可以对日期时间集合使用cut吗? [英] Is it possible to use cut on a collection of datetimes?

问题描述

是否可以使用pandas.cut从datetime邮票中制成垃圾箱?

以下代码:

import pandas as pd
import StringIO

contenttext = """Time,Bid
2014-03-05 21:56:05:924300,1.37275
2014-03-05 21:56:05:924351,1.37272
2014-03-05 21:56:06:421906,1.37275
2014-03-05 21:56:06:421950,1.37272
2014-03-05 21:56:06:920539,1.37275
2014-03-05 21:56:06:920580,1.37272
2014-03-05 21:56:09:071981,1.37275
2014-03-05 21:56:09:072019,1.37272"""

content = StringIO.StringIO(contenttext)
df = pd.read_csv(content, header=0)
df['Time'] = pd.to_datetime(df['Time'], format='%Y-%m-%d %H:%M:%S:%f')

pd.cut(df['Time'], 5)

引发以下错误:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-f5387a84c335> in <module>()
     16 df['Time'] = pd.to_datetime(df['Time'], format='%Y-%m-%d %H:%M:%S:%f')
     17 
---> 18 pd.cut(df['Time'], 5)

/home/???????/sites/varsite/venv/local/lib/python2.7/site-packages/pandas/tools/tile.pyc in cut(x, bins, right, labels, retbins, precision, include_lowest)
     80         else:
     81             rng = (nanops.nanmin(x), nanops.nanmax(x))
---> 82         mn, mx = [mi + 0.0 for mi in rng]
     83 
     84         if mn == mx:  # adjust end points before binning

TypeError: unsupported operand type(s) for +: 'Timestamp' and 'float'

解决方案

旧问题，但是对于任何未来的访问者，我认为这是计算使用cut on的float timedelta的更清晰方法:

import pandas as pd
import datetime as dt

# Get Days Since Date
today = dt.date.today()
df['days ago'] = (today - df['time']).dt.days

# Get Seconds Since Datetime
now = dt.datetime.now()
df['seconds ago'] = (now - df['time']).dt.seconds

# Minutes Since Datetime
# (no dt.minutes attribute, so we use seconds/60)
now = dt.datetime.now()
df['minutes ago'] = (now - df['times']).dt.seconds/60

所有这些列现在都是浮点值，我们可以在

import pandas as pd
import datetime as dt

# Get Days Since Date
today = dt.date.today()
df['days ago'] = (today - df['time']).dt.days

# Get Seconds Since Datetime
now = dt.datetime.now()
df['seconds ago'] = (now - df['time']).dt.seconds

# Minutes Since Datetime
# (no dt.minutes attribute, so we use seconds/60)
now = dt.datetime.now()
df['minutes ago'] = (now - df['times']).dt.seconds/60

上使用

Is it possible to use pandas.cut to make bins out of datetime stamps?

The following code:

import pandas as pd
import StringIO

contenttext = """Time,Bid
2014-03-05 21:56:05:924300,1.37275
2014-03-05 21:56:05:924351,1.37272
2014-03-05 21:56:06:421906,1.37275
2014-03-05 21:56:06:421950,1.37272
2014-03-05 21:56:06:920539,1.37275
2014-03-05 21:56:06:920580,1.37272
2014-03-05 21:56:09:071981,1.37275
2014-03-05 21:56:09:072019,1.37272"""

content = StringIO.StringIO(contenttext)
df = pd.read_csv(content, header=0)
df['Time'] = pd.to_datetime(df['Time'], format='%Y-%m-%d %H:%M:%S:%f')

pd.cut(df['Time'], 5)

Throws the following error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-f5387a84c335> in <module>()
     16 df['Time'] = pd.to_datetime(df['Time'], format='%Y-%m-%d %H:%M:%S:%f')
     17 
---> 18 pd.cut(df['Time'], 5)

/home/???????/sites/varsite/venv/local/lib/python2.7/site-packages/pandas/tools/tile.pyc in cut(x, bins, right, labels, retbins, precision, include_lowest)
     80         else:
     81             rng = (nanops.nanmin(x), nanops.nanmax(x))
---> 82         mn, mx = [mi + 0.0 for mi in rng]
     83 
     84         if mn == mx:  # adjust end points before binning

TypeError: unsupported operand type(s) for +: 'Timestamp' and 'float'

解决方案

Old question, but for any future visitors, I think this is a clearer way to calculate float timedeltas to use cut on:

import pandas as pd
import datetime as dt

# Get Days Since Date
today = dt.date.today()
df['days ago'] = (today - df['time']).dt.days

# Get Seconds Since Datetime
now = dt.datetime.now()
df['seconds ago'] = (now - df['time']).dt.seconds

# Minutes Since Datetime
# (no dt.minutes attribute, so we use seconds/60)
now = dt.datetime.now()
df['minutes ago'] = (now - df['times']).dt.seconds/60

All of these columns are now float values that we can use pd.cut() on

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