Similar questions have been asked already but I still can’t come up with a solution.
I have a pandas Dataframe of such a shape:
1 0 0 0 0
1 0 0 0 0
1 0 0 0 0
1 0 0 0 0
-1 0 0 0 0
-1 0 0 0 0
-1 0 0 0 0
-1 0 0 0 0
And I want to make it into one that looks like this:
1 1 0 0 0
1 0 1 0 0
1 0 0 1 0
1 0 0 0 1
-1 1 0 0 0
-1 0 1 0 0
-1 0 0 1 0
-1 0 0 0 1
I have tried np.fill_diagonal(df.values,1) but that didn’t work.
>Solution :
Assuming the first column is indeed a column and that you want to ignore it.
Use numpy:
n_ignore = 1
a = df.to_numpy()
idx = np.arange(a.shape[0])
col = np.arange(a.shape[0])%(a.shape[1]-n_ignore)+n_ignore
a[idx, col] = 1
df2 = pd.DataFrame(a, index=df.index, columns=df.columns)
If you rather want to use fill_diagonal per group:
def fill_diag(g):
a = g.to_numpy()
np.fill_diagonal(a, 1)
return pd.DataFrame(a, index=g.index, columns=g.columns)
(df.set_index(0)
.groupby(level=0, group_keys=False).apply(fill_diag)
.reset_index()
)
Output:
0 1 2 3 4
0 1 1 0 0 0
1 1 0 1 0 0
2 1 0 0 1 0
3 1 0 0 0 1
4 -1 1 0 0 0
5 -1 0 1 0 0
6 -1 0 0 1 0
7 -1 0 0 0 1