绘制2D数据:具有不同颜色图的热图 [英] Plotting of 2D data : heatmap with different colormaps

问题描述

我想可视化我拥有的2D数据.例如，以下是具有四个属性的数据:

I want to visualize 2D data that I have. For example following is the data with four attributes:

       att1  att2   att3
fun1     10     0      2
fun2      0     1      3
fun3      1    10      5
fun4      2     3     10

我想为每个数据点分配不同的颜色.颜色的强度取决于该列中属性的值，并且每列必须具有不同的颜色.

I want to assign each data point a different colour. The intensity of the color will depend on the value of the attribute in that column, and each column must have a different color.

以下是所需的图像:

有人知道我如何用Python或R制作它吗?

Does anyone have any idea how I can make it in Python or R?

推荐答案

使用Python:

我找到了更好的方法:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm

# data loading
df = pd.read_csv("file.csv", index_col=0) 


# plotting
fig,ax = plt.subplots()
ax.matshow(df.mask(((df == df) | df.isnull()) & (df.columns != "att1")), 
           cmap=cm.Reds) # You can change the colormap here
ax.matshow(df.mask(((df == df) | df.isnull()) & (df.columns != "att2")), 
           cmap=cm.Greens)
ax.matshow(df.mask(((df == df) | df.isnull()) & (df.columns != "att3")), 
           cmap=cm.Blues)
plt.xticks(range(3), df.columns)
plt.yticks(range(4), df.index)
plt.show()

一些细节:

df.mask(((df == df) | df.isnull()) & (df.columns != "att1"))
      att1  att2  att3
fun1    10   NaN   NaN
fun2     0   NaN   NaN
fun3     1   NaN   NaN
fun4     2   NaN   NaN

---

较旧的版本，带有numpy掩码数组:

---

Older version, with numpy masked array :

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from numpy.ma import masked_array
import numpy as np

df = pd.read_clipboard() # just copied your example

# define masked arrays to mask all but the given column
c1 = masked_array(df, mask=(np.ones_like(df)*(df.values[0]!=df.values[0][0]))) 
c2 = masked_array(df, mask=(np.ones_like(df)*(df.values[0]!=df.values[0][1])))
c3 = masked_array(df, mask=(np.ones_like(df)*(df.values[0]!=df.values[0][2])))

fig,ax = plt.subplots()
ax.matshow(c1,cmap=cm.Reds) # You can change the colormap here
ax.matshow(c2,cmap=cm.Greens)
ax.matshow(c3,cmap=cm.Blues)
plt.xticks(range(3), df.columns)
plt.yticks(range(4), df.index)

一些细节:

df是一个数据框:

      att1  att2  att3
fun1    10     0     2
fun2     0     1     3
fun3     1    10     5
fun4     2     3    10

c1，c2，c3是掩码数组(针对第1、2和3列):

c1, c2, c3 are masked arrays (for columns 1, 2 and 3):

>>> c1
masked_array(data =
 [[10 -- --]
 [0 -- --]
 [1 -- --]
 [2 -- --]],
             mask =
 [[False  True  True]
 [False  True  True]
 [False  True  True]
 [False  True  True]],
       fill_value = 999999)

或者，您可以从一个numpy的2D数组开始:

alternatively, you can start from a numpy 2D array :

>> data
array([[10,  0,  2],
       [ 0,  1,  3],
       [ 1, 10,  5],
       [ 2,  3, 10]])

，并将所有df和df.values替换为data(二维数组)，除了标签部分.

and replace all df and df.values with data (the 2D array), except in the labeling part.

这篇关于绘制2D数据:具有不同颜色图的热图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！