在 dataframe 的特定列上的 sum()

Question

I cannot work out how to add a new row at the end. The last row needs to do sum() on specific columns and dividing 2 other columns. While the DF has applied a filter to sum only specific rows.

df:

    Categ    CategID    col3      col4      col5      col6
0   Cat1     1          -65.90    -100.40   -26.91    23.79
1   Cat2     2          -81.91    -15.30    -16.00    10.06
2   Cat3     3          -57.70    -18.62      0.00    0.00

I would like the output to be like so:

3   Total              -123.60   -119.02    -26.91    100*(-119.02/-26.91)

col3,col4,col5 would have sum(), and col6 would be the above formula.

If [CategID]==2, then don't include in the TOTAL

I was able to get it almost as I wanted by using.query(), like so:

#tg is a list

df.loc['Total'] = df.query("categID in @tg").sum()

But with the above I cannot have the 'col6' like this 100*(col4.sum() / col5.sum()) , because they are all sum().

Then I tried with Series like so, but I don't understand how to apply filter.where()

s = pd.Series(  [df['col3'].sum()\
                ,df['col4'].sum()\
                ,df['col5'].sum()\
                ,100*(df['col4'].sum()/df['col5'].sum())\
                ,index = ['col3','col4','col5','col6'])
df.loc['Total'] = s.where('tag1' in tg)

using the above Series() works, until I add.where() this gives the error: ValueError: Array conditional must be same shape as self

So, can I accomplish this with the first method, using.query(), just somehow modify one of the column in TOTAL? Otherwise what am I doing wrong in the second method.where()

Thanks

Answer 1

IIUC, you can try:

s = df.mask(df['CategID'].eq(2)).drop("CategID",1).sum()
s.loc['col6'] = 100*(s['col4'] / s['col5'])
df.loc[len(df)] = s

df = df.fillna({'Categ':'Total',"CategID":''})

---

print(df)

   Categ CategID    col3    col4   col5        col6
0   Cat1       1  -65.90 -100.40 -26.91   23.790000
1   Cat2       2  -81.91  -15.30 -16.00   10.060000
2   Cat3       3  -57.70  -18.62   0.00    0.000000
3  Total         -123.60 -119.02 -26.91  442.289112