数据可视化：随时间变化的效果图

数据处理

获取北京、上海、江苏、广东四省的2008—2012年的GDP数据

在Jupyter Notebook上实现代码如下：

import pandas as pd

data = pd.read_csv("C:/Users/dell/Desktop/gdp.csv", encoding="utf-8",usecols=[9,8,7,6,5,0])
data = data.iloc[[0,8,9,18],0:6]
data

获取北京、浙江、福建、广东四省的2008——2012年的GDP数据，并且依次按2008y到2012y的列的值大小排序

在Jupyter Notebook上实现代码如下：

import pandas as pd
for i in range(2008, 2013):
    data = pd.read_csv("C:/Users/dell/Desktop/gdp.csv", encoding="utf-8",usecols=[9,8,7,6,5,0])
    data = data.iloc[[0,10,12,18],0:6]
    data = data.sort_values( [str(i) + 'y'] ,ascending = False)
    print(data)

Line-Line_yaixs_log

其中，设置折线图效果如下：
线图Y轴log效果，设置每个省使用的点用不同的形状，数据紧贴Y轴，横向zoom缩放条在左下方

import pyecharts.options as opts
from pyecharts.charts import Line
import pandas as pd

data = pd.read_csv("C:/Users/dell/Desktop/gdp.csv", encoding="utf-8",usecols=[9,8,7,6,5,0])
data = data.iloc[[0,8,9,18],0:6]
gdp0 = data.loc[0].values.tolist()
gdp8 = data.loc[8].values.tolist()
gdp9 = data.loc[9].values.tolist()
gdp18 = data.loc[18].values.tolist()

#x轴需转换成str，如果x是纯文本就会出现空白折线图
x = ['2008','2009','2010','2011','2012']

c = (
    Line()
    .add_xaxis(x)
    
    .add_yaxis("北京",gdp0 , symbol="triangle" , symbol_size=12 , linestyle_opts=opts.LineStyleOpts(width=2) , label_opts=opts.LabelOpts(is_show=False), )
    .add_yaxis("上海",gdp8 , linestyle_opts=opts.LineStyleOpts(width=2) , label_opts=opts.LabelOpts(is_show=False), )
    .add_yaxis("江苏",gdp9 , symbol="arrow" ,symbol_size=12 , linestyle_opts=opts.LineStyleOpts(width=2) , label_opts=opts.LabelOpts(is_show=False), )
    .add_yaxis("广东",gdp18 , symbol="pin" ,symbol_size=12 , linestyle_opts=opts.LineStyleOpts(width=2) , label_opts=opts.LabelOpts(is_show=False), )
    
    .set_global_opts(
        datazoom_opts=opts.DataZoomOpts(pos_left='left'),
        title_opts=opts.TitleOpts(title="Line-Line_yaixs_log"),
        xaxis_opts=opts.AxisOpts(name="年份",boundary_gap=False),
        yaxis_opts=opts.AxisOpts(
            type_="log",
            name="GDP",
            splitline_opts=opts.SplitLineOpts(is_show=True),
            is_scale=True,
        ),
    )
)
c.render_notebook()

Timeline

Timeline_Pie

其中，设置时间线轮播多图效果如下：
Timeline饼图，radius类型，轮播年份

from pyecharts import options as opts
from pyecharts.charts import Pie, Timeline
import pandas as pd

tl = Timeline()
for i in range(2008, 2013):
    data = pd.read_csv("C:/Users/dell/Desktop/gdp.csv", encoding="utf-8",usecols=[9,8,7,6,5,0])
    data = data.iloc[[0,8,9,18],0:6]
    city = data['province'].values.tolist()
    time_gdp = data[str(i) + 'y'].values.tolist()
    
    pie = (
        Pie()
        .add(
            "",
            [list(z) for z in zip(city,time_gdp)],
            rosetype="radius",
            radius=["30%", "55%"],
        )
        .set_global_opts(title_opts=opts.TitleOpts("{}年".format(i)))
    )
    tl.add(pie, "{}年".format(i))
tl.render_notebook()

Timeline_Bar

Timeline横向柱状图，按GDP排序，值大的在下，轮播轴纵向在左侧

from pyecharts import options as opts
from pyecharts.charts import Bar, Timeline
import pandas as pd

tl = Timeline()
#北京、福建、浙江、广东四省的2008——2012年的GDP
for i in range(2008, 2013):
    data = pd.read_csv("C:/Users/dell/Desktop/gdp.csv", encoding="utf-8",usecols=[9,8,7,6,5,0])
    data = data.iloc[[0,10,12,18],0:6]
    data = data.sort_values( [str(i) + 'y'] ,ascending = False)
    time_gdp = data[str(i) + 'y'].values.tolist()
    city = data['province'].values.tolist()
    
    bar = (
        Bar()
        .add_xaxis(city)
        .add_yaxis("GDP", time_gdp, label_opts=opts.LabelOpts(position="right"),)
        .reversal_axis()
        .set_global_opts(
            title_opts=opts.TitleOpts("Timeline-Bar-Reversal (时间: {} 年)".format(i)),   
        )
    )
    #轮播组件的设置
    tl.add_schema(orient="vertical",is_timeline_show=True,pos_top='24px',pos_left='30px',pos_right='96%',is_inverse=True)
    tl.add(bar, "{}".format(i))
tl.render_notebook()

Streamlit

其中，设置图形效果如下：
Streamlit任意控件，展示2008–2012年的GDP

在pycharm运行：

import streamlit as st
from pyecharts import options as opts
from pyecharts.charts import Bar
import pandas as pd

st.header("streamlit控件年份+图表")
time = st.select_slider("时间",[2008,2009,2010,2011,2012])

for i  in range(2008,2013):
    data = pd.read_csv("C:/Users/dell/Desktop/gdp.csv", encoding="utf-8",usecols=[0,9,8,7,6,5])
    data = data.iloc[[0,8,9,18],0:6]
    data = data.sort_values( [str(i) + 'y'] ,ascending = True)
    time_gdp = data[str(i) + 'y'].values.tolist()
    city = data['province'].values.tolist()

c = (
    Bar()
    .add_xaxis(city)
    .add_yaxis("gdp", time_gdp)
    .reversal_axis()
    .set_series_opts(label_opts=opts.LabelOpts(position="right"))
    .set_global_opts(title_opts=opts.TitleOpts(title="{}年".format(time)))
)

#饼图
# c = (
#     Pie()
#     .add("".format(time), [list(z) for z in zip(city, time_gdp)])
#     .set_global_opts(title_opts=opts.TitleOpts(title="{}年".format(time)))
#     .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
# )

html = c.render_embed()
st.components.v1.html(html, width=1000, height=800, scrolling=False)