如何根据列中值的差异拆分 pandas 数据框 [英] How to split pandas dataframe based on difference of values in a column

问题描述

我有一个熊猫数据框，其中有几列，称为 strike。如果strike列的行的值大于100加上strike列的前一行，我想将数据框分为两部分到那时（它们仍然具有相同的列名），依此类推。我对pandas很陌生，在查找了一些功能之后找不到一种简单的方法。

I have a pandas dataframe with a few columns, one called 'strike.' If the value of a row of the strike column is greater than 100 plus the previous row of the strike column, I want to split the dataframe into two at that point (they'd still have the same column names) and so on. I'm quite new at pandas and couldn't figure out a simple way to do this after looking up some functions.

示例：以下数据框：

strike crv vol
1400   w   a 
1450   x   b
1600   y   c
1800   z   d

将会是3个数据帧：

strike crv vol
1400   w   a 
1450   x   b

strike crv vol
1600   y   c

strike crv vol
1800   z   d

谢谢！

推荐答案

IIUC，这是compare-cumsum-groupby模式的另一个示例：

IIUC, this is yet another example of the compare-cumsum-groupby pattern:

>>> df
   strike crv vol
0    1400   w   a
1    1450   x   b
2    1600   y   c
3    1800   z   d
>>> group_ids = (df["strike"] > (df["strike"].shift() + 100)).cumsum()
>>> grouped = df.groupby(group_ids)
>>> for k,g in grouped:
...     print("-----")
...     print(g)
...     
-----
   strike crv vol
0    1400   w   a
1    1450   x   b
-----
   strike crv vol
2    1600   y   c
-----
   strike crv vol
3    1800   z   d

您可以如果愿意，可以将其放入列表或字典中。

And you can put this into a list or dictionary if you'd like:

>>> group_list = [g for k,g in grouped]
>>> group_list[2]
   strike crv vol
3    1800   z   d
>>> group_dict = dict(list(grouped))
>>> group_dict[1]
   strike crv vol
2    1600   y   c

---

之所以有用，是因为我们利用True == 1和False == 0的事实来构建组ID：

---

This works because we build the group ids taking advantage of the fact that True == 1 and False == 0:

>>> df["strike"] > (df["strike"].shift() + 100)
0    False
1    False
2     True
3     True
Name: strike, dtype: bool
>>> (df["strike"] > (df["strike"].shift() + 100)).cumsum()
0    0
1    0
2    1
3    2
Name: strike, dtype: int64

然后我们可以将这些值分组

and we can then group on these values.

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