Python data (series?) concatenation with pandas

Question

SP500_stocks = ["AAPL", "BA", "TSLA"]

SP_index = []
for stock in SP500_stocks:
    s_data = pdr.get_data_yahoo(stock, startDate, endDate)
    s_data = s_data["Adj Close"].pct_change()
    SP_index.append(s_data) 


df1 = pd.concat([SP_index[0], SP_index[1], SP_index[2]], axis = 1) 
df1.dropna(inplace=True)`

In this code, I pull data from yahoo for the 3 tickers in SP500_stocks. The for loop returns a series I believe, 3 separate blocks of data, each having a date column and a percent return column. My question is, if I wanted to let's say have 51 stocks. I don't want to index SP_index 50 times. How do I write a for or while loop that does this? I was getting an error when trying to concatenate in the for loop above the df1 block. The df1 block allowed me to have the data side by side (aka 4 columns next to each other, 1st being the date column, followed by the 3 stocks returns).

Answer 1

Your code is generally fine. To simplify it, just use the following line to create the dataframe:

pd.concat(SP_index, axis = 1) 

That way, you don't have to refer to each SP_index[i] separately.