how to union multiple columns from one panda data frame into one series?

Question

I have a data frame that has actually more than 20 columns. The example below give 4 columns. each column has equal number of rows. How to convert to a new dataframe(exmaple shown below) which has only one columns. I will use the new combined dataframe to calculate some metrics. How do I write a neat and efficient code for this? Thank you so much!

data={"col1":[1,2,3,5], "col_2":[6,7,8,9], "col_3":[10,11,12,14], "col_4":[7,8,9,10]}
pd.DataFrame.from_dict(data)

Answer 1

If you start from your dictionary, use itertools.chain:

data={"col1":[1,2,3,5], "col_2":[6,7,8,9], "col_3":[10,11,12,14], "col_4":[7,8,9,10]}

from itertools import chain
pd.DataFrame({'col': chain.from_iterable(data.values())})

Else, ravel the underlying numpy array:

df = pd.DataFrame.from_dict(data)
pd.Series(df.to_numpy().ravel('F'))

Output:

0      1
1      2
2      3
3      5
4      6
5      7
6      8
7      9
8     10
9     11
10    12
11    14
12     7
13     8
14     9
15    10
dtype: int64

Depending on the computation to perform, you might not even need to instantiate a DataFrame/Series and stick to the array:

a = df.to_numpy().ravel('F')

Output: array([ 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 7, 8, 9, 10])