Pandas: multiple columns into one column

Question

I have the following data (2 columns, 4 rows):

Column 1: A, B, C, D

Column 2: E, F, G, H

I am trying to combine columns into one column to look like this (1 column, 8 rows):

Column 3: A, B, C, D, E, F, G, H

I use pandas DataFrame and tried to use different functions without success (append, concat, etc.). Any help would be appreciated!

Answer 1

You could do it this way, although I'm not sure why you want to do this:

In [227]:

df = pd.DataFrame({'Column 1':['A', 'B', 'C', 'D'],'Column 2':['E', 'F', 'G', 'H']})
df
Out[227]:
  Column 1 Column 2
0        A        E
1        B        F
2        C        G
3        D        H

[4 rows x 2 columns]

In [228]:

df['Column 1'].append(df['Column 2']).reset_index(drop=True)
Out[228]:
0    A
1    B
2    C
3    D
4    E
5    F
6    G
7    H
dtype: object

Answer 2

, , - . , , . , , - , - :

import itertools as it, pandas as pd
df = pd.DataFrame({1:['a','b','c','d'],2:['e','f','g','h']})
sorted(it.chain(*df.values))
# -> ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']

Answer 3

You can smooth the values ​​in the direction of the column with ravel, much faster.

In [1238]: df
Out[1238]:
  Column 1 Column 2
0        A        E
1        B        F
2        C        G
3        D        H

In [1239]: pd.Series(df.values.ravel('F'))
Out[1239]:
0    A
1    B
2    C
3    D
4    E
5    F
6    G
7    H
dtype: object

---

More details

Medium

In [1245]: df.shape
Out[1245]: (4000, 2)

In [1246]: %timeit pd.Series(df.values.ravel('F'))
10000 loops, best of 3: 86.2 µs per loop

In [1247]: %timeit df['Column 1'].append(df['Column 2']).reset_index(drop=True)
1000 loops, best of 3: 816 µs per loop

Big

In [1249]: df.shape
Out[1249]: (40000, 2)

In [1250]: %timeit pd.Series(df.values.ravel('F'))
10000 loops, best of 3: 87.5 µs per loop

In [1251]: %timeit df['Column 1'].append(df['Column 2']).reset_index(drop=True)
100 loops, best of 3: 1.72 ms per loop

Answer 4

The trick is to use stack()

df.stack().reset_index()

   level_0   level_1  0
0        0  Column 1  A
1        0  Column 2  E
2        1  Column 1  B
3        1  Column 2  F
4        2  Column 1  C
5        2  Column 2  G
6        3  Column 1  D
7        3  Column 2  H