Several time series to DataFrame

Question

I have problem merging several time series to a common DataFrame. The example code I'm using: ``` import pandas import datetime import numpy as np start = datetime.datetime(2001, 1, 1) end = datetime.datetime(2001, 1, 10) dates = pandas.date_range(start, end) serie_1 = pandas.Series(np.random.randn(10), index = dates) start = datetime.datetime(2001, 1, 2) end = datetime.datetime(2001, 1, 11) dates = pandas.date_range(start, end) serie_2 = pandas.Series(np.random.randn(10), index = dates) start = datetime.datetime(2001, 1, 3) end = datetime.datetime(2001, 1, 12) dates = pandas.date_range(start, end) serie_3 = pandas.Series(np.random.randn(10), index = dates) print 'serie_1' print serie_1 print 'serie_2' print serie_2 print 'serie_3' print serie_3 serie_4 = pandas.concat([serie_1,serie_2], join='outer', axis = 1) print 'serie_4' print serie_4 serie_5 = pandas.concat([serie_4, serie_3], join='outer', axis = 1) print 'serie_5' print serie_5 ``` This gives me the error for serie_5 (the sec…

Accepted Answer

Concatenating a list of `Series` returns a `DataFrame`. Thus, `serie_4` is a `DataFrame`. `serie_3` is a `Series`. Concatenating a `DataFrame` with a `Series` raises the exception.

You could use

```
import pandas as pd
serie_5 = pd.concat([serie_1, serie_2, serie_3], join='outer', axis=1)
```

instead.

For example,

```
import functools
import numpy as np
import pandas as pd

s1 = pd.Series([0,1], index=list('AB'))
s2 = pd.Series([2,3], index=list('AC'))

result = pd.concat([s1, s2], join='outer', axis=1, sort=False)
print(result)
```

yields

```
     0    1
A  0.0  2.0
B  1.0  NaN
C  NaN  3.0
```

Note that you'll get a ValueError if you try to concatenate a series with a non-unique index. For example,

```
s3 = pd.Series([0,1], index=list('AB'), name='s3')
s4 = pd.Series([2,3], index=list('AA'), name='s4') # <-- non-unique index
result = pd.concat([s3, s4], join='outer', axis=1, sort=False)
```

raises

```
ValueError: cannot reindex from a duplicate axis
```

To work around this, reset the index and merge DataFrames instead:

```
import functools   
s3 = pd.Series([0,1], index=list('AB'), name='s3')
s4 = pd.Series([2,3], index=list('AA'), name='s4') # <-- non-unique index

result = functools.reduce(
    lambda left,right: pd.merge(left,right,on='index',how='outer'), 
    [s.reset_index() for s in [s3,s4]])
print(result)
```

yields

```
  index  s3   s4
0     A   0  2.0
1     A   0  3.0
2     B   1  NaN
```

Several time series to DataFrame

Solution

Problem

Related problems