Sum multiple columns to a two column output/dataframe

Question

I am exploring a rugby dataset where I am stuck on a bit of an issue. The dataset contains matches played in the the Six Nations over a year, and the tries are put into two separate columns, which is "Home try's" and "Away try's". I am trying to print out the output of the teams with the largest amount of try`s. Below is an example of how the dataset looks.

Home	Away	Home try's	Away try's
England	France	3	2
Ireland	Scotland	1	4
France	Ireland	1	0
Scotland	England	1	3

I have tried to put the home and away columns to a single column as a list and use the arguments df.explode and df.groupby argument to create a dataframe, but it doesnt work. Any tips on how I could solve this?

The ideal output would be:

England	Scotland	France	Ireland
6	5	3	1

Answer 1

How about this?

series_home = df.groupby('Home').sum()["Home try's"]
series_away = df.groupby('Away').sum()["Away try's"]
combined_series = series_home + series_away
combined_df = pd.DataFrame(combined_series).transpose()

Answer 2

I was able to do this in a way combining the two DFs and using a pivot.

df_Home = df[['Home', 'Home_Try']]
df_Away = df[['Away', 'Away_Try']]
df_Away.columns = [['Team', 'Tries']]
df_Home.columns = [['Team', 'Tries']]
df2 = pd.concat([df_Home, df_Away])
df2.columns = ['Team', 'Tries']
df_pivot = pd.pivot_table(df2, values='Tries', columns=['Team'], aggfunc=np.sum)
df_pivot

Answer 3

Set the "Home" column as an index and sort the dataframe. Create another frame by copying the "Away try's" column into it and setting the "Away" column as the index. I copy the "Home try's" column to the new frame. I count the amounts.

df = df.set_index("Home").sort_index()
df1  = pd.DataFrame(df["Away try's"].values, index=df["Away"]).sort_index()
df1["Home try's"] = df["Home try's"]

print(df1.sum(axis=1))

Output

Away
England     6
France      3
Ireland     1
Scotland    5
dtype: int64