I am attempting to convert a column in a dataframe from an object
to a datetime64[ns]
. I am using to_datetime
to accomplish this, yet at the end of my code, that column is still an object
.
import pandas as pd
from StringIO import StringIO
DATA = StringIO("""id;Date of Event
3574;2015-12-12 22:03:28Z
0657;2015-08-25 17:48:03Z
0408;2015-10-13 12:01:32Z
3043;2015-09-08 16:55:43Z
9397;2015-09-09 09:33:31Z
9291;2015-07-15 08:13:48Z
4263;2015-12-30 09:25:55Z
0200;2015-10-25 13:38:35Z
8576;2015-09-01 02:01:47Z
6023;2015-08-29 20:47:20Z
9975;2015-10-05 15:11:32Z
5202;2015-12-21 23:44:10Z
9278;2015-12-22 05:56:03Z
8520;2015-09-05 01:27:07Z
9048;2015-08-29 18:38:26Z
9624;2015-12-09 01:49:15Z
2659;2015-10-03 01:43:50Z
6230;2015-10-16 11:43:40Z
2272;2015-11-18 14:15:52Z
""")
df = pd.DataFrame.from_csv(DATA, sep=";")
pd.to_datetime(df['Date of Event'], format="%Y-%m-%d %H:%M:%SZ")
print df['Date of Event'].dtype
That final print shows:
object
df.info()
returns this:
Int64Index: 19 entries, 3574 to 2272
Data columns (total 1 columns):
Date of Event 19 non-null object
dtypes: object(1)
memory usage: 304.0+ bytes
Why did my pd.to_datetime(df['Date of Event'], format="%Y-%m-%d %H:%M:%SZ")
fail to convert the column to datetime
objects and how can I correct it?
The format is valid, and I can utilize the datetime
library to test that:
>>> import datetime
>>> s = "2015-11-18 14:15:52Z"
>>> dt = datetime.datetime.strptime(s, "%Y-%m-%d %H:%M:%SZ")
>>> dt
datetime.datetime(2015, 11, 18, 14, 15, 52)
Why did the conversion fail on the entire Pandas column?
to_datetime returns a new result, it doesn't modify its argument in place. Reassign it:
>>> df['Date of Event'] = pd.to_datetime(df['Date of Event'], format="%Y-%m-%d %H:%M:%SZ")
>>> df.dtypes
Date of Event datetime64[ns]
dtype: object
Or use parse_dates
and have it converted at the start (note that it's more common to use read_csv
than pd.DataFrame.from_csv
):
>>> df = pd.read_csv(DATA, sep=";", parse_dates=["Date of Event"])
>>> df.dtypes
id int64
Date of Event datetime64[ns]
dtype: object
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With