数据可视化第二版-03部分-08章-分布

pip install scikit-learn -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install seaborn -i https://pypi.tuna.tsinghua.edu.cn/simple

from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from numpy.random import randn
import matplotlib as mpl
import seaborn as sns
from scipy.stats.kde import gaussian_kde
from scipy.stats import norm
from numpy import linspace, hstack
from pylab import plot, show, hist

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.cm as cm
    
def histeq(image_array,image_bins=256):
    
    # 将图像矩阵转化成直方图数据，返回元组(频数，直方图区间坐标)
    image_array2,bins = np.histogram(image_array.flatten(),image_bins)
    
    # 计算直方图的累积函数
    cdf = image_array2.cumsum()
    
    # 将累积函数转化到区间[0,255]
    cdf = (255.0/cdf[-1])*cdf
    
    # 原图像矩阵利用累积函数进行转化，插值过程
    image2_array = np.interp(image_array.flatten(),bins[:-1],cdf)
    
    # 返回均衡化后的图像矩阵和累积函数
    return image2_array.reshape(image_array.shape),cdf
    
image = Image.open("pika1.jpg").convert("L")
image_array = np.array(image)
plt.subplot(2,2,1)
plt.hist(image_array.flatten(),256)
plt.subplot(2,2,2)
plt.imshow(image,cmap=cm.gray)
plt.axis("off")
    
a = histeq(image_array)  # 利用刚定义的直方图均衡化函数对图像进行均衡化处理
plt.subplot(2,2,3)
plt.hist(a[0].flatten(),256)
plt.subplot(2,2,4)
plt.imshow(Image.fromarray(a[0]),cmap=cm.gray)
plt.axis("off")
    
plt.show()

# 直方图
df = datasets.load_iris()

plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.figure(figsize=(10, 8))  # 设置画布大小
plt.hist(df.data[:, 0],  # 选择鸢尾花数据集的第一个特征
         bins=20,  # 设置分组数量
         alpha=0.5,  # 颜色透明度
         color="r",  # 直方图矩形填充颜色
         edgecolor="black",  # 直方图矩形边框颜色
         range=(4, 8.5))  # 设置直方图边界
plt.xlabel(df.feature_names[0])  # x标签
plt.ylabel("频数密度")  # y标签
plt.title("鸢尾花数据集特征分布直方图")
plt.show()

# 直方图示例
data = np.random.randn(1000)
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(10, 8))  # 设置画布大小
plt.hist(data,
         bins=15,  # 设置分组数量
         alpha=0.5,  # 颜色透明度
         color="blue",  # 直方图矩形填充颜色
         edgecolor="black")  # 直方图矩形边框颜色
plt.xlabel("")  # x标签
plt.ylabel("频数密度")  # y标签
plt.title("直方图示例")
plt.show()

# 直方图与趋势线
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.rcParams['axes.unicode_minus'] = False

sample1 = norm.rvs(loc=-1.0, scale=1, size=320)
sample2 = norm.rvs(loc=2.0, scale=0.6, size=320)
sample = hstack([sample1, sample2])
probDensityFun = gaussian_kde(sample)
x = linspace(-5, 5, 200)
plot(x, probDensityFun(x))
hist(sample, density=True, alpha=0.5, color="purple")
plt.title("直方图与趋势线")
show()

# 直方图与趋势线2
data = randn(250)
sns.set_palette("hls")
plt.rcParams['axes.unicode_minus'] = False

mpl.rc("figure", figsize=(10, 6))
sns.displot(data, bins=10, kde=True,
            rug=True,
            color='b')
plt.title("直方图与趋势线2")
plt.show()

import os
os.chdir(os.path.dirname(__file__))
iris = pd.read_csv("鸢尾花.csv")

plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.figure(figsize=(8, 6))  # 设置画布大小
sns.histplot(data=iris, x="Sepal.Length", hue="Species", alpha=0.5)
plt.title("分组直方图")
plt.xlabel("萼片长度")
plt.show()

# 变形
# 创建数据集
df = pd.DataFrame({
    'var1': np.random.normal(size=1000),
    'var2': np.random.normal(loc=2, size=1000) * -1
})
# 画布大小
plt.rcParams["figure.figsize"] = 10, 6
plt.rcParams['axes.unicode_minus'] = False
# 画变量1的频率分布直方图
sns.histplot(x=df.var1, stat="density", bins=20)
# 画变量2的频率分布直方图
n_bins = 20
# 获得变量2的分组
heights, bins = np.histogram(df.var2, density=True, bins=n_bins)
# 给变量2的高度乘以1
heights *= -1
bin_width = np.diff(bins)[0]
bin_pos = (bins[:-1] + bin_width / 2) * -1
plt.bar(bin_pos, heights, width=bin_width, edgecolor='black')
plt.title("变形")
plt.show()

# 密度图1
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
os.chdir(os.path.dirname(__file__))

iris = pd.read_csv("鸢尾花.csv")
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.figure(figsize=(8, 6))  # 设置画布大小
sns.kdeplot(data=iris, x="Sepal.Length", hue="Species", alpha=0.5, fill="Species")
plt.title("密度图1")
plt.show()

# 密度图2
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
os.chdir(os.path.dirname(__file__))

iris = pd.read_csv("鸢尾花.csv")
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
sns.kdeplot(data=iris.iloc[:, [1, 2, 5]], x="Sepal.Width", hue="Species", common_norm=False, multiple="fill", alpha=1)
plt.title("花萼长度关于花萼宽度的堆积密度图")
plt.xlabel("花萼宽度")
plt.show()

# 密度图3
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
os.chdir(os.path.dirname(__file__))
iris = pd.read_csv("鸢尾花.csv")
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
sns.kdeplot(x=iris.iloc[:, 1], y=iris.iloc[:, 2], cmap="Reds", fill=True, bw_adjust=.5)
plt.xlabel("花萼长度")
plt.ylabel("花萼宽度")
plt.title("密度图3")
plt.show()

# 密度图4
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

iris = pd.read_csv("鸢尾花.csv")
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.rcParams['axes.unicode_minus'] = False
sns.jointplot(x=iris["Petal.Length"], y=iris["Petal.Width"], kind='kde', cmap="Reds", fill=True)
plt.title("密度图4")
plt.show()

# 密度图5

import numpy as np
from numpy import linspace
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde

# 创建数据
df = pd.DataFrame({
    'var1': np.random.normal(size=1000),
    'var2': np.random.normal(loc=2, size=1000) * -1
})
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.rcParams['axes.unicode_minus'] = False
# 画变量1的核密度图
sns.kdeplot(data=df, x="var1", fill=True, alpha=1)
# 画变量2的密度图
kde = gaussian_kde(df.var2)
x_range = linspace(min(df.var2), max(df.var2), len(df.var2))
sns.lineplot(x=x_range * -1, y=kde(x_range) * -1, color='orange')
plt.fill_between(x_range * -1, kde(x_range) * -1, color='orange')
plt.xlabel("数值")
plt.axhline(y=0, linestyle='-', linewidth=1, color='black')
plt.title("密度图5")
# show the graph
plt.show()

# 密度图6

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")

iris = pd.read_csv("鸢尾花.csv")

plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.figure(figsize=(8, 6))  # 设置画布大小
sns.kdeplot(data=iris, x="Sepal.Length", alpha=0.5, fill="red", vertical=True)
plt.title("密度图6")
plt.show()

# 箱线图1
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
os.chdir(os.path.dirname(__file__))

iris = pd.read_csv("鸢尾花.csv")
df = iris
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
sns.boxplot(x=df["Species"], y=df["Petal.Width"])
plt.xlabel("种类")
plt.ylabel("花瓣宽度")
plt.title("箱线图1", fontsize=10)
plt.show()

# 箱线图2
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
os.chdir(os.path.dirname(__file__))

iris = pd.read_csv("鸢尾花.csv")
df = iris
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
sns.boxplot(x=df["Species"], y=df["Petal.Width"])
sns.stripplot(x="Species", y="Petal.Width", data=df, jitter=0.6, color="pink")
plt.xlabel("种类")
plt.ylabel("花瓣宽度")
plt.title("带数据点的盒须图", fontsize=10)
plt.show()

# 箱线图3
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
os.chdir(os.path.dirname(__file__))

iris = pd.read_csv("鸢尾花.csv")
df = iris
sns.boxplot(y=df["Species"], x=df["Petal.Width"], )
plt.xlabel("种类")
plt.ylabel("花瓣宽度")
plt.title("横向盒须图", fontsize=10)
plt.show()

# 箱线图4
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
os.chdir(os.path.dirname(__file__))

plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
iris = pd.read_csv("鸢尾花2.csv")
df = iris
sns.boxplot(x=df["属性"], y=df["指标值"], hue=(df["种类"]), )
plt.xlabel("属性")
plt.ylabel("")
plt.title("分组盒须图", fontsize=10)
plt.show()

# 小提琴图1
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
os.chdir(os.path.dirname(__file__))

iris = pd.read_csv("鸢尾花.csv")
df = iris
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
sns.violinplot(x=df["Species"], y=df["Petal.Width"])
plt.xlabel("种类")
plt.ylabel("花瓣宽度")
plt.title("小提琴图", fontsize=10)
plt.show()

# 小提琴图2
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import warnings
import os
os.chdir(os.path.dirname(__file__))

warnings.filterwarnings("ignore")


score = pd.read_csv("student/student-mat.csv", sep=";")
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
df = score
sns.violinplot(y=df["G3"], x=df["Pstatus"], hue=(df["sex"]), split=True)
plt.xlabel("父母婚姻状况")
plt.ylabel("学生分数")
plt.title("学生成绩与性别以及父母婚姻状况的关系", fontsize=10)
plt.show()

# 小提琴图3
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
os.chdir(os.path.dirname(__file__))

iris = pd.read_csv("鸢尾花.csv")
df = iris
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
fig = plt.figure(figsize=(14, 14))
fig.suptitle("多个小提琴图")
plt.subplot(2, 2, 1)
sns.violinplot(x=df["Species"], y=df["Petal.Width"], inner="box")
plt.subplot(2, 2, 2)
sns.violinplot(x=df["Species"], y=df["Petal.Width"], inner="point")
plt.subplot(2, 2, 3)
sns.violinplot(x=df["Species"], y=df["Petal.Width"], inner="stick")
plt.subplot(2, 2, 4)
sns.violinplot(x=df["Species"], y=df["Petal.Width"], inner="quartile")

plt.show()

# 小提琴图4


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
os.chdir(os.path.dirname(__file__))

iris = pd.read_csv("鸢尾花.csv")
df = iris
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体

sns.violinplot(x=df["Species"], y=df["Petal.Width"])
sns.stripplot(x="Species", y="Petal.Width", data=df, jitter=0.2, color="pink")
plt.xlabel("种类")
plt.ylabel("花瓣宽度")
plt.title("带数据点的小提琴图", fontsize=10)
plt.show()

# 小提琴图5
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
os.chdir(os.path.dirname(__file__))

iris = pd.read_csv("鸢尾花.csv")
df = iris
sns.violinplot(y=df["Species"], x=df["Sepal.Length"], )
plt.xlabel("种类")
plt.ylabel("花瓣宽度")
plt.title("横向小提琴图", fontsize=10)
plt.show()

# 脊线图1
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm  # 色谱
import joypy
import os
os.chdir(os.path.dirname(__file__))

tm1 = pd.read_csv("北京pm2.5数据.csv", sep=",")
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.rcParams['axes.unicode_minus'] = False
tm2 = tm1.iloc[:, [2, 7]]
tm2 = tm2.dropna()
fig, axs = joypy.joyplot(tm2, by="month", fill=True, legend=True, alpha=.8,
                         range_style='own', xlabelsize=22, ylabelsize=22,
                         grid='both', linewidth=.8, linecolor='k', figsize=(8, 6), colormap=(cm.Spectral_r))
plt.title("Ridgeline plot1")
plt.show()

# 脊线图2
import pandas as pd
import matplotlib.pyplot as plt
import joypy
import os
os.chdir(os.path.dirname(__file__))

tm1 = pd.read_csv("北京pm2.5数据.csv", sep=",")
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.rcParams['axes.unicode_minus'] = False
tm2 = tm1.iloc[:, [2, 7]]
tm2 = tm2.dropna()
fig, axs = joypy.joyplot(tm2, by="month", fill=True, legend=True, alpha=.8, hist=True, bins=40,
                         range_style='own', xlabelsize=22, ylabelsize=22,
                         linewidth=.8, linecolor='k', figsize=(8, 6))
plt.title("Ridgeline plot2")
plt.show()

# 脊线图3
import pandas as pd
import matplotlib.pyplot as plt
import joypy
import os
os.chdir(os.path.dirname(__file__))

tm1 = pd.read_csv("鸢尾花.csv", sep=",")
plt.rcParams['font.sans-serif'] = ['SimHei']  # 添加中文字体
plt.rcParams['axes.unicode_minus'] = False
tm2 = tm1.dropna()
fig, axs = joypy.joyplot(tm2, by="Species", column="Sepal.Length", fill=True, legend=True, alpha=.8,
                         range_style='own', xlabelsize=22, ylabelsize=22,
                         grid='both', linewidth=.8, linecolor='k', figsize=(8, 6))
plt.title("Ridgeline plot3")
plt.show()