Question

我想将所有值设置为某个值（例如999）在某个时间段（例如1小时）内超过某个阈值（例如7）出现的某个值。我曾经使用过一些怪异的非矢量化方法，但有些运气，但是必须有一种更好的，泛泛的方法。

一个例子是：

设置随机数据帧：

return new Promise(resolve => {
    let initialCall = true;
    API.addEventListener(newState => {
        cacheStorage.state = newState;
        if (initialCall) {
          resolve(newState);
          initialCall = false;
        }
    }, /* repeating */ true);
  });

随机输出：

hr_rng = pd.date_range(start='7/1/2014 00:00:00', end='7/1/2014 10:00:00', freq='H')
df = pd.DataFrame(hr_rng, columns=['date_time'])
df.set_index(pd.DatetimeIndex(df['date_time']),inplace=True)
df['val0']=np.random.randint(1, 10, df.shape[0])

我想回来的是这个

    date_time   val0
date_time       
2014-07-01 00:00:00     2014-07-01 00:00:00     4
2014-07-01 01:00:00     2014-07-01 01:00:00     8
2014-07-01 02:00:00     2014-07-01 02:00:00     4
2014-07-01 03:00:00     2014-07-01 03:00:00     7
2014-07-01 04:00:00     2014-07-01 04:00:00     2
2014-07-01 05:00:00     2014-07-01 05:00:00     4
2014-07-01 06:00:00     2014-07-01 06:00:00     4
2014-07-01 07:00:00     2014-07-01 07:00:00     9
2014-07-01 08:00:00     2014-07-01 08:00:00     1
2014-07-01 09:00:00     2014-07-01 09:00:00     9
2014-07-01 10:00:00     2014-07-01 10:00:00     5

另一个随机示例：

date_time   val0
date_time       
2014-07-01 00:00:00     2014-07-01 00:00:00     999
2014-07-01 01:00:00     2014-07-01 01:00:00     999
2014-07-01 02:00:00     2014-07-01 02:00:00     999
2014-07-01 03:00:00     2014-07-01 03:00:00     7
2014-07-01 04:00:00     2014-07-01 04:00:00     2
2014-07-01 05:00:00     2014-07-01 05:00:00     4
2014-07-01 06:00:00     2014-07-01 06:00:00     999
2014-07-01 07:00:00     2014-07-01 07:00:00     999
2014-07-01 08:00:00     2014-07-01 08:00:00     999
2014-07-01 09:00:00     2014-07-01 09:00:00     999
2014-07-01 10:00:00     2014-07-01 10:00:00     999

应该变成这样：

    date_time   val0
date_time       
2014-07-01 00:00:00     2014-07-01 00:00:00     5
2014-07-01 01:00:00     2014-07-01 01:00:00     6
2014-07-01 02:00:00     2014-07-01 02:00:00     3
2014-07-01 03:00:00     2014-07-01 03:00:00     2
2014-07-01 04:00:00     2014-07-01 04:00:00     9
2014-07-01 05:00:00     2014-07-01 05:00:00     7
2014-07-01 06:00:00     2014-07-01 06:00:00     6
2014-07-01 07:00:00     2014-07-01 07:00:00     8
2014-07-01 08:00:00     2014-07-01 08:00:00     6
2014-07-01 09:00:00     2014-07-01 09:00:00     7
2014-07-01 10:00:00     2014-07-01 10:00:00     3

Answer 1

IIUC是一种方法：

import pandas as pd
import numpy as np

np.random.seed(42)

hr_rng = pd.date_range(start='7/1/2014 00:00:00', 
                       end='7/1/2014 10:00:00', 
                       freq='H')
df = pd.DataFrame(hr_rng, columns=['date_time'])
df.set_index(pd.DatetimeIndex(df['date_time']),inplace=True)
df['val0']=np.random.randint(1, 10, df.shape[0])

现在，更新等于或大于阈值的行。

threshold = 7

# initialize
df['test'] = df['val0']

mask = df['val0'] >= threshold
df.loc[mask, 'test'] = 999

print(df.head())

                              date_time  val0  test
date_time                                          
2014-07-01 00:00:00 2014-07-01 00:00:00     7   999
2014-07-01 01:00:00 2014-07-01 01:00:00     4     4
2014-07-01 02:00:00 2014-07-01 02:00:00     8   999
2014-07-01 03:00:00 2014-07-01 03:00:00     5     5
2014-07-01 04:00:00 2014-07-01 04:00:00     7   999

您是否有关于查找和更新所选值的问题？还是将观察结果放入一小时的时段中？

根据时间范围设置熊猫值

1 个答案: