How Can Pandas Efficiently Convert String Dates to DateTime Objects and Filter by Date Range?

Converting String Formats to Datetime Format in Pandas

Pandas provides a convenient way to convert string values representing dates and times into datetime objects. The pd.to_datetime() function can handle a variety of input string formats, automatically detecting the correct format based on the value's contents.

Consider the following column of string values representing dates:

I_DATE
28-03-2012 2:15:00 PM
28-03-2012 2:17:28 PM
28-03-2012 2:50:50 PM

To convert I_DATE to datetime format, simply use pd.to_datetime(df['I_DATE']). Since the format is straightforward, pandas will automatically identify it.

In [51]:
pd.to_datetime(df['I_DATE'])

Out[51]:
0    2012-03-28 14:15:00
1    2012-03-28 14:17:28
2    2012-03-28 14:50:50
Name: I_DATE, dtype: datetime64[ns]

You can also access specific components of the datetime object using the dt accessor:

In [54]:
df['I_DATE'].dt.date

Out[54]:
0    2012-03-28
1    2012-03-28
2    2012-03-28
dtype: object

In [56]:    
df['I_DATE'].dt.time

Out[56]:
0    14:15:00
1    14:17:28
2    14:50:50
dtype: object

Filtering Data Based on Date Ranges

Once your data is in datetime format, you can easily filter based on date ranges. For example, to filter the df DataFrame for rows where I_DATE falls within a specific range, you can use:

df[(df['I_DATE'] > '2015-02-04') & (df['I_DATE'] < '2015-02-10')]

Out[59]:
         date
35 2015-02-05
36 2015-02-06
37 2015-02-07
38 2015-02-08
39 2015-02-09

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