How to merge complex data frames in pandas/dataframe-js/etc

I would imagine what I'm trying to do is fairly simple in pandas but I just can't get it. Really I want to do this in dataframe-js (or danfojs) but any help in either pandas or dataframe-js will be helpful.

Essentially:

there can be an array of any number of dataframes with any number of rows and columns.
They should all have at least one matching column uuid.
Most of the dataframes will have data for every uuid but some might be missing.
The only known column name is uuid, so using "merge on" or similar with any other column name isn't an option.

example dataframes:

    let data1 = [
      [['col A', 'uuid'], ['1238', '12']],
      [['col B', 'uuid'], ['42.4', '12']],
      [['col A', 'uuid'], ['1091', '48']],
      [['col B', 'uuid'], ['35.1', '48']],
      [['col B', 'uuid'], ['44.4', '77']],
    ]

desired output (column order doesn't matter):

[
      ['col A', 'uuid', 'col B'],
      ['1238', '12', '42.4'],
      ['1091', '48', '35.1'],
      [null, '77', '44.4'] // null, undefined, NaN...doesn't matter for the gaps
]

please help :)

Solution

Ok I combined @onyambu's answer with the merge function, which now accepts dataframes of different sizes

# create an initial empty df
t = pd.DataFrame(columns=['uuid'])

# reduce list of dataframes into one
df = reduce(lambda x,y: x.merge(pd.DataFrame(y[1:], columns=y[0]), how='outer'), data1, t)

# squash rows on `uuid` index with stack/unstack
df = df.set_index('uuid').stack().unstack().reset_index()

# output in original "table" format
df2 = np.r_[df.columns.values[None],df.iloc[:].values].tolist()
print(df2)