CODEWITHSUNDEEP
pythonpandas
cmp
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Reputation: 568

calculate the difference between pandas rows in pairs

I have a dataframe of orders as below, where the column 'Value' represents cash in/out and the 'Date' column reflects when the transaction occurred.

Each transaction is grouped, so that the 'QTY' out, is always succeeded by the 'QTY' 'in', reflected by the sign in the 'QTY' column:

    Date       Qty  Price       Value       
0   2014-11-18  58  495.775716  -2875499    
1   2014-11-24 -58  484.280147  2808824    
2   2014-11-26  63  474.138699  -2987073  
3   2014-12-31 -63  507.931247  3199966    
4   2015-01-05  59  495.923771  -2925950    
5   2015-02-05 -59  456.224370  2691723

How can I create two columns, 'n_days' and 'price_diff' that is the difference in days between the two dates of each transaction and the 'Value'?

I have tried:

df['price_diff'] = df['Value'].rolling(2).apply(lambda x: x[0] + x[1])

but receiving a key error for the first observation (0).

Many thanks

Upvotes: 2

Views: 1218

Answers (1)

Quang Hoang
Quang Hoang

Reputation: 150745

Why don't you just use sum:

df['price_diff'] = df['Value'].rolling(2).sum()

Although from the name, it looks like

df['price_diff'] = df['Price'].diff()

And, for the two columns:

df[['Date_diff','Price_diff']] = df[['Date','Price']].diff()

Output:

        Date  Qty       Price    Value Date_diff  Price_diff
0 2014-11-18   58  495.775716 -2875499       NaT         NaN
1 2014-11-24  -58  484.280147  2808824    6 days  -11.495569
2 2014-11-26   63  474.138699 -2987073    2 days  -10.141448
3 2014-12-31  -63  507.931247  3199966   35 days   33.792548
4 2015-01-05   59  495.923771 -2925950    5 days  -12.007476
5 2015-02-05  -59  456.224370  2691723   31 days  -39.699401

Updated Per comment, you can try:

df['Val_sum'] = df['Value'].rolling(2).sum()[1::2]

Output:

        Date  Qty       Price    Value   Val_sum
0 2014-11-18   58  495.775716 -2875499       NaN
1 2014-11-24  -58  484.280147  2808824  -66675.0
2 2014-11-26   63  474.138699 -2987073       NaN
3 2014-12-31  -63  507.931247  3199966  212893.0
4 2015-01-05   59  495.923771 -2925950       NaN
5 2015-02-05  -59  456.224370  2691723 -234227.0

Upvotes: 1

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