Python pandas dataframe add previous row values

Question

I have a pandas framework that looks like this:

                     AAPL   IBM  GOOG  XOM
2011-01-10 16:00:00  1500     0     0    0
2011-01-11 16:00:00     0     0     0    0
2011-01-12 16:00:00     0     0     0    0
2011-01-13 16:00:00 -1500  4000     0    0
2011-01-14 16:00:00     0     0     0    0
2011-01-18 16:00:00     0     0     0    0

My goal is to populate the rows by adding the previous row values. The result will look like this:

                     AAPL   IBM  GOOG  XOM
2011-01-10 16:00:00  1500     0     0    0
2011-01-11 16:00:00  1500     0     0    0
2011-01-12 16:00:00  1500     0     0    0
2011-01-13 16:00:00     0  4000     0    0
2011-01-14 16:00:00     0  4000     0    0
2011-01-18 16:00:00     0  4000     0    0

I tried iterating over the dataframe index using

    for date in df.index:

and increase dates with

    dt_nextDate = date + dt.timedelta(days=1)

but there are gaps in the dataframe index that denote weekends.

Is it possible to iterate over an index from the second row to the end, refer to the previous row and add values?

Answer 1

The result of your example is not the result of your example algorithm, so I'm not sure what you are asking for exactly?

, , , :

>>> df.cumsum()
                    AAPL   IBM  GOOG  XOM
index                                    
2011-01-1016:00:00  1500     0     0    0
2011-01-1116:00:00  1500     0     0    0
2011-01-1216:00:00  1500     0     0    0
2011-01-1316:00:00     0  4000     0    0
2011-01-1416:00:00     0  4000     0    0
2011-01-1816:00:00     0  4000     0    0

, , , , , , 2:

>>> result = pd.rolling_sum(df, 2)
>>> result
                    AAPL   IBM  GOOG  XOM
index                                    
2011-01-1016:00:00   NaN   NaN   NaN  NaN
2011-01-1116:00:00  1500     0     0    0
2011-01-1216:00:00     0     0     0    0
2011-01-1316:00:00 -1500  4000     0    0
2011-01-1416:00:00 -1500  4000     0    0
2011-01-1816:00:00     0     0     0    0

NaN, :

>>> result.iloc[0,:] = df.iloc[0,:]
>>> result
                    AAPL   IBM  GOOG  XOM
index                                    
2011-01-1016:00:00  1500     0     0    0
2011-01-1116:00:00  1500     0     0    0
2011-01-1216:00:00     0     0     0    0
2011-01-1316:00:00 -1500  4000     0    0
2011-01-1416:00:00 -1500  4000     0    0
2011-01-1816:00:00     0     0     0    0