├── README.md ├── ecommerce_purchases_.py └── .gitignore /README.md: -------------------------------------------------------------------------------- 1 | # Ecommerce-Purchases- -------------------------------------------------------------------------------- /ecommerce_purchases_.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """Ecommerce Purchases .ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1ZetnkncA2hvQ6s-QfC0TPEt87nU5s3yA 8 | 9 | ___ 10 | 11 | 12 | ___ 13 | # Ecommerce Purchases Exercise 14 | 15 | In this Exercise you will be given some Fake Data about some purchases done through Amazon! Just go ahead and follow the directions and try your best to answer the questions and complete the tasks. Feel free to reference the solutions. Most of the tasks can be solved in different ways. For the most part, the questions get progressively harder. 16 | 17 | Please excuse anything that doesn't make "Real-World" sense in the dataframe, all the data is fake and made-up. 18 | 19 | Also note that all of these questions can be answered with one line of code. 20 | ____ 21 | ** Import pandas and read in the Ecommerce Purchases csv file and set it to a DataFrame called ecom. ** 22 | """ 23 | 24 | import pandas as pd 25 | 26 | ecom = pd.read_csv("/content/Ecommerce_Purchases.csv") 27 | 28 | """**Check the head of the DataFrame.**""" 29 | 30 | ecom.head() 31 | 32 | """** How many rows and columns are there? **""" 33 | 34 | ecom.info() 35 | 36 | """** What is the average Purchase Price? **""" 37 | 38 | ecom['Purchase Price'].mean() 39 | 40 | """** What were the highest and lowest purchase prices? **""" 41 | 42 | ecom["Purchase Price"].max() 43 | 44 | ecom["Purchase Price"].max() 45 | 46 | """** How many people have English 'en' as their Language of choice on the website? **""" 47 | 48 | ecom[ecom["Language"] == "en"].count() 49 | 50 | """** How many people have the job title of "Lawyer" ? ** 51 | 52 | """ 53 | 54 | ecom[ecom["Job"] == "Lawyer"].count() 55 | 56 | """** How many people made the purchase during the AM and how many people made the purchase during PM ? ** 57 | 58 | **(Hint: Check out [value_counts()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) ) ** 59 | """ 60 | 61 | ecom["AM or PM"].value_counts() 62 | 63 | """** What are the 5 most common Job Titles? **""" 64 | 65 | ecom["Job"].value_counts().head(5) 66 | 67 | """** Someone made a purchase that came from Lot: "90 WT" , what was the Purchase Price for this transaction? **""" 68 | 69 | ecom[ecom["Lot"] == "90 WT"]["Purchase Price"] 70 | 71 | """** What is the email of the person with the following Credit Card Number: 4926535242672853 **""" 72 | 73 | ecom[ecom["Credit Card"] == 4926535242672853]["Email"] 74 | 75 | """** How many people have American Express as their Credit Card Provider *and* made a purchase above $95 ?**""" 76 | 77 | ecom[(ecom["CC Provider"] == "American Express") & (ecom["Purchase Price"] > 95)].count() 78 | 79 | """** Hard: How many people have a credit card that expires in 2025? **""" 80 | 81 | ecom[ecom["CC Exp Date"].apply(lambda x : x.split("/")[1]) == "25"].count() 82 | 83 | """** Hard: What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...) **""" 84 | 85 | ecom["Email"].apply(lambda x : x.split("@")[1]).value_counts().head(5) 86 | 87 | """# Great Job!""" -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | --------------------------------------------------------------------------------