David Eccles :
I have stock data that I am trying to normalize but I am having an issue with creating a new column. I need to be able to add a value to the new column called Yahoo Ticker by referencing what is in the Ticker column and getting the corresponding value from the dictionary, using the Ticker value as the key. Here is a sample of the dictionary I am using:
dict = {'AAB': 'AAB.TO',
'ABX': 'ABX.TO',
'XDU': 'XDU-U.TO' }
I want to add a column to my dataframe with the value based off the dictionary key so for example I want to turn this dataframe:
df = pd.DataFrame({'Ticker':['AAB','ABX','XDU'] })
into a dataframe like this:
newdf = pd.DataFrame({'Ticker':['AAB','ABX','AAV'],
'Yahoo Ticker':['AAB.TO','ABX.TO','XDU-U.TO']})
Previously I tried this but it didn't work
for index, value in df.iterrows():
df.loc[df['Ticker'][index] in ticker_dictionary, 'Yahoo Tickers'] = ticker_dictionary[value]
Dani Mesejo :
You could use map:
import pandas as pd
df = pd.DataFrame({'Ticker': ['AAB', 'ABX', 'XDU']})
d = {'AAB': 'AAB.TO',
'ABX': 'ABX.TO',
'XDU': 'XDU-U.TO'}
df['Yahoo Ticker'] = df['Ticker'].map(d)
print(df)
Output
Ticker Yahoo Ticker
0 AAB AAB.TO
1 ABX ABX.TO
2 XDU XDU-U.TO
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