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December 3, 2021 15:35
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Basic StockX pandas analysis data and notebook. You can find the notebook here and the data here: https://s3.amazonaws.com/stockx-sneaker-analysis/wp-content/uploads/2019/02/StockX-Data-Contest-2019-3.xlsx
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"id": "5f497065", | |
"metadata": {}, | |
"source": [ | |
"# StockX\n", | |
"\n", | |
"* **Data:** `StockX-Data-Contest-2019-3.xlsx`\n", | |
"* **Description:** You can buy shoes and resell them later online for more money. Wild world, right? This data is from [StockX](https://stockx.com/)\n", | |
"* **Source:** https://stockx.com/news/the-2019-data-contest/\n", | |
"* **Columns of interest:**\n", | |
" * `Order Date` is the resale order was completed\n", | |
" * `Brand` is the name of the company producing the shoe\n", | |
" * `Sneaker Name` is the name of the shoe itself\n", | |
" * `Sale Price` is how much the shoe went for at resale\n", | |
" * `Retail Price` is how much the shoe originally cost\n", | |
" * `Release Date` is when the shoe was originally released\n", | |
" * `Shoe Size` is the size of the shoe being sold\n", | |
" * `Buyer Region` is where the shoe's buyer is located\n", | |
"\n", | |
"This dataset is topical due to the passing of [Virgil Abloh, founder of Off-White](https://www.newyorker.com/culture/postscript/the-remarkable-life-of-virgil-abloh)." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "a34e18b0", | |
"metadata": {}, | |
"source": [ | |
"## Read in your data\n", | |
"\n", | |
"This Excel file has multiple sheets in it! You'll need to specify the sheet when you read it in with `sheet_name='Raw Data'`." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "e77a0169", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "64b9e32d", | |
"metadata": {}, | |
"source": [ | |
"## What brand had more sales?\n", | |
"\n", | |
"Yes, there are only two of them." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "4586bb21", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "96608e5f", | |
"metadata": {}, | |
"source": [ | |
"## What's the most common shoe size sold?\n", | |
"\n", | |
"I'd like you to write a sentence like `Size ____ is the most common shoe size sold, capturing ___ percent of the market.`" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "7ec30863", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "bc5be5ba", | |
"metadata": {}, | |
"source": [ | |
"## What was the median difference between the sale price and the retail price?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "1afcd0e8", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "025edaed", | |
"metadata": {}, | |
"source": [ | |
"## What were the total sales (in dollars) to South Dakota, New Mexico, and Vermont?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "07d39264", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "2f971a8d", | |
"metadata": {}, | |
"source": [ | |
"## What were the total sales (in dollars) of shoes sized 5, 6 and 7?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "00363d4c", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "f541836c", | |
"metadata": {}, | |
"source": [ | |
"## What sneakers sold, on average, for the highest sale price?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "265eb338", | |
"metadata": { | |
"scrolled": true | |
}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "5f640f47", | |
"metadata": {}, | |
"source": [ | |
"## How many shoes in the dataset were produced by Nike?\n", | |
"\n", | |
"A sneaker with either 'Nike' or 'Jordan' in the name is going to be produced by Nike. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "14d08a1a", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "6e50058c", | |
"metadata": {}, | |
"source": [ | |
"## What are the top 3 months for buying shoes? (This is order date, not release date)\n", | |
"\n", | |
"People like to buy shoes for Christmas, or with money they received during Christmas. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "203a4f1a", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "d40cfed2", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "145ab20a", | |
"metadata": {}, | |
"source": [ | |
"## What month had the most total money spent on the shoes in this dataset?\n", | |
"\n", | |
"Not super-month, but rather instead something like May 2017. And If you get weird decimals like `5,068,067.6894`, don't worry, the data is just a little dirty." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "de14f361", | |
"metadata": { | |
"scrolled": true | |
}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "089dba50", | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3 (ipykernel)", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.9.7" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 5 | |
} |
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