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()
分成几次分别绘制,带颜色
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()
# 密度图
sns.kdeplot(data=iris_data['Petal Length (cm)'], shade=False)
分开绘制密度图
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()
sns.jointplot(x=iris_data['Petal Length (cm)'], y=iris_data['Sepal Width (cm)'],
kind='kde')
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()