Pandas：按日期时间（可能不存在）和返回视图切片数据帧
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Pandas: Slice Dataframe by Datetime (that may not exist) and Return View
提问by PH82
I have a large DataFrame which I would like to slice so that I can perform some calculations on the sliced dataframe so that the values are updated in the original. In addition I am slicing the dataframe by a start and end time that may not exist in the index. Below is a simplified example, but I will actually want to update a number of columns based on different calculations.
我有一个很大的 DataFrame，我想对其进行切片，以便我可以对切片后的数据帧执行一些计算，以便在原始数据中更新这些值。此外，我正在按索引中可能不存在的开始和结束时间对数据帧进行切片。下面是一个简化的例子，但我实际上想根据不同的计算更新一些列。
In [1]: df
Out[1]:
A B C
TIME
2014-01-02 14:00:00 -1.172285 1.706200 NaN
2014-01-02 14:05:00 0.039511 -0.320798 NaN
2014-01-02 14:10:00 -0.192179 -0.539397 NaN
2014-01-02 14:15:00 -0.475917 -0.280055 NaN
2014-01-02 14:20:00 0.163376 1.124602 NaN
2014-01-02 14:25:00 -2.477812 0.656750 NaN
I have tried all of the below statements to create sdf as view for my time range:
我已经尝试了以下所有语句来为我的时间范围创建 sdf 作为视图：
start = datetime.strptime('2014-01-02 14:07:00', '%Y-%m-%d %H:%M:%S')
end = datetime.strptime('2014-01-02 14:22:00', '%Y-%m-%d %H:%M:%S')
sdf = df[start:end]
sdf = df[start < df.index < end]
sdf = df.ix[start:end]
sdf = df.loc[start:end]
sdf = df.truncate(before=start, after=end, copy=False)
sdf[C] == 100
Most return a copy and I get a SettingWithCopyWarning warning. The loc function says the index is incompatible with datetime. Is this something I should be able to do. The result I would like after updating the slice is:
大多数返回副本，我收到 SettingWithCopyWarning 警告。loc 函数表示索引与日期时间不兼容。这是我应该能够做的事情。更新切片后我想要的结果是：
In [1]: df
Out[1]:
A B C
TIME
2014-01-02 14:00:00 -1.172285 1.706200 NaN
2014-01-02 14:05:00 0.039511 -0.320798 NaN
2014-01-02 14:10:00 -0.192179 -0.539397 100
2014-01-02 14:15:00 -0.475917 -0.280055 100
2014-01-02 14:20:00 0.163376 1.124602 100
2014-01-02 14:25:00 -2.477812 0.656750 NaN
Can anyone please suggest a way to this? Am I approaching this the wrong way?
任何人都可以请建议一种方法吗？我是否以错误的方式接近这个？
Thanks
谢谢
回答by EdChum
One way is to use locand wrap your conditions in parentheses and use the bitwise oerator &, the bitwise operator is required as you are comparing an array of values and not a single value, the parentheses are required due to operator precedence. We can then use this to perform label selection using locand set the 'C' column like so:
一种方法是使用loc括号并将条件括起来并使用按位 oerator &，当您比较值数组而不是单个值时需要按位运算符，由于运算符优先级，需要括号。然后我们可以使用它来执行标签选择，loc并像这样设置“C”列：
In [15]:
import datetime as dt
start = dt.datetime.strptime('2014-01-02 14:07:00', '%Y-%m-%d %H:%M:%S')
end = dt.datetime.strptime('2014-01-02 14:22:00', '%Y-%m-%d %H:%M:%S')
df.loc[(df.index > start) & (df.index < end), 'C'] = 100
df
Out[15]:
A B C
TIME
2014-01-02 14:00:00 -1.172285 1.706200 NaN
2014-01-02 14:05:00 0.039511 -0.320798 NaN
2014-01-02 14:10:00 -0.192179 -0.539397 100
2014-01-02 14:15:00 -0.475917 -0.280055 100
2014-01-02 14:20:00 0.163376 1.124602 100
2014-01-02 14:25:00 -2.477812 0.656750 NaN
If we look at each method you tried and why they didn't work:
如果我们查看您尝试过的每种方法以及它们为何不起作用：
sdf = df[start:end] # will raise KeyError if start and end are not present in index
sdf = df[start < df.index < end] # will raise ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all(), this is because you are comparing arrays of values not a single scalar value
sdf = df.ix[start:end] # raises KeyError same as first example
sdf = df.loc[start:end] # raises KeyError same as first example
sdf = df.truncate(before=start, after=end, copy=False) # generates correct result but operations on this will raise SettingWithCopyWarning as you've found
EDIT
编辑
You can set sdfto the mask and use this with locto set your 'C' column:
您可以设置sdf为掩码并使用它loc来设置您的“C”列：
In [7]:
import datetime as dt
start = dt.datetime.strptime('2014-01-02 14:07:00', '%Y-%m-%d %H:%M:%S')
end = dt.datetime.strptime('2014-01-02 14:22:00', '%Y-%m-%d %H:%M:%S')
sdf = (df.index > start) & (df.index < end)
df.loc[sdf,'C'] = 100
df
Out[7]:
A B C
TIME
2014-01-02 14:00:00 -1.172285 1.706200 NaN
2014-01-02 14:05:00 0.039511 -0.320798 NaN
2014-01-02 14:10:00 -0.192179 -0.539397 100
2014-01-02 14:15:00 -0.475917 -0.280055 100
2014-01-02 14:20:00 0.163376 1.124602 100
2014-01-02 14:25:00 -2.477812 0.656750 NaN
