[英]Pandas Dataframe sum row based on column header
I have the following dataframe and want to create two columns, one will show the amount MTD and the other will show the cumulative YTD based on a date parameter for each Account Name.我有以下数据框并希望创建两列,一列将显示 MTD 金额,另一列将根据每个帐户名称的日期参数显示累计 YTD。 This is easily achievable in Excel using a =SUMIFS formula and want to know the Python equivalent.
这很容易在 Excel 中使用 =SUMIFS 公式实现,并且想知道 Python 等价物。
+---------------+------------+------------+------------+------------+
| Account Names | 31/01/2022 | 28/02/2022 | 31/03/2022 | 30/04/2022 |
+---------------+------------+------------+------------+------------+
| Cash At Bank | 100 | 150 | 100 | 150 |
| Debtors | 50 | 50 | 50 | 100 |
| Inventory | 250 | 250 | 350 | 100 |
| PAYG Withheld | 50 | 50 | 10 | 150 |
+---------------+------------+------------+------------+------------+
Ideally, I'd want this to be as efficient as possible ie doesn't require loops.理想情况下,我希望它尽可能高效,即不需要循环。 I went the route of trying to do this using np.select as I've read this is one of the fastest methods, but had no luck.
我尝试使用 np.select 进行此操作,因为我读过这是最快的方法之一,但没有运气。 I get the following error:
我收到以下错误:
ValueError: shape mismatch: objects cannot be broadcast to a single shape
ValueError:形状不匹配:无法将对象广播到单个形状
EndDate = '31/03/2022'
Budget_Assets["MTD_Amount"] = np.select(condlist=[Budget_Assets.columns == EndDate],choicelist=[Budget_Assets[EndDate]],default=0)
For example, the value in the MTD_Amount column for Cash At Bank should be 100 and the YTD_Column will be 350 (sum of numbers from '31/01/2022' to '31/03/2022')例如,银行现金的 MTD_Amount 列中的值应为 100,YTD_Column 将为 350(从 '31/01/2022' 到 '31/03/2022' 的数字总和)
You can try sum(axis=1)
by slicing the datetime like columns to calculate YTD
and just use loc
to get MTD
您可以尝试
sum(axis=1)
通过像列一样切片日期时间来计算YTD
并使用loc
来获取MTD
EndDate = '31/03/2022'
date_cols = df.filter(regex='\d{2}/\d{2}/\d{4}')
date_cols.columns = pd.to_datetime(date_cols.columns, dayfirst=True)
df['YTD_Column'] = date_cols.loc[:, :pd.to_datetime(EndDate, dayfirst=True)].sum(axis=1)
df['MTD_Column'] = df[EndDate]
Account Names 31/01/2022 28/02/2022 31/03/2022 30/04/2022 YTD_Column MTD_Column
0 Cash At Bank 100 150 100 150 350 100
1 Debtors 50 50 50 100 150 50
2 Inventory 250 250 350 100 850 350
3 PAYG Withheld 50 50 10 150 110 10
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