Learn how to exchange data between Python DataFrames and BigQuery.
- [Instructor] In this video,…I will show you how to fetch BigQuery data…into a Pandas DataFrame.…Once you can get the data into a Pandas DataFrame,…you can use regular Python capabilities…for further work on analyze data.…Let use use the BigQuery SDK to accomplish this.…We first import the package for the client SDK…using import google.datalab.bigquery as bq.…
The package is preloaded into the GCP Python environment…during the setup process…so you don't have to install it explicitly.…Now we can create a query object using…the bq.query function call.…We can also set up output options…as to not cache data.…We then execute the query using the execute function call…that extracts the results from the query…and stores them into the cust_results object.…
Then we use the to_dataframe function…to convert the cust_result object…into the cust_df DataFrame.…We them print the DataFrame…to look at the DataFrame contents.…Now you can use this DataFrame…to do further glimpsing, analysis,…exploration, visualization, et cetera…using standard Python capabilities and other packages.…
- Setting up Cloud DataLlb for exploratory data analytics
- Segmentation and profiling
- Reading and writing data from BigQuery
- Managing cloud storage buckets
- Creating visualizations of BigQuery data with the GCP Charting API
- Managing Datalab instances
Skill Level Intermediate
Predictive Customer Analyticswith Kumaran Ponnambalam1h 37m Intermediate
1. Exploration Options in GCP
2. Cloud Datalab Basics
3. Datalab: BigQuery
4. Datalab: Cloud Storage
5. Datalab: Visualizations
6. EDA with GCP: Use Case
7. Managing Datalab
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