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I have a table with 8 columns and 40,000 rows, the following table (DF700) is a small section of the entire table. I need to split the 'sdk_ts' column so that the date and time are separate and the 'UTC' is removed from all rows.

            sdk_ts                                                   y           z 

0   2019-07-02 00:12:32 UTC                  3.455   4.555

1   2019-07-02 00:12:32 UTC                  4.567   6.897

2   2019-07-02 00:12:32 UTC                  9.304   0.440    : : : 

3   2019-07-02 00:12:59.6 UTC               8.909   0.405

4   2019-07-02 00:12:34.789 UTC           10.30   2.344

                               

I've attempted the following code:

DF800 = DF700['sdk_ts'].str.split(n=1, expand=True)

However, the result is:

              0                         1

0   2019-07-02      00:12:32 UTC

1   2019-07-02      00:12:32 UTC

2   2019-07-02     00:12:32 UTC

3   2019-07-02     00:12:59.6 UTC

4   2019-07-02     00:12:34.789 UTC

5   2019-07-02     00:12:35.048 UTCa

Is there another way I can achieve this goal? Splitting the date and time, getting rid of 'UTC' in all rows and making sure that the other columns are still on the table.

1 Answer

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by (41.4k points)

Convert  'sdk_ts' column to datetime format to extract date and time from it.

Here is the code:

df['sdk_ts'] = pd.to_datetime(df['sdk_ts'])

df['date'] = df['sdk_ts'].dt.date

df['time'] = df['sdk_ts'].dt.time

You can refer to our Python online course for more information. 

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