'how to drop duplicated columns data based on column name in pandas

Assume I have a table like below

    A   B   C   B
0   0   1   2   3
1   4   5   6   7

I'd like to drop column B. I tried to use drop_duplicates, but it seems that it only works based on duplicated data not header. Hope anyone know how to do this.

pandas


Solution 1:[1]

Use Index.duplicated with loc or iloc and boolean indexing:

print (~df.columns.duplicated())
[ True  True  True False]

df = df.loc[:, ~df.columns.duplicated()]
print (df)
   A  B  C
0  0  1  2
1  4  5  6

df = df.iloc[:, ~df.columns.duplicated()]
print (df)
   A  B  C
0  0  1  2
1  4  5  6

Timings:

np.random.seed(123)
cols = ['A','B','C','B']
#[1000 rows x 30 columns]
df = pd.DataFrame(np.random.randint(10, size=(1000,30)),columns = np.random.choice(cols, 30))
print (df)

In [115]: %timeit (df.groupby(level=0, axis=1).first())
1000 loops, best of 3: 1.48 ms per loop

In [116]: %timeit (df.groupby(level=0, axis=1).mean())
1000 loops, best of 3: 1.58 ms per loop

In [117]: %timeit (df.iloc[:, ~df.columns.duplicated()])
1000 loops, best of 3: 338 µs per loop

In [118]: %timeit (df.loc[:, ~df.columns.duplicated()])
1000 loops, best of 3: 346 µs per loop

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Solution 2:[2]

You can groupby
We use the axis=1 and level=0 parameters to specify that we are grouping by columns. Then use the first method to grab the first column within each group defined by unique column names.

df.groupby(level=0, axis=1).first()

   A  B  C
0  0  1  2
1  4  5  6

We could have also used last

df.groupby(level=0, axis=1).last()

   A  B  C
0  0  3  2
1  4  7  6

Or mean

df.groupby(level=0, axis=1).mean()

   A  B  C
0  0  2  2
1  4  6  6

Sources

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Source: Stack Overflow

Solution Source
Solution 1 Anton vBR
Solution 2

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