Python: how to avoid loop to convert in a pandas dataframe?

Question

I have the following dataframe:

df:
      y     m   d   val
0   2013    10  1   33.5
1   2013    10  2   37.1
2   2013    10  3   25.9
3   2013    10  4   31.3
4   2013    10  5   35.3
5   2013    10  6   55.4
6   2013    10  7   29.5
7   2013    10  8   31.3
8   2013    10  9   27.7
9   2013    10  10  25.9

where y, m, d correspond to year, month and day respectively. I want to aggregate them and convert to a datetime.

df['date'] =  0
for v in df.index:
    df['date'][v] = datetime.datetime(df.y[v], df.m[v], df.d[v])

I am wondering which is the best way to avoid that loop

I think apply is your friend: pandas.pydata.org/pandas-docs/stable/generated/… — Oleg
– Oleg, Commented Nov 14, 2016 at 19:42

MaxU - stand with Ukraine · Accepted Answer · 2016-11-14 19:50:30Z

3

From docstring:

Assembling a datetime from multiple columns of a DataFrame. The keys can be
common abbreviations like ['year', 'month', 'day', 'minute', 'second',
'ms', 'us', 'ns']) or plurals of the same

>>> df = pd.DataFrame({'year': [2015, 2016],
                       'month': [2, 3],
                       'day': [4, 5]})
>>> pd.to_datetime(df)
0   2015-02-04
1   2016-03-05
dtype: datetime64[ns]

Code:

In [135]: pd.to_datetime(df.rename(columns={'y':'Year','m':'Month','d':'Day'}).iloc[:, :3])
Out[135]:
0   2013-10-01
1   2013-10-02
2   2013-10-03
3   2013-10-04
4   2013-10-05
5   2013-10-06
6   2013-10-07
7   2013-10-08
8   2013-10-09
9   2013-10-10
dtype: datetime64[ns]

edited Nov 14, 2016 at 19:50

answered Nov 14, 2016 at 19:45

MaxU - stand with Ukraine

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Comments

user2285236 · Accepted Answer · 2016-11-14 19:43:43Z

1

Here's a way:

pd.to_datetime((df['y']*10000 + df['m']*100 + df['d']).astype(str))
Out: 
0   2013-10-01
1   2013-10-02
2   2013-10-03
3   2013-10-04
4   2013-10-05
5   2013-10-06
6   2013-10-07
7   2013-10-08
8   2013-10-09
9   2013-10-10
dtype: datetime64[ns]

answered Nov 14, 2016 at 19:43

user2285236

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