根据其他列中的值，将列中的值更改为np.nan

Question

正如标题所述，我希望根据“Cut”中的值，将“Vorgabe”和“Temp”中的值更改为nan

这是一个具有预期结果的示例。我认为这是理解我的问题的最好方法

     OptOpTemp  OpTemp  Grad  Vorgabe  Temp  Cut
0        22.5      24   0.0     22.5    24   0.0
1        22.5      24   0.0     22.5    24   0.0
2        23.5      24   1.0     23.5    24   1.0
3        23.5      24   0.0     23.5    24   0.0
4        23.5      24   0.0     23.5    24   0.0
5        23.5      24   0.0     23.5    24   0.0
6        23.5      24   0.0     23.5    24   0.0
7        23.5      24   0.0     23.5    24   0.0
8        23.5      24   0.0     23.5    24   0.0
9        26.0      24   2.5     26.0    24   3.0
10       26.0      24   0.0     26.0    24   0.0
11       26.0      24   0.0     26.0    24   0.0
12       26.0      24   0.0     26.0    24   0.0
13       26.0      24   0.0     26.0    24   0.0

我想将此更改为：

     OptOpTemp  OpTemp  Grad  Vorgabe  Temp  Cut
0        22.5      24   0.0     22.5    24   0.0
1        22.5      24   0.0     nan    nan   0.0 <- one row above
2        23.5      24   1.0     nan    nan   1.0
3        23.5      24   0.0     nan    nan   0.0 <- one row among
4        23.5      24   0.0     23.5    24   0.0
5        23.5      24   0.0     23.5    24   0.0
6        23.5      24   0.0     nan    nan   0.0 <- three rows above
7        23.5      24   0.0     nan    nan   0.0
8        23.5      24   0.0     nan    nan   0.0
9        26.0      24   2.5     nan    nan   3.0
10       26.0      24   0.0     nan    nan   0.0
11       26.0      24   0.0     nan    nan   0.0
12       26.0      24   0.0     nan    nan   0.0 <- three rows among
13       26.0      24   0.0     26.0    24   0.0

上面和中间的行数基于“剪切”中的值

我想为“Cut”中的每个整数值创建这个。因此，如果出现2，它应该在上面切两行，在中间切两行。因此，我认为需要一个循环，但我不知道如何实现正确的结果

Answer 1

一种方法是创建一个索引数组并用它们屏蔽：

# define in-scope cuts
cuts = df.loc[df['Cut'] != 0, 'Cut'].astype(int)

# calculate array of indices
idx = np.hstack([np.arange(i - val, i + val + 1) for i, val in cuts.items()])

# mask series according to indices
df.loc[idx, ['Vorgabe', 'Temp']] = np.nan

# alternative: use if index ranges may fall out of dataframe index
# df.loc[df.index.isin(idx), ['Vorgabe', 'Temp']] = np.nan

print(df)

#     OptOpTemp  OpTemp  Grad  Vorgabe  Temp  Cut
# 0        22.5      24   0.0     22.5  24.0  0.0
# 1        22.5      24   0.0      NaN   NaN  0.0
# 2        23.5      24   1.0      NaN   NaN  1.0
# 3        23.5      24   0.0      NaN   NaN  0.0
# 4        23.5      24   0.0     23.5  24.0  0.0
# 5        23.5      24   0.0     23.5  24.0  0.0
# 6        23.5      24   0.0      NaN   NaN  0.0
# 7        23.5      24   0.0      NaN   NaN  0.0
# 8        23.5      24   0.0      NaN   NaN  0.0
# 9        26.0      24   2.5      NaN   NaN  3.0
# 10       26.0      24   0.0      NaN   NaN  0.0
# 11       26.0      24   0.0      NaN   NaN  0.0
# 12       26.0      24   0.0      NaN   NaN  0.0
# 13       26.0      24   0.0     26.0  24.0  0.0