Discover and read the best of Twitter Threads about #pythonlearning
Most recents (20)
Who's Monty & what's a White Room got to do with *truly* understanding concepts in #Python #coding?
What’s more challenging in the early days when you learn coding is to join the dots between various topics you learn, viewing them as a coherent set of tools
🧵🪡
#100daysofcode
What’s more challenging in the early days when you learn coding is to join the dots between various topics you learn, viewing them as a coherent set of tools
🧵🪡
#100daysofcode
Here's The White Room analogy in brief:
I call the computer program Monty.
Monty is the one who’s busy doing things, performing all the actions required in your computer program.
I call the computer program Monty.
Monty is the one who’s busy doing things, performing all the actions required in your computer program.
But Monty/the computer program needs some infrastructure to be able to operate.
This infrastructure is the White Room: an empty room with white walls and white ceilings–a blank canvas.
This infrastructure is the White Room: an empty room with white walls and white ceilings–a blank canvas.
Top 10 Github Repositories to Learn Python
🧵 thread ↓
#python #pythonprogramming #pythonlearning #github
🧵 thread ↓
#python #pythonprogramming #pythonlearning #github
1. The Algorithms
The Algorithms/Python repo is one of the most starred and forked Python GitHub repo on and there’s a good reason behind its popularity.
github.com/TheAlgorithms/…
The Algorithms/Python repo is one of the most starred and forked Python GitHub repo on and there’s a good reason behind its popularity.
github.com/TheAlgorithms/…
2. vinta/awesome-python
The Awesome Python repo is the second entry in our list of top GitHub Repos for learning Python to feature such crazy high statistics.
github.com/vinta/awesome-…
The Awesome Python repo is the second entry in our list of top GitHub Repos for learning Python to feature such crazy high statistics.
github.com/vinta/awesome-…
How to Make Small Multiple Charts in Python, with Plotly
🧵
sharpsightlabs.com/blog/plotly-sm…
#datascience #datavisualization #Python #pythonlearning
🧵
sharpsightlabs.com/blog/plotly-sm…
#datascience #datavisualization #Python #pythonlearning
Remember: small multiple charts break out a visualization by a categorical variable.
We take a simple chart and break it out into panels.
#Datavisualization #datascience
We take a simple chart and break it out into panels.
#Datavisualization #datascience
There are a variety of tools to make small multiple charts in Python, including the Seaborn FacetGrid technique.
#Python #Datavisualization #datascience
#Python #Datavisualization #datascience
It is Estimated to have 11,000,000+ job openings 🚀in Data Science industry by 2026.
Building is portfolio to stand out is absolutely necessary.
Tips to build your #datascience portfolio. A Thread 🧵
Building is portfolio to stand out is absolutely necessary.
Tips to build your #datascience portfolio. A Thread 🧵
1. GitHub Profile - @github
▶️Over 65 million developers and more than 3 million companies use GitHub . GitHub says that 72% of Fortune 50 companies use the site.
▶️ There is always debate about GitHub over blog. I started with blog and later created GitHub profile
▶️Over 65 million developers and more than 3 million companies use GitHub . GitHub says that 72% of Fortune 50 companies use the site.
▶️ There is always debate about GitHub over blog. I started with blog and later created GitHub profile
Continuing the #learningpath series
⭐Next skill in #DataScience industry is R programming (One of the most important and my favorite)
⭐There always been a debate around R v/s Python
⭐Step by step learning path for R programming
A Thread 🧵
⭐Next skill in #DataScience industry is R programming (One of the most important and my favorite)
⭐There always been a debate around R v/s Python
⭐Step by step learning path for R programming
A Thread 🧵
1. Introduction to R
⭐Introduction to R
⭐Arithmetic operation
⭐Variable assignment
⭐Basic Data types
⭐Vectors, Matrix, List, Array and Data Frames
⭐Factors, Factor levels, Ordered factors
⭐Introduction to R
⭐Arithmetic operation
⭐Variable assignment
⭐Basic Data types
⭐Vectors, Matrix, List, Array and Data Frames
⭐Factors, Factor levels, Ordered factors
2. Data Input and Output in R
⭐Intro to import data
⭐Reading csv, txt, flat and excel files
⭐Saving output
⭐Intro to import data
⭐Reading csv, txt, flat and excel files
⭐Saving output
Continuing the #learningpath series
⭐Next skill in #DataScience industry is @MSPowerBI (One of the fastest growing #skills 🚀)
⭐Power BI is one of the leaders in Analytics and BI platforms in @gartner magic quadrant
⭐Step by step learning path for Power BI
A Thread 🧵
⭐Next skill in #DataScience industry is @MSPowerBI (One of the fastest growing #skills 🚀)
⭐Power BI is one of the leaders in Analytics and BI platforms in @gartner magic quadrant
⭐Step by step learning path for Power BI
A Thread 🧵
1- Getting Started with @MSPowerBI
⭐Power BI Interface
⭐Design flow
⭐Type of data sources
⭐Data Terminology
⭐Power BI Interface
⭐Design flow
⭐Type of data sources
⭐Data Terminology
2.1- Connecting to Data and Intro to Query Editor
⭐Type of Data Sources
⭐Intro to the @MSPowerBI Query Editor
⭐Table Transformations
⭐Text, Number & Date calculations
⭐Index & Conditional Columns
⭐Grouping & Aggregating Data
⭐Pivoting & Unpivoting
⭐Type of Data Sources
⭐Intro to the @MSPowerBI Query Editor
⭐Table Transformations
⭐Text, Number & Date calculations
⭐Index & Conditional Columns
⭐Grouping & Aggregating Data
⭐Pivoting & Unpivoting
Continuing the #learningpath series.
Next skill in #DataScience industry is SQL (One of the most In-Demand Skills )
⭐There are 1,000,000+ jobs available in USA with requirement of SQL as a skill
Step by step learning SQL for data scientists
A thread🧵
#data #datascience #sql
Next skill in #DataScience industry is SQL (One of the most In-Demand Skills )
⭐There are 1,000,000+ jobs available in USA with requirement of SQL as a skill
Step by step learning SQL for data scientists
A thread🧵
#data #datascience #sql
1. SQL Basics
⭐ Creating Tables
⭐ Inserting values
⭐ Adding a New column in the existing Table
⭐ Deleting a Column from a Table
⭐ Deleting the Table
⭐ Creating Tables
⭐ Inserting values
⭐ Adding a New column in the existing Table
⭐ Deleting a Column from a Table
⭐ Deleting the Table
2. SQL start with Commands
⭐ Learn basic SQL commands: SELECT, FROM and WHERE
⭐ Learn logical operators in SQL
⭐ Learn commands: IN , AND, OR, NOT, LIKE, NULL, ISNULL, BETWEEN
⭐ Learn basic SQL commands: SELECT, FROM and WHERE
⭐ Learn logical operators in SQL
⭐ Learn commands: IN , AND, OR, NOT, LIKE, NULL, ISNULL, BETWEEN
I mentioned projects or books for #Python3 in my other threads but let's talk about the very best resources for #SEO: blogs!
This a thread 🧵
This a thread 🧵
1. holisticseo.digital/python-seo/
You have to know this. Semantic #SEO, clear case studies, topical authority and lots of #Python3. Everything you can ask for in 2022
You have to know this. Semantic #SEO, clear case studies, topical authority and lots of #Python3. Everything you can ask for in 2022
2. seopythonistas.com
It's not a simple list of projects, it's the legacy of a great man. Check it out, it's full of awesome works.
#pythonlearning #DataScience #SEO #Python3
It's not a simple list of projects, it's the legacy of a great man. Check it out, it's full of awesome works.
#pythonlearning #DataScience #SEO #Python3
In Python ...
You can combine Numpy arrays vertically or horizontally using np.concatenate
#Python #pythoncode #datascience
You can combine Numpy arrays vertically or horizontally using np.concatenate
#Python #pythoncode #datascience
The first argument to the function is a list (or collection) of arrays that you want to combine.
You can actually combine many arrays ...just put them inside the list.
You can actually combine many arrays ...just put them inside the list.
The axis parameter controls the direction along which you combine the arrays.
For 2D arrays ...
'axis = 0' combines vertically
'axis = 1' combines horizontally
For 2D arrays ...
'axis = 0' combines vertically
'axis = 1' combines horizontally
In Python, you can visualize images with the Plotly IMshow function.
🧵[1/8]
sharpsightlabs.com/blog/plotly-im…
#Python #pythonlearning #datascience #datavisualization
🧵[1/8]
sharpsightlabs.com/blog/plotly-im…
#Python #pythonlearning #datascience #datavisualization
[2/8]
You can use Plotly IMshow for a few uses.
You can use it to plot heatmaps ...
But you can also use it to plot images.
You can use Plotly IMshow for a few uses.
You can use it to plot heatmaps ...
But you can also use it to plot images.
[3/8]
The syntax for Plotly IMshow is pretty simple.
You call the function as px.imshow and then provide the name of the image file you want to visualize.
(This assumes you've imported Plotly express as px)
#Python #pythoncode
The syntax for Plotly IMshow is pretty simple.
You call the function as px.imshow and then provide the name of the image file you want to visualize.
(This assumes you've imported Plotly express as px)
#Python #pythoncode
In Python ...
You can use the Pandas dropna method to drop rows with missing values.
#Python #pythoncode #datascience
You can use the Pandas dropna method to drop rows with missing values.
#Python #pythoncode #datascience
As seen above, you can limit dropna to specific columns with the 'subset=' parameter.
With 'subset=', you can specify the columns in which dropna will look for missing values
#Python #pythonlearning #datascience
With 'subset=', you can specify the columns in which dropna will look for missing values
#Python #pythonlearning #datascience
If it finds missing values in any of those columns, it will drop the row.
But it will ignore missing values in other columns.
But it will ignore missing values in other columns.
In Python ...
You can use Numpy all to test conditions about the properties of a Numpy array.
#Python #pythoncode #datascience
You can use Numpy all to test conditions about the properties of a Numpy array.
#Python #pythoncode #datascience
☝️
So for instance, in the example above, I test if all of the values are greater than 2, by column.
#Python
So for instance, in the example above, I test if all of the values are greater than 2, by column.
#Python
To do this, you need to know how np.all works ...
But you also need to know how to use axes.
#Python #pythonlearning
But you also need to know how to use axes.
#Python #pythonlearning
Our topic of today is a "PRINT FUNCTION" in python.
#SSOT #PYTHON #pythonlearning #Python #pythonprogramming #CyberSecurity #softwaredevelopment
#SSOT #PYTHON #pythonlearning #Python #pythonprogramming #CyberSecurity #softwaredevelopment
In Python, the command print tells the program to display words or numbers on the screen. Here's a line of code that tells Python to display the words “Hello, World!”
print("Hello, World!")
print("Hello, World!")
print is a keyword—that is, a word that has special meaning for Python. It means, "Display what’s inside the parentheses." Note that print isn't capitalized. If you capitalize it, the program won’t run.
Today we are going to talk about "DATA TYPES IN PYTHON"
Just follow the thread till the end.
#pythonprogramming #pythonlearning #SSOT #python
Just follow the thread till the end.
#pythonprogramming #pythonlearning #SSOT #python
What is data type in python programming?
In python programming, a data type is an attribute of data which tells the compiler or interpreter how the programmer intends to use the data.
In python programming, a data type is an attribute of data which tells the compiler or interpreter how the programmer intends to use the data.
In python programming, data type is an important concept.
Variables can store data of different types, and different types can do different things.
Python has the following data types built-in by default, in these categories:
Variables can store data of different types, and different types can do different things.
Python has the following data types built-in by default, in these categories:
Find the median and the quarts parts of a statistic sequence in #pythonprogramming: a thread
1/ First write an median function which find the half indice of a list using n = len(list) (lenght of list) means (n-1)/2 if n is odd and between n/2 - 1 and n/2 if n is even.
1/ First write an median function which find the half indice of a list using n = len(list) (lenght of list) means (n-1)/2 if n is odd and between n/2 - 1 and n/2 if n is even.
In this function, we particularly use % for design arithmetic rest and int operator to convert float to integer. NB: we suppose that at the beginning, list is range by increase order.
2/ Afterwards we define a new function using same logic, but in more take an list of notes,
2/ Afterwards we define a new function using same logic, but in more take an list of notes,
create a new list knowing that value of first list list[i] design students number who have been the note i. And send the median of the new list.
Hi Reader, I hope you are having a good day and will have a great life from now on.
Day 8 of #100DaysOfCode.
Python
Type 2 of itertools
remember you have to import them using
from itertools import name_of_itertool
Day 8 of #100DaysOfCode.
Python
Type 2 of itertools
remember you have to import them using
from itertools import name_of_itertool
1)takewhile
Take values from iterable while the predicate function remains true,once predicate function returns false it will no
longer take values.
example:
lis=[2,4,1,8,6,14]
lis_even=list(takewhile(lambda x:x % 2 == 0,lis))
returns lis_even=[2,4]
Take values from iterable while the predicate function remains true,once predicate function returns false it will no
longer take values.
example:
lis=[2,4,1,8,6,14]
lis_even=list(takewhile(lambda x:x % 2 == 0,lis))
returns lis_even=[2,4]
2)accumulate
Returns a running total of values in iterable.
lis=[0,1,2,3]
lis_new=list(accumulate(lis))
returns lis_new=[0,1,3,6]
Returns a running total of values in iterable.
lis=[0,1,2,3]
lis_new=list(accumulate(lis))
returns lis_new=[0,1,3,6]
Good morning to you all. Just a quick note to let you know that I am the 67th Pythonista who cracked Bite 317. Pickling objects - on behalf of @pybites I Challenge You! codechalleng.es/bites/317 #Python #CodeChallenges
With almost every challenge, I learn something new. This time how to load (deserialize) and dump (serialize) a data structure from/to a pickle file which supports storing your own objects.
Needless to say, this requires additional practice to fully understand and apply independently.
But that's the whole idea behind learning something new and making it your own.
#learntocode #pythonlearning
But that's the whole idea behind learning something new and making it your own.
#learntocode #pythonlearning
#Learn 🧠🐍#python: Sometimes when programming in python they're situations when you want to copy the contents of an existing list into another. Python has several ways of achieving that. In this thread you will learn different ways of achieving that with the help of examples.
1) Using the equal (=) sign operator:
Using = operator you can copy the contents of an existing list onto another/new list. But there's a problem with this method which I will explain on the next section.
Using = operator you can copy the contents of an existing list onto another/new list. But there's a problem with this method which I will explain on the next section.
The problem with the above method is that if you modify the new copied_fruits list the original list (fruits) is modified too, this is because the copied list (copied_fruits) is referencing/ pointing to same fruits list in memory.
Hi Reader, I hope you are having a good day and will have a great life from now on.
Day 7 of #100DaysOfCode.
Python
Day 7 of #100DaysOfCode.
Python
1)Recursive Functions:
Every recursive function has a base case that stops it from going into the fourth dimension(that's what I like to think 😅).
Example 1:
Every recursive function has a base case that stops it from going into the fourth dimension(that's what I like to think 😅).
Example 1:
Recursive functions can be implemented with more than one function. Here's an example for checking even and odd numbers
Example 2:
Example 2:
Flask or Django?
Read thread...
Read thread...
If you've a bit of Python experience, yu know exactly what yu want, & its a fairly standard web application that involves displaying some content, storing some stuff in a database, having users register, & having administrators b able to control what's on the website: use Django.
If you are a Python beginner: use Flask.
