Python 带颜色条的圆形打印

我尝试用一个颜色条绘制一个圆形图，几乎像这样：

但是，颜色条的最小值当前为1；我希望能够将其设置为0

import pandas            as pd
import matplotlib.pyplot as plt
import matplotlib.cm     as cm
from sklearn import preprocessing

df = pd.DataFrame({'A':[1,2,1,2,3,4,2,1,4], 
                   'B':[3,1,5,1,2,4,5,2,3], 
                   'C':[4,2,4,1,3,3,4,2,1]})

# set the Colour
x              = df.values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled       = min_max_scaler.fit_transform(x)
df_S           = pd.DataFrame(x_scaled)
c1             = df['C']
c2             = df_S[2]
colors         = [cm.jet(color) for color in c2]

# Graph
plt.figure()
ax = plt.gca()
for a, b, color in zip(df['A'], df['B'], colors):
    circle = plt.Circle((a, 
                         b), 
                         1, # Size
                         color=color, 
                         lw=5, 
                         fill=False)
    ax.add_artist(circle)

plt.xlim([0,5])
plt.ylim([0,5])
plt.xlabel('A')
plt.ylabel('B')
ax.set_aspect(1.0)

sc = plt.scatter(df['A'], 
                 df['B'], 
                 s=0, 
                 c=c1, 
                 cmap='jet', 
                 facecolors='none')
plt.grid()

cbar = plt.colorbar(sc)
cbar.set_label('C', rotation=270, labelpad=10)

plt.show()

原问题：

您可以通过摆弄来获得此输出：

fraction = 1/3 # colorbar axis min is 1, max is 4, steps are 0.5 
               # => 2*(1/6) to get to 0
cbar = plt.colorbar(sc, extend="min", extendfrac=fraction, extendrect=True)

---

但是扩展名将被取消标记。

只需在

plt.scatter（）中添加
vmin
和
vmax
参数即可


如果要根据颜色贴图调整圆的颜色，则需要使用` Normalize（vmin，vmax）并将颜色贴图传递给具有规格化值的圆图

代码如下：

import pandas            as pd
import matplotlib.pyplot as plt
import matplotlib.cm     as cm
from sklearn import preprocessing
from matplotlib.colors import Normalize

df = pd.DataFrame({'A':[1,2,1,2,3,4,2,1,4], 
                   'B':[3,1,5,1,2,4,5,2,3], 
                   'C':[4,2,4,1,3,3,4,2,1]})

# set the Colour
x              = df.values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled       = min_max_scaler.fit_transform(x)
df_S           = pd.DataFrame(x_scaled)
c1             = df['C']
c2             = df_S[2]
cmap = cm.jet
vmin = 0
vmax = 5 #your max Y is 5, not 4
norm = Normalize(vmin, vmax)

# Graph
plt.figure()
ax = plt.gca()
for a, b in zip(df['A'], df['B']):
    circle = plt.Circle((a, 
                         b), 
                         1, # Size
                         color=cmap(norm(b)), 
                         lw=5, 
                         fill=False)
    ax.add_artist(circle)

plt.xlim([0,5])
plt.ylim([0,5])
plt.xlabel('A')
plt.ylabel('B')
ax.set_aspect(1.0)

sc = plt.scatter(df['A'], 
                 df['B'], 
                 s=0, 
                 c=c1, 
                 cmap='jet',
                 vmin = vmin,
                 vmax = vmax,
                 facecolors='none')
plt.grid()

cbar = plt.colorbar(sc)
cbar.set_label('C', rotation=270, labelpad=10)

plt.show()

多亏了alec_djinn，这个答案是：


设置颜色栏的最小值和最大值
在与颜色栏相同的范围内控制圆圈（变量C）的颜色


这改变了颜色条的最小值，这正是我所要求的。然而，它并没有改变圆圈的颜色。因此，圆圈的颜色必须与颜色栏不一致。我想我需要知道如何在两张图上做相同的事情。是的，这是另一个问题。我在答案中添加了一个解决方案。我不知道为什么，但它现在使用B来设置圆圈的颜色，而不是C
color=cmap（norm（b））
它根据
b
变量设置颜色，您可以设置任何想要的变量。我假设您想使用
b
这是圆中心的Y ax值。
import pandas            as pd
import matplotlib.pyplot as plt
import matplotlib.cm     as cm
from sklearn import preprocessing
from matplotlib.colors import Normalize

df = pd.DataFrame({'A':[1,2,1,2,3,4,2,1,4], 
                   'B':[3,1,5,1,2,4,5,2,3], 
                   'C':[4,2,4,1,3,3,4,2,1]})

# set the Colour
x              = df.values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled       = min_max_scaler.fit_transform(x)
df_S           = pd.DataFrame(x_scaled)
c1             = df['C']
c2             = df_S[2]
cmap = cm.jet
vmin = 0
vmax = 5 #your max Y is 5, not 4
norm = Normalize(vmin, vmax)

# Graph
plt.figure()
ax = plt.gca()
for a, b in zip(df['A'], df['B']):
    circle = plt.Circle((a, 
                         b), 
                         1, # Size
                         color=cmap(norm(b)), 
                         lw=5, 
                         fill=False)
    ax.add_artist(circle)

plt.xlim([0,5])
plt.ylim([0,5])
plt.xlabel('A')
plt.ylabel('B')
ax.set_aspect(1.0)

sc = plt.scatter(df['A'], 
                 df['B'], 
                 s=0, 
                 c=c1, 
                 cmap='jet',
                 vmin = vmin,
                 vmax = vmax,
                 facecolors='none')
plt.grid()

cbar = plt.colorbar(sc)
cbar.set_label('C', rotation=270, labelpad=10)

plt.show()

import pandas            as pd
import matplotlib.pyplot as plt
import matplotlib.cm     as cm
from sklearn import preprocessing
from matplotlib.colors import Normalize

df = pd.DataFrame({'A':[1,2,1,2,3,4,2,1,4], 
                   'B':[3,2,5,1,2,4,5,2,3], 
                   'C':[4,2,4,1,3,3,4,2,1]})

# set the Colour
x              = df[['C']].values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled       = min_max_scaler.fit_transform(x)
df_S           = pd.DataFrame(x_scaled)
c1             = df['C']
c2             = df_S[0]
cmap           = cm.jet # Use the same Cmap

# Set the Colour Scale
vmin = 0
vmax = 5
norm = Normalize(vmin, vmax)

# Graph
plt.figure()
ax = plt.gca()
for a, b, c in zip(df['A'], df['B'], df['C']):
    circle = plt.Circle((a, 
                         b), 
                         1, # Size
                         color=cmap(norm(c)), 
                         lw=5, 
                         fill=False)
    ax.add_artist(circle)
plt.xlim([0,5])
plt.ylim([0,5])
plt.xlabel('A')
plt.ylabel('B')
ax.set_aspect(1.0)
sc = plt.scatter(df['A'], 
                 df['B'], 
                 s=0, 
                 c=c1, 
                 cmap='jet', # Use the same Cmap
                 vmin = vmin,
                 vmax = vmax,
                 facecolors='none')
plt.grid()
cbar = plt.colorbar(sc)
cbar.set_label('C', rotation=270, labelpad=20)

plt.show()