在条件上设置pandas数据帧的值

Question

I can't find a similar question for this query. However, I have a pandas dataframe where I want to use two of the columns to make conditional and if its true, replace the values in one of these columns.

For example. One of my columns is the 'itemname' and the other is the 'value'. the 'itemname' may be repeated many times. I want to check for each 'itemname', if all other items with the same name have value 0, then replace these 'value' with 100.

I know this should be simple, however I cannot get my head around it.

Just to make it clearer, here

    itemname value
0      a       0
1      b       100
2      c       0
3      a       0
3      b       75
3      c       90

I would like my statement to change this data frame to

    itemname value
0      a       100
1      b       100
2      c       0
3      a       100
3      b       75
3      c       90

Hope that makes sense. I check if someone else has asked something similar and couldnt find something in this case.

Answer 1

You can use GroupBy + transform to create a mask. Then assign via pd.DataFrame.loc and Boolean indexing:

mask = df.groupby('itemname')['value'].transform(lambda x: x.eq(0).all())
df.loc[mask.astype(bool), 'value'] = 100

print(df)

  itemname  value
0        a    100
1        b    100
2        c      0
3        a    100
3        b     75
3        c     90

Answer 2

Using transform with any :

df.loc[~df.groupby('itemname').value.transform('any'), 'value'] = 100

Using numpy.where :

s = ~df.groupby('itemname').value.transform('any')
df.assign(value=np.where(s, 100, df.value))

Using addition and multiplication:

s = ~df.groupby('itemname').value.transform('any')
df.assign(value=df.value + (100 * s))

Both produce the correct output, however, np.where and the final solution don't modify the DataFrame in place:

  itemname  value
0        a    100
1        b    100
2        c      0
3        a    100
3        b     75
3        c     90

---

Explanation

~df.groupby('itemname').value.transform('any')

0     True
1    False
2    False
3     True
3    False
3    False
Name: value, dtype: bool

Since 0 is a falsey value, we can use any , and negate the result, to find groups where all values are equal to 0 .

Answer 3

If all your values are positive or 0

Could use transform with sum and check if 0:

m = (df.groupby('itemname').transform('sum') == 0)['value']
df.loc[m, 'value'] = 100