If the keys are unique, within each dataframe, you can do a concat then groupby:

list_dfs = [df1, df2, df3]
pd.concat(list_dfs).groupby('Col1').first()

In general, you can combine a reduce and merge:

from functools import reduce

# find common columns
commons = reduce(lambda x,y: set(x).intersection(set(y)), list_dfs)

reduce(lambda x,y: x.merge(y, on=commons), list_dfs)

We can use reduce and merge like so :

from functools import reduce

reduce(lambda left, right: pd.merge(left, right, on=list(left.columns.intersection(right.columns)), how='outer'), [df1, df2, df3])

Here the reduce apply function of two arguments cumulatively to the items of iterable, from left to right, so as to reduce the iterable to a single value.
The left argument, left, is the accumulated value and the right argument, right, is the update value from the iterable.

The trick here is to merge on the list of common columns from the DataFrames to get the expected result. @QuangHoang did something similar and find the trick before me.

Output :

    Col1    Col2    Col3    Col4
0   BLAH1   A       NaN     NaN
1   BLAH2   Z       B       C
2   BLAH3   Q       K       W

Let's use functools.reduce with pd.DataFrame.combine_first and comprehension:

from functools import reduce
reduce(lambda x, y: x.combine_first(y), 
       (df.set_index('Col1') for df in [df1,df2,df3])).reset_index()

Output:

    Col1 Col2 Col3 Col4
0  BLAH1    A  NaN  NaN
1  BLAH2    Z    B    C
2  BLAH3    Q    K    W

Given input dataframes as:

df1 = pd.DataFrame({'Col1':['BLAH1', 'BLAH2'],
                   'Col2':[*'AZ']})
df2 = pd.DataFrame({'Col1':['BLAH2', 'BLAH3'],
                   'Col2':[*'ZQ'],
                   'Col3':[*'BK']})
df3 = pd.DataFrame({'Col1':['BLAH2', 'BLAH3'],
                    'Col4':[*'CW']})