带多种配色方案的带注释的热图 [英] Annotated heatmap with multiple color schemes
问题描述
我有以下数据框，并希望通过热图中的不同配色方案区分每个步骤"中的较小十进制差异.
I have the following dataframe and would like to differentiate the minor decimal differences in each "step" with a different color scheme in a heatmap.
样本数据:
Sample Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Step 8
A 64.847 54.821 20.897 39.733 23.257 74.942 75.945
B 64.885 54.767 20.828 39.613 23.093 74.963 75.928
C 65.036 54.772 20.939 39.835 23.283 74.944 75.871
D 64.869 54.740 21.039 39.889 23.322 74.925 75.894
E 64.911 54.730 20.858 39.608 23.101 74.956 75.930
F 64.838 54.749 20.707 39.394 22.984 74.929 75.941
G 64.887 54.781 20.948 39.748 23.238 74.957 75.909
H 64.903 54.720 20.783 39.540 23.028 74.898 75.911
I 64.875 54.761 20.911 39.695 23.082 74.897 75.866
J 64.839 54.717 20.692 39.377 22.853 74.849 75.939
K 64.857 54.736 20.934 39.699 23.130 74.880 75.903
L 64.754 54.746 20.777 39.536 22.991 74.877 75.902
M 64.798 54.811 20.963 39.824 23.187 74.886 75.895
我要寻找的示例:
推荐答案
我的第一种方法是基于具有多个子图的图形.地块数等于数据框中的列数；情节之间的差距可以缩小到零:
My first approach would be based on a figure with multiple subplots. Number of plots would equal number of columns in your dataframe; the gap between the plots could be shrinked down to zero:
cm = ['Blues', 'Reds', 'Greens', 'Oranges', 'Purples', 'bone', 'winter']
f, axs = plt.subplots(1, df.columns.size, gridspec_kw={'wspace': 0})
for i, (s, a, c) in enumerate(zip(df.columns, axs, cm)):
sns.heatmap(np.array([df[s].values]).T, yticklabels=df.index, xticklabels=[s], annot=True, fmt='.2f', ax=a, cmap=c, cbar=False)
if i>0:
a.yaxis.set_ticks([])
结果:
不确定是否会导致有用的甚至是自我描述的数据可视化，但这是您的选择-也许这有助于启动...
Not sure if this will lead to a helpful or even self describing visualization of data, but that's your choice - perhaps this helps to start...
补充:
关于添加颜色条:当然可以.但是-除了不知道您的数据背景和可视化的目的之外，我还想对所有这些添加一些想法:
Regarding adding the colorbars: of course you can. But - besides not knowing the background of your data and the purpose of the visualization - I'd like to add some thoughts on all that:
第一:将所有这些颜色条作为单独的一组条形添加到热图的一侧或下方是可能的，但是我发现读取数据已经非常困难，而且:所有这些注释-我想这一切都弄糟了.
另外:在此期间，@ ImportanceOfBeingErnest提供了关于该主题的如此漂亮的解决方案，以至于在这里这并不是太有意义.
First: adding all those colorbars as a separate bunch of bars on one side or below the heatmap is probably possible, but I find it already quite hard to read the data, plus: you already have all those annotations - it would mess all up I think.
Additionally: in the meantime @ImportanceOfBeingErnest provided such a beutiful solution on that topic, that this would be not too meaningful imo here.
第二:如果您真的想坚持使用热图技术，也许可以拆分并为每个列提供其颜色条会更适合:
Second: if you really want to stick to the heatmap thing, perhaps splitting up and giving every column its colorbar would suit better:
cm = ['Blues', 'Reds', 'Greens', 'Oranges', 'Purples', 'bone', 'winter']
f, axs = plt.subplots(1, df.columns.size, figsize=(10, 3))
for i, (s, a, c) in enumerate(zip(df.columns, axs, cm)):
sns.heatmap(np.array([df[s].values]).T, yticklabels=df.index, xticklabels=[s], annot=True, fmt='.2f', ax=a, cmap=c)
if i>0:
a.yaxis.set_ticks([])
f.tight_layout()
但是，所有这些-我敢怀疑这是您数据的最佳可视化.当然，我不知道您想对这些图说什么，看到或找到什么，但这就是要点:如果可视化类型适合需求，我想我会知道(至少可以想象).
However, all that said - I dare to doubt that this is the best visualization for your data. Of course, I don't know what you want to say, see or find with these plots, but that's the point: if the visualization type would fit to the needs, I guess I'd know (or at least could imagine).
例如:
一个简单的 df.plot()产生
我认为，这比热图能在十分之一秒的时间内显示出更多有关您列的不同特征的信息.
and I feel that this tells more about different characteristics of your columns within some tenths of a second than the heatmap.
还是您明确地追求每列均值的差异?
Or are you explicitely after the differences to each columns' means?
(df - df.mean()).plot()
...还是围绕它们的每一列的分布?
... or the distribution of each column around them?
(df - df.mean()).boxplot()
我想说的是:在开始/必须解释任何事情之前，当情节开始向某人讲述底层数据时，数据可视化将变得强大起来.
What I want to say: data visualization becomes powerful when a plot begins to tell sth about the underlying data before you begin/have to explain anything...
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