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Pulling JSON from url #4

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197 changes: 197 additions & 0 deletions notebooks/enviroCar_python.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pandas.io.json import json_normalize\n",
"import json\n",
"import urllib.request, json "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with urllib.request.urlopen(\"https://envirocar.org/api/stable/tracks/52714d4ee4b000fe0582a292/measurements\") as url:\n",
" data_json = json.loads(url.read().decode())\n",
" #print(data)\n",
"data=data_json['features']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preparing column list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"good_columns = [\n",
" \"Coordinates\",\n",
" \"Geometry Type\",\n",
" \"ID\",\n",
" \"CO2(kg/h)\",\n",
" \"Calculated MAF\",\n",
" \"Consumption\",\n",
" \"GPS Accuracy\",\n",
" \"GPS Altitude\",\n",
" \"GPS Bearing\",\n",
" \"GPS HDOP\",\n",
" \"GPS PDOP\",\n",
" \"GPS Speed\",\n",
" \"GPS VDOP\",\n",
" \"Intake Pressure\",\n",
" \"Intake Temperature\",\n",
" \"Rpm\",\n",
" \"Speed\",\n",
" \"Construction_Year\",\n",
" \"EngineDisplacement\",\n",
" \"FuelType\",\n",
" \"Sensor ID\",\n",
" \"Manufacturer\",\n",
" \"Model\",\n",
" \"Sensor type'\",\n",
" \"Time\",\n",
" \"Track\",\n",
" \"Type\"\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preparing Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_1 = pd.DataFrame.from_dict(json_normalize(data), orient='columns')\n",
"\n",
"#Filtering columns\n",
"df=df_1.filter(regex='^(?!.*unit).*$', axis=1)\n",
"\n",
"#Attaching custom columns\n",
"df.columns = good_columns\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Convert dates to datetime\n",
"df['Time'] = pd.to_datetime(df.Time,format = '%Y-%m-%dT%H:%M:%SZ')\n",
"\n",
"#Setting Time column as Index\n",
"df.set_index('Time', drop=True)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Extracting Longitude and Latitude from Coordinates\n",
"a['Coordinates'].tolist()\n",
"length=len(a['Coordinates'])\n",
"longitude = list()\n",
"latitude= list()\n",
"for ele in range(10):\n",
" #parts=ele.split(\",\")\n",
" #longitude=parts[1]\n",
" #latitude =parts[2] \n",
" #print(parts[0])\n",
" print(['Coordinates'][ele][0])\n",
" #latitude= (a['Coordinates'][i][1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Show variable types\n",
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Descriptive statistics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}