albion | Migrated to : https : //gitlab.com/Oslandia/albion

Use zip to iterate over multiple objects at once instead of nested loops. You will get a tuple of (point, team). Also, eliminate the loop counter variable n by using enumerate. This makes your code more pythonic. Check out the corrected code below:

The simplest example I can think of is to let the image take its 'natural' dimensions but have it in a container that is smaller - i.e. don't do any of the normal things like have object-fit: cover or contain. In that way the user can get the best definition that is available, can move the image within its container and can zoom.

Of course, for the actual webpage, without the user interacting with the image, you may want more of the image to show in the 'static' state. I don't know exactly what the requirement is. On a small device perhaps giving the user the option of using the whole screen to view the image (before zooming it) might be possible as this use (spot the ball) needs as clear definition as possible.

Here's the simple snippet to get things started:

To loop over matches use:

If you use named aggregation, you can specify a column name to sort on:

You can use for-loop to iterate through your fixposts & posts then check if the value match if yes increase count value and store in new array .

Demo code :

You can create a boolean mask to filter out values of full_time_result with None in '1' and '2'. Tp extract values we can use operator.itemgetter then use __eq__ to check equality i.e check if it's (None, None)

It has been working by changing the '"ytsearch2000:" "Ford Festiva"` :

to ' "ytsearch50":"Ford Festiva" as you can see below:

Create filtered data as a reactive object once, and use it in table and plot. Try this

A few key concepts

1. do a Cartesian product between two data frames to get all combinations (joining on identical value between two data frames is approach to this foo=1)
2. once both sets of data is together, have both sets of lat/lon to calculate distance) geopy has been used for this
3. cleanup the columns, use sort_values() to find smallest distance
4. finally a groupby() and agg() to get first values for shortest distance

There are two data frames for use

1. dfdist contains all the combinations and distances
2. dfnearest which contains result