1623452400

# BEGINNER BYBIT TUTORIAL | HOW TO GET STARTED

📺 The video in this post was made by Jayson Casper
The origin of the article: https://www.youtube.com/watch?v=GIvCKFQ1Ne0
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
Cryptocurrency trading is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
(There is no limit to the amount of credit you can earn through referrals)
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#bitcoin #blockchain #bybit #beginner bybit tutorial #beginner bybit tutorial | how to get started

1657081614

## 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")``````

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

#### 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")``````

``````#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']``````

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

ws['B2'] = 367

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

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

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.

#### 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.

#### 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

ws = wb['Sheet']
img = Image('logo.png')
img.height = 130
img.width = 200

wb.save("new2.xlsx")``````

Result:

### 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

ws = wb.active

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

Result

## 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
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
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 📄
Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial

1623452400

## BEGINNER BYBIT TUTORIAL | HOW TO GET STARTED

📺 The video in this post was made by Jayson Casper
The origin of the article: https://www.youtube.com/watch?v=GIvCKFQ1Ne0
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
Cryptocurrency trading is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
(There is no limit to the amount of credit you can earn through referrals)
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#bitcoin #blockchain #bybit #beginner bybit tutorial #beginner bybit tutorial | how to get started

1596728880

## Tutorial: Getting Started with R and RStudio

In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.

If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.

#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials

1594399440

## Getting Started with R Markdown — Guide and Cheatsheet

In this blog post, we’ll look at how to use R Markdown. By the end, you’ll have the skills you need to produce a document or presentation using R Mardown, from scratch!

We’ll show you how to convert the default R Markdown document into a useful reference guide of your own. We encourage you to follow along by building out your own R Markdown guide, but if you prefer to just read along, that works, too!

R Markdown is an open-source tool for producing reproducible reports in R. It enables you to keep all of your code, results, plots, and writing in one place. R Markdown is particularly useful when you are producing a document for an audience that is interested in the results from your analysis, but not your code.

R Markdown is powerful because it can be used for data analysis and data science, collaborating with others, and communicating results to decision makers. With R Markdown, you have the option to export your work to numerous formats including PDF, Microsoft Word, a slideshow, or an HTML document for use in a website.

Turn your data analysis into pretty documents with R Markdown.

We’ll use the RStudio integrated development environment (IDE) to produce our R Markdown reference guide. If you’d like to learn more about RStudio, check out our list of 23 awesome RStudio tips and tricks!

Here at Dataquest, we love using R Markdown for coding in R and authoring content. In fact, we wrote this blog post in R Markdown! Also, learners on the Dataquest platform use R Markdown for completing their R projects.

We included fully-reproducible code examples in this blog post. When you’ve mastered the content in this post, check out our other blog post on R Markdown tips, tricks, and shortcuts.

Okay, let’s get started with building our very own R Markdown reference document!

## 1. Install R Markdown

R Markdown is a free, open source tool that is installed like any other R package. Use the following command to install R Markdown:

``````install.packages("rmarkdown")
``````

Now that R Markdown is installed, open a new R Markdown file in RStudio by navigating to `File > New File > R Markdown…`. R Markdown files have the file extension “.Rmd”.

## 2. Default Output Format

When you open a new R Markdown file in RStudio, a pop-up window appears that prompts you to select output format to use for the document.

The default output format is HTML. With HTML, you can easily view it in a web browser.

We recommend selecting the default HTML setting for now — it can save you time! Why? Because compiling an HTML document is generally faster than generating a PDF or other format. When you near a finished product, you change the output to the format of your choosing and then make the final touches.

One final thing to note is that the title you give your document in the pop-up above is not the file name! Navigate to `File > Save As..` to name, and save, the document.

#data science tutorials #beginner #r #r markdown #r tutorial #r tutorials #rstats #rstudio #tutorial #tutorials

1599097440

## Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials