{"id":22089517,"url":"https://github.com/fatihilhan42/starbucks_analysis_turkey_and_world_with_python","last_synced_at":"2026-04-29T14:04:26.550Z","repository":{"id":154677292,"uuid":"528850932","full_name":"fatihilhan42/Starbucks_Analysis_Turkey_and_World_with_Python","owner":"fatihilhan42","description":"In this project, firstly the brands for coffee in the world and then these brands in Turkey were examined. The data from the dataset, which you can find in the repo, was first organized using data cleaning algorithms. These cleaned data were then graphically extracted using data visualization algorithms.","archived":false,"fork":false,"pushed_at":"2022-08-25T12:55:47.000Z","size":1662,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-15T08:51:07.257Z","etag":null,"topics":["data-analysis","data-cleaning","data-science","data-visualization","jupyter-notebook","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/fatihilhan42.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-08-25T12:55:39.000Z","updated_at":"2022-10-27T10:53:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"80c94366-793e-4834-a61d-f6257ffdb604","html_url":"https://github.com/fatihilhan42/Starbucks_Analysis_Turkey_and_World_with_Python","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/fatihilhan42/Starbucks_Analysis_Turkey_and_World_with_Python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fatihilhan42%2FStarbucks_Analysis_Turkey_and_World_with_Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fatihilhan42%2FStarbucks_Analysis_Turkey_and_World_with_Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fatihilhan42%2FStarbucks_Analysis_Turkey_and_World_with_Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fatihilhan42%2FStarbucks_Analysis_Turkey_and_World_with_Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fatihilhan42","download_url":"https://codeload.github.com/fatihilhan42/Starbucks_Analysis_Turkey_and_World_with_Python/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fatihilhan42%2FStarbucks_Analysis_Turkey_and_World_with_Python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32428622,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-29T13:34:34.882Z","status":"ssl_error","status_checked_at":"2026-04-29T13:34:29.830Z","response_time":110,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-cleaning","data-science","data-visualization","jupyter-notebook","python"],"created_at":"2024-12-01T02:13:09.985Z","updated_at":"2026-04-29T14:04:26.544Z","avatar_url":"https://github.com/fatihilhan42.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Starbucks Analysis Turkey and World\n\nIn this project, firstly the brands for coffee in the world and then these brands in Turkey were examined. The data from the dataset, which you can find in the repo, was first organized using data cleaning algorithms. These cleaned data were then graphically extracted using data visualization algorithms.\n\n\n# Importing Libraries\n```Python\nimport numpy as np \nimport pandas as pd\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\nimport warnings\nfrom mpl_toolkits.basemap import Basemap\n%matplotlib inline\nsns.set(style=\"white\", context=\"talk\")\nwarnings.simplefilter(action='ignore', category=FutureWarning)\nwarnings.filterwarnings(\"ignore\",category=plt.cbook.mplDeprecation)\n\n```\n\n# Read Dataset(head)\n\n```Python\ndata.head()\n```\n![1](https://user-images.githubusercontent.com/77057546/186663466-37eabd6f-0999-497a-89c9-549aa87cf7d6.png)\n\n# How many brands are available in the data\n\n## Distribution of Brands\n```Python\nx=data['Brand'].value_counts()\nprint(x)\nplt.figure(figsize=(8, 6))\nfig=sns.barplot(x.index,x.values)\nplt.xlabel('Brands')\nplt.ylabel('Density')\nfig.set_title('Distribution of Brands')\n```\n\n![2](https://user-images.githubusercontent.com/77057546/186663776-6e1a8627-5c4c-4651-a1b0-82aad0af7e0b.png)\n\n### Which country does 'Starbucks' brand use?\n\n```Python\nStarbucks=data[data['Brand']=='Starbucks']['Country'].value_counts()[1:10]\nprint(Starbucks)\nplt.figure(figsize=(8, 6))\nfig=sns.barplot(Starbucks.index,Starbucks.values)\nplt.xlabel('Country')\nplt.ylabel('Density')\nfig.set_title('Distribution of Starbucks Brand')\n```\n\n![4](https://user-images.githubusercontent.com/77057546/186664406-12afe0ea-2423-4f49-98f9-8697868aec84.png)\n\n\n# How many cities are Starbucks stores available?\n\n\n```Python\nCity=data['City'].value_counts()\nprint(City)\nprint('Total number of cities is',len(City))\nplt.figure(figsize=(12,8))\nfig=sns.barplot(City.index[:10],City.values[:10])\nplt.xlabel('City')\nplt.ylabel('Density')\nfig.set_title('Distribution of City')\n```\n\n![5](https://user-images.githubusercontent.com/77057546/186665247-84b8e788-8424-4588-8ed1-72e144d9c986.png)\n\n## How many state or province are Starbucks stores available?\n\n\n```Python\nState=data['State/Province'].value_counts()\nprint(State)\nprint('Total number of states is',len(State))\nplt.figure(figsize=(12,8))\nfig=sns.barplot(State.index[:10],State.values[:10])\nplt.xlabel('State')\nplt.ylabel('Density')\nfig.set_title('Distribution of State')\n```\n\n![6](https://user-images.githubusercontent.com/77057546/186665523-9649147b-9792-4f64-8d73-3af58c0288bb.png)\n\n## Timezone\n\n```Python\nTimezone=data['Timezone'].value_counts()\nprint(Timezone)\nprint('Total number of timezone is',len(Timezone))\nplt.figure(figsize=(25,8))\nfig=sns.barplot(Timezone.index[:5],Timezone.values[:5])\nplt.xlabel('State')\nplt.ylabel('Density')\nfig.set_title('Top 10 Country Distribution of Timezone')\n```\n![7](https://user-images.githubusercontent.com/77057546/186665842-e02844bb-cbe3-4327-8689-45854a825d29.png)\n\n# World Map of Starbucks Store\n\n```Python\nplt.figure(figsize=(16,16))\nworldmap = Basemap(projection='mill', \n              llcrnrlat=-80,\n              urcrnrlat=80,\n              llcrnrlon=-180,  \n              urcrnrlon=180, \n              resolution='l')\nworldmap.drawcoastlines()\nworldmap.drawcountries()\nworldmap.drawmapboundary(fill_color='white')\n\n# Load in Longitude and Latitude data\nLongitude = data[\"Longitude\"].astype(float)\nLatitude = data[\"Latitude\"].astype(float)\nx, y = worldmap(list(Longitude), list(Latitude))\nworldmap.plot(x, y,'bo',markersize =5,color=\"green\" )\nplt.title('The World Map of Starbucks Store')\nplt.show()\n```\n\n![8](https://user-images.githubusercontent.com/77057546/186666123-6e5d8d53-277f-4e5f-91ab-53a7292aac50.png)\n\n\n# Specific Approximation for Turkey Country\n\n```Python\nTurkeyData=data[data['Country']=='TR']\nTurkeyData.head()\n```\n\n![9](https://user-images.githubusercontent.com/77057546/186666376-8df73c49-af29-4536-a14a-c2b57c2cd0ed.png)\n\n# Distribution of City\n\n\n```Python\nTurkeyCity=TurkeyData['City'].value_counts()\nprint('Total number of city is', len(TurkeyCity))\nplt.figure(figsize=(14,9))\nfig=sns.barplot(TurkeyCity.index[:10],TurkeyCity.values[:10])\nplt.xlabel('Cities')\nplt.ylabel('Density')\nfig.set_title('Distribution of City')\n```\n\n![10](https://user-images.githubusercontent.com/77057546/186666663-6dbece8c-5402-4969-b31e-7b5e9916ff8f.png)\n\n\n# Turkey Starbucks Map\n\n```Python\nplt.figure(figsize=(16,16))\nmap = Basemap(projection='stere', \n              lat_0=38, lon_0=37,\n              llcrnrlon=25, \n              llcrnrlat=34, \n              urcrnrlon=50, \n              urcrnrlat=43,resolution='l',area_thresh=10000,rsphere=6371200.)\nmap.drawcoastlines()\nmap.drawcountries()\nmap.drawmapboundary(fill_color='white')\n\n# Load in Longitude and Latitude data\nLongitude = TurkeyData[\"Longitude\"].astype(float)\nLatitude = TurkeyData[\"Latitude\"].astype(float)\nx, y = map(list(Longitude), list(Latitude))\nmap.plot(x, y,'bo',markersize =7,color=\"red\" )\nplt.title('The Turkey Map of Starbucks Store')\nplt.show()\n```\n\n![11](https://user-images.githubusercontent.com/77057546/186667330-62f76b9c-e072-47a6-bda0-20039ad40464.png)\n\nSee you on another project.\n\n## MAY THE FORCE BE WITH YOU!!!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffatihilhan42%2Fstarbucks_analysis_turkey_and_world_with_python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffatihilhan42%2Fstarbucks_analysis_turkey_and_world_with_python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffatihilhan42%2Fstarbucks_analysis_turkey_and_world_with_python/lists"}