python - 如何使用seaborn防止条形图重叠？

Question

month   cate_cnt1_members   cate_cnt2_members   cate_cnt3_members   cate_cnt4_members   cate_cnt5_members   cate_cnt6_members   cate_cnt7_members   cate_cnt8_members   cate_cnt9_members   cate_cnt10_members  cate_cnt11_members  cate_cnt12_members  cate_cnt13_members  cate_cnt14_members
201501  93.525692   5.989799    0.455098    0.027863    0.001548    0.000000    0.000000    0.000000    0.000000    0.000000    0.000000    0.000000    0.000000    0.0
201502  90.515995   8.396707    0.971026    0.107892    0.008380    0.000000    0.000000    0.000000    0.000000    0.000000    0.000000    0.000000    0.000000    0.0
201503  82.525162   14.066414   2.836065    0.505229    0.061750    0.005380    0.000000    0.000000    0.000000    0.000000    0.000000    0.000000    0.000000    0.0
201504  75.546295   18.279664   4.884050    1.102780    0.172282    0.013621    0.001199    0.000109    0.000000    0.000000    0.000000    0.000000    0.000000    0.0
201505  71.142107   20.954861   6.278794    1.401423    0.206386    0.015837    0.000593    0.000000    0.000000    0.000000    0.000000    0.000000    0.000000    0.0
201506  63.783161   23.386509   9.241094    2.914457    0.601408    0.067921    0.005178    0.000273    0.000000    0.000000    0.000000    0.000000    0.000000    0.0
201507  62.361179   23.364693   9.888232    3.445630    0.812055    0.116408    0.010563    0.001240    0.000000    0.000000    0.000000    0.000000    0.000000    0.0

上面显示了 pandas 数据框，其中显示了不同类别的百分比随月变化。我想使用seaborn 得到一个条形图，其条形由从第2 列到末尾的14 个类别成员的百分比组成。 这是我的代码:

flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]
f, ax = plt.subplots(figsize=(6, 15))
sns.barplot(x = df['month'], y = df['cate_cnt1_members'], label='cate_cnt1_members',   color=sns.color_palette("Set2", 10)[0])
sns.barplot(x = df['month'], y = df['cate_cnt2_members'], label='cate_cnt2_members',   color=sns.color_palette("Set2", 10)[1])
sns.barplot(x = df['month'], y = df['cate_cnt3_members'], label='cate_cnt3_members',   color=sns.color_palette("Set2", 10)[2])
sns.barplot(x = df['month'], y = df['cate_cnt4_members'], label='cate_cnt4_members',   color=sns.color_palette("Set2", 10)[3])
sns.barplot(x = df['month'], y = df['cate_cnt5_members'], label='cate_cnt5_members',   color=sns.color_palette("Set2", 10)[4])
sns.barplot(x = df['month'], y = df['cate_cnt6_members'], label='cate_cnt6_members',   color=sns.color_palette("Set2", 10)[5])
sns.barplot(x = df['month'], y = df['cate_cnt7_members'], label='cate_cnt7_members',   color=sns.color_palette("Set2", 10)[6])
sns.barplot(x = df['month'], y = df['cate_cnt8_members'], label='cate_cnt8_members',   color=sns.color_palette("Set2", 10)[7])
sns.barplot(x = df['month'], y = df['cate_cnt9_members'], label='cate_cnt9_members',   color=sns.color_palette("Set2", 10)[8])
sns.barplot(x = df['month'], y = df['cate_cnt10_members'], label='cate_cnt10_members', color=sns.color_palette("Set2", 10)[9])
sns.barplot(x = df['month'], y = df['cate_cnt11_members'], label='cate_cnt11_members', color=sns.color_palette("Paired")[0])
sns.barplot(x = df['month'], y = df['cate_cnt12_members'], label='cate_cnt12_members', color=sns.color_palette("Paired")[1])
sns.barplot(x = df['month'], y = df['cate_cnt13_members'], label='cate_cnt13_members', color=sns.color_palette("Paired")[4])
sns.barplot(x = df['month'], y = df['cate_cnt14_members'], label='cate_cnt14_members', color=sns.color_palette(flatui)[0])
plt.ylabel("percentage of category scope count")
plt.xlabel(" Month")
ax.legend(ncol=7, loc="topper middle", frameon=True)
sns.despine(left=True, bottom=True)

结果如下。但我不希望它们彼此重叠。我希望 14 个分量之和为 100，并将 100 填满。那么我怎样才能实现这一目标呢？

Answer 1

在 pandas 中有一种非常简单的方法可以做到这一点。首先，您必须将索引设置为月份，然后创建一个堆积条形图

df = df.set_index('month')
df.plot.bar(stacked=True)

在seaborn 中执行此操作有点棘手。您必须计算每行的累积和，然后绘制该图

# set the index if you haven't
df = df.set_index('month')
df = df.cumsum(axis=1)

然后对原始代码进行一些小的调整。然后以相反的顺序绘制，以便首先绘制 100% 条形图。

flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]
f, ax = plt.subplots(figsize=(10, 15))
sns.barplot(x = df.index, y = df['cate_cnt14_members'], label='cate_cnt14_members',   color=sns.color_palette("Set2", 10)[0])
sns.barplot(x = df.index, y = df['cate_cnt13_members'], label='cate_cnt13_members',   color=sns.color_palette("Set2", 10)[1])
sns.barplot(x = df.index, y = df['cate_cnt12_members'], label='cate_cnt12_members',   color=sns.color_palette("Set2", 10)[2])
sns.barplot(x = df.index, y = df['cate_cnt11_members'], label='cate_cnt11_members',   color=sns.color_palette("Set2", 10)[3])
sns.barplot(x = df.index, y = df['cate_cnt10_members'], label='cate_cnt10_members',   color=sns.color_palette("Set2", 10)[4])
sns.barplot(x = df.index, y = df['cate_cnt9_members'], label='cate_cnt9_members',   color=sns.color_palette("Set2", 10)[5])
sns.barplot(x = df.index, y = df['cate_cnt8_members'], label='cate_cnt8_members',   color=sns.color_palette("Set2", 10)[6])
sns.barplot(x = df.index, y = df['cate_cnt7_members'], label='cate_cnt7_members',   color=sns.color_palette("Set2", 10)[7])
sns.barplot(x = df.index, y = df['cate_cnt6_members'], label='cate_cnt6_members',   color=sns.color_palette("Set2", 10)[8])
sns.barplot(x = df.index, y = df['cate_cnt5_members'], label='cate_cnt5_members', color=sns.color_palette("Set2", 10)[9])
sns.barplot(x = df.index, y = df['cate_cnt4_members'], label='cate_cnt4_members', color=sns.color_palette("Paired")[0])
sns.barplot(x = df.index, y = df['cate_cnt3_members'], label='cate_cnt3_members', color=sns.color_palette("Paired")[1])
sns.barplot(x = df.index, y = df['cate_cnt2_members'], label='cate_cnt2_members', color=sns.color_palette("Paired")[4])
sns.barplot(x = df.index, y = df['cate_cnt1_members'], label='cate_cnt1_members', color=sns.color_palette(flatui)[0])
plt.ylabel("percentage of category scope count")
plt.xlabel(" Month")
ax.legend(ncol=7, loc="upper center", frameon=True)
sns.despine(left=True, bottom=True)