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我正在分析Iris dataset并在花瓣宽度和花瓣长度之间做了散点图。为了使情节我用这个代码:seaborn regplot删除数据点的颜色
# First, we'll import pandas, a data processing and CSV file I/O library
import pandas as pd
# We'll also import seaborn, a Python graphing library
import warnings # current version of seaborn generates a bunch of warnings that we'll ignore
warnings.filterwarnings("ignore")
import seaborn as sns
import matplotlib.pyplot as plt
import numpy
sns.set(style="dark", color_codes=True)
# Next, we'll load the Iris flower dataset, which is in the "../input/" directory
iris = pd.read_csv("Iris.csv") # the iris dataset is now a Pandas DataFrame
# Let's see what's in the iris data - Jupyter notebooks print the result of the last thing you do
print(iris.head(10))
# Press shift+enter to execute this cell
sns.FacetGrid(iris, hue="Species", size=10) \
.map(plt.scatter, "PetalLengthCm", "PetalWidthCm") \
.add_legend()
后来我绘制的回归线,但绘制这条线后,颜色不清晰可见。我试图改变回归线的颜色,但这没有帮助。我怎样才能绘制回归线而不失去不同物种的颜色?
,使包括回归线情节的代码是:
sns.FacetGrid(iris, hue="Species", size=10) \
.map(plt.scatter, "PetalLengthCm", "PetalWidthCm") \
.add_legend()
sns.regplot(x="PetalLengthCm", y="PetalWidthCm", data=iris)
petal_length_array = iris["PetalLengthCm"]
petal_width_array = iris["PetalWidthCm"]
r_petal = numpy.corrcoef(petal_length_array, petal_width_array) # bereken de correlatie
print ("Correlation is : " + str(r_petal[0][1]))