...
| Before | After |
|---|---|
| socks, socks, socks | ["socks", "socks", "socks"] |
| pants, pants | ["pants", "pants"] |
...
Extract hashtags
...
Extract hashtags
| Include Page | ||||
|---|---|---|---|---|
|
| d-s- |
|---|
...
| also | |
|---|---|
|
...
|
...
Source:
The following dataset contains a customer tweets across different locations.
...
Excited to announce that we’ve transitioned Wrangler from a hybrid desktop application to a completely cloud-based service! #dataprep #businessintelligence #CommitToCleanData # London
...
Learnt more about the importance of identifying issues in your data—early and often #CommitToCleanData #predictivetransformations #realbusinessintelligence
...
Clean data is the foundation of your analysis. Learn more about what we consider the five tenets of sound #dataprep, starting with #1a prioritizing and setting targets. #startwiththeuser #realbusinessintelligence #Paris
...
Learn how #NewYorklife
onboarded as part of their #bigdata #dataprep initiative to unlock hidden insights and make them accessible across departments.
...
How can you quickly determine the number of times a user ID appears in your data?#dataprep #pivot #aggregation#machinelearning initiatives #SFO
Transformation:
The following transformation extracts the hashtag messages from customer tweets.
| D trans | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
Results:
...
["#dataprep", "#businessintelligence", "#CommitToCleanData", " # London"]
...
["#CommitToCleanData", "#predictivetransformations", "#realbusinessintelligence", "0"]
...
["#dataprep", "#startwiththeuser","#realbusinessintelligence", "# Paris"]
...
["#NewYorklife", "dataprep", "bigdata", "0"]
...
|
