep Learning With Python Libraries and Frameworks

❓Asked by one of the members

1⃣ TensorFlow Python. TensorFlow is an open-source library for numerical computation in which it uses data flow graphs. ...

2⃣ Keras Python. A minimalist, modular, Neural Network library, Keras uses Theano or TensorFlow as a backend. ...

3⃣ Apache mxnet. ...

4⃣ Caffe. ...

5⃣ Theano Python. ...

6⃣ Microsoft Cognitive Toolkit. ...

7⃣ PyTorch. ...

8⃣ Eclipse DeepLearning4J

Link: https://dzone.com/articles/11-deep-learning-with-python-libraries-and-framewo

You can put your question via @pythonethbot