如何以预定义的百分比拆分 Pandas 中的 DataFrame? [英] How to split a DataFrame in pandas in predefined percentages?

问题描述

我有一个按多列排序的熊猫数据框.现在我想以预定义的百分比分割数据帧，以便提取和命名一些段.

I have a pandas dataframe sorted by a number of columns. Now I'd like to split the dataframe in predefined percentages, so as to extract and name a few segments.

例如，我想用前 20% 的行来创建第一个段，然后用接下来的 30% 来创建第二个段，剩下的 50% 留给第三个段.

For example, I want to take the first 20% of rows to create the first segment, then the next 30% for the second segment and leave the remaining 50% to the third segment.

我将如何实现这一目标?

How would I achieve that?

推荐答案

使用 numpy.split:

a, b, c = np.split(df, [int(.2*len(df)), int(.5*len(df))])

示例:

np.random.seed(100)
df = pd.DataFrame(np.random.random((20,5)), columns=list('ABCDE'))
#print (df)

a, b, c = np.split(df, [int(.2*len(df)), int(.5*len(df))])
print (a)
          A         B         C         D         E
0  0.543405  0.278369  0.424518  0.844776  0.004719
1  0.121569  0.670749  0.825853  0.136707  0.575093
2  0.891322  0.209202  0.185328  0.108377  0.219697
3  0.978624  0.811683  0.171941  0.816225  0.274074

print (b)
          A         B         C         D         E
4  0.431704  0.940030  0.817649  0.336112  0.175410
5  0.372832  0.005689  0.252426  0.795663  0.015255
6  0.598843  0.603805  0.105148  0.381943  0.036476
7  0.890412  0.980921  0.059942  0.890546  0.576901
8  0.742480  0.630184  0.581842  0.020439  0.210027
9  0.544685  0.769115  0.250695  0.285896  0.852395

print (c)
           A         B         C         D         E
10  0.975006  0.884853  0.359508  0.598859  0.354796
11  0.340190  0.178081  0.237694  0.044862  0.505431
12  0.376252  0.592805  0.629942  0.142600  0.933841
13  0.946380  0.602297  0.387766  0.363188  0.204345
14  0.276765  0.246536  0.173608  0.966610  0.957013
15  0.597974  0.731301  0.340385  0.092056  0.463498
16  0.508699  0.088460  0.528035  0.992158  0.395036
17  0.335596  0.805451  0.754349  0.313066  0.634037
18  0.540405  0.296794  0.110788  0.312640  0.456979
19  0.658940  0.254258  0.641101  0.200124  0.657625

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