I have a dataframe column, data['time taken'] ;
02:08:00 02:05:00 02:55:00 03:42:00 01:12:00 01:46:00 03:22:00 03:36:00 How do I get the output in the form of minutes like below?
128 125 175 222 72 106 202 216 可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试):
问题:
回答1:
You could try to convert it to DatetimeIndex
In [58]: time = pd.DatetimeIndex(df['time taken']) In [59]: time.hour * 60 + time.minute Out[59]: array([128, 125, 175, 222, 72, 106, 202, 216], dtype=int32) 回答2:
Assuming this is a string column you can use the str.split method:
In [11]: df['time taken'].str.split(':') Out[11]: 0 [02, 08, 00] 1 [02, 05, 00] 2 [02, 55, 00] 3 [03, 42, 00] 4 [01, 12, 00] 5 [01, 46, 00] 6 [03, 22, 00] 7 [03, 36, 00] Name: time taken, dtype: object And then use apply:
In [12]: df['time taken'].str.split(':').apply(lambda x: int(x[0]) * 60 + int(x[1])) Out[12]: 0 128 1 125 2 175 3 222 4 72 5 106 6 202 7 216 Name: time taken, dtype: int64 回答3:
A bit hacky as we don't directly support reading in timedeltas ATM
In [9]: df = read_csv(StringIO(data),header=None) In [10]: df Out[10]: 0 0 02:08:00 1 02:05:00 2 02:55:00 3 03:42:00 4 01:12:00 5 01:46:00 6 03:22:00 7 03:36:00 Name: time, dtype: datetime64[ns] In [13]: df['time'] = pd.to_datetime(df['time']) In [18]: df['delta'] = df['time']-Timestamp('today') In [19]: df Out[19]: time delta 0 2013-07-30 02:08:00 02:08:00 1 2013-07-30 02:05:00 02:05:00 2 2013-07-30 02:55:00 02:55:00 3 2013-07-30 03:42:00 03:42:00 4 2013-07-30 01:12:00 01:12:00 5 2013-07-30 01:46:00 01:46:00 6 2013-07-30 03:22:00 03:22:00 7 2013-07-30 03:36:00 03:36:00 In [20]: df.dtypes Out[20]: time datetime64[ns] delta timedelta64[ns] dtype: object In [22]: df['delta'].apply(lambda x: x/np.timedelta64(1,'m')) Out[22]: 0 128 1 125 2 175 3 222 4 72 5 106 6 202 7 216 Name: delta, dtype: float64