计算时间序列中的连续值 [英] Count consecutives values in time series

问题描述

这是我第一次在这里问一个问题，所以我希望我会做对的！

It's my first time asking a question here, so I hope I will do it right !

我有一个熊猫数据框:

df2.data
Out[66]: 
date
2016-01-02    0.0
2016-01-03    1.0
2016-01-04    1.0
2016-01-05    1.0
2016-01-06    0.0
2016-01-07    0.0
2016-01-08    1.0
2016-01-09    2.0
2016-01-10    1.0
2016-01-11    0.0
Name: data, dtype: float64

我想要以下结果:

            data  trend  trend_type
date                               
2016-01-02   0.0      0           0
2016-01-03   1.0      0           0
2016-01-04   1.0      1           1
2016-01-05   1.0      2           1
2016-01-06   0.0      0           0
2016-01-07   0.0      1           0
2016-01-08   1.0      0           0
2016-01-09   2.0      0           0
2016-01-10   1.0      0           0
2016-01-11   0.0      0           0

我的问题与到目前为止，我设法掌握了趋势，但是效率不够高(对于750行数据帧，大约需要8秒)

So far, I managed to get the trend, but it is not efficient enough (about 8 sec for a 750 rows dataframe)

df['grp'] = (df.close.diff(1) == 0).astype('int')
df['trend'] = 0
start_time = time.time()
for i in range(2, len(df['grp'])):
    if df.grp.iloc[i] == 1:
        df['trend'].iloc[i] = df['trend'].iloc[i-1] + 1 

推荐答案

第1步
要获取趋势，请执行 groupby + cumcount -

df['trend'] = df.data.groupby(df.data.ne(df.data.shift()).cumsum()).cumcount()
df

            data  trend
2016-01-02   0.0      0
2016-01-03   1.0      0
2016-01-04   1.0      1
2016-01-05   1.0      2
2016-01-06   0.0      0
2016-01-07   0.0      1
2016-01-08   1.0      0
2016-01-09   2.0      0
2016-01-10   1.0      0
2016-01-11   0.0      0

第2步
(IIUC)，以获取 trend_type ，比较连续的行并进行分配.

Step 2
(IIUC), to get trend_type, compare consecutive rows and assign.

df['trend_type'] = 0
m = df.data.eq(df.data.shift())
df.loc[m, 'trend_type'] = df.loc[m, 'data']

df

            data  trend  trend_type
2016-01-02   0.0      0         0.0
2016-01-03   1.0      0         0.0
2016-01-04   1.0      1         1.0
2016-01-05   1.0      2         1.0
2016-01-06   0.0      0         0.0
2016-01-07   0.0      1         0.0
2016-01-08   1.0      0         0.0
2016-01-09   2.0      0         0.0
2016-01-10   1.0      0         0.0
2016-01-11   0.0      0         0.0

这篇关于计算时间序列中的连续值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！