Hi peeps, this question is closely related to this question . Instead of getting the name
of the Series, now I'd like to get the index
of each particular series. I've tried using the x.index
but it returns a list of indices instead of the index
of that particular cell.
In [14]: df = pd.DataFrame({
...: 'X': [1,2,3,4,5],
...: 'Y': [3,4,5,6,7],
...: 'Z': [5,6,7,8,9]}, index=['a', 'b', 'c', 'd', 'e'])
In [15]: df
Out[15]:
X Y Z
a 1 3 5
b 2 4 6
c 3 5 7
d 4 6 8
e 5 7 9
In [15]: df.apply(lambda x: (x.name, x.index), axis=1)
Out[15]:
a (a, [X, Y, Z])
b (b, [X, Y, Z])
c (c, [X, Y, Z])
d (d, [X, Y, Z])
e (e, [X, Y, Z])
dtype: object
I'd like to achieve as the following format. However, I'm not sure how to access the index
of that particular row. If I use x.index
, it returns the list of values of the indices.
As you can see in the example, I just want to get the value of each cell to be (index, column), value
X Y Z
a (a, X), 1 (a, Y), 3 (a, Z), 5
b (b, X), 2 (b, Y), 4 (b, Z), 6
c (c, X), 3 (c, Y), 5 (c, Z), 7
d (d, X), 4 (d, Y), 6 (d, Z), 8
e (e, X), 5 (e, Y), 7 (e, Z), 9
I've tried the following but it won't work since the index
is hardcoded. I've also checked the index docs but couldn't find any attributes that suits this needs.
In [35]: df.apply(lambda x: (x.name, x.index[0]), axis=1)
Out[35]:
a (a, X)
b (b, X)
c (c, X)
d (d, X)
e (e, X)
dtype: object
In [36]: df.apply(lambda x: (x.name, x.index[1]), axis=1)
Out[36]:
a (a, Y)
b (b, Y)
c (c, Y)
d (d, Y)
e (e, Y)
dtype: object
In [37]:
I'm thinking that it might be possible to iterate through each column and reassigns the values in it. However, is there a way to do this with apply()
? Thanks!
You can directly modify the row
Series and return the modified row
Series.
def convert(row):
for col in row.index:
row[col] = f'({row.name}, {col}), {row[col]}'
return row
df = df.apply(convert, axis=1)
print(df)
X Y Z
a (a, X), 1 (a, Y), 3 (a, Z), 5
b (b, X), 2 (b, Y), 4 (b, Z), 6
c (c, X), 3 (c, Y), 5 (c, Z), 7
d (d, X), 4 (d, Y), 6 (d, Z), 8
e (e, X), 5 (e, Y), 7 (e, Z), 9
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