#How to open CSV files in Python – store and retrieve large data sets

[iwebscrapingblogs]

Amazon Product Data - How To Scrape Using Python

Title: Amazon Product Data: How To Scrape Using Python

In the world of e-commerce, Amazon reigns supreme as the largest online retailer, offering a vast array of products to consumers worldwide. For businesses, researchers, or anyone interested in tracking trends and pricing on Amazon, accessing Amazon product data can be a valuable resource. In this blog, we'll explore how to scrape Amazon product data using Python, unlocking a wealth of information for various purposes.

Why Scrape Amazon Product Data?

Amazon product data is a treasure trove of information. It can be used for:

Competitive Analysis: Track pricing, customer reviews, and product availability of your competitors to gain a competitive edge.

Market Research: Analyze product trends and consumer preferences to identify profitable niches.

Price Tracking: Monitor price fluctuations to make informed buying decisions and discover the best time to purchase products.

Content Creation: Collect product descriptions, images, and customer reviews to create content for your website or blog.

The Tools You'll Need

Before we dive into the scraping process, make sure you have the following tools and libraries installed:

Python: Download and install Python from the official website (https://www.python.org/).

Web Scraping Libraries: You'll need libraries like BeautifulSoup and Requests to scrape data. Install them using pip:

pip install beautifulsoup4 pip install requests

IDE or Code Editor: Use an Integrated Development Environment (IDE) like Jupyter Notebook or a code editor like Visual Studio Code for a smoother development experience.

Steps to Scrape Amazon Product Data

1. Choose Your Target URL

Select the Amazon product page you want to scrape. For example, let's say you want to scrape data for a particular laptop. Copy the URL of the laptop's product page.

2. Send a Request

Use the requests library to send an HTTP GET request to the chosen URL. This will retrieve the HTML content of the page.import requests url = 'https://www.amazon.com/dp/B07V5KS95Y' response = requests.get(url) # Check if the request was successful (status code 200) if response.status_code == 200: page_content = response.content else: print("Failed to retrieve the page.")

3. Parse HTML with BeautifulSoup

Use BeautifulSoup to parse the HTML content and extract the data you need. You can locate elements using their HTML tags, classes, or attributes.from bs4 import BeautifulSoup soup = BeautifulSoup(page_content, 'html.parser') # Example: Extract the product title product_title = soup.find('span', {'id': 'productTitle'}).text.strip()

4. Extract Relevant Information

Identify and extract the information you're interested in, such as product title, price, customer reviews, and product images. Be mindful of Amazon's terms of service while scraping.

5. Store Data

Store the scraped data in a structured format, such as CSV or JSON, for further analysis or use.import csv data = { 'Product Title': product_title, 'Price': product_price, 'Customer Reviews': customer_reviews, 'Product Images': product_images } # Example: Write data to a CSV file with open('amazon_product_data.csv', 'w', newline='', encoding='utf-8') as csv_file: writer = csv.DictWriter(csv_file, fieldnames=data.keys()) writer.writeheader() writer.writerow(data)

6. Automate the Process

You can automate scraping by creating functions and scripts that scrape multiple product pages, set up periodic scraping, and manage large datasets efficiently.

Conclusion

Scraping Amazon product data using Python can provide valuable insights for businesses and researchers. However, it's essential to use web scraping responsibly and be aware of Amazon's terms of service to avoid any legal issues. Always respect robots.txt and ensure your scraping activities are within legal and ethical boundaries. With the right tools and techniques, you can harness the power of web scraping to gather valuable information from Amazon and use it to your advantage. Happy scraping!

[fancyhints]

How to open CSV files in Python – store and retrieve large data sets

[because-its-important]

what’s the most annoying question to ask a nun* in 1967?

tl;dr - In 1967, a very long survey was administered to nearly 140,000 American women in Catholic ministry. I wrote this script, which makes the survey data work-ready and satisfies a very silly initial inquiry: Which survey question did the sisters find most annoying?

* The study participants are never referred to as nuns, so I kind of suspect that not all sisters are nuns, but I couldn't find a definitive answer about this during a brief search. 'Nun' seemed like an efficient shorthand for purposes of an already long title, but if this is wrong please holler at me!

During my first week at Recurse I made a quick game using a new language and a new toolset. Making a game on my own had been a long-running item on my list of arbitrary-but-personally-meaningful goals, so being able to cross it off felt pretty good! 

Another such goal I’ve had for a while goes something like this: “Develop the skills to be able to find a compelling data set, ask some questions, and share the results.” As such, I spent last week familiarizing myself with Python 🐍, selecting a fun dataset, prepping it for analysis, and indulging my curiosity.

the process

On recommendation from Robert Schuessler, another Recurser in my batch, I read through the first ten chapters in Python Crash Course and did the data analysis project. This section takes you through comparing time series data using weather reports for two different locations, then through plotting country populations on a world map.

During data analysis study group, Robert suggested that we find a few datasets and write scripts to get them ready to work with as a sample starter-pack for the group. Jeremy Singer-Vines’ collection of esoteric datasets, Data Is Plural, came to mind immediately. I was super excited to finally have an excuse to pour through it and eagerly set about picking a real mixed bag of 6 different data sets.

One of those datasets was The Sister Survey, a huge, one-of-its-kind collection of data on the opinions of American Catholic sisters about religious life. When I read the first question, I was hooked. 

“It seems to me that all our concepts of God and His activity are to some degree historically and culturally conditioned, and therefore we must always be open to new ways of approaching Him.” 

I decided I wanted to start with this survey and spend enough time with it to answer at least one easy question. A quick skim of the Questions and Responses file showed that of the multiple choice answer options, a recurring one was: “The statement is so annoying to me that I cannot answer.” 

I thought this was a pretty funny option, especially given that participants were already tolerant enough to take such an enormous survey! How many questions can one answer before any question is too annoying to answer? 🤔 I decided it’d be fairly simple to find the most annoying question, so I started there. 

I discovered pretty quickly that while the survey responses are in a large yet blessedly simple csv, the file with the question and answers key is just a big ole plain text. My solution was to regex through every line in the txt file and build out a survey_key dict that holds the question text and another dict of the set of possible answers for each question. This works pretty well, though I’ve spotted at least one instance where the txt file is inconsistently formatted and therefore breaks answer retrieval.

Next, I ran over each question in the survey, counted how many responses include the phrase “so annoying” and selected the question with the highest count of matching responses.

the most annoying question

Turns out it’s this one! The survey asks participants to indicate whether they agree or disagree with the following statement:

“Christian virginity goes all the way along a road on which marriage stops half way.”

3702 sisters (3%) responded that they found the statement too annoying to answer. The most popular answer was No at 56% of respondents. 

I’m not really sure how to interpret this question! So far I have two running theories about the responses:

The survey participants were also confused and boy, being confused is annoying!

The sisters generally weren’t down for claiming superiority over other women on the basis of their marital-sexual status.

Both of these interpretations align suspiciously well with my own opinions on the matter, though, so, ymmv.

9x speed improvement in one lil refactor

The first time I ran a working version of the full script it took around 27 minutes. 

I didn’t (still don’t) have the experience to know if this is fast or slow for the size of the dataset, but I did figure that it was worth making at least one attempt to speed up. Half an hour is a long time to wait for a punchline!

As you can see in this commit, I originally had a function called unify that rewrote the answers in the survey from the floats which they'd initially been stored as, to plain text returned from the survey_key. I figured that it made sense to build a dataframe with the complete info, then perform my queries against that dataframe alone. 

However, the script was spending over 80% of its time in this function, which I knew from aggressively outputting the script’s progress and timing it. I also knew that I didn’t strictly need to be doing any answer rewriting at all. So, I spent a little while refactoring find_the_most_annoying_question to use a new function, get_answer_text, which returns the descriptive answer text when passed the answer key and its question. This shaved 9 lines (roughly 12%) off my entire script.

Upon running the script post-refactor, I knew right away that this approach was much, much faster - but I still wasn’t prepared when it finished after only 3 minutes! And since I knew between one and two of those minutes were spent downloading the initial csv alone, that meant I’d effectively neutralized the most egregious time hog in the script. 👍

I still don’t know exactly why this is so much more efficient. The best explanation I have right now is “welp, writing data must be much more expensive than comparing it!” Perhaps this Nand2Tetris course I’ll be starting this week will help me better articulate these sorts of things.

flourishes 💚💛💜

Working on a script that takes forever to run foments at least two desires:

to know what the script is doing Right Now

to spruce the place up a bit

I added an otherwise unnecessary index while running over all the questions in the survey so that I could use it to cycle through a small set of characters. Last week I wrote in my mini-RC blog, "Find out wtf modulo is good for." Well, well, well.

Here’s what my script looks like when it’s iterating over each question in the survey:

I justified my vanity with the (true!) fact that it is easier to work in a friendly-feeling environment.

Plus, this was good excuse to play with constructing emojis dynamically. I thought I’d find a rainbow of hearts with sequential unicode ids, but it turns out that ❤️ 💙 and 🖤 all have very different values. ¯\_(ツ)_/¯

the data set

One of the central joys of working with this dataset has been having cause to learn some history that I’d otherwise never be exposed to. Here’s a rundown of some interesting things I learned:

This dataset was only made accessible in October this year. The effort to digitize and publicly release The Sister Survey was spearheaded by Helen Hockx-Yu, Notre Dame’s Program Manager for Digital Product Access and Dissemination, and Charles Lamb, a senior archivist at Notre Dame. After attending one of her forums on digital preservation, Lamb approached Hockx-Yu with a dataset he thought “would generate enormous scholarly interest but was not publicly accessible.”

Previously, the data had been stored on “21 magnetic tapes dating from 1966 to 1990” (Ibid) and an enormous amount of work went into making it usable. This included both transferring the raw data from the tapes, but also deciphering it once it’d been translated into a digital form.

The timing of the original survey in 1967 was not arbitrary: it was a response to the Second Vatican Council (Vatican II). Vatican II was a Big Deal! Half a century later, it remains the most recent Catholic council of its magnitude. For example, before Vatican II, mass was delivered in Latin by a priest who faced away from his congregation and Catholics were forbidden from attending Protestant services or reading from a Protestant Bible. Vatican II decreed that mass should be more participatory and conducted in the vernacular, that women should be allowed into roles as “readers, lectors, and Eucharistic ministers,” and that the Jewish people should be considered as “brothers and sisters under the same God” (Ibid).

The survey’s author, Marie Augusta Neal, SND, dedicated her life of scholarship towards studying the “sources of values and attitudes towards change” (Ibid)  among religious figures. A primary criticism of the survey was that Neal’s questions were leading, and in particular, leading respondents towards greater political activation. ✊

As someone with next to zero conception of religious history, working with this dataset was a way to expand my knowledge in a few directons all at once. Pretty pumped to keep developing my working-with-data skills.

[webinfolinesolutionin-blog]

14 Technologies Every Web Developer Should Be Able to Explain

1. Browsers

Browsers are the interpreters of the web. They request information and then when they receive it, they show us on the page in a format we can see and understand.

Google Chrome - Currently, the most popular browser brought to you by Google

Safari - Apple’s web browser

Firefox - Open-source browser supported by the Mozilla Foundation

Internet Explorer - Microsoft’s browser. You will most often hear web developers complain about this one.

2. HTML

HTML is a markup language. It provides the structure of a website so that web browsers know what to show.

3. CSS

CSS is a Cascading Style Sheet. CSS let’s web designers change colors, fonts, animations, and transitions on the web. They make the web look good.

LESS - a CSS pre-compiler to make working with CSS easier and add functionality

SASS - a CSS pre-compiler to make working with CSS easier and add functionality

4. Programming Languages

Programming languages are ways to communicate to computers and tell them what to do. There are many different programming languages just like there are many different lingual languages (English, Spanish, French, Chinese, etc). One is not better than the other. Developers typically are just proficient at a couple so they promote those more than others. Below are just some of the languages and links to their homepages

Javascript - used by all web browsers, Meteor, and lots of other frameworks

Coffeescript - is a kind of “dialect” of javascript. It is viewed as simpler and easier on your eyes as a developer but it complies (converts) back into javascript

Python -used by the Django framework and used in a lot of mathematical calculations

Ruby - used by the Ruby on Rails framework

PHP - used by Wordpress

Go - newer language, built for speed.

Objective-C - the programming language behind iOS (your iPhone), lead by Apple

Swift - Apple’s newest programming language

Java - Used by Android (Google) and a lot of desktop applications.

5. Frameworks

Frameworks are built to make building and working with programming languages easier. Frameworks typically take all the difficult, repetitive tasks in setting up a new web application and either does them for you or make them very easy for you to do.

Node.js - a server-side javascript framework

Ruby on Rails - a full-stack framework built using ruby

Django - a full-stack framework built using python

Ionic - a mobile framework

Phonegap / Cordova - a mobile framework that exposes native api’s of iOS and Android for use when writing javascript

Bootstrap - a UI (user interface) framework for building with HTML/CSS/Javascript

Foundation - a UI framework for building with HTML/CSS/Javascript

Wordpress - a CMS (content management system) built on PHP. Currently, about 20% of all websites run on this framework

Drupal - a CMS framework built using PHP.

.NET - a full-stack framework built by Microsoft

Angular.js - a front-end javascript framework.

Ember.js - a front-end javascript framework.

Backbone.js - a front-end javascript framework.

6. Libraries

Libraries are groupings of code snippets to enable a large amount of functionality without having to write it all by yourself. Libraries typically also go through the trouble to make sure the code is efficient and works well across browsers and devices (not always the case, but typically they do).

jQuery

Underscore

7. Databases

Databases are where all your data is stored. It’s like a bunch of filing cabinets with folders filled with files. Databases come mainly in two flavors: SQL and NoSQL. SQL provides more structure which helps with making sure all the data is correct and validated. NoSQL provides a lot of flexibility for building and maintaining applications.

MongoDB - is an open-sourced NoSQL database and is currently the only database supported by Meteor.

Redis - is the most popular key-value store. It is lighting fast for retrieving data but doesn’t allow for much depth in the data storage.

PostgreSQL - is a popular open-sourced SQL database.

MySQL - is another popular open-sourced SQL database. MySQL is used in Wordpress websites.

Oracle - is an enterprise SQL database.

SQL Server - is an SQL server manager created by Microsoft.

8. Client (or Client-side)

A client is one user of an application. It’s you and me when we visit http://google.com. Client’s can be desktop computers, tablets, or mobile devices. There are typically multiple clients interacting with the same application stored on a server.

9. Server (or Server-side)

A server is where the application code is typically stored. Requests are made to the server from clients, and the server will gather the appropriate information and respond to those requests.

10. Front-end

The front-end is comprised of HTML, CSS, and Javascript. This is how and where the website is shown to users.

11. Back-end

The back-end is comprised of your server and database. It’s the place where functions, methods, and data manipulation happens that you don’t want the clients to see.

12. Protocols

Protocols are standardized instructions for how to pass information back and forth between computers and devices.

HTTP - This protocol is how each website gets to your browser. Whenever you type a website like “http://google.com” this protocol requests the website from google’s server and then receives a response with the HTML, CSS, and javascript of the website.

DDP - is a new protocol created in connection with Meteor. The DDP protocol uses websockets to create a consistent connection between the client and the server. This constant connection lets websites and data on those websites update in real-time without refreshing your browser.

REST - is a protocol mainly used for API’s. It has standard methods like GET, POST, and PUT that let information be exchanged between applications.

13. API

An API is an application programming interface. It is created by the developer of an application to allow other developers to use some of the application's functionality without sharing code. Developers expose “end points” which are like inputs and outputs of the application. Using an API can control access with API keys. Examples of good API’s are those created by Facebook, Twitter, and Google for their web services.

14. Data formats

Data formats are the structure of how data is stored.

JSON - is quickly becoming the most popular data format

XML - was the main data format early in the web days and predominantly used by Microsoft systems

CSV - is data formatted by commas. Excel data is typically formatted this way.

[hasnainamjad]

A CSV file is a “comma-separated values” file. In plain English, this is a text file that contains an unusually large amount of data. More often than not, this is used in order to create databases of information, where each unit of data is separated by a comma. Hence the name!

Being able to manipulate, load, and store large amounts of data is a hugely beneficial skill when programming. This is particularly true in Python, seeing as Python is such a popular option for machine learning and data science.

Read on then, and we’ll explore how to read CSV files in Python!

How to read CSV files in Python by importing modules

To get started, we’re first going to create our CSV file.

You can do this in Excel by creating a simple spreadsheet and then choosing to save it as a CSV file. I made a little list of exercises, which looks like so:

Credit: Adam Sinicki/ Android Authority

If we open this up as a text file, we see it is stored like this:

Type of Exercise,Sets and Reps,Weight Bench press,3 x 3,120kg Squat,3 x 3,100kg Deadlift,3 x 3,150kg Curls,3 x 5,25kg Bent rows,3 x 5,80kg Military press ,3 x 5,60kg

The top line defines the values, and each subsequent line includes three entries!

So, how do we open this in Python? Fortunately, there is no need to build a CSV parser by scratch! Rather, we can simply use ready-made modules. The one we’re interested in is called, you guessed it, CSV!

We do that like so:

import csv

Now, we can open the CSV file and print that data to the screen:

with open('c:\\Python\\Exercises.csv') as csv_file: csvFile = csv.reader(csv_file, delimiter=',') for row in csvFile: print(row)

We can also split the data if we want to do fancy things with it:

for row in csvFile: if lineCount > 0: print(f'Perform {row[0]} for {row[1]} sets and reps, using {row[2]}.') lineCount += 1

As you can see, this will simply run through the file, extract each piece of data, and then write it out in plain English.

Or, what if we want to pull out a specific row?

for row in csvFile: if lineCount == 2: print(f'Perform {row[0]} for {row[1]} sets and reps, using {row[2]}.') lineCount += 1

Finally, what if we want to write to a CSV file? In that case, we can use the following code:

with open('C:\\Python\\Exercises2.csv', mode='w') as trainingRoutine: trainingRoutine = csv.writer(trainingRoutine, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) trainingRoutine.writerow(['Exercise', 'Sets and Reps', 'Weight']) trainingRoutine.writerow(['Curls', '3 x 5', '25kg']) trainingRoutine.writerow(['Bench Press', '3 x 3', '120kg'])

How to open CSV files in Python manually

Remember that a CSV file is actually just a text document with a fancy formatting. That means that you actually don’t need to use a module if you want to know how to open CSV files in Python!

Also read: How to become a data analyst and prepare for the algorithm-driven future

You can quite simply write to a text file like so:

myFile = open("Exercises3.csv", "w+") myFile.write("Exercise,Sets and Reps,Weight\nCurls,3 x 5,25kg\nBench Press,3 x 3,120kg") myFile.close()

This actually makes it fairly simple to take the contents of a list, dictionary, or set, and turn them into a CSV! Likewise, we could read our files in a similar way and then simply break the data down by looking for commas. The main reason not to do this, is that some CSV files will use slightly different formating, which can cause problems when opening lots of different files. If you’re just working with your own files though, then you’ll have no trouble!

Also read: How to read a file in Python and more

And there you have it: now you know how to open CSV files in Python! And with that, you’ve dabbled in your first bit of JSON development and even a bit of data science. Feel proud!

What are you going to do with this knowledge? Let us know in the comments below! And if you want to learn more skills like this, then we recommend checking out our list of the best online Python courses. There you’ll be able to further your education with courses like the Python Data Science Bundle. You can get it for $37 right now, which is a huge saving on the usual $115.98!

      source https://www.androidauthority.com/how-to-open-csv-file-python-1140486/

[hasnainamjad]

Writing to files is one of the most important things you will learn in any new programming language. This allows you to save user data for future reference, to manipulate large data sets, or to build useful tools like word processors and spreadsheets. Let’s find out how to write to a file in Python!

How to write to a file in Python – .txt files

The simplest way to write to a file in Python, is to create a new text file. This will allow you to store any string to retrieve later.

To do this, you first open the file, then add the content you want, and then close the file to finish.

myFile = open("NewFile.txt", "w+") myFile.write("Hello World!") myFile.close()

In this example, we have opened a new file, written the words “Hello World!” and then closed the file.

The “w+” tells Python that we are writing to a new file. If the file already exists, then that file is overwritten. If the file doesn’t already exist, then it will be created.

But what if you want to append (add) to a file that already exists? In this case, you simply swap the “w+” for an “a+”.

You can learn more useful tricks in a previous article:

How to create a file in Python and more!

This will show you how to delete and move files too!

To display the contents of the file, just use the following two lines:

myFile = open("NewFile.txt", "r") fileContents == myFile.read()

How to write to other types of file

But what if you have another type of file you want to work with, other than a text file? What if you want to create a new spreadsheet file? Or a new Word document?

In many cases, you simply need to learn the formatting used by a particular file-type and then emulate this. For example, CSV files are used to store spreadsheets. The name “CSV” actually refers to the way this formatting works: “Comma-Separated Values.”

In short, each line represents a row in a database and contains a series of values separated by commas. Each comma represents the start of a new column or cell!

You can, therefore, save a bunch of data using the exact same method you used to create your text file, but ensure to insert commas and new-lines in the right place. If you then save the file as “.CSV” then it will open in Excel when you click on it!

The same goes for many other types of file. For example, you could create a HTML file this way by using triangular tags to define headers, bold text, and other basic formatting!

Many developers will create their own formats for storing data specific to their creations. Now you know how to write to a file in Python regardless of the type of file!

Learn more about CSV files in Python here:

How to open CSV files in Python: store and retrieve large data sets

How to write to a file in Python with modules

Of course, some files are going to contain more complex formatting than others. For example, if you want to write a .Doc file in Python, you’ll come unstuck! Open a Word document in a text editor and you’ll see that Microsoft uses a lot of confusing formatting and annotation to define the layout and add additional information.

This is where modules come in!

First, install the module you want via pip. You can do this by using the following command:

pip install python doc-x

If you are running from a command line in Windows, try:

python –m pip install doc-x

Now in your Python code you can do the following:

import docx mydoc = docx.Document() mydoc.add_paragraph("Hello World!") mydoc.save("D:/NewHelloDoc.docx")

This will write “Hello World!” to a document and then close it! You can also do some other, more complex formatting:

mydoc.add_heading("Header 1", 0) mydoc.add_heading("Header 2", 1) mydoc.add_heading("Header 3", 2) mydoc.add_picture("D:/MyPicture.jpg", width=docx.shared.Inches(5), height=docx.shared.Inches(7))

Regardless of the type of file you want to work with, you’ll almost always find a module that can handle it for you. These are usually free to use and come with documentation you can route through! That’s just another of the amazing things about coding in Python!

And that is how to write to a file in Python! If you’re enjoying learning Python, then why not take your education to the next level? We’ve compiled a list of the best online Python courses where you can find some amazing discounts. Check it out!

source https://www.androidauthority.com/how-to-write-to-a-file-in-python-1141195/