<p>将<a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html" rel="nofollow noreferrer">^{<cd1>}</a>与词典的<code>d.keys()</code>和<code>d.values()</code>一起使用:</p>
<pre><code>from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
df = pd.DataFrame(mlb.fit_transform(d.values()), index=d.keys(),columns=mlb.classes_)
print (df)
col_1 col_2 col_3 col_4
GP 1 1 1 1
MIN 1 1 1 1
PTS 1 1 1 1
FGM 1 1 0 1
FGA 0 1 0 0
FG% 0 1 1 1
3P Made 0 1 1 0
AST 0 1 1 0
STL 0 1 0 0
BLK 0 1 1 0
TOV 0 0 1 0
</code></pre>
<p>Pandas是唯一的解决方案,但是<code>Series</code>、<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.join.html" rel="nofollow noreferrer">^{<cd5>}</a>和<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.get_dummies.html" rel="nofollow noreferrer">^{<cd6>}</a>的速度较慢:</p>
<pre><code>df = pd.Series(d).str.join('|').str.get_dummies()
</code></pre>