18650 | 本项目是一个软硬件设计开源，并且设计软件也是用的开源软件，外壳设计开源，并且外壳设计也是用的开源软件。

Why does adding the count count only Luna Rossa's wins?

Count() is an aggregate function and produces one result per GROUP.

As you have no GROUP BY clause the entire result set is a single group and hence the single result.

• The reason why you got Tournament F is due to

  • If the SELECT statement is an aggregate query without a GROUP BY clause, then each aggregate expression in the result-set is evaluated once across the entire dataset. Each non-aggregate expression in the result-set is evaluated once for an arbitrarily selected row of the dataset. The same arbitrarily selected row is used for each non-aggregate expression. Or, if the dataset contains zero rows, then each non-aggregate expression is evaluated against a row consisting entirely of NULL values. As per SQLite SELECT -

How can I change the query to fix it?

So you need a GROUP BY clause. To create groups upon which the count() function will work on.

You probably want GROUP BY Tournament,TeamName

e.g.

See DataGrid API. The props is called columns, not cols

See if this is what you have in mind;

np.select is a much faster way to do conditionals in Pandas/numpy compared to if/else.

Get the new values for key based on conditions, pivot the data, fillna with default values, and do some cleanups:

You could specify the y-axis limits with coord_cartesian and modify your breaks in the primary y-axis.

Here is what I did to do what you want. Some principle to understand:

• The 2nd Y-Axis in ggplot is just a reference - any drawing on the plots is based on it values to the main Y-Axis.
• To draw reference to 2nd Y-Axis, we need to adjust it values base on the different between 2nd Y-Axis & main Y-Axis. In your cases 2nd Y-Axis is 3 times bigger than main Y-Axis so anything that reference to 2nd Y-Axis need to be divided by 3.

Hope this explaination is clear enough. If need more guidance feel free to comment or ask me in message.

One way would be to fill NA values with previous values in the column. It would be better if we could get the data in long format (tidy) so that it is easier to plot.

I think your solution seems fine to me. Another option would be to join both data frame parts by date, and then pivot longer.

For more than one pair, I'd split the data frame into n chunks of 2 columns, and then rename all date columns, and then use purrr::reduce

Indeed, the pipeline in your script will mask any error from within the subshell.

I'm guessing you are using a subshell just to get the output redirection to apply to the entire command. A better design for that is to put the code in a function, which can also then perform a non-local exit in the case of a fatal error.

when filtering on multiple values,
you will need to combine those into one filter setting.
the data view will not let you have multiple filters on the same column.

in this case, you can use the test callback function, rather than the value key.

here, an array is used to gather the values,
and the test callback to filter the column...