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linanqiu/Cheap Rides.ipynb

Created August 15, 2018 01:19
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Cheap Rides.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cheap Rides\n",
"\n",
"My friend came up to me with a startup idea.\n",
"\n",
"> \"Hey what if you could compare Uber and Lyft prices and get the cheapest one? What if you make that a service? ...*more pitching*...\"\n",
"\n",
"Beyond the clear terms of agreement violation (Uber API literally has a clause saying that you aren't allowed to do that) and the fact that Google Maps already has something like this, I was curious how easy / hard this was via the Uber / Lyft APIs. Turns out this can be done in like half an hour. Both had insanely clearly documented APIs and developer interfaces."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import json\n",
"import pint\n",
"import itertools\n",
"import pandas\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'svg'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get your `CLIENT_TOKEN`s from Uber and Lyft APIs. It's literally less than 5 clicks each.\n",
"\n",
"Now let's try to compare prices for a sample trip: **Columbia University** to **Wow Karaoke on 34th St in Manhattan**, a trip that I should probably have taken less when I was a student (and a perfect use case for this, given that I make most of these trips drunk and unable to navigate two apps). Use Google Maps to get the coordinates."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"payload = {\n",
" 'start_lat': 40.8075395,\n",
" 'start_lng': -73.964761417,\n",
" 'end_lat': 40.7473683,\n",
" 'end_lng': -73.988627417\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We grab both payloads from the APIs. They have really similar API endpoints -- it's almost like as if they're poaching engineers from each other. Or just following really good API standards. I won't know -- I come from the finance industry where the last thing everyone agreed on was [FIX](https://en.wikipedia.org/wiki/Financial_Information_eXchange) and it's absolute manure."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"payload_lyft = dict(payload)\n",
"\n",
"headers_lyft = {\n",
" 'Authorization': 'Bearer %s' % CLIENT_TOKEN_LYFT,\n",
" 'Content-type': 'application/json'\n",
"}\n",
"\n",
"estimate_lyft = requests.get('https://api.lyft.com/v1/cost', params=payload_lyft, headers=headers_lyft)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"payload_uber = dict(((k.replace('lat', 'latitude').replace('lng', 'longitude'), v) for k, v in payload.items()))\n",
"\n",
"headers_uber = {\n",
" 'Authorization': 'Bearer %s' % CLIENT_TOKEN_UBER,\n",
" 'Content-type': 'application/json'\n",
"}\n",
"\n",
"estimate_uber = requests.get('https://api.uber.com/v1.2/estimates/price', params=payload_uber, headers=headers_uber)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"b'{\"cost_estimates\": [{\"currency\": \"USD\", \"ride_type\": \"lyft_line\", \"display_name\": \"Shared\", \"primetime_percentage\": \"0%\", \"primetime_confirmation_token\": null, \"cost_token\": null, \"price_quote_id\": \"ENq21tjTLA==\", \"is_valid_estimate\": true, \"estimated_duration_seconds\": 1498, \"estimated_distance_miles\": 6.25, \"estimated_cost_cents_min\": 900, \"estimated_cost_cents_max\": 1200, \"can_request_ride\": true}, {\"currency\": \"USD\", \"ride_type\": \"lyft\", \"display_name\": \"Lyft\", \"primetime_percentage\": \"0%\", \"primetime_confirmation_token\": null, \"cost_token\": null, \"price_quote_id\": \"ENq21tjTLA==\", \"is_valid_estimate\": true, \"estimated_duration_seconds\": 1498, \"estimated_distance_miles\": 6.25, \"estimated_cost_cents_min\": 2000, \"estimated_cost_cents_max\": 2500, \"can_request_ride\": true}, {\"currency\": \"USD\", \"ride_type\": \"lyft_plus\", \"display_name\": \"Lyft XL\", \"primetime_percentage\": \"0%\", \"primetime_confirmation_token\": null, \"cost_token\": null, \"price_quote_id\": \"ENq21tjTLA==\", \"is_valid_estimate\": true, \"estimated_duration_seconds\": 1498, \"estimated_distance_miles\": 6.25, \"estimated_cost_cents_min\": 3000, \"estimated_cost_cents_max\": 3500, \"can_request_ride\": true}, {\"currency\": \"USD\", \"ride_type\": \"lyft_lux\", \"display_name\": \"Lux Black\", \"primetime_percentage\": \"0%\", \"primetime_confirmation_token\": null, \"cost_token\": null, \"price_quote_id\": \"ENq21tjTLA==\", \"is_valid_estimate\": true, \"estimated_duration_seconds\": 1498, \"estimated_distance_miles\": 6.25, \"estimated_cost_cents_min\": 4200, \"estimated_cost_cents_max\": 4900, \"can_request_ride\": true}, {\"currency\": \"USD\", \"ride_type\": \"lyft_luxsuv\", \"display_name\": \"Lux Black XL\", \"primetime_percentage\": \"0%\", \"primetime_confirmation_token\": null, \"cost_token\": null, \"price_quote_id\": \"ENq21tjTLA==\", \"is_valid_estimate\": true, \"estimated_duration_seconds\": 1498, \"estimated_distance_miles\": 6.25, \"estimated_cost_cents_min\": 6000, \"estimated_cost_cents_max\": 7000, \"can_request_ride\": true}]}\\n'\n",
"b'{\"prices\":[{\"localized_display_name\":\"UberPool\",\"distance\":5.62,\"display_name\":\"UberPool\",\"product_id\":\"929fcc19-8cb4-4007-a54f-3ab34473700f\",\"high_estimate\":16.0,\"low_estimate\":11.0,\"duration\":1380,\"estimate\":\"$11-15\",\"currency_code\":\"USD\"},{\"localized_display_name\":\"UberXL\",\"distance\":5.62,\"display_name\":\"UberXL\",\"product_id\":\"1e0ce2df-4a1e-4333-86dd-dc0c67aaabe1\",\"high_estimate\":36.0,\"low_estimate\":29.0,\"duration\":1380,\"estimate\":\"$29-36\",\"currency_code\":\"USD\"},{\"localized_display_name\":\"UberX\",\"distance\":5.62,\"display_name\":\"UberX\",\"product_id\":\"b8e5c464-5de2-4539-a35a-986d6e58f186\",\"high_estimate\":28.0,\"low_estimate\":22.0,\"duration\":1380,\"estimate\":\"$22-28\",\"currency_code\":\"USD\"},{\"localized_display_name\":\"Car Seat\",\"distance\":5.62,\"display_name\":\"Car Seat\",\"product_id\":\"d6d6d7ad-67f9-43ef-a8de-86bd6224613a\",\"high_estimate\":39.0,\"low_estimate\":31.0,\"duration\":1380,\"estimate\":\"$31-39\",\"currency_code\":\"USD\"},{\"localized_display_name\":\"Black\",\"distance\":5.62,\"display_name\":\"Black\",\"product_id\":\"0e9d8dd3-ffec-4c2b-9714-537e6174bb88\",\"high_estimate\":50.0,\"low_estimate\":40.0,\"duration\":1380,\"estimate\":\"$40-50\",\"currency_code\":\"USD\"},{\"localized_display_name\":\"Black SUV\",\"distance\":5.62,\"display_name\":\"Black SUV\",\"product_id\":\"56487469-0d3d-4f19-b662-234b7576a562\",\"high_estimate\":66.0,\"low_estimate\":53.0,\"duration\":1380,\"estimate\":\"$53-66\",\"currency_code\":\"USD\"}]}'\n"
]
}
],
"source": [
"print(estimate_lyft.content)\n",
"print(estimate_uber.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can normalize these JSON blobs into a canonical form I care about: the `id` for a quote, the `app` of origin of the quote, the `ride_type` (e.g. Lyft Line vs Uber XL), `distance`, `cost_min`, `cost_max` and the currency of the quote `cost_currency`. These information exist in both return contents, but in different formats (be it time, currency, or distance). I use the `pint` package to represent units instead of doing stupid integer math on my own. **Strong typing ftw. Even in `Python`**."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"ureg = pint.UnitRegistry()\n",
"\n",
"estimate_lyft_obj = estimate_lyft.json()\n",
"estimate_lyft_norm = ({\n",
" 'id': est['price_quote_id'],\n",
" 'app': 'lyft',\n",
" 'ride_type': est['ride_type'],\n",
" 'distance': est['estimated_distance_miles'] * ureg.mile,\n",
" 'duration': est['estimated_duration_seconds'] * ureg.second,\n",
" 'cost_min': est['estimated_cost_cents_min'] / 100,\n",
" 'cost_max': est['estimated_cost_cents_max'] / 100,\n",
" 'cost_currency': est['currency']\n",
"} for est in estimate_lyft_obj['cost_estimates'])\n",
"\n",
"estimate_uber_obj = estimate_uber.json()\n",
"estimate_uber_norm = ({\n",
" 'id': est['product_id'],\n",
" 'app': 'uber',\n",
" 'ride_type': est['display_name'],\n",
" 'distance': est['distance'] * ureg.mile,\n",
" 'duration': est['duration'] * ureg.second,\n",
" 'cost_min': est['low_estimate'],\n",
" 'cost_max': est['high_estimate'],\n",
" 'cost_currency': est['currency_code']\n",
"} for est in estimate_uber_obj['prices'])\n",
"\n",
"estimate_norm = list(itertools.chain(estimate_lyft_norm, estimate_uber_norm))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's see what lovely data we have. Turns out we have tons of that."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>app</th>\n",
" <th>cost_currency</th>\n",
" <th>cost_max</th>\n",
" <th>cost_min</th>\n",
" <th>distance</th>\n",
" <th>duration</th>\n",
" <th>id</th>\n",
" <th>ride_type</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>lyft</td>\n",
" <td>USD</td>\n",
" <td>12.0</td>\n",
" <td>9.0</td>\n",
" <td>6.25 mile</td>\n",
" <td>1498 second</td>\n",
" <td>ENq21tjTLA==</td>\n",
" <td>lyft_line</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>lyft</td>\n",
" <td>USD</td>\n",
" <td>25.0</td>\n",
" <td>20.0</td>\n",
" <td>6.25 mile</td>\n",
" <td>1498 second</td>\n",
" <td>ENq21tjTLA==</td>\n",
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" <th>2</th>\n",
" <td>lyft</td>\n",
" <td>USD</td>\n",
" <td>35.0</td>\n",
" <td>30.0</td>\n",
" <td>6.25 mile</td>\n",
" <td>1498 second</td>\n",
" <td>ENq21tjTLA==</td>\n",
" <td>lyft_plus</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>lyft</td>\n",
" <td>USD</td>\n",
" <td>49.0</td>\n",
" <td>42.0</td>\n",
" <td>6.25 mile</td>\n",
" <td>1498 second</td>\n",
" <td>ENq21tjTLA==</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>lyft</td>\n",
" <td>USD</td>\n",
" <td>70.0</td>\n",
" <td>60.0</td>\n",
" <td>6.25 mile</td>\n",
" <td>1498 second</td>\n",
" <td>ENq21tjTLA==</td>\n",
" <td>lyft_luxsuv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>uber</td>\n",
" <td>USD</td>\n",
" <td>16.0</td>\n",
" <td>11.0</td>\n",
" <td>5.62 mile</td>\n",
" <td>1380 second</td>\n",
" <td>929fcc19-8cb4-4007-a54f-3ab34473700f</td>\n",
" <td>UberPool</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>uber</td>\n",
" <td>USD</td>\n",
" <td>36.0</td>\n",
" <td>29.0</td>\n",
" <td>5.62 mile</td>\n",
" <td>1380 second</td>\n",
" <td>1e0ce2df-4a1e-4333-86dd-dc0c67aaabe1</td>\n",
" <td>UberXL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>uber</td>\n",
" <td>USD</td>\n",
" <td>28.0</td>\n",
" <td>22.0</td>\n",
" <td>5.62 mile</td>\n",
" <td>1380 second</td>\n",
" <td>b8e5c464-5de2-4539-a35a-986d6e58f186</td>\n",
" <td>UberX</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>uber</td>\n",
" <td>USD</td>\n",
" <td>39.0</td>\n",
" <td>31.0</td>\n",
" <td>5.62 mile</td>\n",
" <td>1380 second</td>\n",
" <td>d6d6d7ad-67f9-43ef-a8de-86bd6224613a</td>\n",
" <td>Car Seat</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>uber</td>\n",
" <td>USD</td>\n",
" <td>50.0</td>\n",
" <td>40.0</td>\n",
" <td>5.62 mile</td>\n",
" <td>1380 second</td>\n",
" <td>0e9d8dd3-ffec-4c2b-9714-537e6174bb88</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>uber</td>\n",
" <td>USD</td>\n",
" <td>66.0</td>\n",
" <td>53.0</td>\n",
" <td>5.62 mile</td>\n",
" <td>1380 second</td>\n",
" <td>56487469-0d3d-4f19-b662-234b7576a562</td>\n",
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],
"text/plain": [
" app cost_currency cost_max cost_min distance duration \\\n",
"0 lyft USD 12.0 9.0 6.25 mile 1498 second \n",
"1 lyft USD 25.0 20.0 6.25 mile 1498 second \n",
"2 lyft USD 35.0 30.0 6.25 mile 1498 second \n",
"3 lyft USD 49.0 42.0 6.25 mile 1498 second \n",
"4 lyft USD 70.0 60.0 6.25 mile 1498 second \n",
"5 uber USD 16.0 11.0 5.62 mile 1380 second \n",
"6 uber USD 36.0 29.0 5.62 mile 1380 second \n",
"7 uber USD 28.0 22.0 5.62 mile 1380 second \n",
"8 uber USD 39.0 31.0 5.62 mile 1380 second \n",
"9 uber USD 50.0 40.0 5.62 mile 1380 second \n",
"10 uber USD 66.0 53.0 5.62 mile 1380 second \n",
"\n",
" id ride_type \n",
"0 ENq21tjTLA== lyft_line \n",
"1 ENq21tjTLA== lyft \n",
"2 ENq21tjTLA== lyft_plus \n",
"3 ENq21tjTLA== lyft_lux \n",
"4 ENq21tjTLA== lyft_luxsuv \n",
"5 929fcc19-8cb4-4007-a54f-3ab34473700f UberPool \n",
"6 1e0ce2df-4a1e-4333-86dd-dc0c67aaabe1 UberXL \n",
"7 b8e5c464-5de2-4539-a35a-986d6e58f186 UberX \n",
"8 d6d6d7ad-67f9-43ef-a8de-86bd6224613a Car Seat \n",
"9 0e9d8dd3-ffec-4c2b-9714-537e6174bb88 Black \n",
"10 56487469-0d3d-4f19-b662-234b7576a562 Black SUV "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"estimate_df = pandas.DataFrame(estimate_norm)\n",
"estimate_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We get pretty interesting observations: right now, Uber's more expensive than Lyft by two whole bucks. That's interesting! No? Yes? Whatever."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
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"text/plain": [
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],
"source": [
"fig, ax = plt.subplots()\n",
"# sorry for ugly indents. fucking python.\n",
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{
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"metadata": {},
"source": [
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