Skip to content

Instantly share code, notes, and snippets.

@jsoma
Created December 3, 2021 15:35
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save jsoma/f8f6d581a248aed6ba0219c35b81ed90 to your computer and use it in GitHub Desktop.
Save jsoma/f8f6d581a248aed6ba0219c35b81ed90 to your computer and use it in GitHub Desktop.
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
Display the source blob
Display the rendered blob
Raw
{
"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
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment