jpp | JSON Prettier Printer that occupies a minimal number | JSON Processing library

If the page number is inside detail band or footer, the code here will work. But since my requirement was to add the page number in page header, I had to update the code.

If Pandas is too expensive, consider using NumPy with advanced Boolean indexing.

If you only have numeric series you may be lucky and be able to modify the underlying NumPy array directly. This, however, is not documented or recommended. Essentially, it's advisable to do all your calculations in NumPy and only move to Pandas if/when you have specific tasks suited to Pandas.

There are a couple of issues:

• The main problem is your construction of df3 has all three series with dtype object, while df1 and df2 have dtype=int for the first two series.
• Data in Pandas dataframes is organized and stored by series [column]. Therefore, type-casting is performed by series. Hence the logic for summing across "rows and columns" is necessarily different and not necessarily consistent with regards to mixed types.

To understand what's happening with the first issue, you have to appreciate that Pandas doesn't continually check the most appropriate dtype is selected after each operation. This would be prohibitively expensive.

You can check dtypes for yourself:

1. Is looping and using concat on multiple data sources to create one or multiple instance(s) of DataFrame so poor that it is wrong?

Yes! Pandas is great. But you should avoid at all cost the unnecessary production of Pandas objects. Creating Pandas objects can be expensive, DataFrames more than Series but this is probably True for all python. For the "criticized" method: Within a loop you create a Pandas object that will be overwritten in the next iteration of the loop. You should instead think how to gather your data in order to produce the Pandas object at the end of the gathering.

1. Should we always use list comprehension in a case like this?

No! As I said above, think of it as gathering data in preparation for the construction of the Pandas object. A comprehension is only one such way to gather.

1. The docs don't seem to recommend using neither list comprehension or for loop, so what is the recommended way of creating DataFrame(s) from multiple data sources?

This is too broad. A case can be made for many approaches. Just don't use concat or append in a loop. I'd call that wrong just about every time.

And by "every time" I don't actually mean "every time". What I DO mean is that you should never create a dataframe at some point prior to a loop, then loop and at each iteration go through the trouble of appending something to the prior initialized dataframe. Every iteration becomes very expensive. In the case of the "Accepted" answer: it assigns a dataframe to a dictionary key and is then left alone. It isn't repeatedly messed with.

By using cumcount to find the pair:

Here's one way using pd.DataFrame.pipe.

With Python everything is an object and can be passed around with no type-checking. The philosophy is "Don't check if it works, just try it...". Hence you can pass either a string or a function to myfunc and thereon to transform without any harmful side-effects.

Is there a way for me to access these characters of interest based on the index column in a vectorized manner, similar to the dataframe['name'].str.* functions?

There is a misunderstanding here. Despite the documentation, pd.Series.str methods are not vectorised in the conventional sense. They operate in a high-level loop and often reflect the functionality in Python's built-in str methods.

In fact, pd.Series.str methods generally underperform simple list comprehensions when manipulating strings stored in Pandas dataframes. The convenient syntax should not be taken as a sign the underlying implementation is vectorised. This is often the case for series with dtype object.

One approach is to use a list comprehension:

One way is to sort your dataframe by descending contact_code and create a couple of dictionary mappings. Then use these mappings to derive the correct contact_code.

This works because during dictionary construction values for keys are overwritten by later assignments. You are only interested in the minimum mappings, which is applied via our initial sorting.

You can use a combination of str.endswith and index-based slicing. The below solution will delete all occurrences of 'SomeMovieName (extras)' where 'SomeMovieName' exists.