Converting particular columns in dataframe to a numpy array and merging with original data frame

Question

I have a dataframe like this

Company_id  year  dummy_1 dummy_2 dummy_3 dummy_4 dummy_5
1           1990   1       0        1        1      1
1           1991   0       0        1        1      0
1           1992   0       0        1        1      0
1           1993   1       0        1        1      0
1           1994   0       1        1        1      0
1           1995   0       0        1        1      0
1           1996   0       0        1        1      1

I need last 5 columns as a vector and then append it the original dataframe. I know I can slices columns and creata a matrix such as:

df.as_matrix(columns=[df[-5:]])

Here is the resulting output that I want :

 Company_id  year  dummy_1 dummy_2 dummy_3 dummy_4 dummy_5   vector
    1           1990   1       0        1        1      1       [1, 0, 1, 1, 1]
    1           1991   0       0        1        1      0       [0, 0, 1, 1, 0]
    1           1992   0       0        1        1      0       [0, 0, 1, 1, 0]
    1           1993   1       0        1        1      0       [1, 0, 1, 1, 0]
    1           1994   0       1        1        1      0       [0, 1, 1, 1, 0]
    1           1995   0       0        1        1      0       [0, 0, 1, 1, 0]
    1           1996   0       0        1        1      1       [0, 0, 1, 1, 1]

But then how could I add it as an array to original dataset?

Sure, adding now

Dogukan Yılmaz
– Dogukan Yılmaz

2018-08-30 13:41:19 +00:00
Commented Aug 30, 2018 at 13:41 — Dogukan Yılmaz
– Dogukan Yılmaz, Commented Aug 30, 2018 at 13:41

jezrael · Accepted Answer · 2018-08-30 13:47:20Z

2

I believe need select last columns by iloc and assign new column converted to numpy arrays and lists:

df = df.assign(new = df.iloc[:, -5:].values.tolist())
print (df)
   Company_id  year  dummy_1  dummy_2  dummy_3  dummy_4  dummy_5  \
0           1  1990        1        0        1        1        1   
1           1  1991        0        0        1        1        0   
2           1  1992        0        0        1        1        0   
3           1  1993        1        0        1        1        0   
4           1  1994        0        1        1        1        0   
5           1  1995        0        0        1        1        0   
6           1  1996        0        0        1        1        1   

               new  
0  [1, 0, 1, 1, 1]  
1  [0, 0, 1, 1, 0]  
2  [0, 0, 1, 1, 0]  
3  [1, 0, 1, 1, 0]  
4  [0, 1, 1, 1, 0]  
5  [0, 0, 1, 1, 0]  
6  [0, 0, 1, 1, 1]

edited Aug 30, 2018 at 13:47

answered Aug 30, 2018 at 13:41

jezrael

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2 Comments

Dogukan Yılmaz Over a year ago

Sorry, should I say something toarray(), because I will use them for comparsion later on

jezrael Over a year ago

@DogukanYılmaz - Then need df['new'] = df['new'].apply(np.array) ?

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