我想创建一个关联，其中一个变量为自变量，多个其他变量为因变量，然后将其转换为热图。
我正在关注这些directions。
我的代码：
df_corr_interest = df[df.columns[0:11]].corr()['impact_action_yn'][:-1]
#set fig size
fig, ax = plt.subplots(figsize=(20,15))
#plot matrix
sns.heatmap(df_corr_interest, square=True,annot=True, annot_kws={'size':12},cmap="GnBu")
plt.show();
我遇到以下错误：KeyError: 'impact_action_yn'
数据框：
ExternalReference interest_el interest_pl interest_ad interest_ol commitment_elected commitment_policy commitment_advocacy commitment_organizing timeline_elected ... Policy Organizing Engagement Parent Veteran first_gen_american first_gen_college ri_region LGBTQ Gender
0 0034000001RHCU0AAP 1 1 1 1 0 0 0 0 0 ... 0 0 1 0 0 0 0 0 0 Woman
1 00340000015yDbOAAU 1 1 1 2 0 0 0 2 0 ... 0 1 2 0 0 0 0 1 0 Man
2 0034000000y3QjMAAU 1 2 2 2 0 2 3 4 0 ... 5 2 3 0 0 0 0 1 0 Man
3 0034000001qcNXRAA2 1 1 1 3 0 0 0 3 0 ... 0 1 6 0 0 0 0 1 0 Woman
4 0034000001DVPedAAH 1 1 1 1 0 0 0 0 0 ... 0 0 1 0 0 0 0 0 0 Woman
有什么想法吗？
已更新完整的变量列表：
ExternalReference object
interest_el int64
interest_pl int64
interest_ad int64
interest_ol int64
commitment_elected int64
commitment_policy int64
commitment_advocacy int64
commitment_organizing int64
timeline_elected int64
timeline_policy int64
timeline_advocacy int64
timeline_organizing int64
interest_appointed float64
interest_vol_organizing float64
interest_school_organizing float64
impact_action_yn float64
impact_action_public_action float64
impact_action_testified float64
impact_action_met_el float64
impact_action_lobbied float64
impact_action_bill float64
impact_action_other float64
impact_action_other_text object
impact_topic_charter_schools float64
impact_topics_ece float64
impact_topics_postsecondary float64
impact_topics_school_choice float64
impact_topics_student_achv float64
impact_topics_district_perf float64
impact_topics_wraparound float64
impact_topics_school_discipline float64
impact_topics_special_pops float64
impact_topics_teacher_tenure float64
impact_topics_other float64
impact_topics_other_text object
impact_role object
impact_level float64
impact_level_text object
impact_success float64
impact_other_comments object
Advocacy int64
Elected int64
Policy int64
Organizing int64
Engagement int64
Parent int64
Veteran int64
first_gen_american int64
first_gen_college int64
ri_region int64
LGBTQ int64
Gender object
dtype: object
答案 0 :(得分：0)
通过运行df[df.columns[0:11]].corr()，您将获得前11列中每列之间的成对相关性，但是'impact_action_yn'是第15列，则没有任何相关性输入结果。相反，您可以执行df.corr()并随后删除所有不必要的关联。
在那之后，您将遇到sns.reshape需要矩阵的问题；您可以通过将['impact_action_yn']替换为[['impact_action_yn']]来解决此问题。
答案 1 :(得分：0)
解决了。
需要df.to_frame（）
完整代码：
df_corr_impact_action_yn = df[df.columns[0:17]].corr()['impact_action_yn'][:-1]
#set fig size
fig, ax = plt.subplots(figsize=(30,25))
#plot matrix
sns.heatmap(df_corr_impact_action_yn.to_frame(),annot=True, annot_kws={'size':12},cmap="GnBu")
plt.show();