Convert Pandas Series to DateTime in a DataFrame

df=pd.read_csv("filename.csv" , parse_dates=["<column name>"])

type(df.<column name>)

example: if you want to convert day which is initially a string to a Timestamp in Pandas

df=pd.read_csv("weather_data2.csv" , parse_dates=["day"])

type(df.day)

The output will be pandas.tslib.Timestamp

You can't: DataFrame columns are Series, by definition. That said, if you make the dtype (the type of all the elements) datetime-like, then you can access the quantities you want via the .dt accessor (docs):

>>> df["TimeReviewed"] = pd.to_datetime(df["TimeReviewed"])
>>> df["TimeReviewed"]
205  76032930   2015-01-24 00:05:27.513000
232  76032930   2015-01-24 00:06:46.703000
233  76032930   2015-01-24 00:06:56.707000
413  76032930   2015-01-24 00:14:24.957000
565  76032930   2015-01-24 00:23:07.220000
Name: TimeReviewed, dtype: datetime64[ns]
>>> df["TimeReviewed"].dt
<pandas.tseries.common.DatetimeProperties object at 0xb10da60c>
>>> df["TimeReviewed"].dt.year
205  76032930    2015
232  76032930    2015
233  76032930    2015
413  76032930    2015
565  76032930    2015
dtype: int64
>>> df["TimeReviewed"].dt.month
205  76032930    1
232  76032930    1
233  76032930    1
413  76032930    1
565  76032930    1
dtype: int64
>>> df["TimeReviewed"].dt.minute
205  76032930     5
232  76032930     6
233  76032930     6
413  76032930    14
565  76032930    23
dtype: int64

---

If you're stuck using an older version of pandas, you can always access the various elements manually (again, after converting it to a datetime-dtyped Series). It'll be slower, but sometimes that isn't an issue:

>>> df["TimeReviewed"].apply(lambda x: x.year)
205  76032930    2015
232  76032930    2015
233  76032930    2015
413  76032930    2015
565  76032930    2015
Name: TimeReviewed, dtype: int64

Some handy script:

hour = df['assess_time'].dt.hour.values[0]