nan values when creating a pd.Series through a function-CodePudding

Assume we have the following dataframe

ap_comp = pd.DataFrame({'Name': ['Troll', 'Legolas'],'Code': [111, 222]})

and I passed it through the following function

a_mapping = pd.Series(apcompl['Code'], index=apcompl['Name']).to_dict()

my question is why the a_mapping returns as

{'Troll': nan, 'Legolas': nan}

why the nan appears? Should' t it be the following

{'Troll': 111, 'Legolas': 222}

CodePudding user response：

You have NaNs because of index alignment. The Series you pass in has an index that is different from the values passed as index, internally the constructor performs a reindexing, which gives NaN.

You would have needed to pass raw values (with .tolist(), .values or .to_numpy()) to the constructor, not a Series:

a_mapping = pd.Series(ap_comp['Code'].tolist(), index=ap_comp['Name']).to_dict()

But, much better, use:

a_mapping = ap_comp.set_index('Name')['Code'].to_dict()

output:

{'Troll': 111, 'Legolas': 222}

CodePudding user response：

As a complement to mozway's answer (where he indicates the reason why OP is having the problem and presents one solution, and one alternative path), one can also do it as follows

a_mapping = dict(zip(ap_comp['Name'], ap_comp['Code']))

[Out]: 
{'Troll': 111, 'Legolas': 222}

I find that the combination between dict and zip (which are Python's Built-in Functions) are quite convenient for this specific use cases (generating dictionaries from keys and values).