Pandas .apply with conditional if in different columns

Question

我有一个数据框如下。我正在尝试检查 Liq_Factor 中是否有 nan，如果有，则输入 1 否则除以 use/TW。测试列中的结果。

+---+------------+------------+--------+--------+--------+
| 1 |            | Liq_Factor | Zscire | Use    | Tw     |
| 2 | 01/10/2020 | 36.5       | 44     | 43.875 | 11.625 |
| 3 | 02/10/2020 | Nan        | 43.625 | 13.625 | 33.25  |
| 4 | 03/10/2020 | 6.125      | 47.875 | 22.5   | 4.625  |
| 5 | 04/10/2020 | Nan        | 34.25  | 37.125 | 36     |
| 6 | 05/10/2020 | 43.875     | 17.375 | 5.5    | 36.25  |
| 7 | 06/10/2020 | 40         | 14.125 | 21.125 | 14.875 |
| 8 | 07/10/2020 | 42.25      | 44.75  | 21.25  | 31.75  |
+---+------------+------------+--------+--------+--------+

我想知道我是否可以在

的意义上使用 .apply

DF1['Testing']=(DF1['Liq_Factor'].apply(lambda x: x=1 if pd.isna(DF1['Zscore']) else DF1['Use']/DF1['Tw'])

你能帮忙吗？

谢谢， H

Answer 1

您可以使用 apply 或另一个替代方法是 numpy 的 where 函数：

df['Liq_Factor'] = np.where(df['Liq_Factor'] == np.Nan, 1, df['Use']/df['TW'])

根据您在下方的评论，您可以执行以下操作：

# create another column with the calculation
df['calc'] = (1/3)* df['ATV']/df['TW']*100000000
# create two rules (you can use one rule and then the opposite)
mask_0 = (df['calc'] < 1)
mask_1 = (df['calc'] > 1)
# change result value by condition
df.loc[mask_0, 'Liq Factor'] = df['calc']
df.loc[mask_1, 'Liq Factor'] = 1

Answer 2

使用下面的代码-

df['Testing']=df.apply(lambda x: 1 if x['Liq_Factor']=='Nan'  else x['Use']/x['Tw'], axis=1)

根据评论部分的变化

df['Testing']=df.apply(lambda x: 1 if x['Liq_Factor']=='Nan'  else min(x['Use']/x['Tw'],1), axis=1)

Pandas .apply with conditional if in different columns

Pandas .apply with conditional if in different columns

python

apply

pandas