How do I create a sum row and sum column in pandas?

Question

I'm going through the Khan Academy course on Statistics as a bit of a refresher from my college days, and as a way to get me up to speed on pandas & other scientific Python.

I've got a table that looks like this from Khan Academy:

             | Undergraduate | Graduate | Total -------------+---------------+----------+------ Straight A's |           240 |       60 |   300 -------------+---------------+----------+------ Not          |         3,760 |      440 | 4,200 -------------+---------------+----------+------ Total        |         4,000 |      500 | 4,500 

I would like to recreate this table using pandas. Of course I could create a DataFrame using something like

"Graduate": {...}, "Undergraduate": {...}, "Total": {...}, 

But that seems like a naive approach that would both fall over quickly and just not really be extensible.

I've got the non-totals part of the table like this:

df = pd.DataFrame(     {         "Undergraduate": {"Straight A's": 240, "Not": 3_760},         "Graduate": {"Straight A's": 60, "Not": 440},     } ) df 

I've been looking and found a couple of promising things, like:

df['Total'] = df.sum(axis=1) 

But I didn't find anything terribly elegant.

I did find the crosstab function that looks like it should do what I want, but it seems like in order to do that I'd have to create a dataframe consisting of 1/0 for all of these values, which seems silly because I've already got an aggregate.

I have found some approaches that seem to manually build a new totals row, but it seems like there should be a better way, something like:

totals(df, rows=True, columns=True) 

or something.

Does this exist in pandas, or do I have to just cobble together my own approach?

Answer 1

Or in two steps, using the .sum() function as you suggested (which might be a bit more readable as well):

import pandas as pd  df = pd.DataFrame( {"Undergraduate": {"Straight A's": 240, "Not": 3_760},"Graduate": {"Straight A's": 60, "Not": 440},})  #Total sum per column:  df.loc['Total',:]= df.sum(axis=0)  #Total sum per row:  df.loc[:,'Total'] = df.sum(axis=1) 

Output:

              Graduate  Undergraduate  Total Not                440           3760   4200 Straight A's        60            240    300 Total              500           4000   4500