英文:
Convert pandas column values based on groupings of values
问题
我有一个带有值1.0，2.0，3.0，4.0和5.0的pandas列，如下所示：
0 5.0
1 2.0
2 3.0
3 3.0
4 5.0
...
1039 5.0
1040 1.0
1041 2.0
1042 4.0
1043 1.0
我想将值为1.0或2.0的行全部赋值为1.0，将3.0和4.0变为2.0，将5.0变为3.0。根据这些分组，我该如何重新分配这些值。我最初考虑使用np.where()，但现在不确定如何用np.where()逻辑实现，因为那似乎更适合转换为二进制变量。也许只是使用.loc()进行掩码处理？
谢谢。
英文:
I have a pandas columns with values 1.0, 2.0, 3.0, 4.0, and 5.0 like below:
0 5.0
1 2.0
2 3.0
3 3.0
4 5.0
...
1039 5.0
1040 1.0
1041 2.0
1042 4.0
1043 1.0
I want rows with values 1.0 or 2.0 to all have a value of 1.0, 3.0 and 4.0 to become 2.0, and 5.0 to become 3.0. How could I re-assign the values based on these groupings. I was thinking np.where() at first but now I'm not sure how to implement that with np.where() logic because that seems like it would be better suited for conversion to a binary variable. Maybe just masking with .loc()?
Thanks.
答案1
得分: 1
根据你的模式，进行简单的算术运算：加1，然后进行整除以2：
df['new'] = df['col'].add(1).floordiv(2)
或者使用 cut：
df['new'] = pd.cut(df['col'], [0, 2, 4, 6], labels=[1, 2, 3])
请注意，cut 会给你一个分类类型。
示例：
col new
0 5.0 3.0
1 2.0 1.0
2 3.0 2.0
3 3.0 2.0
4 5.0 3.0
1039 5.0 3.0
1040 1.0 1.0
1041 2.0 1.0
1042 4.0 2.0
1043 1.0 1.0
英文:
Given your pattern, use simple arithmetics: add 1, get the floor division by 2:
df['new'] = df['col'].add(1).floordiv(2)
Or use cut:
df['new'] = pd.cut(df['col'], [0, 2, 4, 6], labels=[1, 2, 3])
Note that cut will give you a categorical type.
Example:
col new
0 5.0 3.0
1 2.0 1.0
2 3.0 2.0
3 3.0 2.0
4 5.0 3.0
1039 5.0 3.0
1040 1.0 1.0
1041 2.0 1.0
1042 4.0 2.0
1043 1.0 1.0
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