Pandas: New column with values greater than 0 and operate with these values

user_15226448 0
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I have a big dataframe with more than 2500 columns but the structure is very similar than this:

      A     B     C     D      E
0     1     0     8     0      0
1     0     0     0     0      5
2     1     2     3     0      0
3     0     2     0     1      0

I need to detect all the columns with a value greater than 0 and I have done with this:

df['X'] = df.gt(0).dot(df.columns + ',')

And then I get this:

      A     B     C     D      E    X
0     1     0     8     0      0    A,C
1     0     0     0     0      5    E
2     1     2     9     0      0    A,B,C
3     0     3     0     1      0    B,D

The problem is that I don't need in 'X' the name of the columns but the value of the columns and I need to perform the following mathematical operation:

I want it to detect only the 2 lowest values and subtract the lowest value from the second lowest. In the case that there were not more than 2 values in 'X' it would be enough for me to show the value of the column only.

The final result in my example would be the following:

      A     B     C     D      E    X
0     1     0     8     0      0    7
1     0     0     0     0      5    5
2     1     2     9     0      0    1
3     0     3     0     1      0    2

Any idea how to solve it or any orientation?

pandas python
4 Answers

You can use apply with a function and have to specify axis=1 to apply the function row-wise. I have added a get_diff function without being 100% if that is exactly what you would need. I have also added an assign call to create a new dataframe with a new column name Xthat holds the needed value

def get_diff(in_:pd.Series) -> int | float:
    res = in_[in_ != 0].sort_values(ascending=False)
    if len(res) == 0:
        return 0 # Not sure if this is what you want to do in that case
    return res[-2] - res[-1] if len(res) > 1 else res[0]

df = df.assign(X=lambda df: df.apply(get_diff, axis=1))
user_140787580
user_178531390

We can do nsmallest then follow by np.ptp and condition for those row only have one value not equal to 0

df['new'] = df.apply(lambda x :  np.ptp(pd.Series.nsmallest(x[x!=0],2)) if sum(x!=0) != 1 else x[x!=0].iloc[0],axis=1)
Out[520]: 
0    7
1    5
2    1
3    1
dtype: int64

Or doing two steps

df['new'] = df[df.ne(0).sum(1)>1].apply(lambda x :  np.ptp(pd.Series.nsmallest(x,2)),axis=1)
df['new'].fillna(df.max(1),inplace=True)
df
Out[530]: 
   A  B  C  D  E  new
0  1  0  8  0  0  7.0
1  0  0  0  0  5  5.0
2  1  2  3  0  0  1.0
3  0  2  0  1  0  1.0
user_79645270

One option is to identify values greater than 0, before sorting and trimming to get the values for X:

temp = df.where(df.gt(0), np.nan, axis = 0)
# you could use `np.partition` instead
# which should be more efficient
temp = np.sort(temp, axis = 1)[:, :2]
temp = np.nan_to_num(temp)
temp = np.ptp(temp, axis = 1)
df.assign(X = temp)

   A  B  C  D  E    X
0  1  0  8  0  0  7.0
1  0  0  0  0  5  5.0
2  1  2  3  0  0  1.0
3  0  2  0  1  0  1.0
user_71757130
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