Hi all,
I have a problem trying to match items in a dict and pandas series in
Python.
I have a dict ( called city_dict )of cities and city_id's ; for each city (
which is a key in the dict ), a unique city_id is a value in that dict.
So for example, city_dict = { New York : 1001, LA : 1002, Chicago : 1003 }.
New York is a key, 1001 is a value.
Now I have a panda Series called dfCities. In this series is a bunch of
cities, including the cities in city_dict.
My goal is to replace the cities in dfCities with the city_id's in a brand
new csv file. So if dfCities has New York in it, I want to replace it with
it's value in the dictionary, so 1001.
Approaches I've tried - checking to see if the keys match the cities in
dfCities in a 'if in' statement ( such as "if city_dict.keys() in
dfSeries"), and then doing a straight replace ( can't do that since series
are ambiguous in truth values). Therefore I tried using .any() for Pandas
series (since .all() would strictly want all values in dfCities to match,
and all values don't match )
Afterwards, tried to directly match the series with keys and the clarified
truth value series, but dict_keys are unhashable, so I had to convert the
keys to str and see if I could compare strings ( with a stringified
dfCities )
Then I realized that even if I can get a if statement to start checking
(if dfCities.str.contains(keyss).any(): ) (keyss being the stringified
version of the keys for city_dict ), I don't know how to build a approach
to cross check the values of city_dict with the cities in dfCities ( I have
a vague notion that I should check if the keys of city_dict match with
dfCities, and then replace the cities in dfCities with the values of
city_dict in a new csv file output. However, I don't know how to replace
data in a Series with vaues of a dict ).
So I would like to ask the community what approach I can take to build to
that piece of the puzzle. I feel I have most of the solution, but I'm
missing something.
Thanks for reading and I appreciate the help.
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