Pandas dataframe total row

Question

I have a dataframe, something like:

     foo  bar  qux 0    a    1    3.14 1    b    3    2.72 2    c    2    1.62 3    d    9    1.41 4    e    3    0.58 

and I would like to add a 'total' row to the end of dataframe:

     foo  bar  qux 0    a    1    3.14 1    b    3    2.72 2    c    2    1.62 3    d    9    1.41 4    e    3    0.58 5  total  18   9.47 

I've tried to use the sum command but I end up with a Series, which although I can convert back to a Dataframe, doesn't maintain the data types:

tot_row = pd.DataFrame(df.sum()).T tot_row['foo'] = 'tot' tot_row.dtypes:      foo    object      bar    object      qux    object 

I would like to maintain the data types from the original data frame as I need to apply other operations to the total row, something like:

baz = 2*tot_row['qux'] + 3*tot_row['bar'] 

Answer 1

Append a totals row with

df.append(df.sum(numeric_only=True), ignore_index=True) 

The conversion is necessary only if you have a column of strings or objects.

It's a bit of a fragile solution so I'd recommend sticking to operations on the dataframe, though. eg.

baz = 2*df['qux'].sum() + 3*df['bar'].sum() 

Answer 2

df.loc["Total"] = df.sum() 

works for me and I find it easier to remember. Am I missing something? Probably wasn't possible in earlier versions.

I'd actually like to add the total row only temporarily though. Adding it permanently is good for display but makes it a hassle in further calculations.

Just found

df.append(df.sum().rename('Total')) 

This prints what I want in a Jupyter notebook and appears to leave the df itself untouched.