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[crawlxpert01]

A Comprehensive Guide to Scraping DoorDash Restaurant and Menu Data

Introduction

Absolutely! Data is everything; it matters to any food delivery business that is trying to optimize price, look into customer preferences, and be aware of market trends. Web Scraping DoorDash restaurant Data allows one to bring his business a step closer to extracting valuable information from the platform, an invaluable competitor in the food delivery space.

This is going to be your complete guide walkthrough over DoorDash Menu Data Scraping, how to efficiently Scrape DoorDash Food Delivery Data, and the tools required to scrape DoorDash Restaurant Data successfully.

Why Scrape DoorDash Restaurant and Menu Data?

Market Research & Competitive Analysis: Gaining insights into competitor pricing, popular dishes, and restaurant performance helps businesses refine their strategies.

Restaurant Performance Evaluation: DoorDash Restaurant Data Analysis allows businesses to monitor ratings, customer reviews, and service efficiency.

Menu Optimization & Price Monitoring: Tracking menu prices and dish popularity helps restaurants and food aggregators optimize their offerings.

Customer Sentiment & Review Analysis: Scraping DoorDash reviews provides businesses with insights into customer preferences and dining trends.

Delivery Time & Logistics Insights: Analyzing delivery estimates, peak hours, and order fulfillment data can improve logistics and delivery efficiency.

Legal Considerations of DoorDash Data Scraping

Before proceeding, it is crucial to consider the legal and ethical aspects of web scraping.

Key Considerations:

Respect DoorDash’s Robots.txt File – Always check and comply with their web scraping policies.

Avoid Overloading Servers – Use rate-limiting techniques to avoid excessive requests.

Ensure Ethical Data Use – Extracted data should be used for legitimate business intelligence and analytics.

Setting Up Your DoorDash Data Scraping Environment

To successfully Scrape DoorDash Food Delivery Data, you need the right tools and frameworks.

1. Programming Languages

Python – The most commonly used language for web scraping.

JavaScript (Node.js) – Effective for handling dynamic pages.

2. Web Scraping Libraries

BeautifulSoup – For extracting HTML data from static pages.

Scrapy – A powerful web crawling framework.

Selenium – Used for scraping dynamic JavaScript-rendered content.

Puppeteer – A headless browser tool for interacting with complex pages.

3. Data Storage & Processing

CSV/Excel – For small-scale data storage and analysis.

MySQL/PostgreSQL – For managing large datasets.

MongoDB – NoSQL storage for flexible data handling.

Step-by-Step Guide to Scraping DoorDash Restaurant and Menu Data

Step 1: Understanding DoorDash’s Website Structure

DoorDash loads data dynamically using AJAX, requiring network request analysis using Developer Tools.

Step 2: Identify Key Data Points

Restaurant name, location, and rating

Menu items, pricing, and availability

Delivery time estimates

Customer reviews and sentiments

Step 3: Extract Data Using Python

Using BeautifulSoup for Static Dataimport requests from bs4 import BeautifulSoup url = "https://www.doordash.com/restaurants" headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(url, headers=headers) soup = BeautifulSoup(response.text, "html.parser") restaurants = soup.find_all("div", class_="restaurant-name") for restaurant in restaurants: print(restaurant.text)

Using Selenium for Dynamic Contentfrom selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.chrome.service import Service service = Service("path_to_chromedriver") driver = webdriver.Chrome(service=service) driver.get("https://www.doordash.com") restaurants = driver.find_elements(By.CLASS_NAME, "restaurant-name") for restaurant in restaurants: print(restaurant.text) driver.quit()

Step 4: Handling Anti-Scraping Measures

Use rotating proxies (ScraperAPI, BrightData).

Implement headless browsing with Puppeteer or Selenium.

Randomize user agents and request headers.

Step 5: Store and Analyze the Data

Convert extracted data into CSV or store it in a database for advanced analysis.import pandas as pd data = {"Restaurant": ["ABC Cafe", "XYZ Diner"], "Rating": [4.5, 4.2]} df = pd.DataFrame(data) df.to_csv("doordash_data.csv", index=False)

Analyzing Scraped DoorDash Data

1. Price Comparison & Market Analysis

Compare menu prices across different restaurants to identify trends and pricing strategies.

2. Customer Reviews Sentiment Analysis

Utilize NLP to analyze customer feedback and satisfaction.from textblob import TextBlob review = "The delivery was fast and the food was great!" sentiment = TextBlob(review).sentiment.polarity print("Sentiment Score:", sentiment)

3. Delivery Time Optimization

Analyze delivery time patterns to improve efficiency.

Challenges & Solutions in DoorDash Data Scraping

ChallengeSolutionDynamic Content LoadingUse Selenium or PuppeteerCAPTCHA RestrictionsUse CAPTCHA-solving servicesIP BlockingImplement rotating proxiesData Structure ChangesRegularly update scraping scripts

Ethical Considerations & Best Practices

Follow robots.txt guidelines to respect DoorDash’s policies.

Implement rate-limiting to prevent excessive server requests.

Avoid using data for fraudulent or unethical purposes.

Ensure compliance with data privacy regulations (GDPR, CCPA).

Conclusion

DoorDash Data Scraping is competent enough to provide an insight for market research, pricing analysis, and customer sentiment tracking. With the right means, methodologies, and ethical guidelines, an organization can use Scrape DoorDash Food Delivery Data to drive data-based decisions.

For automated and efficient extraction of DoorDash food data, one can rely on CrawlXpert, a reliable web scraping solution provider.

Are you ready to extract DoorDash data? Start crawling now using the best provided by CrawlXpert!

Know More : https://www.crawlxpert.com/blog/scraping-doordash-restaurant-and-menu-data

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What Feature Can Join Offline Business Systems Data with Online Data Collected by Google Analytics?

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[coursera2022]

Logistic Regression

Assignment: Test a Logistic Regression Model

Following is the Python program I wrote to fulfill the fourth assignment of the Regression Modeling in Practice online course.

I decided to use Jupyter Notebook as it is a pretty way to write code and present results.

Research question for this assignment

For this assignment, I decided to use the NESARC database with the following question : Are people from white ethnicity more likely to have ever used cannabis?

The potential other explanatory variables will be:

Age

Sex

Family income

Data management

The data will be managed to get cannabis usage recoded from 0 (never used cannabis) and 1 (used cannabis). The non-answering recordings (reported as 9) will be discarded.

The response variable having 2 categories, categories grouping is not needed.

The other categorical variable (sex) will be recoded such that 0 means female and 1 equals male. And the two quantitative explanatory variables (age and family income) will be centered.

In [1]:# Magic command to insert the graph directly in the notebook%matplotlib inline # Load a useful Python libraries for handling dataimport pandas as pd import numpy as np import statsmodels.formula.api as smf import seaborn as sns import matplotlib.pyplot as plt from IPython.display import Markdown, display

In [2]:nesarc = pd.read_csv('nesarc_pds.csv') C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:2723: DtypeWarning: Columns (76) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result)

In [3]:canabis_usage = {1 : 1, 2 : 0, 9 : 9} sex_shift = {1 : 1, 2 : 0} white_race = {1 : 1, 2 : 0} subnesarc = (nesarc[['AGE', 'SEX', 'S1Q1D5', 'S1Q7D', 'S3BQ1A5', 'S1Q11A']] .assign(sex=lambda x: pd.to_numeric(x['SEX'].map(sex_shift)), white_ethnicity=lambda x: pd.to_numeric(x['S1Q1D5'].map(white_race)), used_canabis=lambda x: (pd.to_numeric(x['S3BQ1A5'], errors='coerce') .map(canabis_usage) .replace(9, np.nan)), family_income=lambda x: (pd.to_numeric(x['S1Q11A'], errors='coerce'))) .dropna()) centered_nesarc = subnesarc.assign(age_c=subnesarc['AGE']-subnesarc['AGE'].mean(), family_income_c=subnesarc['family_income']-subnesarc['family_income'].mean())

In [4]:display(Markdown("Mean age : {:.0f}".format(centered_nesarc['AGE'].mean()))) display(Markdown("Mean family income last year: {:.0f}$".format(centered_nesarc['family_income'].mean())))

Mean age : 46

Mean family income last year: 45631$

Let's check that the quantitative variable are effectively centered.

In [5]:print("Centered age") print(centered_nesarc['age_c'].describe()) print("\nCentered family income") print(centered_nesarc['family_income_c'].describe()) Centered age count 4.272500e+04 mean -2.667486e-13 std 1.819181e+01 min -2.841439e+01 25% -1.441439e+01 50% -2.414394e+00 75% 1.258561e+01 max 5.158561e+01 Name: age_c, dtype: float64 Centered family income count 4.272500e+04 mean -5.710829e-10 std 5.777221e+04 min -4.560694e+04 25% -2.863094e+04 50% -1.263094e+04 75% 1.436906e+04 max 2.954369e+06 Name: family_income_c, dtype: float64

The means are both very close to 0; confirming the centering.

Distributions visualization

The following plots shows the distribution of all 3 explanatory variables with the response variable.

In [6]:g = sns.factorplot(x='white_ethnicity', y='used_canabis', data=centered_nesarc, kind="bar", ci=None) g.set_xticklabels(['Non White', 'White']) plt.xlabel('White ethnicity') plt.ylabel('Ever used cannabis') plt.title('Ever used cannabis dependance on the white ethnicity');

In [7]:g = sns.factorplot(x='sex', y='used_canabis', data=centered_nesarc, kind="bar", ci=None) g.set_xticklabels(['Female', 'Male']) plt.ylabel('Ever used cannabis') plt.title('Ever used cannabis dependance on the sex');

In [8]:g = sns.boxplot(x='used_canabis', y='family_income', data=centered_nesarc) g.set_yscale('log') g.set_xticklabels(('No', 'Yes')) plt.xlabel('Ever used cannabis') plt.ylabel('Family income ($)');

In [9]:g = sns.boxplot(x='used_canabis', y='AGE', data=centered_nesarc) g.set_xticklabels(('No', 'Yes')) plt.xlabel('Ever used cannabis') plt.ylabel('Age');

The four plots above show the following trends:

More white people tries cannabis more than non-white

Male people tries cannabis more than female

Younger people tries cannabis more than older ones

Man from richer families tries cannabis more than those from poorer families

Logistic regression model

The plots showed the direction of a potential relationship. But a rigorous statistical test has to be carried out to confirm the four previous hypothesis.

The following code will test a logistic regression model on our hypothesis.

In [10]:model = smf.logit(formula='used_canabis ~ family_income_c + age_c + sex + white_ethnicity', data=centered_nesarc).fit() model.summary() Optimization terminated successfully. Current function value: 0.451313 Iterations 6

Out[10]:

Logit Regression ResultsDep. Variable:used_canabisNo. Observations:42725Model:LogitDf Residuals:42720Method:MLEDf Model:4Date:Sun, 24 Jul 2016Pseudo R-squ.:0.07529Time:16:15:55Log-Likelihood:-19282.converged:TrueLL-Null:-20852.LLR p-value:0.000coefstd errzP>|z|[95.0% Conf. Int.]Intercept-2.10430.032-66.7640.000-2.166 -2.043family_income_c2.353e-062.16e-0710.8800.0001.93e-06 2.78e-06age_c-0.03780.001-45.2880.000-0.039 -0.036sex0.50600.02619.7660.0000.456 0.556white_ethnicity0.35830.03211.2680.0000.296 0.421

In [11]:params = model.params conf = model.conf_int() conf['Odds Ratios'] = params conf.columns = ['Lower Conf. Int.', 'Upper Conf. Int.', 'Odds Ratios'] np.exp(conf)

Out[11]:Lower Conf. Int.Upper Conf. Int.Odds RatiosIntercept0.1146250.1296990.121930family_income_c1.0000021.0000031.000002age_c0.9613030.9644550.962878sex1.5774211.7439101.658578white_ethnicity1.3444121.5228731.430863

Confounders analysis

As all four variables coefficient have significant p-value (<< 0.05), no confounders are present in this model.

But as the pseudo R-Square has a really low value, the model does not really explain well the response variable. And so there is maybe a confounder variable that I have not test for.

Summary

From the oods ratios results, we can conclude that:

People with white ethnicity are more likely to have ever used cannabis (OR=1.43, 95% confidence int. [1.34, 1.52], p<.0005)

So the results support the hypothesis between our primary explanatory variable (white ethnicity) and the reponse variable (ever used cannabis)

Male are more likely to have ever used cannabis than female (OR=1.66, 95% CI=[1.58, 1.74], p<.0005)

People aged of less than 46 are more likely to have ever used cannabis (OR=0.963, 95% CI=[0.961, 0.964], p<.0005)

Regarding the last explanatory variable (family income), I don't if I can really conclude. Indeed from the strict resuts, people coming from richer family are more likely to have ever used cannabis (OR=1.000002, 95% CI=[1.000002, 1.000003], p<.0005). But the odds ratio is so close to 1.0 than I don't know if the difference is significant.

[teguhteja]

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Panduan Penggunaan Aplikasi Pendataan Tenaga Non-ASN

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What is the Significance of Investigation Case Management Software?

Investigation case management software provides all the data needed for the investigation. Many companies have found that using a software application is an excellent way to track cases from initiation to resolution. Because of the sensitive nature of any case, having a software tool allows you to describe your process for the investigation and then focus on making sure the initiators of any case are happy and kept safe while maintaining your reputation and keeping status.

If you’re a practicing lawyer in need of an efficient system for any investigation, be it business-related, money-laundering, fraud, or even background check of employees, do consider investigating case management software for the below-mentioned reasons.

Reasons to Use Investigation Case Management Software

Consolidates Data

Important data that can be utilized as actionable intelligence can be centrally managed from the investigation software. This spells out convenience because as the legal field involves so much data handling, and that too in different locations – some hard copy and some soft copy, such a consolidation under a powerful system makes work much easier. It also allows one-stop access to all the appropriate data. The investigation case management software can take data from various systems and information sources that were previously separated from each other under one software.

Simplification of Process

Investigation management software facilitates the process simply by ensuring more organization. Once the data is consolidated, it can be organized, making it easier to analyze and categorize the data. Data is thereby assigned as essential or not. Investigation case management software allows for the sifting of data.

Confidentiality

Investigation case management software restricts access to just users who have the access information. Thereby ensuring that sensitive data does in no way get revealed. Such an option is, for apparent reasons, important in the context of an investigation, where evidence tampering, fraud, and destruction of evidence are all genuine threats to the integrity of the investigation. In such cases, investigation software allows for better security of information gathered.

Saves Time

Investigation case management software helps in managing and accessing data. Typically, the legal field involves scattered data that needs to be pieced together and sometimes escapes any primary forms of organization and efficiency. This causes a lot of time spent, which can be saved by using investigation case management software. As we’ve mentioned above, the software allows us to collate, organize, and analyze the data required for the investigation. This, in turn, will enable us to save time.

ICAC software automates processes and has improved accuracy, real-time reporting, and universal access. This also helps in saving valuable time, which can then be diverted elsewhere.

Improved Collaboration

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Conclusion

Investigation management software is one of the many types of software revolutionizing how lawyers and law enforcement agencies conduct their tasks. For reasons earlier mentioned, the tasks can now be performed with enhanced ease and efficiency. 

[synatkeam]

Assignment week 4

## load the library

import numpy as np

import pandas as pd

import seaborn import matplotlib.pyplot as plt

## read the data

import pandas import numpy radioimmuno= pandas.read_csv("radioimmuno.csv") radioimmuno

# checking the format of your variables

radioimmuno['Mouse.Identification'].dtype radioimmuno['Treatment.group'].dtype radioimmuno['Surviveal.day'].dtype radioimmuno['Number.of.T.cell'].dtype

radioimmuno['Percentage_of_T.cell.activation'].dtype

# setting variables to numeric

radioimmuno['Surviveal.day'] = pandas.to_numeric(radioimmuno['Surviveal.day']) radioimmuno['Number.of.T.cell'] = pandas.to_numeric(radioimmuno['Number.of.T.cell']) radioimmuno['Percentage_of_T.cell.activation'] = pandas.to_numeric(radioimmuno['Percentage_of_T.cell.activation'])

## Creating graph

seaborn.catplot(x='Treatment.group', y='Surviveal.day', data=radioimmuno, kind="bar", ci=None) plt.xlabel('radiation group') plt.ylabel('number of day')

## Interpretation and summary

The bargraph here demonstrated that group that received radiotherapy and immunotherapy combination survived longer than group that received just radiation alone. This suggested that adding radiotherapy improved the efficacy of immunotherapy in cancer.

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