I wish to flatten(I am not sure whether is the correct thing to call it flatten) the columns with rows. multiple rows into single row with column change to column_rows I have a dataframe as below:
data = {"a":[3,4,5,6], "b":[88,77,66,55], "c":["ts", "new", "thing", "here"], "d":[9.1,9.2,9.0,8.4]} df = pd.DataFrame(data)
my current output is:
a b c d 0 3 88 ts 9.1 1 4 77 new 9.2 2 5 66 thing 9.0 3 6 55 here 8.4
my expected otput:
a_0 a_1 a_2 a_3 b_0 b_1 b_2 b_3 c_0 c_1 c_2 c_3 d_0 d_1 d_2 d_3 0 3 4 5 6 88 77 66 55 ts new thing here 9.1 9.2 9.0 8.4
from shape (4,4) to (1, 16)
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Answer
Update let’s use the walrus operator
new in Python 3.8 to create a one-liner:
(df_new := df.unstack().to_frame().T).set_axis( [f"{i}_{j}" for i, j in df_new.columns], axis=1 )
Output:
a_0 a_1 a_2 a_3 b_0 b_1 b_2 b_3 c_0 c_1 c_2 c_3 d_0 d_1 d_2 d_3 0 3 4 5 6 88 77 66 55 ts new thing here 9.1 9.2 9.0 8.4
Try this, using unstack
, to_frame
and transpose. Next, flatten the column headers using list comprehension:
df_new = df.unstack().to_frame().T df_new.columns = [f'{i}_{j}' for i, j in df_new.columns] df_new
Output:
a_0 a_1 a_2 a_3 b_0 b_1 b_2 b_3 c_0 c_1 c_2 c_3 d_0 d_1 d_2 d_3 0 3 4 5 6 88 77 66 55 ts new thing here 9.1 9.2 9.0 8.4