我从这里尝试了许多建议,但没有一个得到解决。 我有两列这样的观察结果:15:08:19
如果我写
df.time_entry.describe()
它出现:
count 814262
unique 56765
top 15:03:00
freq 103
Name: time_entry, dtype: object
我已经运行了以下代码:
df['time_entry'] = pd.to_datetime(df['time_entry'],format= '%H:%M:%S', errors='ignore' ).dt.time
但是重新运行描述代码仍然返回dtype: object
。
答案 0 :(得分:1)
dt.time的目的是什么?
只需删除dt.time,从对象到日期时间的转换就可以正常工作。
df['time_entry'] = pd.to_datetime(df['time_entry'],format= '%H:%M:%S')
答案 1 :(得分:0)
问题是您正在使用具有属性.dt
的日期时间访问器(time
),然后又无法将两列相减。因此,只需省略.dt.time
,它应该可以工作。
这里有一些包含两列字符串的数据
df = pd.DataFrame()
df['time_entry'] = ['12:01:00', '15:03:00', '16:43:00', '14:11:00']
df['time_entry2'] = ['13:03:00', '14:04:00', '19:23:00', '18:12:00']
print(df)
time_entry time_entry2
0 12:01:00 13:03:00
1 15:03:00 14:04:00
2 16:43:00 19:23:00
3 14:11:00 18:12:00
将两列都转换为datetime
dtype
df['time_entry'] = pd.to_datetime(df['time_entry'], format= '%H:%M:%S', errors='ignore')
df['time_entry2'] = pd.to_datetime(df['time_entry2'], format= '%H:%M:%S', errors='ignore')
print(df)
time_entry time_entry2
0 1900-01-01 12:01:00 1900-01-01 13:03:00
1 1900-01-01 15:03:00 1900-01-01 14:04:00
2 1900-01-01 16:43:00 1900-01-01 19:23:00
3 1900-01-01 14:11:00 1900-01-01 18:12:00
print(df.dtypes)
time_entry datetime64[ns]
time_entry2 datetime64[ns]
dtype: object
(可选)Specify timezone
df['time_entry'] = df['time_entry'].dt.tz_localize('US/Central')
df['time_entry2'] = df['time_entry2'].dt.tz_localize('US/Central')
现在执行两列之间的时差(减法)并获得天数的时差(以浮点数表示)
df['Diff_days1'] = (df['time_entry'] - df['time_entry2']).dt.total_seconds()/60/60/24
df['Diff_days2'] = (df['time_entry'] - df['time_entry2']) / np.timedelta64(1, 'D')
df['Diff_days3'] = (df['time_entry'].sub(df['time_entry2'])).dt.total_seconds()/60/60/24
print(df)
time_entry time_entry2 Diff_days1 Diff_days2 Diff_days3
0 1900-01-01 12:01:00 1900-01-01 13:03:00 -0.043056 -0.043056 -0.043056
1 1900-01-01 15:03:00 1900-01-01 14:04:00 0.040972 0.040972 0.040972
2 1900-01-01 16:43:00 1900-01-01 19:23:00 -0.111111 -0.111111 -0.111111
3 1900-01-01 14:11:00 1900-01-01 18:12:00 -0.167361 -0.167361 -0.167361
编辑
如果您尝试访问datetime
属性,则可以直接使用time_entry
列(而不是时差列)来进行访问。这是一个例子
df['day1'] = df['time_entry'].dt.day
df['time1'] = df['time_entry'].dt.time
df['minute1'] = df['time_entry'].dt.minute
df['dayofweek1'] = df['time_entry'].dt.weekday
df['day2'] = df['time_entry2'].dt.day
df['time2'] = df['time_entry2'].dt.time
df['minute2'] = df['time_entry2'].dt.minute
df['dayofweek2'] = df['time_entry2'].dt.weekday
print(df[['day1', 'time1', 'minute1', 'dayofweek1',
'day2', 'time2', 'minute2', 'dayofweek2']])
day1 time1 minute1 dayofweek1 day2 time2 minute2 dayofweek2
0 1 12:01:00 1 0 1 13:03:00 3 0
1 1 15:03:00 3 0 1 14:04:00 4 0
2 1 16:43:00 43 0 1 19:23:00 23 0
3 1 14:11:00 11 0 1 18:12:00 12 0