将每行不同列数的值相加

Question

I have a data frame including number of sold tickets in different price buckets for each flight.

For each record/row, I want to use the value in one column as an index in iloc function, to sum up values in a specific number of columns.

Like, for each row, I want to sum up values from column index 5 to value in ['iloc_index']

I tried df.iloc[:, 5:df['iloc_index']].sum(axis=1) but it did not work.

sample data:

   A  B  C  D  iloc_value  total
0  1  2  3  2     1
1  1  3  4  2     2
2  4  6  3  2     1

for each row, I want to sum up the number of columns based on the value in ['iloc_value']

for example,

• for row0, I want the total to be 1+2
• for row1, I want the total to be 1+3+4
• for row2, I want the total to be 4+6

Answer 1

EDIT: I quickly got the results this way:

First define a function that can do it for one row:

def sum_till_iloc_value(row):
    return sum(row[:row['iloc_value']+1])
    

Then apply it to all rows to generate your output:

df_flights['sum'] = df_flights.apply(sum_till_iloc_value, axis=1)

   A  B  C  D  iloc_value  sum
0  1  2  3  2           1    3
1  1  3  4  2           2    8
2  4  6  3  2           1   10

PREVIOUSLY: Assuming you have information that looks like:

 df_flights = pd.DataFrame({'flight':['f1', 'f2', 'f3'], 'business':[2,3,4], 'economy':[6,7,8]}) df_flights

 flight business economy 0 f1 2 6 1 f2 3 7 2 f3 4 8

you can sum the columns you want as below:

 df_flights['seat_count'] = df_flights['business'] + df_flights['economy']

This will create a new column that you can later select:

 df_flights[['flight', 'seat_count']]

 flight seat_count 0 f1 8 1 f2 10 2 f3 12

Answer 2

Here's a way to do that in a fully vectorized way: melting the dataframe, summing only the relevant columns, and getting the total back into the dataframe:

d = dict([[y, x] for x, y in enumerate(df.columns[:-1])])

temp_df = df.copy()
temp_df = temp_df.rename(columns=d)
temp_df = temp_df.reset_index().melt(id_vars = ["index", "iloc_value"])
temp_df = temp_df[temp_df.variable <= temp_df.iloc_value]
df["total"] = temp_df.groupby("index").value.sum()

The output is:

   A  B  C  D  iloc_value  total
0  1  2  3  2           1      3
1  1  3  4  2           2      8
2  4  6  3  2           1     10