Learn how to cleanse and transform data as part of EDA.
- [Instructor] In a typical exploratory…data analysis project, data is cleansed…and transformed before analysis.…Transformations include creating indicator variables,…binning and aggregation.…Let's see what that looks like.…We are going to create indicator variables…for gender, age and discount columns in the dataframe.…We do so by using the pandas function, get_dummies.…This will create individual columns for each unique value…in these columns and populate a one or zero…for that column based on the row.…
This is an important pre-step for…correlation analysis, as well as machine learning,…since those algorithms require…data to be in numerical format.…Once we create indicator variables…we concatenate them with the original dataframe.…When you do a head command you will see…additional columns populated in the same dataset.…This was a really simple example but you can do…much more including filtering, binning,…creating categorical variables,…joining data frames, et cetera.…
- 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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