Having a collection of data frames, the goal is to identify the duplicated column names and return them as a list.
Example
The input are 3 data frames df1, df2 and df3:
df1 = pd.DataFrame({'a':[1,5], 'b':[3,9], 'e':[0,7]})
a b e
0 1 3 0
1 5 9 7
df2 = pd.DataFrame({'d':[2,3], 'e':[0,7], 'f':[2,1]})
d e f
0 2 0 2
1 3 7 1
df3 = pd.DataFrame({'b':[3,9], 'c':[8,2], 'e':[0,7]})
b c e
0 3 8 0
1 9 2 7
The output is a list [b, e]
Since you are using Pandas, you can use pd.Series.duplicated after concatenating column names:
# concatenate column labels
s = pd.concat([df.columns.to_series() for df in (df1, df2, df3)])
# keep all duplicates only, then extract unique names
res = s[s.duplicated(keep=False)].unique()
print(res)
array(['b', 'e'], dtype=object)
Alternatively, you can extract a series of counts and identify rows which have a count greater than 1:
s = pd.concat([df.columns.to_series() for df in (df1, df2, df3)]).value_counts()
res = s[s > 1].index
print(res)
Index(['e', 'b'], dtype='object')
The classic Python solution is to use collections.Counter followed by a list comprehension. Recall that list(df) returns the columns in a dataframe, so we can use this map and itertools.chain to produce an iterable to feed Counter.
from itertools import chain
from collections import Counter
c = Counter(chain.from_iterable(map(list, (df1, df2, df3))))
res = [k for k, v in c.items() if v > 1]
here is my code for this problem, for comparing with only two data frames, with out concat them.
def getDuplicateColumns(df1, df2):
df_compare = pd.DataFrame({'df1':df1.columns.to_list()})
df_compare["df2"] = ""
# Iterate over all the columns in dataframe
for x in range(df1.shape[1]):
# Select column at xth index.
col = df1.iloc[:, x]
# Iterate over all the columns in DataFrame from (x+1)th index till end
duplicateColumnNames = []
for y in range(df2.shape[1]):
# Select column at yth index.
otherCol = df2.iloc[:, y]
# Check if two columns at x y index are equal
if col.equals(otherCol):
duplicateColumnNames.append(df2.columns.values[y])
df_compare.loc[df_compare["df1"]==df1.columns.values[x], "df2"] = str(duplicateColumnNames)
return df_compare
