Pandas Dataframe for Stock % change
<p>I like to create a pandas df for tracking % change of stock on daily, weekly, monthly and yearly basis.
Here is output what i would like the output to look like:</p>

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<pre class="snippet-code-html lang-html prettyprint-override"><code>stock Close Daily WTD MTD YTD
0 IWM 137.960007 0.847956 0.847956 5.337105 25.406785
1 IBM 167.600006 0.551964 0.551964 4.867976 23.280625</code></pre>
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<p>Here is the code that used to generate it.  I am new to python and panda. Is there a better way of doing this. Also , i am inputing the dates manually, can it be generated automatically.</p>

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<pre class="snippet-code-html lang-html prettyprint-override"><code>import pandas as pd
from datetime import datetime, timedelta
from pandas_datareader import data,wb


start = datetime(2016, 1, 1)
end = datetime.today()

m_start = datetime(2016, 12, 1)

w_start = datetime(2016, 12, 19)

d_start = end - timedelta(days=2)


labels = ['stock','Close','Daily','WTD','MTD','YTD']

dat = []

for ticker in ticker_list:
    prices = data.DataReader(ticker, 'yahoo', start, end)
    closing_prices = prices['Close']
    change = 100 * (closing_prices[-1] - closing_prices[0]) / closing_prices[0]
    
    #get the monthly % gain
    m_price = data.DataReader(ticker, 'yahoo', m_start, end)
    m_close = m_price['Close']
    m_change = 100 * (m_close[-1] - m_close[0]) / m_close[0]

    #get the weekly % gain
    w_price = data.DataReader(ticker, 'yahoo', w_start, end)
    w_close = w_price['Close']
    w_change = 100 * (w_close[-1] - w_close[0]) / w_close[0]

    #get the Daily % gain
    d_price = data.DataReader(ticker, 'yahoo', d_start, end)
    d_close = d_price['Close']
    d_change = 100 * (d_close[-1] - d_close[0]) / d_close[0]
    
    dat.append((ticker,closing_prices[-1],d_change,w_change,m_change,change))
    

df2 = pd.DataFrame.from_records(dat,columns=labels)
df2</code></pre>
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<p>Any help to improve this code is really appreciated.</p>

<p>thanks</p>