Question

I have lots of missing values when calculating rollng_mean with:

import datetime as dt
import pandas as pd
import pandas.io.data as web

stocklist = ['MSFT', 'BELG.BR']

# read historical prices for last 11 years
def get_px(stock, start):
    return web.get_data_yahoo(stock, start)['Adj Close']

today = dt.date.today()
start = str(dt.date(today.year-11, today.month, today.day))

px = pd.DataFrame({n: get_px(n, start) for n in stocklist})
px.ffill()
sma200 = pd.rolling_mean(px, 200)

got following result:

In [14]: px
Out[14]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2836 entries, 2002-01-14 00:00:00 to 2013-01-11 00:00:00
Data columns:
BELG.BR    2270  non-null values
MSFT       2769  non-null values
dtypes: float64(2)

In [15]: sma200
Out[15]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2836 entries, 2002-01-14 00:00:00 to 2013-01-11 00:00:00
Data columns:
BELG.BR    689  non-null values
MSFT       400  non-null values
dtypes: float64(2)

Any idea why most of the sma200 rolling_mean values are missing and how to get the complete list ?

Answer 1

px.ffill() returns a new DataFrame. To modify px itself, use inplace = True.

px.ffill(inplace = True)
sma200 = pd.rolling_mean(px, 200)
print(sma200)

yields

Data columns:
BELG.BR    2085  non-null values
MSFT       2635  non-null values
dtypes: float64(2)