R 序列 function 在 Python

Question

pandas version: 1.2

I am trying to take a python pandas dataframe column pandas and create the same type of logic as in R that would be

ss=sequence(df$los)

Which produces for the first two records

[1]  1  2  3  4  5  1  2  3  4  5 

Example dataframe:

df = pd.DataFrame([('test', 5), ('t2', 5), ('t3', 2), ('t4', 6)],
                  columns=['first', 'los'])
df

  first  los
0  test    5
1    t2    5
2    t3    2
3    t4    6

So the first row is sequenced 1-5 and second row is sequenced 1-5 and third row is sequenced 1-2 etc. In R this becomes one sequenced list. I would like that is python.

What I have been able to do is.

ss = df['los']
ss.apply(lambda x: np.array(range(1, x)))
18                          [1, 2, 3, 4, 5]
90                          [1, 2, 3, 4, 5]
105                                   [1,2]
106                      [1, 2, 3, 4, 5, 6]

Which is close but then I need to combine it into a single pd.Series so that it should be:

[1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 1, 2, 3, 4, 5, 6]

Answer 1

You can just use concatenate :

np.concatenate([np.arange(x)+1 for x in df['los']])

Output:

array([1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 1, 2, 3, 4, 5, 6])

Answer 2

Use explode() :

df.los.apply(lambda x: np.arange(1, x+1)).explode().tolist()

Output:

[1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 1, 2, 3, 4, 5, 6]

Note - you can skip the ss assignment step, and use np.arange to streamline a bit.