<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[MaRin0709的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/MaRin0709</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; MaRin0709]]></copyright><item><title><![CDATA[利用Seaborn库进行简单的画图]]></title><link>https://blog.youkuaiyun.com/MaRin0709/article/details/114477044</link><guid>https://blog.youkuaiyun.com/MaRin0709/article/details/114477044</guid><author>MaRin0709</author><pubDate>Sun, 07 Mar 2021 10:07:51 +0800</pubDate><description><![CDATA[Seaborn练习练习题代码效果图小结
练习题


代码
import seaborn as sns
import pandas as pd
from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ["SimHei"]

"""
读取数据
"""
db_read = pd.read_excel("豆瓣读书.xlsx", index_col=0)
print(db_read.info())
print(db_read.hea]]></description><category></category></item><item><title><![CDATA[利用Pandas库进行简单的数据规整]]></title><link>https://blog.youkuaiyun.com/MaRin0709/article/details/113617015</link><guid>https://blog.youkuaiyun.com/MaRin0709/article/details/113617015</guid><author>MaRin0709</author><pubDate>Wed, 03 Feb 2021 18:40:02 +0800</pubDate><description><![CDATA[Pandas练习4-数据规整练习题练习1练习2练习3练习4练习1代码效果图练习2代码效果图练习3代码效果图练习4代码效果图小结
练习题
练习1

练习2

练习3

练习4

练习1
代码
import pandas as pd
# Exercise 1
# 通过starbucks_store_worldwide.csv数据，分析星巴克在中国分布多还是美国分布多？
# 将 Country 设成 行索引 去匹配 CN(中国)和US(美国)
starbucks_data = pd.read_csv("starb]]></description><category></category></item><item><title><![CDATA[利用Pandas库进行简单的数据分析（数据清洗）]]></title><link>https://blog.youkuaiyun.com/MaRin0709/article/details/113448524</link><guid>https://blog.youkuaiyun.com/MaRin0709/article/details/113448524</guid><author>MaRin0709</author><pubDate>Sat, 30 Jan 2021 23:49:02 +0800</pubDate><description><![CDATA[Pandas练习3练习题练习1练习2练习1代码效果图练习2代码效果图小结
练习题
练习1

练习2

练习1
代码
import pandas as pd
import numpy as np

# Exercise 1
# 读取北向.csv   指定 trade_date 为 行索引
data_north = pd.read_csv("北向.csv", index_col="trade_date")
print("读取后的数据为:")
print(data_north)
print()
# 查看数据的基本]]></description><category></category></item><item><title><![CDATA[Pandas库DataFrame的简单应用2]]></title><link>https://blog.youkuaiyun.com/MaRin0709/article/details/113243829</link><guid>https://blog.youkuaiyun.com/MaRin0709/article/details/113243829</guid><author>MaRin0709</author><pubDate>Wed, 27 Jan 2021 11:22:50 +0800</pubDate><description><![CDATA[Pandas练习2练习题DataFrame的简单应用练习1代码效果图练习2代码效果图小结
练习题
DataFrame的简单应用

练习1
代码
import pandas as pd

# Exercise 1
# 读取 catNames2.csv 文件，完成需求如下：
cat_data_df = pd.read_csv("catNames2.csv")  # 用pandas库读取文件 数据文件和代码在同一个文件夹里面
# • 找到所有的使用次数超过800的猫的名字
counts = cat_data_df]]></description><category></category></item><item><title><![CDATA[Pandas之DataFrame的简单使用]]></title><link>https://blog.youkuaiyun.com/MaRin0709/article/details/113131599</link><guid>https://blog.youkuaiyun.com/MaRin0709/article/details/113131599</guid><author>MaRin0709</author><pubDate>Mon, 25 Jan 2021 19:05:43 +0800</pubDate><description><![CDATA[Pandas练习1练习题DataFrame的简单操作代码效果图小结
练习题
DataFrame的简单操作





代码
import pandas as pd
import numpy as np

# exercise 1
# 1. 创建 学生成绩表.xlsx 文件
stu_names = ["胡歌", "林更新", "金世佳", "丑娟"]
courses = ['语文', '数学', '英语', 'Python', '体育']
data = np.array([[87., 74., 98., 84.]]></description><category></category></item><item><title><![CDATA[利用Numpy提取＋处理数据]]></title><link>https://blog.youkuaiyun.com/MaRin0709/article/details/112797366</link><guid>https://blog.youkuaiyun.com/MaRin0709/article/details/112797366</guid><author>MaRin0709</author><pubDate>Mon, 18 Jan 2021 23:56:02 +0800</pubDate><description><![CDATA[Numpy练习2练习题利用Numpy筛选csv文件里面的数据利用Numpy分析数据＋Matplotlib画图利用Numpy筛选csv文件里面的数据代码原数据片段效果图利用Numpy分析数据＋Matplotlib画图代码练习二效果图小结
练习题
利用Numpy筛选csv文件里面的数据

利用Numpy分析数据＋Matplotlib画图

利用Numpy筛选csv文件里面的数据
代码
import numpy as np
# Question 1: 读取scores.csv的数据 并且
score_data =]]></description><category></category></item><item><title><![CDATA[利用Python进行简单的文件读写操作+Numpy库的简单使用]]></title><link>https://blog.youkuaiyun.com/MaRin0709/article/details/112710199</link><guid>https://blog.youkuaiyun.com/MaRin0709/article/details/112710199</guid><author>MaRin0709</author><pubDate>Sat, 16 Jan 2021 16:27:17 +0800</pubDate><description><![CDATA[Python简单的文件操作+Numpy练习1练习题文件操作＋Numpy的简单使用(1)Numpy的简单使用(2)Numpy优化Matplotlib画图过程Numpy在数组上的简单应用文件操作＋Numpy的简单使用(1)练习一代码方法一方法二练习一效果图练习二方法一方法二练习二效果图练习三代码练习三效果图Numpy的简单使用(2)代码效果图Numpy优化Matplotlib画图过程优化代码（练习一）效果图Numpy在数组上的简单应用练习二练习二代码练习二效果图练习三练习三代码练习三效果图小结
练习题
文件操作]]></description><category></category></item><item><title><![CDATA[利用Python Matplotlib库做简单的视觉化(2)]]></title><link>https://blog.youkuaiyun.com/MaRin0709/article/details/112373063</link><guid>https://blog.youkuaiyun.com/MaRin0709/article/details/112373063</guid><author>MaRin0709</author><pubDate>Fri, 08 Jan 2021 17:39:24 +0800</pubDate><description><![CDATA[Matplotlib作业第三节课题目练习一方法一（条形图）方法一效果图方法二（扇形图）方法二效果图练习二练习二代码练习二效果图小结
第三节课
题目

练习一
方法一（条形图）
from matplotlib import pyplot as plt
from matplotlib.font_manager import FontProperties

# 处理中文字体的问题
font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=12]]></description><category></category></item><item><title><![CDATA[利用Python Matplotlib库做简单的视觉化]]></title><link>https://blog.youkuaiyun.com/MaRin0709/article/details/112308021</link><guid>https://blog.youkuaiyun.com/MaRin0709/article/details/112308021</guid><author>MaRin0709</author><pubDate>Thu, 07 Jan 2021 14:14:09 +0800</pubDate><description><![CDATA[第一&amp;二节课Matplotlib作业第一节课作业题目代码效果图第二节课作业题目练习一代码练习一效果图练习二代码练习二效果图小结
第一节课作业
题目

代码
import matplotlib.pyplot as plt
# 处理中文字体
from matplotlib.font_manager import FontProperties

font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=12)

# 构建x (x是相同]]></description><category></category></item></channel></rss>