使用两种颜色为seaborn heatmap中的不同行着色，将行分成两部分 [英] Use two colors to color different rows in seaborn heatmap split the rows into two

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:

mask: If passed, data will not be shown in cells where mask is True. Cells with missing values are automatically masked.

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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