Pandas - 熊猫默认情况下
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Pandas - Case when & default in pandas
提问by Tom J Muthirenthi
I have the below case statement in python,
我在python中有以下case语句，
pd_df['difficulty'] = 'Unknown'
pd_df['difficulty'][(pd_df['Time']<30) & (pd_df['Time']>0)] = 'Easy'
pd_df['difficulty'][(pd_df['Time']>=30) & (pd_df['Time']<=60)] = 'Meduim'
pd_df['difficulty'][pd_df['Time']>60] = 'Hard'
But when I run the code, it throws an error.
但是当我运行代码时，它会引发错误。
A value is trying to be set on a copy of a slice from a DataFrame
回答by cs95
Option 1
For performance, use a nested np.wherecondition. For the condition, you can just use pd.Series.between, and the default value will be inserted accordingly.
选项 1
为了提高性能，请使用嵌套np.where条件。对于条件，您可以使用pd.Series.between，并相应地插入默认值。
pd_df['difficulty'] = np.where(
pd_df['Time'].between(0, 30, inclusive=False),
'Easy',
np.where(
pd_df['Time'].between(0, 30, inclusive=False), 'Medium', 'Unknown'
)
)
Option 2
Similarly, using np.select, this gives more room for adding conditions:
选项 2
同样，使用np.select，这为添加条件提供了更多空间：
pd_df['difficulty'] = np.select(
[
pd_df['Time'].between(0, 30, inclusive=False),
pd_df['Time'].between(30, 60, inclusive=True)
],
[
'Easy',
'Medium'
],
default='Unknown'
)
Option 3
Another performant solution involves loc:
选项 3
另一种高性能解决方案包括loc：
pd_df['difficulty'] = 'Unknown'
pd_df.loc[pd_df['Time'].between(0, 30, inclusive=False), 'difficulty'] = 'Easy'
pd_df.loc[pd_df['Time'].between(30, 60, inclusive=True), 'difficulty'] = 'Medium'
