How do I convert dates in a Pandas data frame to a 'date' data type?
I have a Pandas data frame, one of the column contains date strings in the format YYYY-MM-DD
For e.g. '2013-10-28'
At the moment the dtype of the column is object.
How do I convert the column values to Pandas date format?
Answer
Essentially equivalent to @waitingkuo, but I would use to_datetime here (it seems a little cleaner, and offers some additional functionality e.g. dayfirst):
In [11]: df
Out[11]:
a time
0 1 2013-01-01
1 2 2013-01-02
2 3 2013-01-03
In [12]: pd.to_datetime(df['time'])
Out[12]:
0 2013-01-01 00:00:00
1 2013-01-02 00:00:00
2 2013-01-03 00:00:00
Name: time, dtype: datetime64[ns]
In [13]: df['time'] = pd.to_datetime(df['time'])
In [14]: df
Out[14]:
a time
0 1 2013-01-01 00:00:00
1 2 2013-01-02 00:00:00
2 3 2013-01-03 00:00:00
Handling ValueErrors
If you run into a situation where doing
df['time'] = pd.to_datetime(df['time'])
Throws a
ValueError: Unknown string format
That means you have invalid (non-coercible) values. If you are okay with having them converted to pd.NaT, you can add an errors='coerce' argument to to_datetime:
df['time'] = pd.to_datetime(df['time'], errors='coerce')