CODEWITHSUNDEEP
pythonpandasdataframe
M_S_N
M_S_N

Reputation: 2810

pandas select rows based on multiple datetime columns

I have two columns StartTime and EndTime, I need to select events occurring between 7-9 and 18-20. What I tried so far is this:

+------------+--------------------------------+-------------------------------+
|            |                StartTime       |            EndTime            |
+------------+--------------------------------+-------------------------------+
|        25  | 2018-05-17 11:52:21.769491600  | 2018-05-17 23:08:35.731376400 |
|        32  | 2018-05-19 14:22:24.141359000  | 2018-05-19 18:37:04.003643800 |
|        42  | 2018-05-22 08:25:01.015975500  | 2018-05-22 22:32:34.249869500 |
|        43  | 2018-05-22 08:46:06.187427200  | 2018-05-22 21:29:17.397438000 |
|        44  | 2018-05-22 13:38:37.289871700  | 2018-05-22 18:38:36.498623500 |
+------------+--------------------------------+-------------------------------+

I extracted hours from data used them to calculate following 

df = df[((df['start_hr']<=7) & (df['end_hr']>=9)) | ((df['start_hr']<=18) & (df['end_hr']>=20))]

Is there a more accurate and fast alternative to it?

Upvotes: 2

Views: 372

Answers (2)

Diego
Diego

Reputation: 36176

It will increase your memory consumption for a while but you can do something like this where you create two temp columns and use "df.query" on them. Make sure to delete the columns later.

df = df.assign(start_hr=df.start_hr.dt.hour, end_hr=df.end_hr.dt.hour)

df.query('(start_hr <= 7  and end_hr >=9) or (start_hr <= 18  and end_hr >=20) ')

Upvotes: 1

Barış Can Tayiz
Barış Can Tayiz

Reputation: 87

You can use this:


df['start_hr'] = pd.to_datetime(df['start_hr']) 
df['end_hr'] = pd.to_datetime(df['end_hr'])

df['start_hr_day'] = df['start_hr'].dt.day
df['end_hr_day'] = df['start_hr'].dt.day 

df.loc[((df['start_hr_day']<=7) & (df['end_hr_day']>=9))|((df['start_hr_day']<=18) & (df['end_hr_day']>=20))]

Upvotes: 0

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