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Binning Pandas列的时间戳

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我试图在数据帧中加入一列时间戳 . 时间戳的格式为0:00:00,我认为它们是字符串 . 我尝试使用 uber.dtypes() 但它一直返回错误:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-b4120eada070> in <module>()
----> 1 uber.dtypes()

TypeError: 'Series' object is not callable

picture of dataframe for reference

uber["Time"].head().to_dict() 返回以下内容:

{0: '0:11:00', 1: '0:17:00', 2: '0:21:00', 3: '0:28:00', 4: '0:33:00'}

当我使用这些箱子和标签时:

bins = np.arange(0, 25, 1)
labels = [
    "0:00-1:00",
    "1:01-2:00",
    "2:01-3:00",
    "3:01-4:00",
    "4:01-5:00",
    "5:01-6:00",
    "6:01-7:00",
    "7:01-8:00",
    "8:01-9:00",
    "9:01-10:00",
    "10:01-11:00",
    "11:01-12:00",
    "12:01-13:00",
    "13:01-14:00",
    "14:01-15:00",
    "15:01-16:00",
    "16:01-17:00",
    "17:01-18:00",
    "18:01-19:00",
    "19:01-20:00",
    "20:01-21:00",
    "21:01-22:00",
    "22:01-23:00",
    "23:01-24:00"
]

uber["Hour"] = pd.cut(uber["Time"], bins, labels = labels)

我收到以下错误:

TypeError: '<' not supported between instances of 'int' and 'str'

如果我将箱子更改为:

bins = str(np.arange(0, 25, 1)

我收到此错误:

AxisError: axis -1 is out of bounds for array of dimension 0

我意识到我可以将这些转换为秒和我们pd.to_numeric()将列转换为整数以便它们可以被分箱,但我已经在文档中查看并且仍然不清楚如何使用datetime或time来做到这一点(我可以做很长的路,然后乘以秒和分钟) .

1)如何使用日期时间或时间将这些时间戳转换为秒?

2)有没有办法在不将时间戳转换为秒的情况下将它们分开?

我也尝试将uber [“Time”]中的值转换为datetime.time对象,然后在binning之前将它们插入新列[“Time Object”]:

for i in range(len(uber["Time"])):
    uber.loc[i, "Time Object"] = datetime.datetime.strptime(uber.loc[i, "Time"], "%H:%M:%S").time()

如果我尝试使用[“Time Object”]列进行bin:

uber["Hour"] = pd.cut(uber["Time Object"], bins = 24, labels = labels)

然后我收到此错误:

TypeError: '<=' not supported between instances of 'datetime.time' and 'str'

如果我尝试使用[“Time Object”]列的小时进行bin:

uber [“Hour”] = pd.cut(uber [“Time Object”] . 小时,bins = 24,labels = labels)

我收到此错误:

AttributeError: 'Series' object has no attribute 'hour'

1 回答

  • 1

    您可以尝试花几分钟时间和垃圾桶

    uber = pd.DataFrame()
    
    labels = [str(i)+':01-'+str(i+1)+':00' for i in range(59)]    
    uber['Time'] = {0: '0:11:00', 1: '0:17:00', 2: '0:21:00', 3: '0:28:00', 4: '0:33:00'}.values()
    uber.Time = pd.to_timedelta(uber.Time)
    pd.cut(uber.Time.dt.seconds/60,bins,labels=labels)
    

    日期:

    0    10:01-11:00
    1    16:01-17:00
    2    20:01-21:00
    3    27:01-28:00
    4    32:01-33:00
    Name: Time, dtype: category
    Categories (59, object): [0:01-1:00 < 1:01-2:00 < 2:01-3:00 < 3:01-4:00 ... 55:01-56:00 < 56:01-57:00 < 57:01-58:00 < 58:01-59:00]
    

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