Pandas 用于数据分析的小提琴图

Attribute Information:
   -> sepal length in cm
   -> sepal width in cm
   -> petal length in cm
   -> petal width in cm
   -> class: 
              Iris Setosa
              Iris Versicolour
              Iris Virginica

Number of Instances: 150 

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!)

Class Distribution: 33.3% for each of 3 classes.

import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot
import seaborn

data = pd.read_csv("Iris.csv")
  
print (data.head(10))

data.describe()

data.info()

data["SepalLengthCm"].describe()

count    150.000000
mean       5.843333
std        0.828066
min        4.300000
25%        5.100000
50%        5.800000
75%        6.400000
max        7.900000
Name: SepalLengthCm, dtype: float64

fig, ax = pyplot.subplots(figsize =(9, 7))
sns.violinplot( ax = ax, y = data["SepalLengthCm"] )

fig, ax = pyplot.subplots(figsize =(9, 7))
sns.violinplot(ax = ax,  y = data["SepalWidthCm"] )

fig, ax = pyplot.subplots(figsize =(9, 7))
sns.violinplot(ax = ax, data = data.iloc[:, 1:3])

fig, ax = pyplot.subplots(figsize =(9, 7))
sns.violinplot(ax = ax, x = data["Species"], 
                  y = data["SepalLengthCm"] )