熊猫:如何从现有值中添加用于循环的行

Question

所以我有个密码：

monthly['differences'] = monthly.GMV.diff()
monthly['percentage'] = monthly.differences / (monthly.GMV - monthly.differences) *100
monthly

它产生了这个数据：

    GMV         differences percentage
date            
1   69793.30    NaN         NaN
2   65159.60    -4633.70    -6.639176
3   70397.10    5237.50     8.037956
4   68736.80    -1660.30    -2.358478
5   71402.75    2665.95     3.878490
6   68230.20    -3172.55    -4.443176
7   72557.90    4327.70     6.342792
8   68278.25    -4279.65    -5.898255
9   64180.05    -4098.20    -6.002204
10  64027.60    -152.45     -0.237535
11  70395.35    6367.75     9.945320
12  64701.15    -5694.20    -8.088887

我想从现有数据中添加新行with循环，我希望第13行具有以下值：

GMV13 = GMV12 + (GMV12 * percent12)
diff13= GMV13 - GMV12
percent13= diff13 / (GMV13 * diff13) * 100

对于第14行和开始，直到数据看起来有点像这样：(忽略值，这只是一个例子)

    GMV         differences percentage
date            
1   69793.30    NaN         NaN
2   65159.60    -4633.70    -6.639176
3   70397.10    5237.50     8.037956
4   68736.80    -1660.30    -2.358478
5   71402.75    2665.95     3.878490
6   68230.20    -3172.55    -4.443176
7   72557.90    4327.70     6.342792
8   68278.25    -4279.65    -5.898255
9   64180.05    -4098.20    -6.002204
10  64027.60    -152.45     -0.237535
11  70395.35    6367.75     9.945320
12  64701.15    -5694.20    -8.088887
13  70397.10    5237.50     8.037956
14  68736.80    -1660.30    -2.358478
15  71402.75    2665.95     3.878490
16  68230.20    -3172.55    -4.443176
17  72557.90    4327.70     6.342792
18  68278.25    -4279.65    -5.898255
19  64180.05    -4098.20    -6.002204

Answer 1

您可以在范围13.19中循环以从前面的行中计算新行：

def calc_new_val(old_row):
    new_row = pd.Series()
    new_row['GMV'] = old_row['GMV'] * (1 + old_row['percentage'] / 100)
    new_row['differences'] = new_row['GMV'] - old_row['GMV']
    new_row['percentage'] = new_row['differences'] / old_row['GMV'] * 100
    return new_row

for i in range(13, 20):
    monthly.loc[i, :] = calc_new_val(monthly.loc[i-1, :])

输出：

             GMV  differences  percentage
1   69793.300000          NaN         NaN
2   65159.600000 -4633.700000   -6.639176
3   70397.100000  5237.500000    8.037956
4   68736.800000 -1660.300000   -2.358478
5   71402.750000  2665.950000    3.878490
6   68230.200000 -3172.550000   -4.443176
7   72557.900000  4327.700000    6.342792
8   68278.250000 -4279.650000   -5.898255
9   64180.050000 -4098.200000   -6.002204
10  64027.600000  -152.450000   -0.237535
11  70395.350000  6367.750000    9.945320
12  64701.150000 -5694.200000   -8.088887
13  59467.547089 -5233.602911   -8.088887
14  54657.284403 -4810.262686   -8.088887
15  50236.118430 -4421.165973   -8.088887
16  46172.575577 -4063.542853   -8.088887
17  42437.728114 -3734.847463   -8.088887
18  39004.988241 -3432.739873   -8.088887
19  35849.918818 -3155.069423   -8.088887