Matplotlib 按分类因素散点颜色

Question

我有一个基本的散点图，其中 x 和 y 是浮动的。但我想根据第三个分类变量更改标记的颜色。分类变量是字符串形式。这似乎引起了一个问题。

要使用 iris 数据集-这是我想我会使用的代码：

#Scatter of Petal
x=df['Petal Length']
y=df['Petal Width']
z=df['Species']
plt.scatter(x, y, c=z, s=15, cmap='hot')
plt.xlabel('Petal Width')
plt.ylabel('Petal Length')
plt.title('Petal Width vs Length')

但我得到一个错误：无法将字符串转换为浮点数：iris-setosa

我是否必须在运行之前将分类变量更改为数字变量，或者我可以对当前格式的数据做些什么？

谢谢

更新：整个追溯是：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-47-d67ee3bffc3b> in <module>()
      3 y=df['Petal Width']
      4 z=df['Species']
----> 5 plt.scatter(x, y, c=z, s=15, cmap='hot')
      6 plt.xlabel('Petal Width')
      7 plt.ylabel('Petal Length')

/Users/mpgartland1/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.pyc in scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, hold, **kwargs)
   3198         ret = ax.scatter(x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm,
   3199                          vmin=vmin, vmax=vmax, alpha=alpha,
-> 3200                          linewidths=linewidths, verts=verts, **kwargs)
   3201         draw_if_interactive()
   3202     finally:

/Users/mpgartland1/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, **kwargs)
   3605 
   3606         if c_is_stringy:
-> 3607             colors = mcolors.colorConverter.to_rgba_array(c, alpha)
   3608         else:
   3609             # The inherent ambiguity is resolved in favor of color

/Users/mpgartland1/anaconda/lib/python2.7/site-packages/matplotlib/colors.pyc in to_rgba_array(self, c, alpha)
    420             result = np.zeros((nc, 4), dtype=np.float)
    421             for i, cc in enumerate(c):
--> 422                 result[i] = self.to_rgba(cc, alpha)
    423             return result
    424 

/Users/mpgartland1/anaconda/lib/python2.7/site-packages/matplotlib/colors.pyc in to_rgba(self, arg, alpha)
    374         except (TypeError, ValueError) as exc:
    375             raise ValueError(
--> 376                 'to_rgba: Invalid rgba arg "%s"\n%s' % (str(arg), exc))
    377 
    378     def to_rgba_array(self, c, alpha=None):

ValueError: to_rgba: Invalid rgba arg "Iris-setosa"
to_rgb: Invalid rgb arg "Iris-setosa"
could not convert string to float: iris-setosa

Answer 1

正如你的回溯告诉你的，你不能将字符串传递给颜色参数。您可以传递颜色或将解释为颜色本身的值数组。

见： http://matplotlib.org/api/pyplot_api.html?highlight=plot#matplotlib.pyplot.plot

可能有一种更优雅的方式，但一种实现方式如下（我使用了以下数据集：https://raw.githubusercontent.com/pydata/pandas/master/pandas/tests/data/iris.csv）：

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
from pandas import read_csv

df = read_csv('iris.csv')

#Scatter of Petal
x=df['PetalLength']
y=df['PetalWidth']

# Get unique names of species
uniq = list(set(df['Name']))

# Set the color map to match the number of species
z = range(1,len(uniq))
hot = plt.get_cmap('hot')
cNorm  = colors.Normalize(vmin=0, vmax=len(uniq))
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=hot)

# Plot each species
for i in range(len(uniq)):
    indx = df['Name'] == uniq[i]
    plt.scatter(x[indx], y[indx], s=15, color=scalarMap.to_rgba(i), label=uniq[i])

plt.xlabel('Petal Width')
plt.ylabel('Petal Length')
plt.title('Petal Width vs Length')
plt.legend(loc='upper left')
plt.show()

给出如下内容：

编辑：为图例明确添加标签。

Answer 2

根据@jonnybazookatone 的回答，这是我的方法。我使用 groupby 创建一个小数据框，用于在 Name 和 name_id 之间查找。然后我再次分组，遍历组...

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
from pandas import read_csv

df = read_csv('iris.csv')

# map Name to integer
pos = df.loc[:,["Name"]].groupby("Name").count().reset_index()

# create a new column in the dataframe which contains the numeric value
tag_to_index = lambda x: pos.loc[pos.Name == x.Name].index[0]
df.loc[:,"name_index"]=df.loc[:,["Name"]].apply(tag_to_index, axis=1)

# Set the color map to match the number of species
hot = plt.get_cmap('hot')
cNorm  = colors.Normalize(vmin=0, vmax=len(pos))
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=hot)

# Get unique names of species
for (name, group) in df.groupby("name_index"):
    plt.scatter(group.PetalWidth, group.PetalLength, s=15, label=pos.iloc[name].get("Name"), color=scalarMap.to_rgba(name))

plt.xlabel('Petal Width')
plt.ylabel('Petal Length')
plt.title('Petal Width vs Length')
plt.legend()
plt.show()

Answer 3

Altair 在这里应该是轻而易举的事。

from altair import *
import pandas as pd

df = datasets.load_dataset('iris')
Chart(df).mark_point().encode(x='petalLength',y='sepalLength', color='species')

Answer 4

最简单的方法是简单地将整数类别级别数组传递给 plt.scatter() 颜色参数。

import pandas as pd
import matplotlib.pyplot as plt 

iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')

plt.scatter(iris['petal_length'], iris['petal_width'], c=pd.factorize(iris['species'])[0])
plt.gca().set(xlabel='Petal Width', ylabel='Petal Length', title='Petal Width vs Length')

这会创建一个没有图例的图，使用默认值 "viridis" colormap。

要选择自己的 colormap and add a legend，最简单的方法是：

import matplotlib.patches

levels, categories = pd.factorize(iris['species'])
colors = [plt.cm.tab10(i) for i in levels] # using the "tab10" colormap
handles = [matplotlib.patches.Patch(color=plt.cm.tab10(i), label=c) for i, c in enumerate(categories)]

plt.scatter(iris['petal_length'], iris['petal_width'], c=colors)
plt.gca().set(xlabel='Petal Width', ylabel='Petal Length', title='Petal Width vs Length')
plt.legend(handles=handles, title='Species')

我在这里选择了“tab10”离散（又名定性）颜色图。

加分项：
在第一个图中，通过将 min-max scaled 值从类别级别整数数组 pd.factorize(iris['species'])[0] 传递到 [=14= 的 call 方法来选择默认颜色] 颜色图对象。

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