Here’s a one line solution to remove columns based on duplicate column names:
df = df.loc[:,~df.columns.duplicated()].copy()
How it works:
Suppose the columns of the data frame are ['alpha','beta','alpha']
df.columns.duplicated() returns a boolean array: a True or False for each column. If it is False then the column name is unique up to that point, if it is True then the column name is duplicated earlier. For example, using the given example, the returned value would be [False,False,True].
Pandas allows one to index using boolean values whereby it selects only the True values. Since we want to keep the unduplicated columns, we need the above boolean array to be flipped (ie [True, True, False] = ~[False,False,True])
Finally, df.loc[:,[True,True,False]] selects only the non-duplicated columns using the aforementioned indexing capability.
The final .copy() is there to copy the dataframe to (mostly) avoid getting errors about trying to modify an existing dataframe later down the line.
Note: the above only checks columns names, not column values.
To remove duplicated indexes
Since it is similar enough, do the same thing on the index:
df = df.loc[~df.index.duplicated(),:].copy()
To remove duplicates by checking values without transposing
df = df.loc[:,~df.apply(lambda x: x.duplicated(),axis=1).all()].copy()
This avoids the issue of transposing. Is it fast? No. Does it work? Yeah. Here, try it on this:
# create a large(ish) dataframe
ldf = pd.DataFrame(np.random.randint(0,100,size= (736334,1312)))
#to see size in gigs
#ldf.memory_usage().sum()/1e9 #it's about 3 gigs
# duplicate a column
ldf.loc[:,'dup'] = ldf.loc[:,101]
# take out duplicated columns by values
ldf = ldf.loc[:,~ldf.apply(lambda x: x.duplicated(),axis=1).all()].copy()