Discover how to produce time series analysis with data stored in Cloud Storage.
- [Instructor] Let's take a look at how we can use data…from cloud storage objects…to create a TimeSeries chart with a charting API.…First, we need to read data…from cloud storage into a Python variable.…We have already loaded the campaign_data.csv file…that comes with the exercise into cloud storage…under the exercise-lil bucket…under the data data tree.…We read that object into a variable called campaign_data.…
Even though the source data is a CSV file,…it is read as one large string…into the campaign_data variable.…We need to convert this into a Pandas DataFrame…before it can be used for charting.…We read the string data into a Pandas DataFrame…using the CSV reader.…Then we perform aggregations.…We group the data by OFFER_DATE…and find the sum of conversations by OFFER_DATE.…We then print the summary data to verify its accuracy.…
We also need to convert OFFER_DATE…into a date data type…since the charting API requires date variable…for drawing the TimeSeries chart.…Finally, we call the charting API.…We provide the type as annotation.…
- 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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