CODEWITHSUNDEEP
pythonpandas
AME
AME

Reputation: 5300

Cumulative Sum Function on Pandas Data Frame

I am attempting to capture a "running" cumulative sum given a series of period amounts.

See example:

enter image description here

df = df[1:4].cumsum() # this doesn't return the desired result

Upvotes: 5

Views: 9003

Answers (2)

Brian
Brian

Reputation: 138

You're looking for the axis parameter. Many Pandas functions take this argument to apply an operation across the columns or across the rows. Use axis=0 to apply row-wise and axis=1 to apply column-wise. This operation is actually traversing the columns, so you want axis=1.

df.cumsum(axis=1) by itself works on your example to produce the output table.

In [3]: df.cumsum(axis=1)
Out[3]:
      1   2   3   4
10   16  30  41  61
51   13  29  40  50
13   11  30  45  61
321  12  27  37  52

I suspect you're interested in restricting to a specific range of columns, though. To do that, you can use .loc with the column labels (strings in mine).

In [4]: df.loc[:, '2':'3'].cumsum(axis=1)
Out[4]:
      2   3
10   14  25
51   16  27
13   19  34
321  15  25

.loc is label-based and is inclusive of the bounds. If you want to find out more about indexing in Pandas, check the docs.

Upvotes: 4

chrisb
chrisb

Reputation: 52246

You want axis=1 to sum across the rows.

df.cumsum(axis=1)

Side-note - doing [1:4] slices the rows by default (i.e. numpy or list-like semantics). If you want to select columns by label, use df.loc[:, 1:4]

Upvotes: 3

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