Practical cases of data visualization (timeline rotation diagram, streamlit control year metabase visualization tutorial) 2.0

Reference documents

Starting with a simple

 Time , place 1, place 2, place 3, place 4
1982,5323,1777,2383,1163
1983,5963,1978,2646,1338
1984,7208,2316,3106,1786
1985,9016,2564,3867,2585
1986,10275,2789,4493,2994
1987,12059,3233,5252,3574
1988,15043,3865,6587,4590
1989,16992,4266,7278,5448
1990,18668,5062,7717,5888
1991,21781,5342,9102,7337
1992,26923,5867,11700,9357

 !pip install --index-url https://pypi.douban.com/simple pandas
 !pip install --index-url https://pypi.douban.com/simple pyecharts

timeline Rotation year

Columnar rotation chart

import pandas as pd
df1 = pd.read_csv('gdp edition 1.csv')
# Read the header location 
x = []
for i in df1.iloc[0:0]:
    x.append(i)
del x[0]
# Read time 
t1=[]
for i in df1[' Time ']:
    t1.append(i)
# Read location GDP
v1 = []
for i in df1.iterrows():
    v = (i[1][1],i[1][2],i[1][3],i[1][4])
    c = []
    for k in v:
        c.append(int(k))
    v1.append(c)

visualization

from pyecharts import options as opts
from pyecharts.charts import Bar, Timeline
from pyecharts.globals import ThemeType
tl = Timeline()
for i in range(11):
    bar = (
        Bar({
    "theme": ThemeType.MACARONS})
        .add_xaxis(x)
        .add_yaxis("",v1[i])
        .set_global_opts(title_opts=opts.TitleOpts(" A region {} year GDP".format(t1[i])))
    )
    tl.add(bar, "{} year ".format(t1[i]))
tl.render_notebook()

The pie chart ：

from pyecharts import options as opts
from pyecharts.charts import Pie, Timeline
from pyecharts.faker import Faker

tl = Timeline()
for i in range(11):
    pie = (
        Pie()
        .add(
            " merchants A",
            [list(z) for z in zip(x,v1[i])],
            rosetype="radius",
            radius=["30%", "55%"],
        )
        .set_global_opts(title_opts=opts.TitleOpts(" A store {} Annual turnover ".format(t1[i])))
    )
    print(v1[i])
    tl.add(pie, "{} year ".format(t1[i]))
tl.render_notebook()

Line graph + zoom slider

"""
 Line graph  + zoom slider 
"""
import pandas as pd
df1 = pd.read_csv('gdp edition 1.csv')
# Read the header location 
x1 = []
for i in df1.iloc[0:0]:
    x1.append(i)
del x1[0]
# Read time 
t1=[]
for i in df1[' Time ']:
    t1.append(str(i))
# Read location GDP
v1 = []
for i in range(1,5):
    g = []
    for k in df1[' place {}'.format(i)]:
        g.append(int(k))
    
    v1.append(g)
import pandas as pd
import numpy as np
from pyecharts.charts import Line
from pyecharts import options as opts


# Line graph 
def show_line():
    line = Line().add_xaxis(t1)

    for i in range(len(x1)):
        line.add_yaxis(x1[i],list(v1[i]),is_smooth=True)

    line.set_global_opts(title_opts=opts.TitleOpts(title=" Linear slider "),
                         datazoom_opts=opts.DataZoomOpts(pos_left = True),
                         yaxis_opts=opts.AxisOpts(type_="log",is_scale=True))
    return line

show_line().render_notebook()

GDP Assessment of csv Handle , Examination film

province,2016y,2015y,2014y,2013y,2012y,2011y,2010y,2009y,2008y,2007y,2006y,2005y,2004y,2003y,2002y,2001y,2000y,1999y,1998y,1997y
 The Beijing municipal ,25669.13,23014.59,21330.83,19800.81,17879.4,16251.93,14113.58,12153.03,11115,9846.81,8117.78,6969.52,6033.21,5007.21,4315,3707.96,3161.66,2678.82,2377.18,2077.09
 tianjin ,17885.39,16538.19,15726.93,14442.01,12893.88,11307.28,9224.46,7521.85,6719.01,5252.76,4462.74,3905.64,3110.97,2578.03,2150.76,1919.09,1701.88,1500.95,1374.6,1264.63
 Hebei Province ,32070.45,29806.11,29421.15,28442.95,26575.01,24515.76,20394.26,17235.48,16011.97,13607.32,11467.6,10012.11,8477.63,6921.29,6018.28,5516.76,5043.96,4514.19,4256.01,3953.78
 Shanxi Province ,13050.41,12766.49,12761.49,12665.25,12112.83,11237.55,9200.86,7358.31,7315.4,6024.45,4878.61,4230.53,3571.37,2855.23,2324.8,2029.53,1845.72,1667.1,1611.08,1476
 Inner Mongolia ,18128.1,17831.51,17770.19,16916.5,15880.58,14359.88,11672,9740.25,8496.2,6423.18,4944.25,3905.03,3041.07,2388.38,1940.94,1713.81,1539.12,1379.31,1262.54,1153.51
 Liaoning Province ,22246.9,28669.02,28626.58,27213.22,24846.43,22226.7,18457.27,15212.49,13668.58,11164.3,9304.52,8047.26,6672,6002.54,5458.22,5033.08,4669.06,4171.69,3881.73,3582.46
 Jilin Province ,14776.8,14063.13,13803.14,13046.4,11939.24,10568.83,8667.58,7278.75,6426.1,5284.69,4275.12,3620.27,3122.01,2662.08,2348.54,2120.35,1951.51,1672.96,1577.05,1464.34
 Heilongjiang Province ,15386.09,15083.67,15039.38,14454.91,13691.58,12582,10368.6,8587,8314.37,7104,6211.8,5513.7,4750.6,4057.4,3637.2,3390.1,3151.4,2866.3,2774.4,2667.5
 Shanghai ,28178.65,25123.45,23567.7,21818.15,20181.72,19195.69,17165.98,15046.45,14069.86,12494.01,10572.24,9247.66,8072.83,6694.23,5741.03,5210.12,4771.17,4188.73,3801.09,3438.79
 Jiangsu Province ,77388.28,70116.38,65088.32,59753.37,54058.22,49110.27,41425.48,34457.3,30981.98,26018.48,21742.05,18598.69,15003.6,12442.87,10606.85,9456.84,8553.69,7697.82,7199.95,6680.34
 Zhejiang Province ,47251.36,42886.49,40173.03,37756.59,34665.33,32318.85,27722.31,22990.35,21462.69,18753.73,15718.47,13417.68,11648.7,9705.02,8003.67,6898.34,6141.03,5443.92,5052.62,4686.11
 Anhui Province ,24407.62,22005.63,20848.75,19229.34,17212.05,15300.65,12359.33,10062.82,8851.66,7360.92,6112.5,5350.17,4759.3,3923.11,3519.72,3246.71,2902.09,2712.34,2542.96,2347.32
 Fujian Province ,28810.58,25979.82,24055.76,21868.49,19701.78,17560.18,14737.12,12236.53,10823.01,9248.53,7583.85,6554.69,5763.35,4983.67,4467.55,4072.85,3764.54,3414.19,3159.91,2870.9
 Jiangxi Province ,18499,16723.78,15714.63,14410.19,12948.88,11702.82,9451.26,7655.18,6971.05,5800.25,4820.53,4056.76,3456.7,2807.41,2450.48,2175.68,2003.07,1853.65,1719.87,1605.77
 Shandong Province ,68024.49,63002.33,59426.59,55230.32,50013.24,45361.85,39169.92,33896.65,30933.28,25776.91,21900.19,18366.87,15021.84,12078.15,10275.5,9195.04,8337.47,7493.84,7021.35,6537.07
 Henan province ,40471.79,37002.16,34938.24,32191.3,29599.31,26931.03,23092.36,19480.46,18018.53,15012.46,12362.79,10587.42,8553.79,6867.7,6035.48,5533.01,5052.99,4517.94,4308.24,4041.09
 Hubei province ,32665.38,29550.19,27379.22,24791.83,22250.45,19632.26,15967.61,12961.1,11328.92,9333.4,7617.47,6590.19,5633.24,4757.45,4212.82,3880.53,3545.39,3229.29,3114.02,2856.47
 Hunan province ,31551.37,28902.21,27037.32,24621.67,22154.23,19669.56,16037.96,13059.69,11555,9439.6,7688.67,6596.1,5641.94,4659.99,4151.54,3831.9,3551.49,3214.54,3025.53,2849.27
 Guangdong province, ,80854.91,72812.55,67809.85,62474.79,57067.92,53210.28,46013.06,39482.56,36796.71,31777.01,26587.76,22557.37,18864.62,15844.64,13502.42,12039.25,10741.25,9250.68,8530.88,7774.53
 guangxi ,18317.64,16803.12,15672.89,14449.9,13035.1,11720.87,9569.85,7759.16,7021,5823.41,4746.16,3984.1,3433.5,2821.11,2523.73,2279.34,2080.04,1971.41,1911.3,1817.25
 Hainan ,4053.2,3702.76,3500.72,3177.56,2855.54,2522.66,2064.5,1654.21,1503.06,1254.17,1065.67,918.75,819.66,713.96,642.73,579.17,526.82,476.67,442.13,411.16
 Chongqing City ,17740.59,15717.27,14262.6,12783.26,11409.6,10011.37,7925.58,6530.01,5793.66,4676.13,3907.23,3467.72,3034.58,2555.72,2232.86,1976.86,1791,1663.2,1602.38,1509.75
 Sichuan Province ,32934.54,30053.1,28536.66,26392.07,23872.8,21026.68,17185.48,14151.28,12601.23,10562.39,8690.24,7385.1,6379.63,5333.09,4725.01,4293.49,3928.2,3649.12,3474.09,3241.47
 Guizhou Province ,11776.73,10502.56,9266.39,8086.86,6852.2,5701.84,4602.16,3912.68,3561.56,2884.11,2338.98,2005.42,1677.8,1426.34,1243.43,1133.27,1029.92,937.5,858.39,805.79
 Yunnan Province ,14788.42,13619.17,12814.59,11832.31,10309.47,8893.12,7224.18,6169.75,5692.12,4772.52,3988.14,3462.73,3081.91,2556.02,2312.82,2138.31,2011.19,1899.82,1831.33,1676.17
 Tibet ,1151.41,1026.39,920.83,815.67,701.03,605.83,507.46,441.36,394.85,341.43,290.76,248.8,220.34,185.09,162.04,139.16,117.8,105.98,91.5,77.24
 Shaanxi Province ,19399.59,18021.86,17689.94,16205.45,14453.68,12512.3,10123.48,8169.8,7314.58,5757.29,4743.61,3933.72,3175.58,2587.72,2253.39,2010.62,1804,1592.64,1458.4,1363.6
 Gansu Province ,7200.37,6790.32,6836.82,6330.69,5650.2,5020.37,4120.75,3387.56,3166.82,2703.98,2277.35,1933.98,1688.49,1399.83,1232.03,1125.37,1052.88,956.32,887.67,793.57
 Qinghai Province ,2572.49,2417.05,2303.32,2122.06,1893.54,1670.44,1350.43,1081.27,1018.62,797.35,648.5,543.32,466.1,390.2,340.65,300.13,263.68,239.38,220.92,202.79
 ningxia ,3168.59,2911.77,2752.1,2577.57,2341.29,2102.21,1689.65,1353.31,1203.92,919.11,725.9,612.61,537.11,445.36,377.16,337.44,295.02,264.58,245.44,224.59
 xinjiang ,9649.7,9324.8,9273.46,8443.84,7505.31,6610.05,5437.47,4277.05,4183.21,3523.16,3045.26,2604.19,2209.09,1886.35,1612.65,1491.6,1363.56,1163.17,1106.95,1039.85

timeline_bar

df = pd.read_csv('gdp.csv')
df = df.iloc[[0,8,9,18,12],0:].set_index("province")
df = df.T.sort_index()
t =  df.index.tolist() # Time 
v1 = np.array(df) # value 

x = df.columns.values.tolist() # City 
tl = Timeline()
v = []
for i in v1:
    g = []
    for k in i:
        g.append(k)
    v.append(g)
def timeline_bar() -> Timeline:
    tl = Timeline()
    for i in range(len(t)):
        bar = (
            Bar()
            .add_xaxis(x)
            .add_yaxis("GDP",v[i])
            .set_global_opts(title_opts=opts.TitleOpts(" Four provinces {} year GDP".format(t[i])))
        )
        tl.add(bar, "{} year ".format(t[i]))
    return tl
timeline_bar().render_notebook()

The pie chart

from pyecharts import options as opts
from pyecharts.charts import Pie, Timeline
from pyecharts.faker import Faker
df = pd.read_csv('gdp.csv')
df = df.iloc[[0,8,9,18,12],0:].set_index("province")
df = df.T.sort_index()
t =  df.index.tolist() # Time 
v1 = np.array(df) # value 
x = df.columns.values.tolist() # City 
tl = Timeline()
for i in range(11):
    pie = (
        Pie()
        .add(
            " merchants A",
            [list(z) for z in zip(x,v1[i])],
            rosetype="radius",
            radius=["30%", "55%"],
        )
        .set_global_opts(title_opts=opts.TitleOpts(" A store {} Annual turnover ".format(t1[i])))
    )
    tl.add(pie, "{} year ".format(t1[i]))
tl.render_notebook()

Line graph DataZoomOpts

import pandas as pd
import numpy as np
from pyecharts.charts import Line
from pyecharts import options as opts
df = pd.read_csv('gdp.csv')
df = df.iloc[[0,8,9,18,12],0:].set_index("province")
df = df.T.sort_index()
print(df)
t =  df.index.tolist() # Time 
x = df.columns.values.tolist() # City 
v1 = []
for i in x:
    g = []
    for k in df['{}'.format(i)]:
        g.append(k)
    v1.append(g)
    
print(v1)
# Line graph 
def show_line():
    line = Line().add_xaxis(t)

    for i in range(len(x)):
        line.add_yaxis(x[i],list(v1[i]),is_smooth=True)

    line.set_global_opts(title_opts=opts.TitleOpts(title=" Linear slider "),
                         datazoom_opts=opts.DataZoomOpts(pos_left = True),
                         yaxis_opts=opts.AxisOpts(type_="log",is_scale=True))
    return line

show_line().render_notebook()

Change the position of the line graph slider

def show_line():
    line = Line().add_xaxis(t)
    for i in range(len(x)):
        line.add_yaxis(x1[i],list(v1[i]),is_smooth=True)

    line.set_global_opts(title_opts=opts.TitleOpts(title=" Linear slider "),
                         datazoom_opts=opts.DataZoomOpts(pos_left = True,orient='vertical'),
                         yaxis_opts=opts.AxisOpts(type_="log",is_scale=True))
    return line

show_line().render_notebook()

streamlit Control year + Histogram

""" streamlit  Control year  +  Histogram  """
import pandas as pd
import numpy as np
from pyecharts import options as opts
import streamlit as st
years  = st.slider(' ',2016,1997)
year = years -1997
from pyecharts.charts import Bar
def bar2():
    df = pd.read_csv('gdp.csv')
    df = df.iloc[[0,8,9,18,12],0:].set_index("province")
    df = df.T.sort_index()
    t =  df.index.tolist() # Time 
    v1 = np.array(df) # value 
    x = df.columns.values.tolist() # City 
    bar = (
        Bar().add_xaxis(x)
            .add_yaxis(t[year],list(v1[year]))
            .set_global_opts(title_opts=opts.TitleOpts(" A region {} year GDP".format(t[year])))
    )
    return bar
st.components.v1.html( bar2().render_embed( ) , width = 950 , height = 500 , scrolling = False )

streamlit Control year + The pie chart

import pandas as pd
import numpy as np
from pyecharts import options as opts
import streamlit as st
years  = st.slider(' ',2016,1997)
year = years -1997
from pyecharts.charts import Pie
def pie2():
    df = pd.read_csv('gdp.csv')
    df = df.iloc[[0,8,9,18,12],0:].set_index("province")
    df = df.T.sort_index()
    t =  df.index.tolist() # Time 
    v1 = np.array(df) # value 
    x = df.columns.values.tolist() # City 
    pie = (
        Pie()
        .add(
            "",
            [list(z) for z in zip(x,v1[year])],
            rosetype="radius",
            radius=["30%", "55%"],
        )
        .set_global_opts(title_opts=opts.TitleOpts(" A store {} Annual turnover ".format(t[year])))
    )
    return pie
st.components.v1.html( pie2().render_embed( ) , width = 950 , height = 500 , scrolling = False )

metabase visualization

Visualization tool alicloud disk download
Download below

cmd function

Enter the command ：java -jar metabase.jar


Write something about it

Here we will add... Later

The interface after entering

Here you need to add a database , Or use the first one directly sampleDatabase（ The second one is mine , Never mind ！）

Operate the database here , The database here has no data at present

Metabase Make one panel and two charts , A utility problem （ Line chart or pie chart as required ）, One use SQL Simple groupby（ Pie chart or histogram ）

Look at the topic first

Let's start with the question （ Line chart or pie chart as required ）
use sql Statement to import data
1、 Open database mysql New query

--  Search address 
show variables like '%secure%';
--  My address here is C:\ProgramData\MySQL\MySQL Server 5.7\Uploads\

Put the papers here

Pay attention to my catalog

create database if not exists name_hive;
show databases;
use name_hive;
create table GDP(
    province varchar(10),
    2016y float(9,3),
    2015y float(9,3),
    2014y float(9,3),
    2013y float(9,3),
    2012y float(9,3),
    2011y float(9,3),
    2010y float(9,3),
    2009y float(9,3),
    2008y float(9,3),
    2007y float(9,3),
    2006y float(9,3),
    2005y float(9,3),
    2004y float(9,3),
    2003y float(9,3),
    2002y float(9,3),
    2001y float(9,3),
    2000y float(9,3),
    1999y float(9,3),
    1998y float(9,3),
    1997y float(9,3)
);
set @@sql_mode = ansi;
load data infile 'C:/ProgramData/MySQL/MySQL Server 5.7/Uploads/gdp.csv'  
into table name_hive.gdp    
fields terminated by ','  
lines terminated by '\n'
ignore 1 rows;

Query results


2、 Create a new question

for example

Save it in the dashboard layout

3、sql establish
lose , Actually, the data import is not very good , Really? , Because what do you say? You refer to other sql The data shows how bad this is , So use sql Inquire about group by Group together csv It's all sorted out , Time belongs to time , The place belongs to the place , Something better , For example, the following

place	Time	GDP
place 1	1999	61651
place 1	1997	6151
place 1	1996	6161
place 1	1995	1651
place 1	1994	6151

Ang, so at best, I'll show you this at last