问题：Matplotlib散点图; 颜色作为第三个变量的函数
我想制作一个散点图（使用matplotlib），其中根据第三个变量对点进行阴影处理。我对此非常了解：
plt.scatter(w, M, c=p, marker='s')其中w和M是数据点，而p是我要针对其着色的变量。
但是我想用灰度而不是彩色来做。有人可以帮忙吗？
回答 0
无需手动设置颜色。相反，请指定灰度颜色图…
import numpy as np
import matplotlib.pyplot as plt
# Generate data...
x = np.random.random(10)
y = np.random.random(10)
# Plot...
plt.scatter(x, y, c=y, s=500)
plt.gray()
plt.show()
或者，如果您希望使用更大范围的颜色图，也可以将cmapkwarg 指定为scatter。要使用其中任何一个的反向版本，只需指定其中_r任何一个的“ ”版本即可。例如，gray_r而不是gray。有几种不同的灰度色彩映射预先制作的（如gray，gist_yarg，binary，等）。
import matplotlib.pyplot as plt
import numpy as np
# Generate data...
x = np.random.random(10)
y = np.random.random(10)
plt.scatter(x, y, c=y, s=500, cmap='gray')
plt.show()There’s no need to manually set the colors. Instead, specify a grayscale colormap…
import numpy as np
import matplotlib.pyplot as plt
# Generate data...
x = np.random.random(10)
y = np.random.random(10)
# Plot...
plt.scatter(x, y, c=y, s=500)
plt.gray()
plt.show()
Or, if you’d prefer a wider range of colormaps, you can also specify the cmap kwarg to scatter. To use the reversed version of any of these, just specify the “_r” version of any of them. E.g. gray_r instead of gray. There are several different grayscale colormaps pre-made (e.g. gray, gist_yarg, binary, etc).
import matplotlib.pyplot as plt
import numpy as np
# Generate data...
x = np.random.random(10)
y = np.random.random(10)
plt.scatter(x, y, c=y, s=500, cmap='gray')
plt.show()
回答 1
在matplotlib中，可以将灰色表示为介于0-1之间的数字值。
例如c = '0.1'
然后，您可以将第三个变量转换为该范围内的值，并使用它为点着色。
在以下示例中，我将点的y位置用作确定颜色的值：
from matplotlib import pyplot as plt
x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
y = [125, 32, 54, 253, 67, 87, 233, 56, 67]
color = [str(item/255.) for item in y]
plt.scatter(x, y, s=500, c=color)
plt.show()
In matplotlib grey colors can be given as a string of a numerical value between 0-1.
For example c = '0.1'
Then you can convert your third variable in a value inside this range and to use it to color your points.
In the following example I used the y position of the point as the value that determines the color:
from matplotlib import pyplot as plt
x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
y = [125, 32, 54, 253, 67, 87, 233, 56, 67]
color = [str(item/255.) for item in y]
plt.scatter(x, y, s=500, c=color)
plt.show()
回答 2
有时您可能需要根据x值情况精确绘制颜色。例如，您可能有一个包含3种类型的变量和一些数据点的数据框。您想做以下事情，
- 在RED中绘制对应于物理变量’A’的点。
- 在BLUE中绘制与物理变量“ B”相对应的点。
- 在绿色中绘制对应于物理变量“ C”的点。
在这种情况下，您可能必须编写short函数以将x值映射为对应的颜色名称作为列表，然后将该列表传递给plt.scatter命令。
x=['A','B','B','C','A','B']
y=[15,30,25,18,22,13]
# Function to map the colors as a list from the input list of x variables
def pltcolor(lst):
cols=[]
for l in lst:
if l=='A':
cols.append('red')
elif l=='B':
cols.append('blue')
else:
cols.append('green')
return cols
# Create the colors list using the function above
cols=pltcolor(x)
plt.scatter(x=x,y=y,s=500,c=cols) #Pass on the list created by the function here
plt.grid(True)
plt.show()
Sometimes you may need to plot color precisely based on the x-value case. For example, you may have a dataframe with 3 types of variables and some data points. And you want to do following,
- Plot points corresponding to Physical variable ‘A’ in RED.
- Plot points corresponding to Physical variable ‘B’ in BLUE.
- Plot points corresponding to Physical variable ‘C’ in GREEN.
In this case, you may have to write to short function to map the x-values to corresponding color names as a list and then pass on that list to the plt.scatter command.
x=['A','B','B','C','A','B']
y=[15,30,25,18,22,13]
# Function to map the colors as a list from the input list of x variables
def pltcolor(lst):
cols=[]
for l in lst:
if l=='A':
cols.append('red')
elif l=='B':
cols.append('blue')
else:
cols.append('green')
return cols
# Create the colors list using the function above
cols=pltcolor(x)
plt.scatter(x=x,y=y,s=500,c=cols) #Pass on the list created by the function here
plt.grid(True)
plt.show()