How to convert Dataframe into Series?

user_4684861 0
Viewed 415

I want to convert N columns into one series. How to do it effectively?

Input:

    0   1   2   3
0  64  98  47  58
1  80  94  81  46
2  18  43  79  84
3  57  35  81  31

Expected Output:

0     64
1     80
2     18
3     57
4     98
5     94
6     43
7     35
8     47
9     81
10    79
11    81
12    58
13    46
14    84
15    31
dtype: int64

So Far I tried:

print df[0].append(df[1]).append(df[2]).append(df[3]).reset_index(drop=True)

I'm not satisfied with my solution, moreover it won't work for dynamic columns. Please help me to find a better approach.

pandas series dataframe python
6 Answers

You can use unstack 

pd.Series(df.unstack().values)
user_17622110

you need np.flatten

pd.Series(df.values.flatten(order='F'))

out[]
0     64
1     80
2     18
3     57
4     98
5     94
6     43
7     35
8     47
9     81
10    79
11    81
12    58
13    46
14    84
15    31
dtype: int64
user_54395460

Here's yet another short one.

>>> pd.Series(df.values.ravel(order='F'))                                                                                                               
>>> 
0     64
1     80
2     18
3     57
4     98
5     94
6     43
7     35
8     47
9     81
10    79
11    81
12    58
13    46
14    84
15    31
dtype: int64
user_36200030

Use pd.melt() - 

df.melt()['value']

Output

0     64
1     80
2     18
3     57
4     98
5     94
6     43
7     35
8     47
9     81
10    79
11    81
12    58
13    46
14    84
15    31
Name: value, dtype: int64
user_40980130 
df.T.stack().reset_index(drop=True)

Out:

0     64
1     80
2     18
3     57
4     98
5     94
6     43
7     35
8     47
9     81
10    79
11    81
12    58
13    46
14    84
15    31
dtype: int64
user_82080060

You can also use Series class and .values attribute:

pd.Series(df.values.T.flatten())

Output:

0     64
1     80
2     18
3     57
4     98
5     94
6     43
7     35
8     47
9     81
10    79
11    81
12    58
13    46
14    84
15    31
dtype: int64
user_20996070
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