如何根据条件将一列的值设置为另一列

Question

我有以下 df:

subscription|amount| Total
a           |500   |
x           | 0    | 5000
x           |7500  | 5000
y           |7500  | 5000
y           | 0    | 5000
z           |7500  | 5000
z           | 0    | 5000
b           | 0    | 
b           |1000  |

我想将 'total' 的值转移到 'amount'，其中 'amount' 列中已经有值，当然 'total' 中也有值。

我过滤了：

df.loc[(df['total'].notnull()) & (df['amount']!=0)]

但无法将总计 (5000) 的值转移到金额。

期望的输出：

subscription|amount| Total
a           |500   |
x           | 0    | 5000
x           |5000  | 5000
y           |5000  | 5000
y           | 0    | 5000
z           |5000  | 5000
z           | 0    | 5000
b           | 0    | 
b           |1000  |

Answer 1

还有更多的解决办法，如果column改成float没问题- DataFrame.loc or numpy.where:

mask = (df['Total'].notnull()) & (df['amount']!=0)

df.loc[mask, 'amount'] = df['Total']

df['amount'] = np.where(mask, df['Total'], df['amount'])

print (df)
  subscription  amount   Total
0            a   500.0     NaN
1            x     0.0  5000.0
2            x  5000.0  5000.0
3            y  5000.0  5000.0
4            y     0.0  5000.0
5            z  5000.0  5000.0
6            z     0.0  5000.0
7            b     0.0     NaN
8            b  1000.0     NaN

没有将整数列更改为使用 Series.mask, Series.where 浮动或使用 np.where 转换为 integers 的解决方案：

df['amount'] = df['amount'].mask(mask, df['Total'])

df['amount'] = df['amount'].where(~mask, df['Total'])

df['amount'] = df['amount'].where(~mask, df['Total']).astype(int)

print (df)
  subscription  amount   Total
0            a     500     NaN
1            x       0  5000.0
2            x    5000  5000.0
3            y    5000  5000.0
4            y       0  5000.0
5            z    5000  5000.0
6            z       0  5000.0
7            b       0     NaN
8            b    1000     NaN

如何根据条件将一列的值设置为另一列

how to set the value from a column to another based on a condition

python

dataframe

pandas

pandas-loc