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305 changes: 305 additions & 0 deletions .ipynb_checkpoints/working_with_APIS-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "3be4f6cf",
"metadata": {},
"source": [
"# Lab | Working with APIs"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "e39d3345",
"metadata": {},
"outputs": [],
"source": [
"#Instructions\n",
"#Create a function that returns a Pandas dataframe with the price, names of origin and arrival airports and the name of the company. \n",
"#Do it for all the flights between two dates."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "e8e76a73",
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import pandas as pd\n",
"import json\n",
"import datetime"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "35e16bfd",
"metadata": {},
"outputs": [],
"source": [
"URL = \"https://partners.api.skyscanner.net/apiservices/v3/flights/indicative/search\"\n",
"HEADERS = {\"x-api-key\": \"prtl6749387986743898559646983194\"}"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "9b3b38ae",
"metadata": {},
"outputs": [],
"source": [
"def flatten_dict(dd, separator='_', prefix=''):\n",
" return {f\"{prefix}{separator}{k}\" if prefix else k: v\n",
" for kk, vv in dd.items()\n",
" for k, v in (flatten_dict(vv, separator, kk).items() if isinstance(vv, dict) else {kk: vv}.items())}"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "596cf4f3",
"metadata": {},
"outputs": [],
"source": [
"def flight_finder(start_date, end_date, origin, destination):\n",
" # Create list with [start_date, start_date+1, ...., end_date]\n",
" start_year = start_date // 10000\n",
" start_month = (start_date // 100) % 100\n",
" start_day = start_date % 100\n",
" start_datetime = datetime.datetime(start_year, start_month, start_day)\n",
" \n",
" end_year = end_date // 10000\n",
" end_month = (end_date // 100) % 100\n",
" end_day = end_date % 100\n",
" end_datetime = datetime.datetime(end_year, end_month, end_day)\n",
"\n",
" date_range = []\n",
" current_date = start_datetime\n",
" while current_date <= end_datetime:\n",
" date_range.append(current_date)\n",
" current_date += datetime.timedelta(days=1)\n",
" \n",
" quotes = [] \n",
" for date in date_range: \n",
"\n",
"\n",
" payload = { \"query\": {\n",
" \"market\": \"UK\",\n",
" \"locale\": \"en-GB\",\n",
" \"currency\": \"EUR\",\n",
" \"queryLegs\": [\n",
" {\n",
" \"originPlace\": { \"queryPlace\": { \"iata\": origin } },\n",
" \"destinationPlace\": { \"queryPlace\": { \"iata\": destination } },\n",
" \"fixedDate\": {\n",
" \"year\": date.year,\n",
" \"month\": date.month,\n",
" \"day\": date.day\n",
" }\n",
" }\n",
" ]\n",
" } }\n",
"\n",
" response = requests.post(URL, json=payload, headers=HEADERS)\n",
" output = response.json()\n",
" results = output['content']['results']\n",
" quotes.append(results['quotes'])\n",
"\n",
" dataframes = []\n",
"\n",
" for dictionary in quotes:\n",
" flattened_results = [flatten_dict(dictionary) for dictionary in dictionary.values()]\n",
" df = pd.DataFrame.from_records(flattened_results)\n",
" df.columns = [col.split(\".\")[-1] for col in df.columns]\n",
" dataframes.append(df)\n",
"\n",
" combined_df = pd.concat(dataframes, ignore_index=True)\n",
" \n",
" return combined_df"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "e0298423",
"metadata": {},
"outputs": [],
"source": [
"df = flight_finder(20230501, 20230502, 'TFN', 'LIS')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "f4dac065",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: []\n",
"Index: []"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "17261d44",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[datetime.datetime(2023, 1, 1, 0, 0),\n",
" datetime.datetime(2023, 1, 2, 0, 0),\n",
" datetime.datetime(2023, 1, 3, 0, 0),\n",
" datetime.datetime(2023, 1, 4, 0, 0),\n",
" datetime.datetime(2023, 1, 5, 0, 0),\n",
" datetime.datetime(2023, 1, 6, 0, 0),\n",
" datetime.datetime(2023, 1, 7, 0, 0),\n",
" datetime.datetime(2023, 1, 8, 0, 0),\n",
" datetime.datetime(2023, 1, 9, 0, 0),\n",
" datetime.datetime(2023, 1, 10, 0, 0)]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Testing code\n",
"start_date = 20230101\n",
"end_date = 20230110\n",
"start_year = start_date // 10000\n",
"start_month = (start_date // 100) % 100\n",
"start_day = start_date % 100\n",
"start_datetime = datetime.datetime(start_year, start_month, start_day)\n",
"\n",
"end_year = end_date // 10000\n",
"end_month = (end_date // 100) % 100\n",
"end_day = end_date % 100\n",
"end_datetime = datetime.datetime(end_year, end_month, end_day)\n",
"\n",
"date_range = []\n",
"current_date = start_datetime\n",
"while current_date <= end_datetime:\n",
" date_range.append(current_date)\n",
" current_date += datetime.timedelta(days=1)\n",
"date_range"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "523811a9",
"metadata": {},
"outputs": [],
"source": [
"quotes = [] \n",
"for date in date_range: \n",
" url = \"https://partners.api.skyscanner.net/apiservices/v3/flights/indicative/search\"\n",
"\n",
" payload = { \"query\": {\n",
" \"market\": \"UK\",\n",
" \"locale\": \"en-GB\",\n",
" \"currency\": \"EUR\",\n",
" \"queryLegs\": [\n",
" {\n",
" \"originPlace\": { \"queryPlace\": { \"iata\": 'LIS' } },\n",
" \"destinationPlace\": { \"queryPlace\": { \"iata\": 'MAD' } },\n",
" \"fixedDate\": {\n",
" \"year\": date.year,\n",
" \"month\": date.month,\n",
" \"day\": date.day\n",
" }\n",
" }\n",
" ]\n",
" } }\n",
" headers = {\n",
" \"x-api-key\": \"prtl6749387986743898559646983194\"\n",
" }\n",
" response = requests.post(url, json=payload, headers=headers)\n",
" output = response.json()\n",
" results = output['content']['results']\n",
" quotes.append(results['quotes'])\n",
"\n",
"print(response)\n",
"output\n",
" \n",
"# dataframes = []\n",
"\n",
"# for dictionary in quotes:\n",
"# flattened_results = [flatten_dict(dictionary) for dictionary in dictionary.values()]\n",
"# df = pd.DataFrame.from_records(flattened_results)\n",
"# df.columns = [col.split(\".\")[-1] for col in df.columns]\n",
"# dataframes.append(df)\n",
"\n",
"# combined_df = pd.concat(dataframes, ignore_index=True)\n",
"\n",
"\n",
"# combined_df.head()"
]
}
],
"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": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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