python - "IndexError: positional indexers are out-of-bounds"当他们显然不是

Question

这是我正在使用的一些代码的 MWE。我通过切片和一些条件慢慢地削减了一个初始数据帧，直到我只有我需要的行。每个五行 block 实际上代表一个不同的对象，因此，当我减少内容时，如果每个五行 block 中的任何一行满足条件，我想保留它——这就是循环 keep.index 完成的。无论如何，当我完成后，我可以看到我想要的最终索引存在，但我收到一条错误消息，指出“IndexError:位置索引器超出范围。”这里发生了什么？

import pandas as pd
import numpy as np

temp = np.random.rand(100,5)

df = pd.DataFrame(temp, columns=['First', 'Second', 'Third', 'Fourth', 'Fifth'])

df_cut = df.iloc[10:]

keep = df_cut.loc[(df_cut['First'] < 0.5) & (df_cut['Second'] <= 0.6)]

new_indices_to_use = []
for item in keep.index:
    remainder = (item % 5)
    add = np.arange(0-remainder,5-remainder,1)
    inds_to_use = item + add
    new_indices_to_use.append(inds_to_use)

new_indices_to_use = [ind for sublist in new_indices_to_use for ind in sublist]
final_indices_to_use = []
for item in new_indices_to_use:
    if item not in final_indices_to_use:
        final_indices_to_use.append(item)

final = df_cut.iloc[final_indices_to_use]

Answer 1

来自 .iloc 上的 Pandas 文档(强调我的):

Pandas provides a suite of methods in order to get purely integer based indexing. The semantics follow closely python and numpy slicing. These are 0-based indexing.

您正在尝试按标签使用它，这意味着您需要 .loc

从你的例子:

>>>print df_cut.iloc[89]
...
Name: 99, dtype: float64

>>>print df_cut.loc[89]
...
Name: 89, dtype: float64