project done

.gitignore.txt

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+
+# Created by https://www.toptal.com/developers/gitignore/api/python,jupyternotebooks,macos
+# Edit at https://www.toptal.com/developers/gitignore?templates=python,jupyternotebooks,macos
+
+### JupyterNotebooks ###
+# gitignore template for Jupyter Notebooks
+# website: http://jupyter.org/
+
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Remove previous ipynb_checkpoints
+#   git rm -r .ipynb_checkpoints/
+
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+pytestdebug.log
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+doc/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+
+# IPython
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+pythonenv*
+
+# Spyder project settings
+.spyderproject
+.spyproject
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+# Rope project settings
+.ropeproject
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+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# profiling data
+.prof
+
+# End of https://www.toptal.com/developers/gitignore/api/python,jupyternotebooks,macos

Allergy_friendly_hotels/README.md

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+<img src="https://bit.ly/2VnXWr2" alt="Ironhack Logo" width="100"/>
+
+# Allergy friendly hotels
+*[Daria Gavrilova]*
+
+*[Data analysis, Barcelona & 14.12]*
+
+## Content
+- [Project Description](#project-description)
+- [Hypotheses / Questions](#hypotheses-questions)
+- [Dataset](#dataset)
+- [Cleaning](#cleaning)
+- [Analysis](#analysis)
+- [Conclusion](#conclusion)
+- [Future Work](#future-work)
+- [Workflow](#workflow)
+- [Organization](#organization)
+- [Links](#links)
+
+## Project Description
+As an allergic to pets, planning any trip, I always face a problem - to find a hotel that does not allow accommodation with pets. Booking.com has plenty of different filters such as "Adults only", "Pets allowed" and many others, but not "Pets are not allowed".
+In this project I am analysing data from booking.com in order to define Hotels that do not allowed pets. I check which European city has more “no pets hotels” and compare price and hotel score of “Pets" and “No pets" hotels.
+
+
+## Hypotheses / Questions
+My Null hypotesis are: Prices and hotel's scores of "No pets" and "Pets" hotels are iqual.
+Alternative hypotesis:  A price of "Pets" hotels is higer, as special deep cleaning is required.
+
+
+## Dataset
+With web scraping I collected information about European hotels : names, links, score on booking.com , price for room rate and info “Pets allowed” or “Pets not allowed”.
+I chosed 5 europeans touristic city with more than 500 hotels available on booking.com for the 30.03 : Barcelona, Amsterdam, Lisbon, Paris, Rome. From each of these cities I received information about 500 hotels (first 500 hotels according booking.com search, sorted by booking.com “top picks”).
+
+
+## Cleaning
+After cleaning proces I had data that contained 2367 properties information. Rome 496, Paris 495, Lisabon 472, Barcelona 467, Amsterdam 437.
+In a cleaning proces I droped all the hotels with missed information and also with unrealistic values such as room rete egual to 1 eur. Prices and links were cleaned with regex.
+
+
+## Analysis
+All the information I recived about pets accommondation I divided into two groups ("Pets are allowed", "Pets are not allowed"). 
+Also I divided prices into 3 price groups ("Cheap", "Normal", "Expensive").
+With seaborn ploting I investigated price and score tendancy grouping by city and pets features.
+In statistical analisis I used stats.ttest_ind function.
+
+
+## Conclusion
+My Null hypotesis could be rejected. Price and score differ statisticly significant. 
+The most expensive city is Paris and the cheapest is Lisabon. 
+Most pets friendly is Paris, and not pets friendly is Lisabon.
+Amsterdam has no "Cheap and Pets are allowed" hotels.
+Barcelona, Amsterdam and Lisabon have more hotels in group "Normal price and Pets not allowed".
+Paris - "Expensive and pets allowed". Rome - "Normal and Pets allowed".
+"Pets allowed hotels" are more expensive.
+There are more higly rated "No pets" hotels than "Pets" hotels.
+
+
+## Future Work
+I would add more cities, more features such as stars, pools, locatin and season average price. 
+
+## Workflow
+Considering topic.
+Searching Data (Recursive web scraping)
+    -first I collected links of booking.com hotel search
+    -from this links I collected (hotel name, hotel link, score and price)
+    -from hotel links (link to property) I collected "Pets information".
+Data cleaning (Pandas and regex).
+Analysis (pandas, seaborn and statistical modul).
+Visualisation was made with tableau.
+
+## Organization
+To organize my work I used Trello kanban board.
+Repository contains data folder, code folder, git ignore and read me files.
+Data folder (Hotels data by city and merged data).
+Code folder (Web scraping file, Cleaning file, Analysis file, cleaned data for Tableau visualisation)
+
+## Links
+[Repository](https://github.com/dariagavrilova/Project-Week-8-Final-Project)  
+[Slides](https://slides.com/dariagavrilova-1/deck-9b2441/edit)  
+[Trello](https://trello.com/b/bogPSi6R/final-project)
+[Tableau](https://public.tableau.com/profile/daria4279#!/vizhome/Finalproject_16080338645630)