CODEWITHSUNDEEP
pythonpandasdataframe
gr1zzly be4r
gr1zzly be4r

Reputation: 2162

Pandas Conditional Rolling Sum of Two Columns

I have four columns in a data frame like so:

       A   B     C        D
75472  d1  x    -36.0   0.0
75555  d2  x    -38.0   0.0
75638  d3  x    -18.0   0.0
75721  d4  x    -18.0   1836.0
75804  d5  x    1151.0  0.0
75887  d6  x    734.0   0.0
75970  d7  x    -723.0  0.0

And I want to conditionally sum D by:

So for above, D would be [-36, -74, -92, 1836, 2987, 3721, 2998].

I've been able to do this successfully with a for loop

for i, row in me.iterrows():
    try:
        if row['D'] > 0:
            step1 = me.loc[(me['B'] == row['B']) & (me['A'] == row['A']), 'output'].iloc[0]
            me_copy.iloc[i, me_copy.columns.get_loc('output')] = step1
        else:
            step1 = me.loc[(me['B'] == row['B']) & (me['A'] == (row['A'] - pd.DateOffset(days=1))), 'step1'].iloc[0]
            receipts_adjustments_sales = me.loc[(me['B'] == row['B']) & (me['A'] == row['A']), 'C'].iloc[0]
            me_copy.iloc[i, me_copy.columns.get_loc('output')] = step1 + receipts_adjustments_sales
    except:
        me_copy.iloc[i, me_copy.columns.get_loc('output')] = 0

But the for loop is obviously really expensive, anti-pattern and basically doesn't run over my whole data frame. I'm trying to copy an excel function here that has basically been written over a panel of data, and for the life of me I cannot figure out how to do this with:

I was attempting to do it with shift() for a while, but I realized that I kept having to make a separate column for each row, and that's why I went with a for loop.

Generalized to Groups

df.loc[:, 'A_group'] = df.groupby(['A'])[df['D'] != 0].cumsum()
df.loc[:, 'E'] = df['D'].mask(df['D'] == 0).combine_first(df['C'])
df.loc[:, 'F'] = me.groupby(['A', 'A_group'])['E'].cumsum()

Thanks to Scott Boston for the help!

Upvotes: 4

Views: 312

Answers (2)

Quang Hoang
Quang Hoang

Reputation: 150785

Another, similar to Scott's answer:

groups = df['D'].ne(0).cumsum()
df['new'] = (df['C'].where(df['D'].eq(0), df['D'])
     .groupby(groups)
     .cumsum()
)

Upvotes: 1

Scott Boston
Scott Boston

Reputation: 153510

Here is a way to do it:

grp = (df['D'] != 0).cumsum()
df['D_new'] = df['D'].mask(df['D'] == 0).combine_first(df['C']).groupby(grp).cumsum()
df

Output:

        A  B       C       D   D_new
75472  d1  x   -36.0     0.0   -36.0
75555  d2  x   -38.0     0.0   -74.0
75638  d3  x   -18.0     0.0   -92.0
75721  d4  x   -18.0  1836.0  1836.0
75804  d5  x  1151.0     0.0  2987.0
75887  d6  x   734.0     0.0  3721.0
75970  d7  x  -723.0     0.0  2998.0

Details:

Create grps to help cumsum. Each group is defined the the appears of a value in 'D' hence you stop cumsum before and pick that value of D and continue cumsum until the next value of 'D'

grp = (df['D'] != 0).cumsum()

Output:

        A  B       C       D   D_new  grp
75472  d1  x   -36.0     0.0   -36.0    0
75555  d2  x   -38.0     0.0   -74.0    0
75638  d3  x   -18.0     0.0   -92.0    0
75721  d4  x   -18.0  1836.0  1836.0    1
75804  d5  x  1151.0     0.0  2987.0    1
75887  d6  x   734.0     0.0  3721.0    1
75970  d7  x  -723.0     0.0  2998.0    1

Now, Let's create new column combining 'C' and 'D' when D has a nonzero number

df['newCD'] = df['D'].mask(df['D'] == 0).combine_first(df['C'])

Output:

        A  B       C       D   D_new  grp   newCD
75472  d1  x   -36.0     0.0   -36.0    0   -36.0
75555  d2  x   -38.0     0.0   -74.0    0   -38.0
75638  d3  x   -18.0     0.0   -92.0    0   -18.0
75721  d4  x   -18.0  1836.0  1836.0    1  1836.0
75804  d5  x  1151.0     0.0  2987.0    1  1151.0
75887  d6  x   734.0     0.0  3721.0    1   734.0
75970  d7  x  -723.0     0.0  2998.0    1  -723.0

And, lastly, groupby 'grp' and cumsum newCD:

df['D_new_Details'] = df.groupby('grp')['newCD'].cumsum()

Output:

        A  B       C       D   D_new  grp   newCD  D_new_Details
75472  d1  x   -36.0     0.0   -36.0    0   -36.0          -36.0
75555  d2  x   -38.0     0.0   -74.0    0   -38.0          -74.0
75638  d3  x   -18.0     0.0   -92.0    0   -18.0          -92.0
75721  d4  x   -18.0  1836.0  1836.0    1  1836.0         1836.0
75804  d5  x  1151.0     0.0  2987.0    1  1151.0         2987.0
75887  d6  x   734.0     0.0  3721.0    1   734.0         3721.0
75970  d7  x  -723.0     0.0  2998.0    1  -723.0         2998.0

Upvotes: 5

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