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4. distplot(a=,kde=False)，直方图

kernel density estimate (KDE) kde不写，或者为True，会出现曲线

# 直方图 Histogram filepath = "iris.csv"iris_data = pd.read_csv(filepath, index_col='Id')print(iris_data.head())sns.distplot(a=iris_data['Petal Length (cm)'],kde=False)plt.show()

iris_set_file = "iris_setosa.csv"iris_ver_file = "iris_versicolor.csv"iris_vir_file = "iris_virginica.csv"iris_set_data = pd.read_csv(iris_set_file, index_col="Id")iris_ver_data = pd.read_csv(iris_ver_file, index_col="Id")iris_vir_data = pd.read_csv(iris_vir_file, index_col="Id")sns.distplot(a=iris_set_data["Petal Length (cm)"], label="iris_setosa", kde=False)sns.distplot(a=iris_ver_data['Petal Length (cm)'], label="Iris-versicolor", kde=False)sns.distplot(a=iris_vir_data['Petal Length (cm)'], label="Iris-virginica", kde=False)plt.title("不同种系Petal Lengths直方图")plt.legend()plt.show()

5. kdeplot，密度图

5.1 kdeplot，一维密度图

# 密度图sns.kdeplot(data=iris_data['Petal Length (cm)'], shade=False)

sns.kdeplot(data=iris_set_data['Petal Length (cm)'], label="Iris-setosa", shade=True)sns.kdeplot(data=iris_ver_data['Petal Length (cm)'], label="Iris-versicolor", shade=True)sns.kdeplot(data=iris_vir_data['Petal Length (cm)'], label="Iris-virginica", shade=True)plt.title("不同种系Petal Lengths分布")plt.show()

5.2 jointplot(x=,y=,kind=‘kde’)，二维密度图

sns.jointplot(x=iris_data['Petal Length (cm)'], y=iris_data['Sepal Width (cm)'],              kind='kde')

6. set_style()，设置底色

sns.set_style("dark") # 灰色底色# (1)"darkgrid", (2)"whitegrid", (3)"dark", (4)"white", and (5)"ticks"

style = ["dark", "darkgrid", "white", "whitegrid", "ticks"]plt.figure(figsize=(12, 6))for i in range(5):    sns.set_style(style[i])    f = plt.subplot(2, 3, i + 1)    sns.lineplot(data=data)  # 单个数据可以加 label="label_test"    f.set_title("style_" + style[i])    f.legend()plt.show()

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