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

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

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