Pandas concat yields ValueError: Plan shapes are not aligned

In case it helps, I have also hit this error when I tried to concatenate two data frames (and as of the time of writing this is the only related hit I can find on google other than the source code).

I don't know whether this answer would have solved the OP's problem (since he/she didn't post enough information), but for me, this was caused when I tried to concat dataframe df1 with columns ['A', 'B', 'B', 'C'] (see the duplicate column headings?) with dataframe df2 with columns ['A', 'B']. Understandably the duplication caused pandas to throw a wobbly. Change df1 to ['A', 'B', 'C'] (i.e. drop one of the duplicate columns) and everything works fine.

I recently got this message, too, and I found like user @jason and @user3805082 above that I had duplicate columns in several of the hundreds of dataframes I was trying to concat, each with dozens of enigmatic varnames. Manually searching for duplicates was not practical.

In case anyone else has the same problem, I wrote the following function which might help out.

def duplicated_varnames(df):    """Return a dict of all variable names that     are duplicated in a given dataframe."""    repeat_dict = {}    var_list = list(df) # list of varnames as strings    for varname in var_list:        # make a list of all instances of that varname        test_list = [v for v in var_list if v == varname]         # if more than one instance, report duplications in repeat_dict        if len(test_list) > 1:             repeat_dict[varname] = len(test_list)    return repeat_dict

Then you can iterate over that dict to report how many duplicates there are, delete the duplicated variables, or rename them in some systematic way.

Wrote a small function to concatenate duplicated column names.Function cares about sorting if original dataframe is unsorted, the output will be a sorted one.

def concat_duplicate_columns(df):    dupli = {}    # populate dictionary with column names and count for duplicates     for column in df.columns:        dupli[column] = dupli[column] + 1 if column in dupli.keys() else 1    # rename duplicated keys with °°° number suffix    for key, val in dict(dupli).items():        del dupli[key]        if val > 1:            for i in range(val):                dupli[key+'°°°'+str(i)] = val        else: dupli[key] = 1    # rename columns so that we can now access abmigous column names    # sorting in dict is the same as in original table    df.columns = dupli.keys()    # for each duplicated column name    for i in set(re.sub('°°°(.*)','',j) for j in dupli.keys() if '°°°' in j):        i = str(i)        # for each duplicate of a column name        for k in range(dupli[i+'°°°0']-1):            # concatenate values in duplicated columns            df[i+'°°°0'] = df[i+'°°°0'].astype(str) + df[i+'°°°'+str(k+1)].astype(str)            # Drop duplicated columns from which we have aquired data            df = df.drop(i+'°°°'+str(k+1), 1)    # resort column names for proper mapping    df = df.reindex_axis(sorted(df.columns), axis = 1)    # rename columns    df.columns = sorted(set(re.sub('°°°(.*)','',i) for i in dupli.keys()))    return df