Merging Pandas rows based on index (time series) - python-2.7

Question

I used Pandas .append() to add columns from a number of Pandas timeseries by their index (date). However, instead of combining all data from common dates into one row, the data looks like this:
sve2_all.sort(inplace=True)
print sve2_all['20000101':'20000104']
Hgtot ng/l Q l/s DOC_mg/L Flow_mm/day MeHg ng/l Site \
2000-01-01 NaN NaN NaN 0.18 NaN NaN
2000-01-01 NaN 0.613234 NaN NaN NaN SVE
2000-01-02 NaN NaN NaN 0.18 NaN NaN
2000-01-02 NaN 0.614410 NaN NaN NaN SVE
2000-01-03 NaN NaN NaN NaN NaN 2
2000-01-03 NaN 0.617371 NaN NaN NaN SVE
2000-01-03 NaN NaN NaN NaN NaN NaN
2000-01-03 NaN NaN NaN 0.18 NaN NaN
2000-01-04 NaN 0.627733 NaN NaN NaN SVE
2000-01-04 NaN NaN NaN 0.18 NaN NaN
TOC_filt.TOC TOC_unfilt.TOC Temp oC pH
2000-01-01 NaN NaN NaN NaN
2000-01-01 NaN NaN -12.6117 NaN
2000-01-02 NaN NaN NaN NaN
2000-01-02 NaN NaN -2.3901 NaN
2000-01-03 NaN 8.224648 NaN NaN
2000-01-03 NaN NaN -5.0064 NaN
2000-01-03 NaN NaN NaN NaN
2000-01-03 NaN NaN NaN NaN
2000-01-04 NaN NaN -1.5868 NaN
2000-01-04 NaN NaN NaN NaN
[10 rows x 10 columns]
I've tried to resample this data by day using:
sve2_all.resample('D', how='mean')
And also to group by day using:
sve2_all.groupby(sve2_all.index.map(lambda t: t.day))
However, the DataFrame remains unchanged. How can I collapse the rows for the same date into one date? Thanks.
Additional information: I tried using pd.concat() as suggested by Joris (I had to pass 0 as the axis argument as 1 resulted in ValueError:cannot reindex from a duplicate axis) instead of .append() but the resulting DataFrame is the same as with .append(), a non-uniform non-monotonic time series. I think the index is the problem but I'm not sure what I can do to fix it, I thought that some time stamps might contain hour information while other not so I tried I've also tried using .resample('D',how='mean') on each DataFrame before using .concat() but it didn't make a difference.
Solution: Joris solution was correct, I didn't realise that .resample() wasn't inplace. Once the .resample() was assigned to a new DataFrame Joris' suggestion provided the desired result.

Answer 1

The append method does 'append' the rows to the other dataframe, and does not merge with it based on the index labels. For that you can use concat
Using a toy example:
In [14]: df1 = pd.DataFrame(np.random.randn(3,2), columns=list('AB'), index=pd.date_range('2000-01-01', periods=3))
In [15]: df1
Out[15]:
A B
2000-01-01 1.532085 -1.338895
2000-01-02 -0.016784 -0.270698
2000-01-03 -1.680379 0.838287
In [16]: df2 = pd.DataFrame(np.random.randn(3,2), columns=list('CD'), index=pd.date_range('2000-01-01', periods=3))
In [17]: df2
Out[17]:
C D
2000-01-01 0.375214 -0.812558
2000-01-02 -1.099848 -0.889941
2000-01-03 1.556383 0.870608
.append will append the rows (and columns of df2 that are not in df1 will be added, which is the case here):
In [18]: df1.append(df2)
Out[18]:
A B C D
2000-01-01 1.532085 -1.338895 NaN NaN
2000-01-02 -0.016784 -0.270698 NaN NaN
2000-01-03 -1.680379 0.838287 NaN NaN
2000-01-01 NaN NaN 0.375214 -0.812558
2000-01-02 NaN NaN -1.099848 -0.889941
2000-01-03 NaN NaN 1.556383 0.870608
pd.concat() concatenates the both dataframes along one of the index axises:
In [19]: pd.concat([df1, df2], axis=1)
Out[19]:
A B C D
2000-01-01 1.532085 -1.338895 0.375214 -0.812558
2000-01-02 -0.016784 -0.270698 -1.099848 -0.889941
2000-01-03 -1.680379 0.838287 1.556383 0.870608
Apart from that, the resample should normally work.