.. toctree:: :maxdepth: 1 :hidden: Core Libraries <core/index> Asset Management <asset/index> AutoML <automl/index> BigQuery <bigquery/index> BigQuery Data-Transfer <bigquery_datatransfer/index> BigQuery Storage <bigquery_storage/index> Bigtable <bigtable/index> Container <container/index> Dataproc <dataproc/index> Datastore <datastore/index> Data Loss Prevention <dlp/index> DNS <dns/index> Firestore <firestore/index> IAM <iam/index> IoT <iot/index> Key Management <kms/index> Language <language/index> PubSub <pubsub/index> OSLogin <oslogin/index> Redis <redis/index> Resource Manager <resource-manager/index> Runtime Configuration <runtimeconfig/index> Scheduler <scheduler/index> Security Center <securitycenter/index> Security Scanner <websecurityscanner/index> Spanner <spanner/index> Speech <speech/index> Storage <storage/index> Talent <talent/index> Tasks <tasks/index> Text-to-Speech <texttospeech/index> Translate <translate/index> Vision <vision/index> Video Intelligence <videointelligence/index> Stackdriver Error Reporting <error-reporting/index> Stackdriver Incident Response & Management <irm/index> Stackdriver Logging <logging/index> Stackdriver Monitoring <monitoring/index> Stackdriver Trace <trace/index> Release History <releases>
For more information on setting up your Python development environment,
such as installing pip and virtualenv on your system, please refer
to Python Development Environment Setup Guide for Google Cloud Platform.
Google Cloud Datastore is a fully managed, schemaless database for storing non-relational data.
Install the google-cloud-datastore library using pip:
$ pip install google-cloud-datastorefrom google.cloud import datastore
client = datastore.Client()
key = client.key('Person')
entity = datastore.Entity(key=key)
entity['name'] = 'Your name'
entity['age'] = 25
client.put(entity)Google Cloud Storage allows you to store data on Google infrastructure.
Install the google-cloud-storage library using pip:
$ pip install google-cloud-storagefrom google.cloud import storage
client = storage.Client()
bucket = client.get_bucket('<your-bucket-name>')
blob = bucket.blob('my-test-file.txt')
blob.upload_from_string('this is test content!')