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gpu-jupyter/extra/Getting_Started/ElasticsearchConnection.ipynb

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Installing recommended packages and update conda, sample code with performance test
2019-12-20 10:23:31 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Elasticsearch Data Analytics\n",
"\n",
"This notebook provides sample code to fetch Elasticsearch Data into and analyze it."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://www.antaresnet.com/wp-content/uploads/2018/07/Elasticsearch-Logo-Color-V.png\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"from IPython.core.display import HTML \n",
"Image(url= \"https://www.antaresnet.com/wp-content/uploads/2018/07/Elasticsearch-Logo-Color-V.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading modules and connect to the Elastic Stack"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"import sys\n",
"import json\n",
"import requests\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import pytz\n",
"from datetime import datetime, timedelta\n",
"from dateutil import tz\n",
"\n",
"import matplotlib.pyplot as plt\n",
"sns.set(style=\"darkgrid\")\n",
"plt.rcParams[\"figure.figsize\"] = (18,10)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last run: 2019-12-20 08:12:58.766840 UTC, status: 7.0604683677036 %\n"
]
},
{
"data": {
"text/plain": [
"{'name': 'c185f3ed577c',\n",
" 'cluster_name': 'il.es.cluster',\n",
" 'cluster_uuid': 'sBgbgyRXTvKta2cEJCczKQ',\n",
" 'version': {'number': '6.2.2',\n",
" 'build_hash': '10b1edd',\n",
" 'build_date': '2018-02-16T19:01:30.685723Z',\n",
" 'build_snapshot': False,\n",
" 'lucene_version': '7.2.1',\n",
" 'minimum_wire_compatibility_version': '5.6.0',\n",
" 'minimum_index_compatibility_version': '5.0.0'},\n",
" 'tagline': 'You Know, for Search'}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# connect to our cluster\n",
"from elasticsearch import Elasticsearch\n",
"es = Elasticsearch([{'host': 'elasticsearch', 'port': 9200}])\n",
"print('Last run: {} UTC, status: {} %'.format(\n",
" datetime.utcnow(),\n",
" es.cluster.health()['active_shards_percent_as_number']))\n",
"es.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display our indices and document types saved in elasticsearch.\n",
"\n",
"Update the elasticsearch package."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: elasticsearch<8.0.0,>=7.0.0 in /opt/conda/lib/python3.7/site-packages (7.1.0)\n",
"Requirement already satisfied: urllib3>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from elasticsearch<8.0.0,>=7.0.0) (1.25.7)\n"
]
}
],
"source": [
"!sudo pip install \"elasticsearch>=7.0.0,<8.0.0\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining useful functions"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# %load scroller.py\n",
"def scroller(index, quantity, timerange=timedelta(days=0), startdt=\"\", enddt=\"\"):\n",
" print(\"Starting to scroll\", end='')\n",
" # Retrieve the datetimes, note that timerange has a higher priority\n",
" if timerange.total_seconds() > 0:\n",
" now = datetime.utcnow().replace(tzinfo=pytz.UTC)\n",
" startdt = (now - timerange).isoformat()\n",
" enddt = now.isoformat()\n",
" \n",
" # search the first page and write the result to data\n",
" response = es.search(\n",
" index=index,\n",
" body={\n",
" \"query\": {\n",
" \"bool\": {\n",
" \"must\": [\n",
" {\"range\" : {\n",
" \"phenomenonTime\" : {\n",
" #\"gte\": \"2018-02-20T09:08:34.230693+00:00\", \n",
" \"gte\": startdt,\n",
" \"lte\": enddt, \n",
" \"time_zone\": \"+01:00\"\n",
" }\n",
" }},\n",
" {\n",
" \"match_phrase\": {\n",
" \"Datastream.name.keyword\": quantity\n",
" }\n",
" }\n",
" ]\n",
" }\n",
" }\n",
" },\n",
" scroll='10m'\n",
" )\n",
" data = [[row[\"_source\"][\"phenomenonTime\"], row[\"_source\"][\"result\"]] for row in response['hits']['hits']]\n",
"\n",
" # Append new pages until there aren't any left\n",
" while len(response['hits']['hits']):\n",
" print(\".\", end='')\n",
" # process results\n",
" # print([item[\"_id\"] for item in response[\"hits\"][\"hits\"]])\n",
" response = es.scroll(scroll_id=response['_scroll_id'], scroll='10m')\n",
" data += [[row[\"_source\"][\"phenomenonTime\"], row[\"_source\"][\"result\"]] for row in response['hits']['hits']]\n",
" \n",
" # Convert data to a DataFrame and return it\n",
" df = pd.DataFrame(data, columns=[\"phenomenonTime\", quantity])\n",
" # df.index = pd.to_datetime(df[\"phenomenonTime\"].map(lambda t: t.split(\".\")[0]), utc=True)\n",
" df.index = pd.to_datetime(df[\"phenomenonTime\"].map(lambda t: roundto(t, 1)), utc=True)\n",
" df = df.drop([\"phenomenonTime\"], axis=1)\n",
" print(\"\\nFetched {} tuples.\".format(df.shape[0]))\n",
" return df\n",
"\n",
"def roundto(string, n):\n",
" base = string.split(\".\")[0]\n",
" if n > 0:\n",
" base += \".\" + string.split(\".\")[1][:n]\n",
" return base\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gather data from Elasticsearch\n",
"\n",
"It is supposed that in the Elasticsearch instance, there is data with the Datastream.name \"at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature\" within the index name \"at.srfg.iot-iot4cps-wp5.infraprov.internal-*\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting to scroll\n",
"Fetched 0 tuples.\n"
]
},
{
"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",
" <th>at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature</th>\n",
" </tr>\n",
" <tr>\n",
" <th>phenomenonTime</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature]\n",
"Index: []"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get data for an index and a quantity between two static timestamps\n",
"startdt=\"2019-08-07T08:58:34+00:00\"\n",
"enddt=\"2019-08-07T11:58:34+00:00\"\n",
"df = scroller(\"at.srfg.iot-iot4cps-wp5.infraprov.internal-*\",\n",
" \"at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature\",\n",
" startdt=startdt, enddt=enddt)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting to scroll\n",
"Fetched 0 tuples.\n"
]
},
{
"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",
" <th>at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature</th>\n",
" </tr>\n",
" <tr>\n",
" <th>phenomenonTime</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature]\n",
"Index: []"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get data for an index and a quantity of the latest timerange\n",
"df = scroller(\"at.srfg.iot-iot4cps-wp5.infraprov.internal-*\",\n",
" \"at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature\",\n",
" timerange=timedelta(days=10))\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1296x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the extracted data using pandas and seaborn\n",
"df.plot()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"# Get multiple quantities and (outer) join them to a single DataFrame.\n",
"# There can be a lot of missing values\n",
"used_quantities = [\"at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature\", \n",
" \"at.srfg.iot-iot4cps-wp5.CarFleet2.car_2.Air Temperature\"]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting to scroll.....\n",
"Fetched 48 tuples.\n",
"at.srfg.iot-iot4cps-wp5.CarFleet2.car_2.Air Temperature\n",
"Starting to scroll\n",
"Fetched 0 tuples.\n"
]
}
],
"source": [
"df = scroller(\"at.srfg.iot-iot4cps-wp5.infraprov.internal-*\",\n",
" used_quantities[0],\n",
" timerange=timedelta(days=10))\n",
"for q in used_quantities[1:]:\n",
" print(q)\n",
" df = df.join(scroller(\"at.srfg.iot-iot4cps-wp5.infraprov.internal-*\", q,\n",
" timerange=timedelta(days=10)),\n",
" how=\"outer\")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"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",
" <th>at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature</th>\n",
" <th>at.srfg.iot-iot4cps-wp5.CarFleet2.car_2.Air Temperature</th>\n",
" </tr>\n",
" <tr>\n",
" <th>phenomenonTime</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-08-07 09:32:50.600000+00:00</th>\n",
" <td>-1.396915</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:33:40.700000+00:00</th>\n",
" <td>-2.559881</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:34:10.700000+00:00</th>\n",
" <td>-3.360251</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:32:10.600000+00:00</th>\n",
" <td>-0.112741</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:32:40.600000+00:00</th>\n",
" <td>-0.956904</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature \\\n",
"phenomenonTime \n",
"2019-08-07 09:32:50.600000+00:00 -1.396915 \n",
"2019-08-07 09:33:40.700000+00:00 -2.559881 \n",
"2019-08-07 09:34:10.700000+00:00 -3.360251 \n",
"2019-08-07 09:32:10.600000+00:00 -0.112741 \n",
"2019-08-07 09:32:40.600000+00:00 -0.956904 \n",
"\n",
" at.srfg.iot-iot4cps-wp5.CarFleet2.car_2.Air Temperature \n",
"phenomenonTime \n",
"2019-08-07 09:32:50.600000+00:00 NaN \n",
"2019-08-07 09:33:40.700000+00:00 NaN \n",
"2019-08-07 09:34:10.700000+00:00 NaN \n",
"2019-08-07 09:32:10.600000+00:00 NaN \n",
"2019-08-07 09:32:40.600000+00:00 NaN "
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Store and retrieve the DataFrame in a csv"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"elasticsearchdata.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 56,
"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",
" <th>at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature</th>\n",
" <th>at.srfg.iot-iot4cps-wp5.CarFleet2.car_2.Air Temperature</th>\n",
" </tr>\n",
" <tr>\n",
" <th>phenomenonTime</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-08-07 09:39:01.100000+00:00</th>\n",
" <td>-3.039692</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:39:51.100000+00:00</th>\n",
" <td>-1.599475</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:39:21.100000+00:00</th>\n",
" <td>-2.488179</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:39:31.100000+00:00</th>\n",
" <td>-2.259640</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:40:01.200000+00:00</th>\n",
" <td>-1.246612</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature \\\n",
"phenomenonTime \n",
"2019-08-07 09:39:01.100000+00:00 -3.039692 \n",
"2019-08-07 09:39:51.100000+00:00 -1.599475 \n",
"2019-08-07 09:39:21.100000+00:00 -2.488179 \n",
"2019-08-07 09:39:31.100000+00:00 -2.259640 \n",
"2019-08-07 09:40:01.200000+00:00 -1.246612 \n",
"\n",
" at.srfg.iot-iot4cps-wp5.CarFleet2.car_2.Air Temperature \n",
"phenomenonTime \n",
"2019-08-07 09:39:01.100000+00:00 NaN \n",
"2019-08-07 09:39:51.100000+00:00 NaN \n",
"2019-08-07 09:39:21.100000+00:00 NaN \n",
"2019-08-07 09:39:31.100000+00:00 NaN \n",
"2019-08-07 09:40:01.200000+00:00 NaN "
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"elasticsearchdata.csv\", parse_dates=True, index_col='phenomenonTime')\n",
"df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pre-processing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reduce size and interpolate"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"elasticsearchdata.csv\", parse_dates=True, index_col='phenomenonTime')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"df.index.names = [\"time\"]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"col_mapping = {\"at.srfg.iot-iot4cps-wp5.CarFleet1.car_1.Air Temperature\": \"car1_temp\", \n",
" \"at.srfg.iot-iot4cps-wp5.CarFleet2.car_2.Air Temperature\": \"car2_temp\"}\n",
"df = df.rename(index=str, \n",
" columns=col_mapping)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"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",
" <th>car1_temp</th>\n",
" <th>car2_temp</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-08-07 09:32:50.600000+00:00</th>\n",
" <td>-1.396915</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:33:40.700000+00:00</th>\n",
" <td>-2.559881</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:34:10.700000+00:00</th>\n",
" <td>-3.360251</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:32:10.600000+00:00</th>\n",
" <td>-0.112741</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-07 09:32:40.600000+00:00</th>\n",
" <td>-0.956904</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" car1_temp car2_temp\n",
"time \n",
"2019-08-07 09:32:50.600000+00:00 -1.396915 NaN\n",
"2019-08-07 09:33:40.700000+00:00 -2.559881 NaN\n",
"2019-08-07 09:34:10.700000+00:00 -3.360251 NaN\n",
"2019-08-07 09:32:10.600000+00:00 -0.112741 NaN\n",
"2019-08-07 09:32:40.600000+00:00 -0.956904 NaN"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"# Interpolate forwards and backwaonly up to \n",
"df = df.interpolate(method ='linear', limit_direction ='both', limit=10)\n",
"df = df.interpolate(method ='linear', limit_direction ='both', limit=10)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"# Keep only the rows with at least 2 non-NA values.\n",
"df = df.dropna(thresh=2)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"# make Timestamp unique\n",
"df = df.reset_index()\n",
"df = df.groupby(\"time\").agg({q: \"mean\" for q in col_mapping.values()})"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"# Interpolate again to close gaps, use the smalles value \n",
"df = df.interpolate(method ='zero', limit_direction ='forward')\n",
"df = df.interpolate(method ='zero', limit_direction ='forward')"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.index.min()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 2)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"car1_temp 0\n",
"car2_temp 0\n",
"dtype: int64"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"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",
" <th>car1_temp</th>\n",
" <th>car2_temp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" car1_temp car2_temp\n",
"count 0.0 0.0\n",
"mean NaN NaN\n",
"std NaN NaN\n",
"min NaN NaN\n",
"25% NaN NaN\n",
"50% NaN NaN\n",
"75% NaN NaN\n",
"max NaN NaN"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"# Keep only rows with all filled rows\n",
"df = df.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"elasticsearchdata.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 71,
"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",
" <th>car1_temp</th>\n",
" <th>car2_temp</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [car1_temp, car2_temp]\n",
"Index: []"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"elasticsearchdata.csv\", parse_dates=True, index_col='time')\n",
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"# df.hist()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"# pd.plotting.scatter_matrix(df, alpha=0.2)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"# corr = df.corr() \n",
"cm = sns.light_palette(\"orange\", as_cmap=True) \n",
"cm = sns.diverging_palette(220, 20, sep=20, as_cmap=True) \n",
"# corr.style.background_gradient(cmap=cm).set_precision(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature Engineering\n",
"\n",
"This task is very domain-specific and must be done by an expert."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Analytics"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "75cfeeea6204489e945d894895b2bc30",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=45, description='pitch', max=90), IntSlider(value=45, description='yaw',…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.html.widgets import *\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = (18,10)\n",
"sns.set(style=\"darkgrid\")\n",
"\n",
"def plot3D(pitch, yaw):\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111, projection='3d')\n",
" plot = ax.scatter(df['car1_temp'], df['car1_temp'], df['car2_temp'], c=df[\"car1_temp\"], s=60)\n",
" fig.colorbar(plot)\n",
" ax.view_init(pitch, yaw)\n",
" ax.legend(['Vibration for each 3D position'])\n",
" ax.set_xlabel(\"x-Position\")\n",
" ax.set_ylabel(\"y-Position\")\n",
" ax.set_zlabel(\"z-Position\")\n",
"interact(plot3D, pitch=(0,90,1), yaw=(0,90,1))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"# df[col_mapping.values()].hist()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"# pd.plotting.scatter_matrix(df[[\"vib\", \"distance\", \"projection\", \"v-radial\", \"v-tang\"]], alpha=0.5)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"# bins = np.linspace(0, df['v-radial'].max(), 10)\n",
"# df[\"binned-v-radial\"] = pd.cut(df['v-radial'], bins)\n",
"# df.groupby(\"binned-v-radial\").agg({\"vib\": {\"min\", \"median\", \"mean\", \"max\", \"count\"}})"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"# corr = df[df.columns.sort_values()].corr()[[\"vib\", \"vib-x\", \"vib-y\"]]\n",
"# cm = sns.light_palette(\"orange\", as_cmap=True) \n",
"# cm = sns.diverging_palette(220, 20, sep=20, as_cmap=True) \n",
"# corr.style.background_gradient(cmap=cm).set_precision(2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"## Inline Description:\n",
"\n",
"It is very nice to describe results using Markdown with dynamic values like: **3.141592653589793**.\n",
"\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Markdown \n",
"Markdown(\"\"\"\n",
"## Inline Description:\n",
"\n",
"It is very nice to describe results using Markdown with dynamic values like: **{pi}**.\n",
"\n",
"\"\"\".format(pi=np.pi))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Machine Learning\n",
"\n",
"**Be careful when handling with Artificial Intelligence:**\n",
"\n",
"![Be careful when handling with Artificial Intelligence](https://imgs.xkcd.com/comics/twitter_bot.png)\n"
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Reference in New Issue Copy Permalink