问题描述
根据以下示例，我需要使我的代码忽略第一次匹配时间序列数据后的np.where.
As per the following example I need to make my code ignore the np.where after first match for time series data.
因此，在2014-03-04 14:00:00行上，np.where在test_output列上给出了1.0，并且正如预期的那样，在下一行上也给出了1.0.我只希望这一次触发一次.我将在问题的末尾显示所需的输出.
So on the 2014-03-04 14:00:00 row the np.where gives a 1.0 on the test_output column and, as would be expected, also on the next row. I only want this to trigger once ever. I will show desired output at the end of the question.
感谢您查看问题.
为测试生成的数据帧:
df = pd.DataFrame(index=pd.date_range(start='2014-03-04 09:00:00', end='2014-03-04 16:15:00', freq='1h') + pd.date_range(start='2014-03-05 09:00:00', end='2014-03-05 16:15:00', freq='1h'), data={'test_1': np.nan})
df['test_1'][5:16]=1.0
df['test_output'] = np.where(df['test_1'] == 1.0,1.0,np.nan);
df
test_1 test_output
2014-03-04 09:00:00 NaN NaN
2014-03-04 10:00:00 NaN NaN
2014-03-04 11:00:00 NaN NaN
2014-03-04 12:00:00 NaN NaN
2014-03-04 13:00:00 NaN NaN
2014-03-04 14:00:00 1.0 1.0
2014-03-04 15:00:00 NaN NaN
2014-03-04 16:00:00 1.0 1.0
2014-03-05 09:00:00 1.0 1.0
这是所需的输出:
test_1 test_output
2014-03-04 09:00:00 NaN NaN
2014-03-04 10:00:00 NaN NaN
2014-03-04 11:00:00 NaN NaN
2014-03-04 12:00:00 NaN NaN
2014-03-04 13:00:00 NaN NaN
2014-03-04 14:00:00 1.0 1.0
2014-03-04 15:00:00 NaN NaN
2014-03-04 16:00:00 1.0 NaN
2014-03-05 09:00:00 1.0 NaN
推荐答案
使用 first_valid_index 来设置第一行:
use first_valid_index on the mask to set the first row:
In [30]:
df.loc[df[df['test_1'] == 1.0].first_valid_index(),'test_output'] = 1.0
df
Out[30]:
test_1 test_output
2014-03-04 09:00:00 NaN NaN
2014-03-04 10:00:00 NaN NaN
2014-03-04 11:00:00 NaN NaN
2014-03-04 12:00:00 NaN NaN
2014-03-04 13:00:00 NaN NaN
2014-03-04 14:00:00 1.0 1.0
2014-03-04 15:00:00 1.0 NaN
2014-03-04 16:00:00 1.0 NaN
2014-03-05 09:00:00 1.0 NaN
2014-03-05 10:00:00 1.0 NaN
2014-03-05 11:00:00 1.0 NaN
2014-03-05 12:00:00 1.0 NaN
2014-03-05 13:00:00 1.0 NaN
2014-03-05 14:00:00 1.0 NaN
2014-03-05 15:00:00 1.0 NaN
2014-03-05 16:00:00 1.0 NaN
分解以上内容:
In [32]:
df['test_1'] == 1.0
Out[32]:
2014-03-04 09:00:00 False
2014-03-04 10:00:00 False
2014-03-04 11:00:00 False
2014-03-04 12:00:00 False
2014-03-04 13:00:00 False
2014-03-04 14:00:00 True
2014-03-04 15:00:00 True
2014-03-04 16:00:00 True
2014-03-05 09:00:00 True
2014-03-05 10:00:00 True
2014-03-05 11:00:00 True
2014-03-05 12:00:00 True
2014-03-05 13:00:00 True
2014-03-05 14:00:00 True
2014-03-05 15:00:00 True
2014-03-05 16:00:00 True
Freq: BH, Name: test_1, dtype: bool
In [33]:
df[df['test_1'] == 1.0].first_valid_index()
Out[33]:
Timestamp('2014-03-04 14:00:00', offset='BH')
您可以使用np.where通过再次屏蔽df来做到这一点，从而通过将np数组与1.0进行比较，生成条件为假的NaN:
You can do it using np.where by again masking against the df so it produces NaN where the condition is false by comparing the np array against 1.0:
In [41]:
df.loc[df[np.where(df['test_1'] == 1.0, 1.0, 0) == 1].first_valid_index(), 'test_output'] = 1.0
df
Out[41]:
test_1 test_output
2014-03-04 09:00:00 NaN NaN
2014-03-04 10:00:00 NaN NaN
2014-03-04 11:00:00 NaN NaN
2014-03-04 12:00:00 NaN NaN
2014-03-04 13:00:00 NaN NaN
2014-03-04 14:00:00 1.0 1.0
2014-03-04 15:00:00 1.0 NaN
2014-03-04 16:00:00 1.0 NaN
2014-03-05 09:00:00 1.0 NaN
2014-03-05 10:00:00 1.0 NaN
2014-03-05 11:00:00 1.0 NaN
2014-03-05 12:00:00 1.0 NaN
2014-03-05 13:00:00 1.0 NaN
2014-03-05 14:00:00 1.0 NaN
2014-03-05 15:00:00 1.0 NaN
2014-03-05 16:00:00 1.0 NaN
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