在较新版本的pandas中,pandas.tools.plotting模块已经被移除或更改位置。这意味着,如果你尝试使用from pandas.tools.plotting import scatter_matrix这样的导入方式,会遇到模块导入错误。 替代的导入路径或方法 在新版本的pandas中,scatter_matrix函数已经移动到了pandas.plottin
main :1: FutureWarning: ‘pandas.tools.plotting.scatter_matrix’ 已弃用,改为导入 ‘pandas.plotting.scatter_matrix’。 import pandas.plotting 要么 from pandas.plotting import scatter_matrix https://github.com/pandas-dev/pandas/pull/13579/files/fe8b918a7c7f322a6806d3b262b7b36bbd01da80#diff-...
AI代码解释 from pandas.tools.plottingimportscatter_matrixscatter_matrix(data,alpha=0.2,figsize=(6,6),diagonal='kde') 这使用一个构造函数来创建属性与属性之间的散点图矩阵。每个属性将对其自身绘制的对角线显示该属性的核密度估计: 属性散点图矩阵 这是一个强大的功能,从中可以得出很多有关数据分析的启发。...
用法: pandas.plotting.scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, diagonal='hist', marker='.', density_kwds=None, hist_kwds=None, range_padding=0.05, **kwargs) 绘制散点图矩阵。 参数: frame:DataFrame alpha:浮点数,可选 应用的透明度量。 figsize:(浮点数,浮点数),...
绘制核密度估计图 1df.plot.kde(); pandas.tools.plotting 1#加载iris花的数据集2iris = pd.read_csv('iris.csv')3iris.head() 1pd.tools.plotting.scatter_matrix(iris); 1plt.figure()2pd.tools.plotting.parallel_coordinates(iris,'Name');
# 散点图矩阵 import matplotlib.pyplot as plt import pandas from pandas.tools.plotting import scatter_matrix url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'ped...
Other drawing tools Scatter matrix You can use scatter_matrix in pandas.plotting to draw a scatter matrix chart: In [83]: from pandas.plotting import scatter_matrix In [84]: df = pd.DataFrame(np.random.randn(1000, 4), columns=["a", "b", "c", "d"]) ...
from pandas.plotting import scatter_matrix worked for me too. In fact this may be another lind of issue. For instance: from pandas.tools.plotting import autocorrelation_plot Throws an error:ModuleNotFoundError: No module named 'pandas.tools' ...
import numpy as np import pandas from pandas.tools.plotting import scatter_matrix import matplotlib.pyplot as plt plt.figure() data = pandas.read_csv('energydata_complete.csv') scatter_matrix(data) plt.show() and, we can get a graph like this:编辑...
import pandas as pd import numpy as np import matplotlib.pyplot as plt from pandas.plotting import autocorrelation_plot import seaborn as sns from pandas.plotting import scatter_matrix from pandas.plotting import autocorrelation_plot from pandas.plotting import parallel_coordinates from pandas.plotting imp...