英文:
Pandas new cumulating column based on percentange change and initial value
问题
根据您提供的描述，您想要计算新的"items"列，但没有提供具体的计算方法。如果您需要帮助编写Python代码以在pandas DataFrame中执行此计算，请提供更多细节，以便我可以为您提供准确的代码示例。
英文:
Having an initial dataframe like this, defined with a items columns and a percentage column:
How can I calculate the new items column as shown in the following picture?
In theory this seems an easy task in my mind (and in Excel/Google Sheet), but trying to do this with a pandas dataframe is getting me mad!
Thank you for the help!
答案1
得分: 1
你需要计算累积百分比，方法是除以100，加1，然后计算cumprod，最后乘以"items"列：
df = pd.DataFrame({'label': list('ABCDE'),'items': 5,'perc': [float('nan'), 0, 24.32, -19.57, -91.80]})df['new_items'] = (df['perc'].fillna(0).div(100).add(1).cumprod().mul(df['items']))
输出结果如下：
label items perc new_items0 A 5 NaN 5.0000001 B 5 0.00 5.0000002 C 5 24.32 6.2160003 D 5 -19.57 4.9995294 E 5 -91.80 0.409961
中间结果如下：
label items perc new_items percent cumprod0 A 5 NaN 5.000000 1.0000 1.0000001 B 5 0.00 5.000000 1.0000 1.0000002 C 5 24.32 6.216000 1.2432 1.2432003 D 5 -19.57 4.999529 0.8043 0.9999064 E 5 -91.80 0.409961 0.0820 0.081992
英文:
You need to compute the cumulated percentage by dividing by 100, adding 1 and computing the cumprod, then multiply by "items":
df = pd.DataFrame({'label': list('ABCDE'),'items': 5,'perc': [float('nan'), 0, 24.32, -19.57, -91.80]})df['new_items'] = (df['perc'].fillna(0).div(100).add(1).cumprod().mul(df['items']))
Output:
label items perc new_items0 A 5 NaN 5.0000001 B 5 0.00 5.0000002 C 5 24.32 6.2160003 D 5 -19.57 4.9995294 E 5 -91.80 0.409961
Intermediates:
label items perc new_items percent cumprod0 A 5 NaN 5.000000 1.0000 1.0000001 B 5 0.00 5.000000 1.0000 1.0000002 C 5 24.32 6.216000 1.2432 1.2432003 D 5 -19.57 4.999529 0.8043 0.9999064 E 5 -91.80 0.409961 0.0820 0.081992
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