[英]Displaying pair plot in Pandas data frame
我试图通过在pandas dataframe中创建scatter_matrix来显示一对情节。 这是创建配对图的方式:
# Create dataframe from data in X_train
# Label the columns using the strings in iris_dataset.feature_names
iris_dataframe = pd.DataFrame(X_train, columns=iris_dataset.feature_names)
# Create a scatter matrix from the dataframe, color by y_train
grr = pd.scatter_matrix(iris_dataframe, c=y_train, figsize=(15, 15), marker='o',
hist_kwds={'bins': 20}, s=60, alpha=.8, cmap=mglearn.cm3)
我想显示对情节看起来像这样;
我使用的是Python v3.6和PyCharm,并没有使用Jupyter Notebook。
这段代码使用Python 3.5.2为我工作:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import datasets
iris_dataset = datasets.load_iris()
X = iris_dataset.data
Y = iris_dataset.target
iris_dataframe = pd.DataFrame(X, columns=iris_dataset.feature_names)
# Create a scatter matrix from the dataframe, color by y_train
grr = pd.plotting.scatter_matrix(iris_dataframe, c=Y, figsize=(15, 15), marker='o',
hist_kwds={'bins': 20}, s=60, alpha=.8)
对于pandas版本<v0.20.0。
感谢michael-szczepaniak指出此API已被弃用。
grr = pd.scatter_matrix(iris_dataframe, c=Y, figsize=(15, 15), marker='o',
hist_kwds={'bins': 20}, s=60, alpha=.8)
我只需要删除cmap=mglearn.cm3
,因为我无法使mglearn工作。 sklearn存在版本不匹配问题。
要不显示图像并将其直接保存到文件,您可以使用以下方法:
plt.savefig('foo.png')
也删除
# %matplotlib inline
只是更新了Vikash的优秀答案。 最后两行现在应该是:
grr = pd.plotting.scatter_matrix(iris_dataframe, c=Y, figsize=(15, 15), marker='o',
hist_kwds={'bins': 20}, s=60, alpha=.8)
scatter_matrix函数已移至绘图包,因此原始答案虽然正确,但现已弃用。
所以完整的代码现在是:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import datasets
iris_dataset = datasets.load_iris()
X = iris_dataset.data
Y = iris_dataset.target
iris_dataframe = pd.DataFrame(X, columns=iris_dataset.feature_names)
# create a scatter matrix from the dataframe, color by y_train
grr = pd.plotting.scatter_matrix(iris_dataframe, c=Y, figsize=(15, 15), marker='o',
hist_kwds={'bins': 20}, s=60, alpha=.8)
我终于知道如何用PyCharm做到这一点。
只需将matploblib.plotting
导入为plt
:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import mglearn
from pandas.plotting import scatter_matrix
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris_dataset = load_iris()
X_train,X_test,Y_train,Y_test = train_test_split(iris_dataset['data'],iris_dataset['target'],random_state=0)
iris_dataframe = pd.DataFrame(X_train,columns=iris_dataset.feature_names)
grr = scatter_matrix(iris_dataframe,c = Y_train,figsize = (15,15),marker = 'o',
hist_kwds={'bins':20},s=60,alpha=.8,cmap = mglearn.cm3)
plt.show()
然后它完美如下:
首先使用
pip install mglearn
然后导入mglearn
代码就像这样......
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import pandas as pd
import mglearn
import matplotlib.pyplot as plt
iris_dataframe=pd.DataFrame(X_train,columns=iris_dataset.feature_names)
grr=pd.scatter_matrix(iris_dataframe,
c=y_train,figsize=(15,15),marker='o',hist_kwds={'bins':20},
s=60,alpha=.8,cmap=mglearn.cm3)
plt.show()
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