（1）读入鸢尾花数据，并显示全部鸢尾花数据，然后分别显示花萼数据和花瓣数据 （2）设计程序分别显示鸢尾花每个品种的花萼数据和花瓣数据 （3）使用pandas、numpy、matplotlib实现每个种类鸢尾花的Sepal.Length折线图 （4）使用pandas、numpy、matplotlib实现每个种类鸢尾花的Sepal.Width折线图 （5）使用pandas、numpy、matplotlib实现每个种类鸢尾花的Petal.Length柱状图 （6）使用pandas、numpy、matplotlib实现每个种类鸢尾花的Petal.Width柱状图

好的，以下是回答： 1. 读入鸢尾花数据，并显示全部鸢尾花数据，然后分别显示花萼数据和花瓣数据： ```python import seaborn as sns # 读入数据 iris_df = sns.load_dataset('iris') # 显示全部数据 print(iris_df) # 显示花萼数据 print(iris_df[['sepal_length', 'sepal_width']]) # 显示花瓣数据 print(iris_df[['petal_length', 'petal_width']]) ``` 2. 设计程序分别显示鸢尾花每个品种的花萼数据和花瓣数据： ```python import seaborn as sns # 读入数据 iris_df = sns.load_dataset('iris') # 分别显示每个品种的花萼数据 for species in set(iris_df['species']): print(f'\n{species} Sepal Data:') print(iris_df[iris_df['species'] == species][['sepal_length', 'sepal_width']]) # 分别显示每个品种的花瓣数据 for species in set(iris_df['species']): print(f'\n{species} Petal Data:') print(iris_df[iris_df['species'] == species][['petal_length', 'petal_width']]) ``` 3. 使用pandas、numpy、matplotlib实现每个种类鸢尾花的Sepal.Length折线图： ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # 读入数据 iris_df = sns.load_dataset('iris') # 将数据按品种分类 setosa = iris_df[iris_df['species'] == 'setosa'] versicolor = iris_df[iris_df['species'] == 'versicolor'] virginica = iris_df[iris_df['species'] == 'virginica'] # 绘制折线图 plt.plot(setosa['sepal_length'], label='setosa') plt.plot(versicolor['sepal_length'], label='versicolor') plt.plot(virginica['sepal_length'], label='virginica') plt.legend() # 设定横纵坐标轴标签 plt.xlabel('Index') plt.ylabel('Sepal Length') # 显示图像 plt.show() ``` 4. 使用pandas、numpy、matplotlib实现每个种类鸢尾花的Sepal.Width折线图： ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # 读入数据 iris_df = sns.load_dataset('iris') # 将数据按品种分类 setosa = iris_df[iris_df['species'] == 'setosa'] versicolor = iris_df[iris_df['species'] == 'versicolor'] virginica = iris_df[iris_df['species'] == 'virginica'] # 绘制折线图 plt.plot(setosa['sepal_width'], label='setosa') plt.plot(versicolor['sepal_width'], label='versicolor') plt.plot(virginica['sepal_width'], label='virginica') plt.legend() # 设定横纵坐标轴标签 plt.xlabel('Index') plt.ylabel('Sepal Width') # 显示图像 plt.show() ``` 5. 使用pandas、numpy、matplotlib实现每个种类鸢尾花的Petal.Length柱状图： ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # 读入数据 iris_df = sns.load_dataset('iris') # 将数据按品种分类 setosa = iris_df[iris_df['species'] == 'setosa'] versicolor = iris_df[iris_df['species'] == 'versicolor'] virginica = iris_df[iris_df['species'] == 'virginica'] # 统计每个品种的Petal.Length均值 setosa_mean = np.mean(setosa['petal_length']) versicolor_mean = np.mean(versicolor['petal_length']) virginica_mean = np.mean(virginica['petal_length']) # 绘制柱状图 plt.bar(['setosa', 'versicolor', 'virginica'], [setosa_mean, versicolor_mean, virginica_mean]) # 设定横纵坐标轴标签 plt.xlabel('Species') plt.ylabel('Petal Length') # 显示图像 plt.show() ``` 6. 使用pandas、numpy、matplotlib实现每个种类鸢尾花的Petal.Width柱状图： ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # 读入数据 iris_df = sns.load_dataset('iris') # 将数据按品种分类 setosa = iris_df[iris_df['species'] == 'setosa'] versicolor = iris_df[iris_df['species'] == 'versicolor'] virginica = iris_df[iris_df['species'] == 'virginica'] # 统计每个品种的Petal.Width均值 setosa_mean = np.mean(setosa['petal_width']) versicolor_mean = np.mean(versicolor['petal_width']) virginica_mean = np.mean(virginica['petal_width']) # 绘制柱状图 plt.bar(['setosa', 'versicolor', 'virginica'], [setosa_mean, versicolor_mean, virginica_mean]) # 设定横纵坐标轴标签 plt.xlabel('Species') plt.ylabel('Petal Width') # 显示图像 plt.show() ```