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This is not consumed by json_normalize directly (after my tries). Since the number of BUY and SELL are different, and these record do not neccessarily should match each other (located on a same row), suggestions is to split into two dataframes and then concatenate.

1. It is the main method, so there is no need to cleanup. When main returns, the program exits. If this wasn't the main, then you would be correct.

2. There is no best practice that fits all use cases. The code you show here is a very common pattern. The function creates a goroutine, and returns a channel so that others can communicate with that goroutine. There is no rule that governs how channels must be created. There is no way to terminate that goroutine though. One use case this pattern fits well is reading a large resultset from a database. The channel allows streaming data as it is read from the database. In that case usually there are other means of terminating the goroutine though, like passing a context.

3. Again, there are no hard rules on how channels should be created/closed. A channel can be left open, and it will be garbage collected when it is no longer used. If the use case demands so, the channel can be left open indefinitely, and the scenario you worry about will never happen.

If you check the help of pd.json_normalize(...), it says

• As per Adding plots to the basic mplfinance plot(), section Plotting multiple additional data sets
  • Aside from the example below, for two columns from a dataframe, the documentation shows a number of ways to configure an appropriate object for the addplot parameter.
  • apdict = [mpf.make_addplot(data['Upper Band']), mpf.make_addplot(data['Lower Band'])] works as well. Note it's a list, not a tuple.
• mplfinance/examples

You can concat them:

In the following example code, which you can also run in DartPad, a new item is added to the list every two seconds. The ScrollController is then used to scroll to the end of the list within one second.

The timer is only used to continuously add random items to the list, you could, of course, listen to a stream (or similar) instead.

You called the API in componentWillMount() which is only triggered just before mounting occurs. To reuse the fetch API, make it a method and call it where necessary.

Like this:

looks like you go one level too deep with your subscripts:

ticker = asset[1][0] gives you ticker = [horizontal_label_1, horizontal_input_1] so I think with ticker[0][0] you get the first letter of your horizontal label not the label itself.

I guess what you want is ticker = asset[1] before the line that throws the error and then it should be fine.

Are you trying to iterate both on "df" and "base" rows? If so, you have to slice the "df" to get the value for the columns at the row (using iloc - https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.iloc.html - or loc - https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html). The comparissons on the if and elif conditions returns Series, not a single boolean. If you absolutely have to take this approach on having both "df" and "base", I'd sugest: