Vinish Kapoor

Vinish Kapoor

1636349852

How to Create Sticky, Closable Footer for Anchor Ads? - foxinfotech.in

https://www.foxinfotech.in/2021/11/create-sticky-closable-footer-anchor-ads.html

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How to Create Sticky, Closable Footer for Anchor Ads? - foxinfotech.in
Easter  Deckow

Easter Deckow

1655630160

PyTumblr: A Python Tumblr API v2 Client

PyTumblr

Installation

Install via pip:

$ pip install pytumblr

Install from source:

$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install

Usage

Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(
    '<consumer_key>',
    '<consumer_secret>',
    '<oauth_token>',
    '<oauth_secret>',
)

client.info() # Grabs the current user information

Two easy ways to get your credentials to are:

  1. The built-in interactive_console.py tool (if you already have a consumer key & secret)
  2. The Tumblr API console at https://api.tumblr.com/console
  3. Get sample login code at https://api.tumblr.com/console/calls/user/info

Supported Methods

User Methods

client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

  • state - a string, the state of the post. Supported types are published, draft, queue, private
  • tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
  • tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
  • date - a string, the customized GMT that you want
  • format - a string, the format that your post is in. Support types are html or markdown
  • slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
                    source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",
                    data="/Users/johnb/path/to/my/image.jpg")

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

  • title - a string, the title of post that you want. Supports HTML entities.
  • url - a string, the url that you want to create a link post for.
  • description - a string, the desciption of the link that you have
#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
                    embed="http://www.youtube.com/watch?v=40pUYLacrj4")

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python interactive-console.py

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python setup.py test

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license

#python #api 

Tamale  Moses

Tamale Moses

1669003576

Exploring Mutable and Immutable in Python

In this Python article, let's learn about Mutable and Immutable in Python. 

Mutable and Immutable in Python

Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.

Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.

Mutable Definition

Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.

Immutable Definition

Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.

List of Mutable and Immutable objects

Objects of built-in type that are mutable are:

  • Lists
  • Sets
  • Dictionaries
  • User-Defined Classes (It purely depends upon the user to define the characteristics) 

Objects of built-in type that are immutable are:

  • Numbers (Integer, Rational, Float, Decimal, Complex & Booleans)
  • Strings
  • Tuples
  • Frozen Sets
  • User-Defined Classes (It purely depends upon the user to define the characteristics)

Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.

Objects in Python

In Python, everything is treated as an object. Every object has these three attributes:

  • Identity – This refers to the address that the object refers to in the computer’s memory.
  • Type – This refers to the kind of object that is created. For example- integer, list, string etc. 
  • Value – This refers to the value stored by the object. For example – List=[1,2,3] would hold the numbers 1,2 and 3

While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.

Check out this free python certificate course to get started with Python.

Mutable Objects in Python

I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:

#Creating a list which contains name of Indian cities  

cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]

# Printing the elements from the list cities, separated by a comma & space

for city in cities:
		print(city, end=’, ’)

Output [1]: Delhi, Mumbai, Kolkata

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [2]: 0x1691d7de8c8

#Adding a new city to the list cities

cities.append(‘Chennai’)

#Printing the elements from the list cities, separated by a comma & space 

for city in cities:
	print(city, end=’, ’)

Output [3]: Delhi, Mumbai, Kolkata, Chennai

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [4]: 0x1691d7de8c8

The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.

Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.

Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0. 

#Creating a Tuple with variable name ‘foo’

foo = (1, 2)

#Changing the index[0] value from 1 to 3

foo[0] = 3
	
TypeError: 'tuple' object does not support item assignment 

Immutable Objects in Python

Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:

#Creating a Tuple which contains English name of weekdays

weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’

# Printing the elements of tuple weekdays

print(weekdays)

Output [1]:  (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [2]: 0x1691cc35090

#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’

weekdays  +=  ‘Pythonday’,

#Printing the elements of tuple weekdays

print(weekdays)

Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [4]: 0x1691cc8ad68

This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it.  Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.

Also Read: Understanding the Exploratory Data Analysis (EDA) in Python

Where can you use mutable and immutable objects:

Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.

Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.

Watch outs:  Non transitive nature of Immutability:

OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–

#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements

#The elements (lists) contains the name, age & gender 

person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the tuple

print(person)

Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [2]: 0x1691ef47f88

#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4

person[0][1] = 4

#printing the updated tuple

print(person)

Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [4]: 0x1691ef47f88

In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.

Also Read: Real-Time Object Detection Using TensorFlow

Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–

#creating a list (mutable object) which contains tuples(immutable) as it’s elements

list1 = [(1, 2, 3), (4, 5, 6)]

#printing the list

print(list1)

Output [1]: [(1, 2, 3), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [2]: 0x1691d5b13c8	

#changing object reference at index 0

list1[0] = (7, 8, 9)

#printing the list

Output [3]: [(7, 8, 9), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [4]: 0x1691d5b13c8

As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.

Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’ 

x = 10
 

#printing the value of ‘x’

print(x)

Output [1]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(x)))

Output [2]: 0x538fb560

#creating an object of integer type with value 10 and reference variable name ‘y’

y = 10

#printing the value of ‘y’

print(y)

Output [3]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(y)))

Output [4]: 0x538fb560

As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.

Quick checkPython Data Structures

Immutability of Tuple

Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.

Exceptions in immutability

Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.

Consider a tuple ‘tup’.

Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;

We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.

FAQs

1. Difference between mutable vs immutable in Python?

Mutable ObjectImmutable Object
State of the object can be modified after it is created.State of the object can’t be modified once it is created.
They are not thread safe.They are thread safe
Mutable classes are not final.It is important to make the class final before creating an immutable object.

2. What are the mutable and immutable data types in Python?

  • Some mutable data types in Python are:

list, dictionary, set, user-defined classes.

  • Some immutable data types are: 

int, float, decimal, bool, string, tuple, range.

3. Are lists mutable in Python?

Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)

4. Why are tuples called immutable types?

Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.

A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.

5. Are sets mutable in Python?

A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.

6. Are strings mutable in Python?

Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.

Join Great Learning Academy’s free online courses and upgrade your skills today.


Original article source at: https://www.mygreatlearning.com

#python 

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 

Thierry  Perret

Thierry Perret

1662365538

Les Structures De Données Les Plus Couramment Utilisées En Python

Dans tout langage de programmation, nous devons traiter des données. Maintenant, l'une des choses les plus fondamentales dont nous avons besoin pour travailler avec les données est de les stocker, de les gérer et d'y accéder efficacement de manière organisée afin qu'elles puissent être utilisées chaque fois que cela est nécessaire pour nos besoins. Les structures de données sont utilisées pour répondre à tous nos besoins.

Que sont les Structures de Données ?

Les structures de données sont les blocs de construction fondamentaux d'un langage de programmation. Il vise à fournir une approche systématique pour répondre à toutes les exigences mentionnées précédemment dans l'article. Les structures de données en Python sont List, Tuple, Dictionary et Set . Ils sont considérés comme des structures de données implicites ou intégrées dans Python . Nous pouvons utiliser ces structures de données et leur appliquer de nombreuses méthodes pour gérer, relier, manipuler et utiliser nos données.

Nous avons également des structures de données personnalisées définies par l'utilisateur, à savoir Stack , Queue , Tree , Linked List et Graph . Ils permettent aux utilisateurs d'avoir un contrôle total sur leurs fonctionnalités et de les utiliser à des fins de programmation avancées. Cependant, nous nous concentrerons sur les structures de données intégrées pour cet article.

Structures de données implicites Python

Structures de données implicites Python

LISTE

Les listes nous aident à stocker nos données de manière séquentielle avec plusieurs types de données. Ils sont comparables aux tableaux à l'exception qu'ils peuvent stocker différents types de données comme des chaînes et des nombres en même temps. Chaque élément ou élément d'une liste a un index attribué. Étant donné que Python utilise l' indexation basée sur 0 , le premier élément a un index de 0 et le comptage continue. Le dernier élément d'une liste commence par -1 qui peut être utilisé pour accéder aux éléments du dernier au premier. Pour créer une liste, nous devons écrire les éléments à l'intérieur des crochets .

L'une des choses les plus importantes à retenir à propos des listes est qu'elles sont Mutable . Cela signifie simplement que nous pouvons modifier un élément dans une liste en y accédant directement dans le cadre de l'instruction d'affectation à l'aide de l'opérateur d'indexation. Nous pouvons également effectuer des opérations sur notre liste pour obtenir la sortie souhaitée. Passons en revue le code pour mieux comprendre les opérations de liste et de liste.

1. Créer une liste

#creating the list
my_list = ['p', 'r', 'o', 'b', 'e']
print(my_list)

Production

['p', 'r', 'o', 'b', 'e']

2. Accéder aux éléments de la liste

#accessing the list 
 
#accessing the first item of the list
my_list[0]

Production

'p'
#accessing the third item of the list
my_list[2]
'o'

3. Ajouter de nouveaux éléments à la liste

#adding item to the list
my_list + ['k']

Production

['p', 'r', 'o', 'b', 'e', 'k']

4. Suppression d'éléments

#removing item from the list
#Method 1:
 
#Deleting list items
my_list = ['p', 'r', 'o', 'b', 'l', 'e', 'm']
 
# delete one item
del my_list[2]
 
print(my_list)
 
# delete multiple items
del my_list[1:5]
 
print(my_list)

Production

['p', 'r', 'b', 'l', 'e', 'm']
['p', 'm']
#Method 2:
 
#with remove fucntion
my_list = ['p','r','o','k','l','y','m']
my_list.remove('p')
 
 
print(my_list)
 
#Method 3:
 
#with pop function
print(my_list.pop(1))
 
# Output: ['r', 'k', 'l', 'y', 'm']
print(my_list)

Production

['r', 'o', 'k', 'l', 'y', 'm']
o
['r', 'k', 'l', 'y', 'm']

5. Liste de tri

#sorting of list in ascending order
 
my_list.sort()
print(my_list)

Production

['k', 'l', 'm', 'r', 'y']
#sorting of list in descending order
 
my_list.sort(reverse=True)
print(my_list)

Production

['y', 'r', 'm', 'l', 'k']

6. Trouver la longueur d'une liste

#finding the length of list
 
len(my_list)

Production

5

TUPLE

Les tuples sont très similaires aux listes avec une différence clé qu'un tuple est IMMUTABLE , contrairement à une liste. Une fois que nous avons créé un tuple ou que nous avons un tuple, nous ne sommes pas autorisés à modifier les éléments qu'il contient. Cependant, si nous avons un élément à l'intérieur d'un tuple, qui est une liste elle-même, alors seulement nous pouvons accéder ou changer dans cette liste. Pour créer un tuple, nous devons écrire les éléments entre parenthèses . Comme les listes, nous avons des méthodes similaires qui peuvent être utilisées avec des tuples. Passons en revue quelques extraits de code pour comprendre l'utilisation des tuples.

1. Créer un tuple

#creating of tuple
 
my_tuple = ("apple", "banana", "guava")
print(my_tuple)

Production

('apple', 'banana', 'guava')

2. Accéder aux éléments d'un Tuple

#accessing first element in tuple
 
my_tuple[1]

Production

'banana'

3. Longueur d'un tuple

#for finding the lenght of tuple
 
len(my_tuple)

Production

3

4. Conversion d'un tuple en liste

#converting tuple into a list
 
my_tuple_list = list(my_tuple)
type(my_tuple_list)

Production

list

5. Inverser un tuple

#Reversing a tuple
 
tuple(sorted(my_tuple, reverse=True)) 

Production

('guava', 'banana', 'apple')

6. Trier un tuple

#sorting tuple in ascending order
 
tuple(sorted(my_tuple)) 

Production

('apple', 'banana', 'guava')

7. Supprimer des éléments de Tuple

Pour supprimer des éléments du tuple, nous avons d'abord converti le tuple en une liste comme nous l'avons fait dans l'une de nos méthodes ci-dessus (point n ° 4), puis avons suivi le même processus de la liste et avons explicitement supprimé un tuple entier, juste en utilisant le del déclaration .

DICTIONNAIRE

Dictionary est une collection, ce qui signifie simplement qu'il est utilisé pour stocker une valeur avec une clé et extraire la valeur donnée à la clé. Nous pouvons le considérer comme un ensemble de clés : des paires de valeurs et chaque clé d'un dictionnaire est supposée être unique afin que nous puissions accéder aux valeurs correspondantes en conséquence.

Un dictionnaire est indiqué par l'utilisation d' accolades { } contenant les paires clé : valeur. Chacune des paires d'un dictionnaire est séparée par des virgules. Les éléments d'un dictionnaire ne sont pas ordonnés , la séquence n'a pas d'importance pendant que nous y accédons ou que nous les stockons.

Ils sont MUTABLES ce qui signifie que nous pouvons ajouter, supprimer ou mettre à jour des éléments dans un dictionnaire. Voici quelques exemples de code pour mieux comprendre un dictionnaire en python.

Un point important à noter est que nous ne pouvons pas utiliser un objet mutable comme clé dans le dictionnaire. Ainsi, une liste n'est pas autorisée comme clé dans le dictionnaire.

1. Création d'un dictionnaire

#creating a dictionary
 
my_dict = {
    1:'Delhi',
    2:'Patna',
    3:'Bangalore'
}
print(my_dict)

Production

{1: 'Delhi', 2: 'Patna', 3: 'Bangalore'}

Ici, les entiers sont les clés du dictionnaire et le nom de ville associé aux entiers sont les valeurs du dictionnaire.

2. Accéder aux éléments d'un dictionnaire

#access an item
 
print(my_dict[1])

Production

'Delhi'

3. Longueur d'un dictionnaire

#length of the dictionary
 
len(my_dict)

Production

3

4. Trier un dictionnaire

#sorting based on the key 
 
Print(sorted(my_dict.items()))
 
 
#sorting based on the values of dictionary
 
print(sorted(my_dict.values()))

Production

[(1, 'Delhi'), (2, 'Bangalore'), (3, 'Patna')]
 
['Bangalore', 'Delhi', 'Patna']

5. Ajout d'éléments dans le dictionnaire

#adding a new item in dictionary 
 
my_dict[4] = 'Lucknow'
print(my_dict)

Production

{1: 'Delhi', 2: 'Patna', 3: 'Bangalore', 4: 'Lucknow'}

6. Suppression d'éléments du dictionnaire

#for deleting an item from dict using the specific key
 
my_dict.pop(4)
print(my_dict)
 
#for deleting last item from the list
 
my_dict.popitem()
 
#for clearing the dictionary
 
my_dict.clear()
print(my_dict)

Production

{1: 'Delhi', 2: 'Patna', 3: 'Bangalore'}
(3, 'Bangalore')
{}

POSITIONNER

Set est un autre type de données en python qui est une collection non ordonnée sans éléments en double. Les cas d'utilisation courants d'un ensemble consistent à supprimer les valeurs en double et à effectuer des tests d'appartenance. Les accolades ou la set()fonction peuvent être utilisées pour créer des ensembles. Une chose à garder à l'esprit est que lors de la création d'un ensemble vide, nous devons utiliser set(), et . Ce dernier crée un dictionnaire vide. not { }

Voici quelques exemples de code pour mieux comprendre les ensembles en python.

1. Créer un ensemble

#creating set
 
my_set = {"apple", "mango", "strawberry", "apple"}
print(my_set)

Production

{'apple', 'strawberry', 'mango'}

2. Accéder aux éléments d'un ensemble

#to test for an element inside the set
 
"apple" in my_set

Production

True

3. Longueur d'un ensemble

print(len(my_set))

Production

3

4. Trier un ensemble

print(sorted(my_set))

Production

['apple', 'mango', 'strawberry']

5. Ajout d'éléments dans Set

my_set.add("guava")
print(my_set)

Production

{'apple', 'guava', 'mango', 'strawberry'}

6. Suppression d'éléments de Set

my_set.remove("mango")
print(my_set)

Production

{'apple', 'guava', 'strawberry'}

Conclusion

Dans cet article, nous avons passé en revue les structures de données les plus couramment utilisées en python et avons également vu diverses méthodes qui leur sont associées.

Lien : https://www.askpython.com/python/data

#python #datastructures

August  Larson

August Larson

1662480600

The Most Commonly Used Data Structures in Python

In any programming language, we need to deal with data.  Now, one of the most fundamental things that we need to work with the data is to store, manage, and access it efficiently in an organized way so it can be utilized whenever required for our purposes. Data Structures are used to take care of all our needs.

What are Data Structures?

Data Structures are fundamental building blocks of a programming language. It aims to provide a systematic approach to fulfill all the requirements mentioned previously in the article. The data structures in Python are List, Tuple, Dictionary, and Set. They are regarded as implicit or built-in Data Structures in Python. We can use these data structures and apply numerous methods to them to manage, relate, manipulate and utilize our data.

We also have custom Data Structures that are user-defined namely Stack, Queue, Tree, Linked List, and Graph. They allow users to have full control over their functionality and use them for advanced programming purposes. However, we will be focussing on the built-in Data Structures for this article.

Implicit Data Structures Python

Implicit Data Structures Python

LIST

Lists help us to store our data sequentially with multiple data types. They are comparable to arrays with the exception that they can store different data types like strings and numbers at the same time. Every item or element in a list has an assigned index. Since Python uses 0-based indexing, the first element has an index of 0 and the counting goes on. The last element of a list starts with -1 which can be used to access the elements from the last to the first. To create a list we have to write the items inside the square brackets.

One of the most important things to remember about lists is that they are Mutable. This simply means that we can change an element in a list by accessing it directly as part of the assignment statement using the indexing operator.  We can also perform operations on our list to get desired output. Let’s go through the code to gain a better understanding of list and list operations.

1. Creating a List

#creating the list
my_list = ['p', 'r', 'o', 'b', 'e']
print(my_list)

Output

['p', 'r', 'o', 'b', 'e']

2. Accessing items from the List

#accessing the list 
 
#accessing the first item of the list
my_list[0]

Output

'p'
#accessing the third item of the list
my_list[2]
'o'

3. Adding new items to the list

#adding item to the list
my_list + ['k']

Output

['p', 'r', 'o', 'b', 'e', 'k']

4. Removing Items

#removing item from the list
#Method 1:
 
#Deleting list items
my_list = ['p', 'r', 'o', 'b', 'l', 'e', 'm']
 
# delete one item
del my_list[2]
 
print(my_list)
 
# delete multiple items
del my_list[1:5]
 
print(my_list)

Output

['p', 'r', 'b', 'l', 'e', 'm']
['p', 'm']
#Method 2:
 
#with remove fucntion
my_list = ['p','r','o','k','l','y','m']
my_list.remove('p')
 
 
print(my_list)
 
#Method 3:
 
#with pop function
print(my_list.pop(1))
 
# Output: ['r', 'k', 'l', 'y', 'm']
print(my_list)

Output

['r', 'o', 'k', 'l', 'y', 'm']
o
['r', 'k', 'l', 'y', 'm']

5. Sorting List

#sorting of list in ascending order
 
my_list.sort()
print(my_list)

Output

['k', 'l', 'm', 'r', 'y']
#sorting of list in descending order
 
my_list.sort(reverse=True)
print(my_list)

Output

['y', 'r', 'm', 'l', 'k']

6. Finding the length of a List

#finding the length of list
 
len(my_list)

Output

5

TUPLE

Tuples are very similar to lists with a key difference that a tuple is IMMUTABLE, unlike a list. Once we create a tuple or have a tuple, we are not allowed to change the elements inside it. However, if we have an element inside a tuple, which is a list itself, only then we can access or change within that list. To create a tuple, we have to write the items inside the parenthesis. Like the lists, we have similar methods which can be used with tuples. Let’s go through some code snippets to understand using tuples.

1. Creating a Tuple

#creating of tuple
 
my_tuple = ("apple", "banana", "guava")
print(my_tuple)

Output

('apple', 'banana', 'guava')

2. Accessing items from a Tuple

#accessing first element in tuple
 
my_tuple[1]

Output

'banana'

3. Length of a Tuple

#for finding the lenght of tuple
 
len(my_tuple)

Output

3

4. Converting a Tuple to List

#converting tuple into a list
 
my_tuple_list = list(my_tuple)
type(my_tuple_list)

Output

list

5. Reversing a Tuple

#Reversing a tuple
 
tuple(sorted(my_tuple, reverse=True)) 

Output

('guava', 'banana', 'apple')

6. Sorting a Tuple

#sorting tuple in ascending order
 
tuple(sorted(my_tuple)) 

Output

('apple', 'banana', 'guava')

7. Removing elements from Tuple

For removing elements from the tuple, we first converted the tuple into a list as we did in one of our methods above( Point No. 4) then followed the same process of the list, and explicitly removed an entire tuple, just using the del statement.

DICTIONARY

Dictionary is a collection which simply means that it is used to store a value with some key and extract the value given the key. We can think of it as a set of key: value pairs and every key in a dictionary is supposed to be unique so that we can access the corresponding values accordingly.

A dictionary is denoted by the use of curly braces { } containing the key: value pairs. Each of the pairs in a dictionary is comma separated. The elements in a dictionary are un-ordered the sequence does not matter while we are accessing or storing them.

They are MUTABLE which means that we can add, delete or update elements in a dictionary. Here are some code examples to get a better understanding of a dictionary in python.

An important point to note is that we can’t use a mutable object as a key in the dictionary. So, a list is not allowed as a key in the dictionary.

1. Creating a Dictionary

#creating a dictionary
 
my_dict = {
    1:'Delhi',
    2:'Patna',
    3:'Bangalore'
}
print(my_dict)

Output

{1: 'Delhi', 2: 'Patna', 3: 'Bangalore'}

Here, integers are the keys of the dictionary and the city name associated with integers are the values of the dictionary.

2. Accessing items from a Dictionary

#access an item
 
print(my_dict[1])

Output

'Delhi'

3. Length of a Dictionary

#length of the dictionary
 
len(my_dict)

Output

3

4. Sorting a Dictionary

#sorting based on the key 
 
Print(sorted(my_dict.items()))
 
 
#sorting based on the values of dictionary
 
print(sorted(my_dict.values()))

Output

[(1, 'Delhi'), (2, 'Bangalore'), (3, 'Patna')]
 
['Bangalore', 'Delhi', 'Patna']

5. Adding elements in Dictionary

#adding a new item in dictionary 
 
my_dict[4] = 'Lucknow'
print(my_dict)

Output

{1: 'Delhi', 2: 'Patna', 3: 'Bangalore', 4: 'Lucknow'}

6. Removing elements from Dictionary

#for deleting an item from dict using the specific key
 
my_dict.pop(4)
print(my_dict)
 
#for deleting last item from the list
 
my_dict.popitem()
 
#for clearing the dictionary
 
my_dict.clear()
print(my_dict)

Output

{1: 'Delhi', 2: 'Patna', 3: 'Bangalore'}
(3, 'Bangalore')
{}

SET

Set is another data type in python which is an unordered collection with no duplicate elements. Common use cases for a set are to remove duplicate values and to perform membership testing. Curly braces or the set() function can be used to create sets. One thing to keep in mind is that while creating an empty set, we have to use set(), and not { }. The latter creates an empty dictionary.

Here are some code examples to get a better understanding of sets in python.

1. Creating a Set

#creating set
 
my_set = {"apple", "mango", "strawberry", "apple"}
print(my_set)

Output

{'apple', 'strawberry', 'mango'}

2. Accessing items from a Set

#to test for an element inside the set
 
"apple" in my_set

Output

True

3. Length of a Set

print(len(my_set))

Output

3

4. Sorting a Set

print(sorted(my_set))

Output

['apple', 'mango', 'strawberry']

5. Adding elements in Set

my_set.add("guava")
print(my_set)

Output

{'apple', 'guava', 'mango', 'strawberry'}

6. Removing elements from Set

my_set.remove("mango")
print(my_set)

Output

{'apple', 'guava', 'strawberry'}

Conclusion

In this article, we went through the most commonly used data structures in python and also saw various methods associated with them.

Link: https://www.askpython.com/python/data

#python #datastructures