<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Jason960606的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/Jason960606</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Jason960606]]></copyright><item><title><![CDATA[【C++基础语法- Datawhale学习打卡】]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/124163517</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/124163517</guid><author>Jason960606</author><pubDate>Sat, 16 Apr 2022 22:38:36 +0800</pubDate><description><![CDATA[C++基础语法
文章目录
前言一、C++是什么？
二、基本结构
三、数据类型
四、循环和分支结构
五、代码示例
六、总结]]></description><category></category></item><item><title><![CDATA[2020-09-04]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/108412203</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/108412203</guid><author>Jason960606</author><pubDate>Fri, 04 Sep 2020 21:57:24 +0800</pubDate><description><![CDATA[第十二讲练习
练习1

data = pd.read_csv("C:\\Users\\chen\\Desktop\\python-m notebook\\作业数据\\catNames2.csv")
#找出大于800的名字
data2 = data[data['Count_AnimalName']&gt;800]
data2

#找出使用最多的名字
#法1，直接寻找
# data3 = data[data['Count_AnimalName']==data.max()[0]]

###法2，通过排序寻找，默认]]></description><category></category></item><item><title><![CDATA[2020-09-03]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/108387947</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/108387947</guid><author>Jason960606</author><pubDate>Thu, 03 Sep 2020 17:13:27 +0800</pubDate><description><![CDATA[第十讲练习
练习1


stu_names = ["胡歌","林更新","金世佳","丑娟"]
courses = ['语文', '数学', '英语', 'Python', '体育']
data_1 = np.array([[87., 74., 98., 84., np.nan],[79., 69., 61., 99., np.nan],[84., 84., 94., 66., np.nan],[90., 60., 72., 90., np.nan]])
df = pd.DataFrame(data=dat]]></description><category></category></item><item><title><![CDATA[2020-08-31]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/108330457</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/108330457</guid><author>Jason960606</author><pubDate>Mon, 31 Aug 2020 22:04:32 +0800</pubDate><description><![CDATA[第九讲及练习
练习1

import csv
import pandas as pd
import random
import numpy as np
from functools import reduce
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter

#字体设置1：通过全局设置方式使图片可以显示中文刻度，只支持ttf，不能设置ttc字体
import m]]></description><category></category></item><item><title><![CDATA[2020-08-27]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/108266170</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/108266170</guid><author>Jason960606</author><pubDate>Thu, 27 Aug 2020 18:12:46 +0800</pubDate><description><![CDATA[第八讲练习

原始方法：
data1_stock = np.loadtxt("C:\\Users\\chen\\Desktop\\python-m notebook\\图片及数据\\大地电磁EMD数据\\EMD1.csv",delimiter=",",dtype="object")
data2_stock = np.loadtxt("C:\\Users\\chen\\Desktop\\python-m notebook\\图片及数据\\大地电磁EMD数据\\EMD2.csv",delimiter=",",d]]></description><category></category></item><item><title><![CDATA[2020-08-25]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/108225760</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/108225760</guid><author>Jason960606</author><pubDate>Tue, 25 Aug 2020 17:47:48 +0800</pubDate><description><![CDATA[第七讲练习
练习1

def auto_label(x,my_y):
    for x_i,y_i in zip(x,my_y):
        plt.annotate(f"{y_i}",xy=(x_i,y_i),xytext=(x_i,y_i-1))

plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）
plt.rcParams['axes.unicode_minus'] = False   # 步骤二（解决坐标轴负数]]></description><category></category></item><item><title><![CDATA[2020-08-22]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/108165516</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/108165516</guid><author>Jason960606</author><pubDate>Sat, 22 Aug 2020 11:22:53 +0800</pubDate><description><![CDATA[第六讲练习

创建 2*2 的数组arr1 元素自定义，两种方法
#创建 2*2 的数组arr1 元素自定义
arr_1 = np.arange(4)
arr1 = arr_1.reshape(2,2)#使用reshape,注意reshape不会改变原数组，方法1
arr1 = arr_1.reshape(2,-1)#也是方法1，-1为维度根据数据判断（负数就行）
arr1 = np.array([[0,1],[2,3]])#正常创造，方法2


创建 223 的数组arr2 元素自定义，查看arr2的维度]]></description><category></category></item><item><title><![CDATA[2020-08-19]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/108109089</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/108109089</guid><author>Jason960606</author><pubDate>Wed, 19 Aug 2020 20:02:28 +0800</pubDate><description><![CDATA[第五讲及作业
练习1

第一种方法，使用open函数
f = open('D:\\Download\\餐饮.csv', 'r',encoding="gbk")# 给定文件路径

f.read()#读全部
# f.readline()#读一行
# f.readlines()#读全部


第二种方法，使用pandas

# pd.read_csv??
data_1 = pd.read_csv("D:\\Download\\餐饮.csv",encoding="gbk")
print(data_1)


练习2

]]></description><category></category></item><item><title><![CDATA[2020-08-17]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/108067145</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/108067145</guid><author>Jason960606</author><pubDate>Mon, 17 Aug 2020 23:47:11 +0800</pubDate><description><![CDATA[作业二
练习1

#最小值160，最大值189，
height = [160,163,175,180,176,177,168,189,188,177,174,170,173,181]

#求组数
ma = max(height)
mi = min(height)
b = 1
bi = int((ma - mi)/b)

plt.hist(height,bins = bi,density=True)

#标签
plt.xlabel('区间')
plt.ylabel('频数、频率')
plt.title('身高]]></description><category></category></item><item><title><![CDATA[2020-08-15]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/108016978</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/108016978</guid><author>Jason960606</author><pubDate>Sat, 15 Aug 2020 01:55:17 +0800</pubDate><description><![CDATA[第三课作业

p = [60,80,40,30,70,90,95]
s = [100,50,120,135,65,45,40]

a = sorted(zip(p,s)) 
x = []
y = []
for i in range(len(a)):
    x.append(a[i][0])
    y.append(a[i][1])

x_l = range(len(x))

plt.scatter(x_l,y)
plt.plot(x_l,y)

#刻度
s_xt = ['{}元'.format(x[i]]]></description><category></category></item><item><title><![CDATA[python将多图绘制在同一画布中]]></title><link>https://blog.youkuaiyun.com/Jason960606/article/details/107980394</link><guid>https://blog.youkuaiyun.com/Jason960606/article/details/107980394</guid><author>Jason960606</author><pubDate>Thu, 13 Aug 2020 16:54:16 +0800</pubDate><description><![CDATA[python将多图绘制在同一画布中matplotlib绘图axs[3,3]绘制子图subplot(333)绘制子图
matplotlib绘图
最近在上python数据分析课程，结合之前自己自学和会议论文里面遇到的数据可视化总结了一下多图绘制在一块画布的两种写法。
下面是一张七行一列的图片，是我在EMD算法中对原始信号分解得到的，在这之前我已经有七个数组的数据了，我的需求就是把七个数组绘制到同一幅图并且每个y轴都加上信号信息：
![这是一张七行一列的图片，是我在EMD算法中对原始信号分解得到的，在这之前我已经]]></description><category></category></item></channel></rss>