<p>使用<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumcount.html" rel="nofollow noreferrer">^{<cd1>}</a>+<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.where.html" rel="nofollow noreferrer">^{<cd2>}</a></p>
<p><strong>初始数据帧</strong></p>
<pre><code> h_diff l_diff
timestamp
2000-01-18 NaN NaN
2000-01-19 2.75 2.93
2000-01-20 12.75 10.13
2000-01-21 -7.25 -3.31
2000-01-24 -1.50 -5.07
2000-01-25 0.37 -2.75
2000-01-26 1.07 7.38
2000-01-27 1.19 -2.75
2000-01-28 2.13 -6.38
2000-01-31 -7.00 -6.12
</code></pre>
<hr/>
<pre><code>h = df['h_diff'].gt(0)
#h = np.sign(df['h_diff'])
df['monotonic_h']=h.groupby(h.ne(h.shift()).cumsum()).cumcount().add(1).where(h,0)
print(df)
h_diff l_diff monotonic_h
timestamp
2000-01-18 NaN NaN 0
2000-01-19 2.75 2.93 1
2000-01-20 12.75 10.13 2
2000-01-21 -7.25 -3.31 0
2000-01-24 -1.50 -5.07 0
2000-01-25 0.37 -2.75 1
2000-01-26 1.07 7.38 2
2000-01-27 1.19 -2.75 3
2000-01-28 2.13 -6.38 4
2000-01-31 -7.00 -6.12 0
</code></pre>
<hr/>
<pre><code>df['monotonic_h'].max()
#4
</code></pre>
<hr/>
<p><strong>细节</strong></p>
<pre><code>h.ne(h.shift()).cumsum()
timestamp
2000-01-18 1
2000-01-19 2
2000-01-20 2
2000-01-21 3
2000-01-24 3
2000-01-25 4
2000-01-26 4
2000-01-27 4
2000-01-28 4
2000-01-31 5
Name: h_diff, dtype: int64
</code></pre>
<p><strong>更新</strong></p>
<pre><code>df = df.join( h.groupby(h.ne(h.shift()).cumsum()).cumcount().add(1)
.to_frame('values')
.assign(monotic = np.where(h,'monotic_h_greater_0',
'monotic_h_not_greater_0'),
index = lambda x: x.index)
.where(df['h_diff'].notna())
.pivot_table(columns = 'monotic',
index = 'index',
values = 'values',
fill_value=0) )
print(df)
h_diff l_diff monotic_h_greater_0 monotic_h_not_greater_0
timestamp
2000-01-18 NaN NaN NaN NaN
2000-01-19 2.75 2.93 1.0 0.0
2000-01-20 12.75 10.13 2.0 0.0
2000-01-21 -7.25 -3.31 0.0 1.0
2000-01-24 -1.50 -5.07 0.0 2.0
2000-01-25 0.37 -2.75 1.0 0.0
2000-01-26 1.07 7.38 2.0 0.0
2000-01-27 1.19 -2.75 3.0 0.0
2000-01-28 2.13 -6.38 4.0 0.0
2000-01-31 -7.00 -6.12 0.0 1.0
</code></pre>
