python / excel：如何为列A中的所有相同值添加列B的值

Question

Excel Image 1

Excel Image 2

Please look at the "Excel Image 1" to start.

I am using python to pull those first two columns, and storing them in their own separate array. What I want to do is add the values of column B if the value in column are the same. For ex: if the value in column A is "0", then add -200 and 400 and get 200 because "0" is in row 1 and row 2 of column A.

After the addition as shown in "Excel Image 2", store the new values of both the Column in their own separate arrays, so I can use them for later calculations.

import xlrd
excel = '/Users/Bob/Desktop/'

wb1 = xlrd.open_workbook(excel + 'assignment.xlsx')
sh1 = wb1.sheet_by_index(0)

colA,colB = [],[]
for a in range(0,sh1.nrows):
    colA.append(int(sh1.cell(a,0).value))
    colB.append(int(sh1.cell(a,1).value))
print(colA)
print(colB)

for i in colA:
    if i == 0:
        add = colB[0] + colB[1]
print(add)

I want a code that adds those values in column B irrespective of how many number or number of same values are given in column A.

Current output:

[0, 0, 1, 2, 2, 2, 3, 3, 4, 4]
[-200, 400, 30, 600, -70, 10, 20, -90, 40, 40]

Expected output:

[0, 1, 2, 3, 4]
[200, 30, 540, 70, 80]

Thanks!

Answer 1

Use itertools.groupby() :

from itertools import groupby
import xlrd
excel = '/Users/Bob/Desktop/'

wb1 = xlrd.open_workbook(excel + 'assignment.xlsx')
sh1 = wb1.sheet_by_index(0)

sheet_rows = [sh1.row(r) for r in range(0, sh1.nrows)]
groups = groupby(sheet_rows, lambda row: row[1])

key_list = []
sum_list = []
for key, group in groups:
    key_list.append(key)
    sum_list.append(sum(group))

print(key_list)
print(sum_list)

Answer 2

I'd use the row_values method, instead. This is called with the row number and the column number (zero-based). For example to get the first row in a list do this:

first_row = sh1.row_values(0)
[0.0, -200.0]

Slice the list to get values from specific columns. Such as:

sh1.row_values(0)[1]
-200.0

I started by keeping track of the values in the first column ( curr_key ) and running a cumulative sum for a given key— cum_sum . When the key changes, I just update append the cum_sum to sum_list and update curr_key and cum_sum . The last thing to do is to append the last value in the cumulative sum.

curr_key = sh1.row_values(0)[0]
cum_sum = sh1.row_values(0)[1]
sum_list = []
for a in range(1,sh1.nrows):
    if sh1.row_values(a)[0] == curr_key:
        cum_sum += sh1.row_values(a)[1]
        curr_key = sh1.row_values(a)[0]
    else:
        sum_list.append(cum_sum)
        curr_key = sh1.row_values(a)[0]
        cum_sum = sh1.row_values(a)[1]
sum_list.append(cum_sum)

This yields the following in sum_list :

[200.0, 30.0, 540.0, -70.0, 80.0]

BTW, your second file has 70.0 instead of -70.0 in row 3.

After some thought I realized a better way was to use a dictionary (similar in concept to @Austin Hastings answer, but without the groupby ).

key_vals = [x for x in sh1.col_values(0)]
int_vals = [y for y in sh1.col_values(1)]
sum_list = {}
for i,value in enumerate(key_vals):
    if str(value) not in sum_list:
        sum_list[str(value)] = int_vals[i]
    else:
        sum_list[str(value)] += int_vals[i]
for key in sorted(sum_list):
    print('{}, {}'.format(key, sum_list[key]))

This yields:

0.0, 200.0
1.0, 30.0
2.0, 540.0
3.0, -70.0
4.0, 80.0