Madyson  Reilly

Madyson Reilly

1621569732

How to Get Started with Version Control Using Git

In this tutorial, you’ll learn what version control is and how you can contribute to open-source projects using Git.

We’ll get through the fundamentals of Git and I’ll give you the knowledge that is indispensable for any web developer.

What is Git?

Git is a version control software which lets you collaborate with other programmers. The biggest problem Git solves is that it helps developers keep track of the different versions of the codebase they’re working on.

Before the invention of the version control systems, it was quite hard for different developers to synchronize their work.

#git #github #developer #programming

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How to Get Started with Version Control Using Git
Shubham Ankit

Shubham Ankit

1657081614

How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)

How to Automate Excel with Python

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation

What is OPENPYXL

Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

Sheet: A sheet is a single page composed of cells for organizing data.

Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

Row: A row is a horizontal line represented by a number (1,2, etc.).

Column: A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

pip install openpyxl

CREATE A NEW WORKBOOK

We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.

from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws = wb.active
#creating new worksheets by using the create_sheet method

ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position

#Renaming the sheet
ws.title = "Example"

#save the workbook
wb.save(filename = "example.xlsx")

READING DATA FROM WORKBOOK

We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.

#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")

 

GETTING SHEETS FROM THE LOADED WORKBOOK

 

#getting sheet names
wb.sheetnames
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']

#getting a particular sheet
sheet1 = wb["sheet2"]

#getting sheet title
sheet1.title
result = 'sheet2'

#Getting the active sheet
sheetactive = wb.active
result = 'sheet1'

 

ACCESSING CELLS AND CELL VALUES

 

#get a cell from the sheet
sheet1["A1"] <
  Cell 'Sheet1'.A1 >

  #get the cell value
ws["A1"].value 'Segment'

#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)
d.value
10

 

ITERATING THROUGH ROWS AND COLUMNS

 

#looping through each row and column
for x in range(1, 5):
  for y in range(1, 5):
  print(x, y, ws.cell(row = x, column = y)
    .value)

#getting the highest row number
ws.max_row
701

#getting the highest column number
ws.max_column
19

There are two functions for iterating through rows and columns.

Iter_rows() => returns the rows
Iter_cols() => returns the columns {
  min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.

Example:

#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
  for cell in row:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C3 >

  #iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
  for cell in col:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.C3 >

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.


Example:

for row in ws.values:
  for value in row:
  print(value)

 

WRITING DATA TO AN EXCEL FILE

Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.

 

CREATING AND SAVING A NEW WORKBOOK

 

#creates a new workbook
wb = openpyxl.Workbook()

#saving the workbook
wb.save("new.xlsx")

 

ADDING AND REMOVING SHEETS

 

#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")

#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")

#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']

#deleting a sheet
del wb['sheet 0']

#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']

 

ADDING CELL VALUES

 

#checking the sheet value
ws['B2'].value
null

#adding value to cell
ws['B2'] = 367

#checking value
ws['B2'].value
367

 

ADDING FORMULAS

 

We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.
 

For example:

import openpyxl
from openpyxl
import Workbook

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']

ws['A9'] = '=SUM(A2:A8)'

wb.save("new2.xlsx")

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.

image

 

MERGE/UNMERGE CELLS

Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

For example:
Merge cells

#merge cells B2 to C9
ws.merge_cells('B2:C9')
ws['B2'] = "Merged cells"

Adding the above code to the previous example will merge cells as below.

image

UNMERGE CELLS

 

#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')

The above code will unmerge cells from B2 to C9.

INSERTING AN IMAGE

To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.

Example:

import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3

ws.add_image(img, 'A3')

wb.save("new2.xlsx")

Result:

image

CREATING CHARTS

Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:

Example

import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series

wb = openpyxl.load_workbook("example.xlsx")
ws = wb.active

values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
chart.add_data(values)
ws.add_chart(chart, "E3")
wb.save("MyChart.xlsx")

Result
image


How to Automate Excel with Python with Video Tutorial

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling

📄 Resources 📄
OpenPyXL Docs: https://openpyxl.readthedocs.io/en/stable/ 
Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial 
Subscribe: https://www.youtube.com/c/TechWithTim/featured 

#python 

Madyson  Reilly

Madyson Reilly

1604109000

Best Practices for Using Git

Git has become ubiquitous as the preferred version control system (VCS) used by developers. Using Git adds immense value especially for engineering teams where several developers work together since it becomes critical to have a system of integrating everyone’s code reliably.

But with every powerful tool, especially one that involves collaboration with others, it is better to establish conventions to follow lest we shoot ourselves in the foot.

At DeepSource, we’ve put together some guiding principles for our own team that make working with a VCS like Git easier. Here are 5 simple rules you can follow:

1. Make Clean, Single-Purpose Commits

Oftentimes programmers working on something get sidetracked into doing too many things when working on one particular thing — like when you are trying to fix one particular bug and you spot another one, and you can’t resist the urge to fix that as well. And another one. Soon, it snowballs and you end up with so many changes all going together in one commit.

This is problematic, and it is better to keep commits as small and focused as possible for many reasons, including:

  • It makes it easier for other people in the team to look at your change, making code reviews more efficient.
  • If the commit has to be rolled back completely, it’s far easier to do so.
  • It’s straightforward to track these changes with your ticketing system.

Additionally, it helps you mentally parse changes you’ve made using git log.

#open source #git #git basics #git tools #git best practices #git tutorials #git commit

Lenora  Hauck

Lenora Hauck

1593583680

How To Install Git on Ubuntu 16.04 LTS

Git is one of the most popular tools used as a distributed version control system (VCS). Git is commonly used for source code management (SCM) and has become more used than old VCS systems like SVN. In this article, we’ll show you how to install Git on your Ubuntu 16.04 dedicated server.

Installing Git on Ubuntu 16.04 LTS

We have also created a convenient video tutorial that outlines how to install Git on a Ubuntu 16.04 Server.

Now, let’s get started on that install…

Preflight Check

  • You should be running a server with any VPS Ubuntu LTS release.
  • You will need to log in to SSH via the root user.

First, as always, we should start out by running general OS and package updates. On Ubuntu we’ll do this by running:

apt-get update

After you have run the general updates on the server you can get started with installing Git.

  1. Install Git

  2. apt-get install git-core

  3. You may be asked to confirm the download and installation; simply enter y to confirm. It’s that simple, Git should be installed and ready to use!

  4. Confirm Git the installation

  5. With the main installation done, first check to ensure the executable file is set up and accessible. The best way to do this is simply to run Git with the version command.

  6. git --version

  7. git version 2.7.4

  8. Configure Git’s settings (for the root user)

  9. It’s a good idea to setup your user for git now, to prevent any commit errors later. We’ll setup the user testuser with the e-mail address testuser@example.com.

  10. git config --global user.name "testuser" git config --global user.email "testuser@example.com"

Note:

It’s important to know that git configs work on a user by user basis. For example, if you have a ‘david’ Linux user and they will be working with git then David should run the same commands from his user account. By doing this the commits made by the ‘david’ Linux user will be done under his details in Git.

#distributed version control #git #linux #scm #source code management #tools #ubuntu #ubuntu 16.04 #vcs #version control #version control system

Monty  Boehm

Monty Boehm

1659453850

Twitter.jl: Julia Package to Access Twitter API

Twitter.jl

A Julia package for interacting with the Twitter API.

Twitter.jl is a Julia package to work with the Twitter API v1.1. Currently, only the REST API methods are supported; streaming API endpoints aren't implemented at this time.

All functions have required arguments for those parameters required by Twitter and an options keyword argument to provide a Dict{String, String} of optional parameters Twitter API documentation. Most function calls will return either a Dict or an Array <: TwitterType. Bad requests will return the response code from the API (403, 404, etc).

DataFrame methods are defined for functions returning composite types: Tweets, Places, Lists, and Users.

Authentication

Before one can make use of this package, you must create an application on the Twitter's Developer Platform.

Once your application is approved, you can access your dashboard/portal to grab your authentication credentials from the "Details" tab of the application.

Note that you will also want to ensure that your App has Read / Write OAuth access in order to post tweets. You can find out more about this on Stack Overflow.

Installation

To install this package, enter ] on the REPL to bring up Julia's package manager. Then add the package:

julia> ]
(v1.7) pkg> add Twitter

Tip: Press Ctrl+C to return to the julia> prompt.

Usage

To run Twitter.jl, enter the following command in your Julia REPL

julia> using Twitter

Then the a global variable has to be declared with the twitterauth function. This function holds the consumer_key(API Key), consumer_secret(API Key Secret), oauth_token(Access Token), and oauth_secret(Access Token Secret) respectively.

twitterauth("6nOtpXmf...", # API Key
            "sES5Zlj096S...", # API Key Secret
            "98689850-Hj...", # Access Token
            "UroqCVpWKIt...") # Access Token Secret
  • Ensure you put your credentials in an env file to avoid pushing your secrets to the public 🙀.

Note: This package does not currently support OAuth authentication.

Code examples

See runtests.jl for example function calls.

using Twitter, Test
using JSON, OAuth

# set debugging
ENV["JULIA_DEBUG"]=Twitter

twitterauth(ENV["CONSUMER_KEY"], ENV["CONSUMER_SECRET"], ENV["ACCESS_TOKEN"], ENV["ACCESS_TOKEN_SECRET"])

#get_mentions_timeline
mentions_timeline_default = get_mentions_timeline()
tw = mentions_timeline_default[1]
tw_df = DataFrame(mentions_timeline_default)
@test 0 <= length(mentions_timeline_default) <= 20
@test typeof(mentions_timeline_default) == Vector{Tweets}
@test typeof(tw) == Tweets
@test size(tw_df)[2] == 30

#get_user_timeline
user_timeline_default = get_user_timeline(screen_name = "randyzwitch")
@test typeof(user_timeline_default) == Vector{Tweets}

#get_home_timeline
home_timeline_default = get_home_timeline()
@test typeof(home_timeline_default) == Vector{Tweets}

#get_single_tweet_id
get_tweet_by_id = get_single_tweet_id(id = "434685122671939584")
@test typeof(get_tweet_by_id) == Tweets

#get_search_tweets
duke_tweets = get_search_tweets(q = "#Duke", count = 200)
@test typeof(duke_tweets) <: Dict

#test sending/deleting direct messages
#commenting out because Twitter API changed. Come back to fix
# send_dm = post_direct_messages_send(text = "Testing from Julia, this might disappear later $(time())", screen_name = "randyzwitch")
# get_single_dm = get_direct_messages_show(id = send_dm.id)
# destroy = post_direct_messages_destroy(id = send_dm.id)
# @test typeof(send_dm) == Tweets
# @test typeof(get_single_dm) == Tweets
# @test typeof(destroy) == Tweets

#creating/destroying friendships
add_friend = post_friendships_create(screen_name = "kyrieirving")

unfollow = post_friendships_destroy(screen_name = "kyrieirving")
unfollow_df = DataFrame(unfollow)
@test typeof(add_friend) == Users
@test typeof(unfollow) == Users
@test size(unfollow_df)[2] == 40

# create a cursor for follower ids
follow_cursor_test = get_followers_ids(screen_name = "twitter", count = 10_000)
@test length(follow_cursor_test["ids"]) == 10_000

# create a cursor for friend ids - use barackobama because he follows a lot of accounts!
friend_cursor_test = get_friends_ids(screen_name = "BarackObama", count = 10_000)
@test length(friend_cursor_test["ids"]) == 10_000

# create a test for home timelines
home_t = get_home_timeline(count = 2)
@test length(home_t) > 1

# TEST of cursoring functionality on user timelines
user_t = get_user_timeline(screen_name = "stefanjwojcik", count = 400)
@test length(user_t) == 400
# get the minimum ID of the tweets returned (the earliest)
minid = minimum(x.id for x in user_t);

# now iterate until you hit that tweet: should return 399
# WARNING: current versions of julia cannot use keywords in macros? read here: https://github.com/JuliaLang/julia/pull/29261
# eventually replace since_id = minid
tweets_since = get_user_timeline(screen_name = "stefanjwojcik", count = 400, since_id = 1001808621053898752, include_rts=1)

@test length(tweets_since)>=399

# testing get_mentions_timeline
mentions = get_mentions_timeline(screen_name = "stefanjwojcik", count = 300) 
@test length(mentions) >= 50 #sometimes API doesn't return number requested (twitter API specifies count is the max returned, may be much lower)
@test Tweets<:typeof(mentions[1])

# testing retweets_of_me
my_rts = get_retweets_of_me(count = 300)
@test Tweets<:typeof(my_rts[1])

Want to contribute?

Contributions are welcome! Kindly refer to the contribution guidelines.

Linux: Build Status 

CodeCov: codecov

Author: Randyzwitch
Source Code: https://github.com/randyzwitch/Twitter.jl 
License: View license

#julia #api #twitter 

Derek  Champlin

Derek Champlin

1596343860

Getting Started With Version Control

Version Control is an absolutely critical component to developing that every coder should be familiar with. Version Control is a logging system which records the history of your code so that the entire progression can be available at a later time. Version control allows for developers to write, change, delete, and edit new and existing code while keeping a detailed record of all the different “versions” of our file(s). In particular, GitHub uses the version control system “Git”, which is what we will focus on in this post.

Photo by Yancy Min on Unsplash

Local vs Remote

There are two main types of version control systems: local and remote. A local system refers to any information that is stored on your computer itself. Any time you are performing a solo project or some quick coding for practical purposes, local storage will suffice. However, as soon as collaboration becomes a possibility, local version control no longer works because you will rarely be using the same computer as your colleague.

A **distributed version control system **is one where all users have a complete copy of the entire repository (project server). The project can now be worked on independently of any network or connection, so colleagues can access each other’s previous work and version updates no matter where they are working from. Once the changes are completed, the new work is “pushed” to the repository and can now be seen by all collaborators.

Fork & ‘Git Clone’

The first concept to be aware of when using GitHub’s version control system is what is called forking. Forking refers to making a personal copy of something already in the repository. Let’s say that your colleague wrote a quick script to load, clean, and save project data to a Pandas DataFrame. If you just wanted to get familiar with the script and apply it to your own data locally, you could just fork the repository and then you’d have that access. You can fork a repository by clicking ‘fork’ in the top right corner of a repository.

#mac #data-science #git #developer #version-control