How to convert a pandas dataframe into one dimensional array?

Question

I have a dataframe X. I want to convert it into 1D array with only 5 elements. One way of doing it is converting the inner arrays to lists. How can I do that?

      0     1   2          3           4           5
0   1622    95  1717   85.278544    1138.964373 1053.685830
1   62     328  390    75.613900    722.588235  646.974336
2   102    708  810    75.613900    800.916667  725.302767
3   102    862  964    75.613900    725.870370  650.256471
4   129    1380 1509   75.613900    783.711111  708.097211

val = X.values will give a numpy array. I want to convert the inner elements of the array to list. How can I do that? I tried this but failed

M = val.values.tolist()
A = np.array(M,dtype=list)
N = np.array(M,dtype=object)

Nope. I want a one dimensional array with only 5 elements not series. See @Divakar's answer. — Bharath M Shetty
– Bharath M Shetty, Commented Jul 14, 2017 at 9:41

Divakar · Accepted Answer · 2017-07-14 09:35:32Z

8

Here's one approach to have each row as one list to give us a 1D array of lists -

In [231]: df
Out[231]: 
      0     1     2          3            4            5
0  1622    95  1717  85.278544  1138.964373  1053.685830
1    62   328   390  75.613900   722.588235   646.974336
2   102   708   810  75.613900   800.916667   725.302767
3   102   862   964  75.613900   725.870370   650.256471
4   129  1380  1509  75.613900   783.711111   708.097211

In [232]: out = np.empty(df.shape[0], dtype=object)

In [233]: out[:] = df.values.tolist()

In [234]: out
Out[234]: 
array([list([1622.0, 95.0, 1717.0, 85.278544, 1138.964373, 1053.6858300000001]),
       list([62.0, 328.0, 390.0, 75.6139, 722.5882349999999, 646.974336]),
       list([102.0, 708.0, 810.0, 75.6139, 800.916667, 725.302767]),
       list([102.0, 862.0, 964.0, 75.6139, 725.87037, 650.256471]),
       list([129.0, 1380.0, 1509.0, 75.6139, 783.7111110000001, 708.097211])], dtype=object)

In [235]: out.shape
Out[235]: (5,)

In [236]: out.ndim
Out[236]: 1

answered Jul 14, 2017 at 9:35

Divakar

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Bharath M Shetty Over a year ago

Thanks a lot @Divakar this is what I wanted

gbajson · Accepted Answer · 2017-07-14 09:40:16Z

0

Have you tried to use df.as_matrix() and then join rows?

EDIT:

Example:

L=[]
for m in df.as_matrix().tolist():
    L += m

edited Jul 14, 2017 at 9:40

answered Jul 14, 2017 at 9:31

gbajson

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Bharath M Shetty Over a year ago

I wanted a numpy array

zerocool · Accepted Answer · 2020-09-15 08:12:37Z

0

If it has only one column, you can try this

op_col = []
for i in df_name['Column_name']:
    op_col.append(i)
print(op_col)

answered Sep 15, 2020 at 8:12

zerocool

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