Last week, we learned how to create win-loss charts in Excel. In the comments, Dan said,
Incidentally, the fastest way to do this would be using SFE, just reflect your data with 1 for a win, – 1 for a loss. There’s even an option to automatically invert negative numbers. #
Of course, we can use the beautiful Sparklines for Excel addin to do this and several other charts. But if you just have a series of Wins and Losses, like below, you can use a column chart to create win loss charts too.
Your Data:
Lets say you have data like this,
Win Loss Chart in Excel – 5 Steps
Step1: Select Win & Loss columns and Insert a Column Chart
This is the first and easiest step. At the end, your chart looks like this:
Step 2: Adjust the Series Gap & Overlap
- Select either Win or Loss series and press CTRL+1 (or goto format series).
- From here, adjust the gap to 0
- and overlap to 75%, like shown aside.
Step 3: Remove un-necessary chart elements
- Remove grid lines and labels
- Remove horizontal axis
- Select vertical axis and press CTRL+1 (format axis).
- Now, adjust axis min to -1 and max to 1
- Close it and remove vertical axis too
Step 4: Adjust colors
Change the colors if you fancy.
Step 5: That is all
There is no step 5. Your win loss chart is ready. Go ahead and show it off.
Download Win Loss Chart (Improved) Template:
Click here to download the winloss chart template and play with it.
Click here to download the winloss chart template complete with Sinusoid chart template. (Supplied by Hui)
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One Response to “SQL vs. Power Query – The Ultimate Comparison”
Enjoyed your SQL / Power Query podcast (A LOT). I've used SQL a little longer than Chandoo. Power Query not so much.
Today I still use SQL & VBA for my "go to" applications. While I don't pull billions of rows, I do pull millions. I agree with Chandoo about Power Query (PQ) lack of performance. I've tried to benchmark PQ to SQL and I find that a well written SQL will work much faster. Like mentioned in the podcast, my similar conclusion is that SQL is doing the filtering on the server while PQ is pulling data into the local computer and then filtering the data. I've heard about PQ query folding but I still prefer SQL.
My typical excel application will use SQL to pull data from an Enterprise DB. I load data into Structured Tables and/or Excel Power Pivot (especially if there's lot of data).
I like to have a Control Worksheet to enter parameters, display error messages and have user buttons to execute VBA. I use VBA to build/edit parameters used in the SQL. Sometimes I use parameter-based SQL. Sometimes I create a custom SQL String in a hidden worksheet that I then pull into VBA code (these may build a string of comma separated values that's used with a SQL include). Another SQL trick I like to do is tag my data with a YY-MM, YY-QTR, or YY-Week field constructed form a Transaction Date.
In an application, I like to create a dashboard(s) that may contain hyperlinks that allow the end-user to drill into data. Sometimes the hyperlink will point to worksheet and sometimes to a supporting workbook. In some cases, I use a double click VBA Macro that will pull additional data and direct the user to a supplemental worksheet or pivot table.
In recent years I like Dynamic Formulas & Lambda Functions. I find this preferable to pivot tales and slicers. I like to use a Lambda in conjunction with a cube formula to pull data from a power pivot data model. I.E. a Lambda using a cube formula to aggregate Accounting Data by a general ledger account and financial period. Rather than present info in a power pivot table, you can use this combination to easily build financial reports in a format that's familiar to Accounting Professionals.
One thing that PQ does very well is consolidating data from separate files. In the old days this was always a pain.
I've found that using SQL can be very trying (even for someone with experience). It's largely an iterative process. Start simple then use Xlookup (old days Match/Index). Once you get the relationships correct you can then use SQL joins to construct a well behaved SQL statement.
Most professional enterprise systems offer a schema that's very valuable for constructing SQL statements. For any given enterprise system there's often a community of users that will share SQL. I.E. MS Great Plains was a great source (but I haven't used them in years).
Hope this long reply has value - keep up the good work.