统计学第一周打卡：数据图表的展示

一、知识点汇总

描述数据一共有三种方式，分别是文字、表格和图表。在呈现数据图表中，我们首先需要对数据进行清洗、处理，其次将数据整理成表格，最终再根据数据要表达的结果选择合适的图表，进行展示说明。
数据格式分为两种：离散型数据、连续性数据。
图表的选择：可以参考下图。

二、python实现相关图表

以下展示个人使用pyecharts绘制的图表及部分代码。

#折线图
from pyecharts.charts import Line

def line_base() -> Line:
    c = (
        Line()
        .add_xaxis(['2006','2007','2008','2009','2010','2011','2012','2013','2014','2015'])
        .add_yaxis("农村村镇消费水平",['3066','3538','4065','4355','4456','4798','5200','5699','6079','6400'],is_smooth=True)
        .add_yaxis("城镇居民消费水平",['10739','12867','14999','16504','18908','20666','23000','27654','29087','32998'],is_smooth=True)
        .set_global_opts(title_opts=opts.TitleOpts(title="折线图示例"))
    )
    return c
line_base=line_base()
line_base.render()

#散点图
from pyecharts.charts import Scatter

def Scatter_base():
    c=(
    Scatter()
    .add_xaxis(['6','8','10','13','14','16','21'])
    .add_yaxis('降雨量',[25,40,58,68,110,98,120])
    .set_global_opts(title_opts=opts.TitleOpts(title="散点图示例"))
    )
    return c
Scatter_base=Scatter_base()
Scatter_base.render()

#气泡图
from pyecharts.charts import Scatter

def Scatter_base():
    c=(
    Scatter()
    .add_xaxis(['6','8','10','13','14','16','21'])
    .add_yaxis('降雨量',[25,40,58,68,110,98,120])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="气泡图示例"),
        visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20)
         )
    )
    return c
Scatter_base=Scatter_base()
Scatter_base.render()

#柱状图
from pyecharts import options as opts
from pyecharts.charts import Bar

def bar_base():
    c = (
        Bar()
        .add_xaxis(['2006','2007','2008','2009','2010','2011','2012','2013','2014','2015'])
        .add_yaxis("农村村镇消费水平",['3066','3538','4065','4355','4456','4798','5200','5699','6079','6400'])
        .add_yaxis("城镇居民消费水平",['10739','12867','14999','16504','18908','20666','23000','27654','29087','32998'])
        .set_global_opts(title_opts=opts.TitleOpts(title="柱状图示例"))
    )
    return c
bar_base=bar_base()
bar_base.render()

以下为链接数据库后，进行的图表展示代码：

import pymysql # 导入模块
from pyecharts.charts import Bar

numlist1=[]
numlist2=[]
numlist3=[]

conn = pymysql.connect(
    host='localhost', # 主机模块
    port=3306, # 端口号
    user='user',# 用户名
    password='password', # 密码
    database='db_test', # 需要连接的库
    charset='utf8' # 指定编码utf8
)
cursor = conn.cursor() # 获取游标 
try:
    sql = "此处放入sql;"
    ret = cursor.execute(sql) # ret 受影响的行数
    result=cursor.fetchall()  # 取出所有的

    for result in result:
        #print(result)
        numlist1.append(result[0])
        numlist2.append(result[2])
        numlist3.append(result[3])

    numlist22=list(map(int,numlist2))#转换格式
    numlist33=list(map(int,numlist3))#转换格式
    print(numlist1)
    print(numlist22)
    print(numlist33)
except:
    print("未查询到数据！")
    conn.rollback()
finally:
    cursor.close() # 关闭游标
    conn.close() # 关闭连接
bar = Bar()
bar.add_xaxis(numlist1)
bar.add_yaxis("未上线", numlist22)
bar.add_yaxis("未转入", numlist33)
bar.set_global_opts(title_opts=opts.TitleOpts(title='样例', subtitle='test'),xaxis_opts=opts.AxisOpts(name_rotate=60,axislabel_opts={"rotate":45}))

bar.render()
```python