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Accept objects for encoded typedarrays in data_array valType#5230
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 filter
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1 @@ | ||
| - Accept objects for encoded typedarrays in data_array valType [[#5230](https://github.com/plotly/plotly.js/pull/5230)] |
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Turns out if you make a numpy array from a list of integers, even if they're all small, seems like
int64is the default, but if a float is included thenfloat64becomes the default.I don't know if Pandas does anything smarter than this, but I bet just from ^^ we're going to see a good deal of
int64so probably a good idea to turn it on now.There was a problem hiding this comment.
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Good point. These cases would be handled in plotly/plotly.py@86a6c55
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And if int64 (Big Int) is really needed plotly.py layer pass those in regular arrays. So we don't hit a problem.