join two tuples with different index and different shape

Question

I have two tuples which i like to have in one but i always get a lot of NaN's when I try to concat, merge or join them.

my tuple row looks like this :

and my tuple row_ges looks like this (shape: 5 rows, 6 columns):

I would like to have this in one dataframe like this shape (5 rows,301 columns):

i tried

result=row_ges+row
result_1=row_ges.append(row,ignore_index=True)
result_2 = pd.concat([row_ges,row], axis=1, ignore_index=True)

but i always get a shape like (10rows, 301 columns) and for result_2 i get an error code i don't understand:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-209-cdc656a8e828> in <module>()
      4 result=row_ges+row
      5 result_1=row_ges.append(row,ignore_index=True)
----> 6 result_2 = pd.concat([row_ges,row], axis=1, ignore_index=True)
      7 
      8 #gesamt = pd.DataFrame()

/usr/local/lib/python3.6/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)
    224                        verify_integrity=verify_integrity,
    225                        copy=copy, sort=sort)
--> 226     return op.get_result()
    227 
    228 

/usr/local/lib/python3.6/site-packages/pandas/core/reshape/concat.py in get_result(self)
    421             new_data = concatenate_block_managers(
    422                 mgrs_indexers, self.new_axes, concat_axis=self.axis,
--> 423                 copy=self.copy)
    424             if not self.copy:
    425                 new_data._consolidate_inplace()

/usr/local/lib/python3.6/site-packages/pandas/core/internals.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
   5423         blocks.append(b)
   5424 
-> 5425     return BlockManager(blocks, axes)
   5426 
   5427 

/usr/local/lib/python3.6/site-packages/pandas/core/internals.py in __init__(self, blocks, axes, do_integrity_check)
   3280 
   3281         if do_integrity_check:
-> 3282             self._verify_integrity()
   3283 
   3284         self._consolidate_check()

/usr/local/lib/python3.6/site-packages/pandas/core/internals.py in _verify_integrity(self)
   3491         for block in self.blocks:
   3492             if block._verify_integrity and block.shape[1:] != mgr_shape[1:]:
-> 3493                 construction_error(tot_items, block.shape[1:], self.axes)
   3494         if len(self.items) != tot_items:
   3495             raise AssertionError('Number of manager items must equal union of '

/usr/local/lib/python3.6/site-packages/pandas/core/internals.py in construction_error(tot_items, block_shape, axes, e)
   4841         raise ValueError("Empty data passed with indices specified.")
   4842     raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
-> 4843         passed, implied))
   4844 
   4845 

ValueError: Shape of passed values is (301, 29), indices imply (301, 9)

Hi, I think you mean "dataframe" when you say "tuple." Is there a common column between the two dataframes that you could use to join, merge, or union them? See, for example, https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index — Evan, Nov 13 '18 at 15:22
no there is no common column @Evan , I already tried this link before and i couldn't find a solution — raffa_sa, Nov 13 '18 at 15:44
if you run `row.shape` or `row_ges.shape`, what is returned? — Evan, Nov 13 '18 at 17:54
`row.shape` is `(5,295)` and `row_ges.shape` returns `(5,6)` as written above @Evan — raffa_sa, Nov 13 '18 at 17:58
Hmm. I'm not able to duplicate your error. Have you tried the solutions here? https://stackoverflow.com/questions/27719407/pandas-concat-valueerror-shape-of-passed-values-is-blah-indices-imply-blah2 You can also try resetting the index on one or both of your dataframes. — Evan, Nov 13 '18 at 18:00

score 0 · Answer 1 · answered Nov 13 '18 at 18:10

0

row.reset_index(drop=True, inplace=True)
row_ges.reset_index(drop=True, inplace=True)
result = pd.concat([row_ges,row], axis=1)

The problem was the different index. With this code it is working.

answered Nov 13 '18 at 18:10

raffa_sa

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join two tuples with different index and different shape

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