#Finding the indices of matching elements in list in Python | Explore Tumblr posts and blogs

artmmorg · 3 years ago

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Xpath for text after element

#Xpath for text after element how to

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After you have installed selenium and checked out – Navigating links using get method, you might want to play more with Selenium Python. Selenium Python bindings provides a simple API to write functional/acceptance tests using Selenium WebDriver. Selenium’s Python Module is built to perform automated testing with Python. Python program to convert a list to string.

#Xpath for text after element how to

How to get column names in Pandas dataframe.Adding new column to existing DataFrame in Pandas.MoviePy Composite Video – Setting starting time of single clip.Navigating links using get method – Selenium Python.Selenium Python Introduction and Installation.Selenium Basics – Components, Features, Uses and Limitations.Locating multiple elements in Selenium Python.Locating single elements in Selenium Python.Interacting with Webpage – Selenium Python.

#Xpath for text after element driver

find_elements_by_xpath() driver method – Selenium Python.

find_element_by_xpath() driver method – Selenium Python.

find_elements_by_css_selector() driver method – Selenium Python.

find_element_by_css_selector() driver method – Selenium Python.

find_element_by_class_name() driver method – Selenium Python.

Python – find_element_by_id() method in Selenium.

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Some times we may need to work with URLs with href attributes. In Css we rewrite them as css=a:contains('Forgot'), which will find the first anchor that contains 'Forgot'. We can also specify the partial text of the link as //a. We can just use as 'link=Forgot your password?', using xpath we should use as //a Links have anchor tags, we can apply the same as we applied for 'Text', the only difference here is we should add anchor tag. But If you want to match exactly to the text then we should have something like css=a or a The above can be done using css as css=div:contains('Demo Website!'). We find element by using xpath as //div or //div If the HTML is as below: Check Our Demo Website! We can use like this //button as Xpath to find out element containing exactly 'Log In'. As name describes, 'Exactly' will try to find the exact match and Contains looks for multiple matches. When working with text, we will have two scenarios, one is 'Exactly' and other one is 'Contains'. Now lets us look at the examples for 'Text'. How to match on text using CSS locators and Xpath Example css for child / sub child as div a In css this is very simple by using whitespace. In such cases, we can use two slashes to match any subnode for xpath. Css examples of a link inside of a div tag can be identified as div > aĪnd sometimes, if the element is not direct child, may be the element is inside another element. In CSS the child is indicated with a " >". How to access Child elements using css selectors Example XPATH for child elements : //div/a How to access direct child elements using xpathĪ child in XPATH is represented with a "/". We can also define xpath with 'Style' attribute xpath transparent '] Using xpath : - or here first it will check for the id and then it will check for the second.īased on index also, we can define the path as can also define by the using the value attribute or Phone'] Here using xpath / Css, we can combine two locators when ever required, lets see how we can achieve. Identify element using multiple attributes We can directly use them by using id or name locators. With Name - css=input or css=Īll the above syntax are simple. With ID - css=input#email or css=#emailĢ. With ID : - or we can also use as With Name - or we can also use as css we can use as below :ġ. Let us look for xpath examples to use ID and Name effectively with combinationsġ. Though we have some browser plug-ins to generate xpath or css selector, but they are not much useful in real time applications. In many cases like these, we depend locating elements by CSS or by XPath. It is always very important to make test scripts robust with reliable locators that do not break until changes made. You don't need to search for any other locator if there is ID or unique name present in your application.īut with the applications designed using modern JavaScript Frameworks like Angular, React and Vue.js have no proper web elements in DOM. As we know it is always better to use ID and Name to locate the elements which will work for sure. It is very simple to locate elements if the HTML DOM has 'id' or 'name' and they are the safest locators to use. In order to perform any operation on the element like click or type into an element, we need to locate that element.

#Xpath for text after element

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kirensmartservices · 3 years ago

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What to Focus on While Selecting an Ecommerce Website Development Company?

Selecting the Best E-commerce website development company in Delhi is an intricate deed. Recognized, there are many agencies delivering web design & development companies, though this didn't make the choice any easier for you – but we will make that direct method easy with these few main points to prefer an ecommerce development company. Difference between web designer and web developer Both skills are very interconnected with each other whereas they have diverse proficient skill sets. Usually, these services are primarily delivered through the existing agency. Remaining oriented along with how all of these respective term’s standards encourage you to select the perfect option for your vocation. Web designers form the visual factors of websites. They assemble with customers, online or in person, in demand to obtain a precise image of the note that requires it to be portrayed on the website. Mainly they make layouts, formats, & features that exhibit the customer's services in a way that is pleading to the target visitors by using web tools or platforms. And, the Web developer is a specialist over more than one coding languages such as PHP, Ruby on Rails, XML, ActionScript, Python, etc., & also skilled in developing web applications, custom media, and devices from the setting up. Here are some salient features of focus with some points it will assist you to assemble on selecting the potential assistance provider for your respective businesses.

Matching References, testimonials, and feedback Selecting any company in advance you just need to invite & adjoin with open connections, feedback, & testimonials from clients they have a function with. Ensure that you understand their work's quality, efficacy, dedication to deadlines, and what kind of coordination depicts connection. Receive a report of work Ensure that you have gotten into a covenant agreement consider the web developer and designer that must depict the designer's knowledge of your task & the parts and inform about an evaluated schedule during that he made a contract to offer. It must consider the elements listing the actual services to be prepared, the revenue for such in return, & what any variation from the objective would be. Explore the custom-made conduct to serve all your requirements The E-Commerce site or digital retail company has a foundation of the content management system (CMS). Components such as direction processing, information flow, revenue unification, & systematic would be driving on. Exploration for this indication and ensure that your designer/developer is offering you your particular needs. Make a limited list of designer’s experts whom you find that it will provide everything as per as your necessity. Skills & knowledge is as significance as Certificates There are millions of web Designer & developer freelancers who exist at considerable & are especially self-learned. So, while proficient with a specialist's measure or something like equivalent recognizability which is strongly considered to provide work quality, it’s don't fall to watch the self-learned. In case you believe an individual on the basis of his educated certificates, you could be ignoring out on a skillful proficient person. Knowledge and skill are significant as compared to degree. Choose the top E-Commerce development services company in Delhi helps companies customize their online appearance to satisfy the requirements of a client's taste and develop futuristic competitive websites for their customers in any industry they serve!

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huntersc72 · 4 years ago

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Pandas Github

Pandas Challenge Github

Pandas Github

Github Pandas Tutorial

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Up to date remote data access for pandas, works for multiple versions of pandas.

< Data Indexing and Selection | Contents | Handling Missing Data >

Since Python 3.4, pathlib has been included in the Python standard library. Path objects provide a simple and delightful way to interact with the file system. The pandas-path package enables the Path API for pandas through a custom accessor.path.Getting just the filenames from a series of full file paths is as simple as myfiles.path.name. Dask uses existing Python APIs and data structures to make it easy to switch between NumPy, pandas, scikit-learn to their Dask-powered equivalents. You don't have to completely rewrite your code or retrain to scale up. A REST API based on Flask for serving Pandas Dataframes to Grafana. This way, a native Python application can be used to directly supply data to Grafana both easily and powerfully. It was inspired by and is compatible with the simple json datasource. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Install pandas now!

One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions, exponential and logarithmic functions, etc.).Pandas inherits much of this functionality from NumPy, and the ufuncs that we introduced in Computation on NumPy Arrays: Universal Functions are key to this.

Pandas includes a couple useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will preserve index and column labels in the output, and for binary operations such as addition and multiplication, Pandas will automatically align indices when passing the objects to the ufunc.This means that keeping the context of data and combining data from different sources–both potentially error-prone tasks with raw NumPy arrays–become essentially foolproof ones with Pandas.We will additionally see that there are well-defined operations between one-dimensional Series structures and two-dimensional DataFrame structures.

Ufuncs: Index Preservation¶

Because Pandas is designed to work with NumPy, any NumPy ufunc will work on Pandas Series and DataFrame objects.Let's start by defining a simple Series and DataFrame on which to demonstrate this:

If we apply a NumPy ufunc on either of these objects, the result will be another Pandas object with the indices preserved:

ABCD0-1.0000007.071068e-011.000000-1.000000e+001-0.7071071.224647e-160.707107-7.071068e-012-0.7071071.000000e+00-0.7071071.224647e-16

Any of the ufuncs discussed in Computation on NumPy Arrays: Universal Functions can be used in a similar manner.

UFuncs: Index Alignment¶

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For binary operations on two Series or DataFrame objects, Pandas will align indices in the process of performing the operation.This is very convenient when working with incomplete data, as we'll see in some of the examples that follow.

Index alignment in Series¶

As an example, suppose we are combining two different data sources, and find only the top three US states by area and the top three US states by population:

Let's see what happens when we divide these to compute the population density:

The resulting array contains the union of indices of the two input arrays, which could be determined using standard Python set arithmetic on these indices:

Pandas Github

Any item for which one or the other does not have an entry is marked with NaN, or 'Not a Number,' which is how Pandas marks missing data (see further discussion of missing data in Handling Missing Data).This index matching is implemented this way for any of Python's built-in arithmetic expressions; any missing values are filled in with NaN by default:

If using NaN values is not the desired behavior, the fill value can be modified using appropriate object methods in place of the operators.For example, calling A.add(B) is equivalent to calling A + B, but allows optional explicit specification of the fill value for any elements in A or B that might be missing:

Index alignment in DataFrame¶

A similar type of alignment takes place for both columns and indices when performing operations on DataFrames:

Notice that indices are aligned correctly irrespective of their order in the two objects, and indices in the result are sorted.As was the case with Series, we can use the associated object's arithmetic method and pass any desired fill_value to be used in place of missing entries.Here we'll fill with the mean of all values in A (computed by first stacking the rows of A):

The following table lists Python operators and their equivalent Pandas object methods:

Python OperatorPandas Method(s)+add()-sub(), subtract()*mul(), multiply()/truediv(), div(), divide()//floordiv()%mod()**pow()

Ufuncs: Operations Between DataFrame and Series¶

When performing operations between a DataFrame and a Series, the index and column alignment is similarly maintained.Operations between a DataFrame and a Series are similar to operations between a two-dimensional and one-dimensional NumPy array.Consider one common operation, where we find the difference of a two-dimensional array and one of its rows:

According to NumPy's broadcasting rules (see Computation on Arrays: Broadcasting), subtraction between a two-dimensional array and one of its rows is applied row-wise.

In Pandas, the convention similarly operates row-wise by default:

If you would instead like to operate column-wise, you can use the object methods mentioned earlier, while specifying the axis keyword:

Note that these DataFrame/Series operations, like the operations discussed above, will automatically align indices between the two elements:

This preservation and alignment of indices and columns means that operations on data in Pandas will always maintain the data context, which prevents the types of silly errors that might come up when working with heterogeneous and/or misaligned data in raw NumPy arrays.

< Data Indexing and Selection | Contents | Handling Missing Data >

Display pandas dataframes clearly and interactively in a web app using Flask.

Web apps are a great way to show your data to a larger audience. Simple tables can be a good place to start. Imagine we want to list all the details of local surfers, split by gender. This translates to a couple of pandas dataframes to display, such as the dataframe females below.

Transforming dataframes into html tables

Using the pandas function to_html we can transform a pandas dataframe into a html table. All tables have the class dataframe by default. We can add on more classes using the classes parameter. For example, writing

results in a html table with the classes dataframe female as shown below.

Prepare the file structure for flask app

The simple_tables directory will contains all the scripts, css and html needed for the web app to run. The script site_tables.py will sit in this directory, and from here we will run the app and populate the app’s pages. Any html templates must be stored in the templates directory. Any css sheets must be within the static directory.

Below is the file structure I have used for this surfing example.

Create a flask app that pulls the dataframes

We can create a page on our web app called tables. Every time this page loads, we pull the data, filter and format to get two dataframes, females and males.

The dataframes are then transformed into html tables with classes dataframe female and dataframe male respectively. These html tables are sent as a list to the template view.html, which is stored in the templates directory. We also send a list of titles to use as a heading for each table.

Running the app using debug=True allows the app to auto-update every time the code gets edited.

Define the html template using jinja2

The html template view.html pulls css from the style sheet style.css in the static directory. We will check out the css in the next section.

Next, the jinja2 language allows us to loop through the html table list tables. Using loop.index provides the index of the loop. This starts from 1 so we need to convert between python list indices and those for jinja2 loops. Then we can pull out the correct title for each table.

For each table in the list, the table title is shown, and then the table itself. safe tells jinja2 to show this parameter as a html object.

Style the tables with css

We can use the following styling to make the tables a bit more pretty. The classes male and female have been defined with different header colours. This enables us to highlight different groups of tabled data from the initial site_tables.py script.

Github Pandas Tutorial

Some nice touches include using tr:nth-child(odd) and tr:nth-child(even) to have alternate row colours. Also tr:hover gives an interactive feel to the tables.

View the web app

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Running the script site_tables.py from bash will serve the web app on your local host. Your web page should look like the one below.

Pandas Github Issues

Feedback

Pandas Github License

Always feel free to get in touch with other solutions, general thoughts or questions.

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gianagonzalez · 5 years ago

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Within this blog, We're going to talk about a part of the numerous specialized capacity and abilities to develop into a specialist Accountant now and continuously:

Standard Wondering

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You must have the choice to liaise with unique divisions, traders and the customers. For this, bookkeepers are envisioned to acquire solid relational capabilities, for example, buyer the executives, arrangement aptitudes, compassion, and so on.

Presently, the activity in the bookkeepers are not restricted to the calculating, it involves customary gatherings with industry experts, consumers, and companions to match their Tastes. In this particular way, obtaining Each one of these skills will incorporate within the function usefulness and accomplishment of a bookkeeper. One example is, it can be prescribed never to make the most of interior abbreviations or specialized language even though responding to inquiries of The shoppers. Having excellent relational aptitudes will construct good connections and to hold consumers and to draw in new purchasers. This can be a big aptitude to acquire.

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Smarts tend to be the people who can fathom the information, even so additional astute are the ones who may make it being used. To research the information and to consider a plausible arrangement, it is vital to the bookkeepers to learn the intricate particulars of the data. Owning every one of those aptitudes can make you aware with the present desire of the consumers and customers. Greedy the innovation and embellishment of our arrangement of capabilities as per the need of time will probably support.

If the above title confuses you, you may well be lacking out on a lot of the finest opportunities the world wide web provides. Meanwhile, other people are growing their careers, getting ready for new lives as entrepreneurs, or are learning new techniques or expanding their awareness foundation, usually at little or no Price.

How are they undertaking this?

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As an example, if I enter web page:edu MOOC robotics, I get about 7,000 results, together with this Introduction to Robotics Specialization from Penn Engineering. Through the use of this syntax, you are able to typically bypass the clearinghouses and uncover what exactly you're looking for on university sites, even classes the clearinghouses may not offer.

Pleased MOOCing!

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solaceinfotechpvtltd · 6 years ago

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PHP Developers to bring your business to next level

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Now, Let’s understand the why Facebook also shown faith in PHP development.

Here’s an interesting question I found on Quora: “Why hasn’t Facebook migrated away from PHP?” Facebook engineer Yishan Sparklepants Wong worked at the company in various roles between December 2005 and March 2010. Keep in mind that Facebook launched in February 2004 so this particular engineer has been at the company for the majority of its existence.

Without further ado, here’s Wong’s answer to the PHP question:

The reason Facebook hasn’t migrated away from PHP is because it has incumbent inertia (it’s what’s there) and Facebook’s engineers have managed to work around many of its flaws through a combination of patches at all levels of the stack and excellent internal discipline via code convention and style – the worst attributes of the language are avoided and coding style is rigidly enforced through a fairly tight culture of code review (failing to adhere to the style and “going cowboy” by writing sloppy code results in pitiless mockery by one’s peers). Engineering management has never had to take a strong hand here; this arose largely due to key internal technical leaders just sort of corralling everyone else along.

There is also a lot of industry precedent indicating that re-writing an entire code base in another language is usually one of the worst things you can do, so at all levels there is a reluctance to do that. The preferred strategy is to write new components in a De-coupled manner using a better language of choice (C++, python, Erlang, Java, etc); this is easily facilitated by Facebook’s early development of thrift, an efficient multi-language RPC framework. This also helps avoid the worst effects of PHP: it can be totally avoided in sub components where the language is grossly-unsuitable. The broad effect is that the overall code base slowly evolves away from depending as heavily on PHP, with the components that are still in PHP being written in tightly-controlled, disciplined ways by veteran members of the staff.

https://solaceinfotech.com/blog/php-developer-to-bring-a-positive-change-in-your-business/

#php

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jmtapio · 6 years ago

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From time to time I google for the right syntax how to process lists and dictionaries in Python or arrays and objects in JavaScript. So I decided to extend my series of equivalents with those functions. After all, it's me too, who will be using the information I provide here.

All truthful elements

Sometimes we need to check from a list of conditions if all of them are true, or from a list of elements if all of them are not empty.

This can be checked with the following in Python:

items = [1, 2, 3] all_truthy = all(items) # True

And here is an equivalent in JavaScript:

items = [1, 2, 3]; all_truthy = items.every(Boolean); // true

Any truthful elements

Similarly, we can check if at least one of the conditions is true, or there is at least one non-empty element in a list.

It Python we would do that with:

items = [0, 1, 2, 3] some_truthy = any(items) # True

And in JavaScript we would check it like this:

items = [0, 1, 2, 3]; some_truthy = items.some(Boolean); // true

Iterate through each element and its index

Here is an example of how to iterate through a list of items and also check their indices in Python. It is useful for verbose console output when creating different command line tools that process data:

items = ['a', 'b', 'c', 'd'] for index, element in enumerate(items): print(f'{index}: {element};')

In JavaScript an analogous way to do the same would be using the forEach() method. The usual for loop is also an option, but I find the forEach() more elegant and clear.

items = ['a', 'b', 'c', 'd']; items.forEach(function(element, index) { console.log(`${index}: ${element};`); });

Map elements to the results of a function

To process all elements of a list, you can either iterate through them with the for loop and create a new list with modifications, or you can do that in one step by mapping the list items to a modification function. In Python this can be done with the map() function:

items = [0, 1, 2, 3] all_doubled = list(map(lambda x: 2 * x, items)) # [0, 2, 4, 6]

In JavaScript the map() is a method of an array:

items = [0, 1, 2, 3]; all_doubled = items.map(x => 2 * x); // [0, 2, 4, 6]

Filter elements by a function

When you need to search for some elements in a list or array and want to avoid for loops, you can use the filtering functionality. In Python that is doable with the filter() function that accepts the filtering function and the list and returns a new filtered list.

items = [0, 1, 2, 3] only_even = list(filter(lambda x: x % 2 == 0, items)) # [0, 2]

In JavaScript there is a filter() method of the array for that.

items = [0, 1, 2, 3]; only_even = items.filter(x => x % 2 === 0); // [0, 2]

In both cases, the filtering function checks each item if it is matching the filter criteria and returns true in that case.

Reduce elements by a function to a single value

When you want to apply some function to a list of items to get a single result in one go, you can use the reduce function. It works for summing, multiplying, ORing, ANDing, or checking maximums and minimums.

In Python there is a reduce() function for that.

from functools import reduce items = [1, 2, 3, 4] total = reduce(lambda total, current: total + current, items) # 10

In JavaScript there is a reduce() method of the array.

items = [1, 2, 3, 4]; total = items.reduce((total, current) => total + current); // 10

Merge dictionaries

There are multiple ways to merge dictionaries in Python or objects in JavaScript. But these are probably the simplest ones.

In Python it's decomposing dictionaries to tuples of keys and arrays, joining them, and creating a new dictionary.

d1 = {'a': 'A', 'b': 'B'} d2 = {'a': 'AAA', 'c': 'CCC'} merged = dict(list(d1.items()) + list(d2.items())) # {'a': 'AAA', 'b': 'B', 'c': 'CCC'}

Analogously, in JavaScript it's spreading two objects into a new object:

d1 = {a: 'A', b: 'B'} d2 = {a: 'AAA', c: 'CCC'} merged = {...d1, ...d2}; // {a: 'AAA', b: 'B', c: 'CCC'}

The Takeaways

In both languages, you can traverse through lists of items without explicitly incrementing and referencing an index.

For processing list items, you don't necessarily need a loop. The dedicated methods or functions all() / every(), any() / some(), map(), filter(), and reduce() are there to help you.

In both languages, you can merge multiple dictionaries into one. If the same key appears in several dictionaries, the latest one will be used in the merged dictionary.

Of course, I also updated the cheat sheet with the full list of equivalents in Python and JavaScript that you saw here described. This cheat sheet helps me with a good overview next to my laptop, so I believe that it would be helpful to you too. The new revision 10 is with syntax highlighting, so it makes it even better to explore and understand.

Get the Ultimate Cheat Sheet of Equivalents in Python and JavaScript

Use it for good!

Cover photo by Darren Chan.

via The Django community aggregator

#Reading

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foxpeople477 · 4 years ago

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Web Scraping With Django

In this tutorial, we are going to learn about creating Django form and store data into the database. The form is a graphical entity on the website where the user can submit their information. Later, this information can be saved in the database and can be used to perform some other logical operation. Hi,Greetings for the day I have deep knowledge web scraping. Feel free to contact me. I am Python and Website developer I worked on the below technologies: Back End: - Python with Django and Flask Framework - RE More.

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In August this year, Django 3.1 arrived with support for Django async views. This was fantastic news but most people raised the obvious question – What can I do with it? There have been a few tutorials about Django asynchronous views that demonstrate asynchronous execution while calling asyncio.sleep. But that merely led to the refinement of the popular question – What can I do with it besides sleep-ing?

The short answer is – it is a very powerful technique to write efficient views. For a detailed overview of what asynchronous views are and how they can be used, keep on reading. If you are new to asynchronous support in Django and like to know more background, read my earlier article: A Guide to ASGI in Django 3.0 and its Performance.

Django Async Views

Django now allows you to write views which can run asynchronously. First let’s refresh your memory by looking at a simple and minimal synchronous view in Django:

It takes a request object and returns a response object. In a real world project, a view does many things like fetching records from a database, calling a service or rendering a template. But they work synchronously or one after the other.

Web Scraping With Django Using

In Django’s MTV (Model Template View) architecture, Views are disproportionately more powerful than others (I find it comparable to a controller in MVC architecture though these things are debatable). Once you enter a view you can perform almost any logic necessary to create a response. This is why Asynchronous Views are so important. It lets you do more things concurrently.

It is quite easy to write an asynchronous view. For example the asynchronous version of our minimal example above would be:

This is a coroutine rather than a function. You cannot call it directly. An event loop needs to be created to execute it. But you do not have to worry about that difference since Django takes care of all that.

Note that this particular view is not invoking anything asynchronously. If Django is running in the classic WSGI mode, then a new event loop is created (automatically) to run this coroutine. Holy panda switch specs. So in this case, it might be slightly slower than the synchronous version. But that’s because you are not using it to run tasks concurrently.

So then why bother writing asynchronous views? The limitations of synchronous views become apparent only at a certain scale. When it comes to large scale web applications probably nothing beats FaceBook.

Views at Facebook

In August, Facebook released a static analysis tool to detect and prevent security issues in Python. But what caught my eye was how the views were written in the examples they had shared. They were all async!

Note that this is not Django but something similar. Currently, Django runs the database code synchronously. But that may change sometime in the future.

If you think about it, it makes perfect sense. Synchronous code can be blocked while waiting for an I/O operation for several microseconds. However, its equivalent asynchronous code would not be tied up and can work on other tasks. Therefore it can handle more requests with lower latencies. More requests gives Facebook (or any other large site) the ability to handle more users on the same infrastructure.

Even if you are not close to reaching Facebook scale, you could use Python’s asyncio as a more predictable threading mechanism to run many things concurrently. A thread scheduler could interrupt in between destructive updates of shared resources leading to difficult to debug race conditions. Compared to threads, coroutines can achieve a higher level of concurrency with very less overhead.

Misleading Sleep Examples

As I joked earlier, most of the Django async views tutorials show an example involving sleep. Even the official Django release notes had this example:

To a Python async guru this code might indicate the possibilities that were not previously possible. But to the vast majority, this code is misleading in many ways.

Firstly, the sleep happening synchronously or asynchronously makes no difference to the end user. The poor chap who just opened the URL linked to that view will have to wait for 0.5 seconds before it returns a cheeky “Hello, async world!”. If you are a complete novice, you may have expected an immediate reply and somehow the “hello” greeting to appear asynchronously half a second later. Of course, that sounds silly but then what is this example trying to do compared to a synchronous time.sleep() inside a view?

The answer is, as with most things in the asyncio world, in the event loop. If the event loop had some other task waiting to be run then that half second window would give it an opportunity to run that. Note that it may take longer than that window to complete. Cooperative Multithreading assumes that everyone works quickly and hands over the control promptly back to the event loop.

Secondly, it does not seem to accomplish anything useful. Some command-line interfaces use sleep to give enough time for users to read a message before disappearing. But it is the opposite for web applications - a faster response from the web server is the key to a better user experience. So by slowing the response what are we trying to demonstrate in such examples?

The best explanation for such simplified examples I can give is convenience. It needs a bit more setup to show examples which really need asynchronous support. That’s what we are trying to explore here.

Better examples

A rule of thumb to remember before writing an asynchronous view is to check if it is I/O bound or CPU-bound. A view which spends most of the time in a CPU-bound activity for e.g. matrix multiplication or image manipulation would really not benefit from rewriting them to async views. You should be focussing on the I/O bound activities.

Invoking Microservices

Most large web applications are moving away from a monolithic architecture to one composed of many microservices. Rendering a view might require the results of many internal or external services.

In our example, an ecommerce site for books renders its front page - like most popular sites - tailored to the logged in user by displaying recommended books. The recommendation engine is typically implemented as a separate microservice that makes recommendations based on past buying history and perhaps a bit of machine learning by understanding how successful its past recommendations were.

In this case, we also need the results of another microservice that decides which promotional banners to display as a rotating banner or slideshow to the user. These banners are not tailored to the logged in user but change depending on the items currently on sale (active promotional campaign) or date.

Let’s look at how a synchronous version of such a page might look like:

Here instead of the popular Python requests library we are using the httpx library because it supports making synchronous and asynchronous web requests. The interface is almost identical.

The problem with this view is that the time taken to invoke these services add up since they happen sequentially. The Python process is suspended until the first service responds which could take a long time in a worst case scenario.

Let’s try to run them concurrently using a simplistic (and ineffective) await call:

Notice that the view has changed from a function to a coroutine (due to async def keyword). Also note that there are two places where we await for a response from each of the services. You don’t have to try to understand every line here, as we will explain with a better example.

Interestingly, this view does not work concurrently and takes the same amount of time as the synchronous view. If you are familiar with asynchronous programming, you might have guessed that simply awaiting a coroutine does not make it run other things concurrently, you will just yield control back to the event loop. The view still gets suspended.

Let’s look at a proper way to run things concurrently:

If the two services we are calling have similar response times, then this view should complete in _half _the time compared to the synchronous version. This is because the calls happen concurrently as we would want.

Let’s try to understand what is happening here. There is an outer try…except block to catch request errors while making either of the HTTP calls. Then there is an inner async…with block which gives a context having the client object.

The most important line is one with the asyncio.gather call taking the coroutines created by the two client.get calls. The gather call will execute them concurrently and return only when both of them are completed. The result would be a tuple of responses which we will unpack into two variables response_p and response_r. If there were no errors, these responses are populated in the context sent for template rendering.

Microservices are typically internal to the organization hence the response times are low and less variable. Yet, it is never a good idea to rely solely on synchronous calls for communicating between microservices. As the dependencies between services increases, it creates long chains of request and response calls. Such chains can slow down services.

Why Live Scraping is Bad

We need to address web scraping because so many asyncio examples use them. I am referring to cases where multiple external websites or pages within a website are concurrently fetched and scraped for information like live stock market (or bitcoin) prices. The implementation would be very similar to what we saw in the Microservices example.

But this is very risky since a view should return a response to the user as quickly as possible. So trying to fetch external sites which have variable response times or throttling mechanisms could be a poor user experience or even worse a browser timeout. Since microservice calls are typically internal, response times can be controlled with proper SLAs.

Ideally, scraping should be done in a separate process scheduled to run periodically (using celery or rq). The view should simply pick up the scraped values and present them to the users.

Serving Files

Django addresses the problem of serving files by trying hard not to do it itself. This makes sense from a “Do not reinvent the wheel” perspective. After all, there are several better solutions to serve static files like nginx.

But often we need to serve files with dynamic content. Files often reside in a (slower) disk-based storage (we now have much faster SSDs). While this file operation is quite easy to accomplish with Python, it could be expensive in terms of performance for large files. Regardless of the file’s size, this is a potentially blocking I/O operation that could potentially be used for running another task concurrently.

Imagine we need to serve a PDF certificate in a Django view. However the date and time of downloading the certificate needs to be stored in the metadata of the PDF file, for some reason (possibly for identification and validation).

We will use the aiofiles library here for asynchronous file I/O. The API is almost the same as the familiar Python’s built-in file API. Here is how the asynchronous view could be written:

This example illustrates why we need asynchronous template rendering in Django. But until that gets implemented, you could use aiofiles library to pull local files without skipping a beat.

There are downsides to directly using local files instead of Django’s staticfiles. In the future, when you migrate to a different storage space like Amazon S3, make sure you adapt your code accordingly.

Handling Uploads

On the flip side, uploading a file is also a potentially long, blocking operation. For security and organizational reasons, Django stores all uploaded content into a separate ‘media’ directory.

If you have a form that allows uploading a file, then we need to anticipate that some pesky user would upload an impossibly large one. Thankfully Django passes the file to the view as chunks of a certain size. Combined with aiofile’s ability to write a file asynchronously, we could support highly concurrent uploads.

Again this is circumventing Django’s default file upload mechanism, so you need to be careful about the security implications.

Where To Use

Django Async project has full backward compatibility as one of its main goals. So you can continue to use your old synchronous views without rewriting them into async. Asynchronous views are not a panacea for all performance issues, so most projects will still continue to use synchronous code since they are quite straightforward to reason about.

In fact, you can use both async and sync views in the same project. Django will take care of calling the view in the appropriate manner. However, if you are using async views it is recommended to deploy the application on ASGI servers.

This gives you the flexibility to try asynchronous views gradually especially for I/O intensive work. You need to be careful to pick only async libraries or mix them with sync carefully (use the async_to_sync and sync_to_async adaptors).

Disney plus on switch. Hopefully this writeup gave you some ideas.

Web Development With Django

Thanks to Chillar Anand and Ritesh Agrawal for reviewing this post. All illustrations courtesy of Old Book Illustrations

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jjonassevilla · 5 years ago

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Not Using Landing Pages in Your Ecommerce Email Marketing? Here’s Why You Should

We’ve all had it happen. You meticulously craft an ecommerce email marketing campaign that’s gonna help you sell a ton of products. You build a beautiful HTML template, write engaging copy, and A/B test your subject line. You implement an obvious and compelling call to action.

And after all that work, the landing page that your email directs folks to has a high bounce rate—or worse, a low conversion rate.

What gives?

It could be that your emails are writing checks your click-through destination can’t cash. If you send out a 15% off promotion for dog treats and link your audience to someplace with no mention of the discount, visitors are gonna be confused—and they’ll lose interest in a hurry.

Bottom line: Failing to match the messaging in your email with the copy and visuals on your landing page will hurt your conversion rate.

Maybe you already know it’s a problem, but you feel like you don’t have the resources to pair all of your offers with campaign-specific pages. Fortunately, there’s an easy fix. Here’s why you need to match your emails to your landing pages in your next ecommerce campaign, and how you can do it really, really well.

The Real Reasons Your Email Subscribers Aren’t Buying

Why Every Ecommerce Email Campaign Needs Its Own Landing Page

How to Match Your Emails with Your Landing Pages (& Maximize Conversions)

Ecommerce Email Marketing and Landing Page Examples

How *Not* to Match Your Emails with Landing Pages

The Real Reasons Your Email Subscribers Aren’t Buying

Let’s be honest. Sometimes in marketing, you can get away with doing less—and that’s a problem.

Email marketing offers some of the best ROI in the business. When you’ve already got someone’s email address, you can expect them to open 14% of the emails you send, with click-through rates just under 7% overall. Estimates suggest that there’s $44 of revenue generated for every dollar spent on email marketing.

With stats like these, you can just half-butt your ecomm email promotions and still do pretty good, right?

Not exactly. If your emails are paired with landing pages that have high bounce rates or low conversion rates, you’re not just leaving money on the table—you’re also bombarding your potential customers with marketing that just doesn’t resonate.

Here are some of the common reasons email promos underperform:

1. Your storefront product page isn’t enough

Data indicates the average bounce rate is 9%, even with load times of less than two seconds. If you’ve seen higher bounce rates on the destination page of your email promos, it might be that you’re not linking to a relevant enough page in the first place.

Your online store’s product pages are specific no-no’s for this purpose. They’re often short, lack details mentioned in your email, and don’t create a consistent experience from click to click.

2. You’ve got too many escape routes

Another problem with your online store’s product pages is that it’s too easy for customers to get distracted and leave. Think about all of the escape routes: website menus, product navigation, highlighted deals that have nothing to do with your email.

Your ecommerce landing page needs to be built as a distraction-free, conversion-optimized funnel. Always encourage your customers to go forward, not sideways.

3. You’re a victim of the paradox of choice

Even if you cut down on the escape routes, too many options can lead to fewer conversions. As Barry Schwartz explains in his book, The Paradox of Choice: “What we don’t realize is that the very option of being allowed to change our minds seems to increase the chances that we will change our minds.”

The same is true for your visitors. Landing pages with just one call to action have been shown to have 2% higher conversion rates than those with five or more.

4. Your landing page is trying to do too much

When your landing pages are more specific, you can get away with using fewer words. You may also find that it’s better for your conversion rates: landing pages with less copy tend to outperform pages with too much copy at a rate of 14% to 11%.

Josh Garofolo, CRO expert at Sway Copy, explains:

A product page will never do more than an “okay” job because it needs to cater to everyone—every persona, every use case, every traffic source.

Sending subscribers to a focused landing page that leverages everything you know about them—including the context behind the link they’ve just clicked—is the most reliable way to increase conversions.

Why Every Ecommerce Email Marketing Campaign Needs Its Own Landing Page

To summarize some of the things we’ve already covered, here are some of the biggest reasons that you should be pairing email promotions with dedicated landing pages:

Avoid confusion and frustration. When someone clicks a CTA in your email for a specific offer, they don’t want to end up on a page that doesn’t mention that promo. They may wonder if the offer is even valid.

Target specific customer groups. More specific landing pages help you hit on more customer segments. In one example below, you’ll see how Samuraw targeted specific customer groups with unique pages for each.

Maintain purchase momentum. A customer clicking your email offers is further in the sales cycle than a customer who just discovered your product pages. Creating specific landing pages helps you target those customers who are more prepared to buy and streamlines their path to purchase.

B2B email expert Sophia Le makes the case for pairing emails with landing pages this way:

If ecommerce brands take the extra step to make a landing page, it allows them to create a consistent story arc between the email copy and the actual conversion goal.

The more seamless it is, the more likely the conversion. Plus it’s less jarring for the email subscriber when the transition from email to landing page is a smooth one.

How to Match Your Emails with Your Landing Pages (& Maximize Conversions)

Here are some quick tips for creating landing pages that convert more of your email subscribers:

Be consistent in design. The first thing that visitors are going to internalize is how the landing page actually looks. When someone clicks on your CTA in the email, the last thing you want to do is surprise them. To create a seamless experience, include consistent design elements like colors, fonts, and images.

Minimize navigation. This is a landing page, not a launching page. Yet too few ecommerce marketers seem to realize that: only about 16% of landing pages are free of a navigation bar. Be sure you’re in the other 84%.

Reduce friction. Automatically fill in whatever information you can for visitors on your landing page. For example, if they clicked on a coupon code, make sure it’s already applied to their cart. This reduces the amount of clicking a customer has to do when they’re placing an order.

Make one offer per landing page. While 48% of landing pages make multiple offers, you can reinforce the specificity and consistency of your own promotion by focusing on just one offer per page.

Make sure the offers match. Don’t make the mistake of promising a discount in an email without also mentioning it on the landing page. Keep the messaging precisely matched so customers don’t have to wonder if they’re in the right place.

Val Geisler, email expert at FixMyChurn, offers this advice:

Landing pages help you be super specific with your audience, and they help your audience feel seen and heard. You can create custom landing pages for various segments of your email list and—using targeted content based on what you know about them—speak directly to their needs.

So, what should a great ecommerce email landing page look like? Let’s check out some examples.

Ecommerce Email Marketing & Landing Page Examples

Example: Codecademy

Let’s kick things off with an incredible example from Codecademy, an online learning platform with courses in programming languages like JavaScript and Python.

This email promotion offers a 25% Black Friday discount on annual memberships for Codecademy Pro, a paid subscription that unlocks all of the platform’s educational coursework. In addition to the savings, Codecademy’s pitch here is all about reaching your potential: unlock the tools, get an actionable plan, achieve your goals.

Recipients who click on Codecademy’s email call to action are directed to an attention-grabbing landing page that expands on the email offer:

Image courtesy of Codecademy. Click it to see the whole thing.

Yeah, it looks great—but this Codecademy page is also converting almost half of everyone who lands here. This is why the promotion works:

Incredible design from start to finish. Codecademy uses bold colors and layered patterns to create a promo email that jumps right out of your inbox. Those elements carry over to the landing page, delivering a seamless experience throughout.

No introduced distractions. There’s no navigation on the landing page, and none of the ideas are new—just more information about the things we saw in the email. Codecademy repeats its pitch around harnessing your potential, explains its value props, and includes a testimonial as social proof.

Focused call to action. There are three buttons on this landing page, but they all point to the same place: checkout. Codecademy uses a sticky bar to remind visitors about the email discount and keep the savings top-of-mind.

Example: Samuraw

Next is Samuraw, a multivitamin and probiotic formula that comes in two versions: one for children, one for adults. The challenge? Addressing each of those target segments with a single campaign.

Another Black Friday email marketing promotion, Samuraw starts by highlighting its holiday discount. Scrolling down, customers find two specific offers—one for each version of the formula.

When someone clicks either “Add to Cart” buttons, they’re taken to a landing page (built by Webistry) that corresponds with the selected formula.

Image courtesy of Samuraw. Click it to see the whole thing.

Pretty intuitive, huh? But that’s not the only reason this example from Samuraw is awesome. Here are some other things they’re doing right:

Consistent branding and messaging. The offer being highlighted appears above the fold in the email and on the landing page. The color schemes are the same. Even the product pictures don’t vary. It’s hard to imagine any visitor getting confused when they wind up here.

Reduced friction and streamlined checkout. The discounts offered in the email are automatically applied once someone clicks through to the landing page. Samuraw makes it simple for customers to reach the final purchase decision.

Segmented customer messaging. “Add to Cart” is a call to action that almost begs to point to a product page, but Samuraw instead links to two specific landing pages aimed at either adults or kids to close the sale. With added details, these pre-cart landing pages do a better job of selling than online store pages.

Example: Great Wolf Lodge

Next up is Great Wolf Lodge, a family of indoor water parks and resort hotels.

Over the summer, they drive bookings through an email marketing campaign that touts their Summer Camp-In event, which includes campfires, pool parties, BBQs, and all kinds of other outdoor fun—only, y’know, inside.

To spur interest, Great Wolf Lodge sent out this well-designed email campaign that highlighted some of the main activities going on, as well as lots of images showing families having an awesome time.

From here, recipients are invited to “Book Now” through the email’s CTA button, which leads to the following tailor-made landing page:

Image courtesy of Great Wolf Lodge. Click it to see the whole thing.

As they scroll down the page, the potential booker gets lots of details about what’s included during the event, sees compelling visuals that evoke positive feelings, and even gets a coupon code for a summer-themed suite.

So, well else is working well here?

Seamless look and feel. The custom graphics create a consistent experience across the two different touchpoints and generate a feeling of nostalgia with their classic 1950s look.

Strategic call to action. The booking CTA on the landing page becomes a sticky bar as the visitor scrolls, so it’s always right at the top of the page and never out of sight.

Reinforced discount offer. The coupon code offer is consistent and referenced both in the email and the landing page, helping keep the promotion top of mind.

Looking for more ecommerce landing page examples? Check out our Ultimate Ecommerce Landing Page Lookbook, which features pages from 27 of the top online retailers.

How *Not* to Match Your Emails with Landing Pages

The examples above show a few companies who understand that it’s not enough just to send a great email. Your landing page has to reflect that email if you want to convert your subscribers.

Let’s look at an example of an email and landing page mismatch. Motorsport.com recently ran a Cyber Monday email promotion that promised “better than half price” discounts for customers. Here’s a snippet:

Interesting visuals and a clear call to action make this good so far. But when you click “Subscribe Now,” you’re linked to a landing page with this pricing overview:

It’s great that the link to subscribe sends you to a subscription page. But pay attention to the subtle messaging inconsistencies:

Where’s the mention of the “better than half price” sale? Cyber Monday customers that wind up here might wonder if they’ve missed their chance. Are they receiving the discount, or not? This sort of confusion can lead them to bounce.

If a discount was applied, is it the one we were promised? Is $8.60 per month “better than half price”? Is so, there’s no indication of that here.

Why is there a different call to action? “Subscribe Now” becomes “Get the Full Story” and “Select Package.” There’s a missed opportunity here to more carefully match the messaging and imagery from email to landing page.

Visitors who wanted a unique deal might click anyway, but since the landing page doesn’t even mention the discount, lots of people are going to conclude they’re in the wrong place.

Turn Ready-Made Email Clicks Into Ecommerce Sales

Email conversion expert Laura Lupoch sums things up nicely:

To get an email subscriber to make a purchase, you need a series of touchpoints where they keep saying “yes” to you. That sets the stage for the big “yes” at the end when you ask them to buy.

Think of your landing page as another major step in that “yes” journey towards making a purchase.

If you see high click rates on your emails but not high conversion rates on your landing pages, it doesn’t necessarily reflect on the quality of your emails. It might just be that your emails have promised something your landing page failed to deliver—and that’s hard to say “yes” to.

This is where a landing page builder helps. You can quickly drag-and-drop together specific pages for each email promotion (all without a developer) and deliver a consistent purchase path from inbox to checkout.

from Marketing https://unbounce.com/email-marketing/match-ecommerce-offers-with-landing-pages/ via http://www.rssmix.com/

#SEO

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itsjessicaisreal · 5 years ago