使用两种颜色为seaborn heatmap中的不同行着色，将行分成两部分

问题描述

我有以下数据框:

  fruits = {'fruit':['apple1'，'apple2'，'banana1'，'banan2'，'peach1'，'peach2']，'1':[0,0,0，1,0,1]，'2':[1,1,0,1,1,1,1]，'3':[1,1,1,1,0,0]，'4':[0，1,1,1,1,1]}df_fruits = pd.DataFrame(data = fruits)df_fruits = df_fruits.set_index('fruit')>>>1 2 3 4水果apple1 0 1 1 0apple2 0 1 1 1香蕉1 0 0 1 1banan2 1 1 1 1桃子1 0 1 0 1桃子2 1 1 0 1 

我正在尝试创建某种热图，因此如果值是1，它将获得颜色，如果值是0将获得颜色灰色，除此之外，这就是问题所在，我想给所有结果加上一号为蓝色，所有水果与第二号为绿色.我尝试使用提到的脚本

如您所见，苹果1看起来有两行，苹果2看起来有两行，依此类推，而我想每个都有一行.我已经试过了程度，但不能摆脱那些限制.

我的最终目标-在热图中，数据框中的每一行都包含一行，当以1结束的水果为蓝色时，以2结束的水果为绿色(仅当值为1时).如果值为零，它将为灰色.

修改:我已经按照建议使用了ax.grid(False)，但是由于线条消失而仍然没有效果.我也发现密谋是错误的:

如您所见，"banana2"行应该是绿色但是白色.

解决方案

您可以使用

I have the following dataframe:

fruits={'fruit':['apple1','apple2','banana1','banan2','peach1','peach2'],'1':[0,0,0,1,0,1],'2':[1,1,0,1,1,1],'3':[1,1,1,1,0,0],'4':[0,1,1,1,1,1]}
df_fruits=pd.DataFrame(data=fruits)
df_fruits=df_fruits.set_index('fruit')


>>>     1   2   3   4
fruit
apple1  0   1   1   0
apple2  0   1   1   1
banana1 0   0   1   1
banan2  1   1   1   1
peach1  0   1   0   1
peach2  1   1   0   1

I'm Trying to create some kind of heatmap so if value is 1 it will get color and if is zero will get color grey.In addition to that, and here is the problem, I want to give all the fruits with number one color blue and all the fruits with number two color green.I have tried to use the script as mentioned here but I get white lines on the cells in undesired locations that divide each row into two:

N_communities = df_fruits.index.size
N_cols = df_fruits.columns.size
cmaps = ['Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens','Blues','Greens']

fig, ax = plt.subplots(figsize=(10,8))

for i,((idx,row),cmap) in enumerate(zip(df_fruits.iterrows(), cmaps)):
    ax.imshow(np.vstack([row.values, row.values]), aspect='equal', extent=[-0.5,N_cols-0.5,i,i+1], cmap=cmap)
    for j,val in enumerate(row.values):
        vmin, vmax = row.agg(['min','max'])
        vmid = (vmax-vmin)/2
        #if not np.isnan(val):
            #ax.annotate(val, xy=(j,i+0.5), ha='center', va='center', color='black' if (val<=vmid or vmin==vmax) else 'white')
ax.set_ylim(0,N_communities)

ax.set_xticks(range(N_cols))
ax.set_xticklabels(df_fruits.columns, rotation=90, ha='center')

ax.set_yticks(0.5+np.arange(N_communities))
ax.set_yticklabels(df_fruits.index)
ax.set_ylabel('Index')
ax.hlines([2,4],color="black" ,*ax.get_xlim())
ax.invert_yaxis()

fig.tight_layout()

As you can see, it looks like apple 1 has two rows and apple 2 has two rows and etc., while I want to have one row per each.I have tried to play with the extent but could not ger rid of those lines.

My end goal - to have one rows in the heatmap for each row in the dataframe, when fruit finishes with 1 are blue, fruits finishes with 2 are green (only if value is 1) . if value is zero it will be grey.

Edit:I have used the the ax.grid(False) as suggested but is still not goood as the lines dissapear. also I found out that the plotting is wrong:

as you can see, the row "banana2" suppose to get green color but is white.

解决方案

You can use the mask option of sns.heatmap:

So, to plot the blue fruit1 squares, mask out the fruit2 values and vice versa.

The fruit1/fruit2 heatmaps can be plotted together by saving the axes handle ax and reusing it with ax=ax:

import pandas as pd
import seaborn as sns

fruits = {'fruit':['apple1','apple2','banana1','banana2','peach1','peach2'],'1':[0,0,0,1,0,1],'2':[1,1,0,1,1,1],'3':[1,1,1,1,0,0],'4':[0,1,1,1,1,1]}
df_fruits = pd.DataFrame(data=fruits)
df_fruits = df_fruits.set_index('fruit')

# *** this line is needed for seaborn 0.10.1 (not needed for 0.11.1) ***
df_fruits = df_fruits.astype('float')

# common settings: linewidths for grid lines, hide colorbar, set square aspect
kwargs = dict(linewidths=1, cbar=False, square=True)

# plot initial gray squares and save heatmap handle as ax
ax = sns.heatmap(df_fruits, cmap='Greys_r', alpha=0.2, **kwargs)

# iterate ending:cmap pairs
cmaps = {'1': 'Blues_r', '2': 'Greens_r'}
for ending, cmap in cmaps.items():

    # create mask for given fruit ending
    mask = df_fruits.apply(
        lambda x: x if x.name.endswith(ending) else 0,
        result_type='broadcast',
        axis=1,
    ).eq(0)

    # plot masked heatmap on reusable ax
    sns.heatmap(df_fruits, mask=mask, cmap=cmap, ax=ax, **kwargs)

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