pandas from function / loop

Question

I am importing stock data in python from yahoo.

import pandas as pd
import numpy as np
from pandas_datareader import data
from datetime import date
from datetime import timedelta

def Get_Historic_SP(ticker):
    end_date = date.today()
    start_end = end_date - timedelta(weeks=260)
    df = data.DataReader(ticker, data_source='yahoo',start=start_date, end=end_date)
    df.index = pd.to_datetime(df.index)
    df.reset_index().rename(columns={df.index.name:'Date'})
    return df['Close']

This works well. I would like to do this for multiple codes and create one dataframe with the companies in columns. Also each column to have ticker (comps) as the title.

I am using:

    comps = ['AVON.L', 'BYG.L']

example

    Date         AVON.L    BYG.L
    20/03/2019   193       980

Answer 1

Fixing your code as follows:

import pandas as pd
import numpy as np
from pandas_datareader import data
from datetime import date
from datetime import timedelta

def Get_Historic_SP(ticker, start_date):
    end_date = date.today()
    start_end = end_date - timedelta(weeks=260)
    df = data.DataReader(ticker, data_source='yahoo',start=start_date, end=end_date)
    df.index = pd.to_datetime(df.index)
    df.reset_index().rename(columns={df.index.name:'Date'})
    return df['Close']

You can use the following code that first creates a dictionary and then converts it to a DataFrame:

tickers = ['googl', 'aapl']

ticker_dict = {}
for ticker in tickers:
    ticker_dict[ticker] = Get_Historic_SP(ticker, date(2019,3,1))

df = pd.DataFrame(ticker_dict)