How to convert Dataframe into Series?
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4
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.
python pandas dataframe series
edited Nov 15 '18 at 11:11
today
11.8k22542
asked Nov 15 '18 at 10:49
Mohamed Thasin ahMohamed Thasin ah
4,09932042
add a comment |
4
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.
python pandas dataframe series
edited Nov 15 '18 at 11:11
today
11.8k22542
asked Nov 15 '18 at 10:49
Mohamed Thasin ahMohamed Thasin ah
4,09932042
add a comment |
4
4
4
1
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.
python pandas dataframe series
edited Nov 15 '18 at 11:11
today
11.8k22542
asked Nov 15 '18 at 10:49
Mohamed Thasin ahMohamed Thasin ah
4,09932042
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.
python pandas dataframe series
python pandas dataframe series
edited Nov 15 '18 at 11:11
today
11.8k22542
asked Nov 15 '18 at 10:49
Mohamed Thasin ahMohamed Thasin ah
4,09932042
edited Nov 15 '18 at 11:11
today
11.8k22542
asked Nov 15 '18 at 10:49
Mohamed Thasin ahMohamed Thasin ah
4,09932042
edited Nov 15 '18 at 11:11
today
11.8k22542
edited Nov 15 '18 at 11:11
today
11.8k22542
edited Nov 15 '18 at 11:11
today
11.8k22542
11.8k22542
asked Nov 15 '18 at 10:49
Mohamed Thasin ahMohamed Thasin ah
4,09932042
asked Nov 15 '18 at 10:49
Mohamed Thasin ahMohamed Thasin ah
4,09932042
asked Nov 15 '18 at 10:49
Mohamed Thasin ahMohamed Thasin ah
4,09932042
4,09932042
add a comment |
add a comment |
6 Answers
6
active
oldest
votes
1
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
answered Nov 15 '18 at 11:04
todaytoday
11.8k22542
add a comment |
3
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
answered Nov 15 '18 at 11:05
pydpyd
2,13211229
add a comment |
2
You can use unstack
pd.Series(df.unstack().values)
answered Nov 15 '18 at 10:55
MedAliMedAli
7,28174384
add a comment |
2
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
answered Nov 15 '18 at 11:05
timgebtimgeb
51.4k126794
add a comment |
1
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
answered Nov 15 '18 at 10:53
Vivek KalyanaranganVivek Kalyanarangan
5,1141830
add a comment |
1
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
answered Nov 15 '18 at 10:57
Naga KiranNaga Kiran
2,5541617
add a comment |
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6 Answers
6
active
oldest
votes
6 Answers
6
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
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
answered Nov 15 '18 at 11:04
todaytoday
11.8k22542
add a comment |
1
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
answered Nov 15 '18 at 11:04
todaytoday
11.8k22542
add a comment |
1
1
1
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
answered Nov 15 '18 at 11:04
todaytoday
11.8k22542
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
answered Nov 15 '18 at 11:04
todaytoday
11.8k22542
answered Nov 15 '18 at 11:04
todaytoday
11.8k22542
answered Nov 15 '18 at 11:04
todaytoday
11.8k22542
answered Nov 15 '18 at 11:04
todaytoday
11.8k22542
11.8k22542
add a comment |
add a comment |
3
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
answered Nov 15 '18 at 11:05
pydpyd
2,13211229
add a comment |
3
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
answered Nov 15 '18 at 11:05
pydpyd
2,13211229
add a comment |
3
3
3
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
answered Nov 15 '18 at 11:05
pydpyd
2,13211229
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
answered Nov 15 '18 at 11:05
pydpyd
2,13211229
answered Nov 15 '18 at 11:05
pydpyd
2,13211229
answered Nov 15 '18 at 11:05
pydpyd
2,13211229
answered Nov 15 '18 at 11:05
pydpyd
2,13211229
2,13211229
add a comment |
add a comment |
2
You can use unstack
pd.Series(df.unstack().values)
answered Nov 15 '18 at 10:55
MedAliMedAli
7,28174384
add a comment |
2
You can use unstack
pd.Series(df.unstack().values)
answered Nov 15 '18 at 10:55
MedAliMedAli
7,28174384
add a comment |
2
2
2
You can use unstack
pd.Series(df.unstack().values)
answered Nov 15 '18 at 10:55
MedAliMedAli
7,28174384
You can use unstack
pd.Series(df.unstack().values)
answered Nov 15 '18 at 10:55
MedAliMedAli
7,28174384
edited Nov 15 '18 at 10:59
answered Nov 15 '18 at 10:55
MedAliMedAli
7,28174384
answered Nov 15 '18 at 10:55
MedAliMedAli
7,28174384
answered Nov 15 '18 at 10:55
MedAliMedAli
7,28174384
7,28174384
add a comment |
add a comment |
2
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
answered Nov 15 '18 at 11:05
timgebtimgeb
51.4k126794
add a comment |
2
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
answered Nov 15 '18 at 11:05
timgebtimgeb
51.4k126794
add a comment |
2
2
2
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
answered Nov 15 '18 at 11:05
timgebtimgeb
51.4k126794
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
answered Nov 15 '18 at 11:05
timgebtimgeb
51.4k126794
edited Nov 15 '18 at 11:07
answered Nov 15 '18 at 11:05
timgebtimgeb
51.4k126794
answered Nov 15 '18 at 11:05
timgebtimgeb
51.4k126794
answered Nov 15 '18 at 11:05
timgebtimgeb
51.4k126794
51.4k126794
add a comment |
add a comment |
1
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
answered Nov 15 '18 at 10:53
Vivek KalyanaranganVivek Kalyanarangan
5,1141830
add a comment |
1
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
answered Nov 15 '18 at 10:53
Vivek KalyanaranganVivek Kalyanarangan
5,1141830
add a comment |
1
1
1
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
answered Nov 15 '18 at 10:53
Vivek KalyanaranganVivek Kalyanarangan
5,1141830
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
answered Nov 15 '18 at 10:53
Vivek KalyanaranganVivek Kalyanarangan
5,1141830
answered Nov 15 '18 at 10:53
Vivek KalyanaranganVivek Kalyanarangan
5,1141830
answered Nov 15 '18 at 10:53
Vivek KalyanaranganVivek Kalyanarangan
5,1141830
answered Nov 15 '18 at 10:53
Vivek KalyanaranganVivek Kalyanarangan
5,1141830
5,1141830
add a comment |
add a comment |
1
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
answered Nov 15 '18 at 10:57
Naga KiranNaga Kiran
2,5541617
add a comment |
1
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
answered Nov 15 '18 at 10:57
Naga KiranNaga Kiran
2,5541617
add a comment |
1
1
1
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
answered Nov 15 '18 at 10:57
Naga KiranNaga Kiran
2,5541617
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
answered Nov 15 '18 at 10:57
Naga KiranNaga Kiran
2,5541617
answered Nov 15 '18 at 10:57
Naga KiranNaga Kiran
2,5541617
answered Nov 15 '18 at 10:57
Naga KiranNaga Kiran
2,5541617
answered Nov 15 '18 at 10:57
Naga KiranNaga Kiran
2,5541617
2,5541617
add a comment |
add a comment |
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