在 pandas 数据框中基于尾随行进行计算 [英] doing calculations in pandas dataframe based on trailing row

问题描述

是否可以基于不同列中的尾随行在pandas数据框中进行计算?像这样的东西.

Is it possible to do calculations in pandas dataframe based on trailing rows in a different column? Something like this.

frame = pd.DataFrame({'a' : [True, False, True, False],
                  'b' : [25, 22, 55, 35]})

我希望输出为:

A     B     C
True  25    
False 22   44
True  55   55
False 35   70

当A列中的跟踪行为False时，C列与B列相同； C列为B列* 2的情况下，当C列为B列时， A是真的吗?

Where column C is the same as column B when the trailing row in column A is False and where column C is column B * 2 when the trailing row in column A is True?

推荐答案

您可以使用

You could use the where Series method:

In [11]: frame['b'].where(frame['a'], 2 * frame['b'])
Out[11]:
0    25
1    44
2    55
3    70
Name: b, dtype: int64

In [12]: frame['c'] = frame['b'].where(frame['a'], 2 * frame['b'])

或者，您可以使用 apply (但这通常会比较慢):

Alternatively you could use apply (but this will usually be slower):

In [21]: frame.apply(lambda x: 2 * x['b'] if x['a'] else x['b'], axis=1

由于您使用的是尾随行"，因此您将需要使用 shift :

Since you are using the "trailing row" you are going to need to use shift:

In [31]: frame['a'].shift()
Out[31]:
0      NaN
1     True
2    False
3     True
Name: a, dtype: object

In [32]: frame['a'].shift().fillna(False)  # actually this is not needed, but perhaps clearer
Out[32]:
0    False
1     True
2    False
3     True
Name: a, dtype: object

然后使用相反的方式:

In [33]: c = (2 * frame['b']).where(frame['a'].shift().fillna(False), frame['b'])

In [34]: c
Out[34]:
0    25
1    44
2    55
3    70
Name: b, dtype: int64

并在熊猫中更改第一行(例如，更改为NaN，我们使用NaN来丢失数据)

and to change the first row (e.g. to NaN, in pandas we use NaN for missing data)

In [35]: c = c.astype(np.float)  # needs to accept NaN

In [36]: c.iloc[0] = np.nan

In [36]: frame['c'] = c

In [37]: frame
Out[37]:
       a   b   c
0   True  25 NaN
1  False  22  44
2   True  55  55
3  False  35  70

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