如何使用“matplotlib.pyplot.scatter”创建数据散点图
How to create a scatterplot of data using `matplotlib.pyplot.scatter`
我对 matplotlib.pyplot.scatter 有疑问。
首先，我需要下载虹膜分类的数据，贴上头条。
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn')
%matplotlib inline
df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header = None)
df_names = ['sepal length in cm', 'sepal width in cm', 'petal length in cm', 'petal width in cm', 'class']
df.columns = df_names
df
其次，我应该按以下方式使用 matplotlib.pyplot.scatter 创建数据散点图：
* for x and y coordinates use sepal length and width respectively
* for size use the petal length
* for alpha (opacity/transparency) use the petal width
* illustrate iris belonging to each class by using 3 distinct colours (RGB for instance, but be creative if you want)
* *some columns will need to be scaled, to be passed as parameters; you might also want to scale some other columns to increase the readability of the illustration.
然后，我找到了这个网站：https://www.geeksforgeeks.org/matplotlib-pyplot-scatter-in-python/
之后，我将他们的草稿用于我的任务：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn')
%matplotlib inline
# dataset-df
x1 = [4.3, 7.9, 5.84, 0.83, 0.7826]
y1 = [2.0, 4.4, 3.05, 0.43, -0.4194]
plt.scatter(x1, y1, c ="red",
alpha = 1.0, 6.9, 3.76, 1.76, 0.9490,
linewidth = 2,
marker ="s",
s = [1.0, 6.9, 3.76, 1.76, 0.9490])
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()
但是，我总是得到这个错误：
File "C:\Users\felix\AppData\Local\Temp/ipykernel_32284/4113309647.py", line 21
s = [1.0, 6.9, 3.76, 1.76, 0.9490])
^
SyntaxError: positional argument follows keyword argument
你能告诉我如何解决这个问题并完成我的任务吗？
另外，我从iris.names复制了数据：
1. Title: Iris Plants Database
Updated Sept 21 by C.Blake - Added discrepency information
2. Sources:
(a) Creator: R.A. Fisher
(b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
(c) Date: July, 1988
3. Past Usage:
- Publications: too many to mention!!! Here are a few.
1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
to Mathematical Statistics" (John Wiley, NY, 1950).
2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
Structure and Classification Rule for Recognition in Partially Exposed
Environments". IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-2, No. 1, 67-71.
-- Results:
-- very low misclassification rates (0% for the setosa class)
4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
Transactions on Information Theory, May 1972, 431-433.
-- Results:
-- very low misclassification rates again
5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
conceptual clustering system finds 3 classes in the data.
4. Relevant Information:
--- This is perhaps the best known database to be found in the pattern
recognition literature. Fisher's paper is a classic in the field
and is referenced frequently to this day. (See Duda & Hart, for
example.) The data set contains 3 classes of 50 instances each,
where each class refers to a type of iris plant. One class is
linearly separable from the other 2; the latter are NOT linearly
separable from each other.
--- Predicted attribute: class of iris plant.
--- This is an exceedingly simple domain.
--- This data differs from the data presented in Fishers article
(identified by Steve Chadwick, spchadwick@espeedaz.net )
The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
where the error is in the fourth feature.
The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
where the errors are in the second and third features.
5. Number of Instances: 150 (50 in each of three classes)
6. Number of Attributes: 4 numeric, predictive attributes and the class
7. Attribute Information:
1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class:
-- Iris Setosa
-- Iris Versicolour
-- Iris Virginica
8. Missing Attribute Values: None
Summary Statistics:
Min Max Mean SD Class Correlation
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
9. Class Distribution: 33.3% for each of 3 classes.
iris 数据集没有问题，只是您在散点函数中定义了 alpha 参数的部分。您应该按照您的方式更改为参数赋值的方式：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn')
%matplotlib inline
# dataset-df
x1 = [4.3, 7.9, 5.84, 0.83, 0.7826]
y1 = [2.0, 4.4, 3.05, 0.43, -0.4194]
plt.scatter(x1, y1, c ="red",alpha = 1,
linewidth = 2,
marker ="s",
s = [1.0, 6.9, 3.76, 1.76, 0.9490])
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()
请注意，alpha 只取一个数字，可能是 0.9、0.8 甚至 0.823425，而不是列表或其他任何内容。
输出
我对 matplotlib.pyplot.scatter 有疑问。
首先，我需要下载虹膜分类的数据，贴上头条。
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn')
%matplotlib inline
df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header = None)
df_names = ['sepal length in cm', 'sepal width in cm', 'petal length in cm', 'petal width in cm', 'class']
df.columns = df_names
df
其次，我应该按以下方式使用 matplotlib.pyplot.scatter 创建数据散点图：
* for x and y coordinates use sepal length and width respectively
* for size use the petal length
* for alpha (opacity/transparency) use the petal width
* illustrate iris belonging to each class by using 3 distinct colours (RGB for instance, but be creative if you want)
* *some columns will need to be scaled, to be passed as parameters; you might also want to scale some other columns to increase the readability of the illustration.
然后，我找到了这个网站：https://www.geeksforgeeks.org/matplotlib-pyplot-scatter-in-python/
之后，我将他们的草稿用于我的任务：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn')
%matplotlib inline
# dataset-df
x1 = [4.3, 7.9, 5.84, 0.83, 0.7826]
y1 = [2.0, 4.4, 3.05, 0.43, -0.4194]
plt.scatter(x1, y1, c ="red",
alpha = 1.0, 6.9, 3.76, 1.76, 0.9490,
linewidth = 2,
marker ="s",
s = [1.0, 6.9, 3.76, 1.76, 0.9490])
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()
但是，我总是得到这个错误：
File "C:\Users\felix\AppData\Local\Temp/ipykernel_32284/4113309647.py", line 21
s = [1.0, 6.9, 3.76, 1.76, 0.9490])
^
SyntaxError: positional argument follows keyword argument
你能告诉我如何解决这个问题并完成我的任务吗？
另外，我从iris.names复制了数据：
1. Title: Iris Plants Database
Updated Sept 21 by C.Blake - Added discrepency information
2. Sources:
(a) Creator: R.A. Fisher
(b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
(c) Date: July, 1988
3. Past Usage:
- Publications: too many to mention!!! Here are a few.
1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
to Mathematical Statistics" (John Wiley, NY, 1950).
2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
Structure and Classification Rule for Recognition in Partially Exposed
Environments". IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-2, No. 1, 67-71.
-- Results:
-- very low misclassification rates (0% for the setosa class)
4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
Transactions on Information Theory, May 1972, 431-433.
-- Results:
-- very low misclassification rates again
5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
conceptual clustering system finds 3 classes in the data.
4. Relevant Information:
--- This is perhaps the best known database to be found in the pattern
recognition literature. Fisher's paper is a classic in the field
and is referenced frequently to this day. (See Duda & Hart, for
example.) The data set contains 3 classes of 50 instances each,
where each class refers to a type of iris plant. One class is
linearly separable from the other 2; the latter are NOT linearly
separable from each other.
--- Predicted attribute: class of iris plant.
--- This is an exceedingly simple domain.
--- This data differs from the data presented in Fishers article
(identified by Steve Chadwick, spchadwick@espeedaz.net )
The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
where the error is in the fourth feature.
The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
where the errors are in the second and third features.
5. Number of Instances: 150 (50 in each of three classes)
6. Number of Attributes: 4 numeric, predictive attributes and the class
7. Attribute Information:
1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class:
-- Iris Setosa
-- Iris Versicolour
-- Iris Virginica
8. Missing Attribute Values: None
Summary Statistics:
Min Max Mean SD Class Correlation
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
9. Class Distribution: 33.3% for each of 3 classes.
iris 数据集没有问题，只是您在散点函数中定义了 alpha 参数的部分。您应该按照您的方式更改为参数赋值的方式：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn')
%matplotlib inline
# dataset-df
x1 = [4.3, 7.9, 5.84, 0.83, 0.7826]
y1 = [2.0, 4.4, 3.05, 0.43, -0.4194]
plt.scatter(x1, y1, c ="red",alpha = 1,
linewidth = 2,
marker ="s",
s = [1.0, 6.9, 3.76, 1.76, 0.9490])
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()
请注意，alpha 只取一个数字，可能是 0.9、0.8 甚至 0.823425，而不是列表或其他任何内容。