Query of data table to return alias [closed]

Question

For a long time I've used Python strictly to get something done even though I knew there was likely a much more "pythonic" way of accomplishing a given task. In the example below I have a table that is saved as a CSV and when I need to return an alias I use the alias function.

def alias(old,new):
    frame = pd.read_csv(r'C:\Users\UserName\Teams.csv', index_col=[0])
    return dict(zip(frame[old],frame[new]))

FWIW I know one way of improving speed would be to save the data as a dictionary. The only reason I haven't done so is because it's easier for me to make quick additions/changes by opening the Excel file but that's not a deal breaker. That being said, does anyone have ideas on how to improve on this code? And more specifically, is there potential to make it so I only have to indicate which alias I'm looking to return? For example, right now I have to use this line of code to return the RR value for 'ARI'.

alias('TEAM','RR').get('ARI')
>>> 15

What would be great is if I didn't have to specify that I'm referencing the 'TEAM' value and instead just indicate that I'm looking to return the 'RR' value for a given team/teams. I feel like this should be possible especially given the fact that there are no duplicates. Any thoughts on this would be greatly appreciated.

TEAM	MLB	MLB_ID	RW	RR	DBD	FULL_NAME
ARI	ari	109	ARZ	15	Arizona Diamondbacks	Arizona Diamondbacks
ATL	atl	144	ATL	16	Atlanta Braves	Atlanta Braves
BAL	bal	110	BAL	2	Baltimore Orioles	Baltimore Orioles
BOS	bos	111	BOS	3	Boston Red Sox	Boston Red Sox
CHC	chc	112	CHC	17	Chicago Cubs	Chicago Cubs
CHW	cws	145	CHW	4	Chicago White Sox	Chicago White Sox
CIN	cin	113	CIN	18	Cincinnati Reds	Cincinnati Reds
CLE	cle	114	CLE	5	Cleveland Guardians	Cleveland Indians
COL	col	115	COL	19	Colorado Rockies	Colorado Rockies
DET	det	116	DET	6	Detroit Tigers	Detroit Tigers
HOU	hou	117	HOU	21	Houston Astros	Houston Astros
KCR	kc	118	KC	7	Kansas City Royals	Kansas City Royals
LAA	ana	108	LAA	1	LA Angels	Los Angeles Angels
LAD	la	119	LAD	22	LA Dodgers	Los Angeles Dodgers
MIA	mia	146	MIA	20	Miami Marlins	Miami Marlins
MIL	mil	158	MIL	23	Milwaukee Brewers	Milwaukee Brewers
MIN	min	142	MIN	8	Minnesota Twins	Minnesota Twins
NYM	nym	121	NYM	25	New York Mets	New York Mets
NYY	nyy	147	NYY	9	New York Yankees	New York Yankees
OAK	oak	133	OAK	10	Oakland Athletics	Oakland Athletics
PHI	phi	143	PHI	26	Philadelphia Phillies	Philadelphia Phillies
PIT	pit	134	PIT	27	Pittsburgh Pirates	Pittsburgh Pirates
SDP	sd	135	SD	29	San Diego Padres	San Diego Padres
SEA	sea	136	SEA	11	Seattle Mariners	Seattle Mariners
SFG	sf	137	SF	30	San Francisco Giants	San Francisco Giants
STL	stl	138	STL	28	St. Louis Cardinals	St. Louis Cardinals
TBR	tb	139	TB	12	Tampa Bay Rays	Tampa Bay Rays
TEX	tex	140	TEX	13	Texas Rangers	Texas Rangers
TOR	tor	141	TOR	14	Toronto Blue Jays	Toronto Blue Jays
WSN	was	120	WAS	24	Washington Nationals	Washington Nationals

Answer 1

First things first, a potential bug - you have alias('TEAM', 'RR'), but given the structure of the table, you probably want alias('TEAM', 'RW')

For a more elegant way of looking up stuff in a dataframe, realise that they are like a 2D dict, where one set of keys are the columns, and the other set is the index. Something like df.set_index('TEAM'); df.loc[team_name]['RW'], where team_name is the team name whose alias you want to look up, would solve your intermediate dict problem. You don't even need the set_index bit, as you already set the index when loading the CSV file, but setting the index with an explicit column name instead of an column number makes it easier to understand what's happening, and also insures you against some changes in the CSV file.

Rolling it all up,

def get_team_alias(team_name, alias_file = r'C:\Users\UserName\Teams.csv'):
    frame = pd.read_csv(alias_file, index_col='TEAM')
    return frame.loc[team_name, 'RW'] # frame.loc[<row_label_in_index>, <column_name>]