从项列表中创建特定Shape的pandas数据框

Question

I have a pandas dataframe fa specific size say:

   ID  FACTOR    LEVEL
  160  SPM       P                       
  159  SPM2      S                         
  851  ABS       K                        
  415  ABS       P                       
  202  ABS       P 
  205  ABS2      Q
  207  AQE       T 

What I want to do is that I have a list say of two items: X = ['GAB', 'YER']

So what I want to do is that I want to distribute this items in the list as a new column say NewCol with a specific frequency as the same size of the rows in the dataframe. So in this case say I have 7 rows and 2 items and 7/2 integer quotient is 3, so I want to put the first item in 3 rows and the next one in the remaining 4 rows. So the output should like :

         ID  FACTOR    LEVEL  NewCol
        160  SPM       P        GAB               
        159  SPM2      S        GAB                 
        851  ABS       K        GAB               
        415  ABS       P        YER               
        202  ABS       P        YER
        205  ABS2      Q        YER
        207  AQE       T        YER

So what I was able to do so far is to create aa series of two elements using

 df_s = pd.DataFrame(X)

which is giving me

     0
    GAB
    YER

However, I am not able to create a series out of this of the shape of the dataframe with the values in X and neither I am able to find a way to distribute it. I am still working on it, but some help/hints from the experts will be appreciated.

Answer 1

Use np.repeat and assign the result to a new column:

arr = np.repeat(X, len(df) // len(X))
df['NewCol'] = pd.Series(arr, index=df.index[:len(arr)])
df

    ID FACTOR LEVEL NewCol
0  160    SPM     P    GAB
1  159   SPM2     S    GAB
2  851    ABS     K    GAB
3  415    ABS     P    YER
4  202    ABS     P    YER
5  205   ABS2     Q    YER
6  207    AQE     T    NaN

If you want to fill the last NaN, use,

df['NewCol'] = df['NewCol'].ffill()

Answer 2

Some different idea using clip

import numpy as np 
n=len(X)
m=len(df)//n 
s=pd.Series(np.arange(len(df))//m).clip_upper(n-1)
df['New']=s.map(dict(zip(s.unique(),X)))
df
Out[278]: 
    ID FACTOR LEVEL  New
0  160    SPM     P  GAB
1  159   SPM2     S  GAB
2  851    ABS     K  GAB
3  415    ABS     P  YER
4  202    ABS     P  YER
5  205   ABS2     Q  YER
6  207    AQE     T  YER