将 Pandas DataFrame 写入 Google Cloud Storage 或 BigQuery [英] Write a Pandas DataFrame to Google Cloud Storage or BigQuery

问题描述

您好，感谢您的时间和考虑.我正在 Google Cloud Platform/Datalab 中开发 Jupyter Notebook.我已经创建了一个 Pandas DataFrame 并且想将此 DataFrame 写入 Google Cloud Storage(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:

Hello and thanks for your time and consideration. I am developing a Jupyter Notebook in the Google Cloud Platform / Datalab. I have created a Pandas DataFrame and would like to write this DataFrame to both Google Cloud Storage(GCS) and/or BigQuery. I have a bucket in GCS and have, via the following code, created the following objects:

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()  

我尝试了基于 Google Datalab 文档的各种方法，但仍然失败.谢谢

I have tried various approaches based on Google Datalab documentation but continue to fail. Thanks

推荐答案

试试下面的工作示例:

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)

我使用了这个示例，并且_table.py 文件来自 datalab github 站点 作为参考.您可以在 this 中找到其他 datalab 源代码文件链接.

I used this example, and the _table.py file from the datalab github site as a reference. You can find other datalab source code files at this link.

这篇关于将 Pandas DataFrame 写入 Google Cloud Storage 或 BigQuery的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！