擅长:python、mysql、java
<p>您可以删除列<code>Client</code>,因为它没有测试缺失值的百分比,通过<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.isna.html" rel="nofollow noreferrer">^{<cd2>}</a>测试它们,通过<code>Client</code>聚合平均值以替换<code>NaN</code>避免丢失它们,最后通过<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.T.html" rel="nofollow noreferrer">^{<cd5>}</a>进行转置:</p>
<pre><code>print (df)
id type priority Client
0 NaN Incident Low client1
1 NaN NaN High client1
2 56 294 Incident Nan NaN
3 56 197 NaN Low client3
4 NaN Demande NaN client4
df = (df.drop('Client', 1)
.isna()
.groupby(df['Client'].fillna('NaN'))
.mean()
.rename_axis(None)
.T)
print (df)
NaN client1 client3 client4
id 0.0 1.0 0.0 1.0
type 0.0 0.5 1.0 0.0
priority 0.0 0.0 0.0 1.0
</code></pre>