Collecter des données incrémentales minimales

Analyzing IoT Data in Python

Matthias Voppichler

IT Developer

Qu'est-ce que la mise en cache ?

stockage des données

  • Après la collecte du flux de données
  • Observation par observation
    • Forte charge sur les disques
  • Utilisez la mise en cache
Analyzing IoT Data in Python

Mise en cache

cache = []

def on_message(client, userdata, message): data = json.loads(message.payload) cache.append(data)
if len(cache) > MAX_CACHE:
with Path("data.txt").open("a") as f: f.writelines(cache) cache.clear()
# Connect function to mqtt datastream subscribe.callback(on_message, topics="datacamp/energy", hostname=MQTT_HOST)
Analyzing IoT Data in Python

Flux de données simples

C331,6020
M640,104
C331,6129
M640,180
C331,6205
M640,256
Analyzing IoT Data in Python

Horodatage de l'observation

  • « timestamp in payload »
  • message.timestamp
  • datetime.now()
Analyzing IoT Data in Python

Horodatage de l'observation

def on_message(client, userdata, message):
    publishtime = message.timestamp

consume_time = datetime.utcnow()
Analyzing IoT Data in Python

pd.to_datetime()

print(df.head())
   timestamp         device  val
0  1540535443083       C331  347069.305500
1  1540535460858       C331  347069.381205
import pandas as pd
df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
  timestamp                  device  val
0 2018-10-26 06:30:43.083      C331  347069.305500
1 2018-10-26 06:31:00.858      C331  347069.381205
Analyzing IoT Data in Python

Passons à la pratique !

Analyzing IoT Data in Python

Preparing Video For Download...