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Analyze additions and subtractions to/from a data set
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsMethod to create master product database to validate entries, and enrich data setData store for testing data products?How to store and analyze classification results with Python?Data manipulation in SQLmysql could not recieve table from CitespaceWhat are recommended waystools for processing large data from Excel Files?SQL Server and skip first n rowsFind value and return row in excel spreadsheet with over 1 mil rowsHow can discard “peaks” values from a chart?Expectations of the skill set of a Data Scientist
$begingroup$
I have a data set in the following form:
Product | Date
123 | 2019-01-01
456 | 2019-01-01
123 | 2019-01-02
123 | 2019-01-03
456 | 2019-01-03
123 | 2019-01-04
456 | 2019-01-04
789 | 2019-01-04
This is just a simplified version. The full set has ~300 products and four months of data. I want to understand how the product set changed over time. It's obviously easy to calculate the count per day and see that I lost one product on Jan 2nd and gained one on Jan4th, but then I don't know what product it was.
Is there a more systematic way of going about this? Ideally the output would show me a list of days and what products dropped out / were added that day. I thought about min(date), max(date) by product before, but products can drop and be added repeatedly and I wouldn't capture this back and forth this way.
Available environments are Python, SQL, and Excel.
Thanks
python sql excel
asked Apr 5 at 19:12
BerbatovBerbatov
11
$endgroup$
add a comment |
$begingroup$
I have a data set in the following form:
Product | Date
123 | 2019-01-01
456 | 2019-01-01
123 | 2019-01-02
123 | 2019-01-03
456 | 2019-01-03
123 | 2019-01-04
456 | 2019-01-04
789 | 2019-01-04
This is just a simplified version. The full set has ~300 products and four months of data. I want to understand how the product set changed over time. It's obviously easy to calculate the count per day and see that I lost one product on Jan 2nd and gained one on Jan4th, but then I don't know what product it was.
Is there a more systematic way of going about this? Ideally the output would show me a list of days and what products dropped out / were added that day. I thought about min(date), max(date) by product before, but products can drop and be added repeatedly and I wouldn't capture this back and forth this way.
Available environments are Python, SQL, and Excel.
Thanks
python sql excel
asked Apr 5 at 19:12
BerbatovBerbatov
11
$endgroup$
add a comment |
$begingroup$
I have a data set in the following form:
Product | Date
123 | 2019-01-01
456 | 2019-01-01
123 | 2019-01-02
123 | 2019-01-03
456 | 2019-01-03
123 | 2019-01-04
456 | 2019-01-04
789 | 2019-01-04
This is just a simplified version. The full set has ~300 products and four months of data. I want to understand how the product set changed over time. It's obviously easy to calculate the count per day and see that I lost one product on Jan 2nd and gained one on Jan4th, but then I don't know what product it was.
Is there a more systematic way of going about this? Ideally the output would show me a list of days and what products dropped out / were added that day. I thought about min(date), max(date) by product before, but products can drop and be added repeatedly and I wouldn't capture this back and forth this way.
Available environments are Python, SQL, and Excel.
Thanks
python sql excel
asked Apr 5 at 19:12
BerbatovBerbatov
11
$endgroup$
I have a data set in the following form:
Product | Date
123 | 2019-01-01
456 | 2019-01-01
123 | 2019-01-02
123 | 2019-01-03
456 | 2019-01-03
123 | 2019-01-04
456 | 2019-01-04
789 | 2019-01-04
This is just a simplified version. The full set has ~300 products and four months of data. I want to understand how the product set changed over time. It's obviously easy to calculate the count per day and see that I lost one product on Jan 2nd and gained one on Jan4th, but then I don't know what product it was.
Is there a more systematic way of going about this? Ideally the output would show me a list of days and what products dropped out / were added that day. I thought about min(date), max(date) by product before, but products can drop and be added repeatedly and I wouldn't capture this back and forth this way.
Available environments are Python, SQL, and Excel.
Thanks
python sql excel
python sql excel
asked Apr 5 at 19:12
BerbatovBerbatov
11
asked Apr 5 at 19:12
BerbatovBerbatov
11
asked Apr 5 at 19:12
BerbatovBerbatov
11
asked Apr 5 at 19:12
BerbatovBerbatov
11
asked Apr 5 at 19:12
BerbatovBerbatov
11
11
add a comment |
add a comment |
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