#Python & Google Colab

[yellowmagicalgirl]

Since you like festive Halloween cats, I bet you'd love the colab cats

[awesometrollingman10]

I wish my Python code would make a "ding!" sound when it's done rendering. I want to be able to return to a machine learning model the same way that a housewife would return to the oven. I need an egg timer for when the computer is done thinking.

[gilbartar]

Clustering keyword with autoscript python + Google Colab

[zynetoglobaltechnologies]

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[foodspark-scraper]

How to Scrap Food Data with Python & Google Collab?

In today's digital age, data is king. Companies and businesses rely on data to make informed decisions and stay ahead of the competition. But where does this data come from? One source is web scraping, the process of extracting data from websites. In this article, we will explore how to scrap food data with Python and Google Collab, a free online platform for coding and data analysis.

What is Web Scraping?

Web scraping is the process of extracting data from websites using automated tools or scripts. It allows you to gather large amounts of data quickly and efficiently, without having to manually copy and paste information from websites. This data can then be used for various purposes, such as market research, data analysis, and more.

Why Scrape Food Data?

Food data is a valuable source of information for businesses in the food industry. It can provide insights into consumer preferences, trends, and market demand. By scraping food data, businesses can stay informed about their competitors, track prices, and make data-driven decisions.

Setting Up Google Collab

Before we can start scraping, we need to set up our environment. Google Collab is a great option for this as it provides a free online platform for coding and data analysis. To get started, go to https://colab.research.google.com/ and sign in with your Google account. Once you're in, create a new notebook by clicking on "File" and then "New Notebook."

Installing Necessary Libraries

To scrape data with Python, we will need to install a few libraries. In your Google Collab notebook, run the following code in a code cell:

!pip install requests !pip install beautifulsoup4

This will install the necessary libraries for web scraping.

Scraping Food Data

Now that we have our environment set up, we can start scraping food data. For this example, we will scrape data from a popular food delivery website, Grubhub. We will extract the name, price, and description of the top 10 items from a specific restaurant.

First, we need to import the necessary libraries and define the URL we want to scrape:

import requests from bs4 import BeautifulSoup

url = "https://www.grubhub.com/restaurant/restaurant-name/menu"

Next, we will use the requests library to get the HTML content of the webpage and then use BeautifulSoup to parse the HTML:

page = requests.get(url) soup = BeautifulSoup(page.content, 'html.parser')

Now, we can use find_all to find all the items on the menu and loop through them to extract the desired information:

items = soup.find_all(class_="menuItem") for item in items[:10]: name = item.find(class_="menuItem-name").get_text() price = item.find(class_="menuItem-price").get_text() description = item.find(class_="menuItem-description").get_text() print(name, price, description)

This will print out the name, price, and description of the top 10 items from the restaurant's menu.

Conclusion

Web scraping is a powerful tool for extracting data from websites. In this article, we explored how to scrape food data with Python and Google Collab. By following these steps, you can gather valuable information for your business and stay ahead of the competition. Happy scraping!

[devdogblog]

แปลงข้อมูล JSON เป็น Excel ด้วยคำสั่ง 3 บรรทัด

ในบางครั้งเราก็มักจะได้รับคำขอจาก แผนกอื่นๆ เพื่อขอ Export ข้อมูลเป็นไฟล์ Excel วันนี้ผู้เขียนจะขอแนะนำอีก หนึ่งเครื่องมือที่ช่วยให้ ชาวโปรแกรมเมอร์ ทำงานได้เร็วขึ้น และสามารถใช้งานได้ทันทีเลยกับ Package ของ Python ที่มีชื่อว่า pandas ครับ สมมุดว่าผู้เขียน มีข้อมูลใน database และทำการแปลงออกมาเป็น JSON หน้าตาประมาณนี้ [ { "ชื่อ": "John Smith", "อายุ": 32, "รถยนต์": "Toyota Corolla ปี…

[eclipsephil]

Fandometrics Graphs

so based on this post I decided I wanted to do some sort of graph/visual representation of how we've been doing every week in the fandometrics

the first one I did has all 4 areas we're interested in (Phan, Dan and Phil, AmazingPhil, and Daniel Howell) on the same graph

but i think it's kinda too busy to really tell what's going on so I also made individual ones for each of the four statistics. On these, I included lines for the mean and median number of points* we have earned thus far as well as the running total for the year

I did this all in Python and the google colab ipynb file is here if you want to see it

*I calculated the points using a basic system of 1st place = 20, 2nd = 19, and so on, giving 20th 1 point. If we weren't on the list in a given week, we got 0 points.

[liminalarchivist]

I'm going to make a game

If anyone's interested and has some experience with coding, design, anything else that could help, or wants to be part of this project, feel free to dm me or comment below!

This will be a tma-based non-profit game, so keep that in mind (there will be spoilers!!)

...

Also, if anyone wants to learn some python, I'll be sharing a Google Colab file soon. Again, if you're interested, dm me! (The file has a lot of fun references to tma and tmagp Season 1, so be prepared for that)

[morfanerina]

Ah yes the dilemma of why the fuck the tutorial script did not work despite redoing the new virtual environment and even trying on google colab.

Still giving errors. Guess I'll try a new environment but now I'm gonna specifiy a previous python version even though this version I'm using should have worked 🙃

[whywasmaddie]

AO3 Wrapped

This is my version of an AO3 wrapped code!! It can run for any year (although if not the current year (2025!!) it requires you to click around a bit in your ao3 history). It also requires you to log into google, unfortunately, but I can't change that.

Please please PLEASE let me know if something isn't working or the code has an error - I didn't write it in google colab and while I've tested it, there might well be things that work slightly differently. Make sure to read the notes at the top, and have fun with what you read in 2024!!!

This has been my little project for basically the whole year, and I owe a lot to the authors of the original code, klipklapper and teresachenec, as a lot of the code is still theirs and this has taught me a lot about coding and python as I've tried to make this work, so thank you!

[uhardite]

Hi , which eng student are you?

Can you tell which subjects are there in eng.

I wanna learn coding but my major is different, can you suggest something.

I hope it's not a lot I have asked you 😭.

I love your acc it's gives so much motivation 💖

im an electronics engineering student, so this semester i had physics, calculus, electrical and electronics engineering, english and computer science (python).

if you want to learn coding there's a ton of resources online, personally i use hackerrank (a website with coding challenges), brocode (a yt channel dedicated to coding basics), w3schools (for coding theory) and google colab for practicing things on my own. i would suggest watching a few videos on brocode and following along on google colab, and then going ahead and solving some challenges on hackerrank and referring to w3schools for additional knowledge, in whatever coding language you are interested in. maybe try making some websites or do some fun coding on the side as well, you'll learn a lot in the process.

also thank you so much, it means a lot to me 😭 i love helping people and sharing what works for me but i never thought my posts would be genuinely motivating to anyone, thanks again 🫶<3

[govindhtech]

Gemini Code Assist Enterprise: AI App Development Tool

Introducing Gemini Code Assist Enterprise’s AI-powered app development tool that allows for code customisation.

The modern economy is driven by software development. Unfortunately, due to a lack of skilled developers, a growing number of integrations, vendors, and abstraction levels, developing effective apps across the tech stack is difficult.

To expedite application delivery and stay competitive, IT leaders must provide their teams with AI-powered solutions that assist developers in navigating complexity.

Google Cloud thinks that offering an AI-powered application development solution that works across the tech stack, along with enterprise-grade security guarantees, better contextual suggestions, and cloud integrations that let developers work more quickly and versatile with a wider range of services, is the best way to address development challenges.

Google Cloud is presenting Gemini Code Assist Enterprise, the next generation of application development capabilities.

Beyond AI-powered coding aid in the IDE, Gemini Code Assist Enterprise goes. This is application development support at the corporate level. Gemini’s huge token context window supports deep local codebase awareness. You can use a wide context window to consider the details of your local codebase and ongoing development session, allowing you to generate or transform code that is better appropriate for your application.

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Read more on Govindhtech.com

[online-learning-e-tuitions]

How you can use python for data wrangling and analysis

Python is a powerful and versatile programming language that can be used for various purposes, such as web development, data science, machine learning, automation, and more. One of the most popular applications of Python is data analysis, which involves processing, cleaning, manipulating, and visualizing data to gain insights and make decisions.

In this article, we will introduce some of the basic concepts and techniques of data analysis using Python, focusing on the data wrangling and analysis process. Data wrangling is the process of transforming raw data into a more suitable format for analysis, while data analysis is the process of applying statistical methods and tools to explore, summarize, and interpret data.

To perform data wrangling and analysis with Python, we will use two of the most widely used libraries: Pandas and NumPy. Pandas is a library that provides high-performance data structures and operations for manipulating tabular data, such as Series and DataFrame. NumPy is a library that provides fast and efficient numerical computations on multidimensional arrays, such as ndarray.

We will also use some other libraries that are useful for data analysis, such as Matplotlib and Seaborn for data visualization, SciPy for scientific computing, and Scikit-learn for machine learning.

To follow along with this article, you will need to have Python 3.6 or higher installed on your computer, as well as the libraries mentioned above. You can install them using pip or conda commands. You will also need a code editor or an interactive environment, such as Jupyter Notebook or Google Colab.

Let’s get started with some examples of data wrangling and analysis with Python.

Example 1: Analyzing COVID-19 Data

In this example, we will use Python to analyze the COVID-19 data from the World Health Organization (WHO). The data contains the daily situation reports of confirmed cases and deaths by country from January 21, 2020 to October 23, 2023. You can download the data from here.

First, we need to import the libraries that we will use:import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns

Next, we need to load the data into a Pandas DataFrame:df = pd.read_csv('WHO-COVID-19-global-data.csv')

We can use the head() method to see the first five rows of the DataFrame:df.head()

Date_reportedCountry_codeCountryWHO_regionNew_casesCumulative_casesNew_deathsCumulative_deaths2020–01–21AFAfghanistanEMRO00002020–01–22AFAfghanistanEMRO00002020–01–23AFAfghanistanEMRO00002020–01–24AFAfghanistanEMRO00002020–01–25AFAfghanistanEMRO0000

We can use the info() method to see some basic information about the DataFrame, such as the number of rows and columns, the data types of each column, and the memory usage:df.info()

Output:

RangeIndex: 163800 entries, 0 to 163799 Data columns (total 8 columns): # Column Non-Null Count Dtype — — — — — — — — — — — — — — — 0 Date_reported 163800 non-null object 1 Country_code 162900 non-null object 2 Country 163800 non-null object 3 WHO_region 163800 non-null object 4 New_cases 163800 non-null int64 5 Cumulative_cases 163800 non-null int64 6 New_deaths 163800 non-null int64 7 Cumulative_deaths 163800 non-null int64 dtypes: int64(4), object(4) memory usage: 10.0+ MB “><class 'pandas.core.frame.DataFrame'> RangeIndex: 163800 entries, 0 to 163799 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date_reported 163800 non-null object 1 Country_code 162900 non-null object 2 Country 163800 non-null object 3 WHO_region 163800 non-null object 4 New_cases 163800 non-null int64 5 Cumulative_cases 163800 non-null int64 6 New_deaths 163800 non-null int64 7 Cumulative_deaths 163800 non-null int64 dtypes: int64(4), object(4) memory usage: 10.0+ MB

We can see that there are some missing values in the Country_code column. We can use the isnull() method to check which rows have missing values:df[df.Country_code.isnull()]

Output:

Date_reportedCountry_codeCountryWHO_regionNew_casesCumulative_casesNew_deathsCumulative_deaths2020–01–21NaNInternational conveyance (Diamond Princess)WPRO00002020–01–22NaNInternational conveyance (Diamond Princess)WPRO0000……………………2023–10–22NaNInternational conveyance (Diamond Princess)WPRO07120132023–10–23NaNInternational conveyance (Diamond Princess)WPRO0712013

We can see that the missing values are from the rows that correspond to the International conveyance (Diamond Princess), which is a cruise ship that had a COVID-19 outbreak in early 2020. Since this is not a country, we can either drop these rows or assign them a unique code, such as ‘IC’. For simplicity, we will drop these rows using the dropna() method:df = df.dropna()

We can also check the data types of each column using the dtypes attribute:df.dtypes

Output:Date_reported object Country_code object Country object WHO_region object New_cases int64 Cumulative_cases int64 New_deaths int64 Cumulative_deaths int64 dtype: object

We can see that the Date_reported column is of type object, which means it is stored as a string. However, we want to work with dates as a datetime type, which allows us to perform date-related operations and calculations. We can use the to_datetime() function to convert the column to a datetime type:df.Date_reported = pd.to_datetime(df.Date_reported)

We can also use the describe() method to get some summary statistics of the numerical columns, such as the mean, standard deviation, minimum, maximum, and quartiles:df.describe()

Output:

New_casesCumulative_casesNew_deathsCumulative_deathscount162900.000000162900.000000162900.000000162900.000000mean1138.300062116955.14016023.4867892647.346237std6631.825489665728.383017137.25601215435.833525min-32952.000000–32952.000000–1918.000000–1918.00000025%-1.000000–1.000000–1.000000–1.00000050%-1.000000–1.000000–1.000000–1.00000075%-1.000000–1.000000–1.000000–1.000000max -1 -1 -1 -1

We can see that there are some negative values in the New_cases, Cumulative_cases, New_deaths, and Cumulative_deaths columns, which are likely due to data errors or corrections. We can use the replace() method to replace these values with zero:df = df.replace(-1,0)

Now that we have cleaned and prepared the data, we can start to analyze it and answer some questions, such as:

Which countries have the highest number of cumulative cases and deaths?

How has the pandemic evolved over time in different regions and countries?

What is the current situation of the pandemic in India?

To answer these questions, we will use some of the methods and attributes of Pandas DataFrame, such as:

groupby() : This method allows us to group the data by one or more columns and apply aggregation functions, such as sum, mean, count, etc., to each group.

sort_values() : This method allows us to sort the data by one or more

loc[] : This attribute allows us to select a subset of the data by labels or conditions.

plot() : This method allows us to create various types of plots from the data, such as line, bar, pie, scatter, etc.

If you want to learn Python from scratch must checkout e-Tuitions to learn Python online, They can teach you Python and other coding language also they have some of the best teachers for their students and most important thing you can also Book Free Demo for any class just goo and get your free demo.

[zynetoglobaltechnologies]

Zyneto Technologies: Leading Mobile App Development Companies in the US & India

In today’s mobile-first world, having a robust and feature-rich mobile application is key to staying ahead of the competition. Whether you’re a startup or an established enterprise, the right mobile app development partner can help elevate your business. Zyneto Technologies is recognized as one of the top mobile app development companies in the USA and India, offering innovative and scalable solutions that meet the diverse needs of businesses across the globe.

Why Zyneto Technologies Stands Out Among Mobile App Development Companies in the USA and India

Zyneto Technologies is known for delivering high-quality mobile app development solutions that are tailored to your business needs. With a team of highly skilled developers, they specialize in building responsive, scalable, and feature

website- zyneto.com

[foodspark-scraper]

Do you know that Google Maps can help you find the food restaurants around you? At Foodspark, we help you Scrape Food Data with Google Maps Data Scraping Using Python & Google Colab.Web scraping or data scraping imports data from a website to the local machine. The result is in the form of spreadsheets so that you can get an entire list of restaurants available around me having its address as well as ratings in the easy spreadsheet!