Python数据分析实例原创

Python数据分析

Python爬网Web数据爬网Web数据

// An highlighted block
import requests
if __name__=="__main__":
    response = requests.get("https://book.douban.com/subject/26986954/")
    content = response.content.decode("utf-8")
    print(content)

// An highlighted block
import requests
url="https://pro.jd.com/mall/active/4BNKTNkRMHJ48QQ5LrUf6AsydtZ6/index.html"
try:
    r=requests.get(url)
    r.raise_for_status()
    r.encoding=r.apparent_encoding
    print(r.text[:100])
except:
    print("爬取失败")

Python生成条形图生成直方图生成条形图生成条形图

// An highlighted block
import matplotlib.pyplot as plt  

num_list = [1.5,0.6,7.8,6]  
plt.bar(range(len(num_list)), num_list,color=rbgy)  
plt.show()  

Python生成堆叠直方图生成堆叠条形图生成堆叠直方图

// An highlighted block
import matplotlib.pyplot as plt  

name_list = [Monday,Tuesday,Friday,Sunday]  
num_list = [1.5,0.6,7.8,6]  
num_list1 = [1,2,3,1]  
plt.bar(range(len(num_list)), num_list, label=boy,fc = y)  
plt.bar(range(len(num_list)), num_list1, bottom=num_list, label=girl,tick_label = name_list,fc = r)  
plt.legend()  
plt.show()  

Python生成垂直条形图生成垂直条形图生成垂直条形图

// An highlighted block
import matplotlib.pyplot as plt  

name_list = [Monday,Tuesday,Friday,Sunday]  
num_list = [1.5,0.6,7.8,6]  
num_list1 = [1,2,3,1]  
x =list(range(len(num_list)))  
total_width, n = 0.8, 2  
width = total_width / n  

plt.bar(x, num_list, width=width, label=boy,fc = y)  
for i in range(len(x)):  
    x[i] = x[i] + width  
plt.bar(x, num_list1, width=width, label=girl,tick_label = name_list,fc = r)  
plt.legend()  
plt.show()  

Python生成折线图生成折线图生成折线图

// An highlighted block
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(15, 4), columns=[a, b, c, d])
df.plot.area() 

Python生成条形图生成直方图生成条形图生成条形图

// An highlighted block
import pandas as pd
import numpy as np

df = pd.DataFrame(3 * np.random.rand(5), index=[a, b, c, d,e], columns=[x])
df.plot.pie(subplots=True)

Python生成框图生成框图

// An highlighted block
#首先导入基本绘图包先导入基本绘图包
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

#添加成绩表添加成绩表添加成绩列表
plt.style.use("ggplot")
plt.rcParams[axes.unicode_minus] = False
plt.rcParams[font.sans-serif]=[SimHei] 

#创建一个新的空的创建一个空的新的和空的DataFrame
df=pd.DataFrame()

// An highlighted block
df["英语"]=[76,90,97,71,70,93,86,83,78,85,81]
df["经济数学"]=[65,95,51,74,78,63,91,82,75,71,55]
df["西方经济学西方计量经济学"]=[93,81,76,88,66,79,83,92,78,86,78]
df["计算机应用基础计算机应用基础"]=[85,78,81,95,70,67,82,72,80,81,77]
df

// An highlighted block
plt.boxplot(x=df.values,labels=df.columns,whis=1.5)
plt.show()

// An highlighted block
#用pandas自带绘图工具速度更快，自带绘图工具速度更快
df.boxplot()
plt.show()

Python生成正态分布生成正态分布图生成正态分布图

// An highlighted block
# -*- coding:utf-8 -*-
# Python实现正态分布
# 绘制正态分布概率密度函数
import numpy as np
import matplotlib.pyplot as plt
import math

u = 0  # 均值μ
u01 = -2
sig = math.sqrt(0.2)  # 标准差δ

x = np.linspace(u - 3 * sig, u + 3 * sig, 50)
y_sig = np.exp(-(x - u) ** 2 / (2 * sig ** 2)) / (math.sqrt(2 * math.pi) * sig)
print(x)
print("=" * 20)
print(y_sig)
plt.plot(x, y_sig, "r-", linewidth=2)
plt.grid(True)
plt.show()

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