python – 将Pandas DataFrame写入Google Cloud Storage或BigQuery

您好,感谢您的时间和考虑.
我正在Google Cloud Platform / Datalab中开发一个Jupyter笔记本.
我创建了一个Pandas DataFrame,并希望将此DataFrame写入Google云端存储(GCS)和/或BigQuery.我在GCS中有一个存储桶,并通过以下代码创建了以下对象：

import gcp
import gcp.storage as storage
project = gcp.Context.default().project_id    
bucket_name = 'steve-temp'           
bucket_path  = bucket_name   
bucket = storage.Bucket(bucket_path)
bucket.exists()  

我尝试过基于Google Datalab文档的各种方法,但仍然失败.
谢谢

解决方法:

尝试以下工作示例：

from datalab.context import Context
import google.datalab.storage as storage
import google.datalab.bigquery as bq
import pandas as pd

# Dataframe to write
simple_dataframe = pd.DataFrame(data=[{1,2,3},{4,5,6}],columns=['a','b','c'])

sample_bucket_name = Context.default().project_id + '-datalab-example'
sample_bucket_path = 'gs://' + sample_bucket_name
sample_bucket_object = sample_bucket_path + '/Hello.txt'
bigquery_dataset_name = 'TestDataSet'
bigquery_table_name = 'TestTable'

# Define storage bucket
sample_bucket = storage.Bucket(sample_bucket_name)

# Create storage bucket if it does not exist
if not sample_bucket.exists():
    sample_bucket.create()

# Define BigQuery dataset and table
dataset = bq.Dataset(bigquery_dataset_name)
table = bq.Table(bigquery_dataset_name + '.' + bigquery_table_name)

# Create BigQuery dataset
if not dataset.exists():
    dataset.create()

# Create or overwrite the existing table if it exists
table_schema = bq.Schema.from_data(simple_dataframe)
table.create(schema = table_schema, overwrite = True)

# Write the DataFrame to GCS (Google Cloud Storage)
%storage write --variable simple_dataframe --object $sample_bucket_object

# Write the DataFrame to a BigQuery table
table.insert(simple_dataframe)

我使用了this示例,并将datalab github site中的_table.py文件作为参考.您可以在this链接找到其他datalab源代码文件.

标签：python,google-cloud-storage,google-cloud-platform,google-cloud-datalab
来源： https://codeday.me/bug/20190928/1827402.html