I have been learning and working with Dash and Plotly for Python. I see use-cases for each. And I love them.
With Dash and Plotly for \Python, I use Pandas to group, filter,remove columns, and in general build the data frame I need for a particular chart. For example, a common function my clients want is drill down. It works like this:
- load the csv table I mentioned above with four columns
- group sales data by State on one bar chart (remove City or Month columns)
- allow the user to click on a State bar, which acts as a selector
- the selector is used as a callback to filter a City bar chart to just the cities in the State clicked on.
- this City bar chart uses a data frame that has sales grouped by city. It has the columns State, City, Sales, and I filter it by the State value clicked on from the first bar chart.
In Python, I can use Pandas to prepare a State, Sales data frame and a State, City, Sales data frame.
- States, Sales
- State, City, Sales
Or is there a different method anyone has found to be more efficient?
And does anyone have any good resources on the web for working with byusiness data, i.e. SQL tables, csv files, etc. so that I can mimic Pandas if I need to?
Thanks for any help.