英文:
how to make pandas row value to zero when row above values are zeros and below value not equal to zero using python pandas
问题
我有一个pandas数据框，其中包含以下代码部分:
df = pd.DataFrame({
'rpm': [2.0, 4.5, 5.6, 6.0, 7.0, 6.0, 0.0, 0.0, 3.0, 5.0, 9.0, 8.9,9.3,0,0,0,6,7,8,9,13]
})
在上述数值中，如果为零且下一个值不为零，则当前值应为零，如何实现这一点？到目前为止，我使用了以下代码：
for i in range(1, len(df) - 1):
if df['rpm'].iloc[i-1] == 0 and df['rpm'].iloc[i+1] != 0 :
df['rpm'].iloc[i] = 0
使用这段代码，我没有得到预期的输出。
期望的输出是：
df = pd.DataFrame({
'rpm': [2.0, 4.5, 5.6, 6.0, 7.0, 6.0, 0.0, 0.0, 0.0, 5.0, 9.0, 8.9,9.3,0.0,0.0,0.0,0.0,7,8,9,13]
})
如果上述一组值为零，下一组值为非零，那么值3必须被替换为零，下一组中的6必须被替换为零，如何实现这一点？
英文:
I have pandas data frame
df = pd.DataFrame({
'rpm': [2.0, 4.5, 5.6, 6.0, 7.0, 6.0, 0.0, 0.0, 3.0, 5.0, 9.0, 8.9,9.3,0,0,0,6,7,8,9,13]
})
here the the above values are zero and below values not equal to zero means the current value has to be zero how to achieve this so far i used this code
for i in range(1, len(df) - 1):
if df['rpm'].iloc[i-1] == 0 and df['rpm'].iloc[i+1] != 0 :
df['rpm'].iloc[i] = 0
print(df.to_string()) with this code i am not getting expected output
expected output is
df = pd.DataFrame({
'rpm': [2.0, 4.5, 5.6, 6.0, 7.0, 6.0, 0.0, 0.0, 0.0, 5.0, 9.0, 8.9,9.3,0.0,0.0,0.0,0.0,7,8,9,13]
})
if above set of values are zero below set of values are non zero means the value 3 has to be replaced by 0 and in the next set 6 has to replaced by 0 how to do this
答案1
得分: 2
你可以使用 shift 来检查条件：
df.loc[df['rpm'].shift(1).eq(0) & df['rpm'].shift(-1).ne(0), 'rpm'] = 0
print(df)
# 输出：
rpm
0 2.0
1 4.5
2 5.6
3 6.0
4 7.0
5 6.0
6 0.0
7 0.0
8 0.0 # 此处，旧值: 3
9 5.0
10 9.0
11 8.9
12 9.3
13 0.0
14 0.0
15 0.0
16 0.0 # 此处，旧值: 6
17 7.0
18 8.0
19 9.0
20 13.0
细节部分：
m1 = df['rpm'].shift(1).eq(0)
m2 = df['rpm'].shift(-1).ne(0)
out = pd.concat([df['rpm'], m1, m2, m1&m2], keys=['rpm', 'm1', 'm2', 'all'], axis=1)
print(out)
# 输出
rpm rpm rpm rpm
0 2.0 False True False
1 4.5 False True False
2 5.6 False True False
3 6.0 False True False
4 7.0 False True False
5 6.0 False False False
6 0.0 False False False
7 0.0 True True True # 此处，已经是 0
8 3.0 True True True # 此处，设置为 0
9 5.0 False True False
10 9.0 False True False
11 8.9 False True False
12 9.3 False False False
13 0.0 False False False
14 0.0 True False False
15 0.0 True True True # 此处，已经是 0
16 6.0 True True True # 此处，设置为 0
17 7.0 False True False
18 8.0 False True False
19 9.0 False True False
20 13.0 False True False
英文:
You can use shift to check the condition:
df.loc[df['rpm'].shift(1).eq(0) & df['rpm'].shift(-1).ne(0), 'rpm'] = 0
print(df)
# Output:
rpm
0 2.0
1 4.5
2 5.6
3 6.0
4 7.0
5 6.0
6 0.0
7 0.0
8 0.0 # HERE, old value: 3
9 5.0
10 9.0
11 8.9
12 9.3
13 0.0
14 0.0
15 0.0
16 0.0 # HERE, old value: 6
17 7.0
18 8.0
19 9.0
20 13.0
Details:
m1 = df['rpm'].shift(1).eq(0)
m2 = df['rpm'].shift(-1).ne(0)
out = pd.concat([df['rpm'], m1, m2, m1&m2], keys=['rpm', 'm1', 'm2', 'all'], axis=1)
print(out)
# Output
rpm rpm rpm rpm
0 2.0 False True False
1 4.5 False True False
2 5.6 False True False
3 6.0 False True False
4 7.0 False True False
5 6.0 False False False
6 0.0 False False False
7 0.0 True True True # HERE, already 0
8 3.0 True True True # HERE, set to 0
9 5.0 False True False
10 9.0 False True False
11 8.9 False True False
12 9.3 False False False
13 0.0 False False False
14 0.0 True False False
15 0.0 True True True # HERE, already 0
16 6.0 True True True # HERE, set to 0
17 7.0 False True False
18 8.0 False True False
19 9.0 False True False
20 13.0 False True False
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