How to remove last the two digits in a column that is of integer type?

Question

How can I remove the last two digits of a DataFrame column of type int64?

For example df['DATE'] includes:

DATE
20110708
20110709
20110710
20110711
20110712
20110713
20110714
20110815
20110816
20110817

What I would like is:

DATE
201107
201107
201107
201107
201107
201107
201107
201108
201108
201108

What is the simplest way of achieving this?

Answer 1

Convert the dtype to str using astype then used vectorised str method to slice the str and then convert back to int64 dtype again:

In [184]:
df['DATE'] = df['DATE'].astype(str).str[:-2].astype(np.int64)
df

Out[184]:
     DATE
0  201107
1  201107
2  201107
3  201107
4  201107
5  201107
6  201107
7  201108
8  201108
9  201108

In [185]:    
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 1 columns):
DATE    10 non-null int64
dtypes: int64(1)
memory usage: 160.0 bytes

Hmm...

Turns out there is a built in method floordiv:

In [191]:
df['DATE'].floordiv(100)

Out[191]:
0    201107
1    201107
2    201107
3    201107
4    201107
5    201107
6    201107
7    201108
8    201108
9    201108
Name: DATE, dtype: int64

update

For a 1000 row df, the floordiv method is considerably faster:

%timeit df['DATE'].astype(str).str[:-2].astype(np.int64)
%timeit df['DATE'].floordiv(100)

100 loops, best of 3: 2.92 ms per loop
1000 loops, best of 3: 203 µs per loop

Here we observe ~10x speedup

Answer 2

You could use floor division // to drop the last two digits and preserve the integer type:

>>> df['DATE'] // 100
     DATE
0  201107
1  201107
2  201107
3  201107
4  201107
5  201107
6  201107
7  201108
8  201108
9  201108